**4.1. Dynamic informatics**

I selected ecologically significant Taoyuan Tableland associated irrigation ponds as my study area because one fifth of all the bird species find home on these ponds in Taiwan (Chen, 2000; Fang, 2004a, b; Fang & Chang, 2004; Fang et al., 2009; Fang & Huang, 2011; Fang et al., 2011). This tableland, at an area of 757 km2 in size, comprises an area of 2,898 ha of irrigation ponds on the northwestern portion of Taiwan. Located approximately 30 km from the capital city of Taipei, this rural area was easily converted to urban lands due to the aggregated effects of urbanization and commercialization. Socioeconomic benefits are driving public opinion which is urging the government to approve land-use conversion from farmlands into urban uses. The Taoyuan Tableland lies between the northern border of the Linkou Tableland (23°05'N, 121°17'E) and the southern border of the Hukou Tableland (22°55'N, 121°05'E); it borders the town of Yinge in the east (22°56'N, 121°20'E) and the Taiwan Strait in the west (22°75'N, 120°99'E) (Department of Land Administration, Ministry of the Interior, 2002)(see Fig. 1.). It sits at elevations from sea level to 400 m and is composed of tableland up to 303 m and hills with sloping gradients from 303 to 400 m. It runs in a southeast-to-northwest trend, abutting mountains in the southeastern corner and the shore of the Taiwan Strait at the far end. With a high average humidity of 89%, the tableland is located in a subtropical monsoon region with humid winters and warm summers. January temperatures average 13 °C, and July temperatures average 28 °C. Annual average precipitation ranges from 1,500 to 2,000 mm.

The tableland gradually rose approximately 180,000 years ago. At that time, the Tanshui River had not yet captured the flow from the ancient Shihmen Creek, which directly poured out of the northwestern coast forming alluvial fans. Eventually, foothill faults caused by earthquakes during the same era, resulted in the northern region of Taiwan abruptly dropping by 200 m, and thus, the Taipei basin was born. Since the Taipei area had subsided, the ancient Shihmen Creek which meandered across the Taoyuan Tableland was captured by northward-flowing rivers some 30,000 years ago. The middle streams changed their courses because of the subsidence in the Taipei basin. The resulting Tahan Creek, became the upstream portion of the Tanshui River in the Taipei Basin. Due to blockage of water sources, downstream areas on the Taoyuan Tableland were deficient in water. This caused high flushing and drops in water yields. Historically, it was difficult to withdraw and supply irrigated surface water from rivers due to the tableland's unique topography, thus, forming an obstacle for the development of agriculture (Agricultural and Forestry Aerial Survey Institute, 2010) .

This area has a population density of 2,331 persons/km2 and its population is increasing at a rate of 2,000~3,000/month. Population pressures have contributed to reductions in historical areas of farmlands and irrigation ponds (Fang, 2001). Losses of farm-pond and farmland habitats have had series effects on a range of avian communities as well as other fauna and flora. On the Taoyuan Tableland, agricultural practices are intensifying, which is reducing the heterogeneity of the existing landform, and adding pollutants, also resulting from industrial practices (Fang et al., 2011).

**Figure 1.** Location away the city limits more than 2 km of forty-five study ponds in the range of the tableland (see also as Fang et al., 2011).

#### **4.2. Waterbirds sampled**

216 Biodiversity Conservation and Utilization in a Diverse World

hierarchy and expected numbers of functional groups

precipitation ranges from 1,500 to 2,000 mm.

**4. Materials and methods** 

**4.1. Dynamic informatics** 

about the necessary environmental factors that affected avian guilds, cluster analysis was applied in avian studies. It was used to study for grouping avian community of similar kind into respective functional groups. As a set of methods for building groups (clusters) from multivariate data, their aim was to identify groups with habitat preferences for microhabitats. Then, groups were made as homogenous as possible to reduce the differences between them as large as possible. This obtained a result for existing data correlation

I selected ecologically significant Taoyuan Tableland associated irrigation ponds as my study area because one fifth of all the bird species find home on these ponds in Taiwan (Chen, 2000; Fang, 2004a, b; Fang & Chang, 2004; Fang et al., 2009; Fang & Huang, 2011; Fang et al., 2011). This tableland, at an area of 757 km2 in size, comprises an area of 2,898 ha of irrigation ponds on the northwestern portion of Taiwan. Located approximately 30 km from the capital city of Taipei, this rural area was easily converted to urban lands due to the aggregated effects of urbanization and commercialization. Socioeconomic benefits are driving public opinion which is urging the government to approve land-use conversion from farmlands into urban uses. The Taoyuan Tableland lies between the northern border of the Linkou Tableland (23°05'N, 121°17'E) and the southern border of the Hukou Tableland (22°55'N, 121°05'E); it borders the town of Yinge in the east (22°56'N, 121°20'E) and the Taiwan Strait in the west (22°75'N, 120°99'E) (Department of Land Administration, Ministry of the Interior, 2002)(see Fig. 1.). It sits at elevations from sea level to 400 m and is composed of tableland up to 303 m and hills with sloping gradients from 303 to 400 m. It runs in a southeast-to-northwest trend, abutting mountains in the southeastern corner and the shore of the Taiwan Strait at the far end. With a high average humidity of 89%, the tableland is located in a subtropical monsoon region with humid winters and warm summers. January temperatures average 13 °C, and July temperatures average 28 °C. Annual average

The tableland gradually rose approximately 180,000 years ago. At that time, the Tanshui River had not yet captured the flow from the ancient Shihmen Creek, which directly poured out of the northwestern coast forming alluvial fans. Eventually, foothill faults caused by earthquakes during the same era, resulted in the northern region of Taiwan abruptly dropping by 200 m, and thus, the Taipei basin was born. Since the Taipei area had subsided, the ancient Shihmen Creek which meandered across the Taoyuan Tableland was captured by northward-flowing rivers some 30,000 years ago. The middle streams changed their courses because of the subsidence in the Taipei basin. The resulting Tahan Creek, became the upstream portion of the Tanshui River in the Taipei Basin. Due to blockage of water sources, downstream areas on the Taoyuan Tableland were deficient in water. This caused high flushing and drops in water yields. Historically, it was difficult to withdraw and supply irrigated surface water from rivers due to the tableland's unique topography, thus,

Avian observers recorded all bird species seen within a 100-ha radius at 564.19-m basal radius of the bird census point at pond edge associated with line transects along pondedge trails during 30-minute periods (one case of irrigation ponds see Fig. 2.). Sites were visited four times in the winter seasons between November and February. To reduce the effects of bird-observer bias, three to four observers were grouped and rotated between ponds. The observers counted birds that were in any habitats. All counts were conducted between 7:00 a.m. and 10:00 a.m. on days without rainy days when visibility was good (Bookhout, 1996).

Dynamic Informatics of Avian Biodiversity on an Urban and Regional Scale 219

pondscape configuration may vary according to species-area relationships among regions. Therefore, to find differences in the response of species to habitat area and isolation, studies must include multiple analytical approaches to detect which analysis was better based on an

Descriptive statistics for entire communities were used as the first stage of statistical avian data processing. The main aim was initial analysis of the distribution of avian communities sooner, such as an average individual value and; or a guild value was described for specific groups later. Afterwards, avian diversity was described in the result of diversity indices for all communities or a single group. To detect species evenness and abundance, we used Shannon-Wiener diversity index (*H*'*)* (also named for Shannon index or Shannon-Weaver index), which is given a measure of the richness and relative density of a species to calculate diversity (Shannon and Weaver, 1949). This diversity measure conducted by Shannon and Weaver which originally came from information theory and measures the order observed within a particular system. Regarding to my studies, this order was characterized by the number of avian individuals observed for each species in the sampling ponds. The first step was to calculate *Pi* for each category (i.e., avian species), and then we multiplied this number by the log of the number. The index was computed from the negative sum of these numbers.

2

(7)

*i i*

1

This index reflected bird richness in species and evenness amongst the avian community. The benefits of H' was sensitive by the change in threatened birds by avian study than that of Simpson's diversity index (D)(Dean et al., 2002). If the value of *H'* is higher, it means that species is abundant, or species distribution is even. However, species diversity is sometimes difficult to see relationships with spatial heterogeneity by limited survey data. Grouping and classification are required as well as for spatial heterogeneity reduction from the analyzed variables. It is the main procedure in this methodology for invoking avian groups with similar attributes of spatial behavior. The main approach in cluster analysis application is based on the idea to represent the grouping structure by avian data classification, based

Studies of variation in species individuals with relative abundances have been conducted by using species diversity. Although diversity may be measured most directly as the individual numbers, but it has been expressed the interplay of species richness and abundance into a single value (Shannon & Weaver, 1949; MacArthur & MacArthur, 1961; Dean et al., 2002). In this study, diversity was considered over a wide spectrum of spatial scales, from variation across a single pond scale to a regional scale, where temporal patterns were consequences of

*i H P log P* 

*S*

entire community, or on a specific group.

In short, the Shannon-Wiener index *(H')* is defined as (7):

*Pi*: The percentage of the *i* species in avian community

on the similarity in guilds between the species.

**4.4. Simulation for dynamic informatics** 

*S*: avian species richness

**Figure 2.** Avian observers recorded all bird species seen within a 100-ha radius at 564.19-m basal radius of the bird census point at pond edge (photo by Wei-Ta Fang).

Foliage-loving species was also recorded followed the point-count method. Avian presence/absence on foliage strata was recorded in each pond at each of the following height intervals: edge ground, wetland grasses (< 0.5 m in height), bushes (> 0.5- 2.5 m in height), trees (> 2.5 m in height). Points were sampled at 10-m internals along edge trails established down each side of each pond. Waterbirds were grouped into microhabitat guilds based on actual observations on the sites. Foliage-loving species were initially classified into four height categories: pond-edge ground, low foliage (< 0.5 m in height), middle foliage (> 0.5- 2.5 m in height), and high foliage (> 2.5 m in height). Species were subsequently classified into two groups: understory (ground and low foliage groups) and canopy (middle and high foliage groups).

I calculated the number of individuals detected of each species at each pond for each month. Then, we calculated mean values of these variables for each study microhabitat across all study ponds in a wintering season (Fang et al., 2011).

#### **4.3. Waterbird diversity**

There are two traditional bird analyses for entire avian communities and specific avian groups, richness, and diversity. Differences in the characteristics of avian groups and pondscape configuration may vary according to species-area relationships among regions. Therefore, to find differences in the response of species to habitat area and isolation, studies must include multiple analytical approaches to detect which analysis was better based on an entire community, or on a specific group.

Descriptive statistics for entire communities were used as the first stage of statistical avian data processing. The main aim was initial analysis of the distribution of avian communities sooner, such as an average individual value and; or a guild value was described for specific groups later. Afterwards, avian diversity was described in the result of diversity indices for all communities or a single group. To detect species evenness and abundance, we used Shannon-Wiener diversity index (*H*'*)* (also named for Shannon index or Shannon-Weaver index), which is given a measure of the richness and relative density of a species to calculate diversity (Shannon and Weaver, 1949). This diversity measure conducted by Shannon and Weaver which originally came from information theory and measures the order observed within a particular system. Regarding to my studies, this order was characterized by the number of avian individuals observed for each species in the sampling ponds. The first step was to calculate *Pi* for each category (i.e., avian species), and then we multiplied this number by the log of the number. The index was computed from the negative sum of these numbers. In short, the Shannon-Wiener index *(H')* is defined as (7):

$$H' = -\sum\_{i=1}^{S} P\_i \log^2 P\_i \tag{7}$$

*S*: avian species richness

218 Biodiversity Conservation and Utilization in a Diverse World

**Figure 2.** Avian observers recorded all bird species seen within a 100-ha radius at 564.19-m basal radius

Foliage-loving species was also recorded followed the point-count method. Avian presence/absence on foliage strata was recorded in each pond at each of the following height intervals: edge ground, wetland grasses (< 0.5 m in height), bushes (> 0.5- 2.5 m in height), trees (> 2.5 m in height). Points were sampled at 10-m internals along edge trails established down each side of each pond. Waterbirds were grouped into microhabitat guilds based on actual observations on the sites. Foliage-loving species were initially classified into four height categories: pond-edge ground, low foliage (< 0.5 m in height), middle foliage (> 0.5- 2.5 m in height), and high foliage (> 2.5 m in height). Species were subsequently classified into two groups: understory (ground and low foliage groups) and canopy (middle and high

I calculated the number of individuals detected of each species at each pond for each month. Then, we calculated mean values of these variables for each study microhabitat across all

There are two traditional bird analyses for entire avian communities and specific avian groups, richness, and diversity. Differences in the characteristics of avian groups and

of the bird census point at pond edge (photo by Wei-Ta Fang).

study ponds in a wintering season (Fang et al., 2011).

foliage groups).

**4.3. Waterbird diversity** 

*Pi*: The percentage of the *i* species in avian community

This index reflected bird richness in species and evenness amongst the avian community. The benefits of H' was sensitive by the change in threatened birds by avian study than that of Simpson's diversity index (D)(Dean et al., 2002). If the value of *H'* is higher, it means that species is abundant, or species distribution is even. However, species diversity is sometimes difficult to see relationships with spatial heterogeneity by limited survey data. Grouping and classification are required as well as for spatial heterogeneity reduction from the analyzed variables. It is the main procedure in this methodology for invoking avian groups with similar attributes of spatial behavior. The main approach in cluster analysis application is based on the idea to represent the grouping structure by avian data classification, based on the similarity in guilds between the species.

#### **4.4. Simulation for dynamic informatics**

Studies of variation in species individuals with relative abundances have been conducted by using species diversity. Although diversity may be measured most directly as the individual numbers, but it has been expressed the interplay of species richness and abundance into a single value (Shannon & Weaver, 1949; MacArthur & MacArthur, 1961; Dean et al., 2002). In this study, diversity was considered over a wide spectrum of spatial scales, from variation across a single pond scale to a regional scale, where temporal patterns were consequences of

individual habitat selection. The diversity was measured all species. Four regional diversity variations were mapped from experimental semivariogram for avian communities in contour maps. On these maps a successional gradient was indicated to document concentric rings in bird diversity for spatial-temporal analysis (see Equation 8):

$$\gamma(h) = \frac{1}{2N(h)} \left\{ \sum\_{i=1}^{N(h)} \left[ z(\mathbf{x}\_i + h) - z(\mathbf{x}\_i) \right]^2 \right\} \tag{8}$$

The best linear unbiased estimator (BLUE) will be achieved to (9):

$$
\hat{\boldsymbol{z}}(\mathbf{x}\_0) = \sum\_{i=1}^n \lambda\_i \boldsymbol{z}(\mathbf{x}\_j) \tag{9}
$$

Dynamic Informatics of Avian Biodiversity on an Urban and Regional Scale 221

Frequency

*tranquebarica* 509 3.4%

heron *Nycticorax nycticorax* 2,363 15.7%

2 Little Egret *Egretta garzetta* 1,883 12.5% 3 Grey Heron *Ardea cinerea* 1,829 12.2% 4 Light-vented Bulbul *Pycnonotus sinensis* 1,575 10.5% 5 Eurasian Tree Sparrow *Passer montanus* 1,125 7.7% 6 Great Egret *Casmerodius alba* 726 4.8%

8 Japanese White-eye *Zosterops japonica* 504 3.4% 9 Little Ringed Plover *Charadrius dubius* 316 2.1% 10 Little Grebe *Tachybaptus ruficollis* 304 2.0% Totals 11,134 74.1%

Ratio of Frequency

species (3%) were escaped from captivity. The total number of species in the winter seasons in the study area varied. I found greater species richness in wintering migrants (48%) compared with permanent residents (45%). In the microhabitat scale, the species in water regime (vertical structure from water table to aerial space) and waterfront edge were

Avian individual frequencies of occurrence were surveyed (see Table 1). I found significantly higher abundances of ten species, accounted for 74% of the entire species abundance, such as: Black-crowned Night-Heron (*Nycticorax nycticorax*) (occurrence frequency 2,363, occurrence rate of 15.7%, resident species), Little Egret (*Egretta garzetta*)(occurrence frequency 1,883, occurrence rate of 12.5%, resident species), Grey Heron (*Ardea cinerea*) (occurrence frequency 1,829, occurrence rate of 12.2%, wintering migrant species), Light-vented Bulbul (*Pycnonotus sinensis*) (occurrence frequency 1,575, occurrence rate of 10.5%, resident species), Eurasian Tree Sparrow (*Passer montanus*)( occurrence frequency 1,125, occurrence rate of 7.7%, resident species), Great Egret (*Casmerodius alba*)( occurrence frequency 726, occurrence rate of 4.8%, wintering migrant species), Red Collared-dove (*Streptopelia tranquebarica*)(occurrence frequency 509, occurrence rate of 3.4%, resident species), Japanese White-eye (*Zosterops japonica*)( occurrence frequency 504, occurrence rate of 3.3%, resident species), Little Ringed Plover (*Charadrius dubius*)(occurrence frequency 316, occurrence rate of 2.1%,wintering migrant species), and Little Grebe (*Tachybaptus ruficollis*)(occurrence frequency 304, occurrence rate of 2%, resident species), respectively. Other kinds of avian abundance, 84 species, were accounted for the total abundance of 36%. There were 23 species of which above 100 individuals were detected in the entire survey records, fewer than 10 individuals of 40 species were detected throughout the survey (see also the detection of 2003-2004 in Fang et al., 2009).

Place Common Name Scientific Name Individual

7 Red Collared-dove *Streptopelia* 

**Table 1.** The individual frequency and their frequency of ten abundant species.

encountered most frequently.

1 Black-crowned Night-

while *<sup>i</sup>* : weighting of detections; and 1 1 *n i i* 

The estimation value is equal to the true value, such as:

$$\mathbb{E}\{\hat{z}(\mathbf{x}\_0)\} = \mathbb{E}\{z(\mathbf{x}\_0)\}\tag{10}$$

I introduced (Lagrange multiplier), then

$$L = \operatorname{Var}\{\hat{z}(\mathbf{x}\_0) - z(\mathbf{x}\_0)\} - 2\mu(\sum\_{i=1}^n \lambda\_i - 1) \Rightarrow \min\{L\} \tag{11}$$

$$\begin{cases} \frac{\partial L}{\partial \lambda\_i} = 0, & \text{(i = 1, 2, \dots, n)}\\ \frac{\partial L}{\partial \mu} = 0 \end{cases} \tag{12}$$

$$\begin{cases} \sum\_{j=1}^{n} \lambda\_j \eta \left| \mathbf{x}\_i - \mathbf{x}\_j \right| + \mu = \eta \left| \mathbf{x}\_0 - \mathbf{x}\_i \right|\\ \sum\_{j=1}^{n} \lambda\_j = 1 \end{cases} \tag{13}$$

#### **5. Results and discussion**

#### **5.1. Dynamic informatics for individual frequencies**

The avian survey detected ninety-four species in 45 point-count locations associated with line transect of this investigation as a 2003-2004 example (see also Fang et al., 2011). In Taoyuan, forty-five species (48%) species were wintering migrants; forty species (43%) were permanent residents. Five short-transit species (5%) were encountered on the farm-pond sites, one species (1%) was not present at the site previously, defined "missing"; and three species (3%) were escaped from captivity. The total number of species in the winter seasons in the study area varied. I found greater species richness in wintering migrants (48%) compared with permanent residents (45%). In the microhabitat scale, the species in water regime (vertical structure from water table to aerial space) and waterfront edge were encountered most frequently.

220 Biodiversity Conservation and Utilization in a Diverse World

while *<sup>i</sup>* : weighting of detections; and

I introduced (Lagrange multiplier), then

The estimation value is equal to the true value, such as:

0

*, , ,*

**5.1. Dynamic informatics for individual frequencies** 

*jij i*

**5. Results and discussion** 

*(x x ) (x x )*

1

*j n j j*

*n*

1

1

 

individual habitat selection. The diversity was measured all species. Four regional diversity variations were mapped from experimental semivariogram for avian communities in contour maps. On these maps a successional gradient was indicated to document concentric

1

1

*i*

*i j*

1 2 1 *n i i*

(11)

0 (i 1,2, ,n)

The avian survey detected ninety-four species in 45 point-count locations associated with line transect of this investigation as a 2003-2004 example (see also Fang et al., 2011). In Taoyuan, forty-five species (48%) species were wintering migrants; forty species (43%) were permanent residents. Five short-transit species (5%) were encountered on the farm-pond sites, one species (1%) was not present at the site previously, defined "missing"; and three

i 12 n

*n*

*z(x ) z(x )* 

1

*L Var[z(x ) z(x )] ( ) min[L] ˆ*

*i (h) [z(x h) z(x )] N(h)*

0

1

0 0

*i <sup>L</sup> ,*

*L*

 

0

*n i i* 

*ˆ*

*N(h)*

2

(9)

0 0 *E[z(x )] E[z(x )] ˆ* (10)

(13)

(12)

(8)

*i i*

rings in bird diversity for spatial-temporal analysis (see Equation 8):

The best linear unbiased estimator (BLUE) will be achieved to (9):

1 2

Avian individual frequencies of occurrence were surveyed (see Table 1). I found significantly higher abundances of ten species, accounted for 74% of the entire species abundance, such as: Black-crowned Night-Heron (*Nycticorax nycticorax*) (occurrence frequency 2,363, occurrence rate of 15.7%, resident species), Little Egret (*Egretta garzetta*)(occurrence frequency 1,883, occurrence rate of 12.5%, resident species), Grey Heron (*Ardea cinerea*) (occurrence frequency 1,829, occurrence rate of 12.2%, wintering migrant species), Light-vented Bulbul (*Pycnonotus sinensis*) (occurrence frequency 1,575, occurrence rate of 10.5%, resident species), Eurasian Tree Sparrow (*Passer montanus*)( occurrence frequency 1,125, occurrence rate of 7.7%, resident species), Great Egret (*Casmerodius alba*)( occurrence frequency 726, occurrence rate of 4.8%, wintering migrant species), Red Collared-dove (*Streptopelia tranquebarica*)(occurrence frequency 509, occurrence rate of 3.4%, resident species), Japanese White-eye (*Zosterops japonica*)( occurrence frequency 504, occurrence rate of 3.3%, resident species), Little Ringed Plover (*Charadrius dubius*)(occurrence frequency 316, occurrence rate of 2.1%,wintering migrant species), and Little Grebe (*Tachybaptus ruficollis*)(occurrence frequency 304, occurrence rate of 2%, resident species), respectively. Other kinds of avian abundance, 84 species, were accounted for the total abundance of 36%. There were 23 species of which above 100 individuals were detected in the entire survey records, fewer than 10 individuals of 40 species were detected throughout the survey (see also the detection of 2003-2004 in Fang et al., 2009).


**Table 1.** The individual frequency and their frequency of ten abundant species.

## **5.2. Dynamic informatics for biodiversity**

Based on the point-count locations used random samplings in Taoyuan Tableland, the Shannon-Wiener index (*H'*) by the data of ornithology have been caculated from December 2008, January 2009, and Febuary 2009 in migrating winters. This list with only 7 point-count locations within the entire points of the value of *H'* > 2 can be detected during December 2008, such as: No. 2 (2.522), No. 5 (2.152), No. 15 (2.128), No. 24 (2.127), No. 44 (2.062), No. 33 (2.057), No. 39 (2.022), respectively. This is also 7 point-count locations within the entire points of the value of H' > 2 can be detected during January 2009, such as: No. 32 (2.351), No. 27 (2.267), No. 7 (2.259), No. 40 (2.205), No. 19 (2.134) No. 2 (2.123), No. 5 (2.038), respectively. During the February 2009, a total of 14 point-count locations that the value of *H'* > 2 can be detected at the list, such as the numbers of No. 23 (2.575), No. 44 (2.528) No. 40 (2.516), No. 15 (2.360) No. 1 (2.357), No. 20 (2.320), No. 24 (2.312) No. 2 (2.282), No. 36 (2.281), No. 5 (2.219), No. 37 (2.145), No. 30 (2.046), No. 23 (2.042), No. 34 (2.007), respectively. The average value from three months was calculated at a lower value of 1.603 ± 0.494. This represents some seasonal dynamic informatics currently at a relative peak of *H'* in the month of Febuary on an urban and regional scale from anthropogenic influences during migratory seasons.

Dynamic Informatics of Avian Biodiversity on an Urban and Regional Scale 223

**Figure 3.** Scenario Model in Shannon-Wiener Diversity by Kriging Approach (December 2008).

**Figure 4.** Scenario Model in Shannon-Wiener Diversity by Kriging Approach (January 2009).

#### **5.3. Modelling by biodiversity**

Studies of variation in species individuals with relative abundances have been conducted under the calculation of species diversity. Although diversity may be measured most directly as the individual numbers, but it has been expressed the interplay of species richness and abundance into a single value (Shannon & Weaver, 1949; MacArthur & MacArthur, 1961; Dean et al., 2002). In this study, diversity was considered over a wide spectrum of spatial scales, from variation across a single pond scale to a regional scale, where temporal patterns were consequences of individual habitat selection. The spatial scale on diversity was measured, depended on the pondscape mosaic upon the moment of all species considered from December 2008, January 2009, and Feburary 2009. Three regional diversity variations were mapped for avian communities in contour maps (Figs. 3, 4, & 5). These maps were indicated a successional gradient to document concentric rings in bird diversity for spatial-temporal analysis.

Based on the experimental semivariogram for avian communities in contour maps. Diversities (*H'*), markly indicate from this anthropogenic influenced trends decreased with increasing dysfunctional pondscapes (monthly variations, see Figs. 2, 3, & 4). This is to say that pondscape configuration is so important in this situation. Indeed, three-month surveys demonstrated monthly diversity oscillations that horizontal heterogeneity might still occur at microhabitats. Species were able to select their proper habitats and then either overwintered or undertook long migrations by different groups as well as by local generalists in huge assembleges. I, thus, hypothesized that diversities, at meso-scale, varied among different guilds of species from habitat selection. The occurrence rates detected by observers on avian communities were intriguing this hypothesis in different microhabitats, and they were largely examined and classified into groups in the section that follows.

**5.3. Modelling by biodiversity** 

diversity for spatial-temporal analysis.

**5.2. Dynamic informatics for biodiversity** 

Based on the point-count locations used random samplings in Taoyuan Tableland, the Shannon-Wiener index (*H'*) by the data of ornithology have been caculated from December 2008, January 2009, and Febuary 2009 in migrating winters. This list with only 7 point-count locations within the entire points of the value of *H'* > 2 can be detected during December 2008, such as: No. 2 (2.522), No. 5 (2.152), No. 15 (2.128), No. 24 (2.127), No. 44 (2.062), No. 33 (2.057), No. 39 (2.022), respectively. This is also 7 point-count locations within the entire points of the value of H' > 2 can be detected during January 2009, such as: No. 32 (2.351), No. 27 (2.267), No. 7 (2.259), No. 40 (2.205), No. 19 (2.134) No. 2 (2.123), No. 5 (2.038), respectively. During the February 2009, a total of 14 point-count locations that the value of *H'* > 2 can be detected at the list, such as the numbers of No. 23 (2.575), No. 44 (2.528) No. 40 (2.516), No. 15 (2.360) No. 1 (2.357), No. 20 (2.320), No. 24 (2.312) No. 2 (2.282), No. 36 (2.281), No. 5 (2.219), No. 37 (2.145), No. 30 (2.046), No. 23 (2.042), No. 34 (2.007), respectively. The average value from three months was calculated at a lower value of 1.603 ± 0.494. This represents some seasonal dynamic informatics currently at a relative peak of *H'* in the month of Febuary on an

urban and regional scale from anthropogenic influences during migratory seasons.

Studies of variation in species individuals with relative abundances have been conducted under the calculation of species diversity. Although diversity may be measured most directly as the individual numbers, but it has been expressed the interplay of species richness and abundance into a single value (Shannon & Weaver, 1949; MacArthur & MacArthur, 1961; Dean et al., 2002). In this study, diversity was considered over a wide spectrum of spatial scales, from variation across a single pond scale to a regional scale, where temporal patterns were consequences of individual habitat selection. The spatial scale on diversity was measured, depended on the pondscape mosaic upon the moment of all species considered from December 2008, January 2009, and Feburary 2009. Three regional diversity variations were mapped for avian communities in contour maps (Figs. 3, 4, & 5). These maps were indicated a successional gradient to document concentric rings in bird

Based on the experimental semivariogram for avian communities in contour maps. Diversities (*H'*), markly indicate from this anthropogenic influenced trends decreased with increasing dysfunctional pondscapes (monthly variations, see Figs. 2, 3, & 4). This is to say that pondscape configuration is so important in this situation. Indeed, three-month surveys demonstrated monthly diversity oscillations that horizontal heterogeneity might still occur at microhabitats. Species were able to select their proper habitats and then either overwintered or undertook long migrations by different groups as well as by local generalists in huge assembleges. I, thus, hypothesized that diversities, at meso-scale, varied among different guilds of species from habitat selection. The occurrence rates detected by observers on avian communities were intriguing this hypothesis in different microhabitats, and they

were largely examined and classified into groups in the section that follows.

**Figure 3.** Scenario Model in Shannon-Wiener Diversity by Kriging Approach (December 2008).

**Figure 4.** Scenario Model in Shannon-Wiener Diversity by Kriging Approach (January 2009).

Dynamic Informatics of Avian Biodiversity on an Urban and Regional Scale 225

**Figure 6.** Scenario Model in Shannon-Wiener Diversity by Kriging Approach within building areas (December 2008). Based on the experimental semivariogram for avian communities in contour maps,

**Figure 7.** Scenario Model in Shannon-Wiener Diversity by Kriging Approach withing building areas

the same overlapped map layers as Fig. 7 & Fig. 8.

(January 2009).

**Figure 5.** Scenario Model in Shannon-Wiener Diversity by Kriging Approach (Febuary 2009).

#### **5.4. Finding**

Despite their agribusiness value, farm ponds appear to have great influences on the makeup of avian communities in urbanized areas, especially for water-edge avian community (See Figs. 6, 7, & 8). I compared the following community characteristics against the corresponding ratio of constructed area value associated with pond configuration of each site for all functional groups: cumulative waterfowl, cumulative shorebirds, cumulative landbirds, cumulative air feeders, and cumulative water-edge species. Pondscape was a strong and/or moderate correlate in any birds of the ordinations (i.e., water-edge birds, shorebirds, and waterfowl) beyond landbirds and air feeders. The presence of adjoining natural and/or urbanized habitats was probably the most important determinant of wetland avifauna in these areas. Regarding to this detailed study, there may be a number of reasons why some farm ponds do not become a refuge for the more sensitive species. First, the ornamental vegetation covers used for surrounding areas are often too few, and they may support a small insect population. Second, anthropogenic structure is subjected to concrete construction without native trees, and this may make it unattractive to water-edge species that require an intact shrub layer, dead wood, or generally undisturbed microhabitats. Third, small pond size associated with curvilinear shape is not optimum to support for preserving and attracting water-edge birds and other avifauna.

**5.4. Finding** 

**Figure 5.** Scenario Model in Shannon-Wiener Diversity by Kriging Approach (Febuary 2009).

preserving and attracting water-edge birds and other avifauna.

Despite their agribusiness value, farm ponds appear to have great influences on the makeup of avian communities in urbanized areas, especially for water-edge avian community (See Figs. 6, 7, & 8). I compared the following community characteristics against the corresponding ratio of constructed area value associated with pond configuration of each site for all functional groups: cumulative waterfowl, cumulative shorebirds, cumulative landbirds, cumulative air feeders, and cumulative water-edge species. Pondscape was a strong and/or moderate correlate in any birds of the ordinations (i.e., water-edge birds, shorebirds, and waterfowl) beyond landbirds and air feeders. The presence of adjoining natural and/or urbanized habitats was probably the most important determinant of wetland avifauna in these areas. Regarding to this detailed study, there may be a number of reasons why some farm ponds do not become a refuge for the more sensitive species. First, the ornamental vegetation covers used for surrounding areas are often too few, and they may support a small insect population. Second, anthropogenic structure is subjected to concrete construction without native trees, and this may make it unattractive to water-edge species that require an intact shrub layer, dead wood, or generally undisturbed microhabitats. Third, small pond size associated with curvilinear shape is not optimum to support for **Figure 6.** Scenario Model in Shannon-Wiener Diversity by Kriging Approach within building areas (December 2008). Based on the experimental semivariogram for avian communities in contour maps, the same overlapped map layers as Fig. 7 & Fig. 8.

**Figure 7.** Scenario Model in Shannon-Wiener Diversity by Kriging Approach withing building areas (January 2009).

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**Author details** 

Wei-Ta Fang

*Republic of China* 

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**Figure 8.** Scenario Model in Shannon-Wiener Diversity by Kriging Approach within building areas (February 2009).
