**Highlights on** *Anopheles nili* **and** *Anopheles moucheti***, Malaria Vectors in Africa**

Christophe Antonio-Nkondjio and Frédéric Simard

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55153

## **1. Introduction**

[119] Temu EA, Hunt RH, Coetzee M. Microsatellite DNA polymorphism and hetero‐ zygosity in the malaria vector mosquito *Anopheles funestus* (Diptera: Culicidae) in

[120] Garros C, Koekemoer LL, Kamau L, Awolola TS, Van Bortel W, Coetzee M, et al. Re‐ striction fragment length polymorphism method for the identification of major Afri‐ can and Asian malaria vectors within the *Anopheles funestus* and *An. minimus* groups. The American journal of tropical medicine and hygiene. [Research Support, Non-U.S.

[121] Koekemoer LL, Kamau L, Garros C, Manguin S, Hunt RH, Coetzee M. Impact of the Rift Valley on restriction fragment length polymorphism typing of the major African malaria vector *Anopheles funestus* (Diptera: Culicidae). Journal of Medical Entomolo‐ gy. [Comparative Study Research Support, Non-U.S. Gov't]. 2006 Nov;43(6):1178-84.

[122] Lehmann T, Licht M, Elissa N, Maega BT, Chimumbwa JM, Watsenga FT, et al. Pop‐ ulation Structure of *Anopheles gambiae* in Africa. Journal of Heredity. 2003 Mar-Apr;

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220 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

94(2):133-47.

*Anopheles nili* Theobald 1904 and *An. moucheti* Evans 1925 are major human malaria vectors in forested and humid savannah areas of West and Central Africa [1]. Yet, they remain critically understudied and basic knowledge on their biology, ecology and genetics is crucially lacking [2]. To date, most studies of African malaria vectors have focused on *An. gambiae*, *An. arabien‐ sis*, and *An. funestus*, in part, because molecular and cytogenetic tools for characterizing population structure, ecological adaptation, and taxonomic status of other species have been lacking until recently. Further, no laboratory colony is available for experimental work involving these neglected species. This gap in knowledge needs to be addressed for successful implementation of global strategies for malaria elimination and eradication in the Afrotropical region [3].

Recent studiesofthe ecologicalnicheprofileofmajorAfricanmalariavectorsdemonstratedthat the habitats of *An. gambiae*, *An. arabiensis*, and *An. funestus* have more overlap with each other than with the habitat of *An. nili* and *An. moucheti* [4-7]. This results in an unusual geographic distribution of *An. nili* and *An. moucheti* (Figure 1), revealing their crucial role in malaria transmission in forested and degraded forest areas of equatorial Africa [8-13]. Unique aspects of ecological adaptation and behaviour can, in part, explain the increased vectorial capacity of thespecies intheseenvironmentsandmightprotectthemfromconventionalvector controltools targeting highly endophilic and endophagic mosquito species [3, 14]. Moreover, the recent findings of circulation of *Plasmodium falciparum* along with other *Plasmodium* species in great apesandmonkeys[15-17]raiseconcernsaboutpathogentransferbetweenhumansandprimates and further highlight the need to improve our knowledge of forest malaria vectors.

In this chapter, we review knowledge gained so far on mosquitoes from *An. moucheti* and closely related species, as well as the *An. nili* complex. We highlight specific bionomical,

properly cited.

© 2013 Antonio-Nkondjio and Simard; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is © 2013 Antonio-Nkondjio and Simard; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ecological and genetic attributes that distinguish these species from the most well-known major African malaria vectors, providing opportunities for further research on neglected aspects of vector biology and control.

## **2.** *Anopheles moucheti* **and closely related species**

*Anopheles moucheti* belongs to the series Myzomyia and closely resembles *Anopheles marshallii* Theobald of the Marshallii complex. This close morphological similarity resulted in *An. moucheti* being initially considered a variety of *An. marshallii* before it was raised to the rank of full species on the basis of morphological and bionomic differences [18]. However the taxonomic status of *An. moucheti* has been subject to several interpretations during the past decades. Based on morphological similarities between *An. bervoetsi* and *An. moucheti nigerien‐ sis*, *Anopheles moucheti* was later considered by Brunhes *et al.* [19] as a group consisting of three morphological forms, namely *An. moucheti moucheti* (referred to as the type form), *An. moucheti bervoetsi* and *An. moucheti nigeriensis* distinguishable by slight morphological charac‐ ters present at the adult and/or at the larval stages [2, 19, 20]. In their classification, Brunhes *et al*. [19] referred to *An. bervoetsi* as a subspecies of *An. moucheti* while they suggested to put in synonymy *An. m. nigeriensis* and the type form. Genetic analysis conducted subsequently provided evidences against any taxonomic value for this morphological classification [21-23]. Recent classification by Harbach [24] recognizes *An. moucheti* and *An. bervoetsi* as formal species while *An. m. nigeriensis* is considered as a morphological variant within *An. moucheti*.

*Anopheles moucheti* is widely distributed across West and Central Africa (Figure 2) whereas the two other taxa have only been reported so far from their type locality in Nigeria near Lagos (06°27'N; 03°24'E) for *An. moucheti nigeriensis* and in Tsakalakuku (06°34'S; 17°35'E) in the Democratic Republic of Congo (DRC) for *An. bervoetsi* [18].

*Anopheles moucheti* is among the most important human malaria vectors in the equatorial forest region of Africa, particularly in villages situated along slow moving rivers or streams where its larvae develop in and around floating vegetation and debris (Figure 3) [4, 5]. Larval collections to assess ecological factors influencing *An. moucheti* distribution across river networks in south Cameroun showed that *An. moucheti* larvae are frequently associated with lentic rivers, low temperatures and the abundance of aquatic vegetation at the edge of the river (Figure 4) [5]. Increased urbanization and deforestation as well as lower-scale landscape modification such as river banks cleaning for gardening and/or recreational purposes were shown to be highly detrimental to the species, fostering changes in the malaria vector system composition with a higher prevalence of *An. gambiae*, taking the lead over *An. moucheti* [9]. Insecticide susceptibility tests conducted on several populations from South Cameroon in 2007 indicated that *An. moucheti* is fully susceptible to DDT, permethrin and deltamethrin (Etang *et al.*, unpublished data).

In rural villages situated in deep forest areas, *An. moucheti* usually is the major vector of *Plasmodium*, and quite often the only one maintaining a high level of malaria endemicity in humans. Natural infection rates in the range 1–3% are commonly reported in wild females, Highlights on *Anopheles nili* and *Anopheles moucheti*, Malaria Vectors in Africa http://dx.doi.org/10.5772/55153 223

ecological and genetic attributes that distinguish these species from the most well-known major African malaria vectors, providing opportunities for further research on neglected

*Anopheles moucheti* belongs to the series Myzomyia and closely resembles *Anopheles marshallii* Theobald of the Marshallii complex. This close morphological similarity resulted in *An. moucheti* being initially considered a variety of *An. marshallii* before it was raised to the rank of full species on the basis of morphological and bionomic differences [18]. However the taxonomic status of *An. moucheti* has been subject to several interpretations during the past decades. Based on morphological similarities between *An. bervoetsi* and *An. moucheti nigerien‐ sis*, *Anopheles moucheti* was later considered by Brunhes *et al.* [19] as a group consisting of three morphological forms, namely *An. moucheti moucheti* (referred to as the type form), *An. moucheti bervoetsi* and *An. moucheti nigeriensis* distinguishable by slight morphological charac‐ ters present at the adult and/or at the larval stages [2, 19, 20]. In their classification, Brunhes *et al*. [19] referred to *An. bervoetsi* as a subspecies of *An. moucheti* while they suggested to put in synonymy *An. m. nigeriensis* and the type form. Genetic analysis conducted subsequently provided evidences against any taxonomic value for this morphological classification [21-23]. Recent classification by Harbach [24] recognizes *An. moucheti* and *An. bervoetsi* as formal species

while *An. m. nigeriensis* is considered as a morphological variant within *An. moucheti*.

*Anopheles moucheti* is widely distributed across West and Central Africa (Figure 2) whereas the two other taxa have only been reported so far from their type locality in Nigeria near Lagos (06°27'N; 03°24'E) for *An. moucheti nigeriensis* and in Tsakalakuku (06°34'S; 17°35'E) in the

*Anopheles moucheti* is among the most important human malaria vectors in the equatorial forest region of Africa, particularly in villages situated along slow moving rivers or streams where its larvae develop in and around floating vegetation and debris (Figure 3) [4, 5]. Larval collections to assess ecological factors influencing *An. moucheti* distribution across river networks in south Cameroun showed that *An. moucheti* larvae are frequently associated with lentic rivers, low temperatures and the abundance of aquatic vegetation at the edge of the river (Figure 4) [5]. Increased urbanization and deforestation as well as lower-scale landscape modification such as river banks cleaning for gardening and/or recreational purposes were shown to be highly detrimental to the species, fostering changes in the malaria vector system composition with a higher prevalence of *An. gambiae*, taking the lead over *An. moucheti* [9]. Insecticide susceptibility tests conducted on several populations from South Cameroon in 2007 indicated that *An. moucheti* is fully susceptible to DDT, permethrin and deltamethrin (Etang

In rural villages situated in deep forest areas, *An. moucheti* usually is the major vector of *Plasmodium*, and quite often the only one maintaining a high level of malaria endemicity in humans. Natural infection rates in the range 1–3% are commonly reported in wild females,

aspects of vector biology and control.

222 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**2.** *Anopheles moucheti* **and closely related species**

Democratic Republic of Congo (DRC) for *An. bervoetsi* [18].

*et al.*, unpublished data).

**Figure 1.** Habitat suitability maps for the five major malaria vectors in Cameroon. A/ *Anopheles gambiae*, An. arabien‐ *sis*, *An. funestus*,, *An. nili*, *An. moucheti*. Different colors identify four classes of habitat quality including optimal (red), suitable (orange), marginal (yellow) and unsuitable habitat (white). Figure drawn from Ayala *et al.*, 2009 [4].

sustaining annual entomological inoculation rates (EIR) reaching up to 300 infective bites/ human/year [27, 28]. As such, the species has been incriminated in malaria transmission in a number of countries in CentralAfrica, including Nigeria [29], Cameroon[28, 30],Gabon [31, 32], Equatorial Guinea [10, 11], Congo [18], the DRC [18] and Uganda [18]. In these settings, *An. moucheti*frequentlybites indoorsandhighdensitiesofblood-fedfemales canbecollectedresting indoors, over 95% of which had taken their blood meal on humans demonstrating strong anthropophily. However, high mosquito densities might also be collected far from any human settlements, indicating a probable zoophilic behaviour in some forest populations [33, 34].

*Anopheles bervoetsi* has only been reported so far from its type locality and surrounding villages in the DRC. Larvae are found in small rivers sheltered by forest galleries that wind through the valleys in a hilly landscape. Adults are highly anthropophilic and preferentially bite

**Figure 2.** Map of the predicted probability of occurrence of *Anopheles moucheti* in Africa (redrawn from [25]). Black dots represent 69 records of occurrence for *An. moucheti* as described in Hay et al. [26].

**Figure 3.** A typical breeding site for *Anopheles moucheti* larvae along river Nyong in southern Cameroon.

outdoors. However, it can be collected biting and resting indoors when abundance is high at the end of the rainy season (Antonio-Nkondjio et al. unpublished data). Biting occurs at night with a peak of activity usually recorded in the second part of the night. A recent study reported three specimens found infected by *Plasmodium falciparum* out of 237 tested by ELISA, confirm‐ ing its role in malaria parasites transmission [35].

**Figure 4.** Canonical Correspondence Analysis (CCA) diagram showing the ordination of anopheline species along the first two axes and their correlation with environmental variables. The first axis is horizontal, second vertical. Direction and length of arrows shows the degree of correlation between mosquito larvae and the variables. Figure drawn from Antonio-Nkondjio *et al.*[5].

*Anopheles m. nigeriensis* is considered as a synonym to *An. moucheti*, due to the absence of reliable morphological differences at the adult and larval stages between the two morphs [19, 24]. Nothing is known of the species bionomics. The only report of its implication in malaria parasites transmission is from Baber and Olinger in 1931 ([18], *loc. cit.*) who reported 1 in 87 mosquitoes infected with sporozoites. Collections conducted in its type locality in 2005 reported few specimens (<10, Antonio-Nkondjio and Simard, unpublished data), probably reflecting habitat deterioration due to the expansion of the urban domain around Lagos.

From morphological analysis (Figure 5), it appears that the type form could display high morphological variation with variants similar to *An. m. nigeriensis* and *An. bervoetsi*. However, genetic investigations and the follow-up of morphological diversity in the progeny of field collected gravid females demonstrated that a single taxon was represented, at least in Came‐ roon [21]. Population genetic investigations using a set of ten microsatellite markers [36] further strengthened this view, revealing genetic homogeneity between natural populations of *An. moucheti* in South Cameroon and throughout Central Africa, including Uganda and the DRC [36, 37]. Studies comparing sequence variations in nuclear (rDNA Internal Transcribed Spacer 1, ITS2 and the D3 domain of the 28S ribosomal subunit) and mitochondrial (cyto‐ chrome b) DNA regions were also concordant, depicting a low level of genetic diversity and differentiation between specimens from Cameroon, Uganda and the DRC and confirming the high genetic homogeneity of *An. moucheti* populations throughout Central Africa [23]. However, when mosquito samples collected from the type localities of *An. bervoetsi* and *An. m. nigeriensis* were included in the analyses, sequence differences were detected between the three taxa, similar in degree to the differences found previously between sibling species within other anopheline groups or complexes [23]. An allele specific PCR assay based on sequence differences in the rDNA ITS1 region was developed to allow rapid identification of each of these three genetic lineages (Figure 6) [23]. Microsatellite analysis further demonstrated

outdoors. However, it can be collected biting and resting indoors when abundance is high at the end of the rainy season (Antonio-Nkondjio et al. unpublished data). Biting occurs at night with a peak of activity usually recorded in the second part of the night. A recent study reported three specimens found infected by *Plasmodium falciparum* out of 237 tested by ELISA, confirm‐

**Figure 3.** A typical breeding site for *Anopheles moucheti* larvae along river Nyong in southern Cameroon.

**Figure 2.** Map of the predicted probability of occurrence of *Anopheles moucheti* in Africa (redrawn from [25]). Black

dots represent 69 records of occurrence for *An. moucheti* as described in Hay et al. [26].

224 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

ing its role in malaria parasites transmission [35].

significant genetic differentiation between *An. bervoetsi* populations form the DRC and *An. moucheti* populations from Cameroon, suggesting that they represent two different species [35]. In light of accumulating evidences (morphological, behavioral and genetic differences) this taxa was raised to the rank of full species and named *An. bervoetsi* [35] [24]. Yet the issue of the taxonomic status of *An. m. nigeriensis* remains unresolved. It might still be considered as a variant of *An. moucheti* to be further studied.

**Figure 5.** Morphological variations on the wing of *An. moucheti.*

**Figure 6.** An agarose gel stained with ethidium bromide revealing size differences in the PCR amplification products discriminating *An. moucheti* and closely related species: *An. bervoetsi* (lanes 1 and 2), *An. moucheti* (lanes 3 and 4) and *An. m. nigeriensis* (lanes 5 and 6). Figure from Kengne *et al.*, 2007 [23]

## **3. Anopheles nili complex**

Important morphological, ecological and behavioral differences among natural populations of *Anopheles nili* from sub-Saharan Africa suggested the existence of several taxonomic units and resulted in the description of four formal species, namely: *Anopheles nili sensu stricto*, *An. somalicus*, *An. carnevalei* and *An. ovengensis* [20, 21]. Morphologically, these four species are very close from one another, differing only through subtle morphological characters present at the adult and/or at the larval stages (Figure 7) [18, 38, 39]. Apart from *An. somalicus*, which is zoophilic and was never incriminated in human malaria transmission, the three other members of the complex are highly anthropophilic and are vectors of malaria.

significant genetic differentiation between *An. bervoetsi* populations form the DRC and *An. moucheti* populations from Cameroon, suggesting that they represent two different species [35]. In light of accumulating evidences (morphological, behavioral and genetic differences) this taxa was raised to the rank of full species and named *An. bervoetsi* [35] [24]. Yet the issue of the taxonomic status of *An. m. nigeriensis* remains unresolved. It might still be considered as a

 **M 1 2 3 4 5 6 T** 

**Figure 6.** An agarose gel stained with ethidium bromide revealing size differences in the PCR amplification products discriminating *An. moucheti* and closely related species: *An. bervoetsi* (lanes 1 and 2), *An. moucheti* (lanes 3 and 4) and

Important morphological, ecological and behavioral differences among natural populations of *Anopheles nili* from sub-Saharan Africa suggested the existence of several taxonomic units

variant of *An. moucheti* to be further studied.

226 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**Figure 5.** Morphological variations on the wing of *An. moucheti.*

**400 bp** 

**200 bp** 

*An. m. nigeriensis* (lanes 5 and 6). Figure from Kengne *et al.*, 2007 [23]

**3. Anopheles nili complex**

**Figure 7.** Morphological differences between members of the *An. nili* complex. A: wing of *An. nili* and *An. somalicus,* B: wing of *An. carnevalei,* C: wing of *An. ovengensis.*

*Anopheles nili s.s.* is among the most important malaria vectors in sub-Saharan Africa. It has a wide geographic distribution range spreading across most of West, Central and East Africa mainly populating humid savannas and degraded rainforest areas (Figure 8) [1, 4, 20, 40]. Larvae thrive at the sunny edge of fast running streams and rivers, where floating vegetation and debris provide suitable shelters (Figure 9) [32]. Forest populations are usually highly anthropophilic and feed regularly indoors whereas savanna populations are more exophilic and exophagic [12, 28]. Despite feeding preferentially on humans, this mosquito can be, at times highly zoophilic [41]. *Anopheles nili* is usually responsible for a high nuisance to humans in villages along rivers, and abundance rapidly decreases within a few kilometers from the breeding sites [42]. It is also present at the periphery of urban areas.

The prevalence of *Plasmodium* infections in wild females typically ranges between 1 and 3%and transmission rate reaching 200 infective bites/human/year have been reported in the literature for *An. nili* [12, 13, 28, 43]. Reports on its epidemiological role in East Africa however, are scarce, dating back to the 1970s [18, 44]. There is no published record available for insecticide susceptibility in *An. nili* populations, although unpublished results from South Cameroon suggest full susceptibility to DDT and pyrethroids (permethrin and deltamethrin) using the diagnostic doses recommended for assessing *An. gambiae* populations (Etang *et al.*, unpub‐ lished data). The analysis of key ecological factors associated with the distribution of An. nili larvae across 24 hydrographic networks in Cameroon showed that *An. nili* distribution conforms to that of a generalist species which is adapted in exploiting a variety of environ‐ mental conditions (Figure 4).

*Anopheles carnevalei* and *An. ovengensis* are mainly distributed in deep forest areas where they take over *An. nili s.s.* in this environment [4, 41]. *Anopheles carnevalei* has been reported so far only from Côte d'Ivoire, Cameroon and Equatorial Guinea [10, 11, 38]. It is rarely collected resting indoors and bites more frequently outdoors [12]. This mosquito is mostly zoophilic although it regularly feeds on humans in villages situated close to its breeding sites. Interest‐ ingly, although biting activity can be detected all night long, man-biting activity peaks early in the evening, between 6-7 PM, when inhabitants traditionally meet at the river for domestic and body care activities [12]. Studies conducted in Cameroon and Equatorial Guinea reported infection rates *circa* 1% in Cameroon [12, 28], raising up to 24% when using PCR-based protocols for parasite detection in specimens from Equatorial Guinea [10].

**Figure 8.** Map of the predicted probability of occurrence of *Anopheles nili* complex in Africa [25]. Black dots represent 105 records of occurrence for *An. nili* complex as described in Hay et al. [26].

*Anopheles ovengensis*, the most recently described species of the *An. nili* complex, is highly anthropophilic, and bites and rests frequently outdoors [39]. However, studies conducted in Equatorial Guinea reported high densities collected by window exit traps indicating some degree of endophagic and endophilic behavior [11]. *Anopheles ovengensis* usually displays high Highlights on *Anopheles nili* and *Anopheles moucheti*, Malaria Vectors in Africa http://dx.doi.org/10.5772/55153 229

**Figure 9.** A typical breeding site for *An. nili* along the river Sanaga in South Cameroon.

susceptibility in *An. nili* populations, although unpublished results from South Cameroon suggest full susceptibility to DDT and pyrethroids (permethrin and deltamethrin) using the diagnostic doses recommended for assessing *An. gambiae* populations (Etang *et al.*, unpub‐ lished data). The analysis of key ecological factors associated with the distribution of An. nili larvae across 24 hydrographic networks in Cameroon showed that *An. nili* distribution conforms to that of a generalist species which is adapted in exploiting a variety of environ‐

*Anopheles carnevalei* and *An. ovengensis* are mainly distributed in deep forest areas where they take over *An. nili s.s.* in this environment [4, 41]. *Anopheles carnevalei* has been reported so far only from Côte d'Ivoire, Cameroon and Equatorial Guinea [10, 11, 38]. It is rarely collected resting indoors and bites more frequently outdoors [12]. This mosquito is mostly zoophilic although it regularly feeds on humans in villages situated close to its breeding sites. Interest‐ ingly, although biting activity can be detected all night long, man-biting activity peaks early in the evening, between 6-7 PM, when inhabitants traditionally meet at the river for domestic and body care activities [12]. Studies conducted in Cameroon and Equatorial Guinea reported infection rates *circa* 1% in Cameroon [12, 28], raising up to 24% when using PCR-based

**Figure 8.** Map of the predicted probability of occurrence of *Anopheles nili* complex in Africa [25]. Black dots represent

*Anopheles ovengensis*, the most recently described species of the *An. nili* complex, is highly anthropophilic, and bites and rests frequently outdoors [39]. However, studies conducted in Equatorial Guinea reported high densities collected by window exit traps indicating some degree of endophagic and endophilic behavior [11]. *Anopheles ovengensis* usually displays high

105 records of occurrence for *An. nili* complex as described in Hay et al. [26].

protocols for parasite detection in specimens from Equatorial Guinea [10].

mental conditions (Figure 4).

228 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**Figure 10.** A typical breeding site for *An. ovengensis* along river Njoh in South Cameroon (Photo: P Bousses, IRD/MIVE‐ GEC).

biting rates for humans, ranging from 50 to 300 bites/man/night along rivers where its larvae develop (Figure 10). Infection rates by *P. falciparum* ranges between 0.4 to 4.4% in specimens from Cameroon [39] and in Equatorial Guinea [11]. Larvae are often found in sympatry with those of *An. moucheti* with whom it shares most of its distribution area. The distribution range of the species probably extends further East, throughout the Congolese forest basin but this has not been investigated yet.

*Anopheles somalicus* is strictly zoophilic. At the adult stage, *An. somalicus* closely resembles *An. nili* from which it can be morphologically separated at the larval stage only [18]. Adults are rarely recorded in villages although larvae are always found in sympatry with those of *An. nili* [5]. Nothing is known of its bionomics. According to Gillies and De Meillon [18] its distribution range includes Sierra Leone, Guinea, Burkina Faso, Ivory Coast, Cameroon, Somalia and Tanzania.

Genetic studies conducted on the *An. nili* complex using various molecular markers confirmed the high genetic heterogeneity among its members [2]. Multilocus enzyme analysis of the genetic variability detected species-specific alleles and large differences in shared allele frequencies among species of the complex collected in South Cameroon [45]. Analysis of sequence polymorphism in the rDNA ITS2 region estimated genetic distances in the range of 0.11-0.25 between the four species [46]. This heterogeneity in ITS2 DNA sequences was further used to develop a PCR-based protocol for molecular identification of the different species within the complex (Figure 11) [46]. These data provided support for the recent taxonomic classification within the *An. nili* complex [24].

**Figure 11.** An agarose gel stained with ethidium bromide revealing size differences in the PCR amplification products discriminating between members of the *An. nili* complex: *An. nili* (lanes 1 to 4), *An. somalicus* (lanes 5 and 6), An. oven‐ *gensis* (lanes 7 and 8) and *An. carnevalei* (lanes 9 and 10). Figure from Kengne *et al.*, 2003 [46].

Microsatellite loci were developed in 2003 to allow for more in-depth population genetics investigations [47]. A first comprehensive study explored the level of genetic variability and differentiation between nine populations of *An. nili* distributed in West and central Africa, including samples from Senegal, Ivory Coast, Burkina Faso, Nigeria, Cameroon and the DRC using a set of 11 microsatellite markers and sequence variation in four genes within the nuclear rDNA subunit (ITS2 and D3) and mtDNA (COII and ND4). High genetic homogeneity was revealed among *An. nili* populations distributed from Senegal to Cameroon, suggesting shallow population substructure throughout the humid savannas of West Africa, in agreement with a weak effect of geographic distance [48]. However, the population sampled in DRC was highly significantly differentiated from the core of West African populations (*FST*>0.118, P<0.001), and all individuals segregated into a single genetic cluster separated from all other West African populations in Bayesian cluster analysis (Figure 12). Sequence variation in mtDNA genes matched these results, whereas low polymorphism in rDNA genes prevented detection of any population substructure at this geographical scale in savannah populations [48]. Extensive allele sharing between populations and homogeneity across microsatellite loci in the level of genetic differentiation suggested that enhanced genetic drift in the DRC population, rather than selection was responsible for the observed pattern.

*Anopheles somalicus* is strictly zoophilic. At the adult stage, *An. somalicus* closely resembles *An. nili* from which it can be morphologically separated at the larval stage only [18]. Adults are rarely recorded in villages although larvae are always found in sympatry with those of *An. nili* [5]. Nothing is known of its bionomics. According to Gillies and De Meillon [18] its distribution range includes Sierra Leone, Guinea, Burkina Faso, Ivory Coast, Cameroon,

Genetic studies conducted on the *An. nili* complex using various molecular markers confirmed the high genetic heterogeneity among its members [2]. Multilocus enzyme analysis of the genetic variability detected species-specific alleles and large differences in shared allele frequencies among species of the complex collected in South Cameroon [45]. Analysis of sequence polymorphism in the rDNA ITS2 region estimated genetic distances in the range of 0.11-0.25 between the four species [46]. This heterogeneity in ITS2 DNA sequences was further used to develop a PCR-based protocol for molecular identification of the different species within the complex (Figure 11) [46]. These data provided support for the recent taxonomic

**M 1 2 3 4 5 6 7 8 9 10 T M** 

**Figure 11.** An agarose gel stained with ethidium bromide revealing size differences in the PCR amplification products discriminating between members of the *An. nili* complex: *An. nili* (lanes 1 to 4), *An. somalicus* (lanes 5 and 6), An. oven‐

Microsatellite loci were developed in 2003 to allow for more in-depth population genetics investigations [47]. A first comprehensive study explored the level of genetic variability and differentiation between nine populations of *An. nili* distributed in West and central Africa, including samples from Senegal, Ivory Coast, Burkina Faso, Nigeria, Cameroon and the DRC using a set of 11 microsatellite markers and sequence variation in four genes within the nuclear rDNA subunit (ITS2 and D3) and mtDNA (COII and ND4). High genetic homogeneity was revealed among *An. nili* populations distributed from Senegal to Cameroon, suggesting shallow population substructure throughout the humid savannas of West Africa, in agreement with a weak effect of geographic distance [48]. However, the population sampled in DRC was highly significantly differentiated from the core of West African populations (*FST*>0.118, P<0.001), and all individuals segregated into a single genetic cluster separated from all other West African populations in Bayesian cluster analysis (Figure 12). Sequence variation in mtDNA genes matched these results, whereas low polymorphism in rDNA genes prevented

*gensis* (lanes 7 and 8) and *An. carnevalei* (lanes 9 and 10). Figure from Kengne *et al.*, 2003 [46].

Somalia and Tanzania.

classification within the *An. nili* complex [24].

230 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**400 bp 200 b**p

**Figure 12.** Bayesian genetic cluster analysis of microsatellite allele frequencies in *An. nili s.l.* populations. Genetic ho‐ mogeneity within savannah populations of *An. nili s.s.* from West/Central Africa and high genetic drift in the DRC pop‐ ulation.

In Cameroon, the pattern of genetic differentiation was explored among species within the *An. nili* complex and between populations of *An. nili* collected in different ecological settings including the deep evergreen forest, deforested areas and savannah areas. The average observed heterozygosity varied from 0.359 for *An. ovengensis* to 0.661 for *An. nili s.s*. and mean pairwise *FST* over all loci varied from 0.281 (between *An. nili* and *An. carnevalei*) to 0.416 (between *An. somalicus* and *An. ovengensis*) and were highly significant (P<0.0001) [45]. The limited number of loci which could readily amplify and the high proportion of loci departing from Hardy-Weinberg equilibrium in samples collected from the deep forest region suggested the presence of new taxonomic units in this area. Up to seven clusters could be identified in *An. nili* after processing Bayesian cluster analysis (Figure 13). Two of these clusters were specific for *An. nili* populations collected in the East Cameroon forest area, suggesting that *An. nili* from East Cameroon may consist of four new taxa. Data obtained from microsatellites analysis were consistent with the high genetic distance measured with rDNA and mtDNA genes [49].

**Figure 13.** Bayesian genetic cluster analysis of microsatellite allele frequencies in *An. nili s.l.* populations. Genetic het‐ erogeneity between forest populations of *An. nili s.l.* in South Cameroon showing genetic clustering of *An. carnevalei* (yellow), *An. ovengensis* (green), *An. somalicus* (dark blue) and the four genetic clusters suggesting further taxonomic subdivision within *An. nili s.s.* in this area.

Recently, cytogenetic analysis depicted a physical chromosome map for *An. nili* upon which nine microsatellite markers could be mapped (Figure 14)[50, 51]. Chromosomal arm homology with *An. gambiae* was assessed by fluorescent *in situ* hybridization of DNA probes which established that chromosomes X, 2R and 3R are homologous between the two species, while the 2L arm of *An. gambiae* corresponds to the 3L arm of *An. nili*, and vice versa [50]. Preliminary analysis of chromosomal polymorphism in natural *An. nili* populations from Burkina Faso and Cameroon demonstrated that two polymorphic inversions, named 2R*b* and 2R*c*, are often present simultaneously on the right arm of chromosome 2 [50, 51].

**Figure 14.** Physical chromosome map of *An. nili* showing the cytological location of the nine microsatellite markers mapped on polytene chromosomes (arrows). Two chromosomal inversions are indicated by brackets. Figure from Peery *et al.*, 2011 [51].

Frequencies of inverted and standard 2R*b* variants were almost equal in the savannah areas of Burkina Faso, albeit with strong deficit in heterozygotes (Fis=+0.603, P<0.0001). In forest areas of Cameroon, only the standard arrangement was found. It is postulated that this inversion may be involved in local ecological or behavioral adaptation in *An. nili*[50]. Inversion 2R*c* occurred at high frequency in Burkina Faso (83%) while its frequency was only 0.6% in samples from Cameroon, suggesting its involvement in ecogeographic cline from dry to more humid environments. Because *An. nili* is a forest-savannah transition species, polymorphic inversions could provide genetic plasticity that allowed its expansion into dry savannah and deforested areas of central Africa, where most of the human population is present. High frequencies of these inversions in savannah areas make them useful markers for studying ecological adaptations of this important vector.

#### **4. Conclusion**

Most of the work on malaria vectors has been conducted in the savannah environment, whereas principal vectors and their roles in malaria transmission in the immense African rainforest have barely been explored. Therefore, data are crucially lacking for a large part of Africa where malaria transmission is both intense and permanent throughout the year. Recent results demonstrated high levels of differentiation between populations/species of *An. moucheti* and the *An. nili* complex over short geographic distances within the forest block but not in the savannah. These data suggest that, unlike other major vectors, these mosquitoes originated and speciated in the equatorial forest. Because malaria elimination in forested areas is most difficult, detailed understanding of the genetic structure, gene flow, and species diversity of malaria vectors is important. Original information gained on the genetic structure of *An. moucheti* and *An. nili* can further be used to investigate genes for a signature of selection to uncover the genetic mechanisms of ecological adaptations, speciation, and susceptibility to *Plasmodium*, within a comparative framework that will use information available for other major human malaria vectors. Furthermore, because some species/populations within *An. moucheti* and the *An. nili* complex are highly exophagic/exophilic and can bite man as well as other vertebrates in remote areas, they are likely candidates for acting as bridge vectors, providing opportunities for wildlife pathogens to cause zoonosis in humans. These findings raise a concern in the light of recent reports confirming the circulation of various *Plasmodium* species, including strains of *P. falciparum*, in chimpanzees, gorillas, and guenons in the equatorial forest region [52].

## **Acknowledgements**

Recently, cytogenetic analysis depicted a physical chromosome map for *An. nili* upon which nine microsatellite markers could be mapped (Figure 14)[50, 51]. Chromosomal arm homology with *An. gambiae* was assessed by fluorescent *in situ* hybridization of DNA probes which established that chromosomes X, 2R and 3R are homologous between the two species, while the 2L arm of *An. gambiae* corresponds to the 3L arm of *An. nili*, and vice versa [50]. Preliminary analysis of chromosomal polymorphism in natural *An. nili* populations from Burkina Faso and Cameroon demonstrated that two polymorphic inversions, named 2R*b* and 2R*c*, are often

**Figure 14.** Physical chromosome map of *An. nili* showing the cytological location of the nine microsatellite markers mapped on polytene chromosomes (arrows). Two chromosomal inversions are indicated by brackets. Figure from

Frequencies of inverted and standard 2R*b* variants were almost equal in the savannah areas of Burkina Faso, albeit with strong deficit in heterozygotes (Fis=+0.603, P<0.0001). In forest areas of Cameroon, only the standard arrangement was found. It is postulated that this inversion may be involved in local ecological or behavioral adaptation in *An. nili*[50]. Inversion 2R*c* occurred at high frequency in Burkina Faso (83%) while its frequency was only 0.6% in samples from Cameroon, suggesting its involvement in ecogeographic cline from dry to more humid environments. Because *An. nili* is a forest-savannah transition species, polymorphic inversions could provide genetic plasticity that allowed its expansion into dry savannah and deforested areas of central Africa, where most of the human population is present. High frequencies of these inversions in savannah areas make them useful markers for studying

Most of the work on malaria vectors has been conducted in the savannah environment, whereas principal vectors and their roles in malaria transmission in the immense African

present simultaneously on the right arm of chromosome 2 [50, 51].

232 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

Peery *et al.*, 2011 [51].

**4. Conclusion**

ecological adaptations of this important vector.

Part of the work reported in this manuscript was supported by grants no. A00942, A20727, A60347 from the UNDP/World Bank/WHO Special programme for Research and Training in Tropical Diseases (TDR) to C.A.N, a Wellcome Trust Intermediate Fellowship in Public Health and Tropical Medecine (WTO86423MA) to CAN, the NIH grant R21 AI079350, the Pal+ programme from the French Ministry of Research and the French Institut de Recherche pour le Développement (IRD/MIVEGEC).

## **Author details**

Christophe Antonio-Nkondjio1,2\* and Frédéric Simard3

\*Address all correspondence to: antonio\_nk@yahoo.fr

1 Laboratoire de Recherche sur le Paludisme, Organisation de Coordination pour la lutte Contre les Endémies en Afrique Centrale (OCEAC), Yaoundé, Cameroon

2 Faculty of Health Sciences University of Bamenda, Bambili, Cameroon

3 Institut de Recherche pour le Développement (IRD), UMR IRD -CNRS 5290-Université de Montpellier 1-Université de Montpellier 2 MIVEGEC (Maladies Infectieuses et Vecteurs : Ecologie, Génétique, Evolution et Contrôle), Montpellier Cedex 5, France

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**Chapter 9**

## **The Dominant Mosquito Vectors of Human Malaria in India**

Vas Dev and Vinod P. Sharma

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55215

## **1. Introduction**

[48] Ndo C, Antonio-Nkondjio C, Cohuet A, Ayala D, Kengne P, Morlais I, Awono-Am‐ bene P, Couret D, Ngassam P, Fontenille D *et al*: Population genetic structure of the malaria vector *Anopheles nili* in sub-Saharan Africa. *Malaria Journal* 2010, 9(1):161. [49] Ndo C: Bioécologie et structure génétique des populations d'*Anopheles moucheti* sl et d'*Anopheles nili* vecteurs majeurs du paludisme en Afrique subsaharienne. *Thèse de*

*Doctorat PhD Faculté des Sciences Université de Yaoundé I Cameroun* 2011:286p.

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[50] Sharakhova M, Antonio-Nkondjio C, Xia A, Ndo C, Awono-Ambene H, Simard F, Sharakhov I: Cytogenetic map for *Anopheles nili*: Application for population genetics and comparative physical mapping. *Infection, Genetics and Evolution* 2010, 11:746 -

[51] Peery A, Sharakhova M, Antonio-Nkondjio C, Ndo C, Weill M, Simard F, Sharakhov I: Improving the population genetics toolbox for the study of the African malaria vec‐ tor *Anopheles nili*: microsatellite mapping to chromosomes. *Parasites & Vectors* 2011,

[52] Liu W, Li Y, Learn G, Rudicell R, Robertson J, Keele B, Ndjango J, Sanz C, Morgan D, Locatelli S *et al*: Origin of the human malaria parasite *Plasmodium falciparum* in goril‐

> In India malaria endemicity is characterized by diverse ecology and multiple disease vector species [1]. In the Southeast Asian region, India alone contributes to nearly 80% of malaria cases with the largest population of the world living at risk of malaria. In 2011, India reported 1.3 million confirmed malaria cases and 753 attributable deaths, but estimated cases and deaths are 10 to 20 times more [2,3]. Of the two *Plasmodium* prevalent in India, *Plasmodium falcipa‐ rum* incidence has not declined significantly although *P. vivax* has resulting in the rising trend of the former parasite to presently contributing ~50% of the reported cases. Distribution and spread of chloroquine resistance and emergence of multi-drug resistant strains may have contributed to this phenomenon [4]. Even though transmission intensities across India are lowto-moderate, disease remains geographically entrenched in poor marginalized population groups particularly living in remote/ forest fringe/ tribal belts of eastern, central and north‐ eastern states for contributing >65% of malarial episodes [5,6].

> Mosquito fauna is rich in the tropical climate with numerous and diverse breeding resources [7]. Of 58 anophelines in India, only six taxa are major malaria vectors with regional distribu‐ tion (Figure 1). *Anopheles culicifacies s.l*. is the vector of rural malaria in the country and generates about 65% of cases annually. *An. fluviatilis s.l*. is found in the plains and foothills breeding in streams contributing 15% of malaria cases, *An. minimus* breeds in streams of foothills of the northeast, *An. dirus s.l.* is found in jungles of northeastern states, *An. sundai‐ cus* is found in Andaman and Nicobar islands and breeds in brackish water, and *An. stephen‐ si* is the well known vector species of urban malaria. All these mosquito species except *An. stephensi* have been characterized as species complexes with number of morphologically indistinguishable sibling species which vary for their role in malaria transmission [8].

© 2013 Dev and Sharma; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Dev and Sharma; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

India is experiencing rapid ecological changes owing to population explosion, urbanization, development projects, deforestation and human migration affecting mosquito ecology and disease transmission. In the recent past, significant progress has been made in understanding the genetics and bionomics of the disease vectors, and in the development of newer control tools to strengthen primary healthcare services specific to India [9-14]. In this chapter we shall restrict systematic review on dominant *Anopheles* vectors of human malaria and their current bionomics to help develop malaria-risk maps for strengthening malaria control for sustainable interventions with ultimate goal of malaria elimination.

#### **2.** *Anopheles (Cellia) culicifacies* **Giles species complex**

*Anopheles culicifaciess.l.* is widely distributed in India and has been recorded in all mainland zones including Kashmir and high elevations in the Himalayas (up to 3000 meters) except islands of Andaman & Nicobar and Lakshadweep [7,8,11]. It is the most important vector in plains of rural India contributing 60-70% of reported cases annually [15]. Success stories in malaria control during 1950-1960, and malaria resurgence in the 1970s deal primarily with the control of *An. culicifacies s.l*. Biology and genetics of *An. culicifacies* has been extensively studied in India [16-17], and presently characterized to be a species complex with five informally designated species A, B, C, D and E. These five sibling species are spread across India with distinct biological characteristics and role in malaria transmission (Table 1).

**Figure 1.** Map of India showing distribution of major malaria vectors in relation to physiogeographic regions encom‐ passing evergreen tropical forest (wet zone receiving rainfall >200 cm), deciduous wet forest (monsoon forests receiv‐ ing rainfall 100-200 cm), deciduous dry forest (scrub forest receiving rainfall 50-100 cm), and desert forest (arid and semi-arid area receiving rainfall <50 cm) annually.

Sibling species were initially characterized by species specific diagnostic fixed paracentric inversions readable in polytene chromosomes suggestive of pre-mating barriers in field populations [18-24], and further substantiated by number of techniques including post-zygotic isolation mechanisms in laboratory conditions [25], mitotic karyotype Y- chromosome polymorphism [26-28], gene enzyme variation [29], cuticular hydrocarbon profiles [30], and species specific DNA probes [31]. Recently, PCR-based diagnostic assays were developed for sequencing 28S-D3 domain [32], ITS2-PCR-RFLP [33], rDNA ITS2 region [34], which grouped *An. culicifacies* sibling species into two distinct groups namely Group I (species A/D) and Group II (species B/C/E). In another assay from COII region, A/D specific primers distinguished species A and D, and B/C/E specific primers distinguished B, C and E [35]. More recently, a multiplex PCR–based diagnostic assay using D2 domain of 28S rDNA has been reported which can consistently and accurately discriminate members of the species complex forming two unambiguous monophyly clades of species A/D (Group I) and species B/C and E (Group 2) which were supported by strong bootstrap values [36].

India is experiencing rapid ecological changes owing to population explosion, urbanization, development projects, deforestation and human migration affecting mosquito ecology and disease transmission. In the recent past, significant progress has been made in understanding the genetics and bionomics of the disease vectors, and in the development of newer control tools to strengthen primary healthcare services specific to India [9-14]. In this chapter we shall restrict systematic review on dominant *Anopheles* vectors of human malaria and their current bionomics to help develop malaria-risk maps for strengthening malaria control for sustainable

*Anopheles culicifaciess.l.* is widely distributed in India and has been recorded in all mainland zones including Kashmir and high elevations in the Himalayas (up to 3000 meters) except islands of Andaman & Nicobar and Lakshadweep [7,8,11]. It is the most important vector in plains of rural India contributing 60-70% of reported cases annually [15]. Success stories in malaria control during 1950-1960, and malaria resurgence in the 1970s deal primarily with the control of *An. culicifacies s.l*. Biology and genetics of *An. culicifacies* has been extensively studied in India [16-17], and presently characterized to be a species complex with five informally designated species A, B, C, D and E. These five sibling species are spread across India with

**Figure 1.** Map of India showing distribution of major malaria vectors in relation to physiogeographic regions encom‐ passing evergreen tropical forest (wet zone receiving rainfall >200 cm), deciduous wet forest (monsoon forests receiv‐ ing rainfall 100-200 cm), deciduous dry forest (scrub forest receiving rainfall 50-100 cm), and desert forest (arid and

semi-arid area receiving rainfall <50 cm) annually.

interventions with ultimate goal of malaria elimination.

240 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**2.** *Anopheles (Cellia) culicifacies* **Giles species complex**

distinct biological characteristics and role in malaria transmission (Table 1).


**Table 1.** Inversion genotype and biological characteristics of *Anophelesculicifacies* sibling species complex in India\*

The distribution, relative abundance and predominance of sibling species (but not exclusive) is given in Figure 2. Among its sibling species, species B is the most predominant throughout the country and occurs sympatrically in most areas with predominance of species A in the north and species B in the south [37]. In eastern Uttar Pradesh, north Bihar and northeastern states, species B is either predominant or the only prevalent species. Species B and C are sympatric in western and eastern India. Species D is sympatric with species A and B in northwestern region, and with species A, B and C in central southern India. Species E is sympatric with species B in southern Tamilnadu including Rameshwaram islands. The proportions of sibling species, however, varied in different geographical zones and seasons, e.g., in Alwar (state of Rajasthan), species B proportions increased in post-monsoon months; whereas proportions of species D remained the same throughout the year and density of species C remained very low [38].

**Figure 2.** Map of India showing geographical distribution of predominant sibling species of *Anopheles culicifacies* complex (A,B,C,D,E) and *An. fluviatilis* complex (S,T,U, form V), and stratification (Divisions I –VII) for suggested vector control options. For control of *An. culicifacies* malaria vectors in Division I & III: No routine vector control is necessary except for treatment of imported cases of malaria; Division II: Insecticide spraying based on susceptibility status of *An*. *culicifacies* species A or C; Division IV: DDT spraying to continue; Division V–VII: Insecticide spraying based on suscepti‐ bility status of *An*. *culicifacies* species C. For control of *An. fluviatilis* malaria vectors, even though DDT remains the in‐ secticide of choice, in areas where it is sympatric with *An. culicifacies,* insecticide spraying used for control of latter should be applied. Source Reference No. 37.

All member sibling species of the *An. culicifacies* complex are predominantly zoophilic except species E, and rest indoors in human dwellings and cattle sheds [39]. All are night biting species with different peak biting activity (Table 1). The main strategy for malaria control in areas of *An. culicifacies* distribution is by indoor spraying of residual insecticides chosen based on their susceptibility status in the given region. Presently, *An. culicifacies* has developed resistance to most insecticides in use including malathion (except certain areas) leaving the only option of pyrethroid use for which there are already reports of increased tolerance [40-45]. Molecular characterization revealed a low frequency of the *kdr* allele (mostly in heterozygous condition) in field populations that were resistant to DDT and pyrethroids [46,47]. Based on the geo‐ graphical distribution of sibling species, the country is now stratified into seven divisions for benefit of prioritizing control options, e.g., for division I and III, no routine control interven‐ tions are required, whereas for divisions II, IV - VII, insecticide spraying is necessary based on susceptibility status against the dominant vector species (Figure 2).

The distribution, relative abundance and predominance of sibling species (but not exclusive) is given in Figure 2. Among its sibling species, species B is the most predominant throughout the country and occurs sympatrically in most areas with predominance of species A in the north and species B in the south [37]. In eastern Uttar Pradesh, north Bihar and northeastern states, species B is either predominant or the only prevalent species. Species B and C are sympatric in western and eastern India. Species D is sympatric with species A and B in northwestern region, and with species A, B and C in central southern India. Species E is sympatric with species B in southern Tamilnadu including Rameshwaram islands. The proportions of sibling species, however, varied in different geographical zones and seasons, e.g., in Alwar (state of Rajasthan), species B proportions increased in post-monsoon months; whereas proportions of species D remained the same throughout the year and density of

**Figure 2.** Map of India showing geographical distribution of predominant sibling species of *Anopheles culicifacies* complex (A,B,C,D,E) and *An. fluviatilis* complex (S,T,U, form V), and stratification (Divisions I –VII) for suggested vector control options. For control of *An. culicifacies* malaria vectors in Division I & III: No routine vector control is necessary except for treatment of imported cases of malaria; Division II: Insecticide spraying based on susceptibility status of *An*. *culicifacies* species A or C; Division IV: DDT spraying to continue; Division V–VII: Insecticide spraying based on suscepti‐ bility status of *An*. *culicifacies* species C. For control of *An. fluviatilis* malaria vectors, even though DDT remains the in‐ secticide of choice, in areas where it is sympatric with *An. culicifacies,* insecticide spraying used for control of latter

species C remained very low [38].

242 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

should be applied. Source Reference No. 37.

*An. culicifacies* is indeed a prolific breeder and breeding sites are numerous including riverbed pools, rain water collections (Figure 3), streams, rice-fields, seepage water, borrow pits, irrigation channels, etc [7,11]. It has been incriminated by detection of gut and salivary gland infections by numerous independent investigators across its range of distribution throughout India [7]. Further studies using immunoradiometric analysis revealed that sibling species A, C, D and E are vectors of *Plasmodium vivax* and *P. falciparum* malaria, and species B is nonvector or poor vector [48]. Among these, species E was observed to be highly anthropophilic in Rameswaram islands of Tamilnadu [49]. These observations were further supported by comparative reproductive fitness for which sibling species B was observed to be less fit than species A and C of the complex as well as susceptibility to malaria sporogony [50-52].

**Figure 3.** Breeding habitats of *Anopheles culicifacies* (left – rain water pools; right – river bed pools). Courtesy: N. Nan‐ da and R. Namgay.

However, more information on distribution and bionomics of species E is deemed necessary to substantiate its distribution range and role in malaria transmission in India. In addition, understanding population structure of *An. culicifacies* in adjoining countries is also warranted for effective interventions to check spread of drug-resistant malaria across borders. Additional data on crossing experiments between sibling species to demonstrate

post-zygotic isolation and existence of possible morphological differences would help name the individual species formally similar to other well defined species complexes of *An. dirus* and that of *An. maculatus* [8,10]. *An. culicifacies* is indeed a fast invading species in areas hitherto with low density (deforested pockets in Northeast India), and its control has become a formidable challenge with its sibling species developing multiple resistance including pyrethroids (42-45). Regional control strategy would require monitoring the insecticide susceptibility status periodically for any given area that qualifies for residual spraying for effective control of *An. culicifacies* malaria vectors.

## **3.** *Anopheles (Cellia) fluviatilis* **James species complex**

*Anopheles fluviatilis s.l.* is widespread in mainland India and is considered to be an important vector in hills and foothills contributing ~15% of reported cases annually [1]. It has been extensively studied and recognized a species complex comprising three sibling species, i.e., S, T, U and a form 'V' based on cytotaxonomic study for fixed chromosomal inversions readable in the polytene chromosomes arm 2 [7-11,53]; differentiation of S and T, however, not possible due to diagnostic inversion polymorphism but can be characterized by distinct biological characteristics and regional distribution (Table 2). Earlier reports of existence of X and Y sibling species in *An. fluviatilis* based on rDNA-ITS2 polymerase chain reaction assay subsequently correlated X with sibling species S, and Y with T based on chromosomal data [54,55]. To substantiate these observations, robust molecular techniques now have been developed which distinguish sibling species S, T and U unequivocally based on differences in nucleotide sequences within the D3 domain of 28S rDNA [56]. However, contrary to observations of Garros et al [57] and Chen et al [58] on conspecificity of An*. fluviatilis* species S with *An. harrisoni* (species C of *An. minimus*), Indian population of these two species were observed to be distantly related and did not merit synonymy based on pair-wise distance and phylogenetic inferences using ITS2 sequences [59].


\*Source Reference No. 37, \*\*Distribution, bionomics and biology of new sibling form 'V' is being investigated

**Table 2.** Inversion genotype and biological characteristics of *Anopheles fluviatilis* sibling species complex in India\*

Sibling species S is highly anthropophilic and responsible for maintaining hyperendemic malaria predominantly in state of Odisha (formerly Orissa), eastern India [60]. It prefers to rest indoor human dwellings and have been incriminated and proven to be an efficient vector in areas of its distribution [61,62]. Sibling species T is widely distributed but is largely zoophilic and rests in cattle sheds [63]. Sibling U holds similar characteristics but has limited distribution range presently restricted to northern India. Chen et al [58] documented three haplotypes in species T (designated T1, T2, Y) with its distribution in India, Nepal, Pakistan and Iran implicating the existence of additional taxa within the An*. fluviatilis* species complex provi‐ sionally designated as 'V form' in India, and the same has recently been recorded in district Hardwar, Uttarakhand state of North India [63]. Both sibling species T and U are held very close with similar biological characteristics and there exists possibility of hybridization in some areas. Even though both siblings species are poor vectors but have shown inherent ability to support normal sporogony in laboratory feeding experiments [64].

post-zygotic isolation and existence of possible morphological differences would help name the individual species formally similar to other well defined species complexes of *An. dirus* and that of *An. maculatus* [8,10]. *An. culicifacies* is indeed a fast invading species in areas hitherto with low density (deforested pockets in Northeast India), and its control has become a formidable challenge with its sibling species developing multiple resistance including pyrethroids (42-45). Regional control strategy would require monitoring the insecticide susceptibility status periodically for any given area that qualifies for residual

*Anopheles fluviatilis s.l.* is widespread in mainland India and is considered to be an important vector in hills and foothills contributing ~15% of reported cases annually [1]. It has been extensively studied and recognized a species complex comprising three sibling species, i.e., S, T, U and a form 'V' based on cytotaxonomic study for fixed chromosomal inversions readable in the polytene chromosomes arm 2 [7-11,53]; differentiation of S and T, however, not possible due to diagnostic inversion polymorphism but can be characterized by distinct biological characteristics and regional distribution (Table 2). Earlier reports of existence of X and Y sibling species in *An. fluviatilis* based on rDNA-ITS2 polymerase chain reaction assay subsequently correlated X with sibling species S, and Y with T based on chromosomal data [54,55]. To substantiate these observations, robust molecular techniques now have been developed which distinguish sibling species S, T and U unequivocally based on differences in nucleotide sequences within the D3 domain of 28S rDNA [56]. However, contrary to observations of Garros et al [57] and Chen et al [58] on conspecificity of An*. fluviatilis* species S with *An. harrisoni* (species C of *An. minimus*), Indian population of these two species were observed to be distantly related and did not merit synonymy based on pair-wise distance and phylogenetic

> **Feeding preference**

Anthropophilic

Almost totally zoophilic

\*Source Reference No. 37, \*\*Distribution, bionomics and biology of new sibling form 'V' is being investigated

**Table 2.** Inversion genotype and biological characteristics of *Anopheles fluviatilis* sibling species complex in India\*

U +q'r' mesoendemic

**Preferred adult habitat**

Human dwellings

> Cattle sheds

**Prevalence**

**Ecotype Endemicity**

& foothills Hyperendemic

Hypo -

Hilly forests

Foothills & plains

spraying for effective control of *An. culicifacies* malaria vectors.

244 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**3.** *Anopheles (Cellia) fluviatilis* **James species complex**

inferences using ITS2 sequences [59].

**Inversion genotypes on Chromosome arm 2**

T q'+r' High (up to

S +q'+r'

**Mosquito densities (per person hour)**

Low to Moderate (1-40)

200)

**Sibling Species\*\*** Preferred breeding habitats are seepage water streams with perceptible flow of water, river margins, irrigation channels, shallow wells, terraced rice fields along foothills etc [7,11,65]. Peak biting activity occurs between 20:00 to 24:00 hours but it may vary in different seasons and locations. Both *An. fluviatilis* species S and *An. minimus* share similar resting and breeding habitats and are efficient vectors in their respective zones of distribution [66]. Both are subject to misidentification due to morphological variation to the extent that the earlier records of prevalence and seasonal abundance of *An. fluviatilis*in northeast India have now been proven to be hypermelanic variant of *An. minimus s*.s.by molecular assays [67].

For control of *An. fluviatilis,* the choice of insecticide should be based on the susceptibility status of prevalent sibling of *An. culicifacies* in endemic areas where species of both complexes share similar indoor resting behavior and sympatric distribution records (Figure 1). More investi‐ gations are, however, warranted for precise distribution of different sibling species of this complex especially in areas hitherto unexplored, particularly 'form V' and its role in malaria transmission. Similar to *An. culicifacies* species complex, there is dearth of data for morpho‐ logical differentiation and crossing experiments to distinguish member sibling species enabling binomial nomenclature.

## **4.** *Anopheles (Cellia) minimus* **Theobald species complex**

*Anopheles minimus s.l.* is considered to be the predominant malaria vector in the oriental region [68]. It is a major vector in sub-Himalayan foothills of eastern and northeastern region of India. In the pre-DDT era (1940s), it was extensively studied in Assam and Bengal for its bionomics and control, and it was widely incriminated across its range of distribution [69-74]. With the advent of DDT and large scale application for residual spraying to control, *An. minimus* disappeared from Terai of Uttarakhand (formerly Uttar Pradesh), eastern Odisha, northeastern states and Nepal [75,76]. Subsequently besides *An. dirus s.l*., *An. philippinensis* was implicated in malaria transmission in northeastern region of India [77]. However, return of malaria required containment of persistent transmission and spread of drug-resistant malaria. Towards this objective, systematic investigations were initiated *denovo* during 1980s to incriminate vectors of malaria and to ascertain their relative importance [78,79].

Consequently, systematic studies by independent investigators revealed the reappearance of *An. minimus* in vast areas of northeast. *An. minimus* was re-incriminated in almost all states of the northeast India except in Terai area of Uttarakhand (North India) where it did not return [80-86]. It is only recently that *An. minimus* has been reported to have resurfaced in Odisha (eastern India) after a lapse of 45 years and were observed to be abundant sharing *An. fluviatilis* habitats, and both vectors were incriminated [87,88]. It is presently the most efficient vector in foothill valley areas of northeastern states accounting for nearly 50% reported cases in the region annually, and responsible for focal disease outbreaks characterized by high rise in cases and attributable deaths [89-94]. *An. minimus* is the predominant vector in rice-growing foothill valley areas, and it supplements transmission in forest fringe areas (adjoining to undisturbed forest reserve) predominated by *An. baimaii* [95].

Ever since initial recognition of *An. minimus* as species complex for its three morphological forms [96] and subsequent characterization by population genetic evidence for two isomorphic species [97], *An. minimus s.l.* has been identified to a species complex comprising three formally named species, *An. minimus s.s*. (species A), *An. harrisoni* Harbach & Manguin (species C), and *An. yaeyamaensis* Somboon & Harbach (species E) with distinct bionomical characteristics and distribution [98-101]. The natural distribution range of these species is given in Figure 4. Even though based on classical taxonomy, three designated species are difficult to distinguish due to overlapping morphological characters, yet these can be identified reliably by number of molecular assays [102-107].

Based on DNA sequences of internal transcribed spacer 2 (ITS2) and D3 domain of 28S rDNA (28S-D3) of morphologically identified *An. minimus s.l.* across Indian states of Assam, Aruna‐ chal Pradesh, Meghalaya and Nagaland [108] and that of Odisha [87], it has now been clearly established that these populations are indeed *An. minimus* (species A), whereas *An. harrisoni* and *An. yaeyamaensis* are not recorded from India. Correct identification of *An. minimus* is further complicated by the existence of morphological variants which closely resemble *An. varuna* and *An. fluviatilis s.l*., and these species share similar distribution range and habitats. In northeast India, morphologically identified populations of *An. fluviatilis s.l.* (formerly designated species U based on polytene chromosome banding pattern) have now been genetically characterized as the hypermelanic seasonal variant of *An. minimus* prevalent during cooler months [67]. The ITS2 and 28S-D3 rDNA sequences of morphologically identi‐ fied *An. fluviatilis* populations of from Assam were observed homologous to that of *An. minimus s.s*. and different from that of any member of the *An. fluviatilis* complex.

*An. minimus* is primarily an endophilic and endophagic species with a strong predilection for human host for blood meal [85]. It is a perennial species with seasonal peak density during April to August (wet season), and is the most predominant collection in human bait landing catches (13.7 per person/night) with peak biting activity during 01:00–04:00 hours. It has been incriminated in all months of the year (sporozoite infection rate 3.31%) but relative abundance and entomologic inoculation rates (EIRs) vary across malaria en‐ demic districts [85,109]. The relative abundance and risk of malaria is high in localities near to breeding habitat (<1km) suggestive of poor flight range (Figure 5). *An. minimus* breeding were primarily recorded in perennial seepage water foothill streams with grassy margins in all seasons but occasionally recorded in paddy field water pools with percepti‐ ble flow of water [110].

Towards this objective, systematic investigations were initiated *denovo* during 1980s to

Consequently, systematic studies by independent investigators revealed the reappearance of *An. minimus* in vast areas of northeast. *An. minimus* was re-incriminated in almost all states of the northeast India except in Terai area of Uttarakhand (North India) where it did not return [80-86]. It is only recently that *An. minimus* has been reported to have resurfaced in Odisha (eastern India) after a lapse of 45 years and were observed to be abundant sharing *An. fluviatilis* habitats, and both vectors were incriminated [87,88]. It is presently the most efficient vector in foothill valley areas of northeastern states accounting for nearly 50% reported cases in the region annually, and responsible for focal disease outbreaks characterized by high rise in cases and attributable deaths [89-94]. *An. minimus* is the predominant vector in rice-growing foothill valley areas, and it supplements transmission in forest fringe areas (adjoining to

Ever since initial recognition of *An. minimus* as species complex for its three morphological forms [96] and subsequent characterization by population genetic evidence for two isomorphic species [97], *An. minimus s.l.* has been identified to a species complex comprising three formally named species, *An. minimus s.s*. (species A), *An. harrisoni* Harbach & Manguin (species C), and *An. yaeyamaensis* Somboon & Harbach (species E) with distinct bionomical characteristics and distribution [98-101]. The natural distribution range of these species is given in Figure 4. Even though based on classical taxonomy, three designated species are difficult to distinguish due to overlapping morphological characters, yet these can be identified reliably by number of

Based on DNA sequences of internal transcribed spacer 2 (ITS2) and D3 domain of 28S rDNA (28S-D3) of morphologically identified *An. minimus s.l.* across Indian states of Assam, Aruna‐ chal Pradesh, Meghalaya and Nagaland [108] and that of Odisha [87], it has now been clearly established that these populations are indeed *An. minimus* (species A), whereas *An. harrisoni* and *An. yaeyamaensis* are not recorded from India. Correct identification of *An. minimus* is further complicated by the existence of morphological variants which closely resemble *An. varuna* and *An. fluviatilis s.l*., and these species share similar distribution range and habitats. In northeast India, morphologically identified populations of *An. fluviatilis s.l.* (formerly designated species U based on polytene chromosome banding pattern) have now been genetically characterized as the hypermelanic seasonal variant of *An. minimus* prevalent during cooler months [67]. The ITS2 and 28S-D3 rDNA sequences of morphologically identi‐ fied *An. fluviatilis* populations of from Assam were observed homologous to that of *An.*

*minimus s.s*. and different from that of any member of the *An. fluviatilis* complex.

*An. minimus* is primarily an endophilic and endophagic species with a strong predilection for human host for blood meal [85]. It is a perennial species with seasonal peak density during April to August (wet season), and is the most predominant collection in human bait landing catches (13.7 per person/night) with peak biting activity during 01:00–04:00 hours. It has been incriminated in all months of the year (sporozoite infection rate 3.31%) but relative abundance and entomologic inoculation rates (EIRs) vary across malaria en‐ demic districts [85,109]. The relative abundance and risk of malaria is high in localities

incriminate vectors of malaria and to ascertain their relative importance [78,79].

undisturbed forest reserve) predominated by *An. baimaii* [95].

246 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

molecular assays [102-107].

**Figure 4.** Distribution map of member species of the *Anopheles minimus* complex in Southeast Asia based on molecu‐ lar identification (Courtesy: Dr. S. Manguin). *An. minimus* has wide distribution extending from East India to Northeast and eastwards to China including Taiwan, and occurs in sympatry with *An. harrisoni* over large areas in southern Chi‐ na, Vietnam, Laos and Thailand. *An. yaeyamaensis* is restricted to Ishigaki island of Ryukyu Archipelago of Japan.

**Figure 5.** Breeding and resting habitats of *Anopheles minimus* (left- seepage water foothill streams are preferred breeding habitat; right – mud house with thatched roofing located often adjacent to breeding resource is the ideal resting habitat for which relative risk of malaria is high).

*An. minimus* is susceptible to DDT despite decades of insecticide residual spraying (IRS) by virtue of its physiological resistance and high behavioral plasticity [93]. It avoids resting indoors and instead establishes extra-domiciliary transmission only to return to original habitat after 10 to 12 week post-spray. With the introduction of pyrethroid coated/ incorpo‐ rated long-lasting insecticidal nets (LLINs) and enhanced population coverage in high-risk areas, the populations of *An. minimus* are once again fast diminishing particularly in broken forest reserve erstwhile domains of this anthropophilic species [111-113]. The niche thus vacated is being accessed by *An. culicifacies* populations which are tolerant to multiple insecticides posing a new challenge for effective vector control and associated transmission (unpublished observations).

It is suggested that in areas with *An. minimus* and *An. fluviatilis* sympatric populations, viz., Odisha and West Bengal, there is need to apply integrated vector management for sustainable interventions [114,115]. Given the adaptability of *An. minimus* to varied environments, there is continued need to monitor its bionomical characteristics in the changing ecological context due to rapid socio-economic development and diminishing malaria transmission in erstwhile areas of high receptivity [116]. Additional data are warranted for analyses of mitotic karyo‐ types, polytene chromosome maps and cross-breeding experiments which may of diagnostic importance. Equally important would be to understand the population dynamics of member species of the *An. minimus* complex in the adjoining countries of Myanmar, Bangladesh and Bhutan for developing cross-border initiative to institute appropriate interventions to contain drug-resistant malaria.

### **5.** *Anopheles (Cellia) dirus* **Peyton & Harrison species complex**

*Anopheles dirus s.l.* comprises eight sibling species, seven of which have been formally named, i.e., *An. diruss.s.* (species A), *An. cracens* (species B), *An. scanloni* (species C), *An. baimaii* (species D), *An. elegans* (species E), *An. nemophilous* (species F), *An. takasagoensis,* and a cryptic species tentatively designated as *An.* aff. *takasagoensis* (Figure 6). Each of the seven named species has morphological description (117), distribution range and have varied epidemiological signifi‐ cance in Southeast Asia [10,118], whereas the eighth species, reported in northern Vietnam, is morphological similar but phylogenetically distant from both *An. dirus* and *An. takasagoensis* [119]. All these sibling species except *An.* aff. *takasagoensis* have been well characterized by a number of techniques including cross-mating experiments, karyotypic studies, polytene chromosome banding patterns, gene enzyme variation, DNA probes and egg morphology (8,10,120-122]. In addition, PCR assays have been developed based on ITS2 sequences and SCAR (sequence characterized amplified region) based PCR which distinguishes five of its member species unambiguously [123,124]. Further investigations, however, are warranted to characterize *An.* aff. *takasagoensis* to formally name this as valid species of the *An. dirus* species complex.

Among these member species, only *An. baimaii* and *An. elegans* are prevalent in India with distinct distribution range and epidemiological significance [8]. *An. baimaii* is widely abundant in northeastern states and is an efficient vector of human malaria contributing the remaining 50% of reported cases in the region annually [1]. It has been widely incriminated across northeastern states (sporozoite infection rate 1.9%) and its neighboring countries associated with transmission of drug-resistant malaria [125-132]. In earlier records what was initially described as *An. balabacensis balabacensis* and later *An. dirus* (species D) in India are now referred as *An. baimaii* for all purposes. *An. baimaii* is very closely related to *An. dirus,* populations of both species are of significance in understanding evolution and history of expansion in geological time scale [133,134].

*An. minimus* is susceptible to DDT despite decades of insecticide residual spraying (IRS) by virtue of its physiological resistance and high behavioral plasticity [93]. It avoids resting indoors and instead establishes extra-domiciliary transmission only to return to original habitat after 10 to 12 week post-spray. With the introduction of pyrethroid coated/ incorpo‐ rated long-lasting insecticidal nets (LLINs) and enhanced population coverage in high-risk areas, the populations of *An. minimus* are once again fast diminishing particularly in broken forest reserve erstwhile domains of this anthropophilic species [111-113]. The niche thus vacated is being accessed by *An. culicifacies* populations which are tolerant to multiple insecticides posing a new challenge for effective vector control and associated transmission

It is suggested that in areas with *An. minimus* and *An. fluviatilis* sympatric populations, viz., Odisha and West Bengal, there is need to apply integrated vector management for sustainable interventions [114,115]. Given the adaptability of *An. minimus* to varied environments, there is continued need to monitor its bionomical characteristics in the changing ecological context due to rapid socio-economic development and diminishing malaria transmission in erstwhile areas of high receptivity [116]. Additional data are warranted for analyses of mitotic karyo‐ types, polytene chromosome maps and cross-breeding experiments which may of diagnostic importance. Equally important would be to understand the population dynamics of member species of the *An. minimus* complex in the adjoining countries of Myanmar, Bangladesh and Bhutan for developing cross-border initiative to institute appropriate interventions to contain

**5.** *Anopheles (Cellia) dirus* **Peyton & Harrison species complex**

*Anopheles dirus s.l.* comprises eight sibling species, seven of which have been formally named, i.e., *An. diruss.s.* (species A), *An. cracens* (species B), *An. scanloni* (species C), *An. baimaii* (species D), *An. elegans* (species E), *An. nemophilous* (species F), *An. takasagoensis,* and a cryptic species tentatively designated as *An.* aff. *takasagoensis* (Figure 6). Each of the seven named species has morphological description (117), distribution range and have varied epidemiological signifi‐ cance in Southeast Asia [10,118], whereas the eighth species, reported in northern Vietnam, is morphological similar but phylogenetically distant from both *An. dirus* and *An. takasagoensis* [119]. All these sibling species except *An.* aff. *takasagoensis* have been well characterized by a number of techniques including cross-mating experiments, karyotypic studies, polytene chromosome banding patterns, gene enzyme variation, DNA probes and egg morphology (8,10,120-122]. In addition, PCR assays have been developed based on ITS2 sequences and SCAR (sequence characterized amplified region) based PCR which distinguishes five of its member species unambiguously [123,124]. Further investigations, however, are warranted to characterize *An.* aff. *takasagoensis* to formally name this as valid species of the *An. dirus* species

Among these member species, only *An. baimaii* and *An. elegans* are prevalent in India with distinct distribution range and epidemiological significance [8]. *An. baimaii* is widely abundant

(unpublished observations).

248 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

drug-resistant malaria.

complex.

**Figure 6.** Distribution map of member species of the *Anopheles dirus* complex in Southeast Asia (Courtesy: Dr. S. Man‐ guin). *An. dirus* has a wide distribution in eastern Asia including Myanmar, Thailand, Cambodia, Laos, Vietnam and Hainan Island. *An. cracens* occurs in southern Thailand, peninsular Malaysia and Sumatra (Indonesia). *An. scanloni* dis‐ tribution is restricted along border of southern Myanmar and western Thailand. *An. baimaii* distribution extends from southwest China to northeast India through western Thailand, Myanmar, Bangladesh and Andaman Islands (India). *An. elegans* distribution is restricted to hilly forests of southwestern India. *An. nemophilous* has a patchy distribution along Thai-Malaya peninsula and Thai border with Myanmar and Cambodia. *An. takasagoensis* is restricted to Taiwan and *An*. aff. *takasagoensis* has recently been reported from northern Vietnam.

*An. baimaii* is a forest dweller and actively transmits malaria during monsoons in forest fringe population groups particularly along inter-state and inter-country border areas (Figure 7). It is a hygrophilic species (flight range <1km) and demonstrates phenomenon of 'horizontal' pulsation, i.e., population expansion from 'mother foci' in deep forests to periphery during monsoons (June–October) and then retracting to 'mother foci' in dry seasons (November– March) accounting for its high and low prevalence in respective season, and 'vertical' pulsation for its ability to feed on alternate host to humans in the changing environmental conditions [135]. It is a highly anthropophilic species for its predilection to human host and bites through‐ out night both indoors and outdoors (36.1 bites/person/night) with peak infective biting activity during second quartile (21:00–24:00) of the night hours [136,137]. The relative risk of infective bite, however, was estimated to be much greater in the post-monsoon season. It is largely an exophilic species and breeds in a variety of habitats in forest including small transient pools, elephant foot prints [138]. It is highly susceptible to all residual insecticides but avoids contact with sprayed surfaces making vector control a difficult proposition [139].

**Figure 7.** A typical housing structure receptive for *Anopheles baimaii* transmitted malaria located along Indo-Bangla‐ desh border in northeast India

Even though populations of *An. baimaii* from northeast India had high genetic diversity, these populations were genetically distinct from those of the adjoining countries of Bangladesh, Myanmar and Thailand suggesting significant barrier to gene flow [140]. However, there was no significant genetic differentiation between populations of northeast (except for population in the Barail hill range of northeast), thus be considered one entity for implementation of control interventions [141]. Yet owing to continued deforestation and possible disruption of gene flow between populations, there is possibility of existence of another taxon tentatively designated as 'species x' which call for additional investigations. *An. baimaii* is also known to inhabit forests of Andaman and Nicobar islands but there is dearth of data on population genetic structure and role in malaria transmission. *An. elegans* is exclusively found in south‐ western India but there is no evidence of its role in malaria transmission [8].

## **6.** *Anopheles (Cellia) sundaicus* **(Rodenwaldt) species complex**

March) accounting for its high and low prevalence in respective season, and 'vertical' pulsation for its ability to feed on alternate host to humans in the changing environmental conditions [135]. It is a highly anthropophilic species for its predilection to human host and bites through‐ out night both indoors and outdoors (36.1 bites/person/night) with peak infective biting activity during second quartile (21:00–24:00) of the night hours [136,137]. The relative risk of infective bite, however, was estimated to be much greater in the post-monsoon season. It is largely an exophilic species and breeds in a variety of habitats in forest including small transient pools, elephant foot prints [138]. It is highly susceptible to all residual insecticides but avoids contact with sprayed surfaces making vector control a difficult proposition [139].

250 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**Figure 7.** A typical housing structure receptive for *Anopheles baimaii* transmitted malaria located along Indo-Bangla‐

Even though populations of *An. baimaii* from northeast India had high genetic diversity, these populations were genetically distinct from those of the adjoining countries of Bangladesh, Myanmar and Thailand suggesting significant barrier to gene flow [140]. However, there was no significant genetic differentiation between populations of northeast (except for population in the Barail hill range of northeast), thus be considered one entity for implementation of control interventions [141]. Yet owing to continued deforestation and possible disruption of gene flow between populations, there is possibility of existence of another taxon tentatively designated as 'species x' which call for additional investigations. *An. baimaii* is also known to inhabit forests of Andaman and Nicobar islands but there is dearth of data on population genetic structure and role in malaria transmission. *An. elegans* is exclusively found in south‐

western India but there is no evidence of its role in malaria transmission [8].

desh border in northeast India

*Anopheles sundaicuss.l*. is an important vector of malaria throughout its range of distribution in the oriental region (Figure 8). It is currently a complex of four species, i.e., *An*. *sundaicus s.s*., *An. epiroticus* Linton & Harbach (formerly species A), *An. sundaicus* species D and *An. sundaicus* species E [8,10,13,14,142,143]. In India, it has disappeared from the mainland eastern coastal belt of West Bengal and Orissa except small focus in the Kutch area of Gujarat [144], and is widely prevalent in Andaman and Nicobar islands populations of which have been characterized to be cytotype species D [145-147]. It is largely a brackish water species and breeds in a variety of habitats including swamps, salt water lagoons, creeks, pits along embankments but breeding in fresh water collections has also been recorded. Molecular characterization of cytotype D, however, did not reveal any difference between fresh water and brackish water populations but were different from *An. epiroticus* of Vietnam and *An*. *sundaicus s.s* from Borneo, Malaysia [148].

**Figure 8.** Distribution map of the four member species of the *Anopheles sundaicus* complex in Southeast Asia (Courte‐ sy: Dr. S. Manguin). *An. sundaicus s.s*. is distributed along the coast of Borneo. *An. epiroticus* occurs in coastal brackish water sites extending from southern Vietnam to peninsular Malaysia. *An. sundaicus* species E occurs in Sumatra and Java (Indonesia). *An. sundaicus* species D distribution is restricted to Andaman and Nicobar islands in India.

In Andaman and Nicobar islands, *An. sundaicus* is predominantly zoophilic except for indoor restingpopulationsinhumandwellingswhichhadasignificantlyhigherpredilectionforhuman host[149].Therelativeabundanceisreportedtobehigherinmonsoonandpost-monsoonmonths, populationsofwhichrestbothindoorsandoutdoors[149,150].Bitingactivityoccurredallthrough the night but peak biting was during 21:00 till 04:00 hours. The species is susceptible to DDT and malathion. It is possible that given the richness of fauna of evergreen equatorial forest in the Andaman and Nicobar group of islands, additional sibling species of the *An. sundaicus* complex doexistwithdistinctbionomicalcharacteristics,thusadditionalinvestigationsarewarrantedfor formulating appropriate control interventions [151].

## **7.** *Anopheles (Cellia) stephensi* **Liston – A complex of variants**

*Anopheles stephensi* is an important vector of urban malaria and has been widely incriminated in most metropolitan cities by detection of gland and gut infections [7]. It is not considered a species complex but instead comprises three ecological variants, i.e., 'type form', 'intermediate form' and variety '*mysorensis*' characterized by egg morphometrics [152-154]. The 'type form' is an efficient vector of malaria in urban areas, and the variety '*mysorensis'* is largely zoophilic and has no role in malaria transmission [155-157]. The 'intermediate form' is typically recorded in rural and peri-urban localities but its role in malaria transmission is not known. The existence of ecological variants is further evidenced by Y–chromosome variation [158], spiracular index [159], and frequencies of inversion polymorphism in urban and rural populations in range of its distribution [160,161]. However, results of cross-mating experi‐ ments were variable ranging from infertility to reduced fertility [162,163] as opposed to full compatibility between populations [152].

*An. stephensi* is prevalent throughout the year but most abundant during months of rainfall (June–August) which coincides with the transmission period. In urban areas, it is generally endophilic and endophagic and breeds in domestic containers, building construction sites, overhead tanks, underground cement tanks, and evaporator coolers [155,164]. It is largely the 'type form' that is responsible for malaria outbreaks in urban areas related to construction projects and associated tropical aggregation of labor from malaria endemic areas. It is a thermophilic species and has longer flight range, and maintains a high degree of contact with human population [151]. In rural areas it is predominantly a zoophilic species and rests outdoors in cattle sheds, barracks, poorly constructed houses, and breeds in fresh water ponds, stream beds, seepage canals, wells etc. Peak biting activity is recorded between 22:00 to 24:00 hours but varies seasonally in different localities [7,165]. It is an invasive species and enters new towns and settlements.

The species is resistant to multiple insecticides but indoor residual spraying is not used for control. Instead recommended control measures are (i) source reduction, (ii) minor engineer‐ ing interventions (iii) anti-larval methods including chemical and biological larvicides, (iv) application of larvivorous fish, i.e., guppy and gambusia, (v) aerosol space spraying for control of adult vector populations, (vi) legislative bylaws for preventing mosquito breeding [2]. In the face of rapid urbanization, unplanned growth and mushrooming of urban slums, rationed water supply and unsafe water storage practices; urban malaria is a growing problem presently accounting for >10% reported malaria cases in the country [166]. Overall, malaria cases in the rural and urban areas are grossly underestimated due to scanty surveillance and unreliable laboratory services.

## **8. Prospects of vector control and research priorities**

the night but peak biting was during 21:00 till 04:00 hours. The species is susceptible to DDT and malathion. It is possible that given the richness of fauna of evergreen equatorial forest in the Andaman and Nicobar group of islands, additional sibling species of the *An. sundaicus* complex doexistwithdistinctbionomicalcharacteristics,thusadditionalinvestigationsarewarrantedfor

*Anopheles stephensi* is an important vector of urban malaria and has been widely incriminated in most metropolitan cities by detection of gland and gut infections [7]. It is not considered a species complex but instead comprises three ecological variants, i.e., 'type form', 'intermediate form' and variety '*mysorensis*' characterized by egg morphometrics [152-154]. The 'type form' is an efficient vector of malaria in urban areas, and the variety '*mysorensis'* is largely zoophilic and has no role in malaria transmission [155-157]. The 'intermediate form' is typically recorded in rural and peri-urban localities but its role in malaria transmission is not known. The existence of ecological variants is further evidenced by Y–chromosome variation [158], spiracular index [159], and frequencies of inversion polymorphism in urban and rural populations in range of its distribution [160,161]. However, results of cross-mating experi‐ ments were variable ranging from infertility to reduced fertility [162,163] as opposed to full

*An. stephensi* is prevalent throughout the year but most abundant during months of rainfall (June–August) which coincides with the transmission period. In urban areas, it is generally endophilic and endophagic and breeds in domestic containers, building construction sites, overhead tanks, underground cement tanks, and evaporator coolers [155,164]. It is largely the 'type form' that is responsible for malaria outbreaks in urban areas related to construction projects and associated tropical aggregation of labor from malaria endemic areas. It is a thermophilic species and has longer flight range, and maintains a high degree of contact with human population [151]. In rural areas it is predominantly a zoophilic species and rests outdoors in cattle sheds, barracks, poorly constructed houses, and breeds in fresh water ponds, stream beds, seepage canals, wells etc. Peak biting activity is recorded between 22:00 to 24:00 hours but varies seasonally in different localities [7,165]. It is an invasive species and enters

The species is resistant to multiple insecticides but indoor residual spraying is not used for control. Instead recommended control measures are (i) source reduction, (ii) minor engineer‐ ing interventions (iii) anti-larval methods including chemical and biological larvicides, (iv) application of larvivorous fish, i.e., guppy and gambusia, (v) aerosol space spraying for control of adult vector populations, (vi) legislative bylaws for preventing mosquito breeding [2]. In the face of rapid urbanization, unplanned growth and mushrooming of urban slums, rationed water supply and unsafe water storage practices; urban malaria is a growing problem presently accounting for >10% reported malaria cases in the country [166]. Overall, malaria cases in the rural and urban areas are grossly underestimated due to scanty surveillance and unreliable

**7.** *Anopheles (Cellia) stephensi* **Liston – A complex of variants**

formulating appropriate control interventions [151].

252 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

compatibility between populations [152].

new towns and settlements.

laboratory services.

India has about a billion population at risk of malaria and accounts for the highest disease burden in Southeast Asia for estimated loss of disability adjusted life years [3,6]. Malaria transmission is complex due to multi-species co-existence and variable species dominance and bionomical characteristics [13,14]. Although, transmission trends seem to be declining (Figure 9), National Vector Borne Disease Vector Control Programme (NVBDCP) is faced with new emerging challenges. Some of these are (i) multiple insecticide resistance against target disease vector mosquito species, (ii) emerging multi-drug resistance and steadily rising proportions of *P. falciparum,* (iii) shortage of antimalarial drugs and insecticides, and (iv) human resource attrition of skilled personnel to meet the future challenges.

Indoor residual spraying (IRS) for vector control has become less effective and operationally difficult proposition [9,94]. In addition, ecological driven changes, population migration across borders, deforestation, developmental projects, and poor infrastructure have led to the opportunities for vector proliferation and increased malaria receptivity. Due to poor com‐ munity acceptance for IRS and spray coverage of target population groups [167], India has embarked upon large scale implementation of Insecticide-treated netting materials / longlasting insecticidal nets (LLINs) prioritizing high-risk population in malaria endemic states/ districts. Disease transmission trends are declining in beneficiary population groups (formerly intractable high-risk areas); hence it is the right time to siege the opportunity for up scaling LLIN based intervention coupled with appropriate drug policy in place to combat the malaria illness and preventing spread of drug-resistant malaria [112,113,168-170]. It is worrisome, however, that the LLINs presently in use employ only pyrethroids, and *An. culicifacies* that is multi-resistant, is fast invading new territories making a malaria control a complex enterprise. What would be tantamount to vector control is the management of insecticide resistance for increased duration of its efficacy against target disease vector species by strategic application, insecticide rotation and mosaic application, and integrating bio-environmental approaches which should all be considered [171,172]. These approaches combined with environment management methods which are situation-specific and community-based would yield long term dividend for sustainable vector control [173,174]. Among alternate methods of vector control, large scale application of larvivorous fish, i.e., *Poecilia reticulata* and *Gambusia affinis* have been proven to be effective against *An*. *culicifacies* transmitted malaria in South Indian state of Karnataka [175,176], and inspired by the success story as role model, other malaria endemic states are also contemplating incorporating this method as component of the integrated approach for vector control [177].

Besides dominant proven vector species, sporadic gut/ gland infections have also been recorded in *An. maculatus s.l., An. annularis s.l., An. nivipes/philippinensis*, and *An. subpictus s.l.* substantiated by variable levels of anthropophily and detection of circumsporozoite proteins [8,69,77,109,178,179]. These mosquito species, however, are considered of lesser significance for their role in malaria transmission except in areas reporting diminishing population densities of dominant vector species. Among these, *An. maculatus*, has been investigated in depth for spatial distribution and molecular characterization of its member species for possible role in malaria transmission specific to northeast India [180]. Of the nine formally named species of *An. maculatus* complex [181], six species namely, *An. pseudowillmori* and *An. macu‐ latus* (most abundant), and *An. willmori, An. sawadwongporni, An. rampae, An. dravidicu*s (restricted distribution) have been recorded to exist in northeast region; none of these, however, found positive for human malaria parasite [180]. Of the five species in the *Anopheles annularis* group of mosquito species, *An. annularis, An. nivpipes, An. philippinensis* and *An. pallidus* are widely prevalent in India. Among these, *An. annularis* comprises two cryptic species provisionally designated as species A and B with variable distribution records [182]. It has been incriminated in certain localities but it is a predominantly zoophilic species [183]. *An. nivipes* and An. *philippinensis* are morphologically very similar, yet can be characterized by cytogenetic and molecular techniques [184-186]. Both are also predominantly zoophilic. *An. subpictus* that is widely abundant in mainland India has been characterized to be complex of four sibling species provisionally designated as A, B, C and D identified by distinctive morphology, species specific diagnostic inversion genotypes and breeding characteristics [8,187]. It has been incriminated in coastal villages of South India, Central India, and Sri Lanka but additional investigations are warranted for distribution of individual sibling species and role in malaria transmission [188-190].

**Figure 9.** Malaria cases in India (1970-2011) recorded by the Directorate of National Vector Borne Disease Control Programme (NVBDCP). Cases started rising in 1970, reporting 6.45 million cases in 1976 and following the implemen‐ tation of the Modified Plan of Operation in 1977, malaria cases declined but mainly *Plasmodium vivax* malaria due to its sensitivity to chloroquine. Beginning 2005 with increased allocation of resources for strengthening interventions, cases are gradually declining. *Plasmodium falciparum* proportions, however, that was about 10% in 1977, has risen to about 50% and the parasite has become mono to multi-drug resistant (data source: NVBDCP).

In moving forward for achieving ambitious goal of malaria elimination in feasible districts/ states, lot more needs to be accomplished in understanding vector bionomics in the altered ecology. The future priority area should include developing malaria-risk maps for focused interventions, ecological succession of disease vector species, monitoring insecticide resist‐ ance, cross-border initiative with neighboring countries for data sharing and coordinated control efforts, development of evidence-based newer tools for vector control, strengthening health systems for improved surveillance and monitoring, and universal access to malaria treatment and prevention which would help meeting the Millennium Development Goal in reducing malaria morbidity and mortality by 2015 [191-193].

## **9. Conclusions**

role in malaria transmission specific to northeast India [180]. Of the nine formally named species of *An. maculatus* complex [181], six species namely, *An. pseudowillmori* and *An. macu‐ latus* (most abundant), and *An. willmori, An. sawadwongporni, An. rampae, An. dravidicu*s (restricted distribution) have been recorded to exist in northeast region; none of these, however, found positive for human malaria parasite [180]. Of the five species in the *Anopheles annularis* group of mosquito species, *An. annularis, An. nivpipes, An. philippinensis* and *An. pallidus* are widely prevalent in India. Among these, *An. annularis* comprises two cryptic species provisionally designated as species A and B with variable distribution records [182]. It has been incriminated in certain localities but it is a predominantly zoophilic species [183]. *An. nivipes* and An. *philippinensis* are morphologically very similar, yet can be characterized by cytogenetic and molecular techniques [184-186]. Both are also predominantly zoophilic. *An. subpictus* that is widely abundant in mainland India has been characterized to be complex of four sibling species provisionally designated as A, B, C and D identified by distinctive morphology, species specific diagnostic inversion genotypes and breeding characteristics [8,187]. It has been incriminated in coastal villages of South India, Central India, and Sri Lanka but additional investigations are warranted for distribution of individual sibling species and

role in malaria transmission [188-190].

254 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

0

1970

1972

1974

1976

1978

1980

1982

1984

about 50% and the parasite has become mono to multi-drug resistant (data source: NVBDCP).

1986

1988

Malaria Positive Cases P.falciparum %

1990

**Figure 9.** Malaria cases in India (1970-2011) recorded by the Directorate of National Vector Borne Disease Control Programme (NVBDCP). Cases started rising in 1970, reporting 6.45 million cases in 1976 and following the implemen‐ tation of the Modified Plan of Operation in 1977, malaria cases declined but mainly *Plasmodium vivax* malaria due to its sensitivity to chloroquine. Beginning 2005 with increased allocation of resources for strengthening interventions, cases are gradually declining. *Plasmodium falciparum* proportions, however, that was about 10% in 1977, has risen to

1992

**Year**

1994

1996

1998

2000

2002

2004

2006

2008

2010

0

10

20

30

*P. falciparum* **%**

40

50

60

1000000

2000000

3000000

**Malaria Positive Cases**

4000000

5000000

6000000

7000000

During the past decade, there has been significant progress in development of molecular techniques in identification of sibling species of the dominant mosquito vector taxa, under‐ standing their bionomical characteristics and role in malaria transmission in India. Among these, for *An. culicifacies* and *An. fluviatilis* which account for nearly 80% of malaria cases, vector control strategy has been formulated for judicious application of insecticide and saving operational costs. In the changing ecological context, *An. culicifacies* that is fast invading new territories is reportedly developing resistance to multiple insecticides including pyrethroids and inter-alia rising proportions and spread of multi-drug resistant *P. falciparum* malaria are some of the major concerns which call for continued research efforts for newer interventions that are evidence-based, community oriented and sustainable. Future priority area of research in vector control should include developing malaria-risk maps for focused interventions, monitoring insecticide resistance, cross-border initiative with neighboring countries for data sharing and coordinated control efforts for achieving substantial transmission reduction, and help check spread of drug-resistant malaria.

## **Abbreviations used**

DDT: Dichloro-diphenyl-trichloroethane; rDNA: Ribosomal deoxyribonucleic acid; ITS2: Internal Transcribed Spacer 2; PCR: Polymerase Chain Reaction; RFLP: Restricted Fragment Length Polymorphism; CO II: Cytochrome Oxidase II; IRS: Indoor Residual Spray; LLIN: Long-lasting Insecticidal Net; MPO: Modified Plan of Operation; NVBDCP: National Vector Borne Disease Control Programme.

## **Acknowledgements**

We are thankful to Drs. T. Adak, K. Raghavendra, O.P. Singh, N. Nanda, A. Das, A. Kumar, S.K. Ghosh for access to the valued literature and consultations. We are also indebted to Dr. S. Manguin for encouragement and advice for development of the manuscript. This submis‐ sion has been approved by the Institute Publication Screening Committee and bears the approval No. 022/2012.

## **Author details**

Vas Dev1\* and Vinod P. Sharma2

\*Address all correspondence to: mrcassam@hotmail.com

1 National Institute of Malaria Research (Field Station), Guwahati, Assam, India

2 Centre for Rural Development & Technology, Indian Institute of Technology, New Delhi, India

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## **Chapter 10**

## **Vector Biology and Malaria Transmission in Southeast Asia**

Wannapa Suwonkerd, Wanapa Ritthison, Chung Thuy Ngo, Krajana Tainchum, Michael J. Bangs and Theeraphap Chareonviriyaphap

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56347

**1. Introduction**

There are two primary geographical divisions within an area collectively called Southeast Asia, Mainland SEA and Maritime SEA. Mainland SEA includes Cambodia, Lao PDR, Myanmar, Thailand, Vietnam and Peninsular Malaysia. The first five countries including Yunnan Province, southern China is referred to as the Greater Mekong Subregion (GMS). Maritime SEA consists of Eastern Malaysia (Sarawak and Sabah States located on Borneo Island), Brunei Darussalam, Indonesia, Philippines, Singapore and East Timor (Timor-Leste). Most of these areas are at risk for a variety of vector-borne diseases, especially malaria, one of the most important diseases transmitted by mosquitoes in the genus *Anopheles*.

Despite over 100 years of scientific investigation, malaria remains the leading cause of death among children living in Sub-Saharan Africa and every year is responsible for more than 200 million clinical infections worldwide. The World Malaria Report in 2011 estimated that the number of malaria cases rose from 233 million in 2000 to 244 million in 2005 then dropped to 225 and 219 million in 2009 and 2010, respectively [1, 2].However, mortality from malaria has decreased by over 26% globally since 2000 due to the increased availability of long-lasting insecticide-treated nets, indoor residual spraying, and better access to diagnostic and effective treatment using artemisinin-based therapies (ACTs) [3]. In Thailand, the malaria incidence has markedly decreased over the past 60 years in response to organized malaria control programs [4, 5] and other countries like Vietnam have made great strides in reducing both incidence and mortality in recent decades [6,7]. During the past two decades, significant reduction in malaria

© 2013 Suwonkerd et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Suwonkerd et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

cases has also been reported in Cambodia, Laos, and eastern Malaysia, [8, 9,10]. During this same period, Myanmar and East Timor reported either no change or an increase in the number of cases; however the coverage of control activities appeared to be limited in relation to the total population at risk. The confirmed malaria cases in Myanmar increased by more than 16 fold between 2000 and 2009, primarily the result of an increased availability of parasitological diagnosis by both microscopy and RDTs [1, 9, 10]. Several countries have advanced a great deal in tackling malaria transmission and providing ready access to diagnosis and treatment using artemisinin-based combination therapies (ACTs) against *Plasmodium falciparum,* the most deadly form of malaria parasite, with treatment success >90% of cases. However, resistance to artemisinin-based compounds has already emerged along the Thai-Cambodia border, a similar pattern of resistance that begun with chloroquine, followed by sulfadoxinepyrimethamine and mefloquine, common drug treatments used in malaria control years ago [11,12].

Malaria transmission continues with high risk in refractory foci, especially areas near the international borders between countries, such areas are commonly associated with rural, forested, undeveloped and sylvan environments compounded by frequent uncontrolled human population movement across shared borders for economic and socio-political reasons [13,14,15,16,17,18]. Other contributing epidemiological factors have either maintained or even enhanced transmission potential in certain areas including various factors that contribute to malaria mosquito vector distribution, vector competency and capacity for transmission, bionomics, adult behavior, and abundance. Contributing factors also include physical and topographical changes such as new development projects including dam and road construc‐ tion, mining, reforestation, deforestation and commercial plantations (e.g., rubber, palm oil). Deforestation is particularly severe and widespread in Southeast Asia, the highest relative rate of deforestation of any major tropical region in the world. By year 2100, it is estimated that over three quarters of the original forests and up to 42% of the associated biodiversity will result in massive species declines and outright extinctions [19].

Outdoor transmission and biting immediately after dusk and early morning hours continue to pose a major prevention and control challenge. Additionally, population movement and congregation increase the likelihood of exposure to malaria and reintroduction of transmission in receptive areas. To understand malaria risk in an area, the *Anopheles* fauna and bionomics of the important species including those composed of complexes must be better understood. Unfortunately, there are only a few recent studies in each country which cannot provide a complete picture on malaria vectors in this region. Because the accurate identification of vector species and knowledge of their ecology and behavior is essential for epidemiologic studies and the design and implementation of vector control strategies, a major challenge in most countries in the region is the lack of trained entomologists and budgets supporting essential field and laboratory work. Our aim in this chapter is to provide an overview on the malaria vectors of the Greater Mekong Subregion, in which 6 countries are reviewed. Thailand represents the epicenter of the Mekong countries from northwest to southwest (Myanmar), the eastern border (Cambodia & Vietnam), northeast (Lao PDR) and the southern border (Malaysia). The focus will be on reviewing the current malaria transmission in relation to the various malaria vectors, with an emphasis on the geographic variation, vector biology and ecology of each species and how these factors promote malaria transmission in the region.

cases has also been reported in Cambodia, Laos, and eastern Malaysia, [8, 9,10]. During this same period, Myanmar and East Timor reported either no change or an increase in the number of cases; however the coverage of control activities appeared to be limited in relation to the total population at risk. The confirmed malaria cases in Myanmar increased by more than 16 fold between 2000 and 2009, primarily the result of an increased availability of parasitological diagnosis by both microscopy and RDTs [1, 9, 10]. Several countries have advanced a great deal in tackling malaria transmission and providing ready access to diagnosis and treatment using artemisinin-based combination therapies (ACTs) against *Plasmodium falciparum,* the most deadly form of malaria parasite, with treatment success >90% of cases. However, resistance to artemisinin-based compounds has already emerged along the Thai-Cambodia border, a similar pattern of resistance that begun with chloroquine, followed by sulfadoxinepyrimethamine and mefloquine, common drug treatments used in malaria control years ago

Malaria transmission continues with high risk in refractory foci, especially areas near the international borders between countries, such areas are commonly associated with rural, forested, undeveloped and sylvan environments compounded by frequent uncontrolled human population movement across shared borders for economic and socio-political reasons [13,14,15,16,17,18]. Other contributing epidemiological factors have either maintained or even enhanced transmission potential in certain areas including various factors that contribute to malaria mosquito vector distribution, vector competency and capacity for transmission, bionomics, adult behavior, and abundance. Contributing factors also include physical and topographical changes such as new development projects including dam and road construc‐ tion, mining, reforestation, deforestation and commercial plantations (e.g., rubber, palm oil). Deforestation is particularly severe and widespread in Southeast Asia, the highest relative rate of deforestation of any major tropical region in the world. By year 2100, it is estimated that over three quarters of the original forests and up to 42% of the associated biodiversity will

Outdoor transmission and biting immediately after dusk and early morning hours continue to pose a major prevention and control challenge. Additionally, population movement and congregation increase the likelihood of exposure to malaria and reintroduction of transmission in receptive areas. To understand malaria risk in an area, the *Anopheles* fauna and bionomics of the important species including those composed of complexes must be better understood. Unfortunately, there are only a few recent studies in each country which cannot provide a complete picture on malaria vectors in this region. Because the accurate identification of vector species and knowledge of their ecology and behavior is essential for epidemiologic studies and the design and implementation of vector control strategies, a major challenge in most countries in the region is the lack of trained entomologists and budgets supporting essential field and laboratory work. Our aim in this chapter is to provide an overview on the malaria vectors of the Greater Mekong Subregion, in which 6 countries are reviewed. Thailand represents the epicenter of the Mekong countries from northwest to southwest (Myanmar), the eastern border (Cambodia & Vietnam), northeast (Lao PDR) and the southern border (Malaysia). The focus will be on reviewing the current malaria transmission in relation to the

result in massive species declines and outright extinctions [19].

[11,12].

274 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

## **2. Malaria transmission and primary vectors in mainland Southeast Asia**

Review of mosquito biogeography has shown that the greatest mosquito biodiversity occurs in the SEA region and the Neotropics, with high species richness in Indonesia, Malaysia and Thailand [20, 21, 22]. The basic malaria transmission equation (model) indicates a positive correlation between vector density (and life span) in relation to attack on humans and number of malaria cases; however, even small changes in vector density can result in substantial changes in the proportion of humans infected [23]. This is more apparent in areas of relatively lower transmission than those with stable high attack rates. Malaria stability over time is generally greater in areas with highly efficient vector(s) and those having multiple primary vector species present throughout the year or alternating activity patterns based on seasonal changes and local conditions. However, the primary inter-dependent relationship between Human – Vector – Pathogen is influenced by a fourth set of factors, namely demography (human placement and movement), numerous environmental factors, landscape (vector habitat), socioeconomic conditions, that can greatly impact malaria transmission in each country and specific locations (foci) [24, 25, 26, 27, 28, 29, 30,16]. In general, SEA is faced with a complex vector system whose members are difficult to distinguish morphologically that often include a diverse array of non-vectors, potential vectors and malaria vectors [31, 32]. As members of a species complex usually exhibit significant behavioral differences, understand‐ ing the biological, behavioral and ecological characteristics of each species will be relevant to the epidemiology and disease control methods used. Three main malaria vectors are recog‐ nized on the SEA mainland: *Anopheles dirus* sensu lato (s.l.) (Dirus Complex), *An. minimus* s.l. (Minimus Complex), *An. sundaicus* s.l. (Sundaicus Complex). The Minimus Complex compris‐ es of three sibling species; *An. minimus* (formerly species A), *An. harrisoni* (species C) and *An. yaeyamaensis* (species E). Whereas the latter species is found only in Japan, *An.minimus* and *An. harrisoni* have a broad distribution in SEA and are known vectors of malaria throughout their respective distributions [33, 34]. *An. minimus* s.l. is widespread in the hill forested areas, utilizing mainly margins of slow running streamsunder partial shade and grassy margins [35, 36, 37, 38, 7, 34].

In these forested areas of SEA, malaria transmission can be perennial because of the presence of both *An. dirus* s.l. during rainy season and *An. minimus* s.l. during the drier periods of the year. The Dirus Complex currently includes eight species [39, 40]. Among them two main malaria vectors, *An. dirus* and *An. baimaii* which are considered forest and forest-fringe malaria vectors with an anthropophilic and exophagic behaviors. Their reproduction takes place in and near forested areas (primary and secondary evergreen, deciduous and bamboo forests) with plentiful rain water pools, puddles, as well as artificial containers. Both species are also found in dense mono-agricultural environments, in particular rubber, fruit, and manioc/ cassava plantations [18, 32, 33, 41, 42]. One of the factors that make *An. dirus* an important and efficient malaria vector is its strong attraction to humans [32, 43]. The Sundaicus Complex comprises 4 members, however only *An. epiroticus* is reported on the SEA mainland [44]. These four species are coastal vectors, developing primarily in brackish water while some popula‐ tions can exist in freshwater habitats. *An. epiroticus*, has adapted to a diverse array of biotopes, but also share some common features such as brackish water (optimum 1-7 g NaCl/litre), moderate sun exposure, stagnant or slightly moving water, with floating green algae and presence of vegetation [44, 45]. *Anopheles epiroticus* exhibits both endo- and exophagy while being mainly endophilic and anthropophilic in resting and feeding preference, respectively, although both exophily and zoophily have also been demonstrated [7, 32, 46].

New insights into malaria vectors, in terms of vector bionomics and malaria transmission, are detailed within each country and are framed by the inherent complexity of the epidemiology and the current challenges faced in SEA for implementation of appropriate vector control as one of the key approaches of integrated control for eventual malaria elimination in the region.

#### **2.1. Cambodia**

#### *2.1.1. Overview*

The Kingdom of Cambodia covers an area of approximately 181,000 km² with 15 million inhabitants, comprised mainly of ethnic Khmer (90%), along with Vietnamese, Chinese and other minorities. This country is bounded on the north by Thailand and Lao PDR, on the east and southeast by Vietnam, and on the west by Thailand and the Gulf of Thailand. Much of the country's topography consists of rolling plains. Dominant geo-physical features include the large, centrally located, Tonle Sap (Great Lake) and the Mekong River, which traverses the entire country from north to south. The climate is monsoonal and has marked wet and dry seasons of relatively equal length. Both ambient air temperatures and relative humidity generally are high throughout the year. Forest covers about two-thirds of the country, but it has been degraded in the more readily accessible areas by burning (slash-and-burn agricul‐ ture), and by traditional shifting agricultural practices. Approximately 44% of the population live in high malaria risk areas among which approximately half (~3 million people) live in or around forested areas where there is potentially intense transmission [2]. *Plasmodium falcipa‐ rum* is the dominant malaria infection reported (63%) followed by *P. vivax* [3]. Between 2001-2009, the number of reported cases detected by the official health system in Cambodia (confirmed cases by MOH) fell from 121,612 to 80,644 and further declined to 44,659 in 2010 [47, 1]. The main provinces with endemic malaria are Battambang, Kampong Speu, Pursat, Peah Vihear, Mondulkiri, Rattanakiri, Pailin and Siem Reab [10, 48]. Malaria transmission is seasonal with a peak occurring during May–July and October–November in the forested and forest-fringe areas of the north, west and northeast, and also in the rubber plantations located in the east and northeast parts of the country. In the rice growing areas of the south and central regions, transmission is typically low or non-existent. There is no reported endemic transmis‐ sion in urban areas. Low intensity transmission is found focally in coastal areas. Malaria incidence is highest in the eastern provinces of Mondulkiri and Rattanakiri where the disease disproportionately affects ethnic minorities and migrants [8]. According to the Health Management Information System (HMIS), confirmed malaria cases is predominantly observed in males aged 15-49 years (51%), and regarded an occupational risk [49]. Because of the decades long civil war, including the brutal genocide in the 1970's and systematic destruction of infrastructure under the Khmer Rouge regime, Cambodia was left with a very limited health infrastructure and capacity, particularly in rural areas. In recent years, this situation has seen a remarkable rebound, with the public sector providing the majority of diagnosis and treat‐ ment through both community-based and government health centers. Over the last decade, many of Cambodia's key health indicators have improved dramatically with the increased resources. Universal diagnostic testing for malaria, primarily using malaria microscopy and Rapid diagnostic test (RDTs) formats, is now common practice in the majority of Cambodian public sector facilities [50]. In addition, with both Global Fund against HIV/AIDS, Tuberculosis and Malaria and USAID support, village malaria workers and mobile malaria workers have been trained and equipped with RDTs and artemisinin-based combination treatments (ACTs) to more accurately diagnose and effectively treat malaria, thereby improving access to these services in remote rural communities. In spite of this, the quality of malaria microscopy in many facilities is regarded sub-optimal, particularly in remote locations. In facilities where both microscopy and RDTs are available, the staff prefers using RDTs because of the ease of use. Additionally, the majority of persons with fever are reported to go to private sector providers where the availability of high-quality diagnostic testing is limited and where there is a financial incentive to provide treatment (sometimes outdated, ineffective chemotherapies) to a patient with a negative test. Another challenge is that an increased prevalence of *Plasmo‐ dium vivax* would have implications on the severity of illness, risk of death, and provision of optimal drug therapies to eliminate latent, relapsing forms of the parasite; therefore identifying the parasite species is crucial for case management [51]. Further progress in reducing the burden of the disease will require improved access to reliable diagnosis and effective treatment of both blood-stage and latent parasites and more detailed characterization of the epidemiol‐ ogy, morbidity and economic impact of vivax malaria.

#### *2.1.2. Malaria vectors and biodiversity of* Anopheles *in Cambodia*

comprises 4 members, however only *An. epiroticus* is reported on the SEA mainland [44]. These four species are coastal vectors, developing primarily in brackish water while some popula‐ tions can exist in freshwater habitats. *An. epiroticus*, has adapted to a diverse array of biotopes, but also share some common features such as brackish water (optimum 1-7 g NaCl/litre), moderate sun exposure, stagnant or slightly moving water, with floating green algae and presence of vegetation [44, 45]. *Anopheles epiroticus* exhibits both endo- and exophagy while being mainly endophilic and anthropophilic in resting and feeding preference, respectively,

New insights into malaria vectors, in terms of vector bionomics and malaria transmission, are detailed within each country and are framed by the inherent complexity of the epidemiology and the current challenges faced in SEA for implementation of appropriate vector control as one of the key approaches of integrated control for eventual malaria elimination in the region.

The Kingdom of Cambodia covers an area of approximately 181,000 km² with 15 million inhabitants, comprised mainly of ethnic Khmer (90%), along with Vietnamese, Chinese and other minorities. This country is bounded on the north by Thailand and Lao PDR, on the east and southeast by Vietnam, and on the west by Thailand and the Gulf of Thailand. Much of the country's topography consists of rolling plains. Dominant geo-physical features include the large, centrally located, Tonle Sap (Great Lake) and the Mekong River, which traverses the entire country from north to south. The climate is monsoonal and has marked wet and dry seasons of relatively equal length. Both ambient air temperatures and relative humidity generally are high throughout the year. Forest covers about two-thirds of the country, but it has been degraded in the more readily accessible areas by burning (slash-and-burn agricul‐ ture), and by traditional shifting agricultural practices. Approximately 44% of the population live in high malaria risk areas among which approximately half (~3 million people) live in or around forested areas where there is potentially intense transmission [2]. *Plasmodium falcipa‐ rum* is the dominant malaria infection reported (63%) followed by *P. vivax* [3]. Between 2001-2009, the number of reported cases detected by the official health system in Cambodia (confirmed cases by MOH) fell from 121,612 to 80,644 and further declined to 44,659 in 2010 [47, 1]. The main provinces with endemic malaria are Battambang, Kampong Speu, Pursat, Peah Vihear, Mondulkiri, Rattanakiri, Pailin and Siem Reab [10, 48]. Malaria transmission is seasonal with a peak occurring during May–July and October–November in the forested and forest-fringe areas of the north, west and northeast, and also in the rubber plantations located in the east and northeast parts of the country. In the rice growing areas of the south and central regions, transmission is typically low or non-existent. There is no reported endemic transmis‐ sion in urban areas. Low intensity transmission is found focally in coastal areas. Malaria incidence is highest in the eastern provinces of Mondulkiri and Rattanakiri where the disease disproportionately affects ethnic minorities and migrants [8]. According to the Health Management Information System (HMIS), confirmed malaria cases is predominantly observed

although both exophily and zoophily have also been demonstrated [7, 32, 46].

**2.1. Cambodia**

276 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

*2.1.1. Overview*

In 1975, the list of anophelines known from Cambodia was revised to include 37 species [52]. Between 1959-1963, *An. dirus* s.l*., An. minimus* s.l., *An. maculatus* and *An. sundaicus* s.l. were reported as main malaria vectors in Cambodia [53, 34]. However, there has been no record of entomology activities in the following 25 years due to socio-political issues in the country. In 1997, two years of vector surveys reported 19 and 25 species of anopheline mosquitoes in Kompong Speu and Kratie Provinces, respectively in which *An. dirus* s.l, *An. minimus* s.l and *An. maculatus* were included [53]. With molecular techniques having been developed for identifying members within the species complexes, a significant increase of anopheline species have been recorded in Cambodia. *An. minimus* has been the only species of the Minimus Complex recorded in Cambodia [54, 55, 31]. *An. minimus* was recorded as a late evening biter and more anthropophilic where cattle were scarce with the ratio of indoor to outdoor human landing collections ranging between 0.62 and 7.95 [32]. *Anopheles* specimens were found sporozoite positive by ELISA tests for the detection of circumsporozoite protein (CSP) of *Plasmodium falciparum* and *P. vivax* [7, 32]. Distribution and abundance of this primary malaria vector has changed in response to land-use modifications, deforestation, climate change, and

#### **Incidence rate of malaria treated cases per 1000 population, Cambodia, 2000 to 2011**

**Figure 1.** Malaria Incidence Rate per 1,000 population (solid line) and a total treated cases (bar) in Cambodia between 2000 and 2011. Source: Meeting on Outdoor Malaria Transmission in the Mekong Countries for 13 countries during 12-13 March 2012, Bangkok, Thailand. [http://www.rbm.who.int/partnership/wg/wg\_itn/ppt/ws2/m4SivSovannar‐ oth.pdf.]

possibly due to insecticides used as part of vector control in malaria endemic areas [35, 34, 38, 27, 56, 41, 57]. The Dirus Complex in Cambodia is represented by *An. dirus* only which plays an important role in malaria transmission [31] with CSP rates having been reported above 1% [7]. *An. sundaicus* s.l. has been recorded along the southern coastal areas of Cambodia [58] and later identified as *An. epiroticus* (*An. sundaicus* A). Larvae of *An. epiroticus* are found in large open stagnant brackish water areas, sunlit pools, and often occurring in distinct foci along the coast [59]. In Cambodia, suspected and potential malaria vectors include *An. annularis* s.l., *An. barbirostris* s.l, *An. culicifacies* B although this latter species is mostly considered as a poor or non-vector (collected in Rattanakiri Province, northeast of Cambodia), *An. nivipes*, *An. philippinensis*, *An. sinensis*, and *An. subpictus* s.l. [54, 60]. Within the Maculatus Group, a recent study recorded for the first time *An. sawadwongporni* in the Kampong Spoe Province [31], yet its vector status in Cambodia is unknown. The Subpictus Complex has a coastal distribution in southern Cambodia [59].

#### *2.1.3. Distribution of malaria vectors and behavior of* Anopheles *species in Cambodia*

Forest cover is a very strong determinant of malaria risk. In SEA, forest malaria remains a big challenge for malaria control and in Cambodia malaria risk has increased within 2 to 3 km from the forest border. It is important to note that forest-related malaria covers a wide epidemiological spectrum regard varying vector species and bionomics, human demographics and behavior and control [61]. In Cambodia, malaria transmission is closely associated with two primary malaria vectors that inhabit the forest and forest fringe, *An.dirus* which inhabits predominantly forested areas, and *An. minimus*, a relatively less efficient malaria vector, that occurs in and around rice fields near the forest fringe [7,34,31]. *An. dirus*, *An. minimus* and *An. maculatus* are mainly outdoor biters [32]. This exophagic tendency of vectors is associated with the persistence of malaria transmission among populations with outdoor activities during night time. Intraspecific behavior differences have been observed among different populations of *Anopheles* species. However, in Cambodia, *An. dirus* has shown a higher degree of anthro‐ pophily than other malaria vector species [32]. The inoculation rate of *An. dirus* has been recorded over 1% in Rattanakiri Province indicating this species is a very efficient vector and plays an important role for perennial malaria transmission [7]. *Anopheles minimus* has been found less anthropophilic, preferentially attacking animals more than humans, whereas *An. dirus* showed a higher degree of anthropophily and early biting before 22.00 hr [32]. The host and temporal feeding patterns of malaria vectors are important factors in determining the vector status of *Anopheles* species, both influenced by host availability and location (indoors or outside)[62]. The abundance of malaria vectors in Cambodia is site-specific, for example in Pailin Province, among the three main malaria vectors, *An.minimus* (67.2%) was found more predominant than *An. maculatus* (20.6%) and *An. dirus* (9.9%), while in Pursat Province, 52% of the vector species were *An. dirus*, probably influenced by the suitability of the local envi‐ ronmental conditions and topography [63].

The current vector control methods against indoor feeding and resting vectors include indoor residual spraying (IRS) and insecticide-treated nets (ITNs), but where the vectors primarily feed and rest outdoors, these vector control methods are ineffective, except possibly in those cases where the insecticide used has a high spatial repellent effect [64, 65]. A recent study showed a nearly 45% reduction of blood feeding *An. minimus* in two villages after introduction of long-lasting insecticide-treated hammocks (LLIH) in study sites in Pailin and Pursat Provinces [63]. The obvious risk of regular insecticide use is the development of insecticide resistance in the vector populations. However, so far insecticide resistance has not been a major problem for the primary malaria vectors, *An. dirus* and *An. minimus*. Both species remain susceptible to permethrin, only one site study in Cambodia found *An. dirus* DDT resistant, but this was only based on 23 specimens tested [56]. *Anopheles epiroticus* remains susceptible to permethrin but shown some evidence of possible deltamethrin resistance. The monitoring of the susceptibility status of *Anopheles* to insecticides should be performed regularly as this provides essential information for the correct choice of insecticide to be most effective in vector control. Most studies suggest that ITNs can provide a fair degree of protection if properly used [66, 63, 67, 68, 69]. Therefore, Cambodia has actively distributed ITNs to many at-risk popu‐ lations. Overall, ITNs ownership improved from 43% in high risk areas in 2007 to 75% in 2011 [63, 3]. Cambodia has recently drafted a new strategic plan following the Prime Minister's announcement that Cambodia's goal would be to eliminate malaria by 2025 [70, 48].

possibly due to insecticides used as part of vector control in malaria endemic areas [35, 34, 38, 27, 56, 41, 57]. The Dirus Complex in Cambodia is represented by *An. dirus* only which plays an important role in malaria transmission [31] with CSP rates having been reported above 1% [7]. *An. sundaicus* s.l. has been recorded along the southern coastal areas of Cambodia [58] and later identified as *An. epiroticus* (*An. sundaicus* A). Larvae of *An. epiroticus* are found in large open stagnant brackish water areas, sunlit pools, and often occurring in distinct foci along the coast [59]. In Cambodia, suspected and potential malaria vectors include *An. annularis* s.l., *An. barbirostris* s.l, *An. culicifacies* B although this latter species is mostly considered as a poor or non-vector (collected in Rattanakiri Province, northeast of Cambodia), *An. nivipes*, *An. philippinensis*, *An. sinensis*, and *An. subpictus* s.l. [54, 60]. Within the Maculatus Group, a recent study recorded for the first time *An. sawadwongporni* in the Kampong Spoe Province [31], yet its vector status in Cambodia is unknown. The Subpictus Complex has a coastal distribution

**Figure 1.** Malaria Incidence Rate per 1,000 population (solid line) and a total treated cases (bar) in Cambodia between 2000 and 2011. Source: Meeting on Outdoor Malaria Transmission in the Mekong Countries for 13 countries during 12-13 March 2012, Bangkok, Thailand. [http://www.rbm.who.int/partnership/wg/wg\_itn/ppt/ws2/m4SivSovannar‐

*2.1.3. Distribution of malaria vectors and behavior of* Anopheles *species in Cambodia*

Forest cover is a very strong determinant of malaria risk. In SEA, forest malaria remains a big challenge for malaria control and in Cambodia malaria risk has increased within 2 to 3 km from the forest border. It is important to note that forest-related malaria covers a wide

in southern Cambodia [59].

0

Source: Epidemiology Unit,CNM, 31 Jan 2012

20000

40000

60000

80000

**Number of Malaria Treated Cases**

oth.pdf.]

100000

120000

140000

11.03

278 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

9.6

8.6

10.8

7.5

5.5

**Incidence rate of malaria treated cases per 1000 population, Cambodia, 2000 to 2011**

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

7.2

4.2

4.1

Total Treated Cases Incidence Rate per 1000

6.22

4.1 4.3

0

2

4

6

**Incidence Rate per 1000 population**

8

10

12

#### *2.1.4. Implication of changing social and environment conditions on vectors and transmission*

Environmental factors can have a pronounced impact on the distribution and behavior of malaria vectors [71]. *Anopheles dirus* occurs in forest areas but has an ability to adapt to changing environmental conditions from natural forest habitats to cultivated forests, such as rubber and tea plantations and various types fruit orchards [72, 73, 27]. Deforestation is one of the most potent factors either promoting or reducing infectious diseases, in particular malaria in SEA [74, 75, 57]. Deforestation is caused by a wide variety of human activities, including logging, land clearance for agricultural development, transmigration programs, road construction, mining and hydropower development [76, 77]. Globally, estimates of deforestation range from 36,000-69,000 km2 /year. Deforestation in SEA has been extensive with the mean annual rate of deforestation of 0.71 to 0.79% of land cover and is higher than reported in Latin America (0.33%-0.51%) or Africa (0.34%-0.36%) [78].The forest vector species that transmit malaria are among the most sensitive to environmental changes [27]. The extensive clearing of forests has had enormous impact on local natural ecosystems, in particular dramatically altering micro‐ climates by reducing shade, humidity, and rainfall patterns [38, 79]. For anopheline species that use shaded water bodies, deforestation can reduce larval habitats, thus their propagation and adult densities [38]. In Cambodia, the forest area was reduced from 93,000 km2 in 2003 to 66,959 km2 in 2005 [57], and this possibly has had a direct influence on the richness of ano‐ pheline mosquito fauna including some malaria vectors.

#### **2.2. Lao People Democratic Republic (Lao PDR, Laos)**

#### *2.2.1. Overview*

Lao PDR is a land-locked country, which borders five countries, China, Vietnam, Cambodia, Thailand and Myanmar, respectively. Most of the western border of Laos is demarcated by the Mekong River, which is an important artery for transportation and commerce. Two-thirds of Laos is covered by primary and secondary forests with a mountainous landscape and an abundance of rivers and natural resources which remain intact. The country has a tropical climate with high humidity throughout the year. The Mekong has not been an obstacle but a facilitator for communication between Laos and northeast Thai society (same people, same language) reflecting the close contact that has existed along the river for centuries.

Malaria is considered endemic throughout the country, but intensity of transmission is known to vary between different ecological zones; from relatively low transmission in the plains near the Mekong River and in areas of high altitude, to intense transmission in more remote, hilly and forested areas. Malaria has long been a leading cause of mortality and morbidity in the country. Transmission of malaria is perennial, but with large seasonal and regional variations. Peak transmission occurs between May and October, coinciding with the hot and rainy season. Malaria is also a problem in the dry season in certain areas of Laos [80]. In 1992, *P. falciparum* was the predominant species accounting for 95% of all recorded malaria cases [81] and remains so with 93% of all reported cases [3] representing leading cause of morbidity and mortality in Laos. A field survey for malaria prevalence in southern Laos using molecular-based parasite detection assay showed that mixed species infections were common with all 4 human plas‐ modia species detected among 23.1% of positive samples [82]. A recent national survey of the malaria distribution revealed that approximately 41% of the country's population is living in areas of no malaria transmission, particularly large areas in the central regions of the country while malaria incidence of more than 1 per 1,000 population is occurring in seven provinces, Saravane, Savannakhet, Sekong, Attapeu, Champasack, Khammouan, Phongsaly, collectively representing 36% of the Lao population [3, 69]. Significant reductions have been reported following investments in malaria control, in particular the large-scale introduction of artemi sinin-based combination therapy (ACT) beginning in 2004, ITNs introduced in 2000, and IRS in 2010, in conjunction with socio-economic and environmental changes [3]. In 2008, only 11 deaths among 18,743 confirmed malaria cases were reported (population ~6 million), com‐ pared with 600 deaths and 70,000 confirmed cases in 1997 (Center for Malariology, Parasitology and Entomology [CMPE] unpublished data). However, malaria still continues to be a serious public health problem in some focal areas such as remote areas in southern Laos [8].

*2.1.4. Implication of changing social and environment conditions on vectors and transmission*

36,000-69,000 km2

280 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

66,959 km2

*2.2.1. Overview*

Environmental factors can have a pronounced impact on the distribution and behavior of malaria vectors [71]. *Anopheles dirus* occurs in forest areas but has an ability to adapt to changing environmental conditions from natural forest habitats to cultivated forests, such as rubber and tea plantations and various types fruit orchards [72, 73, 27]. Deforestation is one of the most potent factors either promoting or reducing infectious diseases, in particular malaria in SEA [74, 75, 57]. Deforestation is caused by a wide variety of human activities, including logging, land clearance for agricultural development, transmigration programs, road construction, mining and hydropower development [76, 77]. Globally, estimates of deforestation range from

deforestation of 0.71 to 0.79% of land cover and is higher than reported in Latin America (0.33%-0.51%) or Africa (0.34%-0.36%) [78].The forest vector species that transmit malaria are among the most sensitive to environmental changes [27]. The extensive clearing of forests has had enormous impact on local natural ecosystems, in particular dramatically altering micro‐ climates by reducing shade, humidity, and rainfall patterns [38, 79]. For anopheline species that use shaded water bodies, deforestation can reduce larval habitats, thus their propagation

Lao PDR is a land-locked country, which borders five countries, China, Vietnam, Cambodia, Thailand and Myanmar, respectively. Most of the western border of Laos is demarcated by the Mekong River, which is an important artery for transportation and commerce. Two-thirds of Laos is covered by primary and secondary forests with a mountainous landscape and an abundance of rivers and natural resources which remain intact. The country has a tropical climate with high humidity throughout the year. The Mekong has not been an obstacle but a facilitator for communication between Laos and northeast Thai society (same people, same

Malaria is considered endemic throughout the country, but intensity of transmission is known to vary between different ecological zones; from relatively low transmission in the plains near the Mekong River and in areas of high altitude, to intense transmission in more remote, hilly and forested areas. Malaria has long been a leading cause of mortality and morbidity in the country. Transmission of malaria is perennial, but with large seasonal and regional variations. Peak transmission occurs between May and October, coinciding with the hot and rainy season. Malaria is also a problem in the dry season in certain areas of Laos [80]. In 1992, *P. falciparum* was the predominant species accounting for 95% of all recorded malaria cases [81] and remains so with 93% of all reported cases [3] representing leading cause of morbidity and mortality in Laos. A field survey for malaria prevalence in southern Laos using molecular-based parasite detection assay showed that mixed species infections were common with all 4 human plas‐

and adult densities [38]. In Cambodia, the forest area was reduced from 93,000 km2

language) reflecting the close contact that has existed along the river for centuries.

pheline mosquito fauna including some malaria vectors.

**2.2. Lao People Democratic Republic (Lao PDR, Laos)**

/year. Deforestation in SEA has been extensive with the mean annual rate of

in 2005 [57], and this possibly has had a direct influence on the richness of ano‐

in 2003 to

Between 2005 and 2008, the National Malaria Control Programme introduced a new strategy to improve case management at the community level, which involved training of 12,404 village health volunteers (VHVs) in 6,202 villages in the use of *P. falciparum*-specific malaria rapid diagnostic tests and to guide administration of ACT to infected patients. The VHVs represent the most peripheral level of the public health care system in Laos. Volunteers are selected by villagers and a village committee to provide primary health care services, including diagnosis and management of respiratory diseases, diarrhea, and uncomplicated malaria. Activities also include performing health education, assist in vaccination campaigns, and report morbidity and mortality data to the local health center or the district health office [69]. In Laos, insecticideimpregnated bednets have been reported to reduce malaria transmission successfully [68]. Much of the support has focused on the distribution of ITNs. The CMPE is now in the process of scaling up bed net coverage with a projected target of 3.6 million units reaching the most vulnerable ethnic minority groups, other persons at risk, and together with implementing appropriate diagnosis and effective treatment programs. Improving access to effective malaria treatment has become one of the greatest challenges. In recent years, artemisinin-derivative combination therapy (ACT; artemether-lumefantrine) has been adopted as the first-line treatment for uncomplicated malaria in many countries including Lao [83, 84, 85]. Recent data has shown that 89% of patients with malaria received a parasitological-confirmed diagnosis and were treated with an ACT [69, 86]. Furthermore, as the government public health system in Laos provides the vast bulk of primary health care, a private system for health access is growing, especially in the peripheral areas.

#### *2.2.2. Malaria vectors and biodiversity of* Anopheles *in Laos*

South-East Asia is one of the world's richest regions in terms of biodiversity. The species distribution and factors shaping it are not well understood, yet essential for identifying conservation priorities for the region's highly threatened flora and fauna. Several malaria vectors belong to sibling species that may greatly differ in their biology, behavior and other characteristics of epidemiological importance, such as resistance to insecticides. The sibling

[http://www.rbm.who.int/partnership/wg/wg\_itn/ppt/ws2/m4LaoPDR.pdf.]

**Figure 2.** Annual Parasite Incidence (API/1,000 population), Annual Case Incidence (ACI/1,000 population) and malar‐ ia deaths in Laos from 1987 to 2011. Source: Meeting on Outdoor Malaria Transmission in the Mekong Countries for 13 countries during 12-13 March 2012, Bangkok, Thailand.

species have been described as having individual distribution patterns depending on the landscape and seasonal environmental changes.

There are four recognized malaria vectors in Laos: *An. dirus, An. minimus s.l., An. maculatus s.l.,* and *An. jeyporiensis*. Among these *An. dirus* and *An. minimus* are considered the primary vector species. The anopheline situation in Laos is regarded as complex because of taxonomic and ecological variations that affect malaria transmission in the country [80,86]. *Anopheles minimus* and *An. harrisoni* are known to occur largely in sympatry (i.e., occurring together in the same area) in northern Laos [34]. Anopheline abundance and species composition are sitespecific and can vary throughout the year depending on conditions. A mosquito survey in Khammouane in 1996 and 1999-2000 found 19 and 28 different anopheline species, respec‐ tively. Studies have shown that the vectorial capacity (a transmission probability index) of *An. dirus* was 0.009-0.428, while *An. minimus* s.l was 0.048-0.186, *An. vagus, An. philippinensis, An. nivipes* were predominant species but mostly zoophilic [87, 88]. Three other species belonged to *An. maculatus* Group, including *An. notanandai, An. sawadwongporni,* and *An. willmori* along with *An. hodgkini* (Barbirostris Subgroup), a species reported for first time in Khammouane Province [88]. In 1999, an entomological survey covering 8 provinces, found that out of 19 anopheline species collected, *An. aconitus* was the predominant one, especially in the month of December, yet only 3 species, *An. dirus, An. maculatus* s.l. and *An. minimus* s.l. were found infected with malaria oocysts [86]. In 2000-2001, 16 anopheline species from Sekong Province were captured with only *An. dirus, An. maculatus* s.l. and *An. jeyporiensis* found positive for human malaria sporozoites [89]. *Anopheles dirus* was found to be the primary vector and sporozoite rates were highest during the transitional dry season. Two years of mosquito surveys, from 2002-2004, were conducted in Attapeu, the southern-most province bordering Vietnam and Cambodia, and a town located in a large valley surrounded with forest. It is one of the endemic malaria provinces which documented 8,945 mosquitoes belonging to 14 genera and 57 species, of which 21 species were *Anopheles*. Maculatus Group, *An. sawadwongporni* and *An. notanandai*, were found in large numbers but only *An. minimus* was found malaria sporozoite positive [90, 91]. There is very limited information about adult behavior and breeding habitats of anophelines in Laos. Recently, information has also been provided on non-vector species, for example, *An. annularis* s.l., *An. philippinensis,* and *An. sinensis* [60].

#### *2.2.3. Distribution of malaria vectors and behavior of* Anopheles *in Laos*

species have been described as having individual distribution patterns depending on the

**Figure 2.** Annual Parasite Incidence (API/1,000 population), Annual Case Incidence (ACI/1,000 population) and malar‐ ia deaths in Laos from 1987 to 2011. Source: Meeting on Outdoor Malaria Transmission in the Mekong Countries for

There are four recognized malaria vectors in Laos: *An. dirus, An. minimus s.l., An. maculatus s.l.,* and *An. jeyporiensis*. Among these *An. dirus* and *An. minimus* are considered the primary vector species. The anopheline situation in Laos is regarded as complex because of taxonomic and ecological variations that affect malaria transmission in the country [80,86]. *Anopheles minimus* and *An. harrisoni* are known to occur largely in sympatry (i.e., occurring together in the same area) in northern Laos [34]. Anopheline abundance and species composition are sitespecific and can vary throughout the year depending on conditions. A mosquito survey in Khammouane in 1996 and 1999-2000 found 19 and 28 different anopheline species, respec‐ tively. Studies have shown that the vectorial capacity (a transmission probability index) of *An. dirus* was 0.009-0.428, while *An. minimus* s.l was 0.048-0.186, *An. vagus, An. philippinensis, An. nivipes* were predominant species but mostly zoophilic [87, 88]. Three other species belonged to *An. maculatus* Group, including *An. notanandai, An. sawadwongporni,* and *An. willmori* along

landscape and seasonal environmental changes.

13 countries during 12-13 March 2012, Bangkok, Thailand.

282 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

[http://www.rbm.who.int/partnership/wg/wg\_itn/ppt/ws2/m4LaoPDR.pdf.]

*An. minimus* s.l. is widespread in the country and has been identified in all malaria endemic provinces in Laos. It primarily breeds in slow running streams closely associated with forested hilly areas, irrigation ditches, and rice fields. The mosquito feeds predominantly on humans but also on cattle and other animals and is regarded as primarily endophagic and endophilic. A recent study found both *An. minimus* and *An. harrisoni* present in northern Laos [56]. While *An. dirus* is most common in the central and southern parts of the country, it is considered rare in the north. *Anopheles dirus* is the most important malaria vector in the southern part of Laos. It breeds preferentially in stagnant and shaded waters (e.g. hoof prints, small rain-fed ground pools) in the rainforest, forested foothills and agricultural plantations, but has also been found to breed in scrub lands with lower vegetation. Population densities for this species typically increase during the wet season of the year while also having higher sporozoite infective rates at the end of the rainy season [89, 90]. The species is predominantly anthropophilic making it an ideal vector, but it will also feed on domestic animals with an indoor: outdoor blood feeding ratio of 1.6 [90]. The biting cycle of *An. dirus* has been documented to begin early evening, from 19:00 and remaining active through the night until 06:00, with peak activity around 22:00 [90, 92].

#### *2.2.4. Implications of changing social and environment conditions on vector and transmission*

*Anopheles dirus* is the most capable and dangerous malaria vector in Laos, particularly in southern Laos associated with forest-related habitats. This species has also become well adapted to human-induced environmental change, for example utilization of disturbed scrub areas containing low standing vegetation [90]. Laos' national forest coverage has dropped from 70% in 1940, at around 17 million hectares, to 41% in 2001, when a ban on timber exports was enacted, yet illegal deforestation has remained rampant over the past decade. From 2002 to 2010, central Laos's forestry cover decreased by 3.5%, while 9% of the southern forests disappeared [(http://www.nationmultimedia.com/home/Laos-to-increase-forest-cover‐ age-30145391html (December 2010)]. The government plans to increase forestry cover in Laos to 65% by 2015 and 70% by 2020 (The National Assembly, seventh five-year economic plan for 2011-2015). The current reforestation programmes have concentrated on allowing investments in large rubber plantations in Laos' border regions with southern China and Vietnam. For example, 10,000 hectares have been allocated for rubber plantation development in one area, and this has attracted populations from the Laos highlands to migrate to the plains, especially in Sanamxay District, to work in the rubber and sugar cane plantations. From October to December 2011, a total of 11,833 persons tested for malaria found 3,091 infected as reported from all facilities in the area including Attapeu Province villages. Up to the end of January 2012, 8 deaths due to malaria were reported from Attapeu.This outbreak of malaria has been attributed to the large scale development projects in the province, mainly concentrated in Phuvong and Sanamxay Districts, and the resulting population movements into the areas. In Phuvong District, extensive land clearing for Nam Kong 2 and 3 hydroelectric dams have been completed with dam construction beginning in 2013. The surge in logging activities associated with land clearing, primarily for the prized 'MaiKhayung' (rosewood), has attracted both local populations as well as people from other provinces to Attapeu. Most malaria patients admitted to provincial and district hospitals have been from other provinces or neighboring countries. In Phuvong District, from October to December 2011, 68% of the non-local malaria cases were from Vietnam and approximately 10% of cases were seen in children under the age of 5 years. This should be the lesson for other neighboring malaria-endemic provinces of Savannakhet, Saravane, Sekong and Champasack in southern Laos, where significant development projects are also planned, as well as other neighboring countries that are either initiating, planning or contemplating major development projects that would create extensive environment changes to design strategies to prevent or mitigate the occurrence of disease outbreaks as a result.

#### **2.3. Malaysia**

#### *2.3.1. Overview*

The Federation of Malaysia, a federal constitutional monarchy in Southeast Asia, consists of thirteen states and three federal territories and has a total landmass of 329,847 km² separated by the South China Sea into two similarly sized areas, Peninsular Malaysia located on mainland SEA and Malaysian Borneo. National borders are shared with Thailand, Indonesia, and Brunei, and maritime borders exist with Singapore, Vietnam, and the Philippines. Malaysia is a multiracial country consisting of Malays, Chinese, Indians, Ibans, Kadazans and smaller ethnic groups with total population of approximately 28.3 million [93]. Several vector-borne diseases remain serious concerns in Malaysia, including malaria.

During the 1960s, the number of malaria cases were estimated at 300,000 annually before the Malaria Eradication Program (MEP) was launched. The program was successful in dramati‐ cally reducing malaria transmission with number of cases decreasing from 181,495 at the start of MEP in 1967 to 44,226 cases at the end of the program in 1980. In 1983, the country changed strategy to one focused on 'control' by adopting the Malaria Control Program (MCP). The MCP continued the fight against malaria before reorganizing to the Vector-Borne Disease Control Program (VBDCP) in 2010. The key objective of the current program is to continue the reduction of malaria morbidity and mortality and to prevent the recurrence of malaria in nonendemic areas. The VBDCP also includes activities for the prevention and control of other vector-borne diseases like dengue fever and lymphatic filariasis [94, 95]. The MCP activities had been successful in reducing the number of malaria cases in Malaysia from 48,007 cases in 1986 to 7,010 cases in 2009 [96, 97].

Currently, malaria is still one of the most important vector-borne diseases in the country, primarily in Malaysian Borneo (Sarawak and Sabah states), although only 4% of the population is living in areas within active malaria transmission foci [3]. These refractory areas are partly attributed to anti-malarial drug resistance, insecticide resistance and cross border migration. In 2005, there were almost two million legal migrant workers in Malaysia. Most of these foreigners came from malaria endemic countries, a majority being from Indonesia (68.9%), followed by Nepal (9.9%), India (6.9%) and Myanmar (4.6%) [98,99]. In addition, the risk of malaria is high among the aboriginal groups such as Orang Asli, who lived in the interior of Peninsular Malaysia in remote hilly, cleared jungle areas [96]. In 2009, 7,010 malaria cases were reported in the country with approximately 57.2% of cases occurring in Sabah, 26% in Sarawak and 16.8% in Peninsular Malaysia. Most cases were caused by *Plasmodium vivax* (48.15%), followed by *P. falciparum* (26.75%), *P. knowlesi* (13.01%), *P. malariae* (8.37%) and mixed species infections (3.68%) [97,2]. *Plasmodium knowlesi* has more recently been recognized as an important zoonotic malaria species in eastern Malaysia (Borneo) and outbreaks have been found primarily in Borneo, Sarawak and Sabah and West Malaysia, [100] as well as other countries in SEA (see the Chapter by Vythilingam & Hii). In Malaysia, *An. latens* and *An. cracens* (both members of the *An. leucosphyrus* Subgroup) have been incriminated as vectors of *P. knowlesi* [101, 102, 103].

Malaysia has launched a national vector control program to include use of targeted indoor residual spraying (IRS), ITN distribution, artemisinin-based combination anti-malarial drugs, larviciding aquatic habitats harboring immature stages of vector species, environmental management measures, and personal protection methods [104]. After years of insecticide use to control vectors, development of physiological resistance to insecticides has been detected in some malaria vectors. Hii (1984) reported that *An. balabacensis* was tolerant to DDT and years later that several other anopheline species had also developed resistance to DDT and perme‐ thrin [105].

#### *2.3.2. Malaria vectors in Malaysia*

2010, central Laos's forestry cover decreased by 3.5%, while 9% of the southern forests disappeared [(http://www.nationmultimedia.com/home/Laos-to-increase-forest-cover‐ age-30145391html (December 2010)]. The government plans to increase forestry cover in Laos to 65% by 2015 and 70% by 2020 (The National Assembly, seventh five-year economic plan for 2011-2015). The current reforestation programmes have concentrated on allowing investments in large rubber plantations in Laos' border regions with southern China and Vietnam. For example, 10,000 hectares have been allocated for rubber plantation development in one area, and this has attracted populations from the Laos highlands to migrate to the plains, especially in Sanamxay District, to work in the rubber and sugar cane plantations. From October to December 2011, a total of 11,833 persons tested for malaria found 3,091 infected as reported from all facilities in the area including Attapeu Province villages. Up to the end of January 2012, 8 deaths due to malaria were reported from Attapeu.This outbreak of malaria has been attributed to the large scale development projects in the province, mainly concentrated in Phuvong and Sanamxay Districts, and the resulting population movements into the areas. In Phuvong District, extensive land clearing for Nam Kong 2 and 3 hydroelectric dams have been completed with dam construction beginning in 2013. The surge in logging activities associated with land clearing, primarily for the prized 'MaiKhayung' (rosewood), has attracted both local populations as well as people from other provinces to Attapeu. Most malaria patients admitted to provincial and district hospitals have been from other provinces or neighboring countries. In Phuvong District, from October to December 2011, 68% of the non-local malaria cases were from Vietnam and approximately 10% of cases were seen in children under the age of 5 years. This should be the lesson for other neighboring malaria-endemic provinces of Savannakhet, Saravane, Sekong and Champasack in southern Laos, where significant development projects are also planned, as well as other neighboring countries that are either initiating, planning or contemplating major development projects that would create extensive environment changes to design strategies to prevent or mitigate the occurrence of disease outbreaks as a result.

284 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

The Federation of Malaysia, a federal constitutional monarchy in Southeast Asia, consists of thirteen states and three federal territories and has a total landmass of 329,847 km² separated by the South China Sea into two similarly sized areas, Peninsular Malaysia located on mainland SEA and Malaysian Borneo. National borders are shared with Thailand, Indonesia, and Brunei, and maritime borders exist with Singapore, Vietnam, and the Philippines. Malaysia is a multiracial country consisting of Malays, Chinese, Indians, Ibans, Kadazans and smaller ethnic groups with total population of approximately 28.3 million [93]. Several vector-borne diseases

During the 1960s, the number of malaria cases were estimated at 300,000 annually before the Malaria Eradication Program (MEP) was launched. The program was successful in dramati‐ cally reducing malaria transmission with number of cases decreasing from 181,495 at the start of MEP in 1967 to 44,226 cases at the end of the program in 1980. In 1983, the country changed

remain serious concerns in Malaysia, including malaria.

**2.3. Malaysia**

*2.3.1. Overview*

Seventy-five species of *Anopheles* have been recorded in the country, only 9 of which are reported as malaria vectors to include *An balabacensis* and *An. latens* (both Leucosphyrus Complex), *An. cracens* (Dirus Complex), *An. maculatus* (Maculatus Group), *An. letifer*, *An. campestris*, *An. sundaicus* and *An. epiroticus* (both Sundaicus Complex), *An. donaldi*, and *An. flavirostris* [96]. Each species is considered a malaria vector in various areas of the country, sometimes existing in sympatry (Table 1).


**Table 1.** Anopheline vectors in Malaysia [33,59]

*Anopheles maculatus* is within a species group that comprises at least nine genetically-related species [39]. Historically, *An. maculatus* has been the principal vector of malaria in West Malaysia, particularly in hilly areas not covered with dense forest [106,107,108]. This species prefers to breed in pools formed along the still banks of rivers and small streams. The larval breeding habitats include shallow pools (5-15cm depth) of clear water, with muddy substrate and plants or flotage [109]. In Borneo, this species appears to be more zoophilic and is not regarded a malaria vector of any importance [106].

*Anopheles campestris* belongs to the Barbirostris Subgroup (subgenus *Anopheles*) and is a potential vector of malaria and filariasis, particularly along the west coast of Peninsular Malaysia [110]. The larvae commonly breed in rice fields, burrow pits, stagnant ditches in coconut plantations, earthen wells, and sometimes in slightly brackish water [111]. Reid (1968) reported that this species could be found in deep water with some vegetation and light shade. Adults are generally anthropophilic, will enter houses to blood feed and rest.

*Anopheles cracens* (formerly *An. dirus* species B), is a member of the Dirus Complex, found exclusively in the Thai-Malaysian peninsular area of mainland SEA. *An. cracens* is the vector of *P. knowlesi* in Kuala Lipis of peninsular Malaysia [102]. Larvae typically inhabit small, usually temporary, shaded bodies of fresh, stagnant water, including ground pools, puddles, animal footprints, and wells. This species is found in hilly and mountainous areas containing primary or secondary evergreen and deciduous forests, bamboo, and fruit and rubber plantations [112, 113, 114].

*Anopheles letifer* larvae prefer to breed in stagnant dark-brown (often acidic) water found in peat swamps, especially in jungle clearings along forest edges, with or without shade. Oil palm cultivation areas are also habitats for *An. letifer* associated with open and blocked swamps [115]. In peninsular Malaysia, *An. letifer* is regarded a vector of human malaria and Bancroftian filariasis [106, 96, 116], particularly at low elevations on the coastal plains.

*Anopheles epiroticus* (formerly *An. sundaicus* species A) and *An. sundaicus* s.s. are members of the Sundaicus Complex [117] and considered important vectors of malaria in coastal areas [106, 118, 44]. In Peninsular Malaysia, *An. epiroticus* occurs mostly along coastal areas while *An. epiroticus* is found in Sarawak (Borneo) [46]. The immature stages are typically found in sunlit pools of brackish water, containing filamentous and floating algal mats, and sparse vegetation. Particularly favorable habitats include ponds, swamps, lagoons, open mangrove, rock pools and abandoned or poorly maintained coastal shrimp and fish ponds [46]. Adults rest by day both outdoors and indoors and readily bite people indoors. Sporozoite rates can often be relatively low but are compensated by large adult densities [106].

**Anopheline species Peninsular Malaysia Sarawak and Sabah**

*Anopheles maculatus* is within a species group that comprises at least nine genetically-related species [39]. Historically, *An. maculatus* has been the principal vector of malaria in West Malaysia, particularly in hilly areas not covered with dense forest [106,107,108]. This species prefers to breed in pools formed along the still banks of rivers and small streams. The larval breeding habitats include shallow pools (5-15cm depth) of clear water, with muddy substrate and plants or flotage [109]. In Borneo, this species appears to be more zoophilic and is not

*Anopheles campestris* belongs to the Barbirostris Subgroup (subgenus *Anopheles*) and is a potential vector of malaria and filariasis, particularly along the west coast of Peninsular Malaysia [110]. The larvae commonly breed in rice fields, burrow pits, stagnant ditches in coconut plantations, earthen wells, and sometimes in slightly brackish water [111]. Reid (1968) reported that this species could be found in deep water with some vegetation and light shade.

*Anopheles cracens* (formerly *An. dirus* species B), is a member of the Dirus Complex, found exclusively in the Thai-Malaysian peninsular area of mainland SEA. *An. cracens* is the vector of *P. knowlesi* in Kuala Lipis of peninsular Malaysia [102]. Larvae typically inhabit small, usually temporary, shaded bodies of fresh, stagnant water, including ground pools, puddles, animal footprints, and wells. This species is found in hilly and mountainous areas containing primary or secondary evergreen and deciduous forests, bamboo, and fruit and rubber

*Anopheles letifer* larvae prefer to breed in stagnant dark-brown (often acidic) water found in peat swamps, especially in jungle clearings along forest edges, with or without shade. Oil palm cultivation areas are also habitats for *An. letifer* associated with open and blocked swamps [115]. In peninsular Malaysia, *An. letifer* is regarded a vector of human malaria and Bancroftian

Adults are generally anthropophilic, will enter houses to blood feed and rest.

filariasis [106, 96, 116], particularly at low elevations on the coastal plains.

*An. balabacensis* +

*An. donaldi* + + *An. flavirostris* + *An. letifer* + + *An. latens (=An. leucosphyrus* A) + *An. maculatus* + + *An. epiroticus/An. sundaicus* + +

*An. campestris* + *An. cracens* (*=An. dirus* B) +

286 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**Table 1.** Anopheline vectors in Malaysia [33,59]

plantations [112, 113, 114].

regarded a malaria vector of any importance [106].

*Anopheles donaldi* is one of the primary malaria vectors in Sarawak with a reported sporozoite rate of 0.23% [119]. This species prefers small streams and ground pools, containing clean and shaded fresh water, occasionally rice fields and open marshlands [106,115]. The adults are found in forest fringes in hilly areas and near tree-covered swamps in the lowlands [106]. *Anopheles balabacensis*, a member of the Leucosphyrus Complex,is regarded as the main vector of malaria in Sabah [120,111]. This species occurs in forested area of Malaysian Borneo (eastern Sarawak and Sabah). The immature stages are principally found in shaded temporary pools of stagnant fresh water, including ground puddles, animal footprints, wheel tracks, ditches and rock pools [59]. In addition, *An. balabacensis* is also a vector of *Wuchereria bancrofti* responsible for lymphatic filariasis [121,116]. In most areas, this species is very anthropophilic and will readily enter houses to blood feed.

*Anopheles flavirostris* is a malaria vector in Sabah along the eastern coast [111] belonging to the Minimus Subgroup [122]. This species demonstrates anthropophilic and endophagic behav‐ iors in Sabah [121]. Characteristically, *An. flavirostris* larvae are found in clear, slow-moving freshwater stream habitats that are partly shaded by over hanging vegetation and margins containing emergent plants or grasses [123]. *Anopheles latens* (formerly *An. leucosphyrus* A), a member of the Leucosphyrus Group, is a primary vector of human malaria in Sarawak. Additionally, *An. latens* also transmits the monkey malaria parasite, *P. knowlesi* to humans in Sarawak [101]. Like all members in the group, this is a forest mosquito and larval habitats of *An. latens* are primarily found in shaded, temporary ground pools, small pools on margins of forest streams, and natural containers of clear or turbid water in forested areas [59]. In Sarawak, [124] this species was commonly found in shaded pools, a forest stream and swampy patches. Adults will enter houses in the evening to bite, generally delaying activity until after 2200 hr.

#### *2.3.3. Effects of changing environmental conditions on malaria vectors and transmission*

In Malaysia, malaria transmission appears more strongly associated with land development rather than water development projects [125]. Land use changes, such as deforestation, increased urbanization and agriculture can directly impact mosquito abundance, species biodiversity, biting behavior, and vector competence [77]. For example, the effect of forest clearance for rubber plantations exposes land and streams to direct sunlight and thus increased and expanded the available breeding habitats for *An. maculatus*, which further led to a marked increase in the incidence and severity of malaria [126]. Vythilingam et al. [119] found that *An. donaldi* appears to have replaced *An. balabacensis* as the main vector in Kinabatangan of Sabah as a result deforestation and malaria control activities. Similarly, the clearing of mangroves and swamps for fish aquaculture or mining resulted in an increase in suitable larval habitats of filariasis vectors and *An. epiroticus* followed by malaria outbreaks [76,119, 127].

#### **2.4. Myanmar**

#### *2.4.1. Overview*

Myanmar (formerly Burma) has a total land area of 678,500 km². The extent of border areas with the 5 surrounding countries include 193 km with Bangladesh; 2,185 km with China; 1,463 km with India; 1,800 km with Thailand, and a relatively small stretch with Laos. Administra‐ tively, the nation is divided into 14 states and divisions, 65 districts, 325 townships. The climate is tropical with the southwest monsoon occurring from June to September and a northeast monsoon from December to April.

Migration across international borders through specific points of entry from Myanmar in‐ cludes, Tachilek, Myawaddy and Kawthaung, Thailand; Muse, Namkhan and Khukok, China; Tamu, India; and Maungdaw, Bangladesh. There are also other less important points of entry into Thailand where Thai and Burmese citizens normally need only a valid border pass to cross at official check points. At Mae Sai, approximately 60,000 Thais and 30,000 Burmese nationals crossed the border in 1997. That is one important reason why malaria morbidity and mortality along the Thai-Myanmar border is especially high and refractory to most control methods [127] and why the disease peaked in intensity between 1988 and 1991 [128].

Malaria is a severe public health problem in Myanmar, in particular along parts of internation‐ alborders [129].Confirmedmalaria cases inMyanmarincreasedfrom120, 029 in2000 to447,073 cases in 2008. The 2009 World Malaria Report (WMR) stated that Myanmar (Burma), with a populationofover50million,had17%ofallmalariacasesrecordedinSoutheastAsia,thehighest percentage in the region [47]. There were 400,000 confirmed malaria cases in the country and about 1,100 deaths due to malaria in 2008, occurring in 284 out of 324 townships [85].

In 2008, Chin State reported the highest morbidity rate of 44.7 per 1,000 inhabitants, whereas the highest number of malaria cases was reported in the Rakhine State, followed by Sagaing State (Figure 4) [130,131].

Generally, malaria transmission peaks just before and after the monsoon rains which normally occur between June and September. The populations most at risk include: 1) people who live or migrate into high malaria risk areas, especially along the borders; 2) international migrants or laborers involved in mining, agriculture (e.g., rubber plantations), the construction of dams, roads, and irrigation projects; 3) those who farm or related work near or in forests and along forest fringes such as wood and bamboo cutters; 4) pregnant women and children under five years old; and 5) ethnic minorities residing in more remote areas with poorer access to primary health care. Out of a total population of 60 million, the proportion of residents living under some degree of malaria risk or none is as follows: high risk 37%, low risk 23% and no risk 40% [3]. Overall 36 townships had higher than 4% mortality in cases diagnosed [132]. Significant numbers of ethnic minorities (approximately 100,000) live in semi-permanent refugee camps

**Figure 3.** Malaria morbidity and mortality rate in Myanmar during 1988-2008. (http://www.actmalaria.net/home/ vector\_control.php#base)

along the Thai-Myanmar border where malaria transmission is rampant. The Thai govern‐ ment's policy is to eventually repatriate Shan and other minorities back to Myanmar.

All four species of human plasmodia (*P. falciparum, P. vivax*, *P. malariae* and *P. ovale*), exist in Myanmar. In 2008, the NMCP Myanmar reported 391,461 *P. falciparum* cases (87.6% of all malaria infections) followed by *P. vivax* at 52,256 (11.7%), while *P. malariae* and *P. ovale* were seen in only 283 and 5 cases, respectively. Currently, *P. falciparum* is still the predominate species at 68% of all cases detected [3]. Additionally, one human infection with *P. knowlesi* was found in a Burmese worker at Ranong Province of Thailand. This zoonotic infection may have been acquired in Kawthoung District, Myanmar, a district close to Ranong Province [133]. *Plasmodium falciparum* resistance to antimalarial drugs is a primary concern in the country. Chloroquine and sulfadoxine-pyrimethamine (S-P) resistance at various levels is now com‐ mon. Also, well documented report of resistance in small case series has appeared. Resistance to chloroquine by *P. vivax* has been reported [134,135].

#### *2.4.2. Malaria vectors and species diversity*

as a result deforestation and malaria control activities. Similarly, the clearing of mangroves and swamps for fish aquaculture or mining resulted in an increase in suitable larval habitats

Myanmar (formerly Burma) has a total land area of 678,500 km². The extent of border areas with the 5 surrounding countries include 193 km with Bangladesh; 2,185 km with China; 1,463 km with India; 1,800 km with Thailand, and a relatively small stretch with Laos. Administra‐ tively, the nation is divided into 14 states and divisions, 65 districts, 325 townships. The climate is tropical with the southwest monsoon occurring from June to September and a northeast

Migration across international borders through specific points of entry from Myanmar in‐ cludes, Tachilek, Myawaddy and Kawthaung, Thailand; Muse, Namkhan and Khukok, China; Tamu, India; and Maungdaw, Bangladesh. There are also other less important points of entry into Thailand where Thai and Burmese citizens normally need only a valid border pass to cross at official check points. At Mae Sai, approximately 60,000 Thais and 30,000 Burmese nationals crossed the border in 1997. That is one important reason why malaria morbidity and mortality along the Thai-Myanmar border is especially high and refractory to most control methods [127]

Malaria is a severe public health problem in Myanmar, in particular along parts of internation‐ alborders [129].Confirmedmalaria cases inMyanmarincreasedfrom120, 029 in2000 to447,073 cases in 2008. The 2009 World Malaria Report (WMR) stated that Myanmar (Burma), with a populationofover50million,had17%ofallmalariacasesrecordedinSoutheastAsia,thehighest percentage in the region [47]. There were 400,000 confirmed malaria cases in the country and

In 2008, Chin State reported the highest morbidity rate of 44.7 per 1,000 inhabitants, whereas the highest number of malaria cases was reported in the Rakhine State, followed by Sagaing

Generally, malaria transmission peaks just before and after the monsoon rains which normally occur between June and September. The populations most at risk include: 1) people who live or migrate into high malaria risk areas, especially along the borders; 2) international migrants or laborers involved in mining, agriculture (e.g., rubber plantations), the construction of dams, roads, and irrigation projects; 3) those who farm or related work near or in forests and along forest fringes such as wood and bamboo cutters; 4) pregnant women and children under five years old; and 5) ethnic minorities residing in more remote areas with poorer access to primary health care. Out of a total population of 60 million, the proportion of residents living under some degree of malaria risk or none is as follows: high risk 37%, low risk 23% and no risk 40% [3]. Overall 36 townships had higher than 4% mortality in cases diagnosed [132]. Significant numbers of ethnic minorities (approximately 100,000) live in semi-permanent refugee camps

about 1,100 deaths due to malaria in 2008, occurring in 284 out of 324 townships [85].

and why the disease peaked in intensity between 1988 and 1991 [128].

of filariasis vectors and *An. epiroticus* followed by malaria outbreaks [76,119, 127].

**2.4. Myanmar**

*2.4.1. Overview*

monsoon from December to April.

288 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

State (Figure 4) [130,131].

Due to Myanmar's diverse geography, there is a relatively large number of dominant malaria vector species. Out of 36 *Anopheles* species distributed in the country, 10 species at 16 locations have been found infected with malarial parasites [136]. In Myanmar, the primary vectors responsible for the majority of infections are *An. dirus* s.l. and *An. minimus* s.l. [59]. Other anopheline species, predominantly zoophilic feeders, may also, under ideal conditions, feed

**Figure 4.** Malaria morbidity rate in States/Divisions of Myanmar in 2008. (http://www.actmalaria.net/home/ vector\_control.php#base)

on man [137,138]. These secondary vectors include *An. aconitus, An.annularis* s.l., *An. culicifa‐ cies* s.l., *An. sinensis, An. jeyporiensis, An. maculatus* s.l., *An. philippinensis,* and *An. sundaicus* s.l. [136].

#### *2.4.3. Anopheline behavior*

Much of the recent work on anopheline bionomics and distribution in Myanmar is attributed to Oo et al. [113, 136] herein. *Anopheles baimaii* is the most common species of the Dirus Complex present in Myanmar, which is also the primary vector species in neighboring Bangladesh [113]. Highest numbers of immature stages were collected during the pre- and post-monsoon periods, while the lowest numbers were seen during the cool-dry and hot-dry months. The larvae were found in rock pools along the banks of thickly shaded streams and in cut bamboo stumps. Adults of this species are plentiful in the monsoon months with a peak densities occurring during September and October. *An. dirus* s.l. was also found daytime resting in the crevices and vegetation around the inner walls of domestic wells and on the underside of banana leaves. Adult behavior indicated this species highly exophilic and it will bite both humans and cattle. A previous study [139] has reported a higher zoophilic tendency despite the breeding sites being found very near human dwellings. Outdoor biting peak has been shown to occur between 21:00 and 03:00 hr. [139]. The results of the dissection both of midgut and salivary glands together for determination of natural infection rates in different localities ranged from 0.4 to 2.8%. The highest infection rate for midgut dissection was 0.4 % (1/250) and salivary gland dissection 2.4% (6/250).

For *An. minimus* s.l., adult densities vary seasonally, although it is also abundant throughout the entire year in many locations [136]. The highest prevalence of *An. minimus* s.l. occurs during the post-monsoon months of October to December. Adults prefer to rest in houses and cattle sheds during daytime. The preference of *An. minimus* s.l. for human blood is well documented during different periods of the year and various locations. Even when cattle are present, only a small proportion of mosquitoes appear to deviate from biting humans. *Anopheles minimus* s.l. feeds mainly during the early hours of the evening, beginning before 21:00 hr and peaking in activity just before or after midnight. However, when adult densities are high, *An. mini‐ mus* s.l. populations will bite throughout the night (both outdoors and indoors) with greater activity during the first quarter of the evening and a gradual decrease in biting till dawn (06:00 hr). The infection rate both in midgut and salivary glands has been reported to vary between 1.1-3.0%. *Anopheles minimus* s.l. is primarily a mosquito of hilly regions, low rolling foothills to narrow river valleys in more mountainous areas; it has not been recorded in locations over 915 m above sea level. When found in lowland plains, it is always in association with irrigation systems.

*Anopheles aconitus* is a secondary vector in certain localities and is a fairly abundant species from October to February, peaking in November [136]. From March to September it is very seldom seen. *An. aconitus* is more commonly seen in hilly tracts, foothills and also in the plains of central and southern Myanmar closely associated with active rice cultivation. Only a few *An. aconitus* females are found resting in houses or stables during daytime preferring to rest outdoors in scrub and other locations. *An. aconitus* appears to prefer cattle for blood meals, although it will bite humans if cattle are not available or very limited in number. It is active in the early evening, biting as early as 18:00 hr, with very little activity after 01:00 hr. *An. aconitus* had a 0.2% (1/350) infection rate [136].

on man [137,138]. These secondary vectors include *An. aconitus, An.annularis* s.l., *An. culicifa‐ cies* s.l., *An. sinensis, An. jeyporiensis, An. maculatus* s.l., *An. philippinensis,* and *An. sundaicus* s.l.

**Figure 4.** Malaria morbidity rate in States/Divisions of Myanmar in 2008. (http://www.actmalaria.net/home/

Much of the recent work on anopheline bionomics and distribution in Myanmar is attributed to Oo et al. [113, 136] herein. *Anopheles baimaii* is the most common species of the Dirus Complex present in Myanmar, which is also the primary vector species in neighboring Bangladesh [113]. Highest numbers of immature stages were collected during the pre- and post-monsoon periods, while the lowest numbers were seen during the cool-dry and hot-dry months. The larvae were found in rock pools along the banks of thickly shaded streams and in cut bamboo stumps. Adults of this species are plentiful in the monsoon months with a peak densities occurring during September and October. *An. dirus* s.l. was also found daytime resting in the crevices and vegetation around the inner walls of domestic wells and on the underside of banana leaves. Adult behavior indicated this species highly exophilic and it will bite both humans and cattle. A previous study [139] has reported a higher zoophilic tendency despite the breeding sites being found very near human dwellings. Outdoor biting peak has been

[136].

*2.4.3. Anopheline behavior*

vector\_control.php#base)

290 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

*An*. *annularis* s.l. has been found in few localities with high adult densities. Stagnant water with thick grassy edges in permanent ponds, ground pits, tanks, swamps, stagnant drains and rice fields are common larval habitats of *An. annularis*. Its abundance varies according to rainfall patterns. In coastal areas with heavy rainfall (between 3,800 mm to 5,150 mm annually)*, An. annularis* densities typically increase from October to January. This species appears to prefer‐ entially feed on cattle with a far greater proportion (80-90%) of biting activity seen during the first half of the night (18:00-24:00 hr). The midgut dissection records on *An. annularis* have seen 0.1-0.2% (350-700 samples) plasmodia infection rates [136].

*An. culicifacies* s.l. is a suspected malaria vector in central Myanmar, especially in irrigated areas. The larval stage of this species breeds in fresh (unpolluted) water and also in artificial water containers and unused swimming pools. *An. culicifacies* is more abundant in August and September, dropping of in October and virtually none from November to March. Adults prefer to rest in cattle sheds and houses during the day, but it may take shelter in paddy-sheds, stacked fire-wood and piles of straw near the stables and outside houses. *Anopheles culicifa‐* *cies* is primarily a cattle feeder with generally far fewer numbers attacking humans.This species feeds mostly around midnight with very few biting after 03:00 hr. Midgut infections (with oocysts present) have been recorded at 1.83% (6 infections from 328 examined mosquitoes) [136].

When adult densities are high, *An. sinensis* is a secondary vector along the Myanmar-China border. Larvae are predominately found in stagnant waters and rice fields. This species was found from July to December with a peak in August. It is predominantly zoophilic, preferring cattle over humans. Very few have been caught biting humans at night. The peak biting activity of *An. sinensis* is during the first half of the evening beginning at 18.00 hr. In Shan State, along the Myanmar-China border, 300 specimens of *An. sinensis* were examined of which 2.3% (6/300) were found malaria infected [139]. *Anopheles jeyporiensis* is regarded a secondary vector on the Myanmar-China and Myanmar-Bangladesh borders when adult densities are high. Immature stages are mainly found along margins of slow-moving streams and channels with grassy edges and often sympatric with larvae of *An. aconitus*. Rice fields are also attractive breeding sites for *An. jeyporiensis* when uncultivated or early stages of plant growth but become unfavorable as the plants increase in height. Adults are normally abundant during the premonsoon period of March and April. They will feed on both humans and cattle. The peak biting period has been recorded from 23:30 to 03:00. On the Myanmar-Bangladesh border (Rakhine State), 500 specimens of *An. jeyporiensis* were dissected with four having sporozoite-infected salivary glands (0.8% infection rate). On the Myanmar-China border (Shan State), 500 speci‐ mens of *An. jeyporiensis* were dissected with a 1.2% (6/500) infection rate.

*An*. *maculatus* s.l. has been reported as a primary vector, especially in Tanintharyi Division, and elsewhere as a secondary vector depending on the location. The greatest density of this species in nearly all areas where it occurs is during January (cold dry season). Numbers start to increase at the end of southwest monsoon period in early October and relatively rare during the two annual monsoon seasons.There is only one exception, in Mandalay Division, where *An. maculatus* has been recorded in large numbers during September, at the end of the rainy season. It has not been recorded resting indoors during the day, even though many houses in the foothill areas are semi-enclosed. However, at times of peak densities, *An. maculatus* can be collected in cattle sheds. This species member feeds on both humans and various animals, mainly during the first half of the night beginning at 18:00 hr. The midgut dissections have shown a 0.5% (1/180) infection rate. *Anopheles maculatus* is primarily recorded from forested foothills, around deep forest camps and in mountainous areas at 1, 200m above sea level and typically not found in low lying areas far from foothill environments.

*Anopheles philippinensis* is a vector of minor importance near the Myanmar-Bangladesh border. This species was not found resting in houses and cattle sheds during daytime and presumably selects natural sites outdoors*. An. philippinensis* has only been found resting in houses during morning collections. *An. philippinensis* is a zoophilic species and feeds mainly on cattle. In areas where cattle are either scarce or absent, this species will readily feed on man. In Innwaing Village (Mawlamyine Township, Mon State) and Patheingyi Township *An. philippinensis* has been reported in large numbers during the post-monsoon months from September to Novem‐ ber [136].

*An. sundaicus* s.l. is a secondary vector restricted to coastal areas where larval habitats are‐ mainly located in sunlit lagoons, natural fresh and brackish water impoundments and backup streams, often with dense aquatic vegetation (floating algal mats), and brackish water seepage areas. The seasonal abundance of *An. sundaicus* s.l. often increases between May and July and again in October to February. This species was recorded in moderate numbers from houses and cattle sheds from daytime collections. They feed on both human and cattle. In Chaungthar and Seikgyi areas in Ayeyarwady Division, *An. sundaicus* s.l. had a 0.4 % midgut infection rate (1 oocyst positive /220 sampled and 1 positive per 230, respectively). Along the Myanmar-Bangladesh border in Rakhine State, a total of 202 specimens were dissected from which 0.5% had positive salivary gland infections [136].

*cies* is primarily a cattle feeder with generally far fewer numbers attacking humans.This species feeds mostly around midnight with very few biting after 03:00 hr. Midgut infections (with oocysts present) have been recorded at 1.83% (6 infections from 328 examined mosquitoes)

When adult densities are high, *An. sinensis* is a secondary vector along the Myanmar-China border. Larvae are predominately found in stagnant waters and rice fields. This species was found from July to December with a peak in August. It is predominantly zoophilic, preferring cattle over humans. Very few have been caught biting humans at night. The peak biting activity of *An. sinensis* is during the first half of the evening beginning at 18.00 hr. In Shan State, along the Myanmar-China border, 300 specimens of *An. sinensis* were examined of which 2.3% (6/300) were found malaria infected [139]. *Anopheles jeyporiensis* is regarded a secondary vector on the Myanmar-China and Myanmar-Bangladesh borders when adult densities are high. Immature stages are mainly found along margins of slow-moving streams and channels with grassy edges and often sympatric with larvae of *An. aconitus*. Rice fields are also attractive breeding sites for *An. jeyporiensis* when uncultivated or early stages of plant growth but become unfavorable as the plants increase in height. Adults are normally abundant during the premonsoon period of March and April. They will feed on both humans and cattle. The peak biting period has been recorded from 23:30 to 03:00. On the Myanmar-Bangladesh border (Rakhine State), 500 specimens of *An. jeyporiensis* were dissected with four having sporozoite-infected salivary glands (0.8% infection rate). On the Myanmar-China border (Shan State), 500 speci‐

*An*. *maculatus* s.l. has been reported as a primary vector, especially in Tanintharyi Division, and elsewhere as a secondary vector depending on the location. The greatest density of this species in nearly all areas where it occurs is during January (cold dry season). Numbers start to increase at the end of southwest monsoon period in early October and relatively rare during the two annual monsoon seasons.There is only one exception, in Mandalay Division, where *An. maculatus* has been recorded in large numbers during September, at the end of the rainy season. It has not been recorded resting indoors during the day, even though many houses in the foothill areas are semi-enclosed. However, at times of peak densities, *An. maculatus* can be collected in cattle sheds. This species member feeds on both humans and various animals, mainly during the first half of the night beginning at 18:00 hr. The midgut dissections have shown a 0.5% (1/180) infection rate. *Anopheles maculatus* is primarily recorded from forested foothills, around deep forest camps and in mountainous areas at 1, 200m above sea level and

*Anopheles philippinensis* is a vector of minor importance near the Myanmar-Bangladesh border. This species was not found resting in houses and cattle sheds during daytime and presumably selects natural sites outdoors*. An. philippinensis* has only been found resting in houses during morning collections. *An. philippinensis* is a zoophilic species and feeds mainly on cattle. In areas where cattle are either scarce or absent, this species will readily feed on man. In Innwaing Village (Mawlamyine Township, Mon State) and Patheingyi Township *An. philippinensis* has been reported in large numbers during the post-monsoon months from September to Novem‐

mens of *An. jeyporiensis* were dissected with a 1.2% (6/500) infection rate.

typically not found in low lying areas far from foothill environments.

[136].

292 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

ber [136].

Myanmar's national malaria control program aims to achieve the WHO Millennium Devel‐ opment Goal of halting the increase in malaria cases by 2015 and significantly reversing the incidence of malaria thereafter. The principle method for malaria vector control in malaria endemic areas of Myanmar relies on the application of ITNs distribution and case management [3]. Biological control using two predacious 'top minnow' fish species, *Poecilia reticulata* and *Aplocheilus panchax* are also effective in certain aquatic habitats and when the correct conditions merit. Inter-sector cooperation, community participation and health education are also part of this integrated approach to reducing disease transmission [131]. Although insecticides are an important component of malaria control operations in Myanmar there is lack information on the status of insecticide resistance in key vector species [12]. Information from the NMCP showed insecticide resistance present in anopheline mosquitoes from Rakhine State. In 2009, both *An. annularis* s.l. and *An. barbirostris* were found resistant to 4% DDT, and *An. barbirost‐ ris* was also resistant to 0.25% permethrin, while both species were susceptible to 5% malathion and 0.05% deltamethrin [132]. Although the threat of malaria must be targeted at the local and regional level, especially in the remaining conflict areas of eastern Myanmar, the government does not yet conduct extensive malaria control programmes in many areas in need [140].

#### *2.4.4. Effects of changing environmental conditions on malaria vectors and transmission*

Since Nay Pyi Taw, the new administrative capital of Myanmar was opened in November 2005 to include relocation of all government ministries approximately 320 km north of Yangon. This major infrastructural change has had a major impact on the land-use characteristics in the area with new buildings a connecting train network, roads and other projects. [85]. Land-use changes could create ideal new habitats ideal for mosquito propagation, the extension or reduction of a vector's distribution, and modify the composition of the mosquito vectors in an area [141]. *An. dirus* s.l. and *An. minimus* are the major malaria vectors in the hilly regions of Myanmar. There is a profound lack of information about the effects of environmental changes on malaria vectors in Myanmar. Currently there are only a few publications that describe [77, 142, 75] the effects of major infrastructural projects (e.g., dam construction), deforestation, vegetation replacement, increased in human population density and movement, modified topography and hydrological characteristics that can affect the epidemiology of malaria and risk of transmission.

Myanmar is the country where the malaria situation is still poorly understood and wellorganized control programs remain lacking in many areas of the country. Current and available information is generally lacking and operational research limited to better assess the epidemiology throughout the country. Both published literature and unpublished depart‐ mental reports by the Department of Medical Research (DMR) and Department of Vector Borne Disease Control (VBDC) are regarded as inadequate to address managing an effective malaria control program.

#### **2.5. Thailand**

#### *2.5.1. Overview*

Thailand is the world's fifty-first largest country in terms of total land area (513,120 km²), and a total population of nearly 67 million people. Thailand shares national boundaries with Myanmar on the west and north, Laos on the north and east, Cambodia on the east, and Malaysia in the south. Gem mining, hunting, logging, agriculture, road construction and other economic activities along Thailand's border areas attract many migrant workers from neigh‐ boring countries. The constant movement of workers and the transient, often poorly con‐ structed dwellings they occupy facilitates cross-border transmission of malaria and complicates efforts to control it, making it one of the most serious vector-borne diseases in these areas.

Despite decades of success in reducing the number of cases of malaria in the country, the disease remains a major cause of morbidity and mortality. Approximately 32 million people in Thailand's border areas (50% of the Thailand's population) are at risk of contracting malaria. All four malaria parasites are present with the most common being *P.vivax* with 60% of all reported infections in 2011 [3]. Since 1997, *P. falciparum* and *P*. *vivax* infections have been recorded at near equal prevalence [70] (Figure 5). The under-developed border areas between Thailand and eastern Myanmar remain the worst affected area for continuing transmission [1, 2]. Non-immune workers who migrate across the international border remain the most susceptible and vulnerable populations. The constant movement of this population involved in gem mining, logging, agriculture, construction and other pursuits, has helped to increase the spread of multi-drug resistant *P. falciparum* malaria in the area and region. Serious outbreaks of malaria have taken place in high risk areas along the Thai-Myanmar border, especially in Kanchanaburi and Tak Provinces [70,143]. In four southern provinces of Thailand, malaria cases have risen to nearly 4,000 per year in the areas bordering Malaysia where social conflict and a local insurgency have greatly complicated control efforts [70]. At the same time, a rapid increase of rubber plantations in northeastern Thailand has become a major concern because of the potential for the reemergence of malaria [144]. Several major malaria vectors, mainly *Anopheles dirus* s.l., *An. maculatus* s.l., and *An. minimus* s.l., can adapt and utilize rubber plantations in place of more typical habitats like hill environments and natural forests [4]. Careful attention and monitoring to land use changes along with climatic and other environ‐ mental changes is essential to help prevent or delay the reemergence of malaria in receptive areas.

**Figure 5.** Trends of malaria in Thailand between 1971 and 2010.'Positives' refer to all malaria cases, "Pf" = P. falcipa‐ *rum* infections only. (http://www.searo.who.int/en/Section10/Section21/Section340\_4027.htm).

Based on recorded malaria surveillance activities in Thailand from 1971 to 2011, the peak of malaria cases was seen in 1981 with the total of 473,210 infections, and has since declined thereafter despite another rise in case load seen in 1988 (349,291 infections). In general, from 1988 to 2010, malaria has declined significantly [143, 70]. Despite the significant achievements in malaria control in Thailand over the past five decades, between 25,000 and 35,000 confirmed malaria cases still occur annually [70]. There were 32,502 confirmed cases of malaria in 2010, a decrease of 61.2% compared to 2000. Mortality has also dramatically declined, dropping from 625 in 2000 to 80 in 2010, a decrease of 87.2%. The decline in malaria cases has been attributed to the effective implementation of selective and targeted indoor residual spray of homes and treated netting as vector control measures. Reduction of malaria in Thailand is also the consequence of expanded programs and access to prompt diagnosis and treatment in rural areas as well as an active disease surveillance program.

#### *2.5.2. Malaria vectors and species diversity*

Myanmar is the country where the malaria situation is still poorly understood and wellorganized control programs remain lacking in many areas of the country. Current and available information is generally lacking and operational research limited to better assess the epidemiology throughout the country. Both published literature and unpublished depart‐ mental reports by the Department of Medical Research (DMR) and Department of Vector Borne Disease Control (VBDC) are regarded as inadequate to address managing an effective malaria

Thailand is the world's fifty-first largest country in terms of total land area (513,120 km²), and a total population of nearly 67 million people. Thailand shares national boundaries with Myanmar on the west and north, Laos on the north and east, Cambodia on the east, and Malaysia in the south. Gem mining, hunting, logging, agriculture, road construction and other economic activities along Thailand's border areas attract many migrant workers from neigh‐ boring countries. The constant movement of workers and the transient, often poorly con‐ structed dwellings they occupy facilitates cross-border transmission of malaria and complicates efforts to control it, making it one of the most serious vector-borne diseases in

Despite decades of success in reducing the number of cases of malaria in the country, the disease remains a major cause of morbidity and mortality. Approximately 32 million people in Thailand's border areas (50% of the Thailand's population) are at risk of contracting malaria. All four malaria parasites are present with the most common being *P.vivax* with 60% of all reported infections in 2011 [3]. Since 1997, *P. falciparum* and *P*. *vivax* infections have been recorded at near equal prevalence [70] (Figure 5). The under-developed border areas between Thailand and eastern Myanmar remain the worst affected area for continuing transmission [1, 2]. Non-immune workers who migrate across the international border remain the most susceptible and vulnerable populations. The constant movement of this population involved in gem mining, logging, agriculture, construction and other pursuits, has helped to increase the spread of multi-drug resistant *P. falciparum* malaria in the area and region. Serious outbreaks of malaria have taken place in high risk areas along the Thai-Myanmar border, especially in Kanchanaburi and Tak Provinces [70,143]. In four southern provinces of Thailand, malaria cases have risen to nearly 4,000 per year in the areas bordering Malaysia where social conflict and a local insurgency have greatly complicated control efforts [70]. At the same time, a rapid increase of rubber plantations in northeastern Thailand has become a major concern because of the potential for the reemergence of malaria [144]. Several major malaria vectors, mainly *Anopheles dirus* s.l., *An. maculatus* s.l., and *An. minimus* s.l., can adapt and utilize rubber plantations in place of more typical habitats like hill environments and natural forests [4]. Careful attention and monitoring to land use changes along with climatic and other environ‐ mental changes is essential to help prevent or delay the reemergence of malaria in receptive

control program.

294 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**2.5. Thailand**

*2.5.1. Overview*

these areas.

areas.

Approximately 73 *Anopheles* species are recognized in Thailand. Members within the Leucos‐ phyrus Group, the Maculatus Group and the Minimus Complex are recognized as the most important malaria vectors in the country [145,146,147,148,149]. Molecular techniques based on polymerase chain reaction (PCR) technology have allowed important malaria vectors com‐ prised of sibling species to be correctly identified [33,59]. Within the Dirus Complex, *An. baimaii* and *An. dirus* are considered to be primary malaria vectors in Thailand [149]. Both are forest and forest-fringe inhabiting mosquitoes that are considered highly anthropophilic [150,112,149]. However, a recent study showed a significantly greater number of *An. dirus* and *An. baimaii* collected from cattle-baited traps as compared to human-landing collections, demonstrating that both species could also show strong zoophilic behavior [151].

Among the members of the Maculatus Group, seven known species have been reported in Thailand, including *An*. *maculatus*, *An. sawadwongporni, An. dravidicus, An. notanandai, An. willmori, An. pseudowillmori,* and *An. rampae* [152,153,154,155,147,149,156]. *Anopheles macula‐ tus* and *An. pseudowillmori* has been implicated as important malaria vectors in southern and western Thailand, respectively [145, 147, 149]. *Anopheles sawadwongporni* is a common species often found in high density throughout Thailand, especially along the border provinces with Myanmar and Malaysia [157]. Based on feeding behavior and the natural infection rate detected in this species, *An. sawadwongporni* appears to be a malaria vector in Thailand [16,158,149]. Plasticity in trophic behavior and host preferences over the geographical range of members of this group have been reported [159,153,160]

*An. minimus* is also an important vector of malaria and is widespread throughout Thailand [161]. Its sibling species, *An. harrisoni* (formerly *An. minimus* C) appears restricted to only two districts of Kanchanaburi Province, western Thailand, where it also occurs in sympatry with *An. minimus* [162]. *Anopheles harrisoni* was previously collected from Mae Sot in Tak Province and Mae Rim in Chiangmai Province, northern Thailand, but no clear confirmation was made at the time [149].

Several other potential secondary or incidental vectors of malaria are also present in Thailand. These mosquitoes can have a close association with humans and include *An. barbirostris* s.l. and *An. epiroticus* (Sundaicus Complex) [163]. Within the *An. barbirostris* Subgroup, *An. campestris* is incriminated as a potential vector of *P. vivax* in Thailand [164]. Additionally, under the correct conditions *An. karwari, An. philippinensis* and *An. tessellatus* are also considered to be potential malaria vectors in Thailand. Recently, *An. cracens* (Dirus Complex) and *An. latens* (Leucosphyrus Complex) have been shown natural vectors of *P. knowlesi* in the south of Thailand [165,166,163]. A list of known and potential malaria vector species in Thailand is provided in Table 2.


+: malaria vector, -: not recorded as vector

**Table 2.** Known and potential malaria vector species in Thailand [163].

#### *2.5.3. Anopheline behavior*

Among the members of the Maculatus Group, seven known species have been reported in Thailand, including *An*. *maculatus*, *An. sawadwongporni, An. dravidicus, An. notanandai, An. willmori, An. pseudowillmori,* and *An. rampae* [152,153,154,155,147,149,156]. *Anopheles macula‐ tus* and *An. pseudowillmori* has been implicated as important malaria vectors in southern and western Thailand, respectively [145, 147, 149]. *Anopheles sawadwongporni* is a common species often found in high density throughout Thailand, especially along the border provinces with Myanmar and Malaysia [157]. Based on feeding behavior and the natural infection rate detected in this species, *An. sawadwongporni* appears to be a malaria vector in Thailand [16,158,149]. Plasticity in trophic behavior and host preferences over the geographical range

*An. minimus* is also an important vector of malaria and is widespread throughout Thailand [161]. Its sibling species, *An. harrisoni* (formerly *An. minimus* C) appears restricted to only two districts of Kanchanaburi Province, western Thailand, where it also occurs in sympatry with *An. minimus* [162]. *Anopheles harrisoni* was previously collected from Mae Sot in Tak Province and Mae Rim in Chiangmai Province, northern Thailand, but no clear confirmation was made

Several other potential secondary or incidental vectors of malaria are also present in Thailand. These mosquitoes can have a close association with humans and include *An. barbirostris* s.l. and *An. epiroticus* (Sundaicus Complex) [163]. Within the *An. barbirostris* Subgroup, *An. campestris* is incriminated as a potential vector of *P. vivax* in Thailand [164]. Additionally, under the correct conditions *An. karwari, An. philippinensis* and *An. tessellatus* are also considered to be potential malaria vectors in Thailand. Recently, *An. cracens* (Dirus Complex) and *An. latens* (Leucosphyrus Complex) have been shown natural vectors of *P. knowlesi* in the south of Thailand [165,166,163]. A list of known and potential malaria vector species in Thailand is

> **Vector in neighboring countries**

**Vector of** *Plasmodium knowlesi* **in Thailand**

of members of this group have been reported [159,153,160]

296 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**Vector in Thailand**

*Anopheles dirus* + + - *Anopheles baimaii* + - - *Anopheles cracens* - - + *Anopheles minimus* + + - *Anopheles maculatus* + + - *Anopheles pseudowillmori* + - - *Anopheles sawadwongporni* + - - *Anopheles epiroticus* + + - *Anopheles campestris* + - - *Anopheles latens* - - +

at the time [149].

provided in Table 2.

*Anopheles* **species**

+: malaria vector, -: not recorded as vector

**Table 2.** Known and potential malaria vector species in Thailand [163].

Knowledge of mosquito behavior is of paramount importance to understand the epidemiology of disease transmission and apply effective vector control. Details on mosquito biology, especially blood feeding activity and host preference of a defined species within its particular group or complex is essential to help identify their respective role in disease transmission in specific areas and help vector control operators to design the most appropriate strategy to reduce biting densities. Numerous observations on biting cycles and host preference of the three complexes/group, *An. dirus*, *An. minimus,* and *An. maculatus*, have been conducted in Thailand [167, 168, 169, 170, 171, 172]. However, nearly all previous ecological and behavior studies were based on species populations identified by morphological characters only. Studies on vectors have recognized additional *Anopheles* species within species complexes in Thailand [150, 173, 161, 149]. Infrastructure development and deforestation along the national borders with other countries in the past two decades has led to a significant reduction in malaria incidence, yet many malaria vectors have apparently and successfully adapted to the environmental changes. Using molecular approaches enables investigators to describe the trophic behavior of each species within a complex. For example, the different biting activities of *An. minimus* and *An. harrisoni* were described from two malaria endemic areas of Tak [143] and Kanchanaburi [174] provinces, respectively. Recently, the biting activity and host prefer‐ ence of *An. dirus* and *An. baimaii* have been described from Kanchanaburi [151]. More mean‐ ingful investigations on population biology, bionomics and blood feeding activity of sympatric sibling species within medically important complexes can now be conducted with greater accuracy.

#### *2.5.4. Effects of changing environmental conditions on malaria vectors and transmission*

Most insect species are generally very sensitive to changes in climatic and environmental conditions, such as ambient temperature, relative humidity, wind speed, and rainfall. The natural environment imposes significant constraints on insect populations [175, 176]. Among the blood-sucking species in the forest-type habitat that transmit diseases to humans, mos‐ quitoes are found to be susceptible to environmental/climatic modifications [144]. Longevity (survival), population density, and ecological distribution of any mosquito can be dramatically influenced by small changes in environmental conditions, and the availability of suitable hosts, larval habitats and adult resting sites. Changes in environmental conditions are directly influenced by modification and increased land use, such as conversion of rice fields to rubber plantations, forested areas to urbanized environments. Human activities are of major concern in changing the patterns of vector-borne diseases. For example, in 1988 a major malaria outbreak along the Thai-Cambodia border was due to transient employment opportunities from gem mining activities with almost 60,000 malaria cases detected in this population [4]. Similarly, between 1998 and 2000 an outbreak of malaria occurred at Suan Ping Village, Ratchaburi Province, western Thailand, in another gem mining area where most of the work force was recruited from Myanmar. This outbreak clearly showed that the man-made activity and population movement could be a significant factor in contributing to disease transmission. In the past three decades, rubber plantations have expanded in most SEA countries, including Thailand. Although Thailand is known as a significant producer of natural rubber, these plantations were generally restricted to southern Thailand. Recently, rubber trees have been planted in the east and northeastern parts of the country. Rubber plantations placed in once forested hill areas provide potential habitats for several primary malaria vectors such as *An. dirus* and *An. maculatus*, two commonly found vectors in southern Thailand [161]. Recent rubber plantation expansion in the northeast has also opened more job opportunity for migrant workers from neighboring countries. Lacking sufficient labor resources in Thailand, over one million registered migrant workers from neighboring countries have entered the country since 2004 [144]. This has undoubtedly resulted in trans-border movement of malaria into Thailand with the potential of re-introduction of transmission in once malaria-free areas and malaria resurgence and outbreaks in more vulnerable environments.

In summary, efforts are being directed to strengthen malaria control activities along the international borders of Thailand. The problem of border malaria due to inter-country human population movement, both legal and not, is known to greatly complicate the control efforts. In addition, land use modifications have a great influence on vector-borne disease transmis‐ sion. Careful attention to land use changes along with the climatic and environmental changes is needed to help predict and prevent the reemergence of malaria in all areas of Thailand. Effective collaborative efforts between neighboring countries with trans-border malaria have to be implemented to mitigate continued high malaria transmission in these sensitive areas of the country.

#### **2.6. Vietnam**

#### *2.6.1. Overview*

Vietnam has a land area of 331,690 km², and 4,550 km long with a total population of approx‐ imately 88.2 million [177]. ) This country shares borders with China in the north, Laos and Cambodia in the west. Malaria is the most important public health burden. A massive epidemic of 1991 resulted in more than one million cases and 4,600 deaths [178]. After this epidemic, the National Malaria Control Program (NMCP) focussed on malaria as its first public health priority and intensive control activities were implemented to help reduce malaria transmission in the country, including mass drug treatment in high endemic areas, indoor residual insecti‐ cide spraying and distribution of insecticide-treated bet nets. The successes of the NMCP have been witnessed in many areas, especially in northern Vietnam where no local malaria cases have been reported and malaria entomological inoculated rate has been nil for many years [6, 7, 32]. While malaria control has been successful in northern Vietnam, malaria continues to be a problem further south, particularly in the hilly-forested areas of central and southern Vietnam, and along the international borders with Cambodia and Lao PDR where frequent human population movements occur [92, 43]. Various ethnic minorities are the populations at greatest risk of malaria, suffering five times more malaria paroxysms than the vast majority of the Vietnamese population [179, 180]. From 2010 to 2011, respectively 36% to 18% of the population were still living in defined high transmission areas, while 54% to 20% were exposed to low transmission and 10% to 63% where in malaria-free, many urbanized, areas [2,3].

In the past three decades, rubber plantations have expanded in most SEA countries, including Thailand. Although Thailand is known as a significant producer of natural rubber, these plantations were generally restricted to southern Thailand. Recently, rubber trees have been planted in the east and northeastern parts of the country. Rubber plantations placed in once forested hill areas provide potential habitats for several primary malaria vectors such as *An. dirus* and *An. maculatus*, two commonly found vectors in southern Thailand [161]. Recent rubber plantation expansion in the northeast has also opened more job opportunity for migrant workers from neighboring countries. Lacking sufficient labor resources in Thailand, over one million registered migrant workers from neighboring countries have entered the country since 2004 [144]. This has undoubtedly resulted in trans-border movement of malaria into Thailand with the potential of re-introduction of transmission in once malaria-free areas and malaria

In summary, efforts are being directed to strengthen malaria control activities along the international borders of Thailand. The problem of border malaria due to inter-country human population movement, both legal and not, is known to greatly complicate the control efforts. In addition, land use modifications have a great influence on vector-borne disease transmis‐ sion. Careful attention to land use changes along with the climatic and environmental changes is needed to help predict and prevent the reemergence of malaria in all areas of Thailand. Effective collaborative efforts between neighboring countries with trans-border malaria have to be implemented to mitigate continued high malaria transmission in these sensitive areas of

Vietnam has a land area of 331,690 km², and 4,550 km long with a total population of approx‐ imately 88.2 million [177]. ) This country shares borders with China in the north, Laos and Cambodia in the west. Malaria is the most important public health burden. A massive epidemic of 1991 resulted in more than one million cases and 4,600 deaths [178]. After this epidemic, the National Malaria Control Program (NMCP) focussed on malaria as its first public health priority and intensive control activities were implemented to help reduce malaria transmission in the country, including mass drug treatment in high endemic areas, indoor residual insecti‐ cide spraying and distribution of insecticide-treated bet nets. The successes of the NMCP have been witnessed in many areas, especially in northern Vietnam where no local malaria cases have been reported and malaria entomological inoculated rate has been nil for many years [6, 7, 32]. While malaria control has been successful in northern Vietnam, malaria continues to be a problem further south, particularly in the hilly-forested areas of central and southern Vietnam, and along the international borders with Cambodia and Lao PDR where frequent human population movements occur [92, 43]. Various ethnic minorities are the populations at greatest risk of malaria, suffering five times more malaria paroxysms than the vast majority of the Vietnamese population [179, 180]. From 2010 to 2011, respectively 36% to 18% of the

resurgence and outbreaks in more vulnerable environments.

298 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

the country.

**2.6. Vietnam**

*2.6.1. Overview*

All four human malaria parasites and *P. knowlesi* have been reported in Vietnam [181, 182, 183]. Reported malaria cases are mostly due to *P. falciparum* (66%), followed by *P. vivax* (34%), while *P. malariae* and *P. ovale* are seldom recorded [3]. Transmission of zoonotic *Plasmodium knowlesi* has been reported in southern-central Vietnam [184, 185, 186, 187, 188, 189]. *Plasmo‐ dium knowlesi* has been found in several *Anopheles* species, especially *An. dirus* considered as the main malaria vector in Vietnam [181, 183].

Insecticide use and mass drug treatment were effective measures for controlling vectors and malaria transmission in Vietnam [190]. However, with decades of insecticide and anti-malarial drug use, both resistance of *Anopheles* to insecticides and malaria parasites to malarial drugs has appeared [191, 192, 56, 193, 194]. Moreover, land use modifications caused by deforesta‐ tion, expansion of agriculture, conversion from rice to shrimp production, have introduced dramatic changes in mosquito habitats and represent new challenges for malaria control strategies in Vietnam. Although considerable effort has been invested applying malaria control activities following the 1991 epidemic, malaria still ranks as an important public health problem. In 2011, 16, 539 malaria cases (6 deaths) were reported in central and highland areas of Vietnam [195]. There are two periods of the year during which malaria transmission is the highest: (1) from the end of the rainy season to the early dry season (September to January) and (2) from the late dry season to the early rainy season (May to August).The dry and rainy seasons may slightly shift from year to year and the intensity of malaria transmission is also dependent on the geographic area and other variables.

The term "forest malaria" is defined within a specific context of transmission epidemiology and involves several sylvatic vectors such as *An. dirus* [7, 196, 43, 183]. The population at greatest risk of infection are the inhabitants of hilly forested areas, particularly ethnic minor‐ ities that have the poorest living standards, low educational background, and where their normal life activities include jungle exploitation and subsistence-level slash and burn cultiva‐ tion practices [196, 71, 180]. Moreover, in both recovered forests and deforested areas, many workers come to live in rudimentary huts and other shelters during harvest time that afford poor protection against mosquitoes. Population movements between different areas, together with generally poor living conditions expose them to high malaria risk. Indeed, the socialecological factors such as living in remoted areas and the logistical difficulties in implementing and sustaining control efforts against highly efficient forest vectors favour malaria transmis‐ sion [17, 196, 18, 197, 26].

After the last local malaria cases were reported in northern Vietnam in 1995, malaria trans‐ mission has apparently not returned despite reports that malaria vectors remain common [7, 198, 199]. A study on the health information system on malaria surveillance activities in Vietnam [200] called into question the accuracy of data captured and that there was likely a great underestimation for the actual malarial burden reported during the past decade. By applying spatial-temporal analytical tools to determine the association among social aspects, environmental factors and malaria risk in Vietnam, Bui et al., (2011) suspected that malaria transmission is still occurring in some focal areas of northern Vietnam, therefore, emphasizing that malaria surveillance activities and control capabilities should be sustained to prevent or respond to the reintroduction of malaria in receptive areas.

The prevalence of human malaria and entomological inoculation rates have been reported in several provinces of southern and central Vietnam, such as Binh Thuan, Ninh Thuan, Khanh Hoa, Quang Binh, Binh Phuoc, Dak Nong, Dak Lak, Bac Lieu [7, 181,42, 180, 43, 183, 195].

Source: Meeting on Outdoor Malaria Transmission in the Mekong Countries for 13 countries during 12-13 March 2012, Bangkok, Thailand. [http://www.rbm.who.int/partnership/wg/wg\_itn/ppt/ws2/m4VuDucChinh.pdf.]

#### *2.6.2. Biodiversity of* Anopheles *vectors in Vietnam*

In Vietnam, 61 *Anopheles* species have been reported using morphological identification methods [201]. Many species of *Anopheles* from SEA belong to a species complex or group [39]. For species complexes, as often there is either no or unreliable morphological characters to accurately distinguish each sibling species from one another. Therefore, their specific role in malaria transmission remains unclear [202, 203, 40]. The *Anopheles* in Vietnam can be divided into three categories based on their vectorial capacity to transmit malaria: (i) the primary vectors include species in the Dirus (*An. dirus)*, Minimus (*An. minimus*, *An*. *harrisoni)* and Sundaicus (*An. epiroticus)* Complexes; (ii) secondary or incidental vectors include *An. aconitus, An. jeyporiensis*, *An. maculatus*, *An. subpictus, An. sinensis, An. pampanai, An. vagus, An. indefi‐ nitus*; and (iii) suspected vectors are *An. interruptus, An. campestris, An. lesteri* and *An. nimpe*. Therefore, 16 (26%) are considered as having some role in malaria transmission in the country.

**Figure 6.** Total population living in risk area, malaria cases and positive cases (confirmed by microscopy) in Vietnam in 2011.

However, more studies are needed to better define the importance and role of each species, especially secondary and suspected vectors. For example, *An. culicifacies* s.l., an important vector in India, was recently found in Vietnam. However, the species identified was *An. culicifacies* species B of the Culicifacies Complex which is primarily zoophilic and thus regarded as not involved in malaria transmission in the country [54]. In addition, extensive environ‐ mental changes have occurred since the 90's, which have modified the *Anopheles* habitats and the presence and prevalence of some species.

### *2.6.3. Distribution of* Anopheles *vectors in Vietnam*

that malaria surveillance activities and control capabilities should be sustained to prevent or

The prevalence of human malaria and entomological inoculation rates have been reported in several provinces of southern and central Vietnam, such as Binh Thuan, Ninh Thuan, Khanh Hoa, Quang Binh, Binh Phuoc, Dak Nong, Dak Lak, Bac Lieu [7, 181,42, 180, 43, 183, 195].

> Parasite Cases (Confirmed by microscopy)

respond to the reintroduction of malaria in receptive areas.

living in the risk area

North 39,723,077 4,498,201 22,598 638 Center 23,695,858 9,071,902 21,557 15,272

South 24,830,313 1,892,751 1,433 522 Total 88,249,248 15,462,854 45,588 16,432

*2.6.2. Biodiversity of* Anopheles *vectors in Vietnam*

Malaria cases

Highly endemic Moderate endemic Low endemic Risk of resurgence No malaria

Source: Meeting on Outdoor Malaria Transmission in the Mekong Countries for 13 countries during 12-13 March 2012, Bangkok, Thailand. [http://www.rbm.who.int/partnership/wg/wg\_itn/ppt/ws2/m4VuDucChinh.pdf.]

**Figure 6.** Total population living in risk area, malaria cases and positive cases (confirmed by microscopy) in Vietnam in

In Vietnam, 61 *Anopheles* species have been reported using morphological identification methods [201]. Many species of *Anopheles* from SEA belong to a species complex or group [39]. For species complexes, as often there is either no or unreliable morphological characters to accurately distinguish each sibling species from one another. Therefore, their specific role in malaria transmission remains unclear [202, 203, 40]. The *Anopheles* in Vietnam can be divided into three categories based on their vectorial capacity to transmit malaria: (i) the primary vectors include species in the Dirus (*An. dirus)*, Minimus (*An. minimus*, *An*. *harrisoni)* and Sundaicus (*An. epiroticus)* Complexes; (ii) secondary or incidental vectors include *An. aconitus, An. jeyporiensis*, *An. maculatus*, *An. subpictus, An. sinensis, An. pampanai, An. vagus, An. indefi‐ nitus*; and (iii) suspected vectors are *An. interruptus, An. campestris, An. lesteri* and *An. nimpe*. Therefore, 16 (26%) are considered as having some role in malaria transmission in the country.

Area Population Population

300 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

2011.

According to Phan (2008), the anopheline fauna in Vietnam has been sorted based on two criteria [204]:


Vectors such as *An. minimus* and *An. dirus* are present in almost all clusters, whereas *An. epiroticus* and *An. subpictus* are vectors restricted along the coast line with varying degrees of brackish water in natural impoundments (e.g., lagoons, blocked coastal streams and small rivers). The SEA distribution of the dominant vector species has recently been well delineated [59]. Many studies have contributed to new insights on the presence, biology and behavior, and distribution of *Anopheles* in Vietnam. The majority of investigations have focused in the central and southern regions where malaria transmission is most endemic. In Ma Noi and Phuoc Binh Communes, a forested area of Binh Thuan Province, central Vietnam, 24 *Anophe‐ les* species were collected between 2004 and 2006. The predominant malaria vectors were *An. dirus* and *An. minimus* s.l. and also included *An. maculatus* s.l.*, An. pampanai, An. aconitus, An. annularis* s.l., *An. nigerrimus, An. philippinensis, An. sinensis, An. annandalei, An. argyropus, An. barbumbrosus, An. crawfordi, An. jamesii, An. jeyporiensis, An. monstrosus, An. tessellatus, An. vagus, An. varuna, An. barbirostris, An. kochi, An. nivipes, An. peditaeniatus,* and *An. splendidus* [43].

A nation-wide study to evaluate the status and the distribution of *Anopheles* malaria vectors in four forested regions in northern Vietnam (northern part of the Hai Van Pass) recorded 30 *Anopheles* species, of which, 20 species were collected in primary forests, 21 in secondary growth forests, 16 in woodland or shrub biomes, and 6 species in tidal mangrove zones. Two main malaria vectors were present, *An. minimus* s.l. and *An. dirus*, as well as potential secondary vectors, including *An. aconitus, An. jeyporiensis, An. maculatus, An. subpictus*, *An. sinensis* and *An. donaldi*, the latter species representing a new country distribution record for Vietnam [205]. Sympatric sibling species, *An. minimus* and *An. harrisoni*, was confirmed in Hoa Binh Province in north-eastern Vietnam [32] as well as 21 other *Anopheles* species near the Son La hydroelectrical dam (Son La Province), including *An. minimus* [199]. This finding showed that even though malaria prevalence in this region is very low, malaria risk still remains and vector control capacity in this region should be sustained to prevent or combat possible malaria outbreaks.

Molecular methods have been developed to resolve identification problems due to overlap in morphological characters among sibling species [206, 207, 208, 55, 209, 210, 211]. The distri‐ bution of species that were once morphologically identical has been clarified for many localities.

In Vietnam, *An. minimus* has an extensive north-south distribution, while *An. harrisoni* has a much more patchy occurrence [212]. The presence of *An. minimus* and *An. harrisoni* occurs from northern to south-central regions where they often occur in sympatry [213, 32, 212, 42]. In central Vietnam, an increase in density of *An. harrisoni* has been seen compared to *An. minimus* which also coincided with the wide use of permethrin-treated bed nets in the study village [7,213]. The dominance of *An. harrisoni* was also reported in Quang Binh Province, northern central Vietnam [42].

Out of the 8 species that make up the Dirus Complex, only two occur in Vietnam: *Anopheles dirus*, the main vector found in hilly forested areas [32, 41, 18, 42, 43] and the recently described cryptic species, *An.* aff. *takasagoensis* collected in northern Vietnam [40]. Khanh Phu Commune (Khanh Hoa Province in south-central Vietnam) is a hilly-forested area where malaria transmission is endemic. Twelve *Anopheles* species were captured in this area in which *An. dirus* was the dominant (83.2%) species present [183].

*Anopheles epiroticus*is considered the main malaria vectors in the southern coastal areas below the 11th parallel. Recent studies have shown extremely low infectious rates for this species [46, 58, 7, 214]. *An. epiroticus* is the only member of the Sundaicus Complex present in Vietnam [58, 117, 32, 44].

*Anopheles nimpe* (Hycarnus Group) is a recently described species which was discovered along the coastal area of southern Vietnam and is suspected as a malaria vector due to its high attraction to humans [45, 215, 32, 42]. To date, very little else is known about this species.

The Maculatus Group has three representatives present in the country, *An. maculatus*, *An. sawadwongporni* and *An. rampae* (Form K), with variable distributions and densities based on geographic area [42, 43]. Only *An. maculatus* is regarded as a vector of minor (secondary) importance [45, 204].

#### *2.6.4. Vector habitats and behavior*

*Anopheles dirus* is primarily a forest malaria vector and the main vector species in many cases. However, in Truong Xuan Commune (Quang Binh Province) and Phuoc Chien Commune (Ninh Thuan Province), locations where malaria transmission is still high, *An. dirus* has not been reported infected [18,42], therefore the role of secondary vectors in malaria transmission may be under estimated [32,42,43].

Species of the Minimus Complex are normally found in forested foothills associated with freshwater streams and canals. *Anopheles minimus* has also been found in sunlit and shaded ponds, rock pools, and rice paddies. On the outskirts of Hanoi, along the Red River Delta, *An. minimus* was found to oviposit in artificial containers such as rainwater tanks near houses [204,206,216]. *Anopheles epiroticus* is an important malaria vector along the coast of southern Vietnam and has been commonly found in man-made fish and shrimp ponds. This species has been observed to bite humans throughout the night [32].

Species of the *An. maculatus* Group has been found in hilly forested areas, especially in the recovered forest areas. Their larval habitats are closely associated with stream pools and drying river beds. They are generally zoophilic being more attracted to cattle than humans and tend to bite from early evening to the early morning hours [32, 42, 43].

#### *2.6.5. Implication of changing social and environment conditions on vectors and transmission*

Extensive environmental changes have occurred in Vietnam since the 1990's [217], which have modified the *Anopheles* habitats and the presence and prevalence of some species. *Anopheles minimus,* known as an endophilic and fairly anthropophilic vector, is abundant mainly during the dry season that generally lasts from November to April in the south and from November to February in northern Vietnam [7]. The use of indoor insecticide residual spraying has been successfully used to reduce malaria transmission as *An. minimus* has a strong behavioral tendency for biting indoors. However, this adaptable vector has shown marked variations in its behavior from endophilic to exophilic and anthropophilic to zoophilic in northern Vietnam where it was more attracted to cattle and other domestic animals kept near the house [32, 34, 42]. In parallel, insecticide use led to the significant increase in density of *An. harrisoni* in Khanh Phu Commune [213].

Human practices are generating important environmental changes throughout the country, such as deforestation, reforestation, plantations, fish and shrimp ponds replacing rice culti‐ vation, road construction, dams, more intensive slash and burn activities, and so on. Such land use changes have an impact on vector habitats, vector diversity and distribution that could either promote or discourage the propagation of some vector species and therefore impact risk of malaria transmission [199,218]. In urban and rural settings, the expansion of electricity to the more mountainous and remote villages encourages people to remain outdoors for longer periods during night time, thereby increasing risk in this unprotected population of being bitten by the *Anopheles* vectors, especially *An. dirus* which is more likely to be exophagic and exophilic [32,43]. Housing construction has implications on malaria transmission. Houses with open construction (e.g., with uncompleted walls, no doors) allow anthropophilic mosquitoes to easily detect human host attractant stimuli and enter the houses to bite [32]. As standard of living and economic development increase in the country, so will the type and quality of houses thus adding additional barriers to host-seeking vectors.

### **3. Conclusions**

though malaria prevalence in this region is very low, malaria risk still remains and vector control capacity in this region should be sustained to prevent or combat possible malaria

Molecular methods have been developed to resolve identification problems due to overlap in morphological characters among sibling species [206, 207, 208, 55, 209, 210, 211]. The distri‐ bution of species that were once morphologically identical has been clarified for many

In Vietnam, *An. minimus* has an extensive north-south distribution, while *An. harrisoni* has a much more patchy occurrence [212]. The presence of *An. minimus* and *An. harrisoni* occurs from northern to south-central regions where they often occur in sympatry [213, 32, 212, 42]. In central Vietnam, an increase in density of *An. harrisoni* has been seen compared to *An. minimus* which also coincided with the wide use of permethrin-treated bed nets in the study village [7,213]. The dominance of *An. harrisoni* was also reported in Quang Binh Province,

Out of the 8 species that make up the Dirus Complex, only two occur in Vietnam: *Anopheles dirus*, the main vector found in hilly forested areas [32, 41, 18, 42, 43] and the recently described cryptic species, *An.* aff. *takasagoensis* collected in northern Vietnam [40]. Khanh Phu Commune (Khanh Hoa Province in south-central Vietnam) is a hilly-forested area where malaria transmission is endemic. Twelve *Anopheles* species were captured in this area in which *An.*

*Anopheles epiroticus*is considered the main malaria vectors in the southern coastal areas below the 11th parallel. Recent studies have shown extremely low infectious rates for this species [46, 58, 7, 214]. *An. epiroticus* is the only member of the Sundaicus Complex present in Vietnam [58,

*Anopheles nimpe* (Hycarnus Group) is a recently described species which was discovered along the coastal area of southern Vietnam and is suspected as a malaria vector due to its high attraction to humans [45, 215, 32, 42]. To date, very little else is known about this species.

The Maculatus Group has three representatives present in the country, *An. maculatus*, *An. sawadwongporni* and *An. rampae* (Form K), with variable distributions and densities based on geographic area [42, 43]. Only *An. maculatus* is regarded as a vector of minor (secondary)

*Anopheles dirus* is primarily a forest malaria vector and the main vector species in many cases. However, in Truong Xuan Commune (Quang Binh Province) and Phuoc Chien Commune (Ninh Thuan Province), locations where malaria transmission is still high, *An. dirus* has not been reported infected [18,42], therefore the role of secondary vectors in malaria transmission

Species of the Minimus Complex are normally found in forested foothills associated with freshwater streams and canals. *Anopheles minimus* has also been found in sunlit and shaded

outbreaks.

localities.

117, 32, 44].

importance [45, 204].

*2.6.4. Vector habitats and behavior*

may be under estimated [32,42,43].

northern central Vietnam [42].

302 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

*dirus* was the dominant (83.2%) species present [183].

Many years of organized malaria control and research have led to some notable successes in reducing the incidence of malaria in countries located on mainland SEA. However, this disease is still a major health risk in rural and remote communities close to forest and forest fringe areas where socioeconomic conditions remain low, the areas more difficult to -reach, and daily human are closely-related or dependant on the subsistence from forests.

More recent and dramatic changes in the local ecology created by development projects, while aiming to improve the standard of living of the local populations, may have profound and negative effects upon human health and vector-borne diseases. In most countries, deforesta‐ tion, and reforestation, is one of the most potent factors in relation to emerging and re-emerging infectious diseases. For example, rubber plantations have had the effect of increasing the density of important malaria vectors in Thailand [75]. Southeast Asia has the highest relative rate of deforestation of any major tropical region in the world, and could deplete three quarters of its native forest cover by 2100, effectively removing up to 42% of its fauna and flora biodiversity [19]. Most of the main malaria vectors occurring in mainland SEA are associated with forests, therefore we can anticipate changes in distribution and population densities of malaria vectors, some possibly disappearing while secondary or potential vectors move to exploit the altered habitats to become primary malaria vectors of the future.

Moreover, the expanding exploitation and over utilization of natural resources, together with other forms of economic development can help to improve living conditions, while simulta‐ neously changing the environment in ways that might increase disease transmission risk of malaria or other vector-borne diseases (e.g., dengue). Together with changes in human practices, the adaptation of vector fauna to altered environments, including vector behaviour, might profoundly alter the dynamics of malaria transmission. These are some of the challenges to be raised by all countries in order to reach the goal of malaria elimination by 2015 (Lao PDR), 2020 (Vietnam), 2025 (Cambodia). Clearly there is a need for more studies on *Anopheles* malaria vectors in some countries of SEA, such as Myanmar, where work is now dated. For instance, in order to better control malaria and its vectors, a trans-border network should be organized at the SEA region scale. A better understanding of the mechanisms linking deforestation and development projects with anopheline ecology and malaria epidemiology, and that to contribute to improved health impact assessments in the future, are challenges for further study. Malaria vector control is still predominantly based on the use of insecticides as residual house spraying and bednet impregnation, and still regarded as the most effective way to attack vectors. Yet relatively little work has been done to exploit the behaviour of mosquito vectors as a means of transmission control (e.g., use of spatial repellents to impact outdoor transmis‐ sion, search of natural substances with insecticide properties respectful of the environment). With expected changes in the distribution and epidemiology of malaria, there will be a critical need to continue to explore and develop new and innovative methods of intervention to complement existing strategies.

## **Acknowledgements**

We would like to thank Dr. Steven Bjorge (World Health Organization, Cambodia) and Prof. Sylvie Manguin (Institut de Recherche pour le Développement (IRD, France) for the critical review of this book chapter. We also thank Vithee Muenworn, Ph.D candidate Kasetsart University for general help. Sincere thanks to Thailand Research Fund (TRF) and Department of Disease Control, MOPH, Thailand for providing financial support over the many fruitful years of entomological research.

## **Author details**

is still a major health risk in rural and remote communities close to forest and forest fringe areas where socioeconomic conditions remain low, the areas more difficult to -reach, and daily

More recent and dramatic changes in the local ecology created by development projects, while aiming to improve the standard of living of the local populations, may have profound and negative effects upon human health and vector-borne diseases. In most countries, deforesta‐ tion, and reforestation, is one of the most potent factors in relation to emerging and re-emerging infectious diseases. For example, rubber plantations have had the effect of increasing the density of important malaria vectors in Thailand [75]. Southeast Asia has the highest relative rate of deforestation of any major tropical region in the world, and could deplete three quarters of its native forest cover by 2100, effectively removing up to 42% of its fauna and flora biodiversity [19]. Most of the main malaria vectors occurring in mainland SEA are associated with forests, therefore we can anticipate changes in distribution and population densities of malaria vectors, some possibly disappearing while secondary or potential vectors move to

Moreover, the expanding exploitation and over utilization of natural resources, together with other forms of economic development can help to improve living conditions, while simulta‐ neously changing the environment in ways that might increase disease transmission risk of malaria or other vector-borne diseases (e.g., dengue). Together with changes in human practices, the adaptation of vector fauna to altered environments, including vector behaviour, might profoundly alter the dynamics of malaria transmission. These are some of the challenges to be raised by all countries in order to reach the goal of malaria elimination by 2015 (Lao PDR), 2020 (Vietnam), 2025 (Cambodia). Clearly there is a need for more studies on *Anopheles* malaria vectors in some countries of SEA, such as Myanmar, where work is now dated. For instance, in order to better control malaria and its vectors, a trans-border network should be organized at the SEA region scale. A better understanding of the mechanisms linking deforestation and development projects with anopheline ecology and malaria epidemiology, and that to contribute to improved health impact assessments in the future, are challenges for further study. Malaria vector control is still predominantly based on the use of insecticides as residual house spraying and bednet impregnation, and still regarded as the most effective way to attack vectors. Yet relatively little work has been done to exploit the behaviour of mosquito vectors as a means of transmission control (e.g., use of spatial repellents to impact outdoor transmis‐ sion, search of natural substances with insecticide properties respectful of the environment). With expected changes in the distribution and epidemiology of malaria, there will be a critical need to continue to explore and develop new and innovative methods of intervention to

We would like to thank Dr. Steven Bjorge (World Health Organization, Cambodia) and Prof. Sylvie Manguin (Institut de Recherche pour le Développement (IRD, France) for the critical

human are closely-related or dependant on the subsistence from forests.

304 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

exploit the altered habitats to become primary malaria vectors of the future.

complement existing strategies.

**Acknowledgements**

Wannapa Suwonkerd1\*, Wanapa Ritthison2,5, Chung Thuy Ngo3,4, Krajana Tainchum2 , Michael J. Bangs6 and Theeraphap Chareonviriyaphap2

\*Address all correspondence to: suwannapa@yahoo.com

1 Office of Disease Prevention and Control # 10, Department of Disease Control, Ministry of Public Health, Chiang Mai, Thailand

2 Department of Entomology, Faculty of Agriculture, Kasetsart University, Bangkok, Thai‐ land

3 Institut de Recherche pour le Développement (IRD), Lab. Immuno-Physiopathologie Mo‐ léculaire Comparée, UMR-MD3, Université Montpellier, Montpellier, France

4 National Institute of Veterinary Research, Ha Noi, Viet Nam

5 Office of Disease Prevention and Control # 3, Department of Disease Control, Ministry of Public Health, Chonburi, Thailand

6 Public Health & Malaria Control, International SOS, Kuala Kencana, Papua, Indonesia

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324 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

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## **Understanding** *Anopheles* **Diversity in Southeast Asia and Its Applications for Malaria Control**

Katy Morgan, Pradya Somboon and Catherine Walton

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55709

## **1. Introduction**

#### **1.1. Why study** *Anopheles* **diversity: Relevance for malaria control**

The need to understand diversity in *Anopheles* mosquitoes to win the fight against malaria first became apparent with the paradox of 'anophelism without malaria', as it became evident that there is a vast diversity of *Anopheles* species and that not all species transmit malaria [1]. For example, in Europe it was eventually deduced that the mosquito *Anopheles maculipennis* existed as a species complex comprising several species that differed in their breeding, feeding and resting habitats, which resulted not only in differences in malaria epidemiology but also the success or failure of malaria control efforts [2]. This realisation resulted in countless studies around the world to distinguish and characterise *Anopheles* species, often using molecular or chromosomal characters in the absence of reliable morphological characters [3-4]. Such studies have played an invaluable role in improving malaria control and have, in turn, revealed another layer of complexity. This is exemplified most clearly in the *Anopheles gambiae* Complex, which includes several important African malaria vectors. Taxa within the *An. gambiae* Complex can exist as recently diverged species such as *An. gambiae* and *An. arabiensis*, which still have the potential to exchange genes [5]; as incipient species such as the S and M molecular forms, or as genetically divergent locally adapted forms, e.g. adapted to forest or savannah [6]. Recent genomic studies of the *An. gambiae* Complex are revealing patterns of differential divergence and introgression across the genome between species [7-8]; such phenomena are likely to further complicate the definition of species boundaries within *Anopheles* complexes. Differences in characteristics relevant to malaria control may be present at even the subspecific level (e.g. larval habitat and insecticide resistance both within and between the S and M

© 2013 Morgan et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Morgan et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

molecular forms [9-11]), demonstrating the need to understand the generation and mainte‐ nance of *Anopheles* diversity at all levels.

This chapter focuses on the need to not only characterise species boundaries, ecology and distributions, but also to understand the potential for divergence and the extent of gene flow within and between species of *Anopheles* in Southeast Asia. Southeast Asia is characterised by having numerous vector taxa and epidemiological settings, and though there has been great progress in reducing malaria in Southeast Asia, it has proved difficult or impossible to completely eradicate in many places, e.g. [12-13]. A complete understanding of transmission dynamics in Southeast Asia and the best approach to interrupt them is complicated by several factors, including intraspecific variation in ecology and vector status across species distribu‐ tions, potential interactions between species in malaria transmission (i.e. the fact that the vectorial capacity of one species may vary depending on the presence of a second vector species), and by the potential for ongoing gene flow between species. In this chapter, we argue that understanding the complexity and diversity of *Anopheles* species in this region and the nature of isolation, ecological variation and gene flow in driving divergence or homogenising variation within and between them is key to a complete understanding of malaria transmission dynamics and our attempts to interrupt it via vector control. This involves determining the historical processes that have driven diversification to understand both current intraspecific and interspecific variation and the potential for future change (e.g. in adaptation to environ‐ mental change) that could affect malaria transmission and/or vector control efforts.

#### **2. Diversity of** *Anopheles* **species across Southeast Asia**

This chapter primarily focuses on the diversity of *Anopheles* species in Southeast Asia, which encompasses the geographical area east of India, south of China and west of New Guinea. Southeast Asia is further subdivided into two sub regions: mainland Southeast Asia, com‐ prised of Myanmar, Thailand, Cambodia, Lao People's Democratic Republic, Vietnam and peninsular Malaysia; and insular Southeast Asia, comprised of Indonesia, East Timor, Singapore, East Malaysia, Brunei and the Philippines. However, as many of the vector species found within Southeast Asia, e.g. members of the *An. minimus*, *An. dirus* and *An. subpictus* Complexes, and Funestus and Maculatus Groups, also overlap into India (particularly northeast India), Sri Lanka and China we have included these regions where relevant in order to achieve a more complete understanding of *Anopheles* diversity in Southeast Asia.

The diversity of Anopheline fauna that exists within Southeast Asia is richer than in any other region of the world [14], and at least 19 species, some of which comprise cryptic species complexes, are known to play some role in malaria transmission [15]. Exactly 50% of the 24 currently recognised *Anopheles* species complexes are found within Asia, which when com‐ pared with the 21%, 13%, 13% and 4% found in the Americas, Africa, Australia-Pacific and Europe, respectively, emphasises the complexity of diversity found within the Asian continent [14]. The considerable variation that exists between species in terms of habitat preference and feeding behaviour makes the characterisation of species distributions highly relevant to malaria control efforts. Malaria transmission characteristics and the effectiveness of control efforts such as insecticide treated bednets (ITNs), larvicides, and indoor residual spraying (IRS), will depend to a large extent on the vector species present in a given area [14], and since the effectiveness of a given vector species can be influenced by other species present in the region, malaria transmission dynamics also depend on species composition. Hence consider‐ able effort has been focussed on the stratification of malaria units for effectively targeted malaria control, with the ecological characteristics and geographical distributions of species having particular relevance [16]. In this section we discuss the geographical features that appear to define and limit species distributions, and the relevance of this information for malaria control.

molecular forms [9-11]), demonstrating the need to understand the generation and mainte‐

This chapter focuses on the need to not only characterise species boundaries, ecology and distributions, but also to understand the potential for divergence and the extent of gene flow within and between species of *Anopheles* in Southeast Asia. Southeast Asia is characterised by having numerous vector taxa and epidemiological settings, and though there has been great progress in reducing malaria in Southeast Asia, it has proved difficult or impossible to completely eradicate in many places, e.g. [12-13]. A complete understanding of transmission dynamics in Southeast Asia and the best approach to interrupt them is complicated by several factors, including intraspecific variation in ecology and vector status across species distribu‐ tions, potential interactions between species in malaria transmission (i.e. the fact that the vectorial capacity of one species may vary depending on the presence of a second vector species), and by the potential for ongoing gene flow between species. In this chapter, we argue that understanding the complexity and diversity of *Anopheles* species in this region and the nature of isolation, ecological variation and gene flow in driving divergence or homogenising variation within and between them is key to a complete understanding of malaria transmission dynamics and our attempts to interrupt it via vector control. This involves determining the historical processes that have driven diversification to understand both current intraspecific and interspecific variation and the potential for future change (e.g. in adaptation to environ‐

mental change) that could affect malaria transmission and/or vector control efforts.

to achieve a more complete understanding of *Anopheles* diversity in Southeast Asia.

The diversity of Anopheline fauna that exists within Southeast Asia is richer than in any other region of the world [14], and at least 19 species, some of which comprise cryptic species complexes, are known to play some role in malaria transmission [15]. Exactly 50% of the 24 currently recognised *Anopheles* species complexes are found within Asia, which when com‐ pared with the 21%, 13%, 13% and 4% found in the Americas, Africa, Australia-Pacific and Europe, respectively, emphasises the complexity of diversity found within the Asian continent [14]. The considerable variation that exists between species in terms of habitat preference and feeding behaviour makes the characterisation of species distributions highly relevant to

This chapter primarily focuses on the diversity of *Anopheles* species in Southeast Asia, which encompasses the geographical area east of India, south of China and west of New Guinea. Southeast Asia is further subdivided into two sub regions: mainland Southeast Asia, com‐ prised of Myanmar, Thailand, Cambodia, Lao People's Democratic Republic, Vietnam and peninsular Malaysia; and insular Southeast Asia, comprised of Indonesia, East Timor, Singapore, East Malaysia, Brunei and the Philippines. However, as many of the vector species found within Southeast Asia, e.g. members of the *An. minimus*, *An. dirus* and *An. subpictus* Complexes, and Funestus and Maculatus Groups, also overlap into India (particularly northeast India), Sri Lanka and China we have included these regions where relevant in order

**2. Diversity of** *Anopheles* **species across Southeast Asia**

nance of *Anopheles* diversity at all levels.

328 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

Early attempts for a geographical stratification of malaria units [17] were based on the biogeographical realms of Wallace (1876). However, Wallace's Oriental Realm is largely inappropriate for South Asia and Southeast Asia due to the exceptionally high biodiversity and high heterogeneity of spatial distribution of vectors in this region [14-15]. On a smaller spatial scale there are multiple biogeographical subregions within Southeast Asia, including the biodiversity hotspot regions of IndoBurma, Sundaland, the Philippines and Wallacea ([18]; see figure 1). These hotspots were defined in part on the basis of endemism so it is not surprising that they appear to define the distributions of many malaria vectors, with clear patterns of species turnover apparent at each of the biogeographical boundaries.

**Figure 1.** Topological map of Southeast Asia, indicating the four main biogeographical zones as defined by Myers *et al.* (2000) [17].

The first biogeographical boundary that shows a clear association with species distributions is that separating IndoBurma from southwestern Asia (Figure 1). It should be noted that northeast India, although politically part of India, is biogeographically and ecologically aligned with IndoBurma rather than southwestern Asia. The *Anopheles* fauna on either side of this boundary is generally distinct, for example several vector species that are distributed across IndoBurma, including *An. baimaii*, *An. sawadwongporni* and *An. maculatus*(Figures 2 and 3), have distributions that extend little further than this western border. The closely related *An. minimus* and *An. fluviatilis* Complexes show largely parapatric distributions that overlap along the western border of IndoBurma, with the distribution of the *An. minimus* Complex being primarily restricted to IndoBurma and that of the *An. fluviatilis* Complex being mostly limited to southwestern Asia (Figure 4).

**Figure 2.** The distribution of species within the *Anopheles dirus* Complex.

Understanding *Anopheles* Diversity in Southeast Asia and Its Applications for Malaria Control http://dx.doi.org/10.5772/55709 331

**Figure 3.** The distribution of species within the Maculatus Group.

The first biogeographical boundary that shows a clear association with species distributions is that separating IndoBurma from southwestern Asia (Figure 1). It should be noted that northeast India, although politically part of India, is biogeographically and ecologically aligned with IndoBurma rather than southwestern Asia. The *Anopheles* fauna on either side of this boundary is generally distinct, for example several vector species that are distributed across IndoBurma, including *An. baimaii*, *An. sawadwongporni* and *An. maculatus*(Figures 2 and 3), have distributions that extend little further than this western border. The closely related *An. minimus* and *An. fluviatilis* Complexes show largely parapatric distributions that overlap along the western border of IndoBurma, with the distribution of the *An. minimus* Complex being primarily restricted to IndoBurma and that of the *An. fluviatilis* Complex being mostly

limited to southwestern Asia (Figure 4).

330 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

*An. nemophilous An. elegans An. scanloni An. dirus An. cracens An. baimaii An. takasagoensis*

**Figure 2.** The distribution of species within the *Anopheles dirus* Complex.

**Figure 4.** The distribution of species within the Minimus Subgroup (which encompasses the *An. minimus* and An. flu‐ *viatilis* Complexes).

The boundary between the biodiversity hotspot regions of IndoBurma and Sundaland (Figure 1) represents a second major biogeographic transition in Southeast Asia, and is characterised by high species turnover in a number of taxonomic groups (e.g. birds, mammals and reptiles [19-21]). This long-recognised biogeographic transition was first noted by Wallace in 1869, and though its exact position along the Thai-Malay Peninsula is debated, with some dispute as to whether the transition occurs at the Isthmus of Kra (10º30'N) or the Kangar-Pattani line (6-7ºN) further south [22], its biogeographical significance is unquestioned. The transition is associated with dramatic climate and phytological changes. IndoBurma has a very seasonal climate in terms of both temperature and rainfall, whereas that of Sundaland is much more stable, with precipitation levels remaining high throughout the year. Whereas mixed moist deciduous forest is the dominant forest habitat type of IndoBurma, that of Sundaland is perhumid evergreen forest [23-24]. Thus it seems unsurprising that this is a region of high species turnover, as the selective pressures on either side of the Isthmus of Kra biogeographic transition would differ considerably, potentially driving rapid adaptive change and subse‐ quent ecological speciation following the dispersal of taxa from one side to the other.

Again, the majority of *Anopheles* species are limited in distribution to either side of the IndoBurma-Sundaland biogeographical transition. Within the Leucosphyrus Group (which encompasses both the *An. dirus* and *An. leucosphyrus* Complexes), for example, *An. baimaii* and *An. dirus* are found to the north of this biogeographical boundary whereas many other species in the Leucosphyrus Group occur only to the south, with many species spanning from the mainland of peninsular Malaysia into the major islands e.g. *An. macarthuri, An. cracens, An. introlatus* and *An. latens* (Figures 2 and 5). Again, the major vector species of the *An. minimus* Complex, *An. minimus* and *An. harrisoni*, are limited in distribution to IndoBurma, as are the majority of species within the Maculatus Group (Figures 3 and 4). Although there does appear to be species turnover between the mainland and each of the islands (e.g. *An. nemophilous* is found within peninsular Malaysia but on none of the islands (Figure 2); *An. leucosphyrus* is found only on Sumatra (Figure 5)), several species are found on more than one of the major landmasses but are limited to only one of the biogeographical zones (e.g. *An. balabacensis* is found on both Borneo and Java). This suggests that whilst sea barriers play a role in limiting dispersal, the mainland biogeographical transition is clearly important in limiting species distributions despite the lack of such an obvious physical barrier.

The final distinct biodiversity hotspot regions of Southeast Asia are those of Wallacea and the Philippines, each of which harbours a unique assemblage of *Anopheles* species. Although separated from Borneo by only a narrow sea barrier, the Philippines are thought to share few of the major vector species of Southeast Asia. The Minimus Subgroup (which comprises the *An. minimus* and *An. fluviatilis* Complexes) appears not to have colonised the Philippines, and the species within both the *An. leucosphyrus* Complex and the Maculatus Group found in the Philippines (*An. baisasi*, and *An. greeni* and *An. dispar,* respectively) are limited in distribution to these islands (Figures 3 and5). *An. balabacensis*provides somewhat of an exception, being found on both Borneo and within the Philippines, although its distribution within the Philippines is limited to the small, western islands between Borneo and the major Philippine Island of Luzon (Figure 5).*Anopheles annulariss.l.,*ontheotherhand,isdistributedwithinthePhilippines aswell

Understanding *Anopheles* Diversity in Southeast Asia and Its Applications for Malaria Control http://dx.doi.org/10.5772/55709 333

The boundary between the biodiversity hotspot regions of IndoBurma and Sundaland (Figure 1) represents a second major biogeographic transition in Southeast Asia, and is characterised by high species turnover in a number of taxonomic groups (e.g. birds, mammals and reptiles [19-21]). This long-recognised biogeographic transition was first noted by Wallace in 1869, and though its exact position along the Thai-Malay Peninsula is debated, with some dispute as to whether the transition occurs at the Isthmus of Kra (10º30'N) or the Kangar-Pattani line (6-7ºN) further south [22], its biogeographical significance is unquestioned. The transition is associated with dramatic climate and phytological changes. IndoBurma has a very seasonal climate in terms of both temperature and rainfall, whereas that of Sundaland is much more stable, with precipitation levels remaining high throughout the year. Whereas mixed moist deciduous forest is the dominant forest habitat type of IndoBurma, that of Sundaland is perhumid evergreen forest [23-24]. Thus it seems unsurprising that this is a region of high species turnover, as the selective pressures on either side of the Isthmus of Kra biogeographic transition would differ considerably, potentially driving rapid adaptive change and subse‐

332 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

quent ecological speciation following the dispersal of taxa from one side to the other.

distributions despite the lack of such an obvious physical barrier.

Again, the majority of *Anopheles* species are limited in distribution to either side of the IndoBurma-Sundaland biogeographical transition. Within the Leucosphyrus Group (which encompasses both the *An. dirus* and *An. leucosphyrus* Complexes), for example, *An. baimaii* and *An. dirus* are found to the north of this biogeographical boundary whereas many other species in the Leucosphyrus Group occur only to the south, with many species spanning from the mainland of peninsular Malaysia into the major islands e.g. *An. macarthuri, An. cracens, An. introlatus* and *An. latens* (Figures 2 and 5). Again, the major vector species of the *An. minimus* Complex, *An. minimus* and *An. harrisoni*, are limited in distribution to IndoBurma, as are the majority of species within the Maculatus Group (Figures 3 and 4). Although there does appear to be species turnover between the mainland and each of the islands (e.g. *An. nemophilous* is found within peninsular Malaysia but on none of the islands (Figure 2); *An. leucosphyrus* is found only on Sumatra (Figure 5)), several species are found on more than one of the major landmasses but are limited to only one of the biogeographical zones (e.g. *An. balabacensis* is found on both Borneo and Java). This suggests that whilst sea barriers play a role in limiting dispersal, the mainland biogeographical transition is clearly important in limiting species

The final distinct biodiversity hotspot regions of Southeast Asia are those of Wallacea and the Philippines, each of which harbours a unique assemblage of *Anopheles* species. Although separated from Borneo by only a narrow sea barrier, the Philippines are thought to share few of the major vector species of Southeast Asia. The Minimus Subgroup (which comprises the *An. minimus* and *An. fluviatilis* Complexes) appears not to have colonised the Philippines, and the species within both the *An. leucosphyrus* Complex and the Maculatus Group found in the Philippines (*An. baisasi*, and *An. greeni* and *An. dispar,* respectively) are limited in distribution to these islands (Figures 3 and5). *An. balabacensis*provides somewhat of an exception, being found on both Borneo and within the Philippines, although its distribution within the Philippines is limited to the small, western islands between Borneo and the major Philippine Island of Luzon (Figure 5).*Anopheles annulariss.l.,*ontheotherhand,isdistributedwithinthePhilippines aswell

**Figure 5.** The distribution of species within the *Anopheles leucosphyrus* Complex and *Anopheles macarthuri* of the Leucosphyrus Group

as throughout mainland and insular Southeast Asia, although the limited available evidence suggests that the Philippine populations of this species show strong differentiation from those inotherregions of SoutheastAsia [25].As a result ofthedescribedspecies turnoverpatterns,the subregions differ in terms of major malaria vectors, with the *An. dirus* and *An. minimus* Com‐ plexes,andMaculatusGroupdominatingthroughoutIndoBurma,the*An.leucosphyrus*Complex dominating withinthe SundaicRegion, and*An. flavirostris* being themainmalariavector within the Philippines and a major malaria vector within Indonesia [15].

In addition to the divisions between the biogeographic regions discussed above, there are some apparent transitions within biogeographic regions. As previously discussed, there is some distinction between the species composition of each of the major Sundaic Islands and the mainland, although several species within the *An. dirus* and *An. leucosphyrus* Complexes are found on more than one of the landmasses. An apparent distinction in species composition between the landmasses is seeninothertaxa fromshrike babblers [26]tomacaques [27].Besides this pattern, there is also an apparent distinction within IndoBurma, between the distribution of geneticdiversity east and west oftheThai-Myanmar border.The closely relatedsister species *An. dirus* and *An. baimaii* have parapatric distributions within Southeast Asia, which overlap alongthisborderregion(Figure 2).*An.sawadwongporni* and*An.rampae* are a secondpairof sister species that show a similar pattern, with *An. rampae* having a primarily easterly distribution, which extends from eastern Thailand towards Vietnam and does not overlap the Thai-Myan‐ mar border(Figure 4). *An.rampae* has, however,recently been recordedatlow frequency within northeasternIndia, suggesting thedistributionandpopulationstructure ofthis species warrant further attention [28]. The Thai-Myanmar border region is also the site of a suture zone be‐ tween highly divergent intraspecific lineages within species including *An. splendidus, An. minimus* and *An. annularis* [29]. The patterns in species distribution discussed throughout this section, with closely related species often falling on either side of biogeographical divisions that lack obvious geographical barriers, clearly indicate a role for vicariance and/or ecology in generating biodiversity within Southeast Asia, as will be discussed later in this chapter.

Although the distributions of the majority of *Anopheles* taxa appear to be defined by biogeo‐ graphical boundaries, there are some taxa with relatively wide distributions that span many of the biogeographic subregions discussed above. For example, *An. maculatus* is distributed throughout Nepal, Pakistan, Bhutan and India and throughout the IndoBurma (including Taiwan) and Sundaic Regions of Southeast Asia, and *An. vagus* has a similar distribution throughout India, IndoBurma and the Sundaic Region. These species appear to be largely panmictic throughout their distributions [29-30], suggesting an ability to combine high dispersal capacities with generalist habitat requirements.

The distinctiveness of the Anopheline fauna of each of the major biogeographic regions of Southeast Asia, which occurs despite the continuity of landmass between these regions, suggests that ecological factors, such as climate and dominant habitat type, play a key role in defining species distributions. Malaria stratifications based on ecological biomes, such as forest, foothill and urban regions, are therefore especially useful in designating control efforts [16]. The clear ecological similarity between many closely related vector species also suggests a strong conservation of ecological niche. Species within the *An. dirus* and *leucosphyrus* Complexes, for example, show a strong association with forest habitat [31-33]. Thus in the IndoBurma and Sundaic Regions, where species within these complexes are distributed, malaria is often most prevalent in villages that are in close proximity to the forest fringe, and people involved in forest activities are often most at risk [16]. Species within the Minimus Complex, on the other hand, are prevalent within foothill regions and generally breed in slow running streams [31, 33-34], leading to the designation of a 'foothill' malaria stratification. The brackish water tolerant species *An. sundaicus* and *An. epiroticus,* which are also major vectors of malaria throughout Southeast Asia, dominate malaria transmission in coastal regions [35-37]. Thus the characterisation of species relationships, ecology and distributions has clearly facilitated great improvements to malaria control efforts. However, understanding of malaria transmission dynamics is still complicated by the potential for interactions between vector species, variation in vector capacity across a species range, and remaining taxonomical confusion in some groups (e.g. the *An. culicifacies* Complex) (reviewed in [33]). Thus the previously discussed high diversity of cryptic species within Southeast Asia may be one of the factors making malaria difficult to eliminate in parts of Southeast Asia.

## **3. Processes driving the diversification of the Anopheline fauna of Southeast Asia**

#### **3.1. The role of historical environmental change**

As discussed in the first section of this chapter, as well as an understanding of extant species distribution and ecology, the characterisation of population dynamics and levels and patterns of gene flow both within and between species is essential, as the effective size and connectivity of populations will influence the speed at which traits relevant to malaria control evolve and spread between them [38]. The release of genetically modified mosquitoes has been proposed for the control of vector populations in Africa [39]; if such approaches were developed for Southeast Asia, population genetic studies would be necessary to determine the number of genetically modified individuals and release sites needed for a successful program [39-40]. The estimation of levels of contemporary gene flow is greatly complicated, however, by the historical genetic structuring of mosquito populations [41-42]. In order to reliably infer patterns of contemporary gene flow, it is therefore essential that we first gain a thorough understanding of the population history of the *Anopheles* fauna.

Although the distributions of the majority of *Anopheles* taxa appear to be defined by biogeo‐ graphical boundaries, there are some taxa with relatively wide distributions that span many of the biogeographic subregions discussed above. For example, *An. maculatus* is distributed throughout Nepal, Pakistan, Bhutan and India and throughout the IndoBurma (including Taiwan) and Sundaic Regions of Southeast Asia, and *An. vagus* has a similar distribution throughout India, IndoBurma and the Sundaic Region. These species appear to be largely panmictic throughout their distributions [29-30], suggesting an ability to combine high

The distinctiveness of the Anopheline fauna of each of the major biogeographic regions of Southeast Asia, which occurs despite the continuity of landmass between these regions, suggests that ecological factors, such as climate and dominant habitat type, play a key role in defining species distributions. Malaria stratifications based on ecological biomes, such as forest, foothill and urban regions, are therefore especially useful in designating control efforts [16]. The clear ecological similarity between many closely related vector species also suggests a strong conservation of ecological niche. Species within the *An. dirus* and *leucosphyrus* Complexes, for example, show a strong association with forest habitat [31-33]. Thus in the IndoBurma and Sundaic Regions, where species within these complexes are distributed, malaria is often most prevalent in villages that are in close proximity to the forest fringe, and people involved in forest activities are often most at risk [16]. Species within the Minimus Complex, on the other hand, are prevalent within foothill regions and generally breed in slow running streams [31, 33-34], leading to the designation of a 'foothill' malaria stratification. The brackish water tolerant species *An. sundaicus* and *An. epiroticus,* which are also major vectors of malaria throughout Southeast Asia, dominate malaria transmission in coastal regions [35-37]. Thus the characterisation of species relationships, ecology and distributions has clearly facilitated great improvements to malaria control efforts. However, understanding of malaria transmission dynamics is still complicated by the potential for interactions between vector species, variation in vector capacity across a species range, and remaining taxonomical confusion in some groups (e.g. the *An. culicifacies* Complex) (reviewed in [33]). Thus the previously discussed high diversity of cryptic species within Southeast Asia may be one of the

dispersal capacities with generalist habitat requirements.

334 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

factors making malaria difficult to eliminate in parts of Southeast Asia.

**3.1. The role of historical environmental change**

**Southeast Asia**

**3. Processes driving the diversification of the Anopheline fauna of**

As discussed in the first section of this chapter, as well as an understanding of extant species distribution and ecology, the characterisation of population dynamics and levels and patterns of gene flow both within and between species is essential, as the effective size and connectivity of populations will influence the speed at which traits relevant to malaria control evolve and spread between them [38]. The release of genetically modified mosquitoes has been proposed for the control of vector populations in Africa [39]; if such approaches were developed for

As with all organisms, the genetic structuring of *Anopheles* populations through time is likely to have been greatly impacted by the influence of geographical features on patterns of gene flow and dispersal. Geographical barriers such as mountains, rivers or sea can restrict or prevent gene flow between populations, so causing them to become increasingly differentiated from one another due to the processes of neutral genetic drift and differential natural selection [38]. Many of the *Anopheles* taxa of Southeast Asia, including those within the Minimus and the Leucosphyrus subgroups and the Maculatus Group, are forest associated [31]. Hence for these taxa, expanses of open habitat such as grassland or savannah can constitute an important barrier to gene flow and dispersal. In the absence of gene flow, reproductive barriers may accumulate between isolated populations and cause allopatric speciation [43]. Geographical barriers can shift over time, leading to patterns of repeated expansion and contraction in the ranges of species constrained by them. The biogeographical history of Southeast Asia is especially dynamic, featuring tectonic activity [44], substantial sea-level fluctuations, large shifts in the region's landmass configuration [45], and climate-associated fluctuations in the distribution and extent of forest habitat [46-47]. The time-line below indicates the major biogeographic events inferred to have influenced Anopheline diversification from the mid-Miocene onwards (see figure 6).

#### *3.1.1. Miocene (23.0 – 5.3 mya): Dispersal of Pyretophorus series and Myzomyia series from Africa to Asia*

The collisions of the Indian, African and Australian plates with Eurasia all had substantial impacts on the landscape and fauna of Southeast Asia. India initially collided with Southeast Asia approximately 50 million years ago (mya), and the subsequent northwards push of the Indian plate resulted in the formation and uplift of the Himalayas [44], forming a geographical barrier between Southeast Asia and the rest of the Asian continent. The second major period of tectonic activity, which involved the uplift of the Himalayas approximately 25mya, coincided with the collision of the African and Eurasian plates. This latter event resulted in the closure of the Tethys Sea and so created a land connection between the continents of Africa and Asia [48]. Although this region is now characterised by arid desert habitat, a corridor of tropical forest is thought to have persisted during the humid periods of the early and mid-Miocene [48]. Combined with low sea-levels, this allowed forest taxa such as the ancestors of the Oriental Myzomyia and Pyretophorus Series to disperse from their African origins into Southeast Asia [49-50]. Increasingly arid conditions and the consequent desertification of East Asia during the late Miocene (6.2 – 5mya) restricted this exchange [48, 51], effectively isolating the forest fauna of Asia and Africa. The Oriental and African taxa within the Myzomyia and Pyretophorus Series form monophyletic groups in both cases (with the exception of the

**Figure 6.** Timeline showing the major biogeographic events inferred to have driven speciation and divergence in the Anopheline fauna of Southeast Asia.

placement of the African species *An. leesoni* within the Oriental Myzomyia clade), and are estimated to have diverged during the late Miocene [49-50]. This suggests that dispersal from Africa to Asia occurred during the humid mid Miocene in both cases, and was followed by the isolation of Asian and African lineages after the late-Miocene expansion of desert across East Asia (Figure 6). As *Anopheles* species rely on water bodies for their larval habitats, desert habitat is likely to pose an extremely effective barrier to dispersal. The close relationship of the African species *An. leesoni* with the Oriental Myzomyia species, from which it is estimated to have diverged just 2-3 mya, is somewhat of a mystery, and suggests some faunal exchange during the mid Pliocene despite the dominance of desert habitat throughout East Asia [49].

#### *3.1.2. Late Miocene and Pliocene (6 – 2mya): Forest fragmentation drives allopatric speciation*

The increasingly cool and arid climate responsible for extensive desertification across East Asia during the late Miocene also resulted in the expansion of grassland and savannah habitat across Southeast Asia [52]. The consequent reduction in available *Anopheles* larval habitats likely to have occurred during this time, and the potential consequent fragmentation and isolation of populations in allopatry, is hypothesised to have driven late Miocene speciation (dated to 7.1 mya +/- 1.4 my) within the Neocellia Series Annularis Group [25] (Figure 6). This trend of increasing aridification was reversed during the early Pliocene (5-2.8 mya), which was characterised by increasingly warm and humid conditions, with global temperatures reaching approximately 3°C above current temperatures [53-54]. Tropical forest would have expanded across Southeast Asia during this period, and *Anopheles* habitats would have been more abundant and widespread. A subsequent major climatic transition towards a substantially cooler and more arid climate began approximately 2.8 mya, and culminated in the first of the Pleistocene glacial maxima, 1.8 mya [55]. Once again, tropical forest habitat would have been replaced by large areas of grassland and savannah, fragmenting and isolating populations of forest-dependent *Anopheles* species across Southeast Asia. The consequent divergence of populations in allopatry is thought to have driven speciation within the forest-associated Maculatus Group [25], with contemporary species distributions in this group being fairly distinct (although exhibiting large areas of overlap), and the majority of speciation events dating to within the 2.8-1.8 mya period of major climatic cooling (Figure 6).

#### *3.1.3. Pleistocene (1.8 mya – 11,000 ya): Changes in landmass configuration drive dispersal and divergence within species*

During the Pleistocene, the ongoing fluctuations in the extent of forest cover across Southeast Asia were exacerbatedby thedramatic impact of glacio-eustatic sea level change onthe region's climate[45-46].Thesesea-levelfluctuations,whichinvolveddropsofbetween50and200meters during each of the Pleistocene glaciations [56], had a more dramatic effect on the climate and habitats of Southeast Asia than those of any other tropical region [46]. Sea level regressions of 60 meters or more result in the exposure of the Gulf of Thailand, and dramatically reduce the surface area of the South China Sea [45] (Figure 7). This reduction in the surface area of ocean across Southeast Asia would have reduced evaporation from the ocean's surface, and conse‐ quently the levels of moisture carried across the mainland by the monsoon rains. Due to the coincidence of periods of reduced sea level with glacial maxima, the reduction in the monsoon moisture content would have been exacerbated by the cool temperature and consequently reduced moisture-carrying capacity of the air [46]. The distribution of forest across Southeast Asia was in turn affected by the reduced precipitation levels, as regions with sufficient mois‐ ture to support them shrank [47, 57]. Reconstructions of the dominant habitat types across Southeast Asia during the Last Glacial Maximum (LGM), which are based on palynonlogical and sedimentological data, indicate that tropical forest became restricted to small and isolated pockets, often at intermediate altitudes and at the base of mountains, where precipitation runoff ensured moisture levels remained high enough to support it [58-59]. Substantial areas of forest habitat were replaced by grassland and savannah, although larger areas of forest are thought to have persisted in insular relative to mainland Southeast Asia [47, 57].

placement of the African species *An. leesoni* within the Oriental Myzomyia clade), and are estimated to have diverged during the late Miocene [49-50]. This suggests that dispersal from Africa to Asia occurred during the humid mid Miocene in both cases, and was followed by the isolation of Asian and African lineages after the late-Miocene expansion of desert across East Asia (Figure 6). As *Anopheles* species rely on water bodies for their larval habitats, desert habitat is likely to pose an extremely effective barrier to dispersal. The close relationship of the African species *An. leesoni* with the Oriental Myzomyia species, from which it is estimated to have diverged just 2-3 mya, is somewhat of a mystery, and suggests some faunal exchange during

**Figure 6.** Timeline showing the major biogeographic events inferred to have driven speciation and divergence in the

Anopheline fauna of Southeast Asia.

336 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

the mid Pliocene despite the dominance of desert habitat throughout East Asia [49].

*3.1.2. Late Miocene and Pliocene (6 – 2mya): Forest fragmentation drives allopatric speciation*

The increasingly cool and arid climate responsible for extensive desertification across East Asia during the late Miocene also resulted in the expansion of grassland and savannah habitat across Southeast Asia [52]. The consequent reduction in available *Anopheles* larval habitats likely to have occurred during this time, and the potential consequent fragmentation and isolation of populations in allopatry, is hypothesised to have driven late Miocene speciation (dated to 7.1 mya +/- 1.4 my) within the Neocellia Series Annularis Group [25] (Figure 6). This trend of increasing aridification was reversed during the early Pliocene (5-2.8 mya), which was characterised by increasingly warm and humid conditions, with global temperatures reaching approximately 3°C above current temperatures [53-54]. Tropical forest would have expanded

The reduction of forest habitat to small and isolated patches would have resulted in the fragmentation of forest-associated *Anopheles* populations, and their subsequent divergence in allopatry through genetic drift and differential local adaptation (see figure 8). The repeated climatic fluctuations during the Pleistocene are thought to have led to repeated cycles of forest fragmentation during the cool and arid glacial periods, and expansion during the warm and humid interglacials. This would have caused associated repeated cycles of *Anopheles* popula‐ tion range reduction and fragmentation, and subsequent divergence of populations in allopa‐ try, followed by range expansion and secondary contact between the now genetically differentiated populations. The 'refuge hypothesis' of Haffer [52] was originally put forward to propose a scenario of increased allopatric speciation driven by such repeated cycles of popula‐ tion divergence during periods of major climatic fluctuation such as that characterising the Pleistocene. This hypothesis has since been frequently discussed in the literature and often contested as an explanation for Pleistocene tropical diversification events, due to evidence that speciation in tropical taxa generally predates the Pleistocene, and that forest habitat was not reduced in tropical regions to the extent originally thought [60-62]. As previously discussed, however, the biogeographical changes within Southeast Asia during the Pleistocene were more severe than in other tropical regions, due to the substantial impact of the sea level changes on the region's climate [45]. The likelihood of allopatric speciation driven by such biogeographi‐ cal change could therefore be expected to be greater. Indeed, speciation dated to within the Pleistocenehas beeninferredinboththe forest-dependentLeucosphyrusGroup[63-64] andthe MinimusSubgroup[49],aswellasthecoastal*An.sundaicus*Complex[65],andhasbeenattributed to the repeated isolation of populations following the reduction of forest habitat and on sealevel fluctuations, respectively, across mainland Southeast Asia during glacial periods [25, 49].

**Figure 7.** Maps showing the IndoBurma and Sundaic Regions of Southeast Asia, a. 21 kya, the Last Glacial Maximum (LGM), when sea levels were 116 m below the current level, and b. 6.07 kya, when sea levels were the same as at present. Figures taken from [66]).

The evidence for allopatric speciation associated with Pleistocene environmental change is especially strong between the cryptic sister species *An. dirus* and *An. baimaii*, which are classified within the *An. dirus* Complex of the Leucosphyrus Subgroup. As discussed in the previous section, these species are major malaria vectors throughout mainland Southeast Asia, and have a parapatric distribution that overlaps along the Thai-Myanmar border. Although Understanding *Anopheles* Diversity in Southeast Asia and Its Applications for Malaria Control http://dx.doi.org/10.5772/55709 339

propose a scenario of increased allopatric speciation driven by such repeated cycles of popula‐ tion divergence during periods of major climatic fluctuation such as that characterising the Pleistocene. This hypothesis has since been frequently discussed in the literature and often contested as an explanation for Pleistocene tropical diversification events, due to evidence that speciation in tropical taxa generally predates the Pleistocene, and that forest habitat was not reduced in tropical regions to the extent originally thought [60-62]. As previously discussed, however, the biogeographical changes within Southeast Asia during the Pleistocene were more severe than in other tropical regions, due to the substantial impact of the sea level changes on the region's climate [45]. The likelihood of allopatric speciation driven by such biogeographi‐ cal change could therefore be expected to be greater. Indeed, speciation dated to within the Pleistocenehas beeninferredinboththe forest-dependentLeucosphyrusGroup[63-64] andthe MinimusSubgroup[49],aswellasthecoastal*An.sundaicus*Complex[65],andhasbeenattributed to the repeated isolation of populations following the reduction of forest habitat and on sealevel fluctuations, respectively, across mainland Southeast Asia during glacial periods [25, 49].

338 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**Figure 7.** Maps showing the IndoBurma and Sundaic Regions of Southeast Asia, a. 21 kya, the Last Glacial Maximum (LGM), when sea levels were 116 m below the current level, and b. 6.07 kya, when sea levels were the same as at

The evidence for allopatric speciation associated with Pleistocene environmental change is especially strong between the cryptic sister species *An. dirus* and *An. baimaii*, which are classified within the *An. dirus* Complex of the Leucosphyrus Subgroup. As discussed in the previous section, these species are major malaria vectors throughout mainland Southeast Asia, and have a parapatric distribution that overlaps along the Thai-Myanmar border. Although

present. Figures taken from [66]).

**Figure 8.** The influence of Pleistocene climatic change on *Anopheles* diversity within Southeast Asia.

characterisation of their divergence is complicated by mitochondrial introgression and consequent widespread haplotype sharing between the species [42, 67], application of an isolation-with-migration model to data from three nuclear genes supported their divergence within the last 1.5 my of the Pleistocene [63]. The east-west divide between the distributions of these species suggests that their common ancestor was restricted to habitat fragments in the west and east of the Southeast Asian mainland, and that the subsequently differentiated lineages expanded from these restricted distributions during the warm and moist interglacials to meet along the Thai-Myanmar border (figure 8) [63].

Although the above examples provide exceptions, the majority of speciation events within the Anopheline fauna of Southeast Asia are estimated to pre-date the Pleistocene [25, 30, 49], and the environmental fluctuations of the Pleistocene appear to have been much more influential in driving divergence and shaping population structure within, rather than between, *Anoph‐ eles* species. Patterns of genetic divergence between largely allopatric eastern and western lineages, and signals of Pleistocene population expansion, have been reported within several *Anopheles* species (e.g. *An. minimus* [68]); *An. annularis* and *An. splendidus* [25]). These patterns have generally been attributed to the restriction of populations to isolated forest 'refugia' during the glacial periods, and expansion from these regions during the interglacials (Figure 8). Chen *et al*. [68] investigated this hypothesis further in the forest-associated *An. minimus*, using a modelling approach to compare the hypotheses of a single panmictic population, a stable but spatially structured population, and past fragmentation into eastern and western refugia followed by growth and range expansion. The latter hypothesis was strongly support‐ ed, providing further evidence for an evolutionary history shaped by Pleistocene climatic change [68].

Such an influence of Pleistocene climatic change might be expected to be shared across multiple forest-dependent taxa. This hypothesis has been statistically evaluated in several *Anopheles* species, which exhibit varying degrees of forest-dependency, using a comparative phylogeo‐ graphical approach [29]. Simultaneous divergence of eastern and western lineages within four *Anopheles* species (*An. annularis, An. splendidus, An. minimus* and *An. maculatus*), dated to the mid-Pleistocene and attributed to the similarly-timed restriction of populations to allopatric forest refugia, was strongly supported. Patterns of isolation in allopatry followed by secondary contact across the ranges of these species resulted in the formation of a common suture-zone along the Thai-Myanmar border [29]. Various hypotheses of Pleistocene demographic history were further evaluated using a spatially explicit modelling approach, in which the simulation of demographic and spatial expansions, incorporating environmental information, is followed by the generation of simulated genetic datasets through coalescent theory [69]. Comparison of real to simulated datasets best supported scenarios in which populations were restricted to allopatric eastern and western refugia, before expanding their ranges during the warm and moist interglacials, in all seven species examined (*An. aconitus, An. philippinensis, An. maculatus, An. sawadwongporni, An. annularis, An. baimaii,* and *An. minimus*). Similarly timed population expansions dating to the mid-Pleistocene were inferred in all species, further supporting this scenario [29]. Hence there is substantial evidence supporting a common role of historical environmental change in driving vicariance, and shaping the intraspecific population struc‐ ture that we see today.

Besides driving divergence between isolated populations, the restriction of populations to refugial regions is also likely to have influenced patterns of genetic diversity across the landscape. The long-term persistence of populations within refugial regions leads to the accumulation of high genetic diversity and population structure. Since only a fraction of the gene pool is generally involved in range expansion, regions that are repeatedly re-colonised following local extinction are expected to harbour substantially lower genetic diversity [70-71]. These predicted patterns can be used to identify potential refugial regions, and in Southeast Asia have led to the identification of the mountainous regions of northeastern India, northern Myanmar, northern Thailand, southern China and northern Vietnam as potential Pleistocene glacial refugia for *Anopheles* mosquitoes [25, 29, 42, 68, 72]. Indeed, mountain foothills are the most likely regions to support the persistence of forest habitat during cool and arid climatic periods, due to the interception of precipitation by the mountains surrounding them [46]. The prediction and characterisation of these historically driven patterns, of high diversity and spatially structured populations within formal refugial regions and more homogeneous populations in more recently colonised regions, is important if contemporary levels of gene flow are to be reliably estimated and used to predict malaria transmission dynamics.

Although the above examples provide exceptions, the majority of speciation events within the Anopheline fauna of Southeast Asia are estimated to pre-date the Pleistocene [25, 30, 49], and the environmental fluctuations of the Pleistocene appear to have been much more influential in driving divergence and shaping population structure within, rather than between, *Anoph‐ eles* species. Patterns of genetic divergence between largely allopatric eastern and western lineages, and signals of Pleistocene population expansion, have been reported within several *Anopheles* species (e.g. *An. minimus* [68]); *An. annularis* and *An. splendidus* [25]). These patterns have generally been attributed to the restriction of populations to isolated forest 'refugia' during the glacial periods, and expansion from these regions during the interglacials (Figure 8). Chen *et al*. [68] investigated this hypothesis further in the forest-associated *An. minimus*, using a modelling approach to compare the hypotheses of a single panmictic population, a stable but spatially structured population, and past fragmentation into eastern and western refugia followed by growth and range expansion. The latter hypothesis was strongly support‐ ed, providing further evidence for an evolutionary history shaped by Pleistocene climatic

Such an influence of Pleistocene climatic change might be expected to be shared across multiple forest-dependent taxa. This hypothesis has been statistically evaluated in several *Anopheles* species, which exhibit varying degrees of forest-dependency, using a comparative phylogeo‐ graphical approach [29]. Simultaneous divergence of eastern and western lineages within four *Anopheles* species (*An. annularis, An. splendidus, An. minimus* and *An. maculatus*), dated to the mid-Pleistocene and attributed to the similarly-timed restriction of populations to allopatric forest refugia, was strongly supported. Patterns of isolation in allopatry followed by secondary contact across the ranges of these species resulted in the formation of a common suture-zone along the Thai-Myanmar border [29]. Various hypotheses of Pleistocene demographic history were further evaluated using a spatially explicit modelling approach, in which the simulation of demographic and spatial expansions, incorporating environmental information, is followed by the generation of simulated genetic datasets through coalescent theory [69]. Comparison of real to simulated datasets best supported scenarios in which populations were restricted to allopatric eastern and western refugia, before expanding their ranges during the warm and moist interglacials, in all seven species examined (*An. aconitus, An. philippinensis, An. maculatus, An. sawadwongporni, An. annularis, An. baimaii,* and *An. minimus*). Similarly timed population expansions dating to the mid-Pleistocene were inferred in all species, further supporting this scenario [29]. Hence there is substantial evidence supporting a common role of historical environmental change in driving vicariance, and shaping the intraspecific population struc‐

Besides driving divergence between isolated populations, the restriction of populations to refugial regions is also likely to have influenced patterns of genetic diversity across the landscape. The long-term persistence of populations within refugial regions leads to the accumulation of high genetic diversity and population structure. Since only a fraction of the gene pool is generally involved in range expansion, regions that are repeatedly re-colonised following local extinction are expected to harbour substantially lower genetic diversity [70-71]. These predicted patterns can be used to identify potential refugial regions, and in Southeast

change [68].

340 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

ture that we see today.

Although the majority of main *Anopheles* malaria vectors within Southeast Asia show a strong association with forest habitat, this is not true of all species. The influence of historical environmental change on species such as *An. vagus* and *An. sundaicus*, which typically inhabit open habitat and coastal habitat [31, 37, 73], respectively, are likely to have differed substan‐ tially from the effects on forest-associated species discussed above. Relative to the majority of forest-associated species, *An. vagus* shows relatively little population structure, and appears to be a single, widespread and highly diverse species that is distributed throughout the biogeographic realms of IndoBurma, Sundaland and the Philippines. The expanse of the open grassland habitat favoured by this species throughout much of the Pleistocene is thought to have facilitated gene flow and dispersal, maintaining population connectivity and homoge‐ nising population genetic structure [30]. The Pleistocene evolutionary history of the coastal species *An. sundaicus,* meanwhile, is likely to have been influenced by changes to the landmass configuration, as is discussed below. This illustrates the importance of taking species ecology into account when predicting patterns of historical intraspecific genetic structure across a landscape.

#### *3.1.4. The formation of land-bridges and consequent creation and destruction of dispersal routes during the Pleistocene*

Besides substantially influencing climatic conditions across Southeast Asia, the alterations in landmass configuration during the Pleistocene also had a considerable effect on the availability of migration routes across Southeast Asia. The Sunda Shelf is thought to have been dominated by grassland and savannah habitats during periods of exposure, and thus was important in allowing the exchange of open-habitat species such as early hominins and hoofed mammals between the mainland and the Sundaic Islands [56, 74]. Although the open habitat is thought to have acted as a barrier to dispersal of forest-associated taxa between Borneo and Sumatra, the persistence of gallery forests along the major river systems of the Sunda Shelf is thought to have provided narrow dispersal corridors for such taxa [74]. The repeated exposure and submergence of the Sunda Shelf is thought to have promoted allopatric speciation in a number of Sundaic taxa, with periods of dispersal facilitated by the exposure of the Sundaland bridge being followed by the isolation of populations on different landmasses as sea levels rose, e.g. [26, 75]. Although as previously mentioned, there is some species turnover within *Anopheles* between each of the islands and the mainland, several species of the *An. leucosphyrus* Complex are found on more than one land mass. This suggests that the intermittent presence of forest corridors between the mainland and insular regions during the Pleistocene was sufficient to allow some dispersal and gene flow between current land masses [64].

Inferred speciation events within the *An. sundaicus* Complex have also been attributed to patterns of dispersal and isolation driven by the Pleistocene exposure and submergence of sea barriers, with the subsequent isolation and divergence of the nominal species *An. sundaicus, An. sundaicus* E and *An. epiroticus* within Borneo, Sumatra and Java, and mainland Southeast Asia, respectively [65]. These species designations have since been disputed, however, and evidence supporting the existence of only a single, widespread species within the *An. sundai‐ cus* species Complex was presented after more intensive sampling, sequencing of additional markers, and more comprehensive analysis [50]. An alternative scenario of Pleistocene evolutionary history was also presented for this littoral species. Although the current species distribution extends along the coast of mainland Southeast Asia, with the Thai-Malay Penin‐ sula coast connecting that of southern Thailand with Cambodia and Vietnam [31, 37], the exposure of the Sunda Shelf would have eliminated habitat availability through the Gulf of Thailand and isolated populations on the east and west of the glacial insular landmass (Figure 7). This would have limited gene flow between the current coastal regions of Thailand, Cambodia and Vietnam, and facilitated dispersal between the mainland and insular regions. The detection of allopatric eastern and western mitochondrial and nuclear genetic lineages within *An. sundaicus* s.l., the closer relationship of Vietnamese populations with populations from Borneo and Indonesia than with those from Thailand and Myanmar, and the detection of Pleistocene gene flow between Borneo and Vietnam, and between Indonesia and the mainland, strongly support the influence of sea-level changes on the dispersal and population genetics of *An. sundaicus* s.l. [37, 50], although evidence suggests speciation has not resulted in this case.

#### **3.2. Ecological factors**

The rich diversity of habitat types and host species available within Southeast Asia is likely to have driven differential local adaptation leading to divergence between ecologically isolated populations and consequent ecological speciation [43]. Characterisation of the bionomics, habitat and feeding preferences of vector species, and of interspecific and intraspecific variation in these traits, is an important step in defining appropriate vector control strategies. Additionally, through the relation of species biology and ecology to phylogenetic relationships we may infer the ecological adaptations that are likely to have driven divergence and specia‐ tion, and given rise to the most effective malaria vectors within Southeast Asia. This may also give an indication of the characters that are evolutionarily labile and those that show niche conservatism, which may allow the prediction of how species may respond to anthropogenic change such as urbanisation and an expansion of agriculture. The Leucosphyrus Group provides one example of ecological differentiation between closely related species. This group includes several important vectors of both human and simian malaria, and due to its medical importance, has been well characterised in terms of taxonomy, phylogeny and ecology ([76]; reviewed in [33] and [32]). The mapping of species feeding preferences onto a phylogenetic tree supported two independent host-switching events, each leading to the evolution of anthropophilic taxa from their zoophilic ancestors, which fed on non-human primates in the forest canopy [64]. This switch in host preference is likely to have involved a change in behaviour, from feeding in the forest canopy to feeding on the forest floor, as well as changes in host detection. This host switch was estimated to have occurred during the late Pliocene/ early Pleistocene, which has important implications for human evolution, suggesting that hominins were present within Southeast Asia as early as 2.2 million years ago (mya), and that their arrival shaped the evolution of malaria vectors [64].

corridors between the mainland and insular regions during the Pleistocene was sufficient to

Inferred speciation events within the *An. sundaicus* Complex have also been attributed to patterns of dispersal and isolation driven by the Pleistocene exposure and submergence of sea barriers, with the subsequent isolation and divergence of the nominal species *An. sundaicus, An. sundaicus* E and *An. epiroticus* within Borneo, Sumatra and Java, and mainland Southeast Asia, respectively [65]. These species designations have since been disputed, however, and evidence supporting the existence of only a single, widespread species within the *An. sundai‐ cus* species Complex was presented after more intensive sampling, sequencing of additional markers, and more comprehensive analysis [50]. An alternative scenario of Pleistocene evolutionary history was also presented for this littoral species. Although the current species distribution extends along the coast of mainland Southeast Asia, with the Thai-Malay Penin‐ sula coast connecting that of southern Thailand with Cambodia and Vietnam [31, 37], the exposure of the Sunda Shelf would have eliminated habitat availability through the Gulf of Thailand and isolated populations on the east and west of the glacial insular landmass (Figure 7). This would have limited gene flow between the current coastal regions of Thailand, Cambodia and Vietnam, and facilitated dispersal between the mainland and insular regions. The detection of allopatric eastern and western mitochondrial and nuclear genetic lineages within *An. sundaicus* s.l., the closer relationship of Vietnamese populations with populations from Borneo and Indonesia than with those from Thailand and Myanmar, and the detection of Pleistocene gene flow between Borneo and Vietnam, and between Indonesia and the mainland, strongly support the influence of sea-level changes on the dispersal and population genetics of *An. sundaicus* s.l. [37, 50], although evidence suggests speciation has not resulted

The rich diversity of habitat types and host species available within Southeast Asia is likely to have driven differential local adaptation leading to divergence between ecologically isolated populations and consequent ecological speciation [43]. Characterisation of the bionomics, habitat and feeding preferences of vector species, and of interspecific and intraspecific variation in these traits, is an important step in defining appropriate vector control strategies. Additionally, through the relation of species biology and ecology to phylogenetic relationships we may infer the ecological adaptations that are likely to have driven divergence and specia‐ tion, and given rise to the most effective malaria vectors within Southeast Asia. This may also give an indication of the characters that are evolutionarily labile and those that show niche conservatism, which may allow the prediction of how species may respond to anthropogenic change such as urbanisation and an expansion of agriculture. The Leucosphyrus Group provides one example of ecological differentiation between closely related species. This group includes several important vectors of both human and simian malaria, and due to its medical importance, has been well characterised in terms of taxonomy, phylogeny and ecology ([76]; reviewed in [33] and [32]). The mapping of species feeding preferences onto a phylogenetic tree supported two independent host-switching events, each leading to the evolution of

allow some dispersal and gene flow between current land masses [64].

342 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

in this case.

**3.2. Ecological factors**

As well as the change in host preference, several other ecological adaptations are likely to have driven divergence within the Leucosphyrus Group. The distribution of the group overlaps the biogeographical transition zone that lies between IndoBurma and Sundaland (figure 1;[21]), with the majority of species being limited in distribution to the region either south, or north, of this divide. All basal species are limited in distribution to insular Southeast Asia, suggesting that this region represents the group's ancestral origin [64]. Despite the existence of several species within peninsular Malaysia only two northwards dispersal events into IndoBurma were supported, suggesting that this dispersal required some kind of ecological adaptation. It has been suggested that this may have involved an adaptation specific to the more seasonal climate of Southeast Asia, such as the increased resistance of larvae to desiccation observed in *An. dirus* and *An. baimaii* [32, 64]. Whatever the nature of the ecological adaptation, it is likely to have driven divergence between Indo-Burmese and Sundaic taxa, facilitated the spread of the Leucosphyrus Group throughout mainland IndoBurma, and maintained the distinction between Indo-Burmese and Sundaic species assemblages.

All species within the Leucosphyrus Group show a strong association with tropical forest habitat and are remarkably similar in terms of habitat preference; however *An. scanloni* and *An. nemophilous* do show a unique specialisation to specific habitat types. *An. scanloni* is found in association with limestone karst habitats, whereas *An. nemophilous* is found within man‐ grove swamp habitats [31], thus specialisation and ecological divergence is likely to have played a role in the history of these species. The divergence of *An. scanloni*from its sister species *An. dirus* occurred despite inferred uni-directional gene flow from *An. scanloni* into *An. dirus* [63]. The uni-directional nature of this gene flow is thought to have resulted from a unique ecological adaptation of *An. scanloni* to limestone karst habitat, which confers a fitness advantage to this species in regions of sympatry with *An. dirus*, reducing hybrid fitness. The accumulation and maintenance of reproductive isolation between *An. scanloni* and *An. dirus* is therefore likely to have been driven by ecological adaptation [63].

The likely involvement of ecological variation in species divergence has also been assessed within the Maculatus Group, within which the phylogenetic mapping of species' altitudinal distribution supported a scenario of ecological speciation through altitudinal replacement[25]. This is a phenomenon in which the distribution of one species replaces that of its sister species along an altitudinal gradient, as populations become adapted to the environmental conditions within their altitudinal zone [77-78]. Species within the Maculatus Group typically lay their eggs within streams or the rock pools associated with them. Various characteristics of these typical larval habitats, such as the water temperature and the speed of water flow, are likely to vary with altitude. Adaptation to these specific larval habitats may therefore have played a role in the ecological divergence of populations at higher altitudes [25].

Whilst ecological differences between species may provide clues as to the factors driving past speciation events, investigation of intraspecific ecological variation within a species range may give an indication of the processes involved in the early stages of ecological divergence and speciation. Variation in traits such as anthropophilic vs. zoophilic, or exophagic vs. endophagic feeding preferences have the potential to greatly influence vector status, and there are several species in which vector status is reported to vary across the range. *Anopheles minimus,* for example, is reported to show strong anthropophily within central Vietnam and Laos, but is more attracted to cattle in northern Vietnam and Cambodia [79]. This behavioural variation is thought to be related to the availability of cattle hosts in a region, and will considerably impact the role of *An. minimus* in malaria transmission. Variation in anthropophily, endophagy, biting cycle and endophily in both *An. dirus* and *An. minimus* across the species' ranges have been related to regional variation in human land-use and habits [79], and may be driving intraspe‐ cific adaptive divergence between vector populations. Although it is not currently known whether this variation is the result of phenotypic plasticity or genetic adaptation, any rapid ecological diversification may affect patterns of disease transmission. Thus uncovering the processes involved in the generation of ecological divergence within a species may have considerable relevance for malaria control.

Although several examples of species-specific differences in ecology can be found, there does seem to be considerable ecological similarity between species within each of the major groups, as was discussed earlier in this chapter. All species within the Leucosphyrus Group, for example, show an extremely strong association with forest habitat, laying their eggs within temporary forest pools [31-32]. Although species vary in their feeding preferences, and *An. scanloni* and *An. nemophilous* show previously discussed unique habitat specialism, a number of species within the group show no apparent ecological differentiation from one another. This pattern of apparent 'niche conservatism' is also the case within the Maculatus Group and Minimus Subgroup, with the majority of species within showing preferences for disturbed habitat within forest clearings, and for hilly forest habitats, respectively [31, 80]. It seems surprising that so many apparently ecologically similar species coexist, often with large areas of distributional overlap, and it seems likely that there are subtle ecological differences between species that we are yet to uncover. These ecological differences may involve the bionomics or feeding behaviour of species, and may therefore be of considerable interest in terms of malaria control. The probability of undiscovered ecological differences between species seems especially likely given the fact that methods of cryptic species identification have only recently been developed (e.g. [81-86]), and that early studies of species biology and ecology were marred by incorrect species identifications. Besides the clear direct applications of studies into the biology of *Anopheles* species within Southeast Asia, such studies may shed further light on the role of ecological speciation in the evolutionary history of the region's Anopheline fauna.

## **4. Gene flow within and between species**

to vary with altitude. Adaptation to these specific larval habitats may therefore have played a

Whilst ecological differences between species may provide clues as to the factors driving past speciation events, investigation of intraspecific ecological variation within a species range may give an indication of the processes involved in the early stages of ecological divergence and speciation. Variation in traits such as anthropophilic vs. zoophilic, or exophagic vs. endophagic feeding preferences have the potential to greatly influence vector status, and there are several species in which vector status is reported to vary across the range. *Anopheles minimus,* for example, is reported to show strong anthropophily within central Vietnam and Laos, but is more attracted to cattle in northern Vietnam and Cambodia [79]. This behavioural variation is thought to be related to the availability of cattle hosts in a region, and will considerably impact the role of *An. minimus* in malaria transmission. Variation in anthropophily, endophagy, biting cycle and endophily in both *An. dirus* and *An. minimus* across the species' ranges have been related to regional variation in human land-use and habits [79], and may be driving intraspe‐ cific adaptive divergence between vector populations. Although it is not currently known whether this variation is the result of phenotypic plasticity or genetic adaptation, any rapid ecological diversification may affect patterns of disease transmission. Thus uncovering the processes involved in the generation of ecological divergence within a species may have

Although several examples of species-specific differences in ecology can be found, there does seem to be considerable ecological similarity between species within each of the major groups, as was discussed earlier in this chapter. All species within the Leucosphyrus Group, for example, show an extremely strong association with forest habitat, laying their eggs within temporary forest pools [31-32]. Although species vary in their feeding preferences, and *An. scanloni* and *An. nemophilous* show previously discussed unique habitat specialism, a number of species within the group show no apparent ecological differentiation from one another. This pattern of apparent 'niche conservatism' is also the case within the Maculatus Group and Minimus Subgroup, with the majority of species within showing preferences for disturbed habitat within forest clearings, and for hilly forest habitats, respectively [31, 80]. It seems surprising that so many apparently ecologically similar species coexist, often with large areas of distributional overlap, and it seems likely that there are subtle ecological differences between species that we are yet to uncover. These ecological differences may involve the bionomics or feeding behaviour of species, and may therefore be of considerable interest in terms of malaria control. The probability of undiscovered ecological differences between species seems especially likely given the fact that methods of cryptic species identification have only recently been developed (e.g. [81-86]), and that early studies of species biology and ecology were marred by incorrect species identifications. Besides the clear direct applications of studies into the biology of *Anopheles* species within Southeast Asia, such studies may shed further light on the role of ecological speciation in the evolutionary history of the region's

role in the ecological divergence of populations at higher altitudes [25].

considerable relevance for malaria control.

344 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

Anopheline fauna.

The absence or presence of gene flow between populations and species has a considerable impact on the dynamics of malaria transmission, and on the measures used for vector control. In the absence of gene flow, genetic drift and local adaptation result in the genetic differen‐ tiation of populations, and potentially in divergence at ecological traits likely to influence malaria transmission [38, 43]. The presence of gene flow, on the other hand, homogenises genetic variation and may lead to the exchange of adaptive and potentially medically relevant alleles between populations. Although the accumulation of reproductive barriers generally restricts gene flow between species, gene flow may still continue across certain genomic regions, creating patterns of differential divergence and introgression across the genome [7, 87-89]. Numerous cases of mitochondrial introgression between *Anopheles* species, including the Southeast Asian malaria vectors *An. dirus* and *An. baimaii* [63, 67], reveal that gene flow between species may be fairly common. The adaptive exchange of the 2La inversion between *An. arabiensis* and *An. gambiae* provides evidence of the phenomenon of gene flow across certain regions of the genome [5, 8, 90-91], and recent advances in next generation sequencing and population genomics have enabled more detailed examination, providing comprehensive examples of interspecific gene flow such as between the purported species *An. gambiae* M and S [92-93], and between the diverged species *An. gambaie* and *An. arabiensis* [7]. An understand‐ ing of patterns of contemporary gene flow both within and between species, and of the landscape features that facilitate or restrict this exchange, is of great importance for malaria control efforts. Characterisation of gene flow within and between species will also be relevant to the design of control efforts involving the release of genetically modified mosquitoes, as it will enable prediction of spread of relevant alleles (such as those influencing vectorial capacity) throughout *Anopheles* populations [39].

The dynamic demographic histories of the major malaria vector species, as discussed previ‐ ously in this chapter, complicate the inference of contemporary gene flow. For example, population bottlenecks and subsequent expansions, which appear to be common in the Anopheline fauna of Southeast Asia (e.g. [29, 42]), can homogenise genetic variation and thus eliminate accumulated genetic diversity between isolated populations, giving false signal of ongoing gene flow [94]. Knowledge of the historical patterns of divergence, range restriction and expansion in *Anopheles* populations, as discussed in previously in the chapter, may provide a baseline from which to study contemporary gene flow. Additionally, whereas to date studies of population structure and gene flow within and between species has been primarily restricted to neutral markers, the increasing availability of next generation sequencing (NGS) data will provide the opportunity to study the exchange of adaptive alleles across landscapes (e.g. [8], see below).

## **5. Future directions**

Despite the wealth of knowledge of *Anopheles* diversity within Southeast Asia, there are many directions that remain to be explored. Firstly, although much is known of the historical dynamics of gene flow and divergence and the climatic and landscape features that have been important in defining those patterns, little is known of the impact of contemporary landscape features on dispersal and gene flow. Such questions may be addressed using a landscape genetics approach, which involves the combination of fine-scale, dense spatial sampling with spatial and environmental information [95-96]. This approach has been successful, for example, in revealing the impact of urbanisation and forest corridors on connectivity in amphibian populations [97], and the impact of major roads on the genetic structure of caribou populations [98]. Such an approach may reveal the impact of phenomena such as deforestation and increased urbanisation on the demography of *Anopheles* populations, information which would be beneficial for predicting the impact of future landscape changes on the origin and spread of adaptive alleles relevant to vector control.

Secondly, the investigation of patterns of population structure at a genomic level remains to be performed in the *Anopheles* taxa of Southeast Asia, and will have many potential applica‐ tions. As previously discussed in this chapter, intraspecific phenotypic variation such as that reported within *An. dirus* and *An. minimus* [79] may be due to phenotypic plasticity, or may have an underlying genetic adaptive basis. Patterns of divergence at small numbers of neutral loci, while useful in identifying general population genetic patterns, are insufficient to address such issues comprehensively. Genome-wide approaches can, however, facilitate the identifi‐ cation of loci involved in adaptive response to environmental variation, and may reveal associations between adaptive loci and phenotypic traits (e.g.[99-101]). The availability of the *Anopheles gambiae* reference genome [102] provides additional scope for genomic studies using NGS data, enabling annotation of any identified adaptive loci, and the future availability of 13 additional *Anopheles* genomes, including those of several Southeast Asian species, will aid genomic studies even further [103].

Besides gene flow between populations within a species, the possibility of contemporary interspecific gene flow should also be considered. The identification and characterisation of such contemporary gene flow between species will be vitally important in determining whether medically important traits may spread between them. Again, this issue will benefit from a genome-wide approach, as patterns of introgression and divergence will vary across the genome due to the differential influence of selection [7, 87-89]. Genomic studies have been invaluable in characterising divergence and introgression across the genome, and identifying the targets of selection within the genomes of *An. gambiae* M and S forms [8]. For example, in contrast to the *kdr* mutation, which is responsible for pyrethroid resistance to insecticide and is thought to have spread from the S to the M form of *An. gambiae*through introgression [104], different resistance substitutions within the resistance to dieldrin (*rdl*) gene are thought to have evolved independently within *An. gambiae* M and S forms [8]. Genome-wide approaches will enable similar issues to be addressed within recently diverged species pairs such as *An. baimaii* and *An. dirus.*

The possibility of ongoing gene flow or historic introgression between species is also important for the reliable delineation of species boundaries, particularly within complexes of closely related and morphologically identical *Anopheles* species. The importance of selecting appro‐ priate markers for species delineation, and of considering levels of interspecific gene flow has been recently reviewed [105], and highlights the potential benefits of a genome-wide approach. Questions relating to Anopheline taxonomy and ecology remain to be answered within several of the medically important *Anopheles* groups (including the *An. sundaicus, An. subpictus, An. culicifacies* and *An. fluviatilis* Complexes, for example [33]), and the delineation of species boundaries, resolution of species relationships, development of species identification methods and characterisation of species ecology are still vitally important for the design of more traditional methods of vector control. The usefulness of bed nets in reducing malaria, the identification and control of potential larval habitats within a region, and informing of residents of how to reduce exposure, all rely on detailed information of the species present within a region and of their ecology. Zarowiecki [50] has illustrated the importance of taking a systematic approach to delineating and identifying species and resolving taxomonic relationships, and such an approach should be followed for potentially cryptic species complexes in which taxonomy is still uncertain. Thus taken together, the development of NGS technologies and population genomic analytical methods provides great scope for studies into Anopheles diversity in Southeast Asia, which are likely to considerably benefit both the understanding of malaria transmission dynamics and the effectiveness of vector control.

## **Author details**

dynamics of gene flow and divergence and the climatic and landscape features that have been important in defining those patterns, little is known of the impact of contemporary landscape features on dispersal and gene flow. Such questions may be addressed using a landscape genetics approach, which involves the combination of fine-scale, dense spatial sampling with spatial and environmental information [95-96]. This approach has been successful, for example, in revealing the impact of urbanisation and forest corridors on connectivity in amphibian populations [97], and the impact of major roads on the genetic structure of caribou populations [98]. Such an approach may reveal the impact of phenomena such as deforestation and increased urbanisation on the demography of *Anopheles* populations, information which would be beneficial for predicting the impact of future landscape changes on the origin and

Secondly, the investigation of patterns of population structure at a genomic level remains to be performed in the *Anopheles* taxa of Southeast Asia, and will have many potential applica‐ tions. As previously discussed in this chapter, intraspecific phenotypic variation such as that reported within *An. dirus* and *An. minimus* [79] may be due to phenotypic plasticity, or may have an underlying genetic adaptive basis. Patterns of divergence at small numbers of neutral loci, while useful in identifying general population genetic patterns, are insufficient to address such issues comprehensively. Genome-wide approaches can, however, facilitate the identifi‐ cation of loci involved in adaptive response to environmental variation, and may reveal associations between adaptive loci and phenotypic traits (e.g.[99-101]). The availability of the *Anopheles gambiae* reference genome [102] provides additional scope for genomic studies using NGS data, enabling annotation of any identified adaptive loci, and the future availability of 13 additional *Anopheles* genomes, including those of several Southeast Asian species, will aid

Besides gene flow between populations within a species, the possibility of contemporary interspecific gene flow should also be considered. The identification and characterisation of such contemporary gene flow between species will be vitally important in determining whether medically important traits may spread between them. Again, this issue will benefit from a genome-wide approach, as patterns of introgression and divergence will vary across the genome due to the differential influence of selection [7, 87-89]. Genomic studies have been invaluable in characterising divergence and introgression across the genome, and identifying the targets of selection within the genomes of *An. gambiae* M and S forms [8]. For example, in contrast to the *kdr* mutation, which is responsible for pyrethroid resistance to insecticide and is thought to have spread from the S to the M form of *An. gambiae*through introgression [104], different resistance substitutions within the resistance to dieldrin (*rdl*) gene are thought to have evolved independently within *An. gambiae* M and S forms [8]. Genome-wide approaches will enable similar issues to be addressed within recently diverged species pairs such as *An.*

The possibility of ongoing gene flow or historic introgression between species is also important for the reliable delineation of species boundaries, particularly within complexes of closely related and morphologically identical *Anopheles* species. The importance of selecting appro‐ priate markers for species delineation, and of considering levels of interspecific gene flow has

spread of adaptive alleles relevant to vector control.

346 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

genomic studies even further [103].

*baimaii* and *An. dirus.*

Katy Morgan1 , Pradya Somboon2 and Catherine Walton3\*


2 Department of Parasitology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand

3 Faculty of Life Sciences, University of Manchester, Manchester, UK

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**Chapter 12**

## **The Systematics and Bionomics of Malaria Vectors in the Southwest Pacific**

Nigel W. Beebe, Tanya L. Russell, Thomas R. Burkot, Neil F. Lobo and Robert D. Cooper

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55999

## **1. Introduction**

#### **1.1. Malaria in the Southwest Pacific**

The malaria transmission zone in the southwest Pacific ranges from Indonesia (Papua Province) through Papua New Guinea (PNG) and the Solomon Islands to Vanuatu. The island of Tanna in Vanuatu marks the southern and eastern limit of the region's malaria endemic area. The malaria-free island of Aneityum is the most easterly location where anophelines are found (Fig 1). While northern Australia previously experienced regular outbreaks of malaria, the disease was eliminated in 1962 [1] – although it still experiences sporadic outbreaks following reintroductions of the parasites [2]. Malaria remains the most important vectorborne disease in the region with Indonesian Papua, PNG and the Solomon Islands enduring some of the highest attack rates in the world outside Africa [3].

Malaria is endemic below 1000m, with the degree of endemicity ranging from hypoendemic to holoendemic [4, 5]. Above 1000m malaria tends to be unstable with epidemics of varying degrees of severity [6-8]. Serious control efforts were initiated in the 1950s-1960s as part of the WHO Global Eradication Program, with pilot projects implemented in Papua Province (Indonesia) and PNG (late 1950s) and in the Solomon Islands and Vanuatu (late 1960s). The principal strategy was indoor residual spraying (IRS) with DDT supplemented with mass drug administration of chloroquine [9].

In 1969, the malaria eradication was abandoned in Papua Province and PNG as it was realized that this goal was not attainable – instead, various control programs were introduced. In PNG, IRS continued until 1984, after which little more was done in the way of malaria vector control until the early 1990s, when insecticide treated bed nets (ITNs) were trialed [10] prior to

© 2013 Beebe et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Beebe et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

widespread distribution. In the Solomon Islands and Vanuatu, full-scale malaria eradication programs (MEP) commenced in the early 1970s but were also abandoned after three years and replaced with control programs [11]. In both countries pyrethroids replaced DDT in IRS in the early 1990s and ITNs became the main method of control [12]. During the 1990s, malaria was successfully eliminated on Aneityum Island, the most southern island of Vanuatu [13] with mass drug administration as the primary intervention. Recently, renewed efforts at malaria elimination and intensified control were initiated in Tafea Province in Vanuatu and Temotu and Santa Isabel Provinces in the Solomon Islands [14].

#### **1.2. Geography and climate**

This work covers the malarious area of the southwest Pacific as it lies within the Australian faunal region (Fig. 1). This region is made up of numerous islands many of which are moun‐ tainous (>4000m) with ranges extending to the coasts and drained by river systems over a narrow coastal plain. In New Guinea, the ranges are fragmented by river valleys, creating extensive lowlands comprising flood plains and swamps. Throughout the region, the climate is dominated by two wind systems and by the influence of mountain barriers and the sur‐ rounding oceans. From December to April (the wet season), moist northwesterly winds produce the heaviest and most frequent rains. From May to October (the dry season), south‐ easterly winds prevail and conditions are drier. However during this period substantial rainfall occurs wherever prominent mountain barriers exist. Thus the climate for most of the region is continuous hot/wet with rainfall >2000mm p.a. with rainless periods rarely exceeding four days. Exceptions occur in southern Western Province and around Port Moresby in PNG where the climate is more monsoonal, the dry season is more pronounced, and the rainfall is less (1600-2000mm p.a.) (Fig. 1) [15].

Temperature is not a major climatic factor as there is little seasonality and minimal variation throughout each year in a given elevation. However, elevation exerts the main influence on temperature: in coastal and lowland areas (<500m), the mean temperature is 26o C (max 31o C; min 22o C), while in the highland regions (>500m), the mean temperature is 20o C (max 23o C; min 14o C) [15].

### **2. Systematics of the malaria vector Groups**

The anopheline fauna of the Australian Region is delimited in the west by the Weber Line, which runs through the Moluccas, though there is some incursion east and west of this line by anophelines from the Oriental and Australian Regions (Fig 1 and Table 1). The Australian fauna is highly endemic and most likely of Oriental origin. The malaria vectors in the Australian Region are composed of groups and complexes of closely related, morphologically similar, cryptic or sibling anopheline species. Accurate identification of vector species is essential for interpreting the efficacy of interventions in an area. Since the discovery of cryptic sibling species, the use of morphological characters previously used to identify species has been rendered uncertain. Techniques such as cross-mating, chromosome studies and allozyme

widespread distribution. In the Solomon Islands and Vanuatu, full-scale malaria eradication programs (MEP) commenced in the early 1970s but were also abandoned after three years and replaced with control programs [11]. In both countries pyrethroids replaced DDT in IRS in the early 1990s and ITNs became the main method of control [12]. During the 1990s, malaria was successfully eliminated on Aneityum Island, the most southern island of Vanuatu [13] with mass drug administration as the primary intervention. Recently, renewed efforts at malaria elimination and intensified control were initiated in Tafea Province in Vanuatu and Temotu

This work covers the malarious area of the southwest Pacific as it lies within the Australian faunal region (Fig. 1). This region is made up of numerous islands many of which are moun‐ tainous (>4000m) with ranges extending to the coasts and drained by river systems over a narrow coastal plain. In New Guinea, the ranges are fragmented by river valleys, creating extensive lowlands comprising flood plains and swamps. Throughout the region, the climate is dominated by two wind systems and by the influence of mountain barriers and the sur‐ rounding oceans. From December to April (the wet season), moist northwesterly winds produce the heaviest and most frequent rains. From May to October (the dry season), south‐ easterly winds prevail and conditions are drier. However during this period substantial rainfall occurs wherever prominent mountain barriers exist. Thus the climate for most of the region is continuous hot/wet with rainfall >2000mm p.a. with rainless periods rarely exceeding four days. Exceptions occur in southern Western Province and around Port Moresby in PNG where the climate is more monsoonal, the dry season is more pronounced, and the rainfall is

Temperature is not a major climatic factor as there is little seasonality and minimal variation throughout each year in a given elevation. However, elevation exerts the main influence on

The anopheline fauna of the Australian Region is delimited in the west by the Weber Line, which runs through the Moluccas, though there is some incursion east and west of this line by anophelines from the Oriental and Australian Regions (Fig 1 and Table 1). The Australian fauna is highly endemic and most likely of Oriental origin. The malaria vectors in the Australian Region are composed of groups and complexes of closely related, morphologically similar, cryptic or sibling anopheline species. Accurate identification of vector species is essential for interpreting the efficacy of interventions in an area. Since the discovery of cryptic sibling species, the use of morphological characters previously used to identify species has been rendered uncertain. Techniques such as cross-mating, chromosome studies and allozyme

C (max 31o

C (max 23o

C;

C;

temperature: in coastal and lowland areas (<500m), the mean temperature is 26o

C), while in the highland regions (>500m), the mean temperature is 20o

and Santa Isabel Provinces in the Solomon Islands [14].

**1.2. Geography and climate**

358 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

less (1600-2000mm p.a.) (Fig. 1) [15].

**2. Systematics of the malaria vector Groups**

min 22o

min 14o

C) [15].

**Figure 1.** Map of the southwest Pacific region showing regions and sites described in the text. The malaria vectors described in this chapter exist from the Moluccas in the west (approximately at the Weber line) to Vanuatu in the east and south into northern Australia. Note: The green to orange shading represents elevation from 600m to 4,800m.

analysis were initially deployed to resolve the problems of identifying these sibling species, though none of these can match the speed and simplicity of morphological markers which could be applied in the field. Advances in DNA-based technology with high throughput capability during the past two decades allow large and detailed analyses of vector populations. Although more costly and requiring sophisticated laboratory support, methods such as DNA probe hybridization and PCR are both quick, user-friendly and offer advantages in the study of intraspecific differences between species and for phylogenetic studies. Studies of the *Anopheles punctulatus* group of the southwest Pacific provides a prime example of both the application of this technology and how it has progressed.

Because of advances in DNA-based technologies, mosquito taxonomists and systematists can now identify, describe, and classify *Anopheles* biodiversity, in addition to studying and understanding their evolution, distribution, and species' relationships. The practical relevance of such information extends beyond the labeling and ordering of taxa. Studies of malaria transmission reinforce time and again the importance of incorporating an intimate knowledge of *Anopheles* species biology, behavior, and ecology into the design, implementation and evaluation of any successful vector control strategy. Control strategies require information on vector species distribution, their density, and seasonal prevalence as well as data on mating, oviposition, feeding and resting habits, longevity and fecundity, and susceptibility to both parasites and insecticides. Yet measurements of these entomological parameters are only relevant if accurate vector species' identifications are possible. Each species has evolved characteristics that will influence its ability to transmit malaria and its vulnerability to any control strategies depends on these behavioural characteristics. Additionally, systematics and phylogeny can provide useful information on host/parasite evolution, ecological adaptation,


Monsoonal type climate; continuous hot/wet type climate, highlands >300m; SCH: south of the central highlands; NCH: north of the central highlands

xxxx: abundant, xxx: common, xx: uncommon, x: rare

**Table 1.** The *Anopheles* species currently found in the Australian Region, their distribution and vector status.

and biogeography. The following section outlines our current knowledge of the primary and secondary malaria vectors of the southwest Pacific region.

#### **2.1. The** *Anopheles* **(***Cellia***)** *punctulatus* **group**

**Species, Groups, and Complexes**

Subgenus *Anopheles An*. *bancroftii* complex

Subgenus *Cellia An*. *annulipes* complex:

*An*. *longirostris* complex:

*An*. *lungae* complex:

*An*. *punctulatus* group: *An*. *farauti* complex:

north of the central highlands

xxxx: abundant, xxx: common, xx: uncommon, x: rare

**Moluccas**

360 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**nal**

**New Guinea1**

**Highlands**

four species A-D xxx2 xxx xx xxx xxx secondary *An*. *papuensis* x non-vector

*An*. *annulipes* L xxx x non-vector *An*. *annulipes* M xxx non-vector *An*. *hilli* xxx possible *An*. *karwari* (Oriental) xx xxx secondary

nine species 1-9 xxx xxx xxx secondary

*An*. *lungae* xxxx possible *An*. *solomonis* xxxx possible *An*. *nataliae* xxxx possible *An*. *meraukensis* xx possible *Annovaguinensis* xx possible

*An*. *farauti* xxx xxxx xxxx xxxx xxxx xxxx xxxx primary *An*. *hinesorum* x xxxx xxxx x xxxx xxx xxxx secondary *An*. *torresiensis* xx possible *An*. *farauti* 4 xxx xxx secondary *An*. *farauti* 5 x non-vector *An*. *farauti* 6 xxx secondary *An*. *irenicus* xxx non-vector *An*. *farauti* 8 x secondary *An*. *clowi* x x non-vector *An*. *koliensis* xxxx xxxx xxxx x primary *An*. *punctulatus* xxxx xx xxxx xxxx xx primary *An*. sp near *punctulatus* xx xx xx non-vector *An*. *rennellensis* x non-vector *An*. *subpictus* (Oriental) x xx xx xx x possible *An*. *tessellatus* (Oriental) x x x non-vector Monsoonal type climate; continuous hot/wet type climate, highlands >300m; SCH: south of the central highlands; NCH:

**Table 1.** The *Anopheles* species currently found in the Australian Region, their distribution and vector status.

**Hot / wet**

**Solomon Islands**

**status Monsoo-**

**SCH NCH**

**Vanuatu**

**Vector**

The primary vectors of malaria throughout the southwest Pacific region are members of the *Anopheles punctulatus* group. In 1901 Dönitz described the type form [16], *Anopheles punctula‐ tus*, from the Madang area of PNG, while Laveran described *Anophelesfarauti* in Efate, Vanuatu, the following year [17]. Given that the range of the *An. punctulatus* group spans several countries, the early identity and relationship of the members was somewhat confused – a detailed account of this early history is given in Lee et al. [18] and Rozeboom and Knight [19].

Thanks in part to the necessary deployment of Allied defense personnel throughout this region; the taxonomy of this vector group was studied in depth during World War II. Four closely related species were identified – *An. punctulatus* Dönitz, *An. farauti* Laveran, *An. koliensis* Owen and *An*. *clowi* Rozeboom and Knight – and assembled within the Punctulatus Complex [19].

In 1962, Belkin referred to the group in his taxonomic study of South Pacific mosquitoes [20]. However, this study did not include Irian Jaya, Indonesia (now West Papua/Papua Province) or PNG. Rozeboom and Knight [19] provide descriptions of the original four members of the *An. punctulatus* complex and taxonomic keys for the members of the complex. For adult females, the diagnostic characters used were the black and white scaling patterns on the proboscis and, to a lesser extent, on the wings, palpi, and tarsi. Proboscis morphology readily, but unreliably as was later learned, separated the three most common and widespread members, *An. farauti*, *An. punctulatus*, and *An. koliensis*. *Anopheles farauti* displays an all black scaled labium; *An*. *punctulatus* has the apical half of the labium extensively pale scaled; and *An*. *koliensis* has a patch of pale scales, varying in size, on the ventral surface of the apical half of the labium [19]. For *An. clowi,* the tarsi on the fore- and mid-legs were used [19].

Taxonomic and systematic studies of the group were renewed in the 1970's when Bryan showed that cross-mating between two *An. farauti* colonies (from Rabaul in PNG and north Queensland) was incompatible as the species differed by two paracentric inversions [21]. The two species were then called *An. farauti* 1 and *An. farauti* 2. Bryan then collected material from the type locality (Efate, Vanuatu) and identified it as *An. farauti* 1 [22], hereafter referred to as *An. farauti*. Hybridization experiments by Mahon and Miethke in 1982 [23] revealed another species (designated *An. farauti* 3) and also found three sympatric sibling species with no evidence of interbreeding in the Innisfail region south of Cairns in north Queensland. Bryan also confirmed the species status of *An. koliensis* in 1973 by cross-mating experiments [24]. Also in 1973, Maffi described specimens from Rennell Island in the Solomon Islands as belonging to the *An. punctulatus* group [25] and subsequently declared these mosquitoes as a new species, *An. rennellensis* [26]. In the late 1980s, *An. farauti* was identified from the coastal areas around Madang, PNG [27], and Sweeney showed that salt tolerance could be used as a species diagnostic feature [28].

Although proboscis markings are often obvious and easy to detect, proboscis morphology is not a reliable means of distinguishing species in this group. As early as 1945, working in PNG, Woodhill [29] examined the progeny of wild caught females of the "intermediate form" (now called *An. koliensis*) and found both *An. farauti*- and *An. punctulatus-*type proboscis. Similar polymorphisms in this character were also noted by Foley et al. [30] and Cooper et al. [31]. Later morphological studies [32, 33] using specimens from Australia and the Solomon Islands described morphological features for *An. farauti* species and provided preliminary keys. However these keys are problematic as the characters used are both difficult for routine identification and are not 100% accurate. In addition, they were developed using material from a limited range of the species' distributions. Figure 2 and Table 2 summarizes some problems with using proboscis morphology for identifying members of the *An. punctulatus* group.

The *An. punctulatus* group currently consists of 13 species that include: *An. punctulatus*, *An koliensis*, *An*. species near *punctulatus*, *An. clowi*, *An. rennellensis*, and the members of the *An. farauti* complex: *An. farauti* (formally *An. farauti* 1), *An. hinesorum* (formally *An. farauti* 2), *An. torresiensis* (formally *An. farauti* 3), *An. irenicus* (formally *An. farauti* 7) and *An. farauti* 4-6 and 8 [30, 33-37]. Given that the majority of the 13 species currently known in the *An. punctulatus* group were discovered in the 1990's, a great deal of polymorphism can be presumed to exist in the morphological characters previously used to describe the members of this group. As a consequence, field workers who rely on proboscis morphology should also be using the available molecular tools [30, 31, 38-40] (see Fig. 2 and Table 2).


*farauti* - all black scaled labium; *koliensis* - dorsal white patch of scales on the anterior end; *punctulatus*: anterior half all white scaled.

**Table 2.** Proboscis morphology of five common members of the *Anophelespunctulatus* group from the Australian Region and identified using DNA hybridisation and PCR-RFLP analysis.

The distribution of these species is only beginning to be understood as the group ranges over hundreds of small islands with varying landforms and ecotypes, each island providing opportunities for reproductive isolation and consequent speciation. It is possible that further species may be found when the remote and inaccessible areas of the Moluccas, Indonesian Papua, Papua New Guinea, and the Solomon Islands are more thoroughly surveyed.

#### *2.1.1. Molecular genetic markers*

Although proboscis markings are often obvious and easy to detect, proboscis morphology is not a reliable means of distinguishing species in this group. As early as 1945, working in PNG, Woodhill [29] examined the progeny of wild caught females of the "intermediate form" (now called *An. koliensis*) and found both *An. farauti*- and *An. punctulatus-*type proboscis. Similar polymorphisms in this character were also noted by Foley et al. [30] and Cooper et al. [31]. Later morphological studies [32, 33] using specimens from Australia and the Solomon Islands described morphological features for *An. farauti* species and provided preliminary keys. However these keys are problematic as the characters used are both difficult for routine identification and are not 100% accurate. In addition, they were developed using material from a limited range of the species' distributions. Figure 2 and Table 2 summarizes some problems with using proboscis morphology for identifying members of the *An. punctulatus* group.

The *An. punctulatus* group currently consists of 13 species that include: *An. punctulatus*, *An koliensis*, *An*. species near *punctulatus*, *An. clowi*, *An. rennellensis*, and the members of the *An. farauti* complex: *An. farauti* (formally *An. farauti* 1), *An. hinesorum* (formally *An. farauti* 2), *An. torresiensis* (formally *An. farauti* 3), *An. irenicus* (formally *An. farauti* 7) and *An. farauti* 4-6 and 8 [30, 33-37]. Given that the majority of the 13 species currently known in the *An. punctulatus* group were discovered in the 1990's, a great deal of polymorphism can be presumed to exist in the morphological characters previously used to describe the members of this group. As a consequence, field workers who rely on proboscis morphology should also be using the

> **Proboscis Type1 number (%) farauti koliensis punctulatus**

> > 0 (0)

1 (0.1)

472 (56.0)

1,035 (84.7)

> 16 (2.4)

3 (0.3)

1 (0.1)

135 (16.0)

> 37 (3.0)

656 (97.0)

available molecular tools [30, 31, 38-40] (see Fig. 2 and Table 2).

1,128 (99.7)

1,048 (99.8)

235 (28.0)

151 (12.3)

> 4 (0.6)

Region and identified using DNA hybridisation and PCR-RFLP analysis.

*farauti* - all black scaled labium; *koliensis* - dorsal white patch of scales on the anterior end; *punctulatus*: anterior half all

**Table 2.** Proboscis morphology of five common members of the *Anophelespunctulatus* group from the Australian

**Species (number identified by PCR)**

362 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

*An*. *farauti* (n=1,131)

*An*. *hinesorum* (n=1,050)

> *Anfarauti* 4 (n=842)

*An*. *koliensis* (n=1,223)

*An*. *punctulatus* (n=676)

white scaled.

After cross-mating experiments revealed post-mating barriers and the presence of the three species designated *An. farauti*, *An. hinesorum,* and *An*. *torresiensis* [24], mosquito cytogenetics became a more informative and practical method to study and identify these species. In 1971, Bryan and Coluzzi [21] produced preliminary maps of polytene chromosomes from the salivary glands of 4th instar larvae of *An. farauti* and *An. hinesorum*. Taking *An. farauti* as the standard, *An. hinesorum* differed by a paracentric inversion on each of the left and right arms of chromosome 2 [21]. Mahon [41] found that *An. torresiensis* had the standard arrangement for the autosomes but the X chromosome differed by two inversions. The same author also looked at chromosome maps of *An. punctulatus* and *An. koliensis* and predicted chromosomal relationships among the five species and possible ancestral characters [41].

Figure 2. This single most parsimonious phylogenetic tree generated from the structural alignment of the nuclear ssrDNA reveals 11 members of **Figure 2.** This single most parsimonious phylogenetic tree generated from the structural alignment of the nuclear ssrDNA reveals 11 members of the *An. punctulatus* group with *An*. *annulipes* sp. A from the *An. annulipes* outgroup. Proboscis morphologies identified from field-collected specimens are displayed to the right and overt biological char‐ acteristics are also listed.

**2.1.3. Species-specific genomic DNA probes** 

data generated by Cooper and colleagues [31, 44, 48, 49].

**2.1.2. Molecular markers** 

near *punctulatus* [34].

specimens are displayed to the right and overt biological characteristics are also listed.

lab to prevent protein degradation of samples was the most limiting feature of this technology.

the *An. punctulatus* group with *An*. *annulipes* sp. A from the *An. annulipes* outgroup. Proboscis morphologies identified from field-collected

Allozymes: In the 1990's Foley and colleagues [30] executed the first population genetic studies into the group using allozyme electrophoresis methods to show that *An. farauti* specimens from inland areas around Madang were reproductively isolated from the PNG highlands. In doing this, they discovered *An. farauti* 4 from the Madang area and *An. farauti* 5 and 6 from the PNG highlands. Then, also using allozymes, Foley revealed a reproductively isolated *An. farauti*-like species from Guadalcanal in the Solomon Islands and designated it *An. farauti* 7 (now *An. irenicus*) [35]. Furthermore, a population with morphology very like *An. punctulatus* was found in the Western Province of PNG and appeared reproductively isolated; this was named *Anopheles* species

To facilitate the identification of the large numbers of field-collected material required for malaria studies, Mahon [42] developed a starch gel allozyme electrophoresis method using two enzymes, lactate dehydrogenase and octanol dehydrogenase. This method was employed to study the distribution of cryptic species of *An. farauti* throughout northern Australia [43, 44]. The allozyme technique was further refined with cellulose acetate electrophoresis by Foley in 1993 [30, 45] to also identify *An. farauti* 4, 5, 6, *An. irenicus*, and *An.* species near *punctulatus* [30, 34, 35]. Thus electrophoretic keys were now available for ten species in the *An. punctulatus* group – excluding the rarely recorded *An. clowi* and *An. rennellensis* [34]. These allozyme markers represent the first molecular tools to identify the members of the *An. punctulatus* group. The requirement of a cold (frozen) chain from the field to the

Chromosome banding differences discovered while identifying cryptic species revealed a large variations in the genomic DNA of these species, and suggested possible avenues for producing new technologies for identifying cryptic species. Advances in recombinant DNA technology in the early 1980's enabled the isolation of species-specific repetitive DNA sequences. The use of nucleic acids as characters to identify the members of this group began in 1991 with the development of isotopic DNA probes for the Australian species *An. farauti*, *An. hinesorum*, and *An*. *torresiensis* [46]. Genomic DNA probes were developed for use with squash blot techniques for ten species in the *An. punctulatus* group [38, 46, 47]. The "squash blot" (see Fig. 3 for an example) technique requires no DNA extraction; the specimen (or part of specimens) is squashed directly onto the membrane in the presence of a detergent that ruptures the tissue. The liberated DNA then binds to the nylon membrane. Species-specific probes labeled with a reporter molecule such as biotin or 32P hybridize to homologous DNA from the squashed material and are visualized by the reporter molecule [46]. Up to 100 membranes can be probed simultaneously, permitting thousands of field specimens to be identified for a particular species. Over 100,000 species identifications were thereby processed to produce the extensive distribution

5

#### *2.1.2. Molecular markers*

Allozymes: In the 1990's Foley and colleagues [30] executed the first population genetic studies into the group using allozyme electrophoresis methods to show that *An. farauti* specimens from inland areas around Madang were reproductively isolated from the PNG highlands. In doing this, they discovered *An. farauti* 4 from the Madang area and *An. farauti* 5 and 6 from the PNG highlands. Then, also using allozymes, Foley revealed a reproductively isolated *An. farauti*like species from Guadalcanal in the Solomon Islands and designated it *An. farauti* 7 (now *An. irenicus*) [35]. Furthermore, a population with morphology very like *An. punctulatus* was found in the Western Province of PNG and appeared reproductively isolated; this was named *Anopheles* species near *punctulatus* [34].

To facilitate the identification of the large numbers of field-collected material required for malaria studies, Mahon [42] developed a starch gel allozyme electrophoresis method using two enzymes, lactate dehydrogenase and octanol dehydrogenase. This method was employed to study the distribution of cryptic species of *An. farauti* throughout northern Australia [43, 44]. The allozyme technique was further refined with cellulose acetate electrophoresis by Foley in 1993 [30, 45] to also identify *An. farauti* 4, 5, 6, *An. irenicus*, and *An.* species near *punctula‐ tus* [30, 34, 35]. Thus electrophoretic keys were now available for ten species in the *An. punctulatus* group – excluding the rarely recorded *An. clowi* and *An. rennellensis* [34]. These allozyme markers represent the first molecular tools to identify the members of the *An. punctulatus* group. The requirement of a cold (frozen) chain from the field to the lab to prevent protein degradation of samples was the most limiting feature of this technology.

#### *2.1.3. Species-specific genomic DNA probes*

Chromosome banding differences discovered while identifying cryptic species revealed a large variations in the genomic DNA of these species, and suggested possible avenues for producing new technologies for identifying cryptic species. Advances in recombinant DNA technology in the early 1980's enabled the isolation of species-specific repetitive DNA sequences. The use of nucleic acids as characters to identify the members of this group began in 1991 with the development of isotopic DNA probes for the Australian species *An. farauti*, *An. hinesorum*, and *An*. *torresiensis* [46]. Genomic DNA probes were developed for use with squash blot techniques for ten species in the *An. punctulatus* group [38, 46, 47]. The "squash blot" (see Fig. 3 for an example) technique requires no DNA extraction; the specimen (or part of specimens) is squashed directly onto the membrane in the presence of a detergent that ruptures the tissue. The liberated DNA then binds to the nylon membrane. Species-specific probes labeled with a reporter molecule such as biotin or 32P hybridize to homologous DNA from the squashed material and are visualized by the reporter molecule [46]. Up to 100 membranes can be probed simultaneously, permitting thousands of field specimens to be identified for a particular species. Over 100,000 species identifications were thereby processed to produce the extensive distribution data generat‐ ed by Cooper and colleagues [31, 44, 48, 49].

*2.1.2. Molecular markers*

*Anopheles* species near *punctulatus* [34].

364 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

*2.1.3. Species-specific genomic DNA probes*

ed by Cooper and colleagues [31, 44, 48, 49].

Allozymes: In the 1990's Foley and colleagues [30] executed the first population genetic studies into the group using allozyme electrophoresis methods to show that *An. farauti* specimens from inland areas around Madang were reproductively isolated from the PNG highlands. In doing this, they discovered *An. farauti* 4 from the Madang area and *An. farauti* 5 and 6 from the PNG highlands. Then, also using allozymes, Foley revealed a reproductively isolated *An. farauti*like species from Guadalcanal in the Solomon Islands and designated it *An. farauti* 7 (now *An. irenicus*) [35]. Furthermore, a population with morphology very like *An. punctulatus* was found in the Western Province of PNG and appeared reproductively isolated; this was named

To facilitate the identification of the large numbers of field-collected material required for malaria studies, Mahon [42] developed a starch gel allozyme electrophoresis method using two enzymes, lactate dehydrogenase and octanol dehydrogenase. This method was employed to study the distribution of cryptic species of *An. farauti* throughout northern Australia [43, 44]. The allozyme technique was further refined with cellulose acetate electrophoresis by Foley in 1993 [30, 45] to also identify *An. farauti* 4, 5, 6, *An. irenicus*, and *An.* species near *punctula‐ tus* [30, 34, 35]. Thus electrophoretic keys were now available for ten species in the *An. punctulatus* group – excluding the rarely recorded *An. clowi* and *An. rennellensis* [34]. These allozyme markers represent the first molecular tools to identify the members of the *An. punctulatus* group. The requirement of a cold (frozen) chain from the field to the lab to prevent

protein degradation of samples was the most limiting feature of this technology.

Chromosome banding differences discovered while identifying cryptic species revealed a large variations in the genomic DNA of these species, and suggested possible avenues for producing new technologies for identifying cryptic species. Advances in recombinant DNA technology in the early 1980's enabled the isolation of species-specific repetitive DNA sequences. The use of nucleic acids as characters to identify the members of this group began in 1991 with the development of isotopic DNA probes for the Australian species *An. farauti*, *An. hinesorum*, and *An*. *torresiensis* [46]. Genomic DNA probes were developed for use with squash blot techniques for ten species in the *An. punctulatus* group [38, 46, 47]. The "squash blot" (see Fig. 3 for an example) technique requires no DNA extraction; the specimen (or part of specimens) is squashed directly onto the membrane in the presence of a detergent that ruptures the tissue. The liberated DNA then binds to the nylon membrane. Species-specific probes labeled with a reporter molecule such as biotin or 32P hybridize to homologous DNA from the squashed material and are visualized by the reporter molecule [46]. Up to 100 membranes can be probed simultaneously, permitting thousands of field specimens to be identified for a particular species. Over 100,000 species identifications were thereby processed to produce the extensive distribution data generat‐

Figure 3. Mosquito squash blots hybridized with species-specific genomic DNA probes labeled with 32P can distinguish cryptic species in the *An. punctulatus* group. **Panel A:** squash blot of mosquitoes morphologically identified as *An. koliensis* and probed with a species-specific probe reveals that only a subset of samples are *An. koliensis* (*An. farauti* 4 made up the other individuals identified as *An. koliensis*) **Panel B:** Same blot was **Figure 3.** Mosquito squash blots hybridized with species-specific genomic DNA probes labeled with 32P can distinguish cryptic species in the *An. punctulatus* group. **Panel A:** squash blot of mosquitoes morphologically identified as An. ko‐ *liensis* and probed with a species-specific probe reveals that only a subset of samples are *An. koliensis* (*An. farauti* 4 made up the other individuals identified as *An. koliensis*). **Panel B:** same blot was stripped and probed with a panspecies rDNA 18S probe that binds to all species revealing the total amount of gDNA on the blot. **Panel C:** mosquitoes identified as *An. punctulatus* are probed with the *An. punctulatus* species-specific probe and **Panel D** is the same blot stripped and reprobed with the *An*. sp. nr *punctulatus* probe.

reprobed with the *An*. sp. nr *punctulatus* probe.

**2.1.4.1. Ribosomal DNA ITS2** 

610 bp

310 bp

234 bp 192 bp 72 bp

**2.1.4. PCR-based species diagnostics** 

*An. punctulatus, An. koliensis, An. farauti, An. hinesorum,* and *An. farauti* 4 [40].

1 2 3 4 5 6 7 8 9 10

stripped and probed with a pan-species rDNA 18S probe that binds to all species revealing the total amount of gDNA on the blot. **Panel C:**

mosquitoes identified as *An. punctulatus* are probed with the *An. punctulatus* species-specific probe and **Panel D** is the same blot stripped and

The avent of polymerization chain reaction (PCR) for DNA amplification in the late 1980's facilitated technologies for both cryptic

species' identification and within-species population studies. The most popular marker for species-specific PCR-based diagnosis

has been the rDNA gene family. Despite a lack of understanding of the evolution of this non-Mendelian repetitive gene family, its

rapidly evolving transcribed spacers allow a simplistic evaluation of genetic discontinuity within and between species. The internal

transcribed spacer 2 (ITS2) region proved the most useful for developing two different species diagnostic tools for identifying *An*.

*punctulatus* group members [40, 50]. In the first PCR-RFLP (restricted fragment length polymorphism) technology, the size of the

ITS2 region (~710bp) was identical for all *An*. *punctulatus* group members and was thus diagnostic for the group; this means that

mosquito collections of other (non-*An. punctulatus* group) species can be detected simply as RFLPs of different banding profiles.

Digestion of this product with the restriction enzyme *Msp* I generates species-specific DNA fragments for the 11 most abundant

and most widely distributed members of this group, *An. farauti*, *An. hinesorum*, *An. torresiensis, An. farauti* 4-6, *An. irenicus*, *An.* 

*punctulatus*, *An*. species near *punctulatus*, and *An. clowi* (Fig. 4). This species-specific PCR-RFLP has been extensively used both

independently and alongside genomic DNA probes in species distribution studies of the *An. punctulatus* group [31, 44, 48, 51].

However, more recently, a "Luminex®"-based multiplex ligase detection reaction and fluorescent microsphere-based assay method

became available, also based on species-specific ITS2 sequences, and can separate the five common malaria vector species in PNG:

Figure 4. Molecular diagnostic that discriminates over 10 members of the *An. punctulatus* group based on a PCR-RFLP of the ITS2, cut with the

restriction enzyme *Msp* I and run out on a 3% agarose gel. Banding profiles are as follows: Lane 1, *An. farauti*; (formally *An. farauti* 1) Lane 2, *An.* 

6

reprobed with the *An*. sp. nr *punctulatus* probe.

**2.1.4. PCR-based species diagnostics** 

#### *2.1.4. PCR-based species diagnostics* The avent of polymerization chain reaction (PCR) for DNA amplification in the late 1980's facilitated technologies for both cryptic

#### *2.1.4.1. Ribosomal DNA ITS2* species' identification and within-species population studies. The most popular marker for species-specific PCR-based diagnosis

*An. koliensis*

A

B

C

D

Pan‐species 18S

*An. punctulatus*

*An.* sp. nr. *punctulatus*

The advent of polymerization chain reaction (PCR) for DNA amplification in the late 1980's facilitated technologies for both cryptic species' identification and within-species population studies. The most popular marker for species-specific PCR-based diagnosis has been the rDNA gene family. Despite a lack of understanding of the evolution of this non-Mendelian evolving repetitive gene family, its rapidly evolving transcribed spacers allow a simplistic evaluation of genetic discontinuity within and between species. The internal transcribed spacer 2 (ITS2) region proved the most useful for developing two different species diagnostic tools for identifying *An*. *punctulatus* group members [40, 50]. In the first PCR-RFLP (restricted fragment length polymorphism) technology, the size of the ITS2 region (~710bp) was identical for all *An*. *punctulatus* group members and was thus diagnostic for the group; this means that mosquito collections of other (non-*An. punctulatus* group) species can be detected simply as RFLPs of different banding profiles. Digestion of this product with the restriction enzyme *Msp* I generates species-specific DNA fragments for the 11 most abundant and most widely distributed members of this group, *An. farauti*, *An. hinesorum*, *An. torresiensis, An. farauti* 4-6, *An. irenicus*, *An. punctulatus*, *An*. species near *punctulatus*, and *An. clowi* (Fig. 4). This speciesspecific PCR-RFLP has been extensively used both independently and alongside genomic DNA probes in species distribution studies of the *An. punctulatus* group [31, 44, 48, 51]. However, more recently, a "Luminex®"-based multiplex ligase detection reaction and fluo‐ rescent microsphere-based assay method became available, also based on species-specific ITS2 sequences, and can separate the five common malaria vector species in PNG: *An. punctulatus, An. koliensis, An. farauti, An. hinesorum,* and *An. farauti* 4 [40]. has been the rDNA gene family. Despite a lack of understanding of the evolution of this non-Mendelian repetitive gene family, its rapidly evolving transcribed spacers allow a simplistic evaluation of genetic discontinuity within and between species. The internal transcribed spacer 2 (ITS2) region proved the most useful for developing two different species diagnostic tools for identifying *An*. *punctulatus* group members [40, 50]. In the first PCR-RFLP (restricted fragment length polymorphism) technology, the size of the ITS2 region (~710bp) was identical for all *An*. *punctulatus* group members and was thus diagnostic for the group; this means that mosquito collections of other (non-*An. punctulatus* group) species can be detected simply as RFLPs of different banding profiles. Digestion of this product with the restriction enzyme *Msp* I generates species-specific DNA fragments for the 11 most abundant and most widely distributed members of this group, *An. farauti*, *An. hinesorum*, *An. torresiensis, An. farauti* 4-6, *An. irenicus*, *An. punctulatus*, *An*. species near *punctulatus*, and *An. clowi* (Fig. 4). This species-specific PCR-RFLP has been extensively used both independently and alongside genomic DNA probes in species distribution studies of the *An. punctulatus* group [31, 44, 48, 51]. However, more recently, a "Luminex®"-based multiplex ligase detection reaction and fluorescent microsphere-based assay method became available, also based on species-specific ITS2 sequences, and can separate the five common malaria vector species in PNG: *An. punctulatus, An. koliensis, An. farauti, An. hinesorum,* and *An. farauti* 4 [40].

Figure 3. Mosquito squash blots hybridized with species-specific genomic DNA probes labeled with 32P can distinguish cryptic species in the *An. punctulatus* group. **Panel A:** squash blot of mosquitoes morphologically identified as *An. koliensis* and probed with a species-specific probe reveals that only a subset of samples are *An. koliensis* (*An. farauti* 4 made up the other individuals identified as *An. koliensis*) **Panel B:** Same blot was stripped and probed with a pan-species rDNA 18S probe that binds to all species revealing the total amount of gDNA on the blot. **Panel C:** mosquitoes identified as *An. punctulatus* are probed with the *An. punctulatus* species-specific probe and **Panel D** is the same blot stripped and

Figure 4. Molecular diagnostic that discriminates over 10 members of the *An. punctulatus* group based on a PCR-RFLP of the ITS2, cut with the restriction enzyme *Msp* I and run out on a 3% agarose gel. Banding profiles are as follows: Lane 1, *An. farauti*; (formally *An. farauti* 1) Lane 2, *An.*  **Figure 4.** Molecular diagnostic that discriminates over 10 members of the *An. punctulatus* group based on a PCR-RFLP of the ITS2, cut with the restriction enzyme *Msp* I and run out on a 3% agarose gel. Banding profiles are as follows: Lane 1, *An. farauti*; (formally *An. farauti* 1) Lane 2, *An. hinesorum* (formally *An. farauti* 2); Lane 3, *An. torresiensis* (for‐ mally *An. farauti* 3); Lane 4, *An. farauti* 4 (contains no restriction site); Lane 5, *An. farauti* 5; Lane 6, *An. farauti* 6; Lane 7, *An. irenicus* (formally *farauti* 7), Lane 8, *An. koliensis*, Lane 9, *An. punctulatus*; Lane 10, *An*. sp. nr. *punctulatus*. Addition‐ ally *An. clowi* can be distinguished using this method however *An. farauti* 8 produces the same RFLP profile as *An. farauti*, but is distinguishable by ITS1 RFLP[52].

6

The avent of polymerization chain reaction (PCR) for DNA amplification in the late 1980's facilitated technologies for both cryptic species' identification and within-species population studies. The most popular marker for species-specific PCR-based diagnosis has been the rDNA gene family. Despite a lack of understanding of the evolution of this non-Mendelian repetitive gene family, its rapidly evolving transcribed spacers allow a simplistic evaluation of genetic discontinuity within and between species. The internal transcribed spacer 2 (ITS2) region proved the most useful for developing two different species diagnostic tools for identifying *An*. *punctulatus* group members [40, 50]. In the first PCR-RFLP (restricted fragment length polymorphism) technology, the size of the ITS2 region (~710bp) was identical for all *An*. *punctulatus* group members and was thus diagnostic for the group; this means that mosquito collections of other (non-*An. punctulatus* group) species can be detected simply as RFLPs of different banding profiles. Digestion of this product with the restriction enzyme *Msp* I generates species-specific DNA fragments for the 11 most abundant and most widely distributed members of this group, *An. farauti*, *An. hinesorum*, *An. torresiensis, An. farauti* 4-6, *An. irenicus*, *An. punctulatus*, *An*. species near *punctulatus*, and *An. clowi* (Fig. 4). This species-specific PCR-RFLP has been extensively used both independently and alongside genomic DNA probes in species distribution studies of the *An. punctulatus* group [31, 44, 48, 51]. However, more recently, a "Luminex®"-based multiplex ligase detection reaction and fluorescent microsphere-based assay method Analysis of the ITS2 region reveals substantial insertion and deletion events (indels) between species that are probably due to sequence slippage of common, simple, sequence repeat motifs. Interestingly, no ITS2 PCR-RFLP mixed species hybrids have yet been reported, which would be observed as single mosquitoes sharing RFLP profiles of more than one species. The lack of hybrids at the rDNA locus reinforces the species status for members of this group. Additionally evolutionary information about the *An. punctulatus* group has been obtained with studies of the ITS2 region. The undigested ITS2 PCR products from single mosquitoes contain ITS2 sequence copy variants in the multicopy rDNA array and can provide another view on population genetic structure. For example, intraspecific rDNA genotypes of *An. farauti* were found to be geographically structured by the presence of fixed ITS2 copy variants amplified in the PCR [53] (also see Figs. 5, 6, and 7 for examples). Population genetic analyses of *An. farauti* revealed macrogeographic population structure in *An. farauti* throughout the southwest Pacific comprising several distinct genotypes, suggestive of potential barriers to gene flow. Interestingly, only a subset of these geographically structured genotypes were identified at the level of the mitochondrial DNA cytochrome oxidase I (COI) sequence level in a recent population genetic study of this species [54], suggesting that the rDNA array may be a sensitive tool for species-level diagnostics.

became available, also based on species-specific ITS2 sequences, and can separate the five common malaria vector species in PNG: While the ITS1 region has not been examined in as much detail as the ITS2, the ITS1 is an informative marker for intraspecific population studies for some *An. punctulatus* group members, separating *An. farauti* into several geographically and climatically distributed genotypes [52, 53]. For example, Fig. 5 shows how the ITS2 and ITS1 can reveal qualitative information on population genetic discontinuities within *An. farauti* where rDNA genotypes could also be identified within and between landmasses reflecting genetic and geographic structure [53]. This phenomena was most likely possible because of the extended time this species existed in a region with natural barriers to gene flow [54].

#### *2.1.5. Evolutionary and phylogenetic studies*

*2.1.4. PCR-based species diagnostics*

366 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

*An. koliensis, An. farauti, An. hinesorum,* and *An. farauti* 4 [40].

The advent of polymerization chain reaction (PCR) for DNA amplification in the late 1980's facilitated technologies for both cryptic species' identification and within-species population studies. The most popular marker for species-specific PCR-based diagnosis has been the rDNA gene family. Despite a lack of understanding of the evolution of this non-Mendelian evolving repetitive gene family, its rapidly evolving transcribed spacers allow a simplistic evaluation of genetic discontinuity within and between species. The internal transcribed spacer 2 (ITS2) region proved the most useful for developing two different species diagnostic tools for identifying *An*. *punctulatus* group members [40, 50]. In the first PCR-RFLP (restricted fragment length polymorphism) technology, the size of the ITS2 region (~710bp) was identical for all *An*. *punctulatus* group members and was thus diagnostic for the group; this means that mosquito collections of other (non-*An. punctulatus* group) species can be detected simply as RFLPs of different banding profiles. Digestion of this product with the restriction enzyme *Msp* I generates species-specific DNA fragments for the 11 most abundant and most widely distributed members of this group, *An. farauti*, *An. hinesorum*, *An. torresiensis, An. farauti* 4-6, *An. irenicus*, *An. punctulatus*, *An*. species near *punctulatus*, and *An. clowi* (Fig. 4). This speciesspecific PCR-RFLP has been extensively used both independently and alongside genomic DNA probes in species distribution studies of the *An. punctulatus* group [31, 44, 48, 51]. However, more recently, a "Luminex®"-based multiplex ligase detection reaction and fluo‐ rescent microsphere-based assay method became available, also based on species-specific ITS2 sequences, and can separate the five common malaria vector species in PNG: *An. punctulatus,*

*An. punctulatus, An. koliensis, An. farauti, An. hinesorum,* and *An. farauti* 4 [40].

1 2 3 4 5 6 7 8 9 10

**Figure 4.** Molecular diagnostic that discriminates over 10 members of the *An. punctulatus* group based on a PCR-RFLP of the ITS2, cut with the restriction enzyme *Msp* I and run out on a 3% agarose gel. Banding profiles are as follows: Lane 1, *An. farauti*; (formally *An. farauti* 1) Lane 2, *An. hinesorum* (formally *An. farauti* 2); Lane 3, *An. torresiensis* (for‐ mally *An. farauti* 3); Lane 4, *An. farauti* 4 (contains no restriction site); Lane 5, *An. farauti* 5; Lane 6, *An. farauti* 6; Lane 7, *An. irenicus* (formally *farauti* 7), Lane 8, *An. koliensis*, Lane 9, *An. punctulatus*; Lane 10, *An*. sp. nr. *punctulatus*. Addition‐ ally *An. clowi* can be distinguished using this method however *An. farauti* 8 produces the same RFLP profile as *An.*

reprobed with the *An*. sp. nr *punctulatus* probe.

**2.1.4.1. Ribosomal DNA ITS2** 

**2.1.4. PCR-based species diagnostics** 

Figure 3. Mosquito squash blots hybridized with species-specific genomic DNA probes labeled with 32P can distinguish cryptic species in the *An. punctulatus* group. **Panel A:** squash blot of mosquitoes morphologically identified as *An. koliensis* and probed with a species-specific probe reveals that only a subset of samples are *An. koliensis* (*An. farauti* 4 made up the other individuals identified as *An. koliensis*) **Panel B:** Same blot was stripped and probed with a pan-species rDNA 18S probe that binds to all species revealing the total amount of gDNA on the blot. **Panel C:** mosquitoes identified as *An. punctulatus* are probed with the *An. punctulatus* species-specific probe and **Panel D** is the same blot stripped and

*2.1.4.1. Ribosomal DNA ITS2*

610 bp 310 bp 234 bp 192 bp 72 bp

*farauti*, but is distinguishable by ITS1 RFLP[52].

*An. koliensis*

A

B

C

D

Pan‐species 18S

*An. punctulatus*

*An.* sp. nr. *punctulatus*

6 Figure 4. Molecular diagnostic that discriminates over 10 members of the *An. punctulatus* group based on a PCR-RFLP of the ITS2, cut with the restriction enzyme *Msp* I and run out on a 3% agarose gel. Banding profiles are as follows: Lane 1, *An. farauti*; (formally *An. farauti* 1) Lane 2, *An.*  Identifying levels of genetic differences among mosquito taxa and the phylogenetic relation‐ ships of closely related species allows an understanding of the evolutionary forces acting on mosquito populations. Knowing the evolutionary relationships among vector species can provide insights into understanding the dynamics of disease transmission. Initial attempts to generate a species-level phylogeny of the *An. punctulatus* group were based on the DNA sequence of the rDNA ITS2. However, the large amount of sequence variation between each species appearing as insertion or deletion indels made computer-based sequence alignment difficult, and the resulting systematic trees could not resolve all species in the group [55]. The closely linked ssrDNA (rDNA 18S) structural RNA gene with alignment based on established secondary structures proved more useful for resolving the relatedness of this group [36, 56]. An independent assessment of a 684bp section of the mitochondrial cytochrome oxidase II region [57] found the COII useful in resolving most Australian and Oriental anophelines at the species level, but limited in resolving the known members in the *An. punctulatus* group. However, most phylogenetic studies of the group do consistently reveal two main clades, one containing all the *An*. *farauti*-like species (all-black proboscis) except *An*. *farauti* 4, which appears in a second clade with *An. punctulatus* and *An*. species near *punctulatus* (all of which can display a half-black, half-white proboscis) [31, 58] (see Fig. 2 and Table 2). *Anopheles koliensis* is positioned either basal to all species in the COII tree or between the *An. farauti* and *An. punctulatus* clades in the rDNA trees, neither of which branches showed strong support.

The same evolutionary mechanisms that led to the existence of these species have also produced a number of genetically distinct populations within each species that may differ in behaviour and in their potential to transmit malaria parasites. For example, recent investiga‐ tions have revealed that genotypes of *An. hinesorum* exist in the Solomon Islands that do not appear to bite humans while in other parts of this species' range, there are distinct genetic populations that are anthropophilic and are known to transmit malaria [51, 54, 59]. This study revealed restricted gene flow throughout *An. hinesorum*'s distribution and distinct differences in malaria vectoring potential and demonstrates the importance of detailing how species' populations connect to each other through population genetic studies – particularly in light of the design and efficacy of any control strategy [60].

#### **2.2.** *Anopheles* **(***Cellia***)** *longirostris* **complex**

The morphospecies *Anopheles longirostris* Brug is widespread throughout the coastal and inland lowland regions of New Guinea. Subsequent analysis of this morphospecies using both mtDNA and the rDNA ITS2 from 70 sites in PNG revealed up to nine distinct species that appear reproductively isolated at the rDNA locus [61]. Most of these putative species also exist as distinct mtDNA COI lineages and have been designated A, B, C1, C2, D, E, F, G, H [61]. Fig. 6 displays the phylogenetic study and molecular diagnostic developed with the same *Msp* I PCR-RFLP method as used for the *An. punctulatus* group. Of note, the species designated C1 and C2 produce the same ITS2 PCR-RFLP banding profile but curiously display different ITS2 copy variant organization. Where C1 is uncommon and extant only in the Western Province of PNG to date, species C2 appears to be the most common and widespread species in the group [61]. Thus the molecular diagnostic discrimination of C1 and C2 may only be problem‐ atic south of the central highlands in PNG's Western Province. However, species C1 may exist north of the central highlands. As it is only a recently recognized cryptic species group, little is now known about each species' biology and ecology and malaria transmission potential.

#### **2.3.** *Anopheles* **(***Cellia***)** *lungae* **complex**

Initially described by Belkin [20], the *An. lungae* group members show a distribution through‐ out the highly malarious Solomon Islands and Bougainville to the north. Belkin described three distinct morphological forms – *An. lungae*, *An. solomonis* and *An. nataliae* [20] – and variation among geographical populations was also noted. [20]. The three species have white scaling on the halters which readily separates them from the members of the *An*. *punctulatus* group which occur in the Solomon Islands [20]. Within the *An*. *lungae* complex the members can be separated using proboscis morphology though there is some overlap between the species with this character and this method is not reliable. A molecular diagnostic has been developed for the three species based on a *Msp* I digest of the ITS2 (Fig. 7).

appears in a second clade with *An. punctulatus* and *An*. species near *punctulatus* (all of which can display a half-black, half-white proboscis) [31, 58] (see Fig. 2 and Table 2). *Anopheles koliensis* is positioned either basal to all species in the COII tree or between the *An. farauti* and *An. punctulatus* clades in the rDNA trees, neither of which branches showed strong support.

The same evolutionary mechanisms that led to the existence of these species have also produced a number of genetically distinct populations within each species that may differ in behaviour and in their potential to transmit malaria parasites. For example, recent investiga‐ tions have revealed that genotypes of *An. hinesorum* exist in the Solomon Islands that do not appear to bite humans while in other parts of this species' range, there are distinct genetic populations that are anthropophilic and are known to transmit malaria [51, 54, 59]. This study revealed restricted gene flow throughout *An. hinesorum*'s distribution and distinct differences in malaria vectoring potential and demonstrates the importance of detailing how species' populations connect to each other through population genetic studies – particularly in light of

The morphospecies *Anopheles longirostris* Brug is widespread throughout the coastal and inland lowland regions of New Guinea. Subsequent analysis of this morphospecies using both mtDNA and the rDNA ITS2 from 70 sites in PNG revealed up to nine distinct species that appear reproductively isolated at the rDNA locus [61]. Most of these putative species also exist as distinct mtDNA COI lineages and have been designated A, B, C1, C2, D, E, F, G, H [61]. Fig. 6 displays the phylogenetic study and molecular diagnostic developed with the same *Msp* I PCR-RFLP method as used for the *An. punctulatus* group. Of note, the species designated C1 and C2 produce the same ITS2 PCR-RFLP banding profile but curiously display different ITS2 copy variant organization. Where C1 is uncommon and extant only in the Western Province of PNG to date, species C2 appears to be the most common and widespread species in the group [61]. Thus the molecular diagnostic discrimination of C1 and C2 may only be problem‐ atic south of the central highlands in PNG's Western Province. However, species C1 may exist north of the central highlands. As it is only a recently recognized cryptic species group, little is now known about each species' biology and ecology and malaria transmission potential.

Initially described by Belkin [20], the *An. lungae* group members show a distribution through‐ out the highly malarious Solomon Islands and Bougainville to the north. Belkin described three distinct morphological forms – *An. lungae*, *An. solomonis* and *An. nataliae* [20] – and variation among geographical populations was also noted. [20]. The three species have white scaling on the halters which readily separates them from the members of the *An*. *punctulatus* group which occur in the Solomon Islands [20]. Within the *An*. *lungae* complex the members can be separated using proboscis morphology though there is some overlap between the species with this character and this method is not reliable. A molecular diagnostic has been developed for the

the design and efficacy of any control strategy [60].

**2.2.** *Anopheles* **(***Cellia***)** *longirostris* **complex**

368 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**2.3.** *Anopheles* **(***Cellia***)** *lungae* **complex**

three species based on a *Msp* I digest of the ITS2 (Fig. 7).

**Figure 5.** The rDNA genotypes of *An*. *farauti*. **Panel A** shows a map of southwest Pacific and the 21 *An. farauti* collec‐ tion sites. Dotted circles represent the distribution grouping of ITS2 PCR heteroduplex profiles (genotypes) that ap‐ peared in native acrylamide gels shown in **Panel B** (samples 22-24 not shown). **Panel C** is an agarose gel showing individual *An. farauti* ITS1 PCR products with lanes representing collection sites on Panel A. Intragenomic size variation is evident between collection sites and in most cases individuals showed the same ITS1 and ITS2 heteroduplex profiles, exceptions were found in some sites on the north coast of PNG where rDNA profiles are highly polymorphic. This coastally restricted species shows remarkable rDNA turnover throughout its distribution. Cloning and sequencing ITS2 copy variants revealed no phylogenetic information, however the longer ITS1 (up to 2.5kb) revealed a robust phyloge‐ netic signal resolving genotypes into regions [52].

Figure 6. The discovery of nine cryptic species within mosquitoes identified morphologically as *Anopheles longirostris* from PNG. Phylogenetic assessment of *A. longirostris* based on cloned ITS2 DNA sequence from PCR products (Panel A) and directly sequenced mtDNA COI PCR products (Panel B) reveal nine distinct lineages. Bayesian posterior probabilities (converted to percentage) are shown as branch support values above 70%. Panel C: The molecular diagnostic developed revealed nine ITS2 genotypes of *A. longirostris*. Panel C-top, uncut ITS2 PCR products; Panel Cmiddle, ITS2 PCR products cut with *Msp* I and run through a 3% agarose gel; Panel C-bottom, ITS2 PCR products run through a 7.0% 7 acrylamide gel revealing individuals within interbreeding populations contained fixed copy variants, suggesting reproductive isolation at the rDNA locus. Only the RFLP profile for genotype C showed two distinct heteroduplex profiles (designated C1 and C2) thus revealing the presence of two **Figure 6.** The discovery of nine cryptic species within mosquitoes identified morphologically as *Anopheles longirostris* from PNG. Phylogenetic assessment of *A. longirostris* based on cloned ITS2 DNA sequence from PCR products (**Panel A**) and directly sequenced mtDNA COI PCR products (**Panel B**) reveal nine distinct lineages. Bayesian posterior proba‐ bilities (converted to percentage) are shown as branch support values above 70%. **Panel C**: The molecular diagnostic developed revealed nine ITS2 genotypes of *A. longirostris*. Panel C-top, uncut ITS2 PCR products; Panel C-middle, ITS2 PCR products cut with *Msp* I and run through a 3% agarose gel; Panel C-bottom, ITS2 PCR products run through a 7.0% 7 acrylamide gel revealing individuals within interbreeding populations contained fixed copy variants, suggest‐ ing reproductive isolation at the rDNA locus. Only the RFLP profile for genotype C showed two distinct heteroduplex profiles (designated C1 and C2) thus revealing the presence of two independently evolving ITS2 genotypes.

#### **2.4.** *Anopheles* **(***Anopheles***)** *bancroftii* **group** independently evolving ITS2 genotypes.

based on a *Msp* I digest of the ITS2 (Fig. 8).

500 bp

400 bp

300 bp 200 bp 100 bp 1 2 3 4 5

Two morphological species were initially described in the *Anopheles bancroftii* group based on wing fringe patterns –*Anopheles bancroftii* Giles, and *Anopheles pseudobarbirostris* Ludlow [63] – although some confusion as to the distributions of these two morphotypes existed. The ITS2 PCR-RFLP method using the enzyme *Msp* I identified four distinct ITS2 genotypes designated A, B, C and D [39]. ITS2 DNA sequence analysis of this group revealed intragenomic sequence copy variants existing in individual mosquitoes that assist in the identification of these four ITS2 genotypes (Fig. 8). For example, genotype C could be interpreted as a combination (hybrid) RFLP profile between genotypes A and B, however both DNA sequence analysis and intragenomic ITS2 copy variant studies revealed the presence of four independently evolving **2.3. Anopheles (Cellia) lungae complex**  Initially described by Belkin [20], the *An. lungae* group members show a distribution throughout the highly malarious Solomon Islands and Bougainville to the north. Belkin described three distinct morphological forms – *An. lungae*, *An. solomonis* and *An. nataliae* [20] – and variation among geographical populations was also noted. [20]. The three species have white scaling on the halters which readily separates them from the members of the *An*. *punctulatus* group which occur in the Solomon Islands [20]. Within the *An*. *lungae* complex the members can be separated using proboscis morphology though there is some overlap between

in the Solomon Island: Lane 3, *An. nataliae*; Lane 4, *An. lungae*; and Lane 5, *An. solomonis*.

**2.4. Anopheles (Anopheles) bancroftii group** 

the species with this character and this method is not reliable. A molecular diagnostic has been developed for the three species

Figure 7. Molecular diagnostic for *Anopheles* species collected in Santa Isabel Province in the Solomon Islands based again on an ITS2 PCR-RFLP using *Msp* I [62]: Lane 1-2 isomorphic species *An. farauti*, *An. hinesorum*. Lanes 3-5 are cryptic the members of the *Anopheles lungae* complex that exist

Two morphological species were initially described in the *Anopheles bancroftii* group based on wing fringe patterns –*Anopheles bancroftii* Giles, and *Anopheles pseudobarbirostris* Ludlow [63] – although some confusion as to the distributions of these two

9

The Systematics and Bionomics of Malaria Vectors in the Southwest Pacific http://dx.doi.org/10.5772/55999 371

Figure 6. The discovery of nine cryptic species within mosquitoes identified morphologically as *Anopheles longirostris* from PNG. Phylogenetic assessment of *A. longirostris* based on cloned ITS2 DNA sequence from PCR products (Panel A) and directly sequenced mtDNA COI PCR products (Panel B) reveal nine distinct lineages. Bayesian posterior probabilities (converted to percentage) are shown as branch support values above 70%. Panel C: The molecular diagnostic developed revealed nine ITS2 genotypes of *A. longirostris*. Panel C-top, uncut ITS2 PCR products; Panel Cmiddle, ITS2 PCR products cut with *Msp* I and run through a 3% agarose gel; Panel C-bottom, ITS2 PCR products run through a 7.0% 7 acrylamide gel revealing individuals within interbreeding populations contained fixed copy variants, suggesting reproductive isolation at the rDNA locus. Only the RFLP profile for genotype C showed two distinct heteroduplex profiles (designated C1 and C2) thus revealing the presence of two

 **1C4 1**

 **D 1A3 2 D 1A3 5 D 1B8 4 D 1A3 6 D 1B8 5 D 1A7 8 D 1B9 3 D 1B9 4 D 1B8 3 F 1D1 3 F 1C3 1 F 1D1 5 F 1F4 1 A 1A6 8 A 1A6 3 A 1A6 1 A 1A6 4 C1 1A1 1 C1 1A1 4 C1 1A1 11 C1 1A1 7 C1 1A1 9 C1 1A1 6 C2 1A1 8 C2 1A3 3 C2 1A3 10 C2 1A1 12 C2 1H3 1 C2 1B5 1 C2 1B6 1 C2 1A3 7 C2 1G6 1 C2 1A3 1 E 1E3 2 E 1A7 11 E 1D4 1 E 1B10 3 E 1B2 1 E 1A10 3 E 1B1 1 G 1C7 2 G 1B1 9 G 1B1 4 G 1A7 4 G 1B1 5 G 1B1 8R G 1B1 6 G 1C7 1 G 1C1 2 H 1C6 3 H 1C10 9 H 1C6 2 B 1D3 1 B 1D8 2 B 1A4 2 B 1A4 1 B 1D9 1 B 1A7 7 B 1A1 5 B 1A6 2 B 1B5 2 B 1B3 1**

**D**

**C1** 

**C2** 

**E**

**G**

**B**

**H**

**F**

**A**

99 87 53

79

100

**100**

**92**

7 38 100

**100**

**98**

> 83 100

81

**100**

8 8 2

8 5 40

88 94

99

**99 50 29**

99

**99**

**100**

100

65

**65**

92

**92**

0.2

73

**73**

100

**94**

**100**

79 50 100

**100**

Initially described by Belkin [20], the *An. lungae* group members show a distribution throughout the highly malarious Solomon

Islands and Bougainville to the north. Belkin described three distinct morphological forms – *An. lungae*, *An. solomonis* and *An.* 

*nataliae* [20] – and variation among geographical populations was also noted. [20]. The three species have white scaling on the

halters which readily separates them from the members of the *An*. *punctulatus* group which occur in the Solomon Islands [20].

Within the *An*. *lungae* complex the members can be separated using proboscis morphology though there is some overlap between

the species with this character and this method is not reliable. A molecular diagnostic has been developed for the three species

9

based on a *Msp* I digest of the ITS2 (Fig. 8).

independently evolving ITS2 genotypes.

 **L1C6 3 F11 5 1F4 1 5X**

**100 63** 

**H**

 **1F4 1 1X 1D1 5 2X 1C3 1 5 1C3 1 3 1C3 1 4 1C3 1 2 1D1 5 1X 1F4 1 3X 1C3 1 1 1D1 5 5X 1F4 1 2X 1D1 5 4X 1D1 5 3X 1F4 1 4X 1G2 1 5Y 1B8 3 4 1G2 1 3X 1A3 2 2 1B8 3 3 1B8 3 1 1G2 1 2X 1A3 2 1 1G2 1 4X 1A3 2 5 1A3 2 4 1B8 3 2**

 **1A4 1 1X 1A6 4 3**

7 41

**A**

**E**

**G**

**C2**

**C1**

100

**100**

 **1A6 3 2X 1A6 3 5X 1A6 1 2 1A6 3 4X 1A6 3 3X 1A6 4 5 1A6 4 1 1A6 1 3 1A6 1 1 1A6 4 2 1A6 1 4 1A6 3 1X 1A6 1 5 1B1 1 3X 1B10 3 1 1B1 1 4X 1B10 3 3 1B1 1 2X 1B10 3 5 1B1 1 1X 1B10 3 4 1B10 3 2 1B1 1 5X 1C7 1 3 1B1 8 1 1C7 1 2 1A7 4 3 1A7 4 4 1B1 8 4 1C7 1 4 1C7 1 1 1B1 8 2 1C7 1 5 1B1 8 3 1A7 4 1 1A7 4 2 1A7 4 5 1A3 10 1X 1A3 7 5 1A3 7 2 1A3 7 3 1A3 10 5X 1A3 1 2 1A3 10 3X 1A3 10 2X 1A3 1 4 1A3 1 3 1A3 1 5 1A3 1 1 1A3 10 4X 1A1 7 1 1A1 7 2 1A1 7 5 1A1 1 2 1A1 7 4 1A1 9 2 1A1 1 3 1A1 1 1 1A1 9 5 1A1 7 3 1A1 9 1 1A1 9 4 1A1 9 3 1A1 1 4**

47

83

**83**

**100**

**100** 

5 6 100

76 100

**100** 

99

**99**

100

**100**

**92**

0.5

99

**99**

 **1G2 1 1X 1A4 1 3X**

**100**

 **1D9 1 2 1A4 1 2X 1A4 1 5X 1D9 1 3 1D9 1 5 1D9 1 4 1A4 1 4X**

**A B**

**B**

**A B C1 C2 D E F G H**

**C**

**F**

**D**

**2.3. Anopheles (Cellia) lungae complex** 

Figure 7. Molecular diagnostic for *Anopheles* species collected in Santa Isabel Province in the Solomon Islands based again on an ITS2 PCR-RFLP using *Msp* I [62]: Lane 1-2 isomorphic species *An. farauti*, *An. hinesorum*. Lanes 3-5 are cryptic the members of the *Anopheles lungae* complex that exist **Figure 7.** Molecular diagnostic for *Anopheles* species collected in Santa Isabel Province in the Solomon Islands based again on an ITS2 PCR-RFLP using *Msp* I [62]: Lanes 1-2 isomorphic species *An. farauti*, *An. hinesorum*. Lanes 3-5 are cryptic the members of the *Anopheles lungae* complex that exist in the Solomon Island: Lane 3, *An. nataliae*; Lane 4, *An. lungae*; and Lane 5, *An. solomonis*.

Figure 6. The discovery of nine cryptic species within mosquitoes identified morphologically as *Anopheles longirostris* from PNG. Phylogenetic assessment of *A. longirostris* based on cloned ITS2 DNA sequence from PCR products (Panel A) and directly sequenced mtDNA COI PCR products (Panel B) reveal nine distinct lineages. Bayesian posterior probabilities (converted to percentage) are shown as branch support values above 70%. Panel C: The molecular diagnostic developed revealed nine ITS2 genotypes of *A. longirostris*. Panel C-top, uncut ITS2 PCR products; Panel Cmiddle, ITS2 PCR products cut with *Msp* I and run through a 3% agarose gel; Panel C-bottom, ITS2 PCR products run through a 7.0% 7 acrylamide gel revealing individuals within interbreeding populations contained fixed copy variants, suggesting reproductive isolation at the rDNA locus. Only the RFLP profile for genotype C showed two distinct heteroduplex profiles (designated C1 and C2) thus revealing the presence of two ITS2 genotypes with cloned ITS2 sequences showing little phylogenetic information [39]. No correlation was identified with the wing fringe characteristics initially used to identify *An. bancroftii* and *An. pseudobarbirostris* with any of the four genotypes. The distribution of these ITS2 genotypes (putative species) has been further investigated [64], indicating distinct distribution for genotypes A, B, and D. Genotype C is sympatric with B and D without evidence of hybridization, suggesting these genotypes are reproductively isolated and likely biological species. Confirmation of this hypothesis using other nuclear genetic markers is needed. Thus genotype C is sympatric with B and D without evidence of hybridization, suggesting these genotypes are reproductively isolated and likely biological species. Confirmation of this hypothesis using other nuclear genetic markers is needed. in the Solomon Island: Lane 3, *An. nataliae*; Lane 4, *An. lungae*; and Lane 5, *An. solomonis*. **2.4. Anopheles (Anopheles) bancroftii group**  Two morphological species were initially described in the *Anopheles bancroftii* group based on wing fringe patterns –*Anopheles bancroftii* Giles, and *Anopheles pseudobarbirostris* Ludlow [63] – although some confusion as to the distributions of these two

## **3. Species distribution, biology and vectorial status**

9

#### Initially described by Belkin [20], the *An. lungae* group members show a distribution throughout the highly malarious Solomon **3.1. Primary vectors**

**2.4.** *Anopheles* **(***Anopheles***)** *bancroftii* **group**

 **L1C6 3 F11 5 1F4 1 5X**

**100 63** 

**H**

370 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**100**

 **1F4 1 1X 1D1 5 2X 1C3 1 5 1C3 1 3 1C3 1 4 1C3 1 2 1D1 5 1X 1F4 1 3X 1C3 1 1 1D1 5 5X 1F4 1 2X 1D1 5 4X 1D1 5 3X 1F4 1 4X 1G2 1 5Y 1B8 3 4 1G2 1 3X 1A3 2 2 1B8 3 3 1B8 3 1 1G2 1 2X 1A3 2 1 1G2 1 4X 1A3 2 5 1A3 2 4 1B8 3 2**

 **1A4 1 1X 1A6 4 3**

7 41 100

**A**

**E**

**G**

**C2**

**C1**

 **1A6 3 2X 1A6 3 5X 1A6 1 2 1A6 3 4X 1A6 3 3X 1A6 4 5 1A6 4 1 1A6 1 3 1A6 1 1 1A6 4 2 1A6 1 4 1A6 3 1X 1A6 1 5 1B1 1 3X 1B10 3 1 1B1 1 4X 1B10 3 3 1B1 1 2X 1B10 3 5 1B1 1 1X 1B10 3 4 1B10 3 2 1B1 1 5X 1C7 1 3 1B1 8 1 1C7 1 2 1A7 4 3 1A7 4 4 1B1 8 4 1C7 1 4 1C7 1 1 1B1 8 2 1C7 1 5 1B1 8 3 1A7 4 1 1A7 4 2 1A7 4 5 1A3 10 1X 1A3 7 5 1A3 7 2 1A3 7 3 1A3 10 5X 1A3 1 2 1A3 10 3X 1A3 10 2X 1A3 1 4 1A3 1 3 1A3 1 5 1A3 1 1 1A3 10 4X 1A1 7 1 1A1 7 2 1A1 7 5 1A1 1 2 1A1 7 4 1A1 9 2 1A1 1 3 1A1 1 1 1A1 9 5 1A1 7 3 1A1 9 1 1A1 9 4 1A1 9 3 1A1 1 4**

99

**100** 

**99**

0.5

500 bp

400 bp

300 bp 200 bp 100 bp **100**

**92**

47

83

**83**

**100**

**100** 

99

**99**

 **1G2 1 1X 1A4 1 3X**

**100**

 **1D9 1 2 1A4 1 2X 1A4 1 5X 1D9 1 3 1D9 1 5 1D9 1 4 1A4 1 4X**

**A B**

**B**

**A B C1 C2 D E F G H**

**C**

**F**

**D**

independently evolving ITS2 genotypes.

based on a *Msp* I digest of the ITS2 (Fig. 8).

1 2 3 4 5

**2.3. Anopheles (Cellia) lungae complex** 

Two morphological species were initially described in the *Anopheles bancroftii* group based on wing fringe patterns –*Anopheles bancroftii* Giles, and *Anopheles pseudobarbirostris* Ludlow [63] – although some confusion as to the distributions of these two morphotypes existed. The ITS2 PCR-RFLP method using the enzyme *Msp* I identified four distinct ITS2 genotypes designated A, B, C and D [39]. ITS2 DNA sequence analysis of this group revealed intragenomic sequence copy variants existing in individual mosquitoes that assist in the identification of these four ITS2 genotypes (Fig. 8). For example, genotype C could be interpreted as a combination (hybrid) RFLP profile between genotypes A and B, however both DNA sequence analysis and intragenomic ITS2 copy variant studies revealed the presence of four independently evolving

profiles (designated C1 and C2) thus revealing the presence of two independently evolving ITS2 genotypes.

**Figure 6.** The discovery of nine cryptic species within mosquitoes identified morphologically as *Anopheles longirostris* from PNG. Phylogenetic assessment of *A. longirostris* based on cloned ITS2 DNA sequence from PCR products (**Panel A**) and directly sequenced mtDNA COI PCR products (**Panel B**) reveal nine distinct lineages. Bayesian posterior proba‐ bilities (converted to percentage) are shown as branch support values above 70%. **Panel C**: The molecular diagnostic developed revealed nine ITS2 genotypes of *A. longirostris*. Panel C-top, uncut ITS2 PCR products; Panel C-middle, ITS2 PCR products cut with *Msp* I and run through a 3% agarose gel; Panel C-bottom, ITS2 PCR products run through a 7.0% 7 acrylamide gel revealing individuals within interbreeding populations contained fixed copy variants, suggest‐ ing reproductive isolation at the rDNA locus. Only the RFLP profile for genotype C showed two distinct heteroduplex

in the Solomon Island: Lane 3, *An. nataliae*; Lane 4, *An. lungae*; and Lane 5, *An. solomonis*.

**2.4. Anopheles (Anopheles) bancroftii group** 

the species with this character and this method is not reliable. A molecular diagnostic has been developed for the three species

 **1C4 1**

 **D 1A3 2 D 1A3 5 D 1B8 4 D 1A3 6 D 1B8 5 D 1A7 8 D 1B9 3 D 1B9 4 D 1B8 3 F 1D1 3 F 1C3 1 F 1D1 5 F 1F4 1 A 1A6 8 A 1A6 3 A 1A6 1 A 1A6 4 C1 1A1 1 C1 1A1 4 C1 1A1 11 C1 1A1 7 C1 1A1 9 C1 1A1 6 C2 1A1 8 C2 1A3 3 C2 1A3 10 C2 1A1 12 C2 1H3 1 C2 1B5 1 C2 1B6 1 C2 1A3 7 C2 1G6 1 C2 1A3 1 E 1E3 2 E 1A7 11 E 1D4 1 E 1B10 3 E 1B2 1 E 1A10 3 E 1B1 1 G 1C7 2 G 1B1 9 G 1B1 4 G 1A7 4 G 1B1 5 G 1B1 8R G 1B1 6 G 1C7 1 G 1C1 2 H 1C6 3 H 1C10 9 H 1C6 2 B 1D3 1 B 1D8 2 B 1A4 2 B 1A4 1 B 1D9 1 B 1A7 7 B 1A1 5 B 1A6 2 B 1B5 2 B 1B3 1**

**D**

**C1** 

**C2** 

**E**

**G**

**B**

**H**

**F A**

99 87 53

> 7 38 100

**100**

81 99

**100**

99

**99**

**100**

**99 50 29**

65

**65**

92

**92**

0.2

73

**73**

83 100

**98**

100

**94**

**100**

**100**

100

**100**

**92**

Figure 7. Molecular diagnostic for *Anopheles* species collected in Santa Isabel Province in the Solomon Islands based again on an ITS2 PCR-RFLP using *Msp* I [62]: Lane 1-2 isomorphic species *An. farauti*, *An. hinesorum*. Lanes 3-5 are cryptic the members of the *Anopheles lungae* complex that exist

Two morphological species were initially described in the *Anopheles bancroftii* group based on wing fringe patterns –*Anopheles bancroftii* Giles, and *Anopheles pseudobarbirostris* Ludlow [63] – although some confusion as to the distributions of these two

Islands and Bougainville to the north. Belkin described three distinct morphological forms – *An. lungae*, *An. solomonis* and *An. nataliae* [20] – and variation among geographical populations was also noted. [20]. The three species have white scaling on the halters which readily separates them from the members of the *An*. *punctulatus* group which occur in the Solomon Islands [20]. Within the *An*. *lungae* complex the members can be separated using proboscis morphology though there is some overlap between Three species – *An*. *farauti*, *An*. *koliensis*, and *An*. *punctulatus* – are considered the pri‐ mary vectors of malaria in the region. All are widely distributed and can occur in high densities (Fig. 9). They readily feed on humans, and all have been found infected with human malaria parasites.

morphotypes existed. The ITS2 PCR-RFLP method using the enzyme *Msp* I identified four distinct ITS2 genotypes designated A, B, C and D [39]. ITS2 DNA sequence analysis of this group revealed intragenomic sequence copy variants existing in individual mosquitoes that assist in the identification of these four ITS2 genotypes (Fig 9). For example, genotype C could be interpreted as a combination (hybrid) RFLP profile between genotypes A and B, however both DNA sequence analysis and intragenomic ITS2 copy variant studies revealed the presence of four independently evolving ITS2 genotypes with cloned ITS2 sequences showing little phylogenetic information [39]. No correlation was identified with the wing fringe characteristics initially used to identify *An. bancroftii* and *An. pseudobarbirostris* with any of the four genotypes. The distribution of these ITS2 genotypes (putative species) has been further investigated [64], indicating distinct distribution for genotypes A, B, and D. Genotype C is sympatric with B and D without evidence of hybridization, suggesting these genotypes are reproductively isolated and likely biological species. Confirmation of this hypothesis using other nuclear genetic markers is needed. Thus genotype C is sympatric with B and D without evidence of hybridization, suggesting these genotypes are reproductively isolated and likely biological species.

Figure 8. Molecular diagnostic for the cryptic species in the *An. bancroftii* group. Panel A are *Msp* I cut ITS2 PCR-RFLP profiles of *An. bancroftii* electrophoresis run through a 3.0% agarose gel. First lane on the left is a 100bp marker. Lanes 2-5 are the RFLP of genotypes A-D with genotype D revealing no *Msp* I restriction sites and the full length of the PCR product (all genotypes produce a 400bp PCR product). Panel B are the same PCR products electrophoresed through a 7.0% acrylamide gel that is sensitive to double stranded secondary structure. Lanes A, B and D show a single band for the amplified ITS2 (homogenized single sequence or homoduplex). Lane 4 is genotype C showing both a homoduplex (bottom band) and **Figure 8.** Molecular diagnostic for the cryptic species in the *An. bancroftii* group. **Panel A** are *Msp* I cut ITS2 PCR-RFLP profiles of *An. bancroftii* electrophoresis run through a 3.0% agarose gel. First lane on the left is a 100bp marker. Lanes 2-5 are the RFLP of genotypes A-D with genotype D revealing no *Msp* I restriction sites and the full length of the PCR product (all genotypes produce a 400bp PCR product). **Panel B** are the same PCR products electrophoresed through a 7.0% acrylamide gel that is sensitive to double stranded secondary structure. Lanes A, B and D show a single band for the amplified ITS2 (homogenized single sequence or homoduplex). Lane 4 is genotype C showing both a homoduplex (bottom band) and two heteroduplex products (misspairing in double-stranded duplex alters secondary structure re‐ tarding migration). Lane 5 is genotype D that migrates slower due to differences in the secondary structure duplex and not sequence length.

*Anopheles farauti* has the widest distribution of all the anopheline fauna of the region, occurring in the Moluccas, on New Guinea and its associated islands and archipelagos, in northern Australia, throughout the Solomon Islands and Vanuatu. *Anopheles farauti* has been incrimi‐ nated as a vector of malaria throughout this range [59, 65-68]. It is a coastal species, whose larvae tolerate brackish water [28, 69], with preferred breeding sites ranging from small ground pools to large coastal swamps and lagoons formed where runoff to the sea is blocked by sand bars (Fig. 10 E). These large sites are ubiquitous along the coastline throughout the region [58, 62] and are often associated with human habitation. Due to their size, they can support high population numbers [62, 70]. *Anopheles farauti*'s ability to breed in brackish water has facilitated its dispersal across the myriad tiny islands throughout the region [20, 71]. two heteroduplex products (misspairing in double-stranded duplex alters secondary structure retarding migration). Lane 5 is genotype D that migrates slower due to differences in the secondary structure duplex and not sequence length. **3. Species distribution, biology and vectorial status** **3.1. Primary vectors**  Three species – *An*. *farauti*, *An*. *koliensis*, and *An*. *punctulatus* – are considered the primary vectors of malaria in the region. All are widely distributed and can occur in high densities. They readily feed on humans, and all have been found infected with human

malaria parasites.

10

Three species – *An*. *farauti*, *An*. *koliensis*, and *An*. *punctulatus* – are considered the primary vectors of malaria in the region. All are **Figure 9.** Known distributions of the three main species of the *An. punctulatus* group. **Panel A** is *An. farauti* which throughout its distribution is a coastally restricted species rarely found more that 5 km inland. **Panel B** is An. punctula‐ *tus* which is a fresh water species that exists both coastal, inland and at elevation >1500m. **Panel C**. *An. koliensis* is a lowland inland and coastal species.

10

*Anopheles farauti* has the widest distribution of all the anopheline fauna of the region, occurring in the Moluccas, on New Guinea and its associated islands and archipelagos, in northern Australia, throughout the Solomon Islands and Vanuatu. *Anopheles farauti* has been incrimi‐ nated as a vector of malaria throughout this range [59, 65-68]. It is a coastal species, whose larvae tolerate brackish water [28, 69], with preferred breeding sites ranging from small ground pools to large coastal swamps and lagoons formed where runoff to the sea is blocked by sand bars (Fig. 10 E). These large sites are ubiquitous along the coastline throughout the region [58, 62] and are often associated with human habitation. Due to their size, they can support high population numbers [62, 70]. *Anopheles farauti*'s ability to breed in brackish water has facilitated

**Figure 8.** Molecular diagnostic for the cryptic species in the *An. bancroftii* group. **Panel A** are *Msp* I cut ITS2 PCR-RFLP profiles of *An. bancroftii* electrophoresis run through a 3.0% agarose gel. First lane on the left is a 100bp marker. Lanes 2-5 are the RFLP of genotypes A-D with genotype D revealing no *Msp* I restriction sites and the full length of the PCR product (all genotypes produce a 400bp PCR product). **Panel B** are the same PCR products electrophoresed through a 7.0% acrylamide gel that is sensitive to double stranded secondary structure. Lanes A, B and D show a single band for the amplified ITS2 (homogenized single sequence or homoduplex). Lane 4 is genotype C showing both a homoduplex (bottom band) and two heteroduplex products (misspairing in double-stranded duplex alters secondary structure re‐ tarding migration). Lane 5 is genotype D that migrates slower due to differences in the secondary structure duplex

migrates slower due to differences in the secondary structure duplex and not sequence length.

widely distributed and can occur in high densities. They readily feed on humans, and all have been found infected with human

**3. Species distribution, biology and vectorial status**

morphotypes existed. The ITS2 PCR-RFLP method using the enzyme *Msp* I identified four distinct ITS2 genotypes designated A, B, C and D [39]. ITS2 DNA sequence analysis of this group revealed intragenomic sequence copy variants existing in individual mosquitoes that assist in the identification of these four ITS2 genotypes (Fig 9). For example, genotype C could be interpreted as a combination (hybrid) RFLP profile between genotypes A and B, however both DNA sequence analysis and intragenomic ITS2 copy variant studies revealed the presence of four independently evolving ITS2 genotypes with cloned ITS2 sequences showing little phylogenetic information [39]. No correlation was identified with the wing fringe characteristics initially used to identify *An. bancroftii* and *An. pseudobarbirostris* with any of the four genotypes. The distribution of these ITS2 genotypes (putative species) has been further investigated [64], indicating distinct distribution for genotypes A, B, and D. Genotype C is sympatric with B and D without evidence of hybridization, suggesting these genotypes are reproductively isolated and likely biological species. Confirmation of this hypothesis using other nuclear genetic markers is needed. Thus genotype C is sympatric with B and D without evidence of hybridization, suggesting these genotypes are reproductively isolated and likely biological species.

Confirmation of this hypothesis using other nuclear genetic markers is needed.

A B C D 1 2 3 4 5

A B C D

its dispersal across the myriad tiny islands throughout the region [20, 71].

**3.1. Primary vectors** 

malaria parasites.

**500 bp** 

**400 bp** 

**600 bp** 

**100 bp** 

**200 bp 300 bp 400 bp** 

A

372 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

B

and not sequence length.

In PNG, the Solomon Islands, and Vanuatu, where extensive sampling has occurred and the mosquitoes' distribution is well understood, *An. farauti* is known to exist as several genotypes [53]. These genetically distinct populations are separated by overt barriers: climate disjunction between the northern continuous wet and southern monsoonal region in the Southern Plains of New Guinea (see Fig.1), the central highlands in New Guinea; and sea gaps between New Guinea and Manus Island, the Solomon Islands and Vanuatu [58]. All genotypes appear to have similar behaviours and are malaria vectors wherever they occur.

Given that *An. farauti* remains the dominant species collected in coastal villages, past reference to their biology and behaviour prior to identification using molecular techniques is probably still valid. *Anopheles farauti,* while readily feeding on humans, will also feed on other animals, and anthropophilic indices can be quite low in villages where domestic animals, primarily pigs and dogs, are abundant [27, 67]. Populations of this species were found well outside the flight range of human habitation, indicating that this species will readily feed on native birds and animals [31]. The longevity of this species appears quite variable; in the Solomon Islands province of Temotu the proportion of the population that was parous was 0.42 [70] while in Central Province it was 0.76 (T. Russell, unpublished data). In New Guinea, values ranged from 0.58 in Jayapura [65] to 0.49 in Madang, [27] and 0.73 in the D'Entrecasteaux Islands [66]. It will readily enter houses to feed but is primarily exophilic, leaving the house on the night of feeding to rest outdoors [65, 66].

*Anophelespunctulatus* has been recorded from the Moluccas, New Guinea, and the larger islands of Manus, New Britain, New Ireland and Buka – but it does not appear to be present on Bougainville Island [48, 72]. During faunal surveys conducted in the early 1970's, *An. punctu‐ latus* was found on all the main islands in the Solomon Islands except Temotu Province [73]. It was found on Malaita in 1987 [74] and on the north coast of Guadalcanal in 1998 [51]. However, recent surveys of Santa Isabel and Central Provinces failed to find this species (62, T. Russell, unpublished data). In New Guinea, it is mainly found in inland lowland regions but is also common in the foothills of central ranges and in the intermountain highland valleys [8, 31, 75]. Its natural larval habitats are rock pools, pools in rivers and streambeds, and pools along the margins of these waterways. It is a highly invasive species and will readily invade sites created by human activity such as wheel ruts in roads, pools in walking tracks, hoof and foot prints, pig wallows and shallow drains around village houses (Table 3, Fig. 10 A) [31, 76]. These sites all have a clay or gravel substrate; are small or transient and are maintained only by regular rainfall; they lack established aquatic fauna and flora; and they have little or no debris.

Given that many rural communities throughout the region are connected by unsealed dirt roads, these thoroughfares – along with roads and construction associated with logging and mining activities – have created both extensive larval sites for this species and the corridors along which it can move. *Anopheles punctulatus* has adapted to these small transient sites with eggs that can survive desiccation for several days, a short larval stage (relative to other species) and highly synchronized larval development [76, 77]. A preference for transient sites binds *An*. *punctulatus* to areas where the soil contains clay and the rainfall is perennial. Where these conditions exist it can occur in high densities [65]. It is considered the most anthropophilic of all the members of the *An*. *punctulatus* group [67, 78], and is a late night feeder with a feeding peak between midnight and 2am [79].

Of the three primary malaria vectors in the southwest Pacific, *An*. *punctulatus* is the most long lived [80]. It is a dangerous vector responsible for maintaining holoendemic transmission rates in a number of areas [78]. It has been incriminated as a malaria vector throughout its range [8, 59, 65, 67, 68, 75].

*Anopheleskoliensis* has a more complex distribution. It is found throughout New Guinea but not in the Moluccas; it occurs on New Britain and Buka Islands, but not on Bougainville; it was found on all the main islands in the Solomon Islands except those of Temotu Province [31, 72, 73, 81]. However, it can no longer be found in the islands of Santa Isabel, Guadalcanal, and Buka [48, 51, 62]. It was possibly eliminated from most islands in the Solomon Islands by IRS with DDT, with the last occurrence reported on the island of Malaita in 1983 [74]. Predomi‐ nantly an inland species of the lowlands and river valley flood plains below 300m, its main larval habitats are wheel tracks, drains, natural ground pools, and swamps (Table 3, Fig. 10) [31]. Molecular investigations suggest there may be as many as three independently evolving rDNA genotypes (putative species) within this taxon in the Madang/Maprik areas alone [82], and possibly also elsewhere in PNG (N. Beebe, unpublished data). While *An. koliensis* will feed on pigs and dogs, it prefers humans where available and human blood indices of 0.85 and 0.95 have been recorded [27, 67]. It tends to feed late in the night with a peak biting time similar to *An*. *punctulatus* [65, 79]. In the village of Entrop, Papua Province, peak biting was at 7pm in DDT sprayed villages most likely due to the selection pressure to avoid the DTT, where in Arso (~50km away), which was not sprayed, peak biting was around midnight [65].

It is a moderately long-lived mosquito with parity rates ranging between 0.52 and 0.75 [65, 83]. It has been incriminated as a vector throughout its range [8, 59, 65, 67, 68]. Along with *An*. *punctulatus*, it is responsible for maintaining holoendemic transmission in a numbers of areas in New Guinea [65, 78].

#### **3.2. Secondary vectors**

In PNG, the Solomon Islands, and Vanuatu, where extensive sampling has occurred and the mosquitoes' distribution is well understood, *An. farauti* is known to exist as several genotypes [53]. These genetically distinct populations are separated by overt barriers: climate disjunction between the northern continuous wet and southern monsoonal region in the Southern Plains of New Guinea (see Fig.1), the central highlands in New Guinea; and sea gaps between New Guinea and Manus Island, the Solomon Islands and Vanuatu [58]. All genotypes appear to

Given that *An. farauti* remains the dominant species collected in coastal villages, past reference to their biology and behaviour prior to identification using molecular techniques is probably still valid. *Anopheles farauti,* while readily feeding on humans, will also feed on other animals, and anthropophilic indices can be quite low in villages where domestic animals, primarily pigs and dogs, are abundant [27, 67]. Populations of this species were found well outside the flight range of human habitation, indicating that this species will readily feed on native birds and animals [31]. The longevity of this species appears quite variable; in the Solomon Islands province of Temotu the proportion of the population that was parous was 0.42 [70] while in Central Province it was 0.76 (T. Russell, unpublished data). In New Guinea, values ranged from 0.58 in Jayapura [65] to 0.49 in Madang, [27] and 0.73 in the D'Entrecasteaux Islands [66]. It will readily enter houses to feed but is primarily exophilic, leaving the house on the night

*Anophelespunctulatus* has been recorded from the Moluccas, New Guinea, and the larger islands of Manus, New Britain, New Ireland and Buka – but it does not appear to be present on Bougainville Island [48, 72]. During faunal surveys conducted in the early 1970's, *An. punctu‐ latus* was found on all the main islands in the Solomon Islands except Temotu Province [73]. It was found on Malaita in 1987 [74] and on the north coast of Guadalcanal in 1998 [51]. However, recent surveys of Santa Isabel and Central Provinces failed to find this species (62, T. Russell, unpublished data). In New Guinea, it is mainly found in inland lowland regions but is also common in the foothills of central ranges and in the intermountain highland valleys [8, 31, 75]. Its natural larval habitats are rock pools, pools in rivers and streambeds, and pools along the margins of these waterways. It is a highly invasive species and will readily invade sites created by human activity such as wheel ruts in roads, pools in walking tracks, hoof and foot prints, pig wallows and shallow drains around village houses (Table 3, Fig. 10 A) [31, 76]. These sites all have a clay or gravel substrate; are small or transient and are maintained only by regular rainfall; they lack established aquatic fauna and flora; and they have little or no

Given that many rural communities throughout the region are connected by unsealed dirt roads, these thoroughfares – along with roads and construction associated with logging and mining activities – have created both extensive larval sites for this species and the corridors along which it can move. *Anopheles punctulatus* has adapted to these small transient sites with eggs that can survive desiccation for several days, a short larval stage (relative to other species) and highly synchronized larval development [76, 77]. A preference for transient sites binds *An*. *punctulatus* to areas where the soil contains clay and the rainfall is perennial. Where these conditions exist it can occur in high densities [65]. It is considered the most anthropophilic of

have similar behaviours and are malaria vectors wherever they occur.

of feeding to rest outdoors [65, 66].

374 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

debris.

A number of species have been found infected with human malaria sporozoites throughout the southwest Pacific, but because they have limited distributions or are relatively uncommon, they are considered secondary vectors.

*Anopheleshinesorum* (formally *An. farauti* 2) is almost as widespread as *An*. *farauti*, being found from the Moluccas throughout New Guinea, and on Buka and Bougainville Islands; it is also thought to occur in New Britain, New Ireland, and Manus [31, 48]. In the Solomon Islands, it was found on the islands of Santa Isabel, Central Province and the north coast of Guadalcanal, but does not occur in Vanuatu [51, 62, 70]. Any understanding of its distribution is limited by the paucity of faunal surveys in this region, and it is likely that it will be found on all the main islands in the Solomon Islands except Temotu. In Papua New Guinea this species is most frequently found in lowland inland river valleys and flood plains – however it also occurs on the coast and on small offshore islands [31].

Several genetically structured populations were found within *An. hinesorum* [54], with the genotypes found in Buka and Bougainville in PNG and in the Solomon Islands provinces of Santa Isabel, Central, and Guadalcanal being highly zoophilic and rarely biting humans [35, 48, 51, 62]. On mainland PNG, *An. hinesorum* readily bites humans; it was the most common anopheline found throughout the Southern Plains where it can occur in high densities [59]. It has also been found in the highlands of the central highlands (up to 1740m), though it is less common in this region. This is also the case north of the central highlands, possibly due to competition from other species such as *An*. *farauti* 4 and *An*. *koliensis*, which also occur in this region and share similar larval habitats. *Anopheles hinesorum* has been incriminated as a vector in this northern New Guinea region [59].

*Anopheles hinesorum* oviposits in a range of water bodies, both natural – ground pools, swamps and the edges of streams; and rivers – and human-made drains and ditches, wheel ruts and pig wallows (Table 3, Fig. 10) [31]. On Santa Isabel larvae were found in small, shallow, wheel ruts. These transient sites – turbid, with a clay substrate, and devoid of any vegetation – are, at least in Papua New Guinea, normally the exclusive habitat of *An*. *punctulatus*, but *An. hinesorum* now appears to occupy this niche in the Solomon Islands [62].


**Table 3.** Larval habitats of some primary and secondary vectors of malaria in the Australian Region.

Little is known about this vector's behaviour with regards to malaria transmission although in northern PNG it appears that human feeding activity peaks early in the evening and then declines through the rest of the night [82].

Several genetically structured populations were found within *An. hinesorum* [54], with the genotypes found in Buka and Bougainville in PNG and in the Solomon Islands provinces of Santa Isabel, Central, and Guadalcanal being highly zoophilic and rarely biting humans [35, 48, 51, 62]. On mainland PNG, *An. hinesorum* readily bites humans; it was the most common anopheline found throughout the Southern Plains where it can occur in high densities [59]. It has also been found in the highlands of the central highlands (up to 1740m), though it is less common in this region. This is also the case north of the central highlands, possibly due to competition from other species such as *An*. *farauti* 4 and *An*. *koliensis*, which also occur in this region and share similar larval habitats. *Anopheles hinesorum* has been incriminated as a vector

*Anopheles hinesorum* oviposits in a range of water bodies, both natural – ground pools, swamps and the edges of streams; and rivers – and human-made drains and ditches, wheel ruts and pig wallows (Table 3, Fig. 10) [31]. On Santa Isabel larvae were found in small, shallow, wheel ruts. These transient sites – turbid, with a clay substrate, and devoid of any vegetation – are, at least in Papua New Guinea, normally the exclusive habitat of *An*. *punctulatus*, but *An.*

> **Wheel tracks (D)**

**Larval Habitat1 - incidence (%)**

**Swamp brackish (E)**

(49.7) 41 (17.7) 15 (6.5) 34 (14.7) <sup>0</sup> 2 (0.8) 3 (1.3) 21 (9.0) <sup>231</sup>

(37.7) 7 (1.8) 41 (10.9) 12 (3.2) 18 (4.8) 23 (6.1) 52 (13.9) <sup>374</sup>

0 2 (25.0) 2 (25.0) 1 (12.5) 0 0 0 3 (37.5) 8

0 2 (33.3) 0 0 0 1 (16.7) 0 3 (50.0) 6

0 0 0 0 0 7 (70.0) 2 (20.0) 1 (10.0) 10

*An. farauti* 7 (4.5) 48 (30.7) 1 (0.6) 22 (14.10 43 (27.5) 2 (1.2) 12 (7.7) 21 (13.40 156

*koliensis* 5 (7.9) 17 (27.0) 4 (6.3) 15 (23.8) <sup>0</sup> 2 (3.1) 2 (3.1) 18 (28.5) <sup>63</sup>

**Table 3.** Larval habitats of some primary and secondary vectors of malaria in the Australian Region.

**Swamp fresh (F)** **Edge of streams (G)**

**Drains earthen (H)**

**Totals**

*hinesorum* now appears to occupy this niche in the Solomon Islands [62].

**Pig wallows (C)**

in this northern New Guinea region [59].

376 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**Species**

*An. punctulatus*

*An.*

*An. hinesorum*

*An. farauti* 4

*An. farauti* 6

*An. bancroftii* **Transie nt pools (A)**

115

70 (18.7)

**Ground pools (B)**

141

letters after habitat type correspond to illustrations in Fig. 10

*Anophelesfarauti* 4 has been found throughout the inland lowland river valleys and flood plains north of the central highlands in PNG [31, 82]. In some locations it is very abundant, and in villages inland from Lae it can comprise up to 90% of the night-biting catch [31]. It readily utilizes larval sites created by human activity – pig wallows, drains, and wheel ruts (Table 3, Fig. 10) where it was commonly found in association with *An*. *punctulatus* and *An*. *koliensis*. It is a vector throughout its range [59, 82]. Little is known of its behaviour mainly due to the fact that there are no reliable morphological characters that separate it from *An*. *hinesorum* and *An*. *koliensis*, the two species with which it is commonly sympatric.

*Anopheles farauti* 6 is restricted in its distribution to the intermontane plains and upland valleys of the highland regions (>1000m, ranging to the highest points of 2000m) of New Guinea. It has adapted to the cool moist climate that prevails at these altitudes and in this habitat it is quite common. It is noticeably larger than any other members of the *An*. *farauti* complex [31, 84]. In 1960, Peters and Christian [7] found this large *An*. *farauti* to be the most common anopheline biting humans in the Waghi Valley in the highlands of PNG and recorded sporo‐ zoites in 2.2%. It was the most abundant anopheline in human biting catches in the Baliem Valley (Wamena, at 1,500m) in the central highlands of Papua Province [31]. *An*. *farauti* 6 likely plays an important role in malaria transmission within this restricted range.

*Anophelesfarauti* 8, the most recent member of the *An. farauti* complex to be recognized, has to date only been found in the inland lowland areas on the east side of the Gulf of Papua in PNG [37]. However, given that this species has an ITS2 RFLP identical to *An*. *farauti*, it may have been confused with this species in past faunal surveys and its distribution may be more extensive than is currently known. Very little is currently known about this species other than that specimens infected with human malaria parasites have been found [31].

*Anopheleslongirostris* s.l., now known to be a complex of nine species, [61] is found only on the island of New Guinea. It has a wide distribution below 1000m [31, 81], but has been recorded in large numbers only in a few areas. Its generally low abundance may be due to its preference for jungle pools associated with dense vegetation for oviposition. Behavioural studies have found it to be zoophilic in some areas [27] and anthropophilic in others [64] and these differ‐ ences in behaviour may possibly be explained by the presence of cryptic species, each exhib‐ iting different host-feeding preferences [61]. Little is known about the biology of these species and the individual role that each species might play in malaria transmission. It has been incriminated as a vector of malaria in the Southern Plains and north of the central highlands in PNG [59, 75].

*Anophelesbancroftii* s.l. has a wide distribution throughout New Guinea [64, 81]. It is now known to be a species complex containing four independently evolving genotypes [39], although its status with respect to *An. barbiventris* is unknown. *Anopheles bancroftii* A is found throughout northern Australia and the Southern Plains of PNG where it is common, occurring in large numbers and readily biting humans. Genotype B occurs in Papua south of the central highlands and genotype D occurs in the inland river valleys north of the central highlands. The range of *An*. *bancroftii* C overlaps with genotypes B and D. genotypes B, C, and D are rarely collected near the coast and appear to prefer inland, lowland, river flood plains below 150m. In PNG the members of the *An*. *bancroftii* complex are rarely found anywhere in large numbers, except for the Southern Plains. Members of the complex have been incriminated as vectors of malaria at only a few locations [59, 75]. Larval habitats are mainly large permanent water bodies such as fresh water swamps and lagoons (Table 3, Fig.10 F). Nothing is yet known about the biology or behaviour of any of these putative species.

#### **3.3. Possible vectors**

There are several *Anopheles* species found throughout the southwest Pacific that feed on humans but are not very abundant and have limited distributions – in most cases, little is yet known about their biology or behaviour. These include *An*. *meraukensis*, *An*. *novaguinen‐ sis*, *An*. *torresiensis*, and *An*. *hilli* – all of which are found only on the Southern Plains of New Guinea (Fig. 1). All four species are common in northern Australia where a similar climate type also prevails [31, 85, 86]. In Australia these four species will readily bite humans, but in PNG nothing is known about the biology of these species except that *An. hilli* can occur in large numbers, will feed on humans, and will enter houses to do so [87]. *Anopheles hilli* was incriminated as a vector of malaria in Australia during a *Plasmodium vivax* epidemic in Cairns in 1942 [88]. These four species may be involved in malaria transmission but only as minor local vectors at best.

The members of the *Anopheles lungae* complex – *An*. *lungae*, *An*. *solomonis*, and *An*. *nataliae* – are endemic to the Solomon Islands where they are found on all major islands except Temotu, with *An*. *lungae* also being recorded from Bougainville [70, 89]. All three species have been recorded to bite humans and there is some circumstantial evidence incriminating *An*. *lungae* as a malaria vector [18]. On Santa Isabel, *An*. *solomonis* was found to be the dominant human biting anopheline in inland villages although they were also recorded biting pigs. This species fed outdoors, early in the evening (6pm-9pm) but was short-lived. In a sample of 221 mosqui‐ toes collected via human landing catches, the proportion of parous was 0.33 [62]. No member of the *An*. *lungae* complex has been found infected with human malaria parasites although their human biting behaviour would make them possible vectors.

#### **3.4. Non-vectors**

Several *Anopheles* species present in the southwest Pacific are known not to feed on humans and this zoophilic behaviour precludes them from being vectors of malaria. These species include *An*. *annulipes* L and *An*. *annulipes* M, which are part of the *An*. *annulipes* complex – the members of which are widespread throughout Australia [90]. *Anopheles annulipes* L is found in a small enclave of monsoonal climate, which exists along the southern coast of Papua around Port Moresby, and within this limited distribution, it is quite common (Fig. 1). *Anopheles annulipes* M is a highland species common in intermontane valleys above 1000m [64]. Both species are readily found as larvae but are rarely collected feeding on humans [7, 64].

*Anophelesirenicus* (formally *An. farauti* 7) is endemic to the Solomon Islands, being recorded only on Guadalcanal. Larvae are commonly collected but the adults have never been recorded as biting humans [35, 51].

*Anopheles* sp. near *punctulatus* is an uncommon species with a patchy distribution restricted to the upland valleys of the central highlands in Papua New Guinea [31]. Nothing is yet known of its biology though it appears to have little association with humans.

Several species that occur in the region have limited distributions and are too uncommon to play any significant role in malaria transmission. These species include *An*. *papuensis* and *An*. *farauti* 5, two rarely recorded species from the highlands of PNG; *Anopheles clowi*, found on only two occasions since 1946 [19, 91]; and *An*. *rennellensis*, found only on the malaria-free island of Rennell in the Solomon Islands [25].

## **3.5. Oriental species**

and genotype D occurs in the inland river valleys north of the central highlands. The range of *An*. *bancroftii* C overlaps with genotypes B and D. genotypes B, C, and D are rarely collected near the coast and appear to prefer inland, lowland, river flood plains below 150m. In PNG the members of the *An*. *bancroftii* complex are rarely found anywhere in large numbers, except for the Southern Plains. Members of the complex have been incriminated as vectors of malaria at only a few locations [59, 75]. Larval habitats are mainly large permanent water bodies such as fresh water swamps and lagoons (Table 3, Fig.10 F). Nothing is yet known about the biology

There are several *Anopheles* species found throughout the southwest Pacific that feed on humans but are not very abundant and have limited distributions – in most cases, little is yet known about their biology or behaviour. These include *An*. *meraukensis*, *An*. *novaguinen‐ sis*, *An*. *torresiensis*, and *An*. *hilli* – all of which are found only on the Southern Plains of New Guinea (Fig. 1). All four species are common in northern Australia where a similar climate type also prevails [31, 85, 86]. In Australia these four species will readily bite humans, but in PNG nothing is known about the biology of these species except that *An. hilli* can occur in large numbers, will feed on humans, and will enter houses to do so [87]. *Anopheles hilli* was incriminated as a vector of malaria in Australia during a *Plasmodium vivax* epidemic in Cairns in 1942 [88]. These four species may be involved in malaria

The members of the *Anopheles lungae* complex – *An*. *lungae*, *An*. *solomonis*, and *An*. *nataliae* – are endemic to the Solomon Islands where they are found on all major islands except Temotu, with *An*. *lungae* also being recorded from Bougainville [70, 89]. All three species have been recorded to bite humans and there is some circumstantial evidence incriminating *An*. *lungae* as a malaria vector [18]. On Santa Isabel, *An*. *solomonis* was found to be the dominant human biting anopheline in inland villages although they were also recorded biting pigs. This species fed outdoors, early in the evening (6pm-9pm) but was short-lived. In a sample of 221 mosqui‐ toes collected via human landing catches, the proportion of parous was 0.33 [62]. No member of the *An*. *lungae* complex has been found infected with human malaria parasites although

Several *Anopheles* species present in the southwest Pacific are known not to feed on humans and this zoophilic behaviour precludes them from being vectors of malaria. These species include *An*. *annulipes* L and *An*. *annulipes* M, which are part of the *An*. *annulipes* complex – the members of which are widespread throughout Australia [90]. *Anopheles annulipes* L is found in a small enclave of monsoonal climate, which exists along the southern coast of Papua around Port Moresby, and within this limited distribution, it is quite common (Fig. 1). *Anopheles annulipes* M is a highland species common in intermontane valleys above

or behaviour of any of these putative species.

378 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

transmission but only as minor local vectors at best.

their human biting behaviour would make them possible vectors.

**3.3. Possible vectors**

**3.4. Non-vectors**

Five anopheline species – *An*. *annulatus*, *An*. *kochi*, *An*. *indefinitus*, *An*. *vanus*, and *An*. *vagus* – are Oriental species found as far east as the Moluccas Islands, which borders the Australian Region [18]. Two others – *An*. *karwari* and *An*. *subpictus* – have made substantial dispersals into New Guinea. While *An. tessellatus* has also been recorded in Papua Province and more recently in the Jayapura area (N. Lobo, unpublished data), it is not considered a vector in the Australian Region.

*Anopheleskarwari* was first reported in Jayapura in the 1930s where it was believed to be relatively common [92]. In PNG, the first record was from Maprik in 1960 [78], with subsequent confirmation by Hii and colleagues in 1997 [93] who also recorded it from the Maprik area where it made up 14% of the anophelines collected. Its distribution appears to be restricted to inland lowlands, and to foothills (up to 1000m) on the north side of the central highlands in PNG [31]. Nothing is known of its larval habits in PNG, but in Papua Province it was recorded from the edges of slow-moving watercourses, seepages, grassy pools, wheel ruts and hoof prints. *An*. *karwari* was first incriminated as a vector in 1955 in Papua Province [94]; in PNG it was positive for sporozoites in the Watut Valley inland from Lae [64], and in Maprik [75]. *Anopheles karwari* can be abundant but given its limited distribution, it is considered a secon‐ dary vector.

*Anophelessubpictus* occurs in the Moluccas, in Papua Province, and has been found on the islands of Biak and Misool. It has been found in several isolated populations in Papua New Guinea but only appears to be well established and common along the south coast of PNG from the Gulf of Papua to the D'Entrecasteaux Islands [64, 95]. It is a brackish water breeder and so is restricted to the coast. There are records of it biting humans and being infected with malaria at Bereina west of Port Moresby [95-97]. Apart from the population along the southern coastline of PNG, *An*. *subpictus* is uncommon with a limited distribution, and so is considered only a secondary vector.

**Figure 10.** *Anopheles* larval sites as described in Table 3. **Panel A**, transient pool; **Panel B**, ground Pool; **Panel C**, pig wallow; **Panel D**, tyre track; **Panel E**, brackish swamp; **Panel F**, fresh water swamp; **Panel G**, edge of stream; **Panel H**, Drain.

## **4. Vector control**

The strategy behind the use of indoor residual spraying (IRS) and insecticidal treated bed nets (ITNs) is to deliver insecticide to vectors which have entered the house to obtain a blood meal. Given that a female mosquito feeds every second or third night, it will seek a blood meal at least 3 to 5 times during the duration of the extrinsic incubation period, allowing 3 to 5 opportunities to contact the insecticide associated with IRS and ITNs before it develops sporozoites in the salivary glands. Ideally, for IRS and ITNs to successfully control malaria, the vector should exhibit the following behaviours: a) be highly anthropophilic, b) feed indoors late at night when the humans are indoors, and c) rest on the insecticide treated surfaces of ITNs or IRS either before or after feeding.

The primary vectors in the southwest Pacific initially were reported to exhibit this type of behaviour to varying degrees. *Anopheles punctulatus* is the most anthropophilic of the three vectors [78, 98] and has a peak night-biting time around midnight [79]. *An*. *koliensis* is the next most anthropophilic and also feeds late at night [78, 79, 98]. On the other hand, *Anophelesfarauti* is the least anthropophilic or most opportunistic species, and while it also had a peak feeding time around midnight, it starts feeding earlier in the evening at dusk [99] – when hosts are less likely to be inside or under nets. A pattern of early evening blood feeding was reported in the 1960's [65], 1970's [100] and 1980's [101]. While all species will readily enter houses to obtain a blood meal, none remain inside houses after sunrise [65, 99]. Thus while ITNs and IRS control may well be efficacious against late night biting *An. punctulatus* and *An. koliensis*, adaptation of *An. farauti* to feed primarily early in the evening [100] may minimize the opportunities to contract insecticides and thereby circumvent control efforts with IRS and ITNs.

A 
 B 
 C 

380 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

D 
 E 
 F 

H 

**Figure 10.** *Anopheles* larval sites as described in Table 3. **Panel A**, transient pool; **Panel B**, ground Pool; **Panel C**, pig wallow; **Panel D**, tyre track; **Panel E**, brackish swamp; **Panel F**, fresh water swamp; **Panel G**, edge of stream; **Panel H**,

The strategy behind the use of indoor residual spraying (IRS) and insecticidal treated bed nets (ITNs) is to deliver insecticide to vectors which have entered the house to obtain a blood meal. Given that a female mosquito feeds every second or third night, it will seek a blood meal at least 3 to 5 times during the duration of the extrinsic incubation period, allowing 3 to 5 opportunities to contact the insecticide associated with IRS and ITNs before it develops sporozoites in the salivary glands. Ideally, for IRS and ITNs to successfully control malaria,

G 

Drain.

**4. Vector control**

With the implementation of the eradication program and subsequent control programs using DDT with IRS, populations of *An*. *punctulatus* were reduced to the point where adults and larvae of this species were virtually impossible to find. This was not an isolated occurrence but was found across all areas where these programs were implemented and the behaviours of the vectors were studied: Arso and Entrop in Papua Province; Maprik and Wewak in PNG; Rabaul in the islands of PNG; and in the Solomon Islands [100-103]. *Anopheles koliensis* populations were also suppressed by IRS though the extent of this suppression varied: in Arso, the reduction was short lived, while in PNG it appeared to be more sustained and in the Solomon Islands this species may have been eliminated [65, 72, 100].

Where *An*. *farauti*, populations were suppressed by IRS, they returned to pre-spray levels after only a few years [100, 101]. In Wewak, on the coast from Maprik, this happened after the first spray round, and in the Carteret Islands IRS had little effect on the population density of *An*. *farauti* [72].

Slooff [65] studied the house-visiting behaviour of *An*. *farauti* and observed that fewer mosquitoes entered DDT sprayed houses compared to the unsprayed houses and that their feeding success was less in sprayed houses. Thevasagayam [104] found that >45% of indoorfeeding *An*. *farauti* in the Solomon Islands left the house before picking up a lethal dose of insecticide. Slooff [65] suggested that this behaviour was due to an irritant effect of the DDT, a phenomenon that has been understood for some time [105] and which appeared to be pronounced in *An*. *farauti*.

Studies into the failure of IRS to adequately control populations of *An*. *farauti* revealed a major shift in the biting time of this species (and to some extent in *An*. *koliensis* as well) following IRS [65]. Before IRS *An*. *farauti* commenced feeding at dusk and built up to a peak at midnight [66, 79]. However following IRS, the majority of feeding occurred between 6pm and 8pm [66, 100]. A typical example was New Britain in 1963 where *An*. *farauti* before IRS with DDT fed throughout the night with a peak at midnight, but after five spray cycles (across two years) there was a distinct peak of feeding between 6pm-7pm, with 76% of feeding occurring before 9pm. [101]. It is common for the human populations in this region to spend the first hours of the night outdoors and so by feeding early in the night, *An*. *farauti* can obtain a blood meal without entering houses and being exposed to the insecticides used in IRS or ITNs. In the Solomon Islands this change in behaviour was believed responsible, in part, for the inability to interrupt transmission and the eventual failure of the eradication program [106].

This shift in biting time to early in the night appears fixed in some populations: when spraying was withdrawn, the early night-feeding pattern was maintained. In Temotu and Santa Isabel in the Solomon Islands, where DDT IRS was intensively applied during the eradication program of the early 1970s but only intermittently during the subsequent 35 years, *An*. *farauti* still displays the early night-biting pattern [62, 70]. In Temotu, with the resumption of a malaria elimination program in 2009 (based on the use of pyrethroids in IRS and distribution of ITNs), the early night-biting activity was further enforced with an increase in outdoor biting from 43% to 60% without any significant reduction in biting density post-intervention [70].

On Buka Island, in 1961 prior to spraying with DDT, *An*. *farauti*, *An*. *punctulatus,* and *An*. *koliensis* were all present. A post-spray survey conducted one year later found only *An*. *farauti* (see Spencer, unpublished report to the Department of Health, Malaria Control Program, Papua and New Guinea, 1961). DDT IRS on Buka continued for the next 20 years (40 spray rounds). Entomological surveys in 2000 failed to find *An*. *koliensis*; however at this time both *An*. *farauti* and *An*. *punctulatus* were abundant, indicating the reintroduction or recovery of the latter species. The night-biting pattern of *An*. *farauti* at this time showed the classical pre-spray pattern, that is, a rapid build-up in numbers from 6pm to a peak at midnight [48].

There were only a limited number of vector control strategies evaluated in the southwest Pacific in the decades following the cessation of the IRS-based elimination campaigns. While the DDT campaigns did not succeed in eliminating malaria, the campaigns were credited with the elimination of filariasis from the Solomon Islands where that disease was transmitted by the members of the *An. punctulatus* group [107]. Most of the subsequent vector control evaluations were trials of bed nets, either untreated or treated with pyrethroids. Trials evaluated entomological as well as parasitological impacts for malaria and/or filariasis as anophelines vector both of these parasitic diseases. A single-village longitudinal study of untreated bed nets in Madang Province of PNG showed that nets significantly reduced the human blood index of *An. punctulatus*, as well as the infection rates for the *Plasmodium falciparum* CS antigen and *Wuchereria bancrofti* for both early and late stage larvae [108]. On Bagabag Island of Madang, PNG, where *An. farauti* is the vector, one study [109] reported that users of untreated nets had significantly lower microfilariae and filarial antigen positivity rates than individuals not sleeping under bed nets, suggesting that nets were effective in limiting filariasis transmission by *An. farauti.*

The first study of permethrin treated nets in PNG reported significant reductions in the sporozoite rates in the *An. punctulatus* group in two villages as well as a significant reduction in *P. falciparum* incidence in children under the age of four years [10]. At the same time, Charlwood and Dagoro [110], working in a different part of PNG, found that permethrintreated nets deterred members of the *An. punctulatus* group from entering houses. Prolonged ITN use in PNG was associated with reduced sporozoite rates, a result hypothesized to be due to a reduction in mosquito survival [111]. Bockarie and Dagoro [112] reported that ITNs were more effective in protecting against *P. falciparum* in PNG and postulated that this was due to vivax-infected members of the *An. punctulatus* group feeding earlier than falciparum-infected mosquitoes.

there was a distinct peak of feeding between 6pm-7pm, with 76% of feeding occurring before 9pm. [101]. It is common for the human populations in this region to spend the first hours of the night outdoors and so by feeding early in the night, *An*. *farauti* can obtain a blood meal without entering houses and being exposed to the insecticides used in IRS or ITNs. In the Solomon Islands this change in behaviour was believed responsible, in part, for the inability

This shift in biting time to early in the night appears fixed in some populations: when spraying was withdrawn, the early night-feeding pattern was maintained. In Temotu and Santa Isabel in the Solomon Islands, where DDT IRS was intensively applied during the eradication program of the early 1970s but only intermittently during the subsequent 35 years, *An*. *farauti* still displays the early night-biting pattern [62, 70]. In Temotu, with the resumption of a malaria elimination program in 2009 (based on the use of pyrethroids in IRS and distribution of ITNs), the early night-biting activity was further enforced with an increase in outdoor biting from

to interrupt transmission and the eventual failure of the eradication program [106].

382 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

43% to 60% without any significant reduction in biting density post-intervention [70].

pattern, that is, a rapid build-up in numbers from 6pm to a peak at midnight [48].

filariasis transmission by *An. farauti.*

On Buka Island, in 1961 prior to spraying with DDT, *An*. *farauti*, *An*. *punctulatus,* and *An*. *koliensis* were all present. A post-spray survey conducted one year later found only *An*. *farauti* (see Spencer, unpublished report to the Department of Health, Malaria Control Program, Papua and New Guinea, 1961). DDT IRS on Buka continued for the next 20 years (40 spray rounds). Entomological surveys in 2000 failed to find *An*. *koliensis*; however at this time both *An*. *farauti* and *An*. *punctulatus* were abundant, indicating the reintroduction or recovery of the latter species. The night-biting pattern of *An*. *farauti* at this time showed the classical pre-spray

There were only a limited number of vector control strategies evaluated in the southwest Pacific in the decades following the cessation of the IRS-based elimination campaigns. While the DDT campaigns did not succeed in eliminating malaria, the campaigns were credited with the elimination of filariasis from the Solomon Islands where that disease was transmitted by the members of the *An. punctulatus* group [107]. Most of the subsequent vector control evaluations were trials of bed nets, either untreated or treated with pyrethroids. Trials evaluated entomological as well as parasitological impacts for malaria and/or filariasis as anophelines vector both of these parasitic diseases. A single-village longitudinal study of untreated bed nets in Madang Province of PNG showed that nets significantly reduced the human blood index of *An. punctulatus*, as well as the infection rates for the *Plasmodium falciparum* CS antigen and *Wuchereria bancrofti* for both early and late stage larvae [108]. On Bagabag Island of Madang, PNG, where *An. farauti* is the vector, one study [109] reported that users of untreated nets had significantly lower microfilariae and filarial antigen positivity rates than individuals not sleeping under bed nets, suggesting that nets were effective in limiting

The first study of permethrin treated nets in PNG reported significant reductions in the sporozoite rates in the *An. punctulatus* group in two villages as well as a significant reduction in *P. falciparum* incidence in children under the age of four years [10]. At the same time, Charlwood and Dagoro [110], working in a different part of PNG, found that permethrintreated nets deterred members of the *An. punctulatus* group from entering houses. Prolonged In the Solomon Islands, ITNs had significantly greater impacts than IRS on vector infectivity and inoculation rates of *An. farauti* and *An. punctulatus*, however the reductions in the entomological inoculation rates were insufficient to effectively control malaria without additional interventions [68]. Later, Hii and colleagues [113] reported that ITNs in villages extended the length of the oviposition cycle by one day compared to DDT or untreated villages, and in 1993, Kere and colleagues reported a 71% reduction in biting rates of *An. farauti* on Guadalcanal, Solomon Islands following the introduction of ITNs but questioned the effec‐ tiveness of the nets given that people spend considerable time outside [114]. An analysis of facility-based data showed that both IRS with DDT and permethrin-treated ITNs are associated with reductions in malaria incidence and fever, while larviciding with temephos was not [115]. Recently, Bugoro and colleagues [70] found "little, if any, reduction in biting densities and no reduction in the longevity of the vector population" in Temotu Province of the Solomon Islands following the introduction of LLINs and IRS.

In Vanuatu, malaria was successfully eliminated from the island of Aneityum using a strategy of mass drug administration with pyrimethamine/sulfadoxine (Fansidar), and primaquine, ITNs and larvivorous fish. Falciparum malaria disappeared soon after the start of mass drug administrations [13]. The successful elimination was a function, most likely, of a small island population and the seasonality of transmission together with a high participation of the community in the mass drug administration. The impact of larvivorous fish was believed to be "probably marginal" due to the failure to find all breeding sites and the "incompleteness of predation".

Interpretation of the impact of these interventions must consider the period when the studies were conducted as reports of changes in behaviours of the vectors (discussed earlier) are known to have occurred; the effectiveness of an intervention is not static but is also dependent on the vectors' behaviours (e.g., shifts toward early feeding and outdoor biting may reduce the effectiveness of ITNs and IRS, as was demonstrated by Slooff [65], Taylor [100] and Sweeney [101]). Resistance to pyrethroids (and the existence of knock‐ down resistance genes) has not yet been found in the few studies thus far conducted in the southwest Pacific [116]; however, 30% of *An. koliensis* in Papua Province, Indonesia, were found to be resistant to DDT [117].

There is now a renewed interest in malaria control with IRS and ITNs in the Solomon Islands and Vanuatu with elimination programs in some areas and intensified control in all other areas. At the most fundamental level, the intervention measures of IRS and ITNS both rely on the vector feeding late at night when people are indoors. As such, these tools have the potential to provide effective control of late night biting *An. punctulatus* and *An. koliensis.* However it is important to emphasize that this behaviour pattern is no longer universally demonstrated by *An*. *farauti,* the primary coastal vector in the southwest Pacific.

The early biting pattern of the widely distributed *An. farauti* will prevent mosquito control and malaria elimination where this species bites early and outdoors and thereby avoids insecticides in IRS and ITNs. Therefore, additional control measures that target the vectors outside houses are now urgently needed for these programs to achieve effective reduc‐ tions in malaria transmission. Effective larval control may be feasible with species such as *An. farauti*. Unlike *An*. *koliensis* and *An*. *punctulatus*, whose larvae are found in small ground pools that will be difficult to locate and treat where the annual rainfall is >2000mm, the most productive larval sites of *An. farauti* are large permanent coastal swamps and lagoons (Fig. 10 E) [62, 70, 118]. Such sites are easy to locate, few in number and permanent, and thus more easily treated.

#### **5. Conclusion**

In 2007, the Bill and Melinda Gates Foundation challenged the malaria community to once again attempt to achieve malaria eradication. The failure of the previous campaigns was due, in part, to attempting to control many vector species with a single intervention that targeted vectors inside houses. Enhancing our chances of eliminating malaria in the southwest Pacific will require the implementation of novel interventions that target vectors based on our knowledge of their behaviours. However, basic knowledge about the biology and behaviours of some vectors and potential vector species in this region is limited. This knowledge gap must be filled before control strategies can be optimized to exploit the vectors' biological vulnerabilities to control measures. The basic parameters essential to understanding transmission such as feeding habits, host preference, longevity, frequency of feeding and seasonal abundance – which are essential for the selection of effective control strategies –, await discovery for many species. Additionally, we remain uncertain of the complete distribution of species, or the importance of the various genotypes that have been recognized to date in a number of taxon.

Significant advances in DNA technologies have enhanced our ability to both discover and identify cryptic species in the southwest Pacific. These technologies, coupled with immuno‐ logical and molecular assays to detect malaria parasites in mosquitoes, have led to the resurgence in investigations to incriminate vectors and to characterize their behaviors. We now know that there are 13 species in the *An. punctulatus* group (not three); that *An. longirostris* is not one zoophilic mosquito but a complex that includes human-biting malaria vectors; and that *An. bancroftii* is a complex of at least four species (not one as previously thought), two of which are malaria vectors. New studies on species-specific bionomic trails are enabling us to understand the biological basis for how they might be affected by interventions. Because of recent technological advances and their application to field studies, our knowledge on the major vectors in southwest Pacific is much better understood and as a consequence we are now better positioned than ever to study the species in this region and to design and evaluate novel and effective interventions.

## **Author details**

The early biting pattern of the widely distributed *An. farauti* will prevent mosquito control and malaria elimination where this species bites early and outdoors and thereby avoids insecticides in IRS and ITNs. Therefore, additional control measures that target the vectors outside houses are now urgently needed for these programs to achieve effective reduc‐ tions in malaria transmission. Effective larval control may be feasible with species such as *An. farauti*. Unlike *An*. *koliensis* and *An*. *punctulatus*, whose larvae are found in small ground pools that will be difficult to locate and treat where the annual rainfall is >2000mm, the most productive larval sites of *An. farauti* are large permanent coastal swamps and lagoons (Fig. 10 E) [62, 70, 118]. Such sites are easy to locate, few in number and permanent, and

In 2007, the Bill and Melinda Gates Foundation challenged the malaria community to once again attempt to achieve malaria eradication. The failure of the previous campaigns was due, in part, to attempting to control many vector species with a single intervention that targeted vectors inside houses. Enhancing our chances of eliminating malaria in the southwest Pacific will require the implementation of novel interventions that target vectors based on our knowledge of their behaviours. However, basic knowledge about the biology and behaviours of some vectors and potential vector species in this region is limited. This knowledge gap must be filled before control strategies can be optimized to exploit the vectors' biological vulnerabilities to control measures. The basic parameters essential to understanding transmission such as feeding habits, host preference, longevity, frequency of feeding and seasonal abundance – which are essential for the selection of effective control strategies –, await discovery for many species. Additionally, we remain uncertain of the complete distribution of species, or the importance of the various genotypes that

Significant advances in DNA technologies have enhanced our ability to both discover and identify cryptic species in the southwest Pacific. These technologies, coupled with immuno‐ logical and molecular assays to detect malaria parasites in mosquitoes, have led to the resurgence in investigations to incriminate vectors and to characterize their behaviors. We now know that there are 13 species in the *An. punctulatus* group (not three); that *An. longirostris* is not one zoophilic mosquito but a complex that includes human-biting malaria vectors; and that *An. bancroftii* is a complex of at least four species (not one as previously thought), two of which are malaria vectors. New studies on species-specific bionomic trails are enabling us to understand the biological basis for how they might be affected by interventions. Because of recent technological advances and their application to field studies, our knowledge on the major vectors in southwest Pacific is much better understood and as a consequence we are now better positioned than ever to study the species in this region and to design and evaluate

thus more easily treated.

384 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

have been recognized to date in a number of taxon.

novel and effective interventions.

**5. Conclusion**

Nigel W. Beebe1\*, Tanya L. Russell2 , Thomas R. Burkot2 , Neil F. Lobo3 and Robert D. Cooper4

\*Address all correspondence to: n.beebe@uq.edu.au

1 University of Queensland, St Lucia, Brisbane, Australia and CSIRO Ecosystem Sciences, Brisbane, Australia


4 Australian Army Malaria Institute, Brisbane, Australia

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[116] Keven JB, Henry-Halldin CN, Thomsen EK, Mueller I, Siba PM, Zimmerman PA, et al. Pyrethroid susceptibility in natural populations of the *Anopheles punctulatus* Group (Diptera: Culicidae) in Papua New Guinea. Am J Trop Med Hyg. 2010;83(6):

[117] Bangs MJ, Annis BA, Bahang ZH, Hamzah N, Arbani PR. Insecticide susceptibility status of *Anopheles koliensis* (Diptera: Culicidae) in northeastern Irian Jaya, Indonesia. Southeast Asian J Trop Med Public Health. 1993;24(2):357-62. Epub 1993/06/01. [118] Bugoro H, Hii J, Russell TL, Cooper RD, Chan BK, Iro'ofa C, et al. Influence of envi‐ ronmental factors on the abundance of *Anopheles farauti* larvae in large brackish wa‐ ter streams in Northern Guadalcanal, Solomon Islands. Malar J. 2011;10:262. Epub

1259-61.

394 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

2011/09/14.

## **Chapter 13**

## **Ecology of Larval Habitats**

Eliška Rejmánková, John Grieco, Nicole Achee and Donald R. Roberts

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55229

## **1. Introduction**

Mosquito-borne diseases, including malaria are undergoing a global resurgence [1-7]. The factors responsible for the re-emergence are very complex, and management requires inte‐ grated cooperation at many levels, however, a need to better understand the ecology of disease vectors remains critical for any control program to succeed. In the case of malaria, the spatial and temporal changes in anopheline mosquito abundance, quantification of transmission potential of vector populations, characterizations of climatic conditions, and description of distributions of host (human) populations are necessary prerequisites for predicting high-risk malaria areas and implementing an effective disease control program [5, 8]. Tools such as remote sensing and geographic information systems (GIS), which are increasingly being used in studies of disease transmission and vector ecology have greatly enhanced our abilities to analyze landscape level relationships of vectors and diseases. Yet these tools can be success‐ fully used only in combination with a thorough understanding of ecologic and epidemiologic processes of disease transmission.

Among the most important determinants of adult mosquito abundance and distribution is the presence and quality of larval habitats.1 An understanding of the dynamics and productivity of larval habitats in the changing environment is required if efforts to model and predict adult abundance and ultimately limit the disease spread are to succeed [8-12]. While biology of adult mosquitoes has been reviewed from multiple perspectives [13-15], there has been no recent comprehensive review of mosquito larval habitats.2

<sup>2</sup>*Anopheles* species included in Sinka's et al [17] list of dominant vector species plus *An. vestitipennis* have been included in this review.

© 2013 Rejmánková et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Rejmánková et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

<sup>1</sup> terms larval habitat, breeding site, breeding habitat have been used interchangeably for descriptions of places where mosquito females oviposit eggs, larvae hatch, grow and pupate [16]. We will be using the term larval habitat throughout the paper.

A vast amount of literature on malaria vectors is available. More than 60 years ago, Marston Bates wrote in the Introduction to his The Natural History of Mosquitoes: "Mosquitoes in general, and the malaria carriers in particular, have been the subject of a tremendous amount of study, whose results have been reported in the voluminous literature. Much of this literature is an uncritical accumulation of facts that were easy to record, or of facts that were related to some momentarily fashionable subject of study, or of facts that were needed for the attainment of some immediately practical objective. This accumulation awaits to be converted into an orderly and useful structure of knowledge" [18]. It is hard not to feel the same today, with the Web of Science responding with > 600 references to an inquiry for *Anopheles* larval habitats. We won't be able to provide "an orderly and useful structure of knowledge" in this short chapter, but we will attempt to cover a few important topics:


Research and reporting efforts and resulting available information are disproportionately distributed and heavily skewed towards the most important malaria vector, *An. gambiae* with over 5440 references in the Web of Science, followed by *An. stephensi, An. arabiensis* and *An. funestus* with 1557, 744 and 537 references respectively. The majority of remaining species from Sinka's [17] list are referenced < 200 times with the exception of *An. albimanus, An. quadrima‐ culatus, An. darlingi* and *An. dirus* referenced 592, 456, 264 and 255 times, respectively. However, in most cases these species are primary vector species. In considering potential vector replace‐ ment following the environmental change (see examples further in the text) it will be important to keep in mind that secondary, little studied and less efficient, vector species might be found replacing primary malaria vector species.

## **2. History of description of larval habitats**

Much of what we know about the detailed behavior of individual insect vectors resulted from observations made during the pre-DDT era of the 1920's and 1930's [8, 19], when programs for malaria control through environmental management and regular larvicidal treatment of larval habitats were developed across Europe, Middle East, Asia, and the Americas [20, 21]. Examples of successful treatment schemes [21] show that they were all accomplished based on a good knowledge of larval ecology. The concept that the prevalence of malaria can more effectively be reduced by destroying vector mosquitoes in their adult stage than in their aquatic, larval stages became central to antimalarial efforts practiced throughout the world's tropical regions beginning first with pyrethrum and later with DDT spraying. Success of those efforts led startup of the Global Malaria Eradication Strategy, GMES [20, 22]. One of the unfortunate conse‐ quences of GMES was a substantial reduction in funding for research related to larval ecology, it was even credited with "exterminating more medical entomologists than mosquitoes" [20]. However, as early as 1983, Service [23] pointed out that "the general disillusionment with chemical control methods has led to the resurrection of biological control from the pre-DDT era" and although funding has not been easy to come by, the 1990's saw an exponential increase in studies on larval ecology and larval habitats. Laird's The Natural History of Laval Mosquito Habitats [24] provided an important source of information.

A vast amount of literature on malaria vectors is available. More than 60 years ago, Marston Bates wrote in the Introduction to his The Natural History of Mosquitoes: "Mosquitoes in general, and the malaria carriers in particular, have been the subject of a tremendous amount of study, whose results have been reported in the voluminous literature. Much of this literature is an uncritical accumulation of facts that were easy to record, or of facts that were related to some momentarily fashionable subject of study, or of facts that were needed for the attainment of some immediately practical objective. This accumulation awaits to be converted into an orderly and useful structure of knowledge" [18]. It is hard not to feel the same today, with the Web of Science responding with > 600 references to an inquiry for *Anopheles* larval habitats. We won't be able to provide "an orderly and useful structure of knowledge" in this short

Research and reporting efforts and resulting available information are disproportionately distributed and heavily skewed towards the most important malaria vector, *An. gambiae* with over 5440 references in the Web of Science, followed by *An. stephensi, An. arabiensis* and *An. funestus* with 1557, 744 and 537 references respectively. The majority of remaining species from Sinka's [17] list are referenced < 200 times with the exception of *An. albimanus, An. quadrima‐ culatus, An. darlingi* and *An. dirus* referenced 592, 456, 264 and 255 times, respectively. However, in most cases these species are primary vector species. In considering potential vector replace‐ ment following the environmental change (see examples further in the text) it will be important to keep in mind that secondary, little studied and less efficient, vector species might be found

Much of what we know about the detailed behavior of individual insect vectors resulted from observations made during the pre-DDT era of the 1920's and 1930's [8, 19], when programs for malaria control through environmental management and regular larvicidal treatment of larval habitats were developed across Europe, Middle East, Asia, and the Americas [20, 21]. Examples of successful treatment schemes [21] show that they were all accomplished based on a good knowledge of larval ecology. The concept that the prevalence of malaria can more effectively be reduced by destroying vector mosquitoes in their adult stage than in their aquatic, larval

chapter, but we will attempt to cover a few important topics:

–History of description of larval habitats

398 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

–Human impact and adjustment to new habitats

replacing primary malaria vector species.

**2. History of description of larval habitats**

–Determinants of larval habitats

–Implications for vector control

–Habitat selection –Landscape context

–Future priorities

Although earlier papers are not often cited in the contemporary literature, there are several reasons why older papers are important and should not be ignored:

**They provide records of species distributions**: The older papers often describing simple surveys or even just few locations where a particular species was found provide historical evidence of species distribution prior to human interference [25, 26]. *Example:* Positive records of the presence of *An. darlin*gi in southern Belize (then British Honduras) published by Komp [25] and Kumm and Ram [26] and a report of absence of this species 30 years later by Bertram [27], made one of the authors of this chapter (DR) suspects that disappearance of *An. darlingi* was most probably a response to DDT house-spraying [28]. The species was eventually recorded again from Belize (a consequence of the interruption of DDT-spraying?). The whole story points to the need to continuously study changing roles of malaria vectors in different geographical areas.

**They contain important ecological and ecophysiological observations:** Already in the 1940's mosquito entomologists realized what many recent papers present as a new discovery, i.e., that human interference can lead to a vector change. As described by Muirhead-Thomson [29] from the coastal zones of Sierra Leone, draining and dyking of mangroves, which used to be very productive habitats for *An. melas*, and changing land use to rice cultivation, resulted in very productive habitat for *An. gambiae* and eventual replacement of *An. melas* by *An. gam‐ biae*. Goma [30, 31] discarded a long time belief that high incidence of malaria in Uganda is related to the extensive papyrus swamps hypothesizing [30, 31] and eventually experimentally proving [32] that interior of a papyrus swamp is unsuitable for anophelines and only the swamps altered by human activities are significant providers of larval habitats. Numerous interesting observations and results of simple experiments on oviposition and larval devel‐ opment as influenced by environmental factors were published [18, 33] and are well summar‐ ized in Bates's Natural History of Mosquitoes [34].

**There can be a good information on well executed larval control**: A series of detailed studies on larval habitats originated from the US Tennessee Valley Authority, TVA (TVA is a federally owned corporation in the US created in 1933 to provide navigation and flood control, electricity generation, fertilizer manufacturing and economic development in the Tennessee Valley, a region strongly affected by the Great Depression; http://www.tva.com/abouttva/history.htm). This watershed area of the fifth largest river system in the United States was transformed into a series of reservoirs encompassing more than 11,000 miles of shoreline. Because the im‐ poundment of the river provided enhanced breeding opportunities for *An. quadrimaculatus* in (then) malaria-endemic region, antimalarial measures were required as integral parts of all TVA projects. The general philosophy was to control mosquito breeding through natural measures and limit larvicidal and other temporary controls to an absolute minimum [35]. Papers by Hinman et al [36], Penfound [37], Hess and Hall [38], Hall [39] focused on the importance of aquatic vegetation in anopheline larval habitats (see section on Vegetation).

**Older correlative studies can provide a good starting point for hypotheses testing through experimental studies:** Starting in early 1990's there is a progression of studies that include habitat characteristics and attempts to relate the presence of larvae to these characteristics [17, 40-51]. An important change compared to the majority of older papers was that in these correlative studies, environmental characteristics of both, larvae positive and negative habitats were recorded. As more information became available on the relationships between larval presence and habitat characteristics, attempts to classify anopheline larval habitats appeared. As an example Rejmankova et al. [44] classified larval habitats of *An. albimanus* on the coastal plain of Chiapas into 16 habitat-types based on the dominant aquatic vegetation. The goal was a hierarchical system of habitat classification that could be universally used for larval habitat description in the study area and it became a basis for many future studies on larval ecology by the Tapachula-based Center for Malaria Studies [52-54]. The analytical methods and hierarchical system described in Rejmankova et al [44] article are applicable to a wide range of studies on phytoecological relationships of vectors to aquatic habitats.

The need for regional classification of larval habitats into higher units became more urgent with the increasing use of remote sensing technology in malaria vector studies [55-57]. The step-wise approach (paradigm) advocated by Roberts and Rodriguez [58] became widely applied [59, 60]. These steps included the following: 1) developing an understanding of vector ecology and defining the environmental determinants for its presence and abundance (this step is based on field studies); 2) constructing a database that characterizes the landscape elements associated with the important aspects of vector biology and human habitation (RS and GIS are suitable tools for this step); and 3) formulating and verifying predictions of vector abundance.

Recently, studies describing larval habitats of anophelines were included in the global database on 41 dominant vector species, DVS, of human malaria. The contemporary distribution of each of the DVS, alongside a comprehensive description of the ecology and behavior of each species, has been published in a series of papers by Sinka and coauthors [17, 61-63]. The authors stated that simple, universal species-specific statements regarding the biology of these vectors are nearly impossible due to the behavioral plasticity of most species, in some cases sympatric distributions of sibling species, changing taxonomic categorization and the influence of environmental disturbance, all contributing to a high level of complexity.

While the descriptive and correlative studies of larval habitats have mushroomed in the 1990's and 2000's, good experimental studies explaining the hypothetical relationships between larvae and the habitat characteristics are still relatively lacking. They are increasingly called for [11, 22], e.g., by proposing development and application of enclosed, pathogen-free, semifield mesocosms in which vector populations can be experimentally manipulated. There are a few exceptions such as Goma's [31] study from the papyrus swamps in Uganda. Based on his observations on the absence of *An. gambiae* larvae from the swamp interior, Goma hypothe‐ sized that the larvae are not found there because the conditions are unfavorable for their development. He conducted a series of experiments in which known amounts of larvae of different instars were placed in floating cages in different locations throughout a swamp and confirmed that larvae in the swamp interior suffered significantly higher mortality and those surviving took longer to develop into adults than larvae in cages placed at the swamp periphery. The high mortality has been later explained as a result of inhibition of larval breathing due to the surface layer of oil produced by papyrus [64]. For other examples of hypotheses driven experimental studies see, e.g., [10, 65-76] and other examples provided in further text.

This watershed area of the fifth largest river system in the United States was transformed into a series of reservoirs encompassing more than 11,000 miles of shoreline. Because the im‐ poundment of the river provided enhanced breeding opportunities for *An. quadrimaculatus* in (then) malaria-endemic region, antimalarial measures were required as integral parts of all TVA projects. The general philosophy was to control mosquito breeding through natural measures and limit larvicidal and other temporary controls to an absolute minimum [35]. Papers by Hinman et al [36], Penfound [37], Hess and Hall [38], Hall [39] focused on the importance of aquatic vegetation in anopheline larval habitats (see section on Vegetation).

400 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**Older correlative studies can provide a good starting point for hypotheses testing through experimental studies:** Starting in early 1990's there is a progression of studies that include habitat characteristics and attempts to relate the presence of larvae to these characteristics [17, 40-51]. An important change compared to the majority of older papers was that in these correlative studies, environmental characteristics of both, larvae positive and negative habitats were recorded. As more information became available on the relationships between larval presence and habitat characteristics, attempts to classify anopheline larval habitats appeared. As an example Rejmankova et al. [44] classified larval habitats of *An. albimanus* on the coastal plain of Chiapas into 16 habitat-types based on the dominant aquatic vegetation. The goal was a hierarchical system of habitat classification that could be universally used for larval habitat description in the study area and it became a basis for many future studies on larval ecology by the Tapachula-based Center for Malaria Studies [52-54]. The analytical methods and hierarchical system described in Rejmankova et al [44] article are applicable to a wide range

The need for regional classification of larval habitats into higher units became more urgent with the increasing use of remote sensing technology in malaria vector studies [55-57]. The step-wise approach (paradigm) advocated by Roberts and Rodriguez [58] became widely applied [59, 60]. These steps included the following: 1) developing an understanding of vector ecology and defining the environmental determinants for its presence and abundance (this step is based on field studies); 2) constructing a database that characterizes the landscape elements associated with the important aspects of vector biology and human habitation (RS and GIS are suitable tools for this step); and 3) formulating and verifying predictions of vector

Recently, studies describing larval habitats of anophelines were included in the global database on 41 dominant vector species, DVS, of human malaria. The contemporary distribution of each of the DVS, alongside a comprehensive description of the ecology and behavior of each species, has been published in a series of papers by Sinka and coauthors [17, 61-63]. The authors stated that simple, universal species-specific statements regarding the biology of these vectors are nearly impossible due to the behavioral plasticity of most species, in some cases sympatric distributions of sibling species, changing taxonomic categorization and the influence of

of studies on phytoecological relationships of vectors to aquatic habitats.

environmental disturbance, all contributing to a high level of complexity.

abundance.

#### **2.1. Dichotomy between medical entomologists and ecologists in larval studies**

There has been quite a deep divide between medical entomologists and ecologist in their approach to studying mosquito larval habitats [22, 77]. Medical entomologists generally study larval habitats with the focus on design of efficient control interventions and often don't realize that it is the ecological approach to studying larval habitats in the context of other ecosystem components that can eventually lead to a thorough understanding of the larvae – habitat relationships. A relatively small number of researchers realize that filling the gap between ecologically based and epidemiologically based information is a necessity [77]. As Chase and Knight [78] put it: because larval mosquitoes are components of a much larger metacommunity of interacting species, the interplay between biotic interactions (competitors and predators) and abiotic constraints (temperature, habitat drying) is essential for understanding the controls on mosquito abundance. By placing mosquitoes into a broader community context, a much better predictive framework can be developed for understanding and predicting year-to-year variation in mosquito abundances [79, 80]. Ecology should—like other basic disciplines such as molecular biology and bioinformatics—be considered an enabling science essential for defining the target product profiles of completely new control technologies and delivery systems [22].

## **3. Environmental determinants of larval habitats**

Larval habitats or breeding sites - places where eggs are laid, larvae hatch, change instars, pupate, and adults emerge - are primary drivers of adult distribution, abundance and fitness [5, 9, 10, 81]. They are always composed of water bodies, natural or man-made, permanent or temporary, large or small, freshwater or saline. The mosquito reproduction is successful only if larval habitats remain stable for a duration equivalent to the development of immature stages [82]. The great diversity of habitats, often combined with inaccessibility, makes studies of the ecology of larval anopheline mosquitoes methodologically quite difficult [9].

Larval densities are controlled by interactions between abiotic (hydrology, temperature, light/ shade, pH, salinity, nutrient availability) and biotic (predation, competition) factors [78, 83-85]. For comprehensive analyses of patterns in the productivity of larval habitats the studies should incorporate a landscape context, because presence and abundance of mosquito larvae in aquatic habitats and consequently the number of adults capable of malaria transmission are regulated by a variety of ecosystem processes operating and interacting at several organiza‐ tional levels and spatial/temporal scales [86]. The conceptual scheme in Figure 1 summarizes the main factors and processes important for good understanding of interactions between larvae and their habitat characteristics in the larger ecosystem context. Humans can affect habitat availability and quality through ecosystem and landscape changes such deforestation/ reforestation, desertification, irrigation and other hydrological changes, and agricultural practices (see further). In the following text we will focus on the main determinants of larval development.

**Figure 1.** Relationships between larval development and environmental factors on both habitat and ecosystem level. The relationships reviewed in the chapter are indicated in red.

#### **3.1. Temperature**

temporary, large or small, freshwater or saline. The mosquito reproduction is successful only if larval habitats remain stable for a duration equivalent to the development of immature stages [82]. The great diversity of habitats, often combined with inaccessibility, makes studies of the

Larval densities are controlled by interactions between abiotic (hydrology, temperature, light/ shade, pH, salinity, nutrient availability) and biotic (predation, competition) factors [78, 83-85]. For comprehensive analyses of patterns in the productivity of larval habitats the studies should incorporate a landscape context, because presence and abundance of mosquito larvae in aquatic habitats and consequently the number of adults capable of malaria transmission are regulated by a variety of ecosystem processes operating and interacting at several organiza‐ tional levels and spatial/temporal scales [86]. The conceptual scheme in Figure 1 summarizes the main factors and processes important for good understanding of interactions between larvae and their habitat characteristics in the larger ecosystem context. Humans can affect habitat availability and quality through ecosystem and landscape changes such deforestation/ reforestation, desertification, irrigation and other hydrological changes, and agricultural practices (see further). In the following text we will focus on the main determinants of larval

**Figure 1.** Relationships between larval development and environmental factors on both habitat and ecosystem level.

The relationships reviewed in the chapter are indicated in red.

ecology of larval anopheline mosquitoes methodologically quite difficult [9].

402 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

development.

Temperature affects all the important processes such as the rate of larval development and survivorship, pupation rates, larval-to-adult survivorship and larval-to adult development time [81, 87-89]. Water temperature is influenced by various parameters, such as local climate, water depth and movement, habitat size and geometry, land cover type or canopy overgrowth, presence of vegetation and/or algae, soil properties and turbidity [81]. Despite its importance, there are not many detailed outdoor studies on the temperature of larval habitats and the available data are hard to compare due to different methods of temperature measurement (air temperature vs. water temperature; data loggers vs. hand-held thermometers). Available data on *An. gambiae* point to a consensus that one of the main reasons for higher productivity of *An. gambiae* and *An. funestus* in habitats associated with agricultural crops or swamp margins is higher temperature as compared to shaded dense papyrus swamps [72, 90, 91]. Additional proof comes from Wamae et al [88] who compared *An. gambiae* densities in shaded (by napier grass, *Pennisetum purpureum*) and unshaded water channels in reclaimed sites in Western Kenya highlands. In these studies, the shading reduced anopheline larvae by > 75%, apparently due to ~ 3 degrees C reduced water temperature. High water temperature pools (30-33 degrees C) were reported as the most productive habitats for *An. gambiae* in Gambia [92]. In South America, Marten et al [93] found the majority of *An. albimanus* larvae on the coastal plain of Colombia associated with sun-exposed sites with a mid-day temperature range of 27.5 - 30.0° C. Pinault and Hunter [94] report minimum water temperatures that might limit the upper altitudinal distribution of *An. albimanus* (18.7° C) and *An. pseudopunctipennis* (16.0° C). Larvae are not generally able to survive temperatures over 40 degrees C as documented by Muirhead-Thomson [29] for *An. minimus*, (but see *An. bwambae*in hot springs, [95]). Recent detailed study on the longevity and mortality of *An. gambiae* under a wide range of temperatures [87] concluded that under extremely cold (10–12o C) or hot (38–40o C) temperatures all larvae died within a few days. While the low temperature range is rarely experienced in larval habitats of *An. gambiae*, the higher temperatures are frequently encountered in most tropical regions. In nature, however, such high temperatures occur for no more than a few hours and larvae may survive these short periods.

Paaijmans et al [81, 96] stressed the importance of temperature fluctuations for larval devel‐ opment. The authors provided a conceptual model of radiation and energy fluxes at the air– water and soil–water interfaces of small, shallow and clear water pools and did filed meas‐ urements comparing smaller and larger water bodies [81]. In general, the small-sized water pool reacted more dynamically to suddenly changing meteorological variables and experi‐ enced larger fluctuations. Several important conclusions follow from these experiments: The top layer (upper 2 mm) of each water pool differed in temperature from the layers underneath, which has important consequences for larval dynamics as anopheline larvae generally live horizontally near the air–water interface of aquatic habitats [66]. There can be large differences (> 10 degrees C) between air and water temperature. Larger pools had larger buffering capacity. Mosquito immatures can be exposed to a wide temperature range under natural conditions and they are apparently evolutionarily adapted to their direct environment. The observed differences between air and water temperature have important consequences and should be carefully employed for ecological models that use the air temperature as an input parameter for larval development.

#### **3.2. Light**

There are species occurring mostly in sun-exposed environments such as *An. gambiae* s.s., *An. albimanus, An. pseudopunctipennis*, members of the *An. sundaicus* complex*, An. sinensis, An. aconitus* etc., while others seem to prefer shaded water bodies (*An. funestus*, *An. vestitipennis*). The question of whether sun or shade has a direct effect on the development of larvae or impacts them indirectly through the effect of temperature on food source development has not been answered, although some laboratory experiments seem to show that light is not an important direct factor [83, 97]. It is possible that in some instances, larvae are positively correlated with shaded environment only because shade of trees reduces drying speed of the pools [98]. Little is known about the effects of darkness on larval development in *Anopheles* species. It has been shown, however, that light deprivation causes a significant reduction in the development of adult *An. stephensi* when larvae were bred in the absence of light [33]. In the dark treatment group, only about 60% of pupae transformed into adults.

#### **3.3. Salinity**

There are large differences in the tolerance of anopheline larvae to water salinity. While the majority of anopheline larvae are found in fresh waters, there are several species that show high salinity tolerance and are associated with coastal malaria transmission. *Anopheles melas* and *An. merus* within the *An. gambiae* complex are examples from Africa [61]. *Anopheles farauti* s.s. and *An. irenicus* (formerly designated *An. farauti* No. 7) in the Farauti Complex are reported to be salinity-tolerant in Australasia [63, 99]. Malaria vectors of the *An. sundaicus* complex in Southeast Asia are well known brackish water breeders [100, 101]. On the American continent an example of salt tolerant species is *An. aquasalis* [48, 102].

A major challenge faced by all mosquito larvae is the tendency for larval habitats to fluctuate widely in salinity due to changes in rainfall and evaporation [13]. Organisms living in brackish and saline environments have evolved various mechanisms of coping with increased salinity, and in order to survive in these conditions, they have to be able to regulate their osmotic potential. Larvae of salinity tolerant mosquito possess cuticles that are less permeable to water than freshwater forms, and their pupae have thickened and sclerotized cuticles that are impermeable to water and ions. Larval survival depends upon the ability to regulate hemolymph osmolarity by absorbing and excreting ions [103]. Osmoregulatory mechanisms vary among various mosquito genera, for example *An. albimanus* larvae osmoregulate through rectal ion excretion and the larvae undergo a dramatic shift in rectal Na+/K+-ATPase (an enzyme important for ion regulation) localiza‐ tion when reared in freshwater *vs.* saline water [103].

Saltwater tolerance is a trait that involves ionic regulation at the aquatic larval stage, and it appears to have been a factor in the adaptive radiation of the *A. gambiae* complex into diverse larval habitats. A mechanistic understanding of the physiology and genetics of ion regulation is important because it can open up new classes of larvicide [104]. Additionally, increasing amounts of saltwater pools and puddles associated with natural disasters (tsunami), land subsidence, or sea level rise would facilitate increased breeding of brackish water malaria vectors (e.g., *An. sundaicus*) and may increase the risk of malaria outbreaks [105, 106].

#### **3.4. Hydrology and geomorphology**

Hydrology of a region, i.e., distribution and seasonal dynamics of lotic and lentic water bodies is determined by the geomorphology and precipitation patterns [107, 108]. Water quality in these different water bodies is influenced by rock and soil chemistry, vegetation of the surrounding landscape, and human activities. Both hydrology and water chemistry determine the type of aquatic vegetation present in lakes, pools, and streams [42]. Geomorphological parameters such as elevation, slope, aspect, and ruggedness play an important role in malaria transmission as exemplified, e.g., by Atieli [108] who found broad flat-bottomed valleys in Kenya Highlands to have a significantly higher number of *Anopheles*larvae/dip in their habitats than the narrow valleys. Heavy rains in the tropics can be detrimental to larval survival. In particular, rainstorms are known to flush mosquito larvae from their breeding sites [109, 110] – but see Manguin et al. [47] who reported survival of 3rd and 4th instar larvae in clumps of detritus that was stranded in trees and shrubs in the wake of the flood.

#### **3.5. Vegetation**

should be carefully employed for ecological models that use the air temperature as an input

There are species occurring mostly in sun-exposed environments such as *An. gambiae* s.s., *An. albimanus, An. pseudopunctipennis*, members of the *An. sundaicus* complex*, An. sinensis, An. aconitus* etc., while others seem to prefer shaded water bodies (*An. funestus*, *An. vestitipennis*). The question of whether sun or shade has a direct effect on the development of larvae or impacts them indirectly through the effect of temperature on food source development has not been answered, although some laboratory experiments seem to show that light is not an important direct factor [83, 97]. It is possible that in some instances, larvae are positively correlated with shaded environment only because shade of trees reduces drying speed of the pools [98]. Little is known about the effects of darkness on larval development in *Anopheles* species. It has been shown, however, that light deprivation causes a significant reduction in the development of adult *An. stephensi* when larvae were bred in the absence of light [33]. In

There are large differences in the tolerance of anopheline larvae to water salinity. While the majority of anopheline larvae are found in fresh waters, there are several species that show high salinity tolerance and are associated with coastal malaria transmission. *Anopheles melas* and *An. merus* within the *An. gambiae* complex are examples from Africa [61]. *Anopheles farauti* s.s. and *An. irenicus* (formerly designated *An. farauti* No. 7) in the Farauti Complex are reported to be salinity-tolerant in Australasia [63, 99]. Malaria vectors of the *An. sundaicus* complex in Southeast Asia are well known brackish water breeders [100, 101]. On the American

A major challenge faced by all mosquito larvae is the tendency for larval habitats to fluctuate widely in salinity due to changes in rainfall and evaporation [13]. Organisms living in brackish and saline environments have evolved various mechanisms of coping with increased salinity, and in order to survive in these conditions, they have to be able to regulate their osmotic potential. Larvae of salinity tolerant mosquito possess cuticles that are less permeable to water than freshwater forms, and their pupae have thickened and sclerotized cuticles that are impermeable to water and ions. Larval survival depends upon the ability to regulate hemolymph osmolarity by absorbing and excreting ions [103]. Osmoregulatory mechanisms vary among various mosquito genera, for example *An. albimanus* larvae osmoregulate through rectal ion excretion and the larvae undergo a dramatic shift in rectal Na+/K+-ATPase (an enzyme important for ion regulation) localiza‐

Saltwater tolerance is a trait that involves ionic regulation at the aquatic larval stage, and it appears to have been a factor in the adaptive radiation of the *A. gambiae* complex into diverse larval habitats. A mechanistic understanding of the physiology and genetics of ion regulation

the dark treatment group, only about 60% of pupae transformed into adults.

continent an example of salt tolerant species is *An. aquasalis* [48, 102].

tion when reared in freshwater *vs.* saline water [103].

parameter for larval development.

404 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**3.2. Light**

**3.3. Salinity**

Many shallow water bodies are dominated by aquatic plants – both microphytes (algae and cyanobacteria) and macrophytes.

Aquatic macrophytes, often also called hydrophytes, are key components of aquatic and wetland ecosystems. As primary producers, they are at the base of herbivorous and detritiv‐ orous food chains, providing food to invertebrates, fish and birds, and organic carbon for bacteria. Their stems, roots and leaves serve as a substrate for periphyton, and a shelter for numerous invertebrates and different stages of fish, amphibians and reptiles [66, 111]. Biogeochemical processes in the water column and sediments are to a large extent influenced by the presence/absence and type of macrophyte, and macrophytes can also have a profound impact on water movement and sediment dynamics in water bodies [112].

Phytoecological relationships of many species are strong enough to indicate presence or absence of mosquitoes according to presence or absence of associated plants [44]. The effect of aquatic plants on mosquito oviposition and larval survival and development, particularly among the anophelines, has been recognized since the early 1930's [38, 39, 66, 113-115]. Many aquatic plants provide food and protection for mosquito larvae and create favorable conditions for oviposition. Of special importance is the interface of air-plant-water, which has been termed the intersection line [38]. The intersection line is important to anopheline larvae because it is where the larvae find food and shelter and adults find the water surface broken up into numerous quiet cells favorable for ovipositing [19, 66]. A number of studies have documented a positive correlation between larval density and amount of plant cover or intersection line, e.g., [38, 115-118]. Plants provide favorable conditions for anopheline production if they continuously intersect the water surface during the mosquito breeding season. Collins and Resh [118] present a table showing the evaluation of common wetland plants for habitat suitability including the intersection line value.

Aquatic macrophytes are extremely diverse taxonomically, morphologically and functionally. Thus it is not surprising that different groups of macrophytes provide suitable habitats for different mosquito species (Figure 2). Of the four major macrophyte categories, i.e., freely floating, emergent, submerged, and floating-leaved [112], emergents generally provide the largest number of intersection lines. The positive benefits associated with aquatic macrophyte cover, and dense patches of emergent plants in particular, should result in a strong selective advantage (i.e., increased fitness) to individuals that choose high density macrophyte patches as habitat [66]. Selective pressure for such habitat preferences should operate on both larval and adult stages of *Anopheles* and the strong preferences of larvae and ovipositing adults for higher density patches of *Myriophyllum* were indeed observed by Orr and Resh [66].

While the majority of anopheline species are rather generalists and not very selective for a particular type of vegetation, there are others with tighter phytoecological associations. *Anopheles gambiae* is an example of a generalist whose larval habitats are shallow temporary water bodies with algae or short grasses but also devoid of any vegetation [61], see Figure 2H and papers of Mutuku et al. [119] and Ndenga et al. [89] for illustrations. Among examples of an extremely close association are the larval habitats of *An. pseudopunctipennis*, which are typically sun-exposed streams with abundant filamentous algae [42, 94, 120-124], see Figure 2E. The selection of filamentous algae by *An. pseudopunctipennis* has been confirmed by oviposition experiments [125, 126]. Similarly, the presence and abundance of *An. farauti* larvae was positively associated with filamentous algae in Solomon Islands [99]. Another species whose habitat can be clearly defined by vegetation presence is *An. vestitipennis*. Numerous reports confirm its association with tall dense macrophytes and/or flooded swamp forest [127-130] see Figure 2A. It is perhaps the preference of *An. vestitipennis* for a shaded environ‐ ment generally that results in it being associated with these two types of habitats [129]. Preferred habitats for *An. darlingi* are patches of detritus often accumulated behind a fallen stump, or vegetation at the shady edges in slowly running streams and rivers [26, 42, 47, 73, 82] see Figure 2F. Barros et al [82] call these habitats "microdams" and they found the presence of microdams to be the most important parameter determining spatial distribution of *An. darlingi* larvae in northern Brazilian Amazon. Achee et al [73] experimentally evaluated the importance of floating detritus patches and overhanging bamboo for *An. darlingi* habitat selection using floating screened enclosures placed in a river at a location with documented presence of both larval and adult *An. darlingi* populations. The detritus treatment had a significantly higher average count of *An. darlingi* larvae documenting that females preferen‐ tially oviposited in this habitat.

Even with these tight associations, there are often exceptions, e.g., *An. pseudopunctipennis*found in tall dense macrophytes (*Schoenoplectus californicus*) in the coastal zones of Peru (DR, ER unpublished data), or even without vegetation [124], but these snapshot observations on larval presence don't really provide information about survival and adult fitness.

**Figure 2.** Examples of various larval habitat types as defined by vegetation. A: Freshwater marsh with tall dense mac‐ rophyte, *Typha domingensis*, a typical habitat for *Anopheles vestitipennis*; B: River edge vegetation dominated by a dense submersed macrophyte *Cabomba aquatica*, a potential habitat of *An. darlingi*; C: Marsh dominated by floatingleaved macrophyte, *Nymphaea ampla*, an example of an environment where larvae are typically not found; D: Marsh with sparse emergent macrophyte, *Eleocharis cellulosa*, interspersed with floating mats of cyanobacteria, a typical habitat of *An. albimanus*; E: A stream with filamentous green algae, a typical habitat for *An. pseudopunctipennis*; F: Detritus in a protected riverine environment, a typical habitat of *An. darlingi*.G: Small, partially shaded stream with vegetated margins, a tyical habitat for *An. minimus*; H: *An. gambiae* habitat from Equatorial Guinea (Malabo region); I: Stagnant pool of water with floating mats of algae, a habitat of *An. epiroticus* (Sundaicus complex) from southern Vietnam. Note the different scale bars.(Photo G & I courtesy of Sylvie Manguin; photo H courtesy of Pierre Carnevale).

#### **3.6. Rice fields**

continuously intersect the water surface during the mosquito breeding season. Collins and Resh [118] present a table showing the evaluation of common wetland plants for habitat

Aquatic macrophytes are extremely diverse taxonomically, morphologically and functionally. Thus it is not surprising that different groups of macrophytes provide suitable habitats for different mosquito species (Figure 2). Of the four major macrophyte categories, i.e., freely floating, emergent, submerged, and floating-leaved [112], emergents generally provide the largest number of intersection lines. The positive benefits associated with aquatic macrophyte cover, and dense patches of emergent plants in particular, should result in a strong selective advantage (i.e., increased fitness) to individuals that choose high density macrophyte patches as habitat [66]. Selective pressure for such habitat preferences should operate on both larval and adult stages of *Anopheles* and the strong preferences of larvae and ovipositing adults for

higher density patches of *Myriophyllum* were indeed observed by Orr and Resh [66].

While the majority of anopheline species are rather generalists and not very selective for a particular type of vegetation, there are others with tighter phytoecological associations. *Anopheles gambiae* is an example of a generalist whose larval habitats are shallow temporary water bodies with algae or short grasses but also devoid of any vegetation [61], see Figure 2H and papers of Mutuku et al. [119] and Ndenga et al. [89] for illustrations. Among examples of an extremely close association are the larval habitats of *An. pseudopunctipennis*, which are typically sun-exposed streams with abundant filamentous algae [42, 94, 120-124], see Figure 2E. The selection of filamentous algae by *An. pseudopunctipennis* has been confirmed by oviposition experiments [125, 126]. Similarly, the presence and abundance of *An. farauti* larvae was positively associated with filamentous algae in Solomon Islands [99]. Another species whose habitat can be clearly defined by vegetation presence is *An. vestitipennis*. Numerous reports confirm its association with tall dense macrophytes and/or flooded swamp forest [127-130] see Figure 2A. It is perhaps the preference of *An. vestitipennis* for a shaded environ‐ ment generally that results in it being associated with these two types of habitats [129]. Preferred habitats for *An. darlingi* are patches of detritus often accumulated behind a fallen stump, or vegetation at the shady edges in slowly running streams and rivers [26, 42, 47, 73, 82] see Figure 2F. Barros et al [82] call these habitats "microdams" and they found the presence of microdams to be the most important parameter determining spatial distribution of *An. darlingi* larvae in northern Brazilian Amazon. Achee et al [73] experimentally evaluated the importance of floating detritus patches and overhanging bamboo for *An. darlingi* habitat selection using floating screened enclosures placed in a river at a location with documented presence of both larval and adult *An. darlingi* populations. The detritus treatment had a significantly higher average count of *An. darlingi* larvae documenting that females preferen‐

Even with these tight associations, there are often exceptions, e.g., *An. pseudopunctipennis*found in tall dense macrophytes (*Schoenoplectus californicus*) in the coastal zones of Peru (DR, ER unpublished data), or even without vegetation [124], but these snapshot observations on larval

presence don't really provide information about survival and adult fitness.

suitability including the intersection line value.

406 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

tially oviposited in this habitat.

Considering the large extent of rice fields in the areas with endemic malaria, they deserve their own subchapter. The changing crop practices, such as the shift to irrigated wetland rice affect *Anopheles* vector populations, increasing the extent of larval habitats and transmission of malaria [131]. Irrigated rice cultivation extends the time in which vectors breed and in countries with two crops of rice per year, anopheline breeding and biting rates extend well beyond their usual seasons [131, 132].

The aquatic community in rice fields is a dynamic system related closely to rice plant growth, rice cultivation practices, and seasonal climatic changes [133-135]. Each mosquito species often has a preference for a particular phase in rice field development, which may result in an orderly succession of species as the rice plants develop and mature [136]. The pioneer colonizers are typically sun-preferring species, such as *An. gambiae* (Africa) *An. albimanus* (Central America), and *An. fluviatilis* and *An. culicifacies* (Oriental region); but when the rice grows taller it shades the water and shade-preferring species, such as *An. funestus* (Africa), *An. umbrosus* (India), *An. hyrcanus* group (Asia), *An. leucosphyrus* complex (Malaysia), *An. freeborni* (North America), *An. punctimacula* (South America) usually become more abundant [131, 136]. The abundance of aquatic macroinvertebrates, including predators, also changes during the growth of a single rice crop [76, 135, 137]. Compared to Asia and Africa there is less documentation of linkages between rice cultivation and disease in Latin America, although in parts of Mexico and Venezuela rice appears to be associated with seasonal increases in malaria incidence [138].

#### **3.7. Food sources**

Aquatic plants (both micro- and macrophytes) provide protection from predators and, together with trees and shrubs, contribute detritus that supports the bacterial community, which, in turn, serves as food for larvae [139]. An understanding of the spatial and temporal distribution of the dietary resources available to larval mosquitoes in their natural habitats could clarify the relationships among food availability, vector competence, and mosquito fitness [19, 140, 141]. Yet, the quantity and quality of food sources available to larvae is often ignored in the study of larval growth and development [9]. Natural food assemblages of larval mosquitoes are extremely diverse biochemically [142]. Generally, bacteria have been consid‐ ered the most important of the microorganisms that comprise the food of mosquito larvae [19, 24], and mosquito growth can occur on cultures of bacteria alone [19]. In the water column of aquatic ecosystems, bacteria are the major decomposers of organic matter and the presence of particulate heterotrophic bacterial biomass represents an important link between detritus, dissolved organic matter, and higher trophic levels [143]. This bacterial production is control‐ led by or directly related to the supply of decomposable organic material. Thus, larval habitats with ample supplies of autochthonous and/or allochthonous detritus are capable of providing sufficient supplies of larval food resources. Experiments with diets also demonstrated that mosquito larvae can develop solely by drinking dissolved nutrients [19]. Larval food sources are not distributed homogeneously throughout the water column. The surface microlayer contains relatively high amounts of nutrients, organic material both particulate and dissolved, and various microorganisms as compared to subsurface water [144]. Anopheline larvae are well suited to utilize food sources from the enriched surface layer as they typically feed at the surface of the water where they engage in interfacial feeding behavior [13, 144].

Microalgae and/or small cyanobacteria can also serve as an important food source [19, 53, 93, 145]. Gimnig's et al [10] study demonstrated that larval grazing reduced algal abundances and biomass by an order of magnitude, and changed microeukaryote community structure. Changes in this algal food resource due to larval consumption almost certainly led to the observed density-dependent responses in larval development. Kaufman et al [145] conducted experiments to investigate the importance of algal food resources for larval growth and adult emergence of *An. gambiae* in simulated larval habitats in Kenya. Their results confirmed the importance of algal biomass in the surface microlayers of larval habitats to larval development and production of *An*. *gambiae* adults. They also showed that soil quality in these ephemeral larval habitats is important as the growth of algae depends on nutrient availability, particularly phosphorus (P). Thus soils releasing more P after flooding would support more algae that can feed more larvae.

While some microalgae are an important food source, other algae can be harmful to anopheline larvae. Marten's [146] review concludes that many species of green algae in the order Chlor‐ ococcales are resistant to digestion by mosquito larvae. Larvae are unable to complete their development if indigestible algae are numerous enough in the aquatic habitat to prevent the larvae ingesting enough other food to satisfy their nutritional needs. In addition, cyanobacteria (blue-green algae) can potentially kill larvae by toxins they produce [53].

#### **3.8. Essential fatty acids**

*Anopheles* vector populations, increasing the extent of larval habitats and transmission of malaria [131]. Irrigated rice cultivation extends the time in which vectors breed and in countries with two crops of rice per year, anopheline breeding and biting rates extend well beyond their

The aquatic community in rice fields is a dynamic system related closely to rice plant growth, rice cultivation practices, and seasonal climatic changes [133-135]. Each mosquito species often has a preference for a particular phase in rice field development, which may result in an orderly succession of species as the rice plants develop and mature [136]. The pioneer colonizers are typically sun-preferring species, such as *An. gambiae* (Africa) *An. albimanus* (Central America), and *An. fluviatilis* and *An. culicifacies* (Oriental region); but when the rice grows taller it shades the water and shade-preferring species, such as *An. funestus* (Africa), *An. umbrosus* (India), *An. hyrcanus* group (Asia), *An. leucosphyrus* complex (Malaysia), *An. freeborni* (North America), *An. punctimacula* (South America) usually become more abundant [131, 136]. The abundance of aquatic macroinvertebrates, including predators, also changes during the growth of a single rice crop [76, 135, 137]. Compared to Asia and Africa there is less documentation of linkages between rice cultivation and disease in Latin America, although in parts of Mexico and Venezuela rice appears to be associated with seasonal increases in malaria incidence [138].

Aquatic plants (both micro- and macrophytes) provide protection from predators and, together with trees and shrubs, contribute detritus that supports the bacterial community, which, in turn, serves as food for larvae [139]. An understanding of the spatial and temporal distribution of the dietary resources available to larval mosquitoes in their natural habitats could clarify the relationships among food availability, vector competence, and mosquito fitness [19, 140, 141]. Yet, the quantity and quality of food sources available to larvae is often ignored in the study of larval growth and development [9]. Natural food assemblages of larval mosquitoes are extremely diverse biochemically [142]. Generally, bacteria have been consid‐ ered the most important of the microorganisms that comprise the food of mosquito larvae [19, 24], and mosquito growth can occur on cultures of bacteria alone [19]. In the water column of aquatic ecosystems, bacteria are the major decomposers of organic matter and the presence of particulate heterotrophic bacterial biomass represents an important link between detritus, dissolved organic matter, and higher trophic levels [143]. This bacterial production is control‐ led by or directly related to the supply of decomposable organic material. Thus, larval habitats with ample supplies of autochthonous and/or allochthonous detritus are capable of providing sufficient supplies of larval food resources. Experiments with diets also demonstrated that mosquito larvae can develop solely by drinking dissolved nutrients [19]. Larval food sources are not distributed homogeneously throughout the water column. The surface microlayer contains relatively high amounts of nutrients, organic material both particulate and dissolved, and various microorganisms as compared to subsurface water [144]. Anopheline larvae are well suited to utilize food sources from the enriched surface layer as they typically feed at the

surface of the water where they engage in interfacial feeding behavior [13, 144].

usual seasons [131, 132].

408 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**3.7. Food sources**

Lipids are animportantfoodcomponentformosquitolarvaebecause theyprovide a concentrat‐ ed form of energy storage and a source of essential biochemical nutrients. Fatty acid (FA) constituents of lipids are present in a great structural variety, and are increasingly being used as chemical markers of biogeochemical processes and trophic relationships [147]. While the saturated palmitic acid (16:0) is often one of the most abundant fatty acids in lipid extracts, the interest of nutritional studies has concentrated on polyunsaturated fatty acids (PUFA) with two or more double bonds [148]. Some of these PUFAs are essential to the normal function of cells and they or their corresponding precursors have to be obtained in animal diets. In most ani‐ mals, the 18-carbon chain, 18C, PUFAs can be converted to the longer-chain essential PUFAs, specifically arachidonic acid, ARA, eicosapentaenoic acid, EPA, and docosahexaenoic acid, DHA. Mosquitoes seem to be an exception because their dietary FA requirements cannot be satisfied by the C-18 PUFAs [149, 150]. They require some 20- and 22-C polyunsaturated fatty acids, EPA, ARA and DHA and without an adequate supply of these PUFAs they are not able to fly [149, 150]. Adult females may get these from a blood meal [151] but these PUFAs are be‐ lievedessentialinthelarvalstageforflightmuscledevelopment.Theunderstandingofthespatial andtemporaldistributionofdietaryresources available tomosquitolarvae isneededinorderto clarify the relationship among food availability, vector competence, and mosquito fitness. Not only does the nutrient availability within the habitat have to meet a minimum dietary require‐ ment for proper larval development, but the food consumed in the larval stage is critical for a number of physiological processes that impact adult performance [152].

Kominkova et al. [153], in order to reveal the importance of feeding habitats for the nutrition of anophelinelarvae,analyzedtheFAcompositionoflarvaeofthreemalariatransmittingmosquito

species *An. albimanus, An. vestitipennis* and *An. darlingi* and their corresponding habitats. They found that habitats were generally low in essential PUFAs and there were no significant differences among the FA composition of habitat samples. However, there were significant differences in FA composition of larvae. *Anopheles darlingi* contained significantly higher amounts of FA, specifically the linoleic acid. Large differences in PUFA content were found between field collected and laboratory-reared *An. vestitipennis* larvae, however, there were no differences in the total dry weight of the 4th stage larvae between the wild vs. laboratory-reared populations. Total FA in both larvae and samples of habitats of *An. albimanus* and *An. darlingi* were positively correlated with the concentration of particulate organic carbon and nitrogen (POC, PON) in their respective habitats, but no such correlation was found for *An. vestitipennis*. This study revealed that PUFA are a good indicator of nutritional quality although factors controllingthesuccessofanophelinedevelopmentinlarvalhabitatsarelikelytobemorecomplex and include, among others, the presence of predators, pathogens and toxins.

#### **3.9. Species interactions (predation and competition)**

Understanding species interactions such as competition and predation, across environmental gradients provides insight into how assemblages of mosquitoes are structured. This informa‐ tion is then critical for proper application of biological control [154]. The topic of competition and predation is a good example of the dichotomy in the approach to studying larval stages of mosquitoes. Many papers focus on use of predators for larval control [155-157]. There is a lack of studies focusing on larval competition and predation in the ecological context such as habitat size and temporal stability. But it is what influences the prevalence, pattern, and effects of species interactions across freshwater communities [158-160]. Spatial variation in biotic interactions can explain spatial variation in larval mosquito densities and ultimately the abundance of adult mosquitoes [78, 158]. Studies on predators of mosquito larvae go way back into history. Hinman [161] in his summary of predators on mosquito larvae lists over 100 references. Competition on the other hand is less studied even though interspecific competition for limited resources can be quite important and has been shown to have large effects on mosquito larvae. Mosquitoes compete with tadpoles [162, 163], other species of mosquitoes [164] and cladocerans [165].

Relative impacts of competition and predation change across a gradient of habitat size and permanence [159]. Bodies of water that may serve as larval habitats form a gradient from small and highly ephemeral to large and permanent. At the small, ephemeral end of this gradient, large long-lived predatory organisms (namely fish) are often absent, and aquatic organisms need to develop quickly. These conditions favor rapid growth and development, active foraging, movement, and competitive ability. As water bodies become larger and temporally more stable they can support more diverse community of larger, longer-lived predators. This increase of diversity, number, and voracity of predators favors refuge use, inconspicuousness, predator deterrence, and slow growth and development [159]. Organization of mosquito communities can be viewed in the same way. Interspecific competition among mosquitoes can be more important as a determinant of community structure in small ephemeral habitats, whereas predation can be more important in large permanent habitats [159]. Limited evidence suggests interspecific competition and cannibalism among mosquitoes is common in small pools [70], but comprehensive review of the ecology of competitive interactions of mosquitoes is lacking.

species *An. albimanus, An. vestitipennis* and *An. darlingi* and their corresponding habitats. They found that habitats were generally low in essential PUFAs and there were no significant differences among the FA composition of habitat samples. However, there were significant differences in FA composition of larvae. *Anopheles darlingi* contained significantly higher amounts of FA, specifically the linoleic acid. Large differences in PUFA content were found between field collected and laboratory-reared *An. vestitipennis* larvae, however, there were no differences in the total dry weight of the 4th stage larvae between the wild vs. laboratory-reared populations. Total FA in both larvae and samples of habitats of *An. albimanus* and *An. darlingi* were positively correlated with the concentration of particulate organic carbon and nitrogen (POC, PON) in their respective habitats, but no such correlation was found for *An. vestitipennis*. This study revealed that PUFA are a good indicator of nutritional quality although factors controllingthesuccessofanophelinedevelopmentinlarvalhabitatsarelikelytobemorecomplex

Understanding species interactions such as competition and predation, across environmental gradients provides insight into how assemblages of mosquitoes are structured. This informa‐ tion is then critical for proper application of biological control [154]. The topic of competition and predation is a good example of the dichotomy in the approach to studying larval stages of mosquitoes. Many papers focus on use of predators for larval control [155-157]. There is a lack of studies focusing on larval competition and predation in the ecological context such as habitat size and temporal stability. But it is what influences the prevalence, pattern, and effects of species interactions across freshwater communities [158-160]. Spatial variation in biotic interactions can explain spatial variation in larval mosquito densities and ultimately the abundance of adult mosquitoes [78, 158]. Studies on predators of mosquito larvae go way back into history. Hinman [161] in his summary of predators on mosquito larvae lists over 100 references. Competition on the other hand is less studied even though interspecific competition for limited resources can be quite important and has been shown to have large effects on mosquito larvae. Mosquitoes compete with tadpoles [162, 163], other species of mosquitoes

Relative impacts of competition and predation change across a gradient of habitat size and permanence [159]. Bodies of water that may serve as larval habitats form a gradient from small and highly ephemeral to large and permanent. At the small, ephemeral end of this gradient, large long-lived predatory organisms (namely fish) are often absent, and aquatic organisms need to develop quickly. These conditions favor rapid growth and development, active foraging, movement, and competitive ability. As water bodies become larger and temporally more stable they can support more diverse community of larger, longer-lived predators. This increase of diversity, number, and voracity of predators favors refuge use, inconspicuousness, predator deterrence, and slow growth and development [159]. Organization of mosquito communities can be viewed in the same way. Interspecific competition among mosquitoes can be more important as a determinant of community structure in small ephemeral habitats, whereas predation can be more important in large permanent habitats [159]. Limited evidence

and include, among others, the presence of predators, pathogens and toxins.

**3.9. Species interactions (predation and competition)**

410 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

[164] and cladocerans [165].

Natural predators of mosquito larvae are quite diverse and include the tadpole stages of amphibians [166], planktivorous fishes [165] and aquatic insects (Coleoptera, adult Hetero‐ ptera and larval Odonata). There is a range of papers reviewing predators on mosquito larvae and their potential use in biological control. Kumar and Hwang [167] provided an excellent review of larvicidal efficiency of amphibian tadpoles, larvivorous fish, cyclopoid copepods and aquatic insects. Mogi [168] reviewed insects and invertebrate predation on different life stages of mosquito. Quiroz-Martinez and Rodriguez-Castro [169] summarized the information on arthropods (insects, mites and spiders) that prey on mosquito larvae and discussed the potential of these predators in mosquitoes' biological control programs. Shaalan and Canyon's [156] review covered the predation of different insect species on mosquito larvae, predator prey-habitat relationships, co-habitation developmental issues, survival and abundance, oviposition avoidance, predatorial capacity and integrated vector control. Rozendaal [170] and Chandra et al [171] reviewed information on different larvivorous fish species and the present status of their use in mosquito control.

Despite thorough reviews and much information on different types of predators, there is a paucity of well-designed experimental studies verifying the long term effect of predators on mosquito populations. Although predation has been suggested as one of the important regulation mechanisms for malaria vectors in long lasting aquatic habitats, the predatory efficiency of potential predators is largely unknown [22, 157]. Research on predation of mosquito larvae has relied partly on the identification of larvae in the predators' gut – serological methods [172, 173], partly on correlative field observations evaluating the abun‐ dance of larvae and predators in the habitats [52, 174], and partly on laboratory feeding studies [157]. However, many predators that have been shown to be highly successful in eliminating target prey in the laboratory do not show a similar response in their natural habitats [75, 155]. The most basic question is whether predators have an important impact on mosquito popu‐ lations in the field in the presence of alternative prey. Collins and Resh [118] listed the ecological factors affecting predation that should be considered when designing predation experiments: 1) dietary preference for mosquitoes, 2) abundance of alternative pray; 3) degree of congruity between habitats of the predator and target mosquito; 4) density of predators within habitat; 5) density of mosquito population; 6) quality of habitat as a refuge from predator. Among examples of well-designed experimental studies on multiple predator impacts we can cite Kumar et al [155] who compared the control potential of three larvivorous predators commonly co-occurring in the wetlands of tropical and subtropical regions, the mosquito fish *Gambusia affinis,* the cyclopoid copepod *Mesocyclops aspericornis*, and naiads of the dragonfly *Zyxomma petiolatum*, against the larvae of *An. stephensi* in the presence of alternative cladoceran prey. The presence of the alternative prey significantly reduced larval consumption by all three predators. Kumar et al [155] also discuss the issues related to using non-native mosquito fish considering its potential negative impacts on native assemblages and its lower selectivity for mosquito larvae.

Mosquito control using fish has focused on a limited number of species, primarily *Gambusia af*fi*nis* and *Poecilia reticulata* that have traditionally been used for controlling mosquito larvae [175, 176]. One of the most important concerns when introducing exotic fish for mosquito control is their impact on native species [177] and thus information on the predation role of native species is desirable. Louca et al [175] evaluated the role of larval predation by native fishes in Gambia River and they pointed out that the major impact on larvae was actually exerted by a detritivorous *Tilapia*, which is a prevailing species in the system that feeds on larvae only opportunistically in small aquatic habitats.

Blaustein [134] documented an unefficient control of anopheline larvae in the rice fields in California. He pointed out that contrary to what a good system should be composed of, i.e., a relatively permanent habitat, a specialist control agent and a relatively abundant pest species, the fish-mosquito-rice field system does not have any of these attributes. In addition, mosquito fish may have indirect positive effects on mosquito abundance; they also feed on invertebrates which are either natural predators (see [178]) or potential competitors of mosquito immatures [165]. Thus, this strategy attempts to control a relatively rare prey species with a generalist predator. The underlying mechanisms of predator-prey relationships need to be more clearly defined in order to use this biological control agent more effectively. There is a general need for field experiments on competition, predation, and mutualism, and on their context depend‐ ence across species and habitats [159].

Predation at larval stages can have important evolutionary consequences for mosquitoes [179]. For example, the predation of aquatic immature stages has been identified as a major evolution‐ ary force driving habitat segregation and niche partitioning in the malaria mosquito *An. gambiae* in humid savannahs of West Africa [160, 180]. These studies explored behavioral responses to thepresenceofapredatorinwildpopulationsoftheMandSmolecularformsthattypicallybreed in permanent (e.g., rice field paddies) and temporary (e.g., road ruts) water collections. The experiments showed that the M and S forms modify their behavior in the presence of a natural predator by becoming less active and positioning themselves at the wall of the container. These behavioral modifications suggest that mosquitoes are able to detect a predator's presence, through as yet unknown mechanisms which deserve further investigation.

#### **4. Habitat selection**

Habitat selection, defined as a process in which individuals preferentially choose and occupy a nonrandom set of available habitats, is of major importance for interpretation of spatial and temporal distributions of populations [139, 181]. The choice for suitable places for female mosquitoes to lay eggs is a key-factor for the survival of immature stages (eggs and larvae). Oviposition site selection has been recognized as critical both for the survival and population dynamics of mosquitoes. It is influenced by several environmental factors [182], including the salinity and turbidity of the water, the size and degree of permanence of the water body, the amount of sunlight, the presence of emergent/floating vegetation and shade, presence of predators, and distance to human habitation [8, 66]. In general, larvae of anopheline mosqui‐ toes prefer clean rather than polluted water [8, 183], although in urban areas in parts of Africa *An. gambiae* appears to be adapting to new habitats such as rubbish-filled pools, sometimes containing sewage [182, 184]. Larvae of several Asian species (*An. dirus, An. punctulatus, An. subpictus*) have been reported from muddy and/or polluted waters [63].

Mosquito control using fish has focused on a limited number of species, primarily *Gambusia*

Blaustein [134] documented an unefficient control of anopheline larvae in the rice fields in California. He pointed out that contrary to what a good system should be composed of, i.e., a relatively permanent habitat, a specialist control agent and a relatively abundant pest species, the fish-mosquito-rice field system does not have any of these attributes. In addition, mosquito fish may have indirect positive effects on mosquito abundance; they also feed on invertebrates which are either natural predators (see [178]) or potential competitors of mosquito immatures [165]. Thus, this strategy attempts to control a relatively rare prey species with a generalist predator. The underlying mechanisms of predator-prey relationships need to be more clearly defined in order to use this biological control agent more effectively. There is a general need for field experiments on competition, predation, and mutualism, and on their context depend‐

Predation at larval stages can have important evolutionary consequences for mosquitoes [179]. For example, the predation of aquatic immature stages has been identified as a major evolution‐ ary force driving habitat segregation and niche partitioning in the malaria mosquito *An. gambiae* in humid savannahs of West Africa [160, 180]. These studies explored behavioral responses to thepresenceofapredatorinwildpopulationsoftheMandSmolecularformsthattypicallybreed in permanent (e.g., rice field paddies) and temporary (e.g., road ruts) water collections. The experiments showed that the M and S forms modify their behavior in the presence of a natural predator by becoming less active and positioning themselves at the wall of the container. These behavioral modifications suggest that mosquitoes are able to detect a predator's presence,

Habitat selection, defined as a process in which individuals preferentially choose and occupy a nonrandom set of available habitats, is of major importance for interpretation of spatial and temporal distributions of populations [139, 181]. The choice for suitable places for female mosquitoes to lay eggs is a key-factor for the survival of immature stages (eggs and larvae). Oviposition site selection has been recognized as critical both for the survival and population dynamics of mosquitoes. It is influenced by several environmental factors [182], including the salinity and turbidity of the water, the size and degree of permanence of the water body, the amount of sunlight, the presence of emergent/floating vegetation and shade, presence of predators, and distance to human habitation [8, 66]. In general, larvae of anopheline mosqui‐ toes prefer clean rather than polluted water [8, 183], although in urban areas in parts of Africa *An. gambiae* appears to be adapting to new habitats such as rubbish-filled pools, sometimes

through as yet unknown mechanisms which deserve further investigation.

larvae only opportunistically in small aquatic habitats.

412 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

ence across species and habitats [159].

**4. Habitat selection**

*nis* and *Poecilia reticulata* that have traditionally been used for controlling mosquito larvae [175, 176]. One of the most important concerns when introducing exotic fish for mosquito control is their impact on native species [177] and thus information on the predation role of native species is desirable. Louca et al [175] evaluated the role of larval predation by native fishes in Gambia River and they pointed out that the major impact on larvae was actually exerted by a detritivorous *Tilapia*, which is a prevailing species in the system that feeds on

*af*fi

> In choosing sites for oviposition, females have to consider multiple—and possibly conflicting —factors to arrive at a site selection strategy that will optimize their reproductive success [185]. As many other oviparous species, mosquitoes also avoid oviposition in habitats with high risk of predation to their larvae [154, 186]. Females perceive these different characteristics of their habitats through a set of various cues both positive and negative. Among positive cues, volatile substances released from larval habitats have been implicated as potential olfactory cues mediating oviposition [54, 126, 139]. Experimental verification of dose response confirmed that low concentrations of volatile materials extracted from species-specific larval habitat materials increased oviposition, while there was a shift to reduced oviposition at high volatile concen‐ trations. Rejmankova et al [139] also confirmed through reciprocal treatment tests that volatile effect was strongly habitat/species-specific.

> Different mosquito species may rely on distinct chemical cues to avoid predators [187]. Mosquitoes that can detect aquatic predators often do so by sensing predator-released kairomones [187], see also review in Vonesh and Blaustein [188]. This was confirmed by preferential oviposition of *An. gambiae* in containers with clean water rather than water conditioned with predators (backswimmers, *Notonecta* sp. and tadpoles, *Xenopus* sp.) [72]. The experiment with Notonecta was later successfully repeated on other strains of *An. gambiae* by Warburg et al [187].

> After oviposition, the main factors determining larval survival are food availability and refuge from predators. Orr and Resh [66] documented microhabitat selection by larvae of *An. freeborni*. They found that larval distribution throughout the habitat (an emergent macrophyte, *Myriophyllum aquaticum*) was not random, but that the larvae tended to congregate in denser patches of macrophytes. Observational data confirmed an active mechanism of selection, i.e., larvae actively choose patches with higher plant densities.

> Larval habitats of the main malaria vectors in Belize are associated with three distinctly different aquatic environments: marshes with sparse macrophytes and cyanobacterial mats (*An. albimanus*), tall dense macrophyte marshes (*An. vestitipennis*), and floating detritus assemblages within freshwater rivers (*An. darlingi*). To assess species specific habitat suitabil‐ ity, we conducted mosquito transplant experiments [74]. First instar larvae of *An. albimanus, An*. *vestitipenis* and *An. darlingi* were placed in floating containers in the respective habitats of each species. Response of mosquito species to environmental conditions of its own and transplanted habitats clearly showed that each species was performing best in its own habitat. Survivorship of *An*. *vestitipenis* and *An. darlingi* in the *An. albimanus* habitat was extremely low or none.

## **5. Landscape context, remote sensing, GIS**

Larval habitats are not located in a vacuum, they are an integral part of a broader landscape and their environmental requirements should be studied in this context. The landscape level approach gained momentum when technologies such as remote sensing (RS) and GIS became widely used in 1990's [55, 57, 59, 60, 189-191] and it has continued improving with the progress in RS technology (see review in Machault and coauthors [192, 193]. Direct measurements of the Earth's hydrological and biophysical characteristics, its geological features and its climate from space have provided new data layers with spatial and temporal resolutions relevant to landscape-scale habitat characteristics and ecological processes [194, 195]. The landscape, vegetation, and ecosystem attributes derived from the applied remote sensing data contribute significantly to defining habitat characteristics and help discern patterns and gradients that may exist even within seemingly homogeneous environments.

The use of RS may involve various degree of complexity. The simplest case is when larval habitats are large enough to be directly identified within spatial resolution of remote sensors as, e.g., in Wood et al [55] study from irrigated rice in northern and central California. This study [55] provided a model of rice field mosquito population dynamics using spectral and spatial information. Analysis of field data revealed that rice fields with rapid early season vegetation canopy development, located near livestock pastures (i.e. bloodmeal sources), had greater mosquito larval populations than fields with more slowly developing vegetation canopies located further from pastures. Remote sensing reflectance measurements of early season rice canopy development and GIS measurements of distance to livestock pasture were combined to distinguish between high and low mosquito-producing rice fields. These distinctions were made with 90% accuracy nearly two months before anopheline larval populations peaked.

A more complex approach is needed in situations where larval habitats are spatially below the detection limit of RS data. As an example, a hierarchical approach was used to link larval habitat-types with larger land cover units in an integrated RS, GIS and field study in the Pacific coastal plain of Chiapas, Mexico [57]. Using this approach, villages with high *vs*. low risk for malaria transmission were identified and it was demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area [59, 60]. Similarly, RS generated maps of larval habitats in Madagascar rice fields and urban areas were used for predictions of adult densities and definitions of areas that may require indoor insecticide spraying [196, 197]. The landscape determinants of anopheline mosquito larval habitats in Kenya highlands and lowlands and their temporal changes were assessed by Mushinzimana et al. [198], Jacob et al [199], Munga et al [200], Mutuku et al [201], from elsewhere in Central and west Africa by Dambach [193] and Clennon et al [202], and from Malaysia by Ahmad et al [203]. The use of RS as a predictive tool to locate larval habitats has not always been successful as demonstrated by Achee et al [204]. Their results indicated that remotely sensed land cover is not a valuable indicator of the location in which *An. darlingi* larval habitats will form. High-resolution satellite imagery could be used to detect homes along river systems and potentially predict general areas at risk for *An. darlingi* breeding habitat formation based on distances from houses to waterways (Figure 3). The basic idea behind the remotely sensed assessment of larval habitats is to define environmental parameters that can be used to identify areas with increased risk of malaria transmission [193]. Yet, as already stated by

Roberts et al [205], the successful use of RS and GIS technologies to predict potential or actual malaria trouble spots is dependent on clear understandings of environmental factors that determine the presence of malaria vectors.

**Figure 3.** IKONOS 1m-resolution panchromatic image showing three houses (A-C) along a section of the Sibun River. Distance from the river to houses (black lines) was predictive for presence and abundance of *An. darlingi*, the primary malaria vector in Belize.

#### **5.1. Ecological niche models**

approach gained momentum when technologies such as remote sensing (RS) and GIS became widely used in 1990's [55, 57, 59, 60, 189-191] and it has continued improving with the progress in RS technology (see review in Machault and coauthors [192, 193]. Direct measurements of the Earth's hydrological and biophysical characteristics, its geological features and its climate from space have provided new data layers with spatial and temporal resolutions relevant to landscape-scale habitat characteristics and ecological processes [194, 195]. The landscape, vegetation, and ecosystem attributes derived from the applied remote sensing data contribute significantly to defining habitat characteristics and help discern patterns and gradients that

The use of RS may involve various degree of complexity. The simplest case is when larval habitats are large enough to be directly identified within spatial resolution of remote sensors as, e.g., in Wood et al [55] study from irrigated rice in northern and central California. This study [55] provided a model of rice field mosquito population dynamics using spectral and spatial information. Analysis of field data revealed that rice fields with rapid early season vegetation canopy development, located near livestock pastures (i.e. bloodmeal sources), had greater mosquito larval populations than fields with more slowly developing vegetation canopies located further from pastures. Remote sensing reflectance measurements of early season rice canopy development and GIS measurements of distance to livestock pasture were combined to distinguish between high and low mosquito-producing rice fields. These distinctions were made with 90% accuracy nearly two months before anopheline larval

A more complex approach is needed in situations where larval habitats are spatially below the detection limit of RS data. As an example, a hierarchical approach was used to link larval habitat-types with larger land cover units in an integrated RS, GIS and field study in the Pacific coastal plain of Chiapas, Mexico [57]. Using this approach, villages with high *vs*. low risk for malaria transmission were identified and it was demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area [59, 60]. Similarly, RS generated maps of larval habitats in Madagascar rice fields and urban areas were used for predictions of adult densities and definitions of areas that may require indoor insecticide spraying [196, 197]. The landscape determinants of anopheline mosquito larval habitats in Kenya highlands and lowlands and their temporal changes were assessed by Mushinzimana et al. [198], Jacob et al [199], Munga et al [200], Mutuku et al [201], from elsewhere in Central and west Africa by Dambach [193] and Clennon et al [202], and from Malaysia by Ahmad et al [203]. The use of RS as a predictive tool to locate larval habitats has not always been successful as demonstrated by Achee et al [204]. Their results indicated that remotely sensed land cover is not a valuable indicator of the location in which *An. darlingi* larval habitats will form. High-resolution satellite imagery could be used to detect homes along river systems and potentially predict general areas at risk for *An. darlingi* breeding habitat formation based on distances from houses to waterways (Figure 3). The basic idea behind the remotely sensed assessment of larval habitats is to define environmental parameters that can be used to identify areas with increased risk of malaria transmission [193]. Yet, as already

may exist even within seemingly homogeneous environments.

414 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

populations peaked.

stated by

Populations of mosquito larvae are ideally suited to GIS and remote sensing applications due to their close association with their microenvironment. Specifically, larval mosquitoes have three distinct ecological characteristics that are directly related to predictive risk-modeling: 1) specific habitat preferences, 2) microclimate requirements and 3) vegetation-dependent associations to include plant height and density. Spatial-temporal interactions of mosquito larvae with their natural environment are critical to understanding the risk of contact between the vectors and their human hosts. Due to the fact that mosquitoes spend a substantial portion of their life cycle in the larval stage, population structure and vector survival is greatly influenced by the environmental surroundings. One area that is increasingly being applied to disease ecology which takes advantage of these environmental associations is the use ecolog‐ ical niche models [206]. An ecological niche model is an estimate of the distribution of a species and requires two input data sets: the known locations of a species and environmental data in an image format (such as larval habitats, climate data, elevation data, land cover, etc.). The ecological niche modeling program examines the environmental data at the locations where the species occurs to infer the environmental requirements of the species across a much larger area. The requirements of the species are then used to create a map of the predicted distribution of the species. Any species affected by environmental conditions such as climate can be modeled including disease vectors, disease hosts and pathogens. Models of monthly predic‐ tions of dengue fever in Mexico have been created based on mosquito activity [207]. Niche models of malaria vectors in the *An. gambiae* complex have been developed for under-sampled regions of Africa [208]. The benefit of niche modeling is the development of maps showing predicted distribution of an organism based on current and projected vector ecology and environmental data.

## **6. Human impact land use/global change**

Natural ecosystems throughout the world are being severely altered by human intervention. Population pressure results in transformation of natural ecosystems to agriculture, construc‐ tion of roads and hydroelectric dams, irrigation projects, open pit mines, and uncontrolled human colonization [209, 210]. Anthropogenic modification of the ecosystems also contributes to global climate change represented by an increase in temperature and accompanied by extremes of the hydrologic cycle (e.g., floods and droughts) [211, 212]. The global rate of tropical deforestation continues with nearly 2% to 3% of global forests lost each year and land use change for agriculture represents the largest driver of land cover change across the earth [85, 209, 213]. Arthropod vectors in general, and insect vectors in particular are very sensitive to their environment, which determines their presence, development and behavior. As a consequence, climatic, as well as landscape and land cover factors greatly influence the spatial distribution of vectors and the diseases they transmit [214].

Mosquitoes are among the most sensitive insects to environmental change; their survival, density, and distribution are dramatically influenced by small changes in environmental conditions, such as temperature, humidity, and the availability of suitable larval habitats [48, 88, 215-219]. All these changes can alter the incidence, seasonality and intensity of transmis‐ sion, and geographic range of diseases such as malaria. Changes in the distribution of malaria cases and intensities of malaria transmission have been documented by many historical examples. As described by Hackett [220], malaria increased in Malaya as jungle was cleared for rubber plantations. Where forest was removed the sun penetrated and populations of *Anopheles maculatus* mosquitoes proliferated, greatly increasing the incidence of human malaria. The better we are able to assess and explain the distribution and dynamics of vector species in relation to fluctuations in their environments, the more accurate prediction can be made of malaria in the context of ongoing environmental change [221, 222]. This will allow us to evaluate the risks associated with current practices, better explain the patterns of increasing and decreasing disease, better identify measures to mitigate the likelihood and impact of disease emergence, and eventually improve its control [213]. Below are specific examples of changes related to important human activities.

#### **6.1. Deforestation**

the species occurs to infer the environmental requirements of the species across a much larger area. The requirements of the species are then used to create a map of the predicted distribution of the species. Any species affected by environmental conditions such as climate can be modeled including disease vectors, disease hosts and pathogens. Models of monthly predic‐ tions of dengue fever in Mexico have been created based on mosquito activity [207]. Niche models of malaria vectors in the *An. gambiae* complex have been developed for under-sampled regions of Africa [208]. The benefit of niche modeling is the development of maps showing predicted distribution of an organism based on current and projected vector ecology and

Natural ecosystems throughout the world are being severely altered by human intervention. Population pressure results in transformation of natural ecosystems to agriculture, construc‐ tion of roads and hydroelectric dams, irrigation projects, open pit mines, and uncontrolled human colonization [209, 210]. Anthropogenic modification of the ecosystems also contributes to global climate change represented by an increase in temperature and accompanied by extremes of the hydrologic cycle (e.g., floods and droughts) [211, 212]. The global rate of tropical deforestation continues with nearly 2% to 3% of global forests lost each year and land use change for agriculture represents the largest driver of land cover change across the earth [85, 209, 213]. Arthropod vectors in general, and insect vectors in particular are very sensitive to their environment, which determines their presence, development and behavior. As a consequence, climatic, as well as landscape and land cover factors greatly influence the spatial

Mosquitoes are among the most sensitive insects to environmental change; their survival, density, and distribution are dramatically influenced by small changes in environmental conditions, such as temperature, humidity, and the availability of suitable larval habitats [48, 88, 215-219]. All these changes can alter the incidence, seasonality and intensity of transmis‐ sion, and geographic range of diseases such as malaria. Changes in the distribution of malaria cases and intensities of malaria transmission have been documented by many historical examples. As described by Hackett [220], malaria increased in Malaya as jungle was cleared for rubber plantations. Where forest was removed the sun penetrated and populations of *Anopheles maculatus* mosquitoes proliferated, greatly increasing the incidence of human malaria. The better we are able to assess and explain the distribution and dynamics of vector species in relation to fluctuations in their environments, the more accurate prediction can be made of malaria in the context of ongoing environmental change [221, 222]. This will allow us to evaluate the risks associated with current practices, better explain the patterns of increasing and decreasing disease, better identify measures to mitigate the likelihood and impact of disease emergence, and eventually improve its control [213]. Below are specific examples of

environmental data.

**6. Human impact land use/global change**

416 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

distribution of vectors and the diseases they transmit [214].

changes related to important human activities.

Deforestation is one of the most important factors driving emerging and re-emerging infectious diseases. Through the process of clearing forests and subsequent agricultural development, deforestation changes almost every attribute of local ecosystems such as microclimate, soil, and aquatic conditions, and most significantly, the ecology of local flora and fauna, including human disease vectors. Numerous country and area studies have described the influence of deforestation and subsequent land use on the density of local mosquito vectors [223]. One of the most thorough evaluations of the impact of deforestation combined with the prediction of future changes has been presented by Yasuoka and Levins [224] who conducted a metaanalysis of 60 published studies of changes in ecology of 31 anopheline species and malaria incidence as a consequence of deforestation. In comprehensive tables they summarized density changes by land cover, and for larval habitats the niche-width and sun-preference indices of each species. The conclusion was that mechanisms linking deforestation and agricultural development with mosquito ecology and malaria epidemiology are extremely complex. The impacts of deforestation on mosquito density and malaria incidence are influenced by both the nature of the agricultural development and the ecological characteristics of the local vector mosquitoes. Some species were directly affected by deforestation, some favored or could adapt to the different environmental conditions, and some invaded and/or replaced other species in the process of development and cultivation. The results of the statistical analyses showed that deforestation and agricultural development are favorable for sun-loving species, allowing them to increase in or invade deforested areas where water bodies become exposed to sunlight.

As a specific example of the complexity of a malaria vector to deforestation we present the case of *An. darlingi* in the Amazon region. Vittor et al [225] examined the larval breeding habitat of a major South American malaria vector, *An. darlingi,* in areas with varying degrees of ecologic alteration in the Peruvian Amazon and concluded that deforestation and associated ecologic alterations are conducive to *An. darlingi* larval presence, and thereby increase malaria risk. According to Barros et al [82], deforestation and human presence creates a new habitat, a forest fringe ecosystem, by promoting three changes in *An. darlingi* bionomics: (i) increasing contact with humans; (ii) increasing the number of microdams (small river obstruction causing the accumulation of debris), which increases the number of potential larval habitats as well as the breeding season; and (iii) reducing the number of shaded breeding sites in a given geographical area, which results in a concentration of larvae in remaining shaded areas. The ideal breeding site occurs in the forest fringe, where the three factors, shade, microdams and human blood meals, are located close to each other.

Environmental changes caused by deforestation often lead to vector replacement (for examples referenced in older papers see Service [136]). Conn et al. [226] conducted entomological surveys in malaria areas of Macapá, northeastern Amazonia, and found *An. marajoara* replacing *An. darlingi* as the primary vector. It is hypothesized that the observed change in mosquito population densities was caused by deforestation for agriculture that resulted in newly created ground pools favoring *An. marajoara* larvae. For many regions in the Amazon Basin, popula‐ tions of *An. darlingi* have increased because road construction in the forest has considerably expanded the breeding sites—large areas of neutral, partially shaded and unpolluted water. These characteristics also attract human inhabitants. Subsequently, clearing of forests and water pollution reduce the suitability of these for *An. darlingi* breeding. However, these sites, and newly created ponds for agricultural use, attract other mosquito species such as *An. marajoara*. In addition, humans have colonized land near extensive marshy areas, another preferred breeding habitat of *An. marajoara*.

#### **6.2. Dam construction**

Water reservoirs have long been recognized to be a risk factor for malaria transmission [227-231]. Hydroelectric or irrigation dam construction increases the habitat availability by the formation of lakes. Shallow parts of these lakes are typically overgrown with macrophytes that provide excellent breeding sites for anopheline mosquitoes [227]. However, compared to the number of studies on land use change due to deforestation and agricultural expansion, research related to the entomological and ecological determinants of the rising malaria burden in the vicinity of large dams is rather limited [232]. There are historical examples, such as that of Tennessee Valley Authority ([35], see also p. 3) of well executed environmental management measures to control malaria vectors [21, 35]. These successfully executed environmental measures can be adapted to control malaria associated with dam construction in sub-Saharan Africa and elsewhere in malaria endemic regions. Construction of new reservoirs under the tropical, sub-humid climatic conditions should therefore be accompanied by entomologic studies to predict the risk of malaria epidemics [233]. Keiser et al [231] calls for institutionali‐ zation of health impact assessments for future water development projects analogous to environmental impact assessments as well as the employment of monitoring and surveillance systems that would facilitate systematic evaluation of the impact of these ecosystem interven‐ tions over time. The reality is that more dams will be built and thus mitigation strategies to alleviate potential negative health effects are mandatory to reduce the current burden of malaria in settings near irrigation or dam projects.

#### **6.3. Wetland destruction**

Draining wetlands has been extensively practiced and promoted as the easiest solution to localized public health threats posed by malaria vectors [21, 234]. Unfortunately, this practice has not always worked. Among many cases of increasing malaria transmission after destruc‐ tion of natural wetlands are the examples from African papyrus swamps [64]. As stated already by Goma [32, 235] and confirmed recently by others [72, 88, 236], the interior of a papyrus swamp is unsuitable for anophelines, while the swamp periphery and cultivation of natural swamps provides productive larval habitats for *An. gambiae* and consequently, increase the risks of malaria transmission to the human population. Many natural wetlands have been destroyed and changed to brick-making pits – the most abundant habitat type containing *An. gambiae* larvae in Africa [237].

What has not been taken into account when manipulating wetlands for health benefits is the loss of valuable ecosystem services provided by these wetlands, such as water purification, flood control, or provision of food and fiber, and their contributions to human health. This aspect was emphasized by the 2008 Conference of the Contracting Parties to the Ramsar Convention on Wetlands, whose resolution stated among others: "Those concerned with wetland conservation and management should encourage new and ongoing research regard‐ ing the links between wetlands and human health and to bring information on the scientifically proven contributions that functioning wetland ecosystems make to good health to the attention of national ministries and agencies responsible for health, sanitation, and water supply. The human health sector, and all relevant stakeholders should collaborate in assessing the consequences of wetland management linked with human health, and vice versa the conse‐ quences for the ecological character of wetlands of current practices which seek to maintain or improve human health, including the identification of appropriate trade-offs in decisionmaking."

#### **6.4. Wetland creation and restoration**

These characteristics also attract human inhabitants. Subsequently, clearing of forests and water pollution reduce the suitability of these for *An. darlingi* breeding. However, these sites, and newly created ponds for agricultural use, attract other mosquito species such as *An. marajoara*. In addition, humans have colonized land near extensive marshy areas, another

Water reservoirs have long been recognized to be a risk factor for malaria transmission [227-231]. Hydroelectric or irrigation dam construction increases the habitat availability by the formation of lakes. Shallow parts of these lakes are typically overgrown with macrophytes that provide excellent breeding sites for anopheline mosquitoes [227]. However, compared to the number of studies on land use change due to deforestation and agricultural expansion, research related to the entomological and ecological determinants of the rising malaria burden in the vicinity of large dams is rather limited [232]. There are historical examples, such as that of Tennessee Valley Authority ([35], see also p. 3) of well executed environmental management measures to control malaria vectors [21, 35]. These successfully executed environmental measures can be adapted to control malaria associated with dam construction in sub-Saharan Africa and elsewhere in malaria endemic regions. Construction of new reservoirs under the tropical, sub-humid climatic conditions should therefore be accompanied by entomologic studies to predict the risk of malaria epidemics [233]. Keiser et al [231] calls for institutionali‐ zation of health impact assessments for future water development projects analogous to environmental impact assessments as well as the employment of monitoring and surveillance systems that would facilitate systematic evaluation of the impact of these ecosystem interven‐ tions over time. The reality is that more dams will be built and thus mitigation strategies to alleviate potential negative health effects are mandatory to reduce the current burden of

Draining wetlands has been extensively practiced and promoted as the easiest solution to localized public health threats posed by malaria vectors [21, 234]. Unfortunately, this practice has not always worked. Among many cases of increasing malaria transmission after destruc‐ tion of natural wetlands are the examples from African papyrus swamps [64]. As stated already by Goma [32, 235] and confirmed recently by others [72, 88, 236], the interior of a papyrus swamp is unsuitable for anophelines, while the swamp periphery and cultivation of natural swamps provides productive larval habitats for *An. gambiae* and consequently, increase the risks of malaria transmission to the human population. Many natural wetlands have been destroyed and changed to brick-making pits – the most abundant habitat type containing *An.*

What has not been taken into account when manipulating wetlands for health benefits is the loss of valuable ecosystem services provided by these wetlands, such as water purification, flood control, or provision of food and fiber, and their contributions to human health. This aspect was emphasized by the 2008 Conference of the Contracting Parties to the Ramsar

preferred breeding habitat of *An. marajoara*.

418 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

malaria in settings near irrigation or dam projects.

**6.3. Wetland destruction**

*gambiae* larvae in Africa [237].

**6.2. Dam construction**

In addition to rice fields, which are the most extensive human made wetlands and their significance as larval habitat has been already described previously, the use of constructed wetlands for wastewater treatment is expanding [236, 238, 239]. Constructed wetland tech‐ nology has broad applications for the treatment of many types of wastewaters and provides an ecological approach to mitigate the release of nutrients and toxic materials into the envi‐ ronment [240]. However, design features, maintenance activities and the characteristics of the wastewater undergoing treatment contribute differentially to potential levels of mosquito production and, consequently, to threats to human and animal health from mosquito-borne pathogens. Nutrients (nitrogen and phosphorus), and the configuration and maintenance of emergent vegetation can have strong effects on mosquito production. As loading rates of organic matter and nutrients decline, the diversity of mosquitoes produced by treatment wetlands tends to increase and the relative abundance of *Anopheles* species increases in temperate man-made wetlands [239, 241]. A proper design, e.g. subsurface rather than surface flow or flow-through rather than pond-type wetland [242] can help local mosquito problems. Surface-flow wetlands can also be designed to minimize mosquito breeding by increasing macro-invertebrate predators [243]. Greenway [243] concluded that a marsh with a diversity of macrophytes appears optimal for macro-invertebrate biodiversity and the control of mosquito larvae by predation. The key to mosquito management is to ensure a well-balanced ecosystem supporting a diversity of aquatic organisms [240]. A general conclusion from those areas that contain both treatment wetlands and unimpacted natural wetlands is that ade‐ quately designed and appropriately managed treatment wetlands do not pose any greater mosquito threat than the existing natural wetlands [244].

To compensate for a large loss of wetlands in the past, we are now witnessing many projects attempting to restore, rehabilitate, or create various types of wetland habitats. The resulting restored wetland areas provide flood control, improve water quality, and provide habitat for wildlife, especially bird species. However, they create great mosquito habitat and only a few restoration project address this issue properly [234] and there is a need for a better coordination between wetland restoration design and management and mosquito larval management.

#### **6.5. Eutrophication**

Freshwaters are among the most extensively and rapidly altered ecosystems on the planet [213]. Increased use of fertilizers in agriculture and destruction of natural buffer zones leads to runoff of excessive nutrients, specifically nitrogen and phosphorus to lakes, rivers and reservoirs [245-250]. Nutrient increase is generally responsible for plant production resulting in potential changes in other trophic levels. Several studies have shown positive correlations between concentrations of inorganic nutrients in surface waters and larval abundance for *Anopheles* [43, 251]. Nutrient enriched waters are easily invaded by aggressive aquatic weeds such as water hyacinth (*Eichhornia crassipes*), which are known to be very productive anophe‐ line habitats [37, 44, 252].

The authors' research in Belize [56, 86] provided data in support of the hypothesis that eutrophication causes changes in freshwater communities. The Central American country of Belize contains large wetland areas that used to be dominated by phosphorus limited sparse macrophyte communities interspersed with floating mats of cyanobacteria – a typical *An. albimanus* habitat (Figure 4).

**Figure 4.** Schematic representation of the change of plant communities in marshes of Belize caused by increased eu‐ trophication by phosphorus. This change is accompanied by the replacement of *An. albimanus* habitat with An. vestiti‐ *pennis* habitat.

Anthropogenically mediated P enrichment of wetland plant communities through introduc‐ tion of fertilizer runoff from expanding sugar cane fields is causing a switch from sparse macrophytes to tall dense macrophytes represented mostly by *Typha domingensis*. Tall dense macrophytes provide favorable habitat for *An. vestitipennis*, which appears to be a more efficient vector of malaria. Thus human-caused nutrient enrichment of marshes may lead to increased risk of malaria transmission in human settlements in proximity to the impacted marshes.

#### **6.6. Temperature and precipitation changes**

Malaria transmission is very sensitive to both temperature and precipitation, which makes the issue of change in risk due to past and projected warming trends one of the most important climate change-health questions to follow [253, 254]. Large malaria epidemics in the East African highlands during the mid and late 1990s initiated research on the role that global warming might have on malaria transmission. Historically, these highlands have been used as a shelter against malaria because malaria has been naturally absent due to conditions that limit the biology of the parasite [255]. Several authors proposed that spread of malaria into areas that rarely saw malaria transmission could be related to the impacts of small increases in temperature [253, 256]. The issue became hotly debated [255]. Recently, Chaves et al [257] assessed conclusions from both sides of the argument and found that evidence for the role of climate is robust but they also found a large heterogeneity in malaria trends. They argued that over-emphasizing the importance of climate is misleading for setting a research agenda to understand climate change impacts on emerging malaria patterns. The global change is expected to influence rainfall patterns both seasonal rainfall totals and inter-annual variability in malaria endemic regions, and these events will impact larval habitats availability and thus mosquito population dynamics [258].

#### **6.7. Sea level rise**

**6.5. Eutrophication**

420 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

line habitats [37, 44, 252].

*albimanus* habitat (Figure 4).

*pennis* habitat.

Freshwaters are among the most extensively and rapidly altered ecosystems on the planet [213]. Increased use of fertilizers in agriculture and destruction of natural buffer zones leads to runoff of excessive nutrients, specifically nitrogen and phosphorus to lakes, rivers and reservoirs [245-250]. Nutrient increase is generally responsible for plant production resulting in potential changes in other trophic levels. Several studies have shown positive correlations between concentrations of inorganic nutrients in surface waters and larval abundance for *Anopheles* [43, 251]. Nutrient enriched waters are easily invaded by aggressive aquatic weeds such as water hyacinth (*Eichhornia crassipes*), which are known to be very productive anophe‐

The authors' research in Belize [56, 86] provided data in support of the hypothesis that eutrophication causes changes in freshwater communities. The Central American country of Belize contains large wetland areas that used to be dominated by phosphorus limited sparse macrophyte communities interspersed with floating mats of cyanobacteria – a typical *An.*

**Figure 4.** Schematic representation of the change of plant communities in marshes of Belize caused by increased eu‐ trophication by phosphorus. This change is accompanied by the replacement of *An. albimanus* habitat with An. vestiti‐

Along with warming temperatures, any increase in sea levels will affect the extent of saline (>30 ppt) or brackish (0.5-30 ppt) water bodies in coastal areas. These include coastal estuaries, lagoons, marshes and mangroves [106]. An expansion of brackish and saline water bodies in coastal areas, associated with rising sea levels, can increase densities of salinity-tolerant vector mosquitoes and lead to the adaptation of freshwater vectors to breed in brackish and saline waters. Higher vector densities can increase transmission of vector-borne infectious diseases in coastal localities, which can then spread to other areas [106].

The consequences of human-induced ecological changes provide another set of examples. Large-scale shrimp farming in the Mekong delta of Vietnam locally increased the density of *An. sundaicus*[259]. The greater availability of brackish water bodies can also lead to freshwater breeding mosquitoes such as *An. stephensi* and *An. culicifacies* getting adapted to breed in brackish waters as was observed immediately after the 2004 tsunami in India [260] and some years later in eastern Sri Lanka [261].

#### **6.8. Replacability and adaptability**

As already indicated by a few examples in the above text, a change in ecology of a region whether due natural factors or human impact can lead to changes in the quality and quantity of larval habitats. This often leads to changes in mosquito population dynamics and species composition [262]. The original anopheline species can be replaced by species better adapted to new conditions or they can adapt themselves. Mosquito species distributed over broad geographic ranges are more likely to have greater habitat diversity than species distributed over a small range [263] and thus their adaptability can be higher. Except for a few examples, our knowledge on the species adaptability is quite limited. But since at least some species are able to adapt to different environmental conditions, an effort needs to be made to obtain data on anopheline population dynamics before, during, and after ecologic alterations. Further‐ more, the long-term effectiveness of any control strategy will depend on whether vectors respond to the evolutionary selection pressure created by intervention [22]. For example, mosquitoes may respond by phenotypic plasticity, or by evolving traits such as insecticide resistance or behavioral avoidance.

### **7. Implication for vector control**

Malaria vector control targeting the larval stages of mosquitoes was applied successfully against many species of *Anopheles* in malarious countries until the mid-20th Century [3, 8, 264-266]. Since the introduction of DDT in the 1940s and the associated development of indoor residual spraying (IRS), which usually has a more powerful impact on vectorial capacity than larval control, the focus of malaria prevention programs shifted to the control of adult vectors [8, 267]. However, when it became clear that this strategy is not working (Service 1983), an integrated disease management approach including control of larval stages of malaria vectors, i.e., Integrated Vector Management (IVM) began to be reconsidered [21, 268]. A great step in that direction was made by Keiser et al [264] who provided a systematic review and a metaanalysis of malaria control programs, emphasizing environmental management as their main feature. Most of the 40 studies (85%) were implemented before the Global Malaria Eradication Campaign (1955–69). The authors concluded that malaria control programs that emphasize environmental management are highly effective in reducing malaria. Lessons learned from these past successful programs can guide sound and sustainable malaria control approaches and strategies. The conclusions of Keiser's et al [231] meta-analysis of past control strategies are in agreement with recently developed malaria transmission models showing that sub‐ stantial reductions of the entomological inoculation rate are possible when an integrated malaria control program with multiple interventions (e.g., environmental management tools) implemented simultaneously is used [269, 270].

The larval source management (LSM) also termed Environmental management that has been successfully used to control mosquitoes in many developed countries (US, Brazil, Canada) is recently becoming an integral component of malaria control methods in Africa [271]. LSM includes: (1) habitat (or environmental) modification, (2) habitat (environmental) manipula‐ tion, (3) biological control and (4) larviciding [236, 264, 271]. **Habitat modification** is designed to prevent, eliminate, or reduce vector habitat and it involves a permanent change of land and water, including landscaping, drainage of surface water, land reclamation and filling but also coverage of large water storage containers, wells and other potential breeding sites. **Habitat manipulation** refers to activities that reduce larval habitats of the vector mosquito through temporary changes to the aquatic environment in which larvae develop. It is a recurrent activity, such as water-level manipulation, which includes measures such as flushing, drain clearance, shading or exposing habitats to the sun depending on the ecology of the local vector. It may include planting water-intensive tree species such as *Eucalyptus robusta* to reduce standing water in marshy areas. The best strategies are those that are adapted to local vector ecology, epidemiology and resources, guided by operational research and subject to routine monitoring and evaluation [22, 272]. Bond's et al [122, 123] studies can serve as an example of habitat manipulation. They report on how manual algal removal from breeding pools along a river in southern Mexico significantly reduced both larval and adult densities of *An. pseudo‐ punctipennis*. In a follow up study, the abundance of *An. pseudopunctipennis* larvae + pupae was dramatically reduced by this treatment and remained depressed for two to three months. Algal extraction did not reduce the overall abundance of aquatic insects in river pools. **Biological control** of mosquitoes refers to the introduction of natural enemies into aquatic habitats; these are predatory fish or invertebrates, parasites or disease organisms (see the predator section). *Bacillus thuringiensis israelensis* (Bti) and *Bacillus sphaericus* (Bs) are bacterial species reported to be effective against mosquitoes, and have been widely studied and used as biolarvicides [266, 273, 274]. Recently, researchers have focused on the resident microbiota of insect vectors that can potentially impede transmission of human pathogens. These microbes may prove effective agents for manipulating the vector competence of malaria and other important human pathogens [275-278]. Biological control agents should be evaluated with respect to their climatic compatibility and their capability to maintain very close interactions with target populations [155].

## **8. What next?**

**6.8. Replacability and adaptability**

422 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

resistance or behavioral avoidance.

**7. Implication for vector control**

implemented simultaneously is used [269, 270].

As already indicated by a few examples in the above text, a change in ecology of a region whether due natural factors or human impact can lead to changes in the quality and quantity of larval habitats. This often leads to changes in mosquito population dynamics and species composition [262]. The original anopheline species can be replaced by species better adapted to new conditions or they can adapt themselves. Mosquito species distributed over broad geographic ranges are more likely to have greater habitat diversity than species distributed over a small range [263] and thus their adaptability can be higher. Except for a few examples, our knowledge on the species adaptability is quite limited. But since at least some species are able to adapt to different environmental conditions, an effort needs to be made to obtain data on anopheline population dynamics before, during, and after ecologic alterations. Further‐ more, the long-term effectiveness of any control strategy will depend on whether vectors respond to the evolutionary selection pressure created by intervention [22]. For example, mosquitoes may respond by phenotypic plasticity, or by evolving traits such as insecticide

Malaria vector control targeting the larval stages of mosquitoes was applied successfully against many species of *Anopheles* in malarious countries until the mid-20th Century [3, 8, 264-266]. Since the introduction of DDT in the 1940s and the associated development of indoor residual spraying (IRS), which usually has a more powerful impact on vectorial capacity than larval control, the focus of malaria prevention programs shifted to the control of adult vectors [8, 267]. However, when it became clear that this strategy is not working (Service 1983), an integrated disease management approach including control of larval stages of malaria vectors, i.e., Integrated Vector Management (IVM) began to be reconsidered [21, 268]. A great step in that direction was made by Keiser et al [264] who provided a systematic review and a metaanalysis of malaria control programs, emphasizing environmental management as their main feature. Most of the 40 studies (85%) were implemented before the Global Malaria Eradication Campaign (1955–69). The authors concluded that malaria control programs that emphasize environmental management are highly effective in reducing malaria. Lessons learned from these past successful programs can guide sound and sustainable malaria control approaches and strategies. The conclusions of Keiser's et al [231] meta-analysis of past control strategies are in agreement with recently developed malaria transmission models showing that sub‐ stantial reductions of the entomological inoculation rate are possible when an integrated malaria control program with multiple interventions (e.g., environmental management tools)

The larval source management (LSM) also termed Environmental management that has been successfully used to control mosquitoes in many developed countries (US, Brazil, Canada) is recently becoming an integral component of malaria control methods in Africa [271]. LSM includes: (1) habitat (or environmental) modification, (2) habitat (environmental) manipula‐ tion, (3) biological control and (4) larviciding [236, 264, 271]. **Habitat modification** is designed Almost every paper that we reviewed for this chapter ends up with the call for more infor‐ mation on larval stages of malaria vectors, in order to enable a better vector control and more accurate predictions of vector response to changing environment. It is (finally!) becoming clear that understanding the ecology and evolution of mosquito vectors needs to complement epidemiology, genetics and molecular biology in solving malaria problems. Several review papers provide good suggestions for future directions in vector ecology research (see, e.g., Table 2 in Chaves and Koenraadt [255] and Box 3 in Ferguson et al [22]). As stated in the preceding text, almost any factor defining a larval habitat can change as a result of direct human modification (deforestation, agricultural practices, eutrophication) and/or indirectly caused environmental change (temperature, precipitation). In addition, new habitats can be created. All these changes can and will impact the basic environmental determinants of larval habitats – food availability, refuge, predator presence. There are indications that some species will be able to adapt, some will be replaced by other species, and some anophelines that have not traditionally been regarded as vectors may become important ones.

In the context of ecosystem change whether due to nutrient, temperature, precipitation, salinity or vegetation changes, there is a strong need for studies on adaptability of different anopheline species to new conditions. The majority of these studies would be best executed as manipula‐ tive field or semi-field experiments focused not only on changing characteristics of species performance but also on interactions with other species (both competition and predation). To be able to accomplish these types of experiments, systems of enclosed, pathogen-free, semifield mesocosms in which vector populations can be experimentally manipulated will have to be established within environmentally realistic, contained semi-field settings. See, e.g., Ng'habi et al [11] semi-field system of large, netting-enclosed mesocosms, in which vectors can fly freely, feed on natural plant and vertebrate host sources, and access realistic resting and oviposition sites. Ideally, systems of these experimental mesocosms should be established along environmental (temperature, precipitation) gradients or with the capability to experi‐ mentally manipulate these variables so that we can conduct the experiments focused on species response to changing environments.

In addition, there is an ongoing need for regular monitoring and good quality long-term dataset on species distributions. High resolution satellite data enable more detailed observa‐ tions on vegetation changes and regional distribution of precipitations and temperature, which all can results and result in better risk prediction maps [193]. In order to include a temporal component to the risk models, a network of longitudinal population monitoring sites for vector development needs to be established. The ecological niche models [206, 279] mentioned above will undoubtedly play increasingly important role in predictions of disease outbreaks.

## **Acknowledgements**

We thank Stephanie Castle for technical help. Parts of the research referred to in the chapter was supported by the NIH-NSF Ecology of Infectious Diseases program, Grant # R01 AI49726, "Environmental Determinants of Malaria in Belize".

## **Author details**

Eliška Rejmánková1\*, John Grieco2 , Nicole Achee2 and Donald R. Roberts2,3

\*Address all correspondence to: erejmankova@ucdavis.edu

1 Department of Environmental Science and Policy, University of California, Davis, USA

2 Department of Preventive Medicine & Biometrics, Uniformed Services University of the Health Sciences, Bethesda, USA

3 Retired, Professor Emeritus, present address: Clifton Forge, VA, USA

## **References**

In the context of ecosystem change whether due to nutrient, temperature, precipitation, salinity or vegetation changes, there is a strong need for studies on adaptability of different anopheline species to new conditions. The majority of these studies would be best executed as manipula‐ tive field or semi-field experiments focused not only on changing characteristics of species performance but also on interactions with other species (both competition and predation). To be able to accomplish these types of experiments, systems of enclosed, pathogen-free, semifield mesocosms in which vector populations can be experimentally manipulated will have to be established within environmentally realistic, contained semi-field settings. See, e.g., Ng'habi et al [11] semi-field system of large, netting-enclosed mesocosms, in which vectors can fly freely, feed on natural plant and vertebrate host sources, and access realistic resting and oviposition sites. Ideally, systems of these experimental mesocosms should be established along environmental (temperature, precipitation) gradients or with the capability to experi‐ mentally manipulate these variables so that we can conduct the experiments focused on species

In addition, there is an ongoing need for regular monitoring and good quality long-term dataset on species distributions. High resolution satellite data enable more detailed observa‐ tions on vegetation changes and regional distribution of precipitations and temperature, which all can results and result in better risk prediction maps [193]. In order to include a temporal component to the risk models, a network of longitudinal population monitoring sites for vector development needs to be established. The ecological niche models [206, 279] mentioned above will undoubtedly play increasingly important role in predictions of disease outbreaks.

We thank Stephanie Castle for technical help. Parts of the research referred to in the chapter was supported by the NIH-NSF Ecology of Infectious Diseases program, Grant # R01 AI49726,

, Nicole Achee2

1 Department of Environmental Science and Policy, University of California, Davis, USA

2 Department of Preventive Medicine & Biometrics, Uniformed Services University of the

and Donald R. Roberts2,3

response to changing environments.

424 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**Acknowledgements**

**Author details**

Eliška Rejmánková1\*, John Grieco2

Health Sciences, Bethesda, USA

"Environmental Determinants of Malaria in Belize".

\*Address all correspondence to: erejmankova@ucdavis.edu

3 Retired, Professor Emeritus, present address: Clifton Forge, VA, USA


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## **From** *Anopheles* **to Spatial Surveillance: A Roadmap Through a Multidisciplinary Challenge**

Valérie Obsomer, Nicolas Titeux, Christelle Vancustem, Grégory Duveiller, Jean-François Pekel, Steve Connor, Pietro Ceccato and Marc Coosemans

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55622

**1. Introduction**

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mates. BMC Infectious Diseases. 2009; 9, 59.

*Anopheles* mosquito species are diverse and vector of many pathogens. A review of the genus *Anopheles* [1] recently updated [2] listed more than 520 species, some of which including subspecies and cryptic species. Each of them presents ecologic requirements and behaviours that can influence their status as vector for specific pathogens. Pathogen transmission dynam‐ ics vary greatly from one region to another such as documented for the biodiversity of malaria in the world [3]. Acknowledging these variations at local scale within a country through detailed mapping can lead to better targeted measures and improved monitoring. Interactions between vectors, pathogens and humans in a given area can be better comprehended using a spatial framework leading to what we call here a spatial surveillance.

Part of spatial variation is explained by differences in pathogen species or by successful control in some areas. Nevertheless, *Anopheles* species play a major part in the occurrence, seasonality and spatial variation of *Anopheles*-borne diseases. The environment in a given region provides or not support for a given species to breed, thrive and live long enough to be an efficient vector. Because of these variations a species might be an efficient vector in one settlement and then only a secondary vector in another. The need to clarify *Anopheles* distribution is recognised as a crucial step towards malaria eradication [4]. A recent effort to provide detailed maps of malaria and vectors has been carried out [5–8] including a description of ecological require‐ ments. While these distribution maps are essential for an overview, some issues [9] (described further) linked to the data and modelling impede usage in an operational world. Modelling

© 2013 Obsomer et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Obsomer et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

and surveillance are key activities for successful control but integration of all components for effective operational surveillance is not straightforward.

In this chapter we review the challenges posed by spatial surveillance of *Anopheles*-borne diseases with particular attention for malaria surveillance. This challenge will mainly reside in the difficulty of getting the appropriate raw data and the large spectrum of multidisciplinary expertise. We propose here a roadmap from *Anopheles* sampling to a spatial surveillance.

## **2. Problem statement and application area**

When working on vector-borne-diseases, decision makers and researchers often face a lack of specific quality data required for optimal targeting the intervention and surveillance. How‐ ever, the results/decisions are critical as they impact on the lives of many people. Numerous studies use suboptimal vector dataset, and proxies for environmental drivers to map vector distribution or provide basis for vector surveillance. However, the uncertainties linked to the original dataset are not always well documented, in particular regarding proxies for environ‐ mental drivers derived from satellite imagery. Analysis methods also do not always take into account the specificity of species ecological distribution and inaccuracies linked to the vector dataset.

## **3. Research course and method used**

Spatial surveillance of vector-borne diseases should integrate specialised knowledge in entomology, ecology, parasitology, epidemiology, human health, ecological modelling and social sciences. The authors of this chapter are specialized in those different fields and teamed up to offer an overview of the challenges posed by spatial surveillance. As vector-bornediseases are linked to the environment, a spatial analysis using geographical information or remote sensing related technologies seems then appropriate.

### **4. Following the road map**

Reliable outputs to go from *Anopheles* to spatial surveillance first depend on the data entering any analysis or decision process, being the data on *Anopheles* or on environmental factors. Environmental factors provided by remote sensing techniques that could be used to predict distribution or occurrence of malaria and *Anopheles* have already been reviewed [10]. Based on this inventory, we analyse pros and cons of *Anopheles* sampling strategies, various types of data and modelling techniques. Finally, useful initiatives to make research efforts available and operational in the field are discussed. The general scheme is provided in figure 1.

**Figure 1.** General scheme of the roadmap for *Anopheles* spatial surveillance

#### **4.1.** *Anopheles* **and sampling strategies**

and surveillance are key activities for successful control but integration of all components for

In this chapter we review the challenges posed by spatial surveillance of *Anopheles*-borne diseases with particular attention for malaria surveillance. This challenge will mainly reside in the difficulty of getting the appropriate raw data and the large spectrum of multidisciplinary expertise. We propose here a roadmap from *Anopheles* sampling to a spatial surveillance.

When working on vector-borne-diseases, decision makers and researchers often face a lack of specific quality data required for optimal targeting the intervention and surveillance. How‐ ever, the results/decisions are critical as they impact on the lives of many people. Numerous studies use suboptimal vector dataset, and proxies for environmental drivers to map vector distribution or provide basis for vector surveillance. However, the uncertainties linked to the original dataset are not always well documented, in particular regarding proxies for environ‐ mental drivers derived from satellite imagery. Analysis methods also do not always take into account the specificity of species ecological distribution and inaccuracies linked to the vector

Spatial surveillance of vector-borne diseases should integrate specialised knowledge in entomology, ecology, parasitology, epidemiology, human health, ecological modelling and social sciences. The authors of this chapter are specialized in those different fields and teamed up to offer an overview of the challenges posed by spatial surveillance. As vector-bornediseases are linked to the environment, a spatial analysis using geographical information or

Reliable outputs to go from *Anopheles* to spatial surveillance first depend on the data entering any analysis or decision process, being the data on *Anopheles* or on environmental factors. Environmental factors provided by remote sensing techniques that could be used to predict distribution or occurrence of malaria and *Anopheles* have already been reviewed [10]. Based on this inventory, we analyse pros and cons of *Anopheles* sampling strategies, various types of data and modelling techniques. Finally, useful initiatives to make research efforts available and operational in the field are discussed. The general scheme is provided in figure 1.

effective operational surveillance is not straightforward.

448 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**2. Problem statement and application area**

**3. Research course and method used**

**4. Following the road map**

remote sensing related technologies seems then appropriate.

dataset.

Many useful attributes can be collected on *Anopheles* such as the list of species and their vector status in a given area, resistance to insecticides, behaviour influencing human vector contact, control effort avoidance (early biting, outdoor resting). Research and monitoring programmes might be based on existing entomological data whose particularities should be dealt with at the modelling step. However the most direct way is to design the collection protocol in relation to the objective, i.e. mapping the *Anopheles* species. In this last case, the quality of the dataset could be high if some rules are followed. Monitoring data are typically collected in a network of sampling locations according to a variety of standardized procedures [11] and used for mapping species distributions [12]. However, records are generally collected only in a restricted number of locations often loosely distributed across the region of interest, which is inconvenient for documenting species distribution.

Species distribution modelling techniques [13,14] provide assistance to achieve mapping based on monitoring data [15] such as detailed further in the road map. When coupling *Anopheles* monitoring and mapping efforts, defining an optimal sampling strategy becomes of highest interest. Indeed, well-designed monitoring projects have the potential to produce appropriate data to estimate changes in species attributes [16] but also document the distribution in space and time [12]. An appropriate sampling design should address key issues: what constitutes a sampling location? How many are needed? Where do they have to be located? How often to survey? When monitoring data are used to generate species distribution models, designing the sampling strategy is a challenge because these issues are to be addressed relatively to the monitoring and the modelling objectives.

#### *4.1.1. Optimizing sample size*

Sampling locations may be sites, squares, transects or any spatial unit from which the meas‐ urements are made in the field to document attributes (e.g. presence, population density, infected/infective mosquitoes, reproductive status, insecticide resistance) that describe the *Anopheles* species. A sample is a set of sampling locations where attributes of the species are measured to estimate its characteristics over the entire study area. Hence, a sample must be representative of the whole study area and more than one sampling location is needed to account for the variation in the measurements made in the field. For instance, the population density or even the presence of a species depend on environmental conditions and this is to be taken into account to estimate the mean population density or the infection rate of the species in the study area or to document its spatial distribution with sufficient accuracy. Precision (typically measured by standard error) reflects how similar to each other are the different measurements made in the sampling locations, thereby providing a measure of sampling uncertainty. When sample measurements are similar to each other, the sample mean is likely to be estimated with an acceptable level of precision from a few sampling locations. In contrast, when the between-location variation in the measurements is high, a larger number of sampling locations is needed [11]. Achieving a sufficient level of precision is of critical importance: the higher the precision of the estimates, the better the chances to detect temporal changes using statistical hypothesis testing procedures. Sample size is also known to impact on the perform‐ ance of species distribution models [17–19]: predictions based on few records are likely to be less accurate than predictions based on larger sample sizes [18]. A sufficient number of sampling locations is needed to capture in the statistical models the response of the species to the environmental conditions. A balance is, therefore, to be achieved between ensuring statistical robustness (i.e. increasing the sample size) and reducing sampling effort (i.e. decreasing the sample size) because sampling is time- and/or budget-consuming.

For monitoring purpose, a power analysis may be performed to evaluate the number of sampling locations required to detect a given level of change over time in the attributes of the species with a predetermined level of statistical certainty. First, decisions are to be made by the users on (1) the minimum level of change that is to be detected in the analysis (for instance, 10% of change between time t and t+1) and (2) the acceptable chances of making type-1 (i.e. concluding that change is taking place when it is not) and type-2 (i.e. concluding that no change is taking place when it is) errors in hypothesis testing procedures [15]. Such decisions are often based on the precautionary principle and the relative importance of type-1 and type-2 errors also depends on the objective. Then, the analysis integrates information on the precision of the estimates to calculate the optimal sample size needed to detect the desired level of change. A pilot survey is, however, required to obtain an initial approximation of the precision of the estimates linked to the variation in the field measurements. For modelling applications, modelling performance increases with sample size and impact of sample size on modelling performance may strongly depend on the modelling technique used [20]. A series of studies have also recently shown that the performance may be sensitive to particularly small sample sizes and may reach an asymptote level beyond a sufficiently large sample size [18,19]. In order to examine how large the sample size should be to obtain sufficiently well-performing models, different alternative options are available: (1) using readily available datasets in the study area [12] or (2) creating virtual species in real landscapes [19,21]. With such data, it becomes possible to manipulate the number of sampling locations to represent a range of sample size and to examine the impact of restricted sample size on modelling performance.

### *4.1.2. Optimizing sampling strategy in space*

the sampling strategy is a challenge because these issues are to be addressed relatively to the

Sampling locations may be sites, squares, transects or any spatial unit from which the meas‐ urements are made in the field to document attributes (e.g. presence, population density, infected/infective mosquitoes, reproductive status, insecticide resistance) that describe the *Anopheles* species. A sample is a set of sampling locations where attributes of the species are measured to estimate its characteristics over the entire study area. Hence, a sample must be representative of the whole study area and more than one sampling location is needed to account for the variation in the measurements made in the field. For instance, the population density or even the presence of a species depend on environmental conditions and this is to be taken into account to estimate the mean population density or the infection rate of the species in the study area or to document its spatial distribution with sufficient accuracy. Precision (typically measured by standard error) reflects how similar to each other are the different measurements made in the sampling locations, thereby providing a measure of sampling uncertainty. When sample measurements are similar to each other, the sample mean is likely to be estimated with an acceptable level of precision from a few sampling locations. In contrast, when the between-location variation in the measurements is high, a larger number of sampling locations is needed [11]. Achieving a sufficient level of precision is of critical importance: the higher the precision of the estimates, the better the chances to detect temporal changes using statistical hypothesis testing procedures. Sample size is also known to impact on the perform‐ ance of species distribution models [17–19]: predictions based on few records are likely to be less accurate than predictions based on larger sample sizes [18]. A sufficient number of sampling locations is needed to capture in the statistical models the response of the species to the environmental conditions. A balance is, therefore, to be achieved between ensuring statistical robustness (i.e. increasing the sample size) and reducing sampling effort (i.e.

decreasing the sample size) because sampling is time- and/or budget-consuming.

For monitoring purpose, a power analysis may be performed to evaluate the number of sampling locations required to detect a given level of change over time in the attributes of the species with a predetermined level of statistical certainty. First, decisions are to be made by the users on (1) the minimum level of change that is to be detected in the analysis (for instance, 10% of change between time t and t+1) and (2) the acceptable chances of making type-1 (i.e. concluding that change is taking place when it is not) and type-2 (i.e. concluding that no change is taking place when it is) errors in hypothesis testing procedures [15]. Such decisions are often based on the precautionary principle and the relative importance of type-1 and type-2 errors also depends on the objective. Then, the analysis integrates information on the precision of the estimates to calculate the optimal sample size needed to detect the desired level of change. A pilot survey is, however, required to obtain an initial approximation of the precision of the estimates linked to the variation in the field measurements. For modelling applications, modelling performance increases with sample size and impact of sample size on modelling performance may strongly depend on the modelling technique used [20]. A series of studies

monitoring and the modelling objectives.

450 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

*4.1.1. Optimizing sample size*

An appropriate sampling design also involves positioning the sampling locations so that the full range of environmental conditions across the study area may be covered to ensure the representativeness of the sample. Several approaches are available to position the sampling locations (only some are presented below) [21] with different advantages and disadvantages (details in [11]): Those include:

**Expert-based sampling** – Sampling units are located based on a priori knowledge of the study area and the status of the species. This subjective strategy is to be avoided because the sample is most likely not representative of the study area and may thus not be used for statistical inference.

**Random sampling** – Random selection of sampling locations among a list is an easy-to-use procedure that is recommended when the aim of the sampling is to provide a picture of the situation across the study area. However, the precision of the estimates may be much lower than when using a stratified sampling (see below), especially in heterogeneous environments.

**Systematic (or regular) sampling** – A regular distribution of the sampling locations may prove to be appealing because the whole study area is covered with the same sampling effort. However, the sample may provide a biased picture when the fixed distance between sampling locations coincides with a particular structure in the spatial arrangement of the environmental conditions.

**Stratified sampling** – The study area is first divided in strata assumed to influence differently the attributes of the species measured in the field. A random sampling procedure is applied to select a number of sampling locations within the strata in ratio to their relative geographical extent. The main advantage of stratification is that the precision of the estimates based on the sample may be considerably improved compared to a simple random sampling. Stratification requires preliminary survey to be conducted to minimize the within-strata variation in the measurements. In practice, however, stratification is often applied according to environmental layers representing heterogeneity of the environment conditions that are assumed to exert an influence on the attributes of the species.

#### *4.1.3. Optimizing sampling strategy in time*

Presence-only techniques can deal with the issue of false absences in species distribution modelling studies [14,22], and failure to consider the detectability of a species (i.e. the probability of detecting it when present at a site) when designing a monitoring pro‐ gramme might lead to misleading conclusions [23,24]. In order to account for detection probabilities and to provide an unbiased estimate of *Anopheles* species occupancy or infection rate, it becomes necessary to carry out repeated survey at least in some sam‐ pling locations over a single season of data collection. If the emphasis of the programme is on estimating changes in the species occupancy or infection rate over time, it is also required to repeat the surveys from one season to the other. Site occupancy modelling is a statistical framework specifically designed to jointly estimate detectability and occupan‐ cy of the species as well as changes in those parameters over time [24]. Designing effective sampling schemes to estimate *Anopheles* species dynamics in space and time requires decisions to be made about how to allocate sampling effort among spatial and temporal replicates. Power analyses may be implemented to optimize the sampling design in space and time, i.e. to achieve a compromise between the number of sampling locations and the number of repeated surveys within sampling locations in relation to (1) the acceptable level of imprecision associated with the estimates of species occupancy, (2) the occupancy and detectability of the species, (3) the available manpower and possible sampling effort.

#### **4.2. Environmental factors**

Once environmental factors of interest are identified, their importance according to the type of climate (e.g. semi-arid or humid), type of species, and altitude must be further discussed. Any place where surface water is available for breeding and emergence might lead to *Anopheles* occurrence. Vector status requires above plus (1) presence of human/animal host and their disease parasites. Then (2) suitable temperature and humidity which have then an effect on (3) vector dynamics and parasite development. A review [10] of the current state of the art in the context of remote sensing applications for malaria underlines that, temperature, humidity, surface water, climate seasonality, vegetation type and growth stage influence vector abun‐ dance irrespective of their association with rainfall. The vegetation around breeding sites may also determine abundance associated with the breeding site by providing resting sites, sugar feeding supplies for adult mosquitoes and protection from climatic conditions [25]. Further‐ more, vegetation type or land use may influence mosquito abundance by affecting the presence of animal or human hosts and thus availability of blood meals [10]. Factors are of two kinds [9]: (1) abiotic slow changing factors such as long term climatic variables, soils, topography, (2) fast changing biotic factors such as vegetation, presence of predator, hosts, interactions with other *Anopheles*, seasonal temperature/ rainfall, water bodies,….

Remote sensing products provide environmental characteristics on large surfaces even in areas of limited accessibility and can provide recent information on an area compared to commonly available maps. The quality of the information provided is however dependent of the original remote sensing data quality and suitabilility. The processing required to mosaic images in order to cover a large area, to make various types of image correction, cloud screening operations and image interpretation are not straightforward for non-specialists. Derived products, such as land cover maps or composited time series of simple vegetation indices, are therefore often more adapted to the need of the users. However, the process behind the final product must be understood to a certain extent by the users, in order for them to be aware of the assumptions and simplifications done in the processing. Furthermore, different methods are typically available to reach a given goal, and the choice of the method can strongly influence on the quality of the results.

#### *4.2.1. Long term abiotic variables*

gramme might lead to misleading conclusions [23,24]. In order to account for detection probabilities and to provide an unbiased estimate of *Anopheles* species occupancy or infection rate, it becomes necessary to carry out repeated survey at least in some sam‐ pling locations over a single season of data collection. If the emphasis of the programme is on estimating changes in the species occupancy or infection rate over time, it is also required to repeat the surveys from one season to the other. Site occupancy modelling is a statistical framework specifically designed to jointly estimate detectability and occupan‐ cy of the species as well as changes in those parameters over time [24]. Designing effective sampling schemes to estimate *Anopheles* species dynamics in space and time requires decisions to be made about how to allocate sampling effort among spatial and temporal replicates. Power analyses may be implemented to optimize the sampling design in space and time, i.e. to achieve a compromise between the number of sampling locations and the number of repeated surveys within sampling locations in relation to (1) the acceptable level of imprecision associated with the estimates of species occupancy, (2) the occupancy and detectability of the species, (3) the available manpower and possible sampling effort.

Once environmental factors of interest are identified, their importance according to the type of climate (e.g. semi-arid or humid), type of species, and altitude must be further discussed. Any place where surface water is available for breeding and emergence might lead to *Anopheles* occurrence. Vector status requires above plus (1) presence of human/animal host and their disease parasites. Then (2) suitable temperature and humidity which have then an effect on (3) vector dynamics and parasite development. A review [10] of the current state of the art in the context of remote sensing applications for malaria underlines that, temperature, humidity, surface water, climate seasonality, vegetation type and growth stage influence vector abun‐ dance irrespective of their association with rainfall. The vegetation around breeding sites may also determine abundance associated with the breeding site by providing resting sites, sugar feeding supplies for adult mosquitoes and protection from climatic conditions [25]. Further‐ more, vegetation type or land use may influence mosquito abundance by affecting the presence of animal or human hosts and thus availability of blood meals [10]. Factors are of two kinds [9]: (1) abiotic slow changing factors such as long term climatic variables, soils, topography, (2) fast changing biotic factors such as vegetation, presence of predator, hosts, interactions with

Remote sensing products provide environmental characteristics on large surfaces even in areas of limited accessibility and can provide recent information on an area compared to commonly available maps. The quality of the information provided is however dependent of the original remote sensing data quality and suitabilility. The processing required to mosaic images in order to cover a large area, to make various types of image correction, cloud screening operations and image interpretation are not straightforward for non-specialists. Derived products, such as land cover maps or composited time series of simple vegetation indices, are therefore often more adapted to the need of the users. However, the process behind the final product must be understood to a certain extent by the users, in order for them to be aware of the assumptions and simplifications done in the processing. Furthermore, different methods

other *Anopheles*, seasonal temperature/ rainfall, water bodies,….

**4.2. Environmental factors**

452 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

Abiotic slow changing factors might be used to delineate a species distribution area or maximum potential extend for a species. Those factors include topography, soil types, long term climate and ecoregions (Table 1). Available source are not many but cover the world. Consistent topography is available from the USGS GTOPO 30 suite [26] including derived variables such as digital elevation model, flow accumulation, slope or aspect or from the NASA Shuttle Radar Topographic Mission (SRTM) dataset reprocessed by the CGIAR [27]. The digital soils map of the world compiled by the FAO [28] is still a reference. Long term climatic datasets of monthly temperature and rainfall are available from Worldclim [29] which provided also bioclimatic variables. A second dataset CRU CL2.0 [30] provided also monthly temperature and rainfall but also number of monthly rainy days, rainfall monthly variation and relative humidity. The datasets are based on meteorological stations data from 1950 to 1990 or 2000. The quality of the data is high in some areas and less in others due to availability of meteoro‐ logical station which can be quite low, particularly in Africa. The ecoregions [31] are a useful dataset to delineate sample stratification at regional level. Those dataset are mostly not derived from remote sensing (RS) images but grids developed from point data.


**Table 1.** Relevant long term abiotic variables

#### *4.2.2. Monitoring air temperature*

Air temperature *Ta*, is commonly obtained from measurements in weather stations, which depend on the regional infrastructure. Data are collected as point samples whose distribution is rarely designed to capture the range of climate variability within a region especially in developing countries. The data is also not readily available for real time applications and need to be interpolated to obtain information everywhere in a given region. On the other hand satellite images can provide land surface temperature *Ts* which is different from the air temperature and corresponds to the temperature of the top of the features present on the land surface (i.e. snow, ice, grass of a lawn, roof of a building, leaves of the canopy in a forest). Specific methods (split-windows techniques) can derive daily *Ts* at 1 km resolution [32,33] from two types of sensors, namely the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) [33,34] (see description table of MODIS Ts: https://lpdaac.usgs.gov/products/modis\_products\_table). On the contrary, the derivation of air temperature (*Ta*) is far from straightforward. Recent research showed that minimum *Ts* retrieved from MODIS night images provide estimates of minimum *Ta* in different ecosystems in Africa [35]. Information on maximum *Ta* is also needed to study heat waves and can influence the transmission of vector-borne diseases in regions where temper‐ ature is a limiting factor. During daytime the retrieval of maximum *Ta* from *Ts* is more complex due to factors which influence (*Ts-Ta*): i.e. solar radiation, soil moisture and surface brightness. Methods based on Temperature Vegetation index, Normalized Difference Vegetation Index and Solar Zenith Angle to correct (*Ts-Ta*) are not sufficiently accurate to retrieve maximum Ta in different ecosystems [35]. Therefore, a new approach has been recently proposed [36] to estimate maximum *Ta* based on night AQUA-MODIS *Ts* data in combination with Worldclim [29] which provides long term monthly average of maximum and minimum air temperature. These inputs allow to characterize the diurnal cycle (amplitude and phase) and determine maximum *Ta* by extrapolating in time minimum *Ta* according to the determined diurnal cycle. The method is used to produce maximum *Ta* maps at 1km every 8 days over Africa available in real time from the International Research Institute for Climate and Society (IRI). Unfortu‐ nately *Ta* does represent temperature outside but no proxies are available to monitor indoor temperature or other stable microenvironment which can explain transmission in Finland when temperature in -20°c outside and is important in highlands malaria in Africa.

#### *4.2.3. Monitoring rainfall*

In some regions, the spatial distribution of weather stations is limited and the dissemination of rainfall data is variable, therefore limiting their use for real-time applications. If satellitebased data can partly compensate and help to monitor rainfall, unfortunately, no satellite yet exists which can reliably identify rainfall and accurately estimate the rainfall rate in all circumstances. Some sensors can make indirect estimates of rainfall by measuring parameters such as the thickness of clouds or the temperature of the cloud tops. Advantages and draw‐ backs of existing methods are summarized in [37]. Various satellite rainfall products exist at continental or global scales. The most relevant are:


#### *4.2.4. Remote sensing indicators of vegetation status*

surface (i.e. snow, ice, grass of a lawn, roof of a building, leaves of the canopy in a forest). Specific methods (split-windows techniques) can derive daily *Ts* at 1 km resolution [32,33] from two types of sensors, namely the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) [33,34] (see description table of MODIS Ts: https://lpdaac.usgs.gov/products/modis\_products\_table). On the contrary, the derivation of air temperature (*Ta*) is far from straightforward. Recent research showed that minimum *Ts* retrieved from MODIS night images provide estimates of minimum *Ta* in different ecosystems in Africa [35]. Information on maximum *Ta* is also needed to study heat waves and can influence the transmission of vector-borne diseases in regions where temper‐ ature is a limiting factor. During daytime the retrieval of maximum *Ta* from *Ts* is more complex due to factors which influence (*Ts-Ta*): i.e. solar radiation, soil moisture and surface brightness. Methods based on Temperature Vegetation index, Normalized Difference Vegetation Index and Solar Zenith Angle to correct (*Ts-Ta*) are not sufficiently accurate to retrieve maximum Ta in different ecosystems [35]. Therefore, a new approach has been recently proposed [36] to estimate maximum *Ta* based on night AQUA-MODIS *Ts* data in combination with Worldclim [29] which provides long term monthly average of maximum and minimum air temperature. These inputs allow to characterize the diurnal cycle (amplitude and phase) and determine maximum *Ta* by extrapolating in time minimum *Ta* according to the determined diurnal cycle. The method is used to produce maximum *Ta* maps at 1km every 8 days over Africa available in real time from the International Research Institute for Climate and Society (IRI). Unfortu‐ nately *Ta* does represent temperature outside but no proxies are available to monitor indoor temperature or other stable microenvironment which can explain transmission in Finland

when temperature in -20°c outside and is important in highlands malaria in Africa.

In some regions, the spatial distribution of weather stations is limited and the dissemination of rainfall data is variable, therefore limiting their use for real-time applications. If satellitebased data can partly compensate and help to monitor rainfall, unfortunately, no satellite yet exists which can reliably identify rainfall and accurately estimate the rainfall rate in all circumstances. Some sensors can make indirect estimates of rainfall by measuring parameters such as the thickness of clouds or the temperature of the cloud tops. Advantages and draw‐ backs of existing methods are summarized in [37]. Various satellite rainfall products exist at

**•** The Tropical Rainfall Measuring Mission **(TRMM)** products [38] provide better spatial (25 km) and temporal estimation (3 hours) of rainfall in Africa [39] than most products but are

**•** Products from the CPC MORPHing technique **(CMORPH)** [40] cover the world at 8 km resolution every 30 min. This technique uses precipitation estimates derived from low orbite satellite microwave observations obtained entirely from various geostationary satellite infrared (IR) data. The estimation method developed for these products is extremely flexible such that any precipitation estimates from any microwave satellite source can be incorpo‐

*4.2.3. Monitoring rainfall*

454 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

rated.

continental or global scales. The most relevant are:

available only between 35° North and South latitudes.

Monitoring the status of green biomass from space is made possible thanks to the particular spectral properties of green vegetation. In order to drive the exothermic reaction of photosyn‐ thesis, plant pigments absorb electromagnetic radiation over different parts of the visible spectrum (400-700 nm). This is known as photosynthetically active radiation (PAR). Addi‐ tionally, much of the near-infrared light (740-1100 nm) is scattered by green plant tissues to avoid overheating, and this scatter results in strong spectral reflectance at these wavelengths. These unusual spectral properties, which are directly linked to photosynthesis, stomatal resistance and evapotranspiration, facilitate the retrieval of information on plant canopies from the electromagnetic signal measured by satellite remote sensing instruments [46]. Satellites dedicated to vegetation monitoring have been equipped with sensors capable of measuring reflected electromagnetic radiations in various wavebands, with a particular emphasis on the red (Red) and near-infrared (NIR), to assess the green biomass in a canopy.

A common and simple way to resume the information content within these bands is the use of spectral vegetation indices, which is an algebraic combination of the spectral bands designed to be as sensitive to the desired factor (green biomass) and as insensitive as possible to perturbing factors affecting spectral reflectance (such as atmospheric and illumination conditions, soil properties and the viewing geometry of the imaging instrument). Indices based on red and near-infrared reflectance have been shown to be a measure of chlorophyll abun‐ dance and energy absorption [47]. Variations of across one year can help spotting vegetation types, and the quantification of the water content can help identifying areas in a similar vegetation class which retain more humidity and might thus be more favourable to mosquito breeding or survival in dry season. Dozens of vegetation indices assess the state of the vegetation qualitatively and quantitatively on the basis of reflectance values:


Albeit their widespread use, the use of vegetation indices over large geographic extents has its limits for describing canopy status in a fine and robust way, since both the desired infor‐ mation and the perturbing factors vary spatially, temporally and spectrally. Another type of information on canopy status that can be retrieved from remote sensing data is biophysical variables. The most common are the fraction of Absorbed Photosynthetically Active Radiation **(fAPAR)** and the Leaf Area Index **(LAI)**, defined as half the total developed area of green leaves per unit of ground horizontal area [58]. Unlike vegetation indices, which are a convenient way to resume spectral information related to vegetation behaviour, biophysical variables such as fAPAR and LAI have a real physiological meaning. These variables govern the process of photosynthesis and the exchange of energy, water and carbon between the canopy and the atmosphere. To retrieve LAI and fAPAR from satellite remote sensing observations, the radiative transfer of photons within the canopy and through the atmosphere must be modelled. A thorough description of the physical problem, alongside caveats on its application to satellite remote sensing of vegetation, is presented in [59]. Dorigo et al. [60] provide a review of the various methods that exist to use such radiative transfer models to relate satellite observations to LAI and fAPAR. Up to recently, the two main datasets of global fAPAR and LAI are products from MODIS and CYCLOPES with different methodologies described in [61] and [62]. These datasets have been inter-compared and evaluated against ground measurements over different land cover types [63–65]. A combined product has recently been made available, GEOV1, in the framework of the Geoland2 project, in view of providing it as an operational land product service of the Global Monitoring for Environment and Security (GMES) pro‐ gramme [66]. This product is currently based on SPOT-Vegetation, but a compatible long term data record from 1981 to 2000 has been also constructed based on NOAA-AVHRR data (with a spatial resolution of 0.05°) [67], and in the future it is expected to be produced based on the future operational Sentinel3-OLCI mission. Such biophysical products are increasingly used but seldom in epidemiological studies.

#### *4.2.5. Land cover*

**•** The Normalized Difference Vegetation Index **(NDVI)** (NDVI = (NIR - Red) / (NIR + Red)) [48] is the most popular of such vegetation indices. NDVI is easily available because it is based only on Red and NIR bands, which are present in most satellite sensors dedicated to land surface observation. The GIMMS (Global Inventory Modelling and Mapping Studies) NDVI dataset based on NOAA-AVHRR offer the longest coherent dataset from July 1981 to December 2011 which can be useful for long term studies [49]. However the spatial resolution of 8 km limits some applications. SPOT-VEGETATION provides a regular product since 1998 at a better spatial resolution of 1 km and geo-location. Similarly, the MODIS sensor provides NDVI at 250 m resolution. NDVI can also be calculated from images with a higher spatial resolution, such as those from the Landsat or SPOT series. The NDVI is used extensively but has several disadvantages such as its sensibility to atmospheric aerosols and to soil background (particularly in sparsely vegetated areas) [50]. Additionally, NDVI also tends to saturate in forested areas and is therefore not responsive to variations

**•** The Enhanced Vegetation Index **(EVI)** remains sensitive to variations in dense forests where NDVI saturates [52]. EVI calculated from MODIS imagery is provided, alongside NDVI, as standard freely available product. A disadvantage of EVI is that it requires an additional blue band, which is not available in NOAA-AVHRR, thereby blocking the possibility to exploit the long term dataset. To remediate that, a simplified 2-band EVI has also been

**•** The Normalized Difference Water Index (**NDWI** = (NIR - SWIR) / (NIR + SWIR)) [54], where SWIR is the Short wave infrared, is sensitive to vegetation water content and to the spongy mesophyll structure in vegetation canopies. Regarding vegetation water content, [55] summarized the limitations of using the NDVI: a decrease in chlorophyll content does not imply a decrease in vegetation water content and inversely. It might also help target vegetation retaining humidity in the dry season. Few studies have attempted to retrieve directly vegetation water content using operational satellite data such as provided by SPOT-VEGETATION [55], MODIS [56] and Landsat [51]. A disadvantage of NDWI is that several instruments are not equipped with detectors in the SWIR domain, and when they do they

**•** The **Hue index** is a qualitative index proposed recently by [57] for the monitoring of the Locust habitat. This exploits simultaneously three wavelengths (the SWIR, the NIR, and red) and has two main advantages: (i) avoiding confusions between bare soils and vegetation, and (ii) allowing the identification of green vegetation independently from the observation conditions, i.e., atmosphere and acquisition geometry, and from its intrinsic variations, i.e., the phenological stage. Potential for monitoring crops, forests and other applications still

Albeit their widespread use, the use of vegetation indices over large geographic extents has its limits for describing canopy status in a fine and robust way, since both the desired infor‐ mation and the perturbing factors vary spatially, temporally and spectrally. Another type of information on canopy status that can be retrieved from remote sensing data is biophysical variables. The most common are the fraction of Absorbed Photosynthetically Active Radiation

in the full range of canopy vegetation content [51].

456 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

are often at lower spatial resolution than other bands.

proposed [53].

need to be assessed.

Detailed information from land cover maps is generally available in national geographical institutes but this information is often out of date due to the long process implied in developing such dataset for a whole country. Moreover, the diverse origin and scale of these datasets when considering more than one country impeded proper comparison between sites. One could thus consider producing national or regional land cover maps using satellite high-resolution data. This exercise includes the pre-processing, the interpretation of the images, and the validation through field surveys. For instance, Landsat images were used in the framework of the Food and Agriculture Organization of the United Nations (FAO) Africover program [68] to map land cover types at 30 m resolution for 11 countries in Africa. Such land cover maps present a great level of detail, but may suffer for some inconsistencies because of heterogeneity in acquisition dates, images and interpretation from one scene to another. Moreover this approach hardly takes into account the seasonal variation and phenological behaviour of different vegetation types. These datasets are also limited in their spatial coverage and cannot be regularly updated following the methodology commonly used (i.e. visual interpretation). Finally, if the whole Landsat images archive was made freely available in 2009, images from Landsat 7 present gaps since May 2003 and Landsat 5 back to activity in 2003 is now failing since November 2011.

Medium to coarse resolution imagery (250 to 1 km) can improve some major issues: the information is acquired consistently over the whole area and frequent images (every 1 or 3 days) of a same area can be combined to eliminate cloud contamination and angular effects, and characterize the vegetation phenology. These time series can be used to produce global maps such as (i) the Global Land Cover 2000 **(GLC2000)** map that is based on SPOT-VEGE‐ TATION data (1 km) thanks to an international partnership of research groups coordinated by the European Commission's Joint Research Centre (JRC) [69], (ii) the 500 m **MODIS global land cover** derived from collection 5 Nadir BRDF-Adjusted Reflectance (NBAR) and Land Surface Temperature (LST) products [70], (iv) the **GlobCover** map [71] at 300 m derived from a Medium Resolution Imaging Spectrometer (MERIS) time series for year 2005. These types of time series were also used to produce land cover and vegetation maps at national and regional scales such as for example [72]. These types of products have the advantage that the data preprocessing and the methodology used are adapted to the local constraints and application needs but are limited in their spatial coverage. The possibility to regularly update global land cover information has been proved recently with the second run of the GlobCover processing system [73], thus offering the potential to use such product in a monitoring program. The delineation of the vector habitat underlines the essential role of these land cover datasets which makes the necessary link between the technical remote sensing world and application requirements. Land cover dataset are one of the essential variables for the Group on Earth Observations (GEO). A major effort is to be continuously invested in the development and improvement of such dataset. The quality of this dataset can only be really tested if used for applications. Close interactions with final users remain the guarantee for the relevancy of the Earth observation product.

#### *4.2.6. Monitoring water bodies*

In order to identify the presence of water, it is also possible to use satellite-derived products that detect water bodies instead of approximate water availability using rainfall estimates. In the last 10 years, only a few operational methods applied to datasets with a spatial resolution equal or higher than 1 km, were proposed to monitor surface water at continental or global scale. Among these, two most recent offer dynamic detections in near real-time through an operational monitoring system:


to have a robust and reliable image-independent discrimination between water and other land cover types. An automatic processing chain based on SPOT-VEGETATION was designed to provide a dekadal water surface product at the continental scale. The product can be ordered freely through the geoland2 web portal following the link http:// www.geoland2.eu/core-mapping-services/biopar.html.

The analysis of eight years of small water body data demonstrated the capacity of such methods to capture inter-annual water bodies variability and the relation with seasonal rainfall patterns [98]. Nevertheless, the 1 km spatial resolution of products derived from SPOT-VEGETATION is still a strong intrinsic limitation. The operational production of a MODIS based product at 250 m using the second method is in progress and should be available soon.

#### *4.2.7. Caveats on remote sensing data*

days) of a same area can be combined to eliminate cloud contamination and angular effects, and characterize the vegetation phenology. These time series can be used to produce global maps such as (i) the Global Land Cover 2000 **(GLC2000)** map that is based on SPOT-VEGE‐ TATION data (1 km) thanks to an international partnership of research groups coordinated by the European Commission's Joint Research Centre (JRC) [69], (ii) the 500 m **MODIS global land cover** derived from collection 5 Nadir BRDF-Adjusted Reflectance (NBAR) and Land Surface Temperature (LST) products [70], (iv) the **GlobCover** map [71] at 300 m derived from a Medium Resolution Imaging Spectrometer (MERIS) time series for year 2005. These types of time series were also used to produce land cover and vegetation maps at national and regional scales such as for example [72]. These types of products have the advantage that the data preprocessing and the methodology used are adapted to the local constraints and application needs but are limited in their spatial coverage. The possibility to regularly update global land cover information has been proved recently with the second run of the GlobCover processing system [73], thus offering the potential to use such product in a monitoring program. The delineation of the vector habitat underlines the essential role of these land cover datasets which makes the necessary link between the technical remote sensing world and application requirements. Land cover dataset are one of the essential variables for the Group on Earth Observations (GEO). A major effort is to be continuously invested in the development and improvement of such dataset. The quality of this dataset can only be really tested if used for applications. Close interactions with final users remain the guarantee for the relevancy of the

In order to identify the presence of water, it is also possible to use satellite-derived products that detect water bodies instead of approximate water availability using rainfall estimates. In the last 10 years, only a few operational methods applied to datasets with a spatial resolution equal or higher than 1 km, were proposed to monitor surface water at continental or global scale. Among these, two most recent offer dynamic detections in near real-time through an

**•** First, the Small Water Bodies **(SWB)** product based on SPOT-VEGETATION [74] available via the DevCoCast project website makes use of 10 day NDVI, the NDWI and syntheses of the SWIR band data. It is based on a contextual algorithm [75] exploiting the local contrast of the water surface with respect to the surrounding area. The product performs well in subhumid and semi-arid regions, but limitations have been observed over dense vegetation areas. The 1 km spatial resolution is an intrinsic limitation. Nevertheless, the combination of eight years of small water body monitoring data demonstrated the value of multi-annual approaches to capture water bodies that do not replenish every year in relation with seasonal

**• The HSV WATER** product [76] based on Hue Saturation Value (HSV) transformation of SPOT-VEGETATION and MODIS time series allows consistent detection at continental scale. This pixel based approach uses SWIR, NIR and red bands and transform the RGB color space into HSV that decouples chromaticity and luminance. It presents the advantage

Earth observation product.

458 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

*4.2.6. Monitoring water bodies*

operational monitoring system:

rainfall patterns [74][97].

Various issues have to be highlighted when looking from the application angle:


**Spatial resolution**: Remote sensing is typically characterized by a trade-off between the different types of resolutions: spatial, temporal, spectral and to a certain extent also radiometric and angular. High spatial resolution is desired to characterise the land in a detailed way. However, cloud occurrence limits its availability. Basis for land cover map might be a puzzle of images from different seasons or even years thus creating artifacts of land cover differences at the limits between the images. As it is discussed further, coarse spatial resolution imagery, with its frequent revisit and through the use of compositing can partially remediate the problem, but this can be a problem with high resolution imagery where images are costly and revisit not frequent. Having regular observations at fine spatial resolution typically limit the geographic extend that can be monitored. Even over a limited coverage, satellites providing such services are typically commercial ones for which the cost is currently high and for which there is competition for their observation capacity between different geographic sites. Such images are thus often used in studies of limited spatial extend from which the results are difficult to extrapolate to a country level needed for spatial surveillance. It is however just a matter of time before high spatial resolution (5 – 20 m) becomes available for the entire globe


**Table 2.** Some important remote sensing related products

and the European Space Agency is currently preparing its Sentinel-2 constellation (with an expected launch of its first satellite in 2014), which aims at operationally providing multispec‐ tral imagery, at spatial resolutions of 10 to 60 m for different bands, and with a 5-day revisit period. However, the challenge of collecting, processing and delivering this data may still limit its practical use for years.

**Clouds and compositing**: The quality of the spatial and temporal spectral consistency of coarse resolution optical time series may be limited by processing steps of cloud-screening and compositing. The efficiency of the cloud-screening, i.e. its ability to remove clouds while keeping a maximum of useful information, depends on three factors: (i) the methodology used to identify cloud-free pixels, (ii) the type of clouds (thick clouds are easier to overcome than veils of clouds which change surreptitiously radiation values), and (iii) the sensor character‐ istics. The detection of clouds is often based on specific bands, i.e. the blue, the middle infrared and the thermic infrared, and the choice of the wavelengths may vary according to the sensor. Depending on these factors, residuals clouds and haze may still remain after the cloudscreening step. Quality of time series may strongly vary according to the compositing strategy used. The most common method used for producing temporal syntheses consists of selecting the Maximum Value Composite (MVC) NDVI [77] (Figure 2) that minimizes the effect of undetected clouds since these would typically have a lower NDVI value. However, the composited reflectance bands may exhibit substantial radiometric variations, since composite radiances are generally recorded under varying atmospheric and geometric conditions. This may cause serious spatial inconsistencies in the composites and in the subsequent processing.

**Figure 2.** Maximum NDVI standard compositing

A more advanced approach consists of normalizing the bidirectional reflectance by fitting a bi-directional reflectance distribution function (BRDF) model to the available cloud free observations [78] which considerable improve the result. But operational implementation requires a large number of cloud-free observations, the BRDF retrieval has a high sensitivity to residual clouds [79] (Figure 3), the algorithm is complex and requires ancillary data. A more flexible and "user-friendly" compositing approach was recently proposed [80] where cloud free reflectance values are averaged after a quality control.

**Figure 3.** Mean composting method

and the European Space Agency is currently preparing its Sentinel-2 constellation (with an expected launch of its first satellite in 2014), which aims at operationally providing multispec‐ tral imagery, at spatial resolutions of 10 to 60 m for different bands, and with a 5-day revisit period. However, the challenge of collecting, processing and delivering this data may still limit

**Type of variable Sensor/ source Spatial Timing Area Date**

MODIS/ Worldclim 1 km World

Estimation (RFE) produced by NOAA-CPC 10 km daily Africa Since January

Rainfall EUMETSAT/ GRIB MPE From January

fAPAR + LAI Terra/Aqua MODIS 1 km 8 days World Since 2000

NDVI Spot VEGETATION 1 km daily World NDVI MODIS Terra/Aqua MODIS 250 m World EVI Terra/Aqua MODIS 500 m World NDWI Spot VEGETATION 1 km daily World Hue Index Spot-VEGETATION 1 km daily World

1 km World

8 km 30 min World

4 km Africa Since 1983

8 km World Since 1981

1 km 10 days World Since 2000

1km 10 days World Since 2000

5 km 10 days World 1981 - 2000

35° N & 35° S

Since 1998

2002

2001

2007

Since December

Terra/Aqua MODIS

iridl.ldeo.columbia.edu/ maproom/.Health/.Regional/. Africa/.Malaria/.TMR/)

Rainfall TRMM 25 km 3 hours

http://

460 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

Rainfall CMORPH : Mixt IR data,

NOAA-CPC

TARCAT http:// www.met.reading.ac.uk/

~tamsat/data

NDVI GIMMS products from NOAA-AVHRR

fAPAR+LAI CYCLOPES products fSpot-

fAPAR+LAI GEOV1 products from NOAA-AVHRR

**Table 2.** Some important remote sensing related products

faPAR+LAI GEOV1 products Spot-

VEGETATION

VEGETATION

**Clouds and compositing**: The quality of the spatial and temporal spectral consistency of coarse resolution optical time series may be limited by processing steps of cloud-screening and compositing. The efficiency of the cloud-screening, i.e. its ability to remove clouds while keeping a maximum of useful information, depends on three factors: (i) the methodology used to identify cloud-free pixels, (ii) the type of clouds (thick clouds are easier to overcome than

its practical use for years.

Minimum air temperature

Maximum air temperature

African Rainfall

Rainfall

It presents the advantages to reduce both the anisotropy effects and the possible remaining perturbation after atmospheric correction and cloud removal. Despite the benefits of compo‐ siting, for some applications it may be more interesting to avoid it altogether. Indeed, to follow vegetation changes at a finer time scale it may be better to exploit all available observations within a period (typically 10 days or more) instead of combining them together. In agriculture monitoring, considerable changes in biomass or phenology can occur within a week and exploiting all available observations should thus be preferred. Such approach has been used, to provide crop specific biophysical variable time series at regional scale by fitting a simplified model of the canopy dynamics over daily data [81] and might be of use to identify processing occurring in potential *Anopheles* habitat such as rice paddies.

**What is in a pixel?** Coarse spatial resolution satellite imagery has several advantages. Frequent observations enable timely detection of environmental changes that may indicate potential changes in the presence of *Anopheles*. Second, the higher frequency of available observations allows to better address the problem of lack of data due to cloud contamination and anisotropy through compositing or temporal smoothing. Third, their (relatively) long archives enable to have a picture of the past with which the actual conditions can be compared to. In the short coming future, coarse datasets may also serve as a benchmark in order to calibrate products to their signal, which could be more stable thanks to their higher revisit frequency. Finally, coarse spatial resolution data are also often the only data available and there is thus a tendency to use them at the limit of their spatial resolution by looking at individual pixels. A common misconception is that the observational footprint is the geometric projection of a rectangular pixel onto the Earth's surface [82]. The footprint rather depends on some properties of the instrument, resumed under the concept of spatial response [83], and which results in an observation footprint generally larger than the pixel delivered to the user (Figure 4).

This problem is compounded for sensors such as AVHRR, MODIS and VIIRS (the successor of MODIS), which scan the Earth with large angles, leading to an expansion of the observation footprint along the scanline (while the grid in which the data is provided keeps the same size). Furthermore, the pre-processing step of gridding, i.e. assigning an observation to a predefined system of grid, introduces a ``pixel-shift'' [84], which means that the centre of the pixel does not correspond with the centre of the observation. Such gridding artefacts have serious consequences on the quality of the MODIS signal, and more specifically on composites and band-to-band registration across various spatial resolutions [85]. Recent work [86] has further demonstrated the impact of gridding artefacts and the scan angle on the spatial purity of an observation, i.e. on the percentage of the target land cover within an observation footprint that effectively contributes to the signal encoded in the pixel.

**Mosquito Land cover**: Land cover provides the more understandable information to nonspecialist in terms of vegetation and habitat but the classes are not always adapted to the user needs. Instead of choosing between vegetation indices which represent continuous values and land cover of more or less 20 classes, it might be useful to give access to intermediary products of land cover classification. Indeed, processing chains of a land cover such as GlobCover include a correction process, cloud screening and image compositing to improve overall quality of the data [71]. Then vegetation indices and reflectance bands linked to vegetation

From *Anopheles* to Spatial Surveillance: A Roadmap Through a Multidisciplinary Challenge http://dx.doi.org/10.5772/55622 463

It presents the advantages to reduce both the anisotropy effects and the possible remaining perturbation after atmospheric correction and cloud removal. Despite the benefits of compo‐ siting, for some applications it may be more interesting to avoid it altogether. Indeed, to follow vegetation changes at a finer time scale it may be better to exploit all available observations within a period (typically 10 days or more) instead of combining them together. In agriculture monitoring, considerable changes in biomass or phenology can occur within a week and exploiting all available observations should thus be preferred. Such approach has been used, to provide crop specific biophysical variable time series at regional scale by fitting a simplified model of the canopy dynamics over daily data [81] and might be of use to identify processing

**What is in a pixel?** Coarse spatial resolution satellite imagery has several advantages. Frequent observations enable timely detection of environmental changes that may indicate potential changes in the presence of *Anopheles*. Second, the higher frequency of available observations allows to better address the problem of lack of data due to cloud contamination and anisotropy through compositing or temporal smoothing. Third, their (relatively) long archives enable to have a picture of the past with which the actual conditions can be compared to. In the short coming future, coarse datasets may also serve as a benchmark in order to calibrate products to their signal, which could be more stable thanks to their higher revisit frequency. Finally, coarse spatial resolution data are also often the only data available and there is thus a tendency to use them at the limit of their spatial resolution by looking at individual pixels. A common misconception is that the observational footprint is the geometric projection of a rectangular pixel onto the Earth's surface [82]. The footprint rather depends on some properties of the instrument, resumed under the concept of spatial response [83], and which results in an

observation footprint generally larger than the pixel delivered to the user (Figure 4).

This problem is compounded for sensors such as AVHRR, MODIS and VIIRS (the successor of MODIS), which scan the Earth with large angles, leading to an expansion of the observation footprint along the scanline (while the grid in which the data is provided keeps the same size). Furthermore, the pre-processing step of gridding, i.e. assigning an observation to a predefined system of grid, introduces a ``pixel-shift'' [84], which means that the centre of the pixel does not correspond with the centre of the observation. Such gridding artefacts have serious consequences on the quality of the MODIS signal, and more specifically on composites and band-to-band registration across various spatial resolutions [85]. Recent work [86] has further demonstrated the impact of gridding artefacts and the scan angle on the spatial purity of an observation, i.e. on the percentage of the target land cover within an observation footprint that

**Mosquito Land cover**: Land cover provides the more understandable information to nonspecialist in terms of vegetation and habitat but the classes are not always adapted to the user needs. Instead of choosing between vegetation indices which represent continuous values and land cover of more or less 20 classes, it might be useful to give access to intermediary products of land cover classification. Indeed, processing chains of a land cover such as GlobCover include a correction process, cloud screening and image compositing to improve overall quality of the data [71]. Then vegetation indices and reflectance bands linked to vegetation

occurring in potential *Anopheles* habitat such as rice paddies.

462 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

effectively contributes to the signal encoded in the pixel.

**Figure 4.** Some effects influencing what is really in a pixel of a satellite remote sensing image. (a) Schematic mis-align‐ ment between the observation footprint and the arbitrary grid remote sensing observations are encoded into (i.e. the pixel). (b) Illustration of the expansion and overlap of the observation footprint along the scanline for whiskbroom sensors such as AVHRR and MODIS; and (c) representation of how this size increase as the sensor scans with larger viewing angles. Figure adapted from [87] with permission from Elsevier.

status are used to group similar adjacent pixels and assign to those a same class label through clustering method which creates a chosen number of classes. In the next step, each class is compared to classes of a reference existing dataset or other existing data. According to a set of decision rules, the classes are interpreted and grouped in definitive classes. This last step raises several issues. The transformation of continuous dataset and the separation of the continuous landscape into a set of discrete classes are bound to a loss of information and inaccuracy particularly at the border of the classes. For example, the transition from a forest to a meadow might not always present a clear cut border. Other land cover initiatives work with continuous fields to avoid this issue [88]. Moreover, at the end of the process, up to 30% of the pixels are integrated into mosaic classes used when it is impossible to attribute the group of pixels to a single class, the pixel itself being a mixture for example of forest and meadow and thus providing a signal which is neither corresponding to forest, neither corresponding to meadow. Using mosaic classes in models and analysis can create confusion, particularly if several mosaic classes are grouped together. In this context, access to intermediate products such as classes based on cluster of similar pixels produced by remote sensing specialist might allow to integrate those into ecological models integrating ecological information relevant to *Anophe‐ les* into the building of the final land cover would allow to define a better suited product for the purpose. Integrating several sensors to build a Landover might also improve the results. Indeed, GlobCover is based on MERIS satellite images which do not contain the Short Wavelength Infrared (SWIR) useful for discrimination of the forest vegetation. A combination with spot VEGETATION could result into better discrimination power for a similar resolution.

#### **4.3. Model development**

Sampling strategies, detailed field studies and casual observations can provide data which constitute the baseline information for model development. While remote sensing products are still too coarse resolution or maybe not adapted to define microhabitats, they can however provide proxies for environmental factors influencing general habitat and might be used in two ways. (1) Environmental values can be extracted at the sampling sites or in a buffer around the sites and then related to *Anopheles* data in descriptive models. Buffer size is often a compromise between some meaningful ecological feature such as flying range and the spatial resolution of the environmental factors [89]. (2) For question regarding habitat, spatial variation in vector capacity and spatial surveillance, spatial models are needed. In these models, environmental factors are related to the species records collected in the sampling locations and this relation is then used to predict the distribution of the species beyond the sampling locations [90–93].

When working with existing data, sampling protocol cannot be influenced a posteriori but an adapted methodology can be used to take into account potential peculiarity of each dataset. Field data may be obtained as a by-product of existing operational projects. However, depending on the finality which determined sampling design, the data might not be used straightforwardly for spatial surveillance. The dataset might include non standardized data collected during different years, according to a variety of sampling strategies but might be the only data available covering many countries. Existing datasets can consist of a collection of literature records covering wide regions. However, the collection sites are seldom well georeferenced, large areas are not covered by the studies which might use different collection techniques at different seasons. With such datasets lack of records might be linked to inefficient sampling method, wrong timing for the survey or absence of survey and according to the source of data, abundance and absence need to be treated with caution. Even certified presence might not reflect current situation if recorded years ago. These issues may partly be addressed by methods similar to the previously mentioned subsampling procedures to reduce the potential biases in readily available datasets or using adapted modelling techniques.

#### *4.3.1. Species distribution modelling*

Early development in the field of remote sensing and vector-borne diseases risk mapping used the following methodological steps: collecting human cases (or mosquito presence/absence), collectingrelevant environmentalgriddeddata (pixel), extractingdata at samplingsites tobuild a logistic regression model explaining cases of occurrence according to the environmental conditions, then mapping the probabilities by calculation of the model output for each grid‐ ded cell of the original environmental maps [94]. Numerous methods have now been used to model vector-borne diseases spatially [95] and suggestions to improve frequent drawbacks include(1)usingseveralmodelsandselectthebest suitedforpredictionand(2)makeasummary model from the best-fitting models. On the other hand innovative methods are constantly improved in spatial ecology. Quantifying the link between species and their environment is a central research area in quantitative ecology. When absence data are available / reliable, numerous methods now do exist, ranging from logistic regression, ordinary multiple regres‐ sions and its generalized form (GLM), ordination, classification method, distance metrics such as Mahalonobis distances, neural networks, boosted regression tree, random forest and even more sophisticated support vector machines are some examples among the plethora of recently developed methods [14]. Multi-species community modelling methods have also been devel‐ oped. One advantage of this kind of techniques is that it becomes possible to build species assemblage models that take into account the relationships between the different species in the community and so their relative location in the "environmental hyperspace", instead of modelling single species distribution independently from each other [96].

**4.3. Model development**

464 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

sampling locations [90–93].

*4.3.1. Species distribution modelling*

Sampling strategies, detailed field studies and casual observations can provide data which constitute the baseline information for model development. While remote sensing products are still too coarse resolution or maybe not adapted to define microhabitats, they can however provide proxies for environmental factors influencing general habitat and might be used in two ways. (1) Environmental values can be extracted at the sampling sites or in a buffer around the sites and then related to *Anopheles* data in descriptive models. Buffer size is often a compromise between some meaningful ecological feature such as flying range and the spatial resolution of the environmental factors [89]. (2) For question regarding habitat, spatial variation in vector capacity and spatial surveillance, spatial models are needed. In these models, environmental factors are related to the species records collected in the sampling locations and this relation is then used to predict the distribution of the species beyond the

When working with existing data, sampling protocol cannot be influenced a posteriori but an adapted methodology can be used to take into account potential peculiarity of each dataset. Field data may be obtained as a by-product of existing operational projects. However, depending on the finality which determined sampling design, the data might not be used straightforwardly for spatial surveillance. The dataset might include non standardized data collected during different years, according to a variety of sampling strategies but might be the only data available covering many countries. Existing datasets can consist of a collection of literature records covering wide regions. However, the collection sites are seldom well georeferenced, large areas are not covered by the studies which might use different collection techniques at different seasons. With such datasets lack of records might be linked to inefficient sampling method, wrong timing for the survey or absence of survey and according to the source of data, abundance and absence need to be treated with caution. Even certified presence might not reflect current situation if recorded years ago. These issues may partly be addressed by methods similar to the previously mentioned subsampling procedures to reduce the

potential biases in readily available datasets or using adapted modelling techniques.

Early development in the field of remote sensing and vector-borne diseases risk mapping used the following methodological steps: collecting human cases (or mosquito presence/absence), collectingrelevant environmentalgriddeddata (pixel), extractingdata at samplingsites tobuild a logistic regression model explaining cases of occurrence according to the environmental conditions, then mapping the probabilities by calculation of the model output for each grid‐ ded cell of the original environmental maps [94]. Numerous methods have now been used to model vector-borne diseases spatially [95] and suggestions to improve frequent drawbacks include(1)usingseveralmodelsandselectthebest suitedforpredictionand(2)makeasummary model from the best-fitting models. On the other hand innovative methods are constantly improved in spatial ecology. Quantifying the link between species and their environment is a central research area in quantitative ecology. When absence data are available / reliable, numerous methods now do exist, ranging from logistic regression, ordinary multiple regres‐

However, mapping elusive species such as mosquitoes is often a challenge mainly because of the impossible collection of reliable absence data such as described earlier. Discriminant approaches such as logistic regression analysis developed for specific diseases are thus not suited anymore because they compare environmental conditions in sites where the species is present and absent (not recorded). When only occurrence data are available, some niche-based modelling approaches offer adapted solution as they can use presence-only record information to build the statistical models. The concept of ecological niche has been defined [97] as follows: considering the n variables corresponding to all of the ecological factors relevant for the species, an n-dimensional hyper-volume can be defined in the environmental hyperspace between the limiting values permitting a species to survive and reproduce. This volume is called the fundamental niche of the species. This niche can be related to the two-dimensional geographical area of distribution considering that any point of the niche may represent a combination of environmental values that corresponds to some locations in the geographical space. Mechanistic approaches to ecological niche modelling [90] use direct measurements or physical modelling of response of individuals to parameters and infer from them individuals fitness values of different combinations of physical variables. On the contrary, correlative approaches to ecological niche models such as developed for species distribution models intend in a first step to define niches using the environmental variables at sampling point of occurrence, then assess for each spatial location in a study area probability to belong to the niche. Many large-scale species modelling techniques inspired by the principle of environ‐ mental envelopes were developed including BIOCLIM [98] based on a very simple classifica‐ tion tree, DOMAIN [99] based on a measure of multivariate distance, ENFA [100] based on the same principle of distance measure in an environmental hyperspace. Elith *et al.* [14] provide a good overview of most currently used methods including the Maxent method [22] based on presence data which seems to perform particularly well.

In this context classical presence-only modeling can also be integrated [9] into a hierarchical framework [101]. The first step is to model entomological data using environmental data relevant for the same time period. Indeed, mapping *Anopheles* information from literature records dating back several decades should be based on long term environmental factors such as climatic factors and not on factors such as land cover, or NDVI which are changing fast in some regions. The mapping of a first potential distribution based on long term slows changing information and literature records is then refined using a mask of fast changing updatable information such as land cover or current meteorological prediction. This allows producing a risk map or distribution map relevant for a specific date corresponding to the date of envi‐ ronmental factors used to refine the map. The resulting map is thus ecologically meaningful and relevant for a precise date. Recent other improvements in the field of presence only models include selection of pseudo-absence with a spatial bias similar to the potential bias of presence data [102], selection of the environmental factors to enter the model based on ecological requirements, adapted method for species with low number of occurrence [103].

Some issues still need to be tackled however. Ecological model should be based on source populations. Those are sustainable populations in suitable habitat. To the contrary, sink populations are surviving in habitat not suitable for population persistence but persist thanks to immigration from nearby source population. Typical museum records include both sink and source populations [104]. Moreover, current vector-borne disease distribution may be limited by a number of factors both environmental and socio-economic. For example, during the past 100years,malaria riskzonehas reducedfromaroundahalfdowntoaquarteroftheEarth's land surface. However malaria remains prevalent in 106 countries of the tropical and semitropical world,with35countriesincentralAfricabearingthehighestburdenofcasesanddeaths[105,106]. The latitudinal limits apparent today are in effect 'control frontiers' reflecting the interplay of control interventions combined with changes in environmental management and socioeconomic developments that reduce community vulnerability to the disease [107]. Altitudinal limits to malaria transmission have been the subject of much discussion regarding shifting of malariariskintohighlandregions,suchasEastAfrica.Ifdocumentedclimatechange[108]might have add a small impact, major factors for extension to new areas seem to be changes in land use and landscape leading to changes in local ecology for human and vector [109].

#### *4.3.2. Time or space prediction — Evolution in time — Forecast*

While delineation of potential habitat for a species is a first step in risk mapping for *Anophe‐ les*-borne species, forecasting seasonal events and variation in (micro-) habitat suitability and mosquito population is essential. Remote sensing and Geographical Information Systems (GIS) contributed to the development of environmental systems to support vector control or more sophisticated early warning systems. Those systems usually target situations of epidemic malaria which occurs in regions where malaria is not present continuously but associated to climatic events such as a particularly wet season in near desert areas [110] or a hot season in African highlands [111]. Epidemic situation are predicted to increase preparedness in public health [112]. These first experiences are reviewed in [10]. Several trends are observed in current research, but a major effort is targeted towards the prediction of malaria epidemic season based on climatic/meteorological variables, particularly in the context of climate changes and availability of new meteorological data sources [35]. The disease risk is forecasted using seasonal climate prediction and in particular rainfall and sea-surface temperature [110], and influence of climate change analysed [113]. Following the development of the European ENSEMBLE System for seasonal to inter - annual prediction [114], challenging researches are now proposing to integrate the seasonal climate forecasts from climate model into malaria early warnings systems [115]. Regional specificity still needs to be integrated in such models as for example the fact that low rainfall may trigger epidemics in the highlands [116].

#### *4.3.3. Anopheles vector capacity*

ronmental factors used to refine the map. The resulting map is thus ecologically meaningful and relevant for a precise date. Recent other improvements in the field of presence only models include selection of pseudo-absence with a spatial bias similar to the potential bias of presence data [102], selection of the environmental factors to enter the model based on ecological

Some issues still need to be tackled however. Ecological model should be based on source populations. Those are sustainable populations in suitable habitat. To the contrary, sink populations are surviving in habitat not suitable for population persistence but persist thanks to immigration from nearby source population. Typical museum records include both sink and source populations [104]. Moreover, current vector-borne disease distribution may be limited by a number of factors both environmental and socio-economic. For example, during the past 100years,malaria riskzonehas reducedfromaroundahalfdowntoaquarteroftheEarth's land surface. However malaria remains prevalent in 106 countries of the tropical and semitropical world,with35countriesincentralAfricabearingthehighestburdenofcasesanddeaths[105,106]. The latitudinal limits apparent today are in effect 'control frontiers' reflecting the interplay of control interventions combined with changes in environmental management and socioeconomic developments that reduce community vulnerability to the disease [107]. Altitudinal limits to malaria transmission have been the subject of much discussion regarding shifting of malariariskintohighlandregions,suchasEastAfrica.Ifdocumentedclimatechange[108]might have add a small impact, major factors for extension to new areas seem to be changes in land use

requirements, adapted method for species with low number of occurrence [103].

466 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

and landscape leading to changes in local ecology for human and vector [109].

While delineation of potential habitat for a species is a first step in risk mapping for *Anophe‐ les*-borne species, forecasting seasonal events and variation in (micro-) habitat suitability and mosquito population is essential. Remote sensing and Geographical Information Systems (GIS) contributed to the development of environmental systems to support vector control or more sophisticated early warning systems. Those systems usually target situations of epidemic malaria which occurs in regions where malaria is not present continuously but associated to climatic events such as a particularly wet season in near desert areas [110] or a hot season in African highlands [111]. Epidemic situation are predicted to increase preparedness in public health [112]. These first experiences are reviewed in [10]. Several trends are observed in current research, but a major effort is targeted towards the prediction of malaria epidemic season based on climatic/meteorological variables, particularly in the context of climate changes and availability of new meteorological data sources [35]. The disease risk is forecasted using seasonal climate prediction and in particular rainfall and sea-surface temperature [110], and influence of climate change analysed [113]. Following the development of the European ENSEMBLE System for seasonal to inter - annual prediction [114], challenging researches are now proposing to integrate the seasonal climate forecasts from climate model into malaria early warnings systems [115]. Regional specificity still needs to be integrated in such models

as for example the fact that low rainfall may trigger epidemics in the highlands [116].

*4.3.2. Time or space prediction — Evolution in time — Forecast*

When trying to assess disease occurrence risk, not only vector presence is necessary but the capacity and eagerness to transmit the diseases is essential. This capacity is well summarized in the vectorial capacity (VC) concept [117] derived from the Basic reproduction rate of MacDonald [118]. Vectorial capacity is a series of biological features that determine the ability of mosquitoes to transmit *Plasmodium.* It is defined as the daily rate at which future inoculations could arise from a currently infected case [119] and it is generally used as a convenient way to express malaria transmission risk. Interestingly, a spatial version of the VC called VCAP has been developed to propose a spatial version of the formula, allowing assessment of vectorial capacity for each pixel in a given area [120]. To be able to do so, the VCAP is VC only driven by minimum *Ta* and rainfall. Rainfall and temperature are used as inputs to the model because they have an impact on vectorial capacity. Temperature has an effect on both the vector and the parasite. For the vector, it affects the juvenile development rates, the length of the gono‐ trophic cycle and survivorship of larvae and adults with an optimal temperature and upper and lower lethal boundaries. For the parasite, it effects the extrinsic incubation period [121]. *Plasmodium falciparum* (the dominant parasite in Africa) requires warmer minimum tempera‐ ture than *Plasmodium vivax*. This can account for the geographic limits of malaria transmission for this species in Africa [122]. At 26ºC the extrinsic incubation period of this species is about 9-10 days whereas at 20-22ºC it may take as long as 15-20 days. In highlands, where cold temperatures preclude vector and/or parasite development during part/or all of the year, increased prevalence rates may be associated with higher than average minimum tempera‐ tures [123] which might be led by period of low rainfall [116]. It is possible to use minimum *Ta* derived from MODIS for monitoring risks of malaria transmission in highlands regions including Eritrea and Ethiopia where a high proportion of the population lives at risk of epidemic malaria. Currently, the USGS EROS Center uses this temperature derived from MODIS night *Ts* on an 8-day basis jointly with rainfall data derived from the Tropical Rainfall Measuring Mission (TRMM) downscaled to 1 km spatial resolution to produce a 1 km VCAP map every 8-days specifically for the epidemic regions of sub-Saharan Africa [118]. In Eq. 1, the two raster images MODIS night time (Ts) and rainfall (TRMM) are integrated as follows:

$$\text{VCAP} = \frac{-\left(\begin{array}{c} m \ a^2 \end{array}\right) p^n}{\ln\left(p\right)}\tag{1}$$

Where:

m = 10.0 \* TRMM a = 0.7/gonotrophic

gonotrophic =[36.5/( Ts+2.0-9.9)]+0.5

p=0.5(1.0/gonotrophic))

n=111.0/{[2.0\*(36.5/ Ts+2.0-9.9)/gonotrophic]+Ts-18.0}

**Figure 5.** Vectorial capacity map VCAP provided for the epidemic zones of Africa at 1 km spatial resolution.

Parameter, *m* is the density of vectors (per human), *a* is the frequency of daily vector-man contact, *p* is the probability of a mosquito surviving through one whole day, and *n* is the extrinsic incubation period of malaria parasites or 'the time taken for completion of the extrinsic cycle'. Here the density *m* is estimated as a function of rainfall while the duration of the gonotrophic cycle and the extrinsic incubation period *n* are function of the temperature. The coefficients used in the VCAP equation are at this stage not optimized to specific regions. The variability in VCAP is only driven by the *Ts* and rainfall. This is a first attempt to spatially map risk of malaria transmission based on a vectorial capacity model. The product (Figure 5) is made available on a regular basis for the period Jan 2004 to present on the FEWS NET Africa Data Portal: http://earlywarning.usgs.gov/fews/africa/web and IRI data library: (http:// iridl.ldeo.columbia.edu/maproom/.Health/.Regional/.Africa/.Malaria/.VCAP/ )

The analysis of VCAP in relation to rainfall, temperature, and malaria incidence data in Eritrea and Madagascar shows that the VCAP correctly tracks the risk of malaria both in regions where rainfall is the limiting factor and in regions where temperature is the limiting factor [118]. However, in Burundi highlands, low rainfall triggered higher temperature and increased the risk of epidemics [116] and thus lower rainfall might be the trigger particularly because houses provide microenvironment with stable temperature 5°c higher than outside temperature and reduce influence of temperature on epidemic risk. The VCAP could also be further detailed by carrying analysis per vector species.

#### **4.4. Transferring spatial information to health professionals**

Roberts *et al.* [124] demonstrated many potential uses of remotely sensed data in managing and targeting vector and disease control measures. Just mapping the existing *Anopheles* species attributes can already bring information. Recently a map of all existing records for the *Anopheles* *dirus* complex was proposed [125] to document ecological settings, but also to demonstrate that detailed mapping could bring much more information and could lead to more sophisti‐ cated models [9] from those datasets such as developed [6] for *Anopheles* vectors. In this context, major effort were made in the past to provide mapping expertise through customized GIS application to malaria control staff and help them to map their entomological and diseases cases records. Simply overlaying this information with existing environmental information can lead to new working hypotheses better defined by people with experience in the field. Current availability of easy to use packages such as Google earth and Google map offer new opportunities particularly in areas covered by detailed imagery. Studies carried out by scientist devoted to research provide outputs in scientific publications, in pdf format or might target small study areas not representative of the whole country. While this type of output is useful for advances in sciences it is often of little use to the health worker in the field. Two types of approaches are more adapted to the field and complementary. One is to provide ready-to-use product to integrate into operating systems, updated regularly to feed into early warning systems, or informative enough to provide the necessary clues for control and forecast. Those include vector capacity maps. The other approach is to bring the most expertise possible into the hands of the health worker.

However, to be fully operational the development of new products and early warning systems presented above must be integrated into a decision/action framework. There is currently a good deal of policy congruence through international, regional and local levels to support this effort (e.g. the Global Framework for Climate Services whose aims are to develop more effective services to meet the increasing demand coming from climate sensitive sectors including health). The remaining challenge is to get the knowledge into practice and sustaining it where it is needed. It is crucial that appropriate policies are developed and implemented to improve health system performance [126]. This may be helped by enhancing the workforces' ability to detect and treat diseases, monitor and predict spatio-temporal patterns and imple‐ ment intervention and control strategies in a timely and cost-effective manner through the use of tools and analysis informed by climate data.

**Figure 5.** Vectorial capacity map VCAP provided for the epidemic zones of Africa at 1 km spatial resolution.

iridl.ldeo.columbia.edu/maproom/.Health/.Regional/.Africa/.Malaria/.VCAP/ )

by carrying analysis per vector species.

468 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

**4.4. Transferring spatial information to health professionals**

Parameter, *m* is the density of vectors (per human), *a* is the frequency of daily vector-man contact, *p* is the probability of a mosquito surviving through one whole day, and *n* is the extrinsic incubation period of malaria parasites or 'the time taken for completion of the extrinsic cycle'. Here the density *m* is estimated as a function of rainfall while the duration of the gonotrophic cycle and the extrinsic incubation period *n* are function of the temperature. The coefficients used in the VCAP equation are at this stage not optimized to specific regions. The variability in VCAP is only driven by the *Ts* and rainfall. This is a first attempt to spatially map risk of malaria transmission based on a vectorial capacity model. The product (Figure 5) is made available on a regular basis for the period Jan 2004 to present on the FEWS NET Africa Data Portal: http://earlywarning.usgs.gov/fews/africa/web and IRI data library: (http://

The analysis of VCAP in relation to rainfall, temperature, and malaria incidence data in Eritrea and Madagascar shows that the VCAP correctly tracks the risk of malaria both in regions where rainfall is the limiting factor and in regions where temperature is the limiting factor [118]. However, in Burundi highlands, low rainfall triggered higher temperature and increased the risk of epidemics [116] and thus lower rainfall might be the trigger particularly because houses provide microenvironment with stable temperature 5°c higher than outside temperature and reduce influence of temperature on epidemic risk. The VCAP could also be further detailed

Roberts *et al.* [124] demonstrated many potential uses of remotely sensed data in managing and targeting vector and disease control measures. Just mapping the existing *Anopheles* species attributes can already bring information. Recently a map of all existing records for the *Anopheles* In order to get research outcomes into policy and practice it is important to understand the context in which policies are adopted and supported in a practical manner. Below is an example of how policies developed at the district and national level connect to the larger political agenda of international policy makers. At the global scale improved early warning, prevention and control of epidemics is one of the key technical elements of the current Global Strategy for Malaria Control [127] the RBM Partnership referenced earlier in this section. In Africa, Headsof-State declared their support for the Roll Back Malaria initiative in April 2000 with the Abuja Targets [128]. In these targets, national malaria control services are expected to detect sixty per cent of malaria epidemics within two weeks of onset, and respond to sixty per cent of epidemics within two weeks of their detection. With the support of the WHO Regional Office for Africa, the WHO Inter-Country Programme Teams engage in the development of recommendations, guidelines and technical support to improve prevention and control of epidemics and transboundary/cross border within their various sub-regions (e.g. Regional Economic Com‐ munities (RECS) ECOWAS, IGAD and SADC) including collaborative activities with the African Development Bank. As a consequence of these policy developments, nations epidemic prone have enhanced capabilities for delimiting epidemic/endemic prone areas; established epidemic malaria surveillance systems; and strengthening their epidemic response capacities with the help of the Global Fund to Fight AIDS, Tuberculosis and Malaria (GFATM) and other donor support.

In many national malaria control policy documents, countries now recognize that to achieve the Roll Back Malaria (RBM) and Millennium Development Goals (MDG) targets they need better information on where epidemics are most likely to occur, and some indication of when they are likely to happen. As a consequence, they have begun to explore the use of climate information in the development of integrated early warning systems. Thus, there is increasing congruence in policy initiatives from multilateral, bilateral, national and non-governmental agencies in relation to epidemic disease control and a growing demand for climate information and robust early warning systems to support these efforts. This is reflected in the newly emerging Global Framework for Climate Services. This policy congruence extends to the current discussions on adaptation to climate change. Strengthened health systems are also seen as vital to improving the management of climate-sensitive disease in the context of climate change. The IPCC identified building public health infrastructure as: *The most important, cost effective and urgently needed adaptation strategy.* Other measures endorsed by the IPCC include public health training programs, more effective surveillance and emergency response systems, and sustainable prevention and control programs. These measures are familiar to the public health community and are needed regardless of climate change and constitute what is the basis of a *no regrets* adaptation strategy [129,130].

## **5. Further research**

In terms of data, interactions between *Anopheles* species should be investigated, those being sympatric on the same habitat or even breeding site or one dominant species deterring another species. Adapted methodology based on asymmetrical similarity coefficients, indirect clus‐ tering and the search of indicative species [131] have been proposed [132] to identify species association to help assess the risk of presence of elusive species, if another often associated species is present. Caveats and potential improvements to environmental factors have already been discussed. Remote sensing offers already a wide range of useful products but improve‐ ments could target easier delivery of products such as proposed by the IRI data library (http:// iridl.ldeo.columbia.edu/) in similar standardised format and resolution and availability of all useful derived products over the world.

In terms of modelling, various issues have also already been discussed such as the necessity to better integrate ecological issues such as sink and source population [104]. Regarding the outputs, quality assessment could be attached to the resulting maps. Bayesian inference can be used [133] to quantify the uncertainty in the predictions. Rather than mapping the preva‐ lence, what is mapped is the probability, given the data, that a particular location exceeded the predetermined high-risk prevalence threshold for which a change in strategy for control or the delivery of the drug is required. A level of uncertainty attached to each location help the decision maker choose which areas are at risk or not.

There is a necessity to document in details the data entered in models and choices of the modellers particularly when dealing with results which might trigger decision in public health [134]. Indeed, the final results do not only depend on input data but on pre-processing of those data, selection of useful variables, selection of a best model between various potential models, a whole process of model building which leads to one final result dependant on choices of the modeller. More details on dates of satellite images used to derived RS product, or even detailing quality spatially could also improve the final results and potential interpretation. Providing maps of the dataset entered in the model could help spot good spatial consistency or mismatch between adjacent raw images.

While disease occurrence prediction is generally the objective of forecasting, targeting the vector instead of the disease cases might provide several advantages. Indeed, some diseases might be present in a high number of asymptomatic carriers (lymphatic filariasis), or might not be accurately reported because the disease is not notifiable or misdiagnosis is frequent such as confusion between malaria and *Borrelia duttoni* in parts of Senegal and Togo [135]. Targeting the vector can help identify areas where asymptomatic cases might occur, target several diseases at once and predict epidemics or seasonal occurrence of diseases in advance based on fluctuations in mosquito populations.

## **6. Conclusions**

African Development Bank. As a consequence of these policy developments, nations epidemic prone have enhanced capabilities for delimiting epidemic/endemic prone areas; established epidemic malaria surveillance systems; and strengthening their epidemic response capacities with the help of the Global Fund to Fight AIDS, Tuberculosis and Malaria (GFATM) and other

In many national malaria control policy documents, countries now recognize that to achieve the Roll Back Malaria (RBM) and Millennium Development Goals (MDG) targets they need better information on where epidemics are most likely to occur, and some indication of when they are likely to happen. As a consequence, they have begun to explore the use of climate information in the development of integrated early warning systems. Thus, there is increasing congruence in policy initiatives from multilateral, bilateral, national and non-governmental agencies in relation to epidemic disease control and a growing demand for climate information and robust early warning systems to support these efforts. This is reflected in the newly emerging Global Framework for Climate Services. This policy congruence extends to the current discussions on adaptation to climate change. Strengthened health systems are also seen as vital to improving the management of climate-sensitive disease in the context of climate change. The IPCC identified building public health infrastructure as: *The most important, cost effective and urgently needed adaptation strategy.* Other measures endorsed by the IPCC include public health training programs, more effective surveillance and emergency response systems, and sustainable prevention and control programs. These measures are familiar to the public health community and are needed regardless of climate change and constitute what is the basis

In terms of data, interactions between *Anopheles* species should be investigated, those being sympatric on the same habitat or even breeding site or one dominant species deterring another species. Adapted methodology based on asymmetrical similarity coefficients, indirect clus‐ tering and the search of indicative species [131] have been proposed [132] to identify species association to help assess the risk of presence of elusive species, if another often associated species is present. Caveats and potential improvements to environmental factors have already been discussed. Remote sensing offers already a wide range of useful products but improve‐ ments could target easier delivery of products such as proposed by the IRI data library (http:// iridl.ldeo.columbia.edu/) in similar standardised format and resolution and availability of all

In terms of modelling, various issues have also already been discussed such as the necessity to better integrate ecological issues such as sink and source population [104]. Regarding the outputs, quality assessment could be attached to the resulting maps. Bayesian inference can be used [133] to quantify the uncertainty in the predictions. Rather than mapping the preva‐ lence, what is mapped is the probability, given the data, that a particular location exceeded the predetermined high-risk prevalence threshold for which a change in strategy for control or the delivery of the drug is required. A level of uncertainty attached to each location help

donor support.

of a *no regrets* adaptation strategy [129,130].

470 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

useful derived products over the world.

the decision maker choose which areas are at risk or not.

**5. Further research**

In conclusion, providing relevant information to help disease spatial surveillance is not straighforwards and resemble more to a multidisciplinary challenge. In order to improve the current situation, increased sharing of existing data and increase transparency and documen‐ tation in the building of models could help target low quality areas such as places with low information or part of modelling process which could be improved. The quality of the entomological and environmental dataset as well as documentation of the relevant dates of each parameter such as original satellite images included in land cover maps and potential issues such as source-sink population sample could help identify new questions. Meanwhile, the information is still needed for the support of essential activities such as malaria control or for scientific research. A better interaction between research and operational work also seems to be necessary. Research product and results can only be useful if validated in the field and the best research questions are defined by people working in the field. Constant interactions can improve quality of research products and finally improve surveillance. Reinforcing the research capabilities in the region and in the malaria centres is of up-most importance. Indeed malaria workers in-countries have an extended experience of the field. They are in a better position to analyze the situation, identify their needs and find the answers. This would help bringing the data and the expertise where it is mostly needed: in the malaria centres.

#### **Abbreviations**

AVHRR Advanced Very High Resolution Radiometer

BRDF bi-directional reflectance distribution function

CGIAR Consultative Group on International Agricultural Research CMORPH Products from the CPC MORPHing technique CRU Climate Research Unit, University of East Anglia, UK ECOWAS Economic Community Of West African States EUMETSAT European Organisation for the Exploitation of Meteorological Satellites EVI The Enhanced Vegetation Index FAO Food and Agriculture Organization of the United Nations fAPAR fraction of Absorbed Photosynthetically Active Radiation FEWS NET Famines Early Warning Systems Network GFATM Global Fund to Fight AIDS, Tuberculosis and Malaria GIMMS Global Inventory Modelling and Mapping Studies GIS Geographical Information Systems GMES Global Monitoring for Environment and Security GRIB MPE The Multi-sensor Precipitation Estimate HSV Hue saturation value IGAD The Intergovernmental Authority on Development, East Africa IPCC Intergovernmental Panel on Climate Change IR Infrared IRI International Research Institute for Climate and Society LAI Leaf Area Index LST Land Surface Temperature MDG Millennium Development Goals MERIS Medium Resolution Imaging Spectrometer MODIS Moderate Resolution Imaging Spectroradiometer MPE Multisensor Precipitation Estimator MVC Maximum Value Composite NASA National Aeronautic and Space Administration NBAR Nadir BRDF-Adjusted Reflectance NDVI The Normalized Difference Vegetation Index NDWI The Normalized Difference Water Index

NIR Near-Infrared NOAA-CPC National Oceanic and Atmospheric Administration – Climate Prediction Centre RBM Roll Back Malaria RECS Regional Economic Communities RFE African Rainfall Estimation SADC Southern African Development Community SRTM Shuttle Radar Topographic Mission SWIR Short Wavelength Infrared TARCAT The TAMSAT African Rainfall Climatology And Time-series TIR Thermal infra-red TRMM The Tropical Rainfall Measuring Mission USGS United states Geological Survey Agency VC (VCAP) Vectorial Capacity (spatial version) WHO World Health Organisation

## **Author details**

CGIAR Consultative Group on International Agricultural Research

FAO Food and Agriculture Organization of the United Nations fAPAR fraction of Absorbed Photosynthetically Active Radiation

GFATM Global Fund to Fight AIDS, Tuberculosis and Malaria

IGAD The Intergovernmental Authority on Development, East Africa

GIMMS Global Inventory Modelling and Mapping Studies

GMES Global Monitoring for Environment and Security

GRIB MPE The Multi-sensor Precipitation Estimate

IPCC Intergovernmental Panel on Climate Change

MERIS Medium Resolution Imaging Spectrometer

MODIS Moderate Resolution Imaging Spectroradiometer

NASA National Aeronautic and Space Administration

NDVI The Normalized Difference Vegetation Index

NDWI The Normalized Difference Water Index

IRI International Research Institute for Climate and Society

FEWS NET Famines Early Warning Systems Network

EUMETSAT European Organisation for the Exploitation of Meteorological Satellites

CMORPH Products from the CPC MORPHing technique CRU Climate Research Unit, University of East Anglia, UK ECOWAS Economic Community Of West African States

EVI The Enhanced Vegetation Index

472 *Anopheles* Anopheles mosquitoes - New insights into malaria vectors mosquitoes - New insights into malaria vectors

GIS Geographical Information Systems

HSV Hue saturation value

IR Infrared

LAI Leaf Area Index

LST Land Surface Temperature

MDG Millennium Development Goals

MPE Multisensor Precipitation Estimator

NBAR Nadir BRDF-Adjusted Reflectance

MVC Maximum Value Composite

Valérie Obsomer1\*, Nicolas Titeux2 , Christelle Vancustem3 , Grégory Duveiller3 , Jean-François Pekel3 , Steve Connor4 , Pietro Ceccato5 and Marc Coosemans6

\*Address all correspondence to: valerie.obsomer@uclouvain.be

1 Earth and Life Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium

2 Department Environment & Agro-biotechnologies, Public Research Centre - Gabriel Lipp‐ mann, Belvaux, Luxembourg

3 Joint Research Centre of the European Commission (JRC), Ispra, Italy

4 School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom

5 International Research Institute for Climate and Society, The Earth Institute, Columbia University, New York, USA

6 Institute for Tropical Medicine, Antwerp, Belgium

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