**Areas of Endemism: Methodological and Applied Biogeographic Contributions from South America**

Dra Dolores Casagranda and Dra Mercedes Lizarralde de Grosso

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55482

### **1. Introduction**

The geographic distribution of organisms is the subject of Biogeography, a field of biology that naturalists have carried out for over two centuries [1-6]. From the observation of animal and plant distribution, diverse questions emerge; the description of diversity gradients; delimita‐ tion of areas of endemism; identification of ancestral areas and search of relationships among areas, among others, have become major issues to be analyzed, worked out and solved. In this way, biogeography has turned into a multi-layered discipline with both theoretical and analytical frameworks and far-reaching objectives.

However, at the beginning it was closely related to systematics. Taxonomists were the ones who took a keen interest in the geographical distribution of taxa. In other words, because the connection is so close, several analytical tools applied to the treatment of biogeographical problems are adaptations or modifications from methods oriented to solve systematics questions. This apparent panacea may also represent one important analytical obstacle for biogeography. Although some biogeographical questions require systematic information to be solved, the object of study of biogeography, that is, spatial distribution of taxa, as well as its concepts and problems, are different from those of systematics. Hence, methods taken from systematics are not appropriate for the treatment of biogeographical problems. The need for its own methods and its own analytical framework have promoted prolific theoretical discussions and methodological developments throughout the last 20 years. In this context, the concept of areas of endemism is being widely debated and several methods have been proposed to attempt to identify these patterns. Areas of endemism have a central role in biogeography as they are the analytical units in historical biogeography, and are also consid‐ ered quite relevant for biodiversity conservation [7]. It is the aim of this chapter to introduce

© 2013 Casagranda and de Grosso; 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 The Author(s). Licensee InTech. This chapter is 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.

the major discussions around the concept of areas of endemism and focus on analytical problems associated with its identification. A brief revision of contributions on endemism in South America is presented and some limitations associated to empirical analysis are high‐ lighted in order to give an overall picture on the current state of affairs on this controversial subject.

endemism only if it emerged from a vicariant event. This assumption entails new difficulties for the identification of areas of endemism: the causes which originate the patterns must be known a priori, or else, the identification of patterns and processes should be performed simultaneously. Fortunately, most biogeographers follow the generalized concept, which

Areas of Endemism: Methodological and Applied Biogeographic Contributions from South America

http://dx.doi.org/10.5772/55482

5

The identification of such areas has been a major challenge in biogeography and deals with several difficulties, some of them related with the two questions mentioned above. However, in the last decades, several methods for identification of these patterns have been proposed [9, 15-16, 18, 24-25] In general, current methods for recognizing areas of endemism can be classified on the basis of whether they aim to determine (i) species patterns, i.e. groups of species with overlapping distributions, or (ii) geographical patterns, i.e. groups of area units with similar species composition. These approaches assess closely related but slightly different aspects of biogeographical data. Methods dealing with species patterns group species with similar distributions and result in clusters -which may or may not define obvious spatial patterns-. Instead, methods oriented to define geographical patterns, are more related to the classical notion of area of endemism, resulting in geographical areas defined by species

The methods currently in use are many and heterogeneous. While reflecting the multiple conceptions of areas of endemism, these proposals differ in their theoretical bases as well in its mathematical formulations. Following are three of them: Parsimony Analysis of Endemicity (PAE [15 ]), Biotic Elements (BE; Hausdorf and Hennig, 2003[24] ), and Endemicity Analysis (EA; Szumik et al., 2002[9]; Szumik and Goloboff, 2004[18]). Although several modifications of PAE, as well as other hierarchical methods have been proposed (see [16, 26]), this method has been selected as a representative of hierarchical methods because it remains the most

**PAE.** The Parsimony Analysis of Endemicity (PAE) was the first method proposed to formally identify areas of endemism[15]. The input data for PAE consist of a binary matrix in which the presence of a given species (rows) in an area unit (columns) is coded as 1 and its absence as 0. Analogous to a cladistic analysis, PAE hierarchically groups area units (analogous to taxa) based on their shared species (analogous to characters) according to the maximum-parsimony criterion. Therefore, PAE attempts to minimize both ''dispersion events'' (parallelisms) and ''extinctions'' (secondary reversions) of species within a given area. Areas of endemism are defined from the most-parsimonious tree (or strict consensus) as groups of area units sup‐ ported by two or more ''synapomorphic species'' (i.e. endemic species [15]). In its most classical formulation, species that present reversions (i.e. are absent in any of the area units) and ⁄ or parallelisms (i.e. are present elsewhere) in their distributions are not considered endemic. Therefore, PAE is especially strict when penalizing the absence of a species within an area, which makes it more likely to fail to detect a relatively large number of areas of endemism.

supposes that multiple factors affect and define current patterns.

**4. Identifying areas of endemism**

widely used in empirical analyses ([27-33].

distributions.

#### **2. Areas of endemism, its importance**

In biogeography, the term "area of endemism" is used to refer to a particular pattern of distribution delimited by the distribution congruence of, at least, two taxa [ 8). Given that the range of distribution of a taxon is determined by historical, as well as current factors, it can be assumed that those taxa which show similar ranges have been affected by the same factors in a similar way [9]. The identification of areas of endemism is an essential first step to elaborate hypotheses that help to disclose the general history of biota and the places where they inhabit. Because of this, recognition of these patterns has been central to biogeography. Oddly enough, and despite its indisputable importance, endemism involves several problems which reach even its definition (semantic field), not to mention those resulting from the absence of a clear framework (conceptual problem) or those associated to the identification of areas of endemism (analytical issues) [9, 11-19]; While the first two problems are briefly dealt with in the present chapter, identifying and assessing the main areas of endemism will be the main focus.

#### **2.1. Defining the term**

The idea of endemism dates back to more than 200 years, and has been employed, as it is actually understood, by de Candolle [1]). Since then, the concepts of endemicity and areas of endemism have been widely discussed. Some problems around these concepts emerge from the diverse uses and interpretations given to them in literature (e.g. [16, 20-21], Harold and Moii [21], Although differences between diverse uses as regards connotations could seem minor, the lack of precision in the definition of these concepts hinders an unambiguous interpretation and causes confusion. Additionally, numerous expressions, such as "general‐ ized track", "track", "biotic element", "centers of endemicity", "units of co-ocurrence", among others, are commonly used as synonyms of area of endemism, [16, 21-23]. Although basically related with the term "areas of endemism", these concepts refer to different patterns of distribution and are defined on different theoretical grounds.

#### **3. A clear conceptual framework**

As it usually happens in other fields such as morphology and embryology, in the field of biogeography, the identification and description of patterns precede the inference of the causes of its occurrence. However, some biogeographers assume that vicariance must be involved [12, 17]). According to this idea, a pattern of sympatry among species could be defined as area of endemism only if it emerged from a vicariant event. This assumption entails new difficulties for the identification of areas of endemism: the causes which originate the patterns must be known a priori, or else, the identification of patterns and processes should be performed simultaneously. Fortunately, most biogeographers follow the generalized concept, which supposes that multiple factors affect and define current patterns.

### **4. Identifying areas of endemism**

the major discussions around the concept of areas of endemism and focus on analytical problems associated with its identification. A brief revision of contributions on endemism in South America is presented and some limitations associated to empirical analysis are high‐ lighted in order to give an overall picture on the current state of affairs on this controversial

In biogeography, the term "area of endemism" is used to refer to a particular pattern of distribution delimited by the distribution congruence of, at least, two taxa [ 8). Given that the range of distribution of a taxon is determined by historical, as well as current factors, it can be assumed that those taxa which show similar ranges have been affected by the same factors in a similar way [9]. The identification of areas of endemism is an essential first step to elaborate hypotheses that help to disclose the general history of biota and the places where they inhabit. Because of this, recognition of these patterns has been central to biogeography. Oddly enough, and despite its indisputable importance, endemism involves several problems which reach even its definition (semantic field), not to mention those resulting from the absence of a clear framework (conceptual problem) or those associated to the identification of areas of endemism (analytical issues) [9, 11-19]; While the first two problems are briefly dealt with in the present chapter, identifying and assessing the main areas of endemism will be the main focus.

The idea of endemism dates back to more than 200 years, and has been employed, as it is actually understood, by de Candolle [1]). Since then, the concepts of endemicity and areas of endemism have been widely discussed. Some problems around these concepts emerge from the diverse uses and interpretations given to them in literature (e.g. [16, 20-21], Harold and Moii [21], Although differences between diverse uses as regards connotations could seem minor, the lack of precision in the definition of these concepts hinders an unambiguous interpretation and causes confusion. Additionally, numerous expressions, such as "general‐ ized track", "track", "biotic element", "centers of endemicity", "units of co-ocurrence", among others, are commonly used as synonyms of area of endemism, [16, 21-23]. Although basically related with the term "areas of endemism", these concepts refer to different patterns of

As it usually happens in other fields such as morphology and embryology, in the field of biogeography, the identification and description of patterns precede the inference of the causes of its occurrence. However, some biogeographers assume that vicariance must be involved [12, 17]). According to this idea, a pattern of sympatry among species could be defined as area of

distribution and are defined on different theoretical grounds.

**3. A clear conceptual framework**

subject.

4 Current Progress in Biological Research

**2.1. Defining the term**

**2. Areas of endemism, its importance**

The identification of such areas has been a major challenge in biogeography and deals with several difficulties, some of them related with the two questions mentioned above. However, in the last decades, several methods for identification of these patterns have been proposed [9, 15-16, 18, 24-25] In general, current methods for recognizing areas of endemism can be classified on the basis of whether they aim to determine (i) species patterns, i.e. groups of species with overlapping distributions, or (ii) geographical patterns, i.e. groups of area units with similar species composition. These approaches assess closely related but slightly different aspects of biogeographical data. Methods dealing with species patterns group species with similar distributions and result in clusters -which may or may not define obvious spatial patterns-. Instead, methods oriented to define geographical patterns, are more related to the classical notion of area of endemism, resulting in geographical areas defined by species distributions.

The methods currently in use are many and heterogeneous. While reflecting the multiple conceptions of areas of endemism, these proposals differ in their theoretical bases as well in its mathematical formulations. Following are three of them: Parsimony Analysis of Endemicity (PAE [15 ]), Biotic Elements (BE; Hausdorf and Hennig, 2003[24] ), and Endemicity Analysis (EA; Szumik et al., 2002[9]; Szumik and Goloboff, 2004[18]). Although several modifications of PAE, as well as other hierarchical methods have been proposed (see [16, 26]), this method has been selected as a representative of hierarchical methods because it remains the most widely used in empirical analyses ([27-33].

**PAE.** The Parsimony Analysis of Endemicity (PAE) was the first method proposed to formally identify areas of endemism[15]. The input data for PAE consist of a binary matrix in which the presence of a given species (rows) in an area unit (columns) is coded as 1 and its absence as 0. Analogous to a cladistic analysis, PAE hierarchically groups area units (analogous to taxa) based on their shared species (analogous to characters) according to the maximum-parsimony criterion. Therefore, PAE attempts to minimize both ''dispersion events'' (parallelisms) and ''extinctions'' (secondary reversions) of species within a given area. Areas of endemism are defined from the most-parsimonious tree (or strict consensus) as groups of area units sup‐ ported by two or more ''synapomorphic species'' (i.e. endemic species [15]). In its most classical formulation, species that present reversions (i.e. are absent in any of the area units) and ⁄ or parallelisms (i.e. are present elsewhere) in their distributions are not considered endemic. Therefore, PAE is especially strict when penalizing the absence of a species within an area, which makes it more likely to fail to detect a relatively large number of areas of endemism.

Despite the well-known limitations of hierarchical classification models in the delimitation of areas of endemism [9, 33-34]), PAE remains the most widely used method for describing biogeographical patterns [31- 32, 35]).

hypothetical predefined patterns. These patterns represent nested, overlapping, and disjoint

Areas of Endemism: Methodological and Applied Biogeographic Contributions from South America

http://dx.doi.org/10.5772/55482

7

This comparison shows how the application of different analytical methods can lead to identification of different areas of endemism, and reveals some undesirable effects produced by methodological idiosyncrasies in the description of these patterns. Following are the main

PAE shows a poor performance at identifying overlapping and disjoint patterns. In all cases, PAE is able to recover areas defined by perfectly sympatric species, but its performance

**Figure 1.** Noise effect on identification of areas of endemism, results using PAE (Modified from Casagranda *et al.*, 2012.)

As regards BE, it is very sensitive to the degree of congruence among the distributions of the species that define an area, showing a counterintuitive behaviour: while the method cannot recognize patterns defined by perfectly sympatric species, its performance improves with increasing levels of incongruence between the species distributions. BE often report multiple distinct biotic elements for species which actually have very similar distributions (Figure 2 a) as well as reporting a single biotic element including species with completely allopatric distributions (Figure 2 b). These examples show discordance between the theoretical basis of

decreases as the incongruence among the species distributions increases (Figure 1)

areas of endemism supported by species with different degrees of sympatry.

results reported in this contribution:

**BE.** Hausdorf [17] considers areas of endemism in the context of the vicariance model, and argues for the use of ''biotic elements'' defined as ''groups of taxa whose ranges are signifi‐ cantly more similar to each other than to those of taxa of other such groups'' ( p. 651[17]), rather than the more traditional areas of endemism [24]). This method is implemented in the R package Prabclus by Hennig [36], which calculates a Kulczynski dissimilarity matrix [37]) between pairs of species which is then reduced using a nonmetric multidimensional scaling (NMDS; [38]). A Model-Based Gaussian clustering (MBGC) is applied to this matrix to identify clusters of species with similar distributions, or biotic elements. In spatial terms, a biotic element is equivalent to the spatial extent of the distributions of all species included in the cluster.

**EA.** In 2002, Szumik and colleagues proposed an optimality criterion to identify areas of endemism by explicitly assessing the congruence among species distributions. This proposal, improved by Szumik & Goloboff [17]), is implemented in NDM⁄VNDM by Goloboff [39] and Szumik and Goloboff [9]). The congruence between a species distribution and a given area is measured by an Endemicity Index (EI) ranging from 0 to1. The EI is 1 for species that are uniformly distributed in the area under study, and only within that area (''perfect endemism''), and decreases for species that are present elsewhere, and ⁄ or poorly distributed within the area. In turn, the endemicity value of an area (EIA) is calculated as the sum of the EIs of the endemic species included in the area. Therefore, two factors contribute to the EIA: the number of species included in the area and the degree of congruence (measured by the EI) between the species distributions and the area itself (for details see [ 9]).

The emergency of quantitative methods that allow describing these patterns objectively has represented an important advance in the discussion of endemism. However, the contrast between different methodological proposals introduced new questions: are the hypothesis resulting from different analysis homologous? Is there a better method to identify areas of endemism? A few recent contributions attempt to elucidate these queries by testing and exploring the behaviour of some methods, e.g. [34, 40]. Several comparisons between methods have been performed by using real data [41-43]). However, real data provide only a limited assessment of the differences between the procedures. Some characteristics of the distribution of species, e.g. geographical shape or number of records, affect pattern recognition in uncertain ways. Furthermore, sampling bias, which often affects available distributional data, causes problems in the identification of biogeographical patterns [44]). As it is often difficult to distinguish whether the identified patterns result from singularities of the data or properties of the methods, an evaluation based on real datasets, or data simulated under realistic conditions, is not enough to establish general conclusions on the performance of the methods.

Recently, Casagranda et al.[19]) states a comparison by using controlled -hypothetical distributions, pointing differences, advantages and limitations of Endemicity Analysis (EA), Parsimony Analysis of Endemicity (PAE), and Biotic Elements Analysis (BE) In their study, these authors measured the efficiency of the methods according their ability to identify hypothetical predefined patterns. These patterns represent nested, overlapping, and disjoint areas of endemism supported by species with different degrees of sympatry.

Despite the well-known limitations of hierarchical classification models in the delimitation of areas of endemism [9, 33-34]), PAE remains the most widely used method for describing

**BE.** Hausdorf [17] considers areas of endemism in the context of the vicariance model, and argues for the use of ''biotic elements'' defined as ''groups of taxa whose ranges are signifi‐ cantly more similar to each other than to those of taxa of other such groups'' ( p. 651[17]), rather than the more traditional areas of endemism [24]). This method is implemented in the R package Prabclus by Hennig [36], which calculates a Kulczynski dissimilarity matrix [37]) between pairs of species which is then reduced using a nonmetric multidimensional scaling (NMDS; [38]). A Model-Based Gaussian clustering (MBGC) is applied to this matrix to identify clusters of species with similar distributions, or biotic elements. In spatial terms, a biotic element is equivalent to the spatial extent of the distributions of all species included in the

**EA.** In 2002, Szumik and colleagues proposed an optimality criterion to identify areas of endemism by explicitly assessing the congruence among species distributions. This proposal, improved by Szumik & Goloboff [17]), is implemented in NDM⁄VNDM by Goloboff [39] and Szumik and Goloboff [9]). The congruence between a species distribution and a given area is measured by an Endemicity Index (EI) ranging from 0 to1. The EI is 1 for species that are uniformly distributed in the area under study, and only within that area (''perfect endemism''), and decreases for species that are present elsewhere, and ⁄ or poorly distributed within the area. In turn, the endemicity value of an area (EIA) is calculated as the sum of the EIs of the endemic species included in the area. Therefore, two factors contribute to the EIA: the number of species included in the area and the degree of congruence (measured by the EI) between the

The emergency of quantitative methods that allow describing these patterns objectively has represented an important advance in the discussion of endemism. However, the contrast between different methodological proposals introduced new questions: are the hypothesis resulting from different analysis homologous? Is there a better method to identify areas of endemism? A few recent contributions attempt to elucidate these queries by testing and exploring the behaviour of some methods, e.g. [34, 40]. Several comparisons between methods have been performed by using real data [41-43]). However, real data provide only a limited assessment of the differences between the procedures. Some characteristics of the distribution of species, e.g. geographical shape or number of records, affect pattern recognition in uncertain ways. Furthermore, sampling bias, which often affects available distributional data, causes problems in the identification of biogeographical patterns [44]). As it is often difficult to distinguish whether the identified patterns result from singularities of the data or properties of the methods, an evaluation based on real datasets, or data simulated under realistic conditions, is not enough to establish general conclusions on the performance of the methods. Recently, Casagranda et al.[19]) states a comparison by using controlled -hypothetical distributions, pointing differences, advantages and limitations of Endemicity Analysis (EA), Parsimony Analysis of Endemicity (PAE), and Biotic Elements Analysis (BE) In their study, these authors measured the efficiency of the methods according their ability to identify

species distributions and the area itself (for details see [ 9]).

biogeographical patterns [31- 32, 35]).

6 Current Progress in Biological Research

cluster.

This comparison shows how the application of different analytical methods can lead to identification of different areas of endemism, and reveals some undesirable effects produced by methodological idiosyncrasies in the description of these patterns. Following are the main results reported in this contribution:

PAE shows a poor performance at identifying overlapping and disjoint patterns. In all cases, PAE is able to recover areas defined by perfectly sympatric species, but its performance decreases as the incongruence among the species distributions increases (Figure 1)

**Figure 1.** Noise effect on identification of areas of endemism, results using PAE (Modified from Casagranda *et al.*, 2012.)

As regards BE, it is very sensitive to the degree of congruence among the distributions of the species that define an area, showing a counterintuitive behaviour: while the method cannot recognize patterns defined by perfectly sympatric species, its performance improves with increasing levels of incongruence between the species distributions. BE often report multiple distinct biotic elements for species which actually have very similar distributions (Figure 2 a) as well as reporting a single biotic element including species with completely allopatric distributions (Figure 2 b). These examples show discordance between the theoretical basis of the approach [16]) and its practical implementation. Together, these limitations suggest the users should exercise caution when interpreting the results generated by this method.

other than to those of taxa of other such groups'', the method may both group totally allopatric species and fail to recognize biotic elements defined by totally sympatric species (see Fig. 2).

Areas of Endemism: Methodological and Applied Biogeographic Contributions from South America

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9

An inescapable consequence of the application of an optimality criterion is that multiple hypotheses may be obtained in an analysis; in the case of EA, the ''twin'' areas represent small variations of single cells. The ambiguity in the input data often results in multiple ''best'' solutions according to an optimality criterion. The reported alternative and equally optimal

Conclusions of Casagranda et al. show that EA, in conjunction with consensus areas, is the best available option for endemicity analyses, despite other studies indicating that EA is rather sensitive to certain aspects of the data, such as spatial gaps of information [34]. The advantages of EA over other methods are related to considering spatial information during the identifi‐ cation of areas, as well as using the classical definition of area of endemism as the basis for the analysis: [an area of endemism]... is identified by the congruent distributional boundaries of two or more species, where congruent does not demand complete agreement on those limits at all possible scales of mapping, but relatively extensive sympatry is a prerequisite [8].

The knowledge about the distribution of species, as well as the geographical patterns, consti‐ tute crucial information for biodiversity conservation [7]. Because of this, the study of both species distributions and the mechanisms that give them rise have increased since the

In the last few years, endemicity has acquired importance in conservation biology since it is

Due to its particular history and its huge biodiversity, South America is interesting from a biogeographical point of view. Numerous contributions have been made to address diverse aspects of the distribution of South America's biota ([47], [48-49] [50-55] ; however, quantitative

The development of computational methods [8, 14, 17 23, 35] together with the availability of biodiversity data-bases, such as CONABIO[57] GBIF, [58] y SNDB [59], and Jetz contribution [60] has promoted the advance of empirical analyses dealing with the description of areas of endemism. It is reflected in numerous publications focused on different methodological perspectives and including diverse taxa, in various places of South America [33, 40, 61-64]. A remarkable example of these studies is the recent contribution of Szumik et al (2012) [63], framed between parallels 21 and 32 S and meridians 70 and 53 W, (Figure 3) in the North region

Although the idea of an area of endemism implies that different groups of plants and animals should have largely coincident distributions, most studies of this type are focused on analyzing a restricted number of taxa. In this sense, the analysis of Szumik et al. (2012) represents an

considered an outstanding factor for delimitation of conservation areas [45-47]).

patterns often force the researcher to more conservative interpretations.

**5. Areas of endemism in South America**

awareness of biodiversity crisis.

studies are relatively recent.

of Argentina.

**Figure 2.** Special results found by biotic elements. (a) Three species with similar distributions (sp.a, sp.b. and sp.c) are separated in different biotic elements (BE 1, BE 2 and BE 3); (b) three species with completely allopatric distributions (sp.d, sp.e. and sp.f) are grouped in the same biotic element (BE 4) (Modified from Casagranda *et al.*, 2012.).

Regarding EA, it shows a high percentage of success in the recovery of predefined areas with no discrimination of case, whether nested, overlapping or disjoint, of degree of congruence between distributions of species. EA reports frequently redundant ''twin'' areas that have only slight differences in spatial structure and ⁄or in their species composition.

Taking into account that overlapping and disjoint patterns are relatively common in nature, and that, in general, sympatry between species varies widely, PAE is probably not the most suitable method to describe areas of endemism based on real distributional data. Although ideal cases are not frequently observed on the spatial scale used for most biogeographical analyses, the inability of BE to identify a perfect case of the pattern which the method intends to describe is questionable. The flexibility to recognize areas displayed by EA is associated with the fact that, in contrast to the other methods considered here, EA uses both the number of species and the overlap between their distributions as optimality criteria to search for areas of endemism.

One serious problem is that the method relies on an algorithm that is ineffective for its intended purpose. PAE, for example, is a hierarchical method implying that each cell is included in at least one area of endemism; consequently, PAE cannot describe overlapping patterns, such as nested areas. Additionally, the maximum parsimony criterion aims to minimize the number of homoplasies, resulting in PAE hardly identifying any disjoint areas.

Similarly, BE model-based inference requires a series of distributional assumptions which, if not satisfied, may lead to unreliable or erroneous conclusions. Thus, even if, in theory, a biotic element is defined as a ''group of taxa whose ranges are significantly more similar to each other than to those of taxa of other such groups'', the method may both group totally allopatric species and fail to recognize biotic elements defined by totally sympatric species (see Fig. 2).

An inescapable consequence of the application of an optimality criterion is that multiple hypotheses may be obtained in an analysis; in the case of EA, the ''twin'' areas represent small variations of single cells. The ambiguity in the input data often results in multiple ''best'' solutions according to an optimality criterion. The reported alternative and equally optimal patterns often force the researcher to more conservative interpretations.

Conclusions of Casagranda et al. show that EA, in conjunction with consensus areas, is the best available option for endemicity analyses, despite other studies indicating that EA is rather sensitive to certain aspects of the data, such as spatial gaps of information [34]. The advantages of EA over other methods are related to considering spatial information during the identifi‐ cation of areas, as well as using the classical definition of area of endemism as the basis for the analysis: [an area of endemism]... is identified by the congruent distributional boundaries of two or more species, where congruent does not demand complete agreement on those limits at all possible scales of mapping, but relatively extensive sympatry is a prerequisite [8].

### **5. Areas of endemism in South America**

the approach [16]) and its practical implementation. Together, these limitations suggest the users should exercise caution when interpreting the results generated by this method.

**Figure 2.** Special results found by biotic elements. (a) Three species with similar distributions (sp.a, sp.b. and sp.c) are separated in different biotic elements (BE 1, BE 2 and BE 3); (b) three species with completely allopatric distributions

Regarding EA, it shows a high percentage of success in the recovery of predefined areas with no discrimination of case, whether nested, overlapping or disjoint, of degree of congruence between distributions of species. EA reports frequently redundant ''twin'' areas that have only

Taking into account that overlapping and disjoint patterns are relatively common in nature, and that, in general, sympatry between species varies widely, PAE is probably not the most suitable method to describe areas of endemism based on real distributional data. Although ideal cases are not frequently observed on the spatial scale used for most biogeographical analyses, the inability of BE to identify a perfect case of the pattern which the method intends to describe is questionable. The flexibility to recognize areas displayed by EA is associated with the fact that, in contrast to the other methods considered here, EA uses both the number of species and the overlap between their distributions as optimality criteria to search for areas

One serious problem is that the method relies on an algorithm that is ineffective for its intended purpose. PAE, for example, is a hierarchical method implying that each cell is included in at least one area of endemism; consequently, PAE cannot describe overlapping patterns, such as nested areas. Additionally, the maximum parsimony criterion aims to minimize the number

Similarly, BE model-based inference requires a series of distributional assumptions which, if not satisfied, may lead to unreliable or erroneous conclusions. Thus, even if, in theory, a biotic element is defined as a ''group of taxa whose ranges are significantly more similar to each

(sp.d, sp.e. and sp.f) are grouped in the same biotic element (BE 4) (Modified from Casagranda *et al.*, 2012.).

slight differences in spatial structure and ⁄or in their species composition.

of homoplasies, resulting in PAE hardly identifying any disjoint areas.

of endemism.

8 Current Progress in Biological Research

The knowledge about the distribution of species, as well as the geographical patterns, consti‐ tute crucial information for biodiversity conservation [7]. Because of this, the study of both species distributions and the mechanisms that give them rise have increased since the awareness of biodiversity crisis.

In the last few years, endemicity has acquired importance in conservation biology since it is considered an outstanding factor for delimitation of conservation areas [45-47]).

Due to its particular history and its huge biodiversity, South America is interesting from a biogeographical point of view. Numerous contributions have been made to address diverse aspects of the distribution of South America's biota ([47], [48-49] [50-55] ; however, quantitative studies are relatively recent.

The development of computational methods [8, 14, 17 23, 35] together with the availability of biodiversity data-bases, such as CONABIO[57] GBIF, [58] y SNDB [59], and Jetz contribution [60] has promoted the advance of empirical analyses dealing with the description of areas of endemism. It is reflected in numerous publications focused on different methodological perspectives and including diverse taxa, in various places of South America [33, 40, 61-64]. A remarkable example of these studies is the recent contribution of Szumik et al (2012) [63], framed between parallels 21 and 32 S and meridians 70 and 53 W, (Figure 3) in the North region of Argentina.

Although the idea of an area of endemism implies that different groups of plants and animals should have largely coincident distributions, most studies of this type are focused on analyzing a restricted number of taxa. In this sense, the analysis of Szumik et al. (2012) represents an atypical example because the number and diversity of taxa included, more than 800 species of mammals, amphibians, reptiles, birds, insects and plants, representing one of the first approx‐ imations to the analysis of total evidence in a biogeographical context.

The patterns of distribution recognized here depict almost all the main biogeographical units proposed in previous studies [26, 47, 49, 51, 53, 54, 55, 60] the Atlantic Forest the Campos (Grasslands) District, the Chaco shrubland (Fig. 5a), the deciduous tropical Yungas forest the Puna highland, and the tropical tails entering Argentina in two disjoint patches[63]. Each of these tropical tails represents part of a broader area that extends towards the north of the South

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http://dx.doi.org/10.5772/55482

11

Additionaly, the species that support the various areas are consistent in general with previous biogeographical studies based on individual groups (plants [32]; snakes [66]; mammals:[66]; insects: [63]; birds: [65,67]), and should be noted that several of these species are currently on

**Figure 4.** An example of an area of endemism identified under differents grids sides (results of Szumik *et al*., 2012)

The necessity of quantitative methods that allow a formal description of nature on the basis of available evidence has been an important subject in modern biology. In the last 30 years, both the advances in the field of informatics and the development of computational methods to

Biogeography is not foreign to these important advances. When having to compare and evaluate alternative biogeographical hypotheses, biogeographers hold no doubts over the importance of quantitative methods. However, unlike other research areas such as systematics, the richness of biogeography is quite noticeable as far as the number and variety of methodo‐

explore diverse biological questions have been remarkable [74-76].

American subcontinent.

**6. Final comments**

red lists of threatened species [68-73]).

The quality and structure of data influence the identification of biogeographical patterns [19, 43]. Since the knowledge about distribution of organisms is scarce and taxonomical misiden‐ tification and georreferencing errors are commonly observed in available distributional data, an appropriate revision and correction of input information is essential to perform reliable biogeographical descriptions. In this sense, the above mentioned analysis differs from similar studies because the traits of the analyzed data set : "unique among biogeographical studies not only for the number and diversity of plant and animal taxa, but also because it was compiled, edited, and corroborated by 25 practising taxonomists, whose work specializes in the study region Thus, it differs substantially from data sets constructed by downloading data from biodiversity websites" (Szumik et al 2012, p.2[63]; see Figure 3).).

**Figure 3.** Maps of Argentina: a) relief map; b) biogeographical divisions of Argentina according to Cabrera and Willink (1973); the study region is framed in the red square.

The results reported by these authors indicate that when all the evidence is analysed for a given region, it is possible to obtain areas supported by diverse taxonomic groups (Navarro et al., 2009[63]): half of 126 found areas are supported by three or more major groups. Examples of areas of endemism defined by multiple taxa are the Atlantic Forest (Selva Paranaense— Neotropical, Figure 4) and the north Yungas forest sector (tropical Bermejo- Toldo-Calilegua, two of the most diverse ecorregions of the region.

The patterns of distribution recognized here depict almost all the main biogeographical units proposed in previous studies [26, 47, 49, 51, 53, 54, 55, 60] the Atlantic Forest the Campos (Grasslands) District, the Chaco shrubland (Fig. 5a), the deciduous tropical Yungas forest the Puna highland, and the tropical tails entering Argentina in two disjoint patches[63]. Each of these tropical tails represents part of a broader area that extends towards the north of the South American subcontinent.

Additionaly, the species that support the various areas are consistent in general with previous biogeographical studies based on individual groups (plants [32]; snakes [66]; mammals:[66]; insects: [63]; birds: [65,67]), and should be noted that several of these species are currently on red lists of threatened species [68-73]).

**Figure 4.** An example of an area of endemism identified under differents grids sides (results of Szumik *et al*., 2012)

#### **6. Final comments**

atypical example because the number and diversity of taxa included, more than 800 species of mammals, amphibians, reptiles, birds, insects and plants, representing one of the first approx‐

The quality and structure of data influence the identification of biogeographical patterns [19, 43]. Since the knowledge about distribution of organisms is scarce and taxonomical misiden‐ tification and georreferencing errors are commonly observed in available distributional data, an appropriate revision and correction of input information is essential to perform reliable biogeographical descriptions. In this sense, the above mentioned analysis differs from similar studies because the traits of the analyzed data set : "unique among biogeographical studies not only for the number and diversity of plant and animal taxa, but also because it was compiled, edited, and corroborated by 25 practising taxonomists, whose work specializes in the study region Thus, it differs substantially from data sets constructed by downloading data

**Figure 3.** Maps of Argentina: a) relief map; b) biogeographical divisions of Argentina according to Cabrera and Willink

The results reported by these authors indicate that when all the evidence is analysed for a given region, it is possible to obtain areas supported by diverse taxonomic groups (Navarro et al., 2009[63]): half of 126 found areas are supported by three or more major groups. Examples of areas of endemism defined by multiple taxa are the Atlantic Forest (Selva Paranaense— Neotropical, Figure 4) and the north Yungas forest sector (tropical Bermejo- Toldo-Calilegua,

(1973); the study region is framed in the red square.

two of the most diverse ecorregions of the region.

imations to the analysis of total evidence in a biogeographical context.

10 Current Progress in Biological Research

from biodiversity websites" (Szumik et al 2012, p.2[63]; see Figure 3).).

The necessity of quantitative methods that allow a formal description of nature on the basis of available evidence has been an important subject in modern biology. In the last 30 years, both the advances in the field of informatics and the development of computational methods to explore diverse biological questions have been remarkable [74-76].

Biogeography is not foreign to these important advances. When having to compare and evaluate alternative biogeographical hypotheses, biogeographers hold no doubts over the importance of quantitative methods. However, unlike other research areas such as systematics, the richness of biogeography is quite noticeable as far as the number and variety of methodo‐ logical proposals are concerned in the attempt to solve a given biogeographical problem. In contrast, those studies where the capacity to explain differences between methods or the quality of the results are put to the test are scarce, as well as anecdotal. The case referred to in the present chapter on the identification of areas of endemism clearly demonstrates the urge of serious and critical studies on biogeography. The formal recognition of areas of endemism is a complex issue; quite a lot has been done in the last few years in order to understand it, but there is still a lot to be done.In addition, the current impending threat on biological diversity urges for methodological improvements conducive to more realistic descriptions of biogeo‐ graphical patterns.

[5] Darlington P J. Jr. Zoogeography: the geographical distribution of animals. John Wil‐

Areas of Endemism: Methodological and Applied Biogeographic Contributions from South America

http://dx.doi.org/10.5772/55482

13

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### **Acknowledgements**

We thank authors of references, specially our colleagues of INSUE. Helpful comments, constructive criticism and generosity from Claudia Szumik are greatly appreciated. Luisa Montivero helped with the English text and Andres Grosso with illistrations. This work was supported by grant PIP-Conicet Nº 1112- 200801-00696

### **Author details**

Dra Dolores Casagranda1,2 and Dra Mercedes Lizarralde de Grosso2,3


3 Instituto Superior de Entomología (INSUE)-Universidad nacional de Tucumán, Tucumán, Argentina

### **References**


[5] Darlington P J. Jr. Zoogeography: the geographical distribution of animals. John Wil‐ ley & Sons, New York;1957

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We thank authors of references, specially our colleagues of INSUE. Helpful comments, constructive criticism and generosity from Claudia Szumik are greatly appreciated. Luisa Montivero helped with the English text and Andres Grosso with illistrations. This work was

graphical patterns.

**Author details**

Argentina

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1 Instituto de Herpetología, Fundación Miguel Lillo, Tucumán, Argentina

2 Consejo Nacional de Investigaciones Científicas y Técnicas, Tucumán, Argentina

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**Chapter 2**

**Genomic Rearrangements and Evolution**

All genomes in living organisms can change under influence of internal or external factors. That is why genomic materials are commonly defined as dynamic entities and it is believed that they have been repeatedly altered and rearranged since the beginning of the life on the planet [1-4]. Understanding this dynamism is a valuable key to unlock the chest of the mysterious existence story in an evolutionary manner. Therefore, a lot of studies have been conducted on the dynamism of genomic materials in organisms and the count of related researches has gradually risen by the day. An enormous data from these studies call attention to recombinational, tranpositional and mutational processes as three main sources of genomic

Recombinational changes of genomes are mainly dependent on internal factors which are closely associated with a great many of intracellular and intercellular interactions. Enzyme catalyzed pathways and predetermined timing are the most descriptive properties for many types of recombination events. For instance, usual meiotic crossing over, the best known recombinational event, always occurs under control of specified enzymatic reactions at a

Transpositional events are also important sources for sequential rearrangements in genomes and induced by external or internal genomic material pieces that are described as mobile or transposable elements. In mechanism of transposition, a transposable element changes its relative position within the genome. "Copy and Paste" or "Cut and Paste" postulates work in this process. A transpositional event occurring with the copy and paste mechanism is called as replicative transposition that a transposable element is duplicated during the process and copied sequence transferred into the target genomic sequence, and the other one with the cut and paste mechanism is called as non-replicative transposition that duplication of the trans‐

> © 2013 Barış 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,

© 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

Özlem Barış, Mehmet Karadayı, Derya Yanmış and

Additional information is available at the end of the chapter

Medine Güllüce

**1. Introduction**

changes [1,2,5-18].

certain time period in the cell cycle [2,4,19-22].

http://dx.doi.org/10.5772/55456


## **Genomic Rearrangements and Evolution**

Özlem Barış, Mehmet Karadayı, Derya Yanmış and Medine Güllüce

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55456

### **1. Introduction**

[72] Lavilla E O, Richard E, Scrocchi G J. Categorización de los anfibios y reptiles de Ar‐ gentina. Asociación Herpetológica Argentina, Tucumán, Argentina; 2000.

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gentina; 2006. 359 pp

18 Current Progress in Biological Research

model.2005.03.026

All genomes in living organisms can change under influence of internal or external factors. That is why genomic materials are commonly defined as dynamic entities and it is believed that they have been repeatedly altered and rearranged since the beginning of the life on the planet [1-4]. Understanding this dynamism is a valuable key to unlock the chest of the mysterious existence story in an evolutionary manner. Therefore, a lot of studies have been conducted on the dynamism of genomic materials in organisms and the count of related researches has gradually risen by the day. An enormous data from these studies call attention to recombinational, tranpositional and mutational processes as three main sources of genomic changes [1,2,5-18].

Recombinational changes of genomes are mainly dependent on internal factors which are closely associated with a great many of intracellular and intercellular interactions. Enzyme catalyzed pathways and predetermined timing are the most descriptive properties for many types of recombination events. For instance, usual meiotic crossing over, the best known recombinational event, always occurs under control of specified enzymatic reactions at a certain time period in the cell cycle [2,4,19-22].

Transpositional events are also important sources for sequential rearrangements in genomes and induced by external or internal genomic material pieces that are described as mobile or transposable elements. In mechanism of transposition, a transposable element changes its relative position within the genome. "Copy and Paste" or "Cut and Paste" postulates work in this process. A transpositional event occurring with the copy and paste mechanism is called as replicative transposition that a transposable element is duplicated during the process and copied sequence transferred into the target genomic sequence, and the other one with the cut and paste mechanism is called as non-replicative transposition that duplication of the trans‐

© 2013 Barış 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 The Author(s). Licensee InTech. This chapter is 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.

posable element does not occur and the original sequence is transferred from one region into another [5,23-24]. In both cases, a transpositional event is commonly resulted in a mutational phenomenon and alteration in genomic sizes that makes them attractive for genomic evolution studies [6-7,23-26].

**Lysis**

**Lysis**

**a**

Figure 1. Simple mechanism of transformation

**Recipient Bacterium (F ‐ )**

**Recipient Bacterium (F ‐ )**

**Figure 2.** An illustrative scheme for bacterial conjugation of F+ (a) and Hfr (b) cells

**A B c d**

**A B c d**

**+**

**a**

**Fragments of DNA**

**Fragments of DNA**

**B**

**B**

**Taking up DNA fragment**

Bacterial conjugation, discovered in 1946 by Joshua Lederberg and Edward Tatum [43], is another process to transfer the genetic information in Prokaryotes. In its mechanism, the transfer of genetic material involves cell to cell contact and a plasmid encoded pathway. The process occurs between a donor cell, which includes a certain type of conjugative plasmid, and a recipient cell, which does not. In this process, the plasmid plays a key role by carrying all related genes on *tra* region. These genes encode the sex pilus (F pili) formation, which allow specific pairing to take place between the donor cell and the recipient cell. After generation of sex pilus mediated cell to cell contact, a copy of the plasmid is transferred to the recipient under control of various enzyme systems encoded by *tra* region. In most cases, this type of recom‐ bination does not cause genetic variation at high level because the transferred genetic infor‐ mation is restricted by sequential contents of the plasmid. However, in certain circumstances, conjugative plasmid may integrate into the main genomic material, resulting in the formation of Hfr (High Frequency Recombination) cells. These cells, commonly seen in Gram negative bacterial groups, have significant potential for recombination at higher levels due to leading transfer of genes from the host chromosome [2,41]. Figure 2 shows regular bacterial conjuga‐

**Taking up DNA fragment**

Figure 2. An illustrative scheme for bacterial conjugation of F+ (a) and Hfr (b) cells

**a**

**F**

**b C D**

**a**

**b C D**

**A B c d**

**D**

**C**

**A B c d**

**<sup>F</sup> <sup>F</sup>**

**Transfer a copy of F plasmid**

**Transfer a part of DNA**

the same time [2,41]. Both types of transduction events are summarized at Figure 3.

genetic materials [2,5,45-46]. The scheme of meiotic crossing over is showed in Figure 4.

**A b**

**B**

**Recombination**

**A B**

Genomic Rearrangements and Evolution http://dx.doi.org/10.5772/55456 21

**Recombinant Bacterium**

**A B**

**Recombinant Bacterium**

**Recipient Bacterium**

**A b**

**B**

**Recipient Bacterium**

introduced into a prokaryotic cell that result in genomic variation. Transformation occurs in several groups of Gram positive, Gram negative and Archaea. A healthy double strand DNA molecule with a homological property and specific size (mostly smaller than 1000 nucleotides) is the most fundamental requirement for transformation [2,41]. Figure 1 illustrates a summarized scheme for

**Recombination**

Bacterial conjugation, discovered in 1946 by Joshua Lederberg and Edward Tatum [43], is another process to transfer the genetic information in Prokaryotes. In its mechanism, the transfer of genetic material involves cell to cell contact and a plasmid encoded pathway. The process occurs between a donor cell, which includes a certain type of conjugative plasmid, and a recipient cell, which does not. In this process, the plasmid plays a key role by carrying all related genes on *tra* region. These genes encode the sex pilus (F pili) formation, which allow specific pairing to take place between the donor cell and the recipient cell. After generation of sex pilus mediated cell to cell contact, a copy of the plasmid is transferred to the recipient under control of various enzyme systems encoded by *tra* region. In most cases, this type of recombination does not cause genetic variation at high level because the transferred genetic information is restricted by sequential contents of the plasmid. However, in certain circumstances, conjugative plasmid may integrate into the main genomic material, resulting in the formation of Hfr (High Frequency Recombination) cells. These cells, commonly seen in Gram negative bacterial groups, have significant potential for recombination at higher levels due to leading transfer of genes from the host chromosome [2,41]. Figure 2 shows regular bacterial conjugation events and Hfr formation.

**a**

**Recombination**

**Recombination**

**Donor Bacterium (F + )**

**F**

**Donor Bacterium (Hfr)**

**b C D +**

**a**

**b C D F**

**+**

**A B c d F**

**Recombinant Bacterium (F + )**

**Recombinant Bacterium (F ‐ )**

**A B C D**

Transduction, initially discovered by Norton Zinder and Joshua Lederberg in 1951 [44], refers to virus-mediated transfers of genetic materials. There are two fundamental mechanisms as generalized and specialized transduction. In generalized transduction, any bacterial genomic sequence may be transferred to another bacterium via a modified bacteriophage that accidentally involves bacterial DNA instead of viral DNA. However, in specialized transduction, bacteriophage includes both bacterial and viral DNA at

In eukaryotic organisms, meiotic crossing over (chromosomal cross over) is the most well-known example for homologous recombination. This event occurs between homologous chromosomes at prophase I stage in meiosis and results in variation of

**a B**

**Donor Bacterium**

tion events and Hfr formation.

**a)**

**a**

**Donor Bacterium (F + )**

**F**

**Donor Bacterium (Hfr)**

**b C D +**

**a**

**b)**

**b C D F**

**Figure 1.** Simple mechanism of transformation

transformation.

**a B**

**Donor Bacterium**

Mutations are described as sudden changes in genomic materials induced by internal and external factors [27]. They have importance in medicinal, agricultural and other related researches due to their deleterious, beneficial or functional effects on organisms [5,9,28]. Moreover, enormous potential for construction of novel genes and other types of genomic sequences, they are considered as the most attractive subject for genome evolution [2,29-32].

### **2. Recombinations**

Genetic recombination is a process that is catalyzed by many different enzymes called as recombinases. It can take place in all living cells from bacteria to eukaryota as well as viral genomes. This process mainly results in DNA repair, genomic rearrangements, variations and evolutional forces. Genetic recombinations are assigned to one of two groups according to their mechanism, which can be described as either homologous or non-homologous recombination [2,4,20,22,33-35].

#### **2.1. Homologous recombination**

Homologous recombinational events are sequential changes that occur between similar or identical parts of genomic material. In the beginning of 20th century, initial descriptions of homologous recombinations were introduced by W. Bateson and R. Punnett to explain diversions from predicted Mendelian inheritance phenotypic ratios [4,36-37]. This process, which is commonly found in many organisms from bacteria to higher organized eukaryotes, plays a significant role in DNA repair mechanisms and genome evolution by producing variations [2,38-40].

In prokaryotic cellular organisms, the most known types of homologous recombinational events are transformation, conjugation and transduction [41]. All of these events are resulted in genomic variations that have great value for evolution [42].

Transformation was discovered by Frederick Griffith in the late 1920s. His transformation experiments are considered as the beginning mile stone of the molecular biology discipline [5]. In the mechanism of natural prokaryotic transformation, a naked DNA fragment released from a cell is taken up by another under appropriate conditions, thus an exogenous genetic material is introduced into a prokaryotic cell that result in genomic variation. Transformation occurs in several groups of Gram positive, Gram negative and Archaea. A healthy double strand DNA molecule with a homological property and specific size (mostly smaller than 1000 nucleotides) is the most fundamental requirement for transformation [2,41]. Figure 1 illustrates a summar‐ ized scheme for transformation.

negative and Archaea. A healthy double strand DNA molecule with a homological property and specific size (mostly smaller than

These cells, commonly seen in Gram negative bacterial groups, have significant potential for recombination at higher levels due to leading transfer of genes from the host chromosome [2,41]. Figure 2 shows regular bacterial conjugation events and Hfr formation.

Transduction, initially discovered by Norton Zinder and Joshua Lederberg in 1951 [44], refers to virus-mediated transfers of genetic materials. There are two fundamental mechanisms as generalized and specialized transduction. In generalized transduction, any bacterial genomic sequence may be transferred to another bacterium via a modified bacteriophage that accidentally involves bacterial DNA instead of viral DNA. However, in specialized transduction, bacteriophage includes both bacterial and viral DNA at

In eukaryotic organisms, meiotic crossing over (chromosomal cross over) is the most well-known example for homologous recombination. This event occurs between homologous chromosomes at prophase I stage in meiosis and results in variation of

**Figure 1.** Simple mechanism of transformation 1000 nucleotides) is the most fundamental requirement for transformation [2,41]. Figure 1 illustrates a summarized scheme for transformation.

posable element does not occur and the original sequence is transferred from one region into another [5,23-24]. In both cases, a transpositional event is commonly resulted in a mutational phenomenon and alteration in genomic sizes that makes them attractive for genomic evolution

Mutations are described as sudden changes in genomic materials induced by internal and external factors [27]. They have importance in medicinal, agricultural and other related researches due to their deleterious, beneficial or functional effects on organisms [5,9,28]. Moreover, enormous potential for construction of novel genes and other types of genomic sequences, they are considered as the most attractive subject for genome evolution [2,29-32].

Genetic recombination is a process that is catalyzed by many different enzymes called as recombinases. It can take place in all living cells from bacteria to eukaryota as well as viral genomes. This process mainly results in DNA repair, genomic rearrangements, variations and evolutional forces. Genetic recombinations are assigned to one of two groups according to their mechanism, which can be described as either homologous or non-homologous recombination

Homologous recombinational events are sequential changes that occur between similar or identical parts of genomic material. In the beginning of 20th century, initial descriptions of homologous recombinations were introduced by W. Bateson and R. Punnett to explain diversions from predicted Mendelian inheritance phenotypic ratios [4,36-37]. This process, which is commonly found in many organisms from bacteria to higher organized eukaryotes, plays a significant role in DNA repair mechanisms and genome evolution by producing

In prokaryotic cellular organisms, the most known types of homologous recombinational events are transformation, conjugation and transduction [41]. All of these events are resulted

Transformation was discovered by Frederick Griffith in the late 1920s. His transformation experiments are considered as the beginning mile stone of the molecular biology discipline [5]. In the mechanism of natural prokaryotic transformation, a naked DNA fragment released from a cell is taken up by another under appropriate conditions, thus an exogenous genetic material is introduced into a prokaryotic cell that result in genomic variation. Transformation occurs in several groups of Gram positive, Gram negative and Archaea. A healthy double strand DNA molecule with a homological property and specific size (mostly smaller than 1000 nucleotides) is the most fundamental requirement for transformation [2,41]. Figure 1 illustrates a summar‐

in genomic variations that have great value for evolution [42].

studies [6-7,23-26].

20 Current Progress in Biological Research

**2. Recombinations**

[2,4,20,22,33-35].

variations [2,38-40].

**2.1. Homologous recombination**

ized scheme for transformation.

Bacterial conjugation, discovered in 1946 by Joshua Lederberg and Edward Tatum [43], is another process to transfer the genetic information in Prokaryotes. In its mechanism, the transfer of genetic material involves cell to cell contact and a plasmid encoded pathway. The process occurs between a donor cell, which includes a certain type of conjugative plasmid, and a recipient cell, which does not. In this process, the plasmid plays a key role by carrying all related genes on *tra* region. These genes encode the sex pilus (F pili) formation, which allow specific pairing to take place between the donor cell and the recipient cell. After generation of sex pilus mediated cell to cell contact, a copy of the plasmid is transferred to the recipient under control of various enzyme systems encoded by *tra* region. In most cases, this type of recom‐ bination does not cause genetic variation at high level because the transferred genetic infor‐ mation is restricted by sequential contents of the plasmid. However, in certain circumstances, conjugative plasmid may integrate into the main genomic material, resulting in the formation of Hfr (High Frequency Recombination) cells. These cells, commonly seen in Gram negative bacterial groups, have significant potential for recombination at higher levels due to leading transfer of genes from the host chromosome [2,41]. Figure 2 shows regular bacterial conjuga‐ tion events and Hfr formation. Figure 1. Simple mechanism of transformation Bacterial conjugation, discovered in 1946 by Joshua Lederberg and Edward Tatum [43], is another process to transfer the genetic information in Prokaryotes. In its mechanism, the transfer of genetic material involves cell to cell contact and a plasmid encoded pathway. The process occurs between a donor cell, which includes a certain type of conjugative plasmid, and a recipient cell, which does not. In this process, the plasmid plays a key role by carrying all related genes on *tra* region. These genes encode the sex pilus (F pili) formation, which allow specific pairing to take place between the donor cell and the recipient cell. After generation of sex pilus mediated cell to cell contact, a copy of the plasmid is transferred to the recipient under control of various enzyme systems encoded by *tra* region. In most cases, this type of recombination does not cause genetic variation at high level because the transferred genetic information is restricted by sequential contents of the plasmid. However, in certain circumstances, conjugative plasmid may integrate into the main genomic material, resulting in the formation of Hfr (High Frequency Recombination) cells. **Lysis a B Donor Bacterium B a Fragments of DNA Taking up DNA fragment A b Recipient Bacterium B Recombination A B Recombinant Bacterium**

**Figure 2.** An illustrative scheme for bacterial conjugation of F+ (a) and Hfr (b) cells

Figure 2. An illustrative scheme for bacterial conjugation of F+ (a) and Hfr (b) cells

the same time [2,41]. Both types of transduction events are summarized at Figure 3.

genetic materials [2,5,45-46]. The scheme of meiotic crossing over is showed in Figure 4.

Transduction, initially discovered by Norton Zinder and Joshua Lederberg in 1951 [44], refers to virus-mediated transfers of genetic materials. There are two fundamental mechanisms as generalized and specialized transduction. In generalized transduction, any bacterial genomic sequence may be transferred to another bacterium via a modified bacteriophage that acciden‐ tally involves bacterial DNA instead of viral DNA. However, in specialized transduction, bacteriophage includes both bacterial and viral DNA at the same time [2,41]. Both types of transduction events are summarized at Figure 3.

In eukaryotic organisms, meiotic crossing over (chromosomal cross over) is the most wellknown example for homologous recombination. This event occurs between homologous chromosomes at prophase I stage in meiosis and results in variation of genetic materials [2,5,45-46]. The scheme of meiotic crossing over is showed in Figure 4.

**Figure 3.** Mechanism of generalized (a) and specialized (b) transduction events

Fig. 3. Mechanism of generalized (a) and specialized (b) transduction events

**separate in first cell division**

Fig. 4. Mechanism of meiotic crossing over

**The homologous pair moves close together. The chromatids may exchange genes.** Homologous recombination also plays a significant role in DNA repair mechanisms in both prokaryotic and eukaryotic organisms. It is one of the major DNA repair processes in bacteria [2,46]. For example, double-strand breaks in bacteria are repaired by the RecBCD pathway of homologous recombination [42,47-49]. Moreover, it is well known that similar mechanisms work in eukaryotic organisms.

**Genes that have crossed over The homologous pair** Homologous recombination also includes non-allelic ones that have been not well document‐ ed. These events occur between sequences arisen from duplications or deletions that show high homology, but are not alleles. It is believed that non-allelic homologous recombination has a great importance for evolution due to generating a decrease or an increase in copy number of sequences [50-52].

> **The sister chromatids separate in second cell division**

**2.2. Non-homologous recombination**

**Figure 4.** Mechanism of meiotic crossing over

**The homologous pair moves close together. The chromatids may exchange genes.**

> **The homologous pair separate in first cell division**

materials [2,53-55].

**3.1. Transposons**

**3. Mobile genetic elements**

Non-homologous recombination, also named as non-homologous end joining (NHEJ), is a pathway that mainly associated with DNA repair that especially works on double strand breaks. Contrary to the mechanisms of homologous recombination, it does not require sequential homology. However, this pathway has been identified in many groups of living organisms from bacteria to multicellular organisms, even in human being, recent studies have mainly focused on eukaryotes much more than bacteria. One reason for this is that prokaryotic

**Genes that have crossed over**

Genomic Rearrangements and Evolution http://dx.doi.org/10.5772/55456 23

**The sister chromatids separate in second cell division**

Nuclease, polymerase and ligase activities play the major role in NHEJ process. Despite its conservative mechanism, this process is generally resulting in variations of genetic

Mobile genetic elements are described as DNA segments that can move within the genome. These include transposons, group II introns, plasmids and viral elements [56]. All these events

Transposons, also named as transposable elements, are major forces in the evolution and rearrangement of genomes [6,26,56]. Discovery of transposable elements was achieved in 1943 by Barbara McClintock who was awarded with a Nobel Prize after 40 years in 1983 [2,58]. Since

result in genomic alterations that cause rising of evolutional forces [6,8,24-26,57-61].

DNA repair is heavily done by various processes of homologous recombination.

**Figure 4.** Mechanism of meiotic crossing over

Transduction, initially discovered by Norton Zinder and Joshua Lederberg in 1951 [44], refers to virus-mediated transfers of genetic materials. There are two fundamental mechanisms as generalized and specialized transduction. In generalized transduction, any bacterial genomic sequence may be transferred to another bacterium via a modified bacteriophage that acciden‐ tally involves bacterial DNA instead of viral DNA. However, in specialized transduction, bacteriophage includes both bacterial and viral DNA at the same time [2,41]. Both types of

In eukaryotic organisms, meiotic crossing over (chromosomal cross over) is the most wellknown example for homologous recombination. This event occurs between homologous chromosomes at prophase I stage in meiosis and results in variation of genetic materials

**Infection**

**Infection**

Homologous recombination also plays a significant role in DNA repair mechanisms in both prokaryotic and eukaryotic organisms. It is one of the major DNA repair processes in bacteria [2,46]. For example, double-strand breaks in bacteria are repaired by the RecBCD pathway of homologous recombination [42,47-49]. Moreover, it is well known that similar mechanisms

Homologous recombination also includes non-allelic ones that have been not well document‐ ed. These events occur between sequences arisen from duplications or deletions that show high homology, but are not alleles. It is believed that non-allelic homologous recombination has a great importance for evolution due to generating a decrease or an increase in copy

**A b**

**Recombination**

**Recombination**

**A B**

**Genes that have crossed over**

**The sister chromatids separate in second cell division**

**A B**

**Recombinant Bacterium**

**Recombinant Bacterium**

**c <sup>D</sup> <sup>v</sup>**

**B**

**Infected Recipient Bacterium by an Abnormal Phage**

> **Infected Recipient Bacterium by a Phage**

**<sup>v</sup> <sup>v</sup> <sup>D</sup> <sup>B</sup>**

**c d A**

[2,5,45-46]. The scheme of meiotic crossing over is showed in Figure 4.

**Abnormal and Normal Phages**

**v D v v**

**Figure 3.** Mechanism of generalized (a) and specialized (b) transduction events

**Recombinant and Normal Phages**

**B**

**v D**

Fig. 3. Mechanism of generalized (a) and specialized (b) transduction events

transduction events are summarized at Figure 3.

**Lysis**

**v**

**Lysis**

**The homologous pair moves close together. The chromatids may exchange genes.**

> **The homologous pair separate in first cell division**

Fig. 4. Mechanism of meiotic crossing over

**a B**

**a)**

22 Current Progress in Biological Research

**b)**

**Bacteriophage Infected Donor Bacterium**

**Bacteriophage Infected Donor Bacterium**

work in eukaryotic organisms.

number of sequences [50-52].

**b C <sup>D</sup> <sup>v</sup> a**

#### **2.2. Non-homologous recombination**

Non-homologous recombination, also named as non-homologous end joining (NHEJ), is a pathway that mainly associated with DNA repair that especially works on double strand breaks. Contrary to the mechanisms of homologous recombination, it does not require sequential homology. However, this pathway has been identified in many groups of living organisms from bacteria to multicellular organisms, even in human being, recent studies have mainly focused on eukaryotes much more than bacteria. One reason for this is that prokaryotic DNA repair is heavily done by various processes of homologous recombination.

Nuclease, polymerase and ligase activities play the major role in NHEJ process. Despite its conservative mechanism, this process is generally resulting in variations of genetic materials [2,53-55].

### **3. Mobile genetic elements**

Mobile genetic elements are described as DNA segments that can move within the genome. These include transposons, group II introns, plasmids and viral elements [56]. All these events result in genomic alterations that cause rising of evolutional forces [6,8,24-26,57-61].

#### **3.1. Transposons**

Transposons, also named as transposable elements, are major forces in the evolution and rearrangement of genomes [6,26,56]. Discovery of transposable elements was achieved in 1943 by Barbara McClintock who was awarded with a Nobel Prize after 40 years in 1983 [2,58]. Since that time, the importance of transposons has been well established and much more attention has been given to their formation and consequences [62]. To get more easily comprehensive information, they are divided into three main groups as retrotransposons, DNA transposons and insertion sequences.

**3.3. Plasmids**

**3.4. Viral elements**

**4. Mutations**

comprehensive evaluations [1-3,5,29-31,34].

**4.1. Classification of mutations**

and chromosome mutations [5,27].

*4.1.1. Gene mutations*

deletion [2,5,27,34].

Plasmids are circular and extra chromosomal genomic materials naturally found in bacteria, but rarely in several yeasts as eukaryotic organisms [41]. These elements show intracellular or intercellular mobility (see section 2.1.) that result in genomic alterations and evolutional forces.

Genomic Rearrangements and Evolution http://dx.doi.org/10.5772/55456 25

Viral elements are genomic materials transferring between living organisms via virus infec‐ tions. According to the mechanism of infection, viruses are divided into two categories as lytic and lysogenic. Lytic ones complete their eclipse phase in the cell and cause lysis of the host. However, lysogenic ones integrate their genomic materials into the host genome and directly cause genomic alterations [41]. For example, some retroviruses are common type of lysogenic

The "Mutation" term was initially used by Hugo de Vries in 1905 to describe the phenotypic changes in evening-primrose plant (*Oenothera lamarckiana*). However, it commonly describes any sequential change in the genomic material of living organisms in the present day. Their various effects resulting in genotypic and phenotypic alterations that cause diseases, gaining or loss of advantageous or deleterious properties, attract the scientific attention on mutation focused investigations. In these researches, mutations are generally classified according to the effect mechanisms and size of effected genomic sequences to perform more apparent and

Effect size of mutations on genomes is one of the most widely-accepted criteria for classifica‐ tion. According to this, mutations can be divided into two groups named as gene mutations

Gene mutations are small-scale mutations that effect one or few bases in a genome. However, they can induce many important phenomenon depend on properties of effected genomic sequences. For example, a gene mutation in a protein coding region of genomic material can result in synthesis of a non-functional protein that mostly causes deleterious effects for the organism. Gene mutations are also divide subcategories as base substitution and insertion/

**Base Substitutions:** They are also called as point mutations. These types of mutations are characterized by taking place of a different base instead of original one in the genome. When a purine base replaces with another purine or a pyrimidine base with another pyrimidine (A↔G

viral elements and their effect mechanism is similar to retrotransposons.

#### *3.1.1. Retrotransposons*

Retrotransposons can be considered as the biggest group of transposable elements due to their abundance in many eukaryotic genomes (i.e. 49-78% of the total genome in maize and 42% in human) [63-64]. The term "retrotransposon" is attributed to the transposition mechanism that involves via RNA intermediates. In the mechanism, a retrotransposon is initially copied to RNA (transcription), then converted to DNA (reversetranscription) and finally inserted to the genome (integration), and this process is mainly under control of the gene region of retro‐ transposons encoding reverse transcriptase. These elements can increase genome size and induce mutational events by disturbing genes [2,24,26,56,59,62,65].

Retrotransposons are divided into three main groups according to the operation mechanisms: long terminal repeats (LTRs) encode reverse transcriptase, similar to retroviruses; long interspersed elements (LINEs) do not have LTRs and encode reverse transcriptase and small interspersed elements (SINEs) do not encode reverse transcriptase. LINEs and SINEs are transcripted by RNA polymerase II and III, respectively [66-68].

#### *3.1.2. DNA transposons*

DNA transposons are the first discovered ones of transposable elements, initially named as "jumping genes" by Barbara McClintock in 1943 [69]. These are also called as Class II trans‐ posons, operate with a "cut and paste" mechanism. In this mechanism, transposition event mainly requires to transposase enzymes. Under control of the enzymatic processes, a DNA transposon is cut out of its location and inserted into a new location on the genome. Some transposases require a specific sequence as their target site; others can insert the transposon anywhere in the genomic material [2,24,41,62].

#### *3.1.3. Insertion sequences*

These are also known as IS elements. They are short DNA sequences that act as a simple form of transposable elements. Characterized properties of IS elements are that they have shorter sizes than other types of transposable elements (approximately 700 – 2500 bp), and carry some specific genes such as antibiotic resistance. Insertion sequences are usually flanked by inverted repeats [23,24,70].

#### **3.2. Group II introns**

Group II introns were discovered by Alexandre de Lencastre and his teammates in 2005 [71]. These elements, an important group of self-catalytic ribozymes, are generated during RNA splicing, and may cause genetic alterations [71].

#### **3.3. Plasmids**

that time, the importance of transposons has been well established and much more attention has been given to their formation and consequences [62]. To get more easily comprehensive information, they are divided into three main groups as retrotransposons, DNA transposons

Retrotransposons can be considered as the biggest group of transposable elements due to their abundance in many eukaryotic genomes (i.e. 49-78% of the total genome in maize and 42% in human) [63-64]. The term "retrotransposon" is attributed to the transposition mechanism that involves via RNA intermediates. In the mechanism, a retrotransposon is initially copied to RNA (transcription), then converted to DNA (reversetranscription) and finally inserted to the genome (integration), and this process is mainly under control of the gene region of retro‐ transposons encoding reverse transcriptase. These elements can increase genome size and

Retrotransposons are divided into three main groups according to the operation mechanisms: long terminal repeats (LTRs) encode reverse transcriptase, similar to retroviruses; long interspersed elements (LINEs) do not have LTRs and encode reverse transcriptase and small interspersed elements (SINEs) do not encode reverse transcriptase. LINEs and SINEs are

DNA transposons are the first discovered ones of transposable elements, initially named as "jumping genes" by Barbara McClintock in 1943 [69]. These are also called as Class II trans‐ posons, operate with a "cut and paste" mechanism. In this mechanism, transposition event mainly requires to transposase enzymes. Under control of the enzymatic processes, a DNA transposon is cut out of its location and inserted into a new location on the genome. Some transposases require a specific sequence as their target site; others can insert the transposon

These are also known as IS elements. They are short DNA sequences that act as a simple form of transposable elements. Characterized properties of IS elements are that they have shorter sizes than other types of transposable elements (approximately 700 – 2500 bp), and carry some specific genes such as antibiotic resistance. Insertion sequences are usually flanked by inverted

Group II introns were discovered by Alexandre de Lencastre and his teammates in 2005 [71]. These elements, an important group of self-catalytic ribozymes, are generated during RNA

induce mutational events by disturbing genes [2,24,26,56,59,62,65].

transcripted by RNA polymerase II and III, respectively [66-68].

anywhere in the genomic material [2,24,41,62].

splicing, and may cause genetic alterations [71].

and insertion sequences.

24 Current Progress in Biological Research

*3.1.1. Retrotransposons*

*3.1.2. DNA transposons*

*3.1.3. Insertion sequences*

repeats [23,24,70].

**3.2. Group II introns**

Plasmids are circular and extra chromosomal genomic materials naturally found in bacteria, but rarely in several yeasts as eukaryotic organisms [41]. These elements show intracellular or intercellular mobility (see section 2.1.) that result in genomic alterations and evolutional forces.

#### **3.4. Viral elements**

Viral elements are genomic materials transferring between living organisms via virus infec‐ tions. According to the mechanism of infection, viruses are divided into two categories as lytic and lysogenic. Lytic ones complete their eclipse phase in the cell and cause lysis of the host. However, lysogenic ones integrate their genomic materials into the host genome and directly cause genomic alterations [41]. For example, some retroviruses are common type of lysogenic viral elements and their effect mechanism is similar to retrotransposons.

### **4. Mutations**

The "Mutation" term was initially used by Hugo de Vries in 1905 to describe the phenotypic changes in evening-primrose plant (*Oenothera lamarckiana*). However, it commonly describes any sequential change in the genomic material of living organisms in the present day. Their various effects resulting in genotypic and phenotypic alterations that cause diseases, gaining or loss of advantageous or deleterious properties, attract the scientific attention on mutation focused investigations. In these researches, mutations are generally classified according to the effect mechanisms and size of effected genomic sequences to perform more apparent and comprehensive evaluations [1-3,5,29-31,34].

#### **4.1. Classification of mutations**

Effect size of mutations on genomes is one of the most widely-accepted criteria for classifica‐ tion. According to this, mutations can be divided into two groups named as gene mutations and chromosome mutations [5,27].

#### *4.1.1. Gene mutations*

Gene mutations are small-scale mutations that effect one or few bases in a genome. However, they can induce many important phenomenon depend on properties of effected genomic sequences. For example, a gene mutation in a protein coding region of genomic material can result in synthesis of a non-functional protein that mostly causes deleterious effects for the organism. Gene mutations are also divide subcategories as base substitution and insertion/ deletion [2,5,27,34].

**Base Substitutions:** They are also called as point mutations. These types of mutations are characterized by taking place of a different base instead of original one in the genome. When a purine base replaces with another purine or a pyrimidine base with another pyrimidine (A↔G to retrotransposons.

**4. Mutations** 

apparent and comprehensive evaluations [1-3,5,29-31,34].

**4.1. Classification of mutations** 

**4.1.1. Gene mutations** 

or C↔T), it is called as transition. On the other hand, if a purine base replaces with a pyrimidine or apyrimidinebasewithapurine(A↔C,A↔T,G↔C or G↔T),thenitiscalledastransversion. **Base Substitutions:** They are also called as point mutations. These types of mutations are characterized by taking place of a different base instead of original one in the genome. When a purine base replaces with another purine or a pyrimidine base with another pyrimidine (A↔G or C↔T), it is called as transition. On the other hand, if a purine base replaces with a pyrimidine or a

mutations are also divide subcategories as base substitution and insertion/deletion [2,5,27,34].

pyrimidine base with a purine (A↔C, A↔T, G↔C or G↔T), then it is called as transversion.

be divided into two groups named as gene mutations and chromosome mutations [5,27].

lysis of the host. However, lysogenic ones integrate their genomic materials into the host genome and directly cause genomic alterations [41]. For example, some retroviruses are common type of lysogenic viral elements and their effect mechanism is similar

The "Mutation" term was initially used by Hugo de Vries in 1905 to describe the phenotypic changes in evening-primrose plant (*Oenothera lamarckiana*). However, it commonly describes any sequential change in the genomic material of living organisms in the present day. Their various effects resulting in genotypic and phenotypic alterations that cause diseases, gaining or loss of advantageous or deleterious properties, attract the scientific attention on mutation focused investigations. In these researches, mutations are generally classified according to the effect mechanisms and size of effected genomic sequences to perform more

Effect size of mutations on genomes is one of the most widely-accepted criteria for classification. According to this, mutations can

Gene mutations are small-scale mutations that effect one or few bases in a genome. However, they can induce many important

genomic material can result in synthesis of a non-functional protein that mostly causes deleterious effects for the organism. Gene

*4.1.2.1. Numerical alterations*

form after these kind of mutations.

*4.1.2.2. Structural alterations*

cations [5,9,27,72].

piece exchanges.

**14**

**Figure 7.** Structural chromosome mutations

**7**

**Deletion**

losses from the genome.

Euploidy and aneuploidy are two essential subgroups.

These types of mutations mainly cause alterations in chromosome numbers in the living cells.

**Euploidy:** The word "euploidy" refers to cumulative alterations in chromosome numbers. For example, diploid (2n) chromosome number of an organism can be changed to tetraploid (4n)

**Aneuploidy:** The word "aneuploidy" refers to non-cumulative alterations in chromosome numbers. For example, diploid (2n) chromosome number of an organism can be changed to nullisomy (2n-2), monosomy (2n-1) or trisomy (2n+1) form after these kind of mutations.

These types of mutations do not change chromosome numbers. However, their effects are mainly on chromosomal structure. According to their effect mechanisms, structural mutations are grouped in four subcategories including deletions, inversions, duplications and translo‐

**Deletions:** Chromosomal deletions include losing of chromosomal pieces resulting in gene

**Inversions:** An inversion refers to a phenomenon in which a chromosome break following by 180° rotation and reattachment of the broken piece on the same chromosomal region. It does

**Duplications:** Duplication is a case having two or more copies of a chromosomal region.

**Translocations:** These types of alterations are arisen from non-homologues chromosomal

**Invertion**

**Translocation**

**b**

**e a b c d f g h i j**

**7**

**7**

**7 8**

**c d f g h i j**

**e a**

**Duplication**

**7**

**9 10 11**

Genomic Rearrangements and Evolution http://dx.doi.org/10.5772/55456 27

not cause gene losses, but results in an inverted genetic material.

**7**

**7**

**Insertions/Deletions:** The insertion term means addition of one or few bases into a genomic material. Contrary to this, deletions

Figure 5. Base substitutions type of gene mutations **Figure 5.** Base substitutions type of gene mutations

from a genome.

are defined as removing of one or few bases from a genome. **Insertions/Deletions:** The insertion term means addition of one or few bases into a genomic material. Contrary to this, deletions are defined as removing of one or few bases from a genome. **Insertions/Deletions:** The insertion term means addition of one or few bases into a genomic material. Contrary to this, deletions are defined as removing of one or few bases

Fig. 6. Insertion/Deletions type of gene mutations **Figure 6.** Insertion/Deletions type of gene mutations

**4.1.2. Chromosome Mutations** 

tetraploid (4n) form after these kind of mutations.

**4.1.2.2. Structural Alterations** 

translocations [5,9,27,72].

in gene losses from the genome.

#### Chromosomal mutations are described as phenomenon that causes bigger sequence alterations than gene mutations. These are also called as macro-mutations due to their *4.1.2. Chromosome mutations*

mutations.

microscopically examination capabilities. There are two main subcategories as structural and numerical alterations in chromosomal mutations [5,9,27,34]. **4.1.2.1. Numerical Alterations**  These types of mutations mainly cause alterations in chromosome numbers in the living cells. Euploidy and aneuploidy are two essential subgroups. Chromosomal mutations are described as phenomenon that causes bigger sequence alterations than gene mutations. These are also called as macro-mutations due to their microscopically examination capabilities. There are two main subcategories as structural and numerical alterations in chromosomal mutations [5,9,27,34].

> These types of mutations do not change chromosome numbers. However, their effects are mainly on chromosomal structure. According to their effect mechanisms, structural mutations are grouped in four subcategories including deletions, inversions, duplications and

> **Euploidy:** The word "euploidy" refers to cumulative alterations in chromosome numbers. For example, diploid (2n) chromosome number of an organism can be changed to

> **Aneuploidy:** The word "aneuploidy" refers to non-cumulative alterations in chromosome numbers. For example, diploid (2n) chromosome number of an organism can be changed to nullisomy (2n-2), monosomy (2n-1) or trisomy (2n+1) form after these kind of

**Deletions:** Chromosomal deletions include losing of chromosomal pieces resulting

#### *4.1.2.1. Numerical alterations*

or C↔T), it is called as transition. On the other hand, if a purine base replaces with a pyrimidine or apyrimidinebasewithapurine(A↔C,A↔T,G↔C or G↔T),thenitiscalledastransversion.

**…ATGGGCAAATATAGCATTCCATAAAAATATATA…**

**Insertions/Deletions:** The insertion term means addition of one or few bases into a genomic material. Contrary to this, deletions are defined as removing of one or few bases from a

genomic material. Contrary to this, deletions are defined as removing of one or few bases

Chromosomal mutations are described as phenomenon that causes bigger sequence alterations than gene mutations. These are also called as macro-mutations due to their microscopically examination capabilities. There are two main subcategories as structural

Chromosomal mutations are described as phenomenon that causes bigger sequence alterations than gene mutations. These are also called as macro-mutations due to their microscopically examination capabilities. There are two main subcategories as structural and numerical

These types of mutations mainly cause alterations in chromosome numbers in the living

These types of mutations do not change chromosome numbers. However, their effects are mainly on chromosomal structure. According to their effect mechanisms, structural mutations are grouped in four subcategories including deletions, inversions, duplications and

**Euploidy:** The word "euploidy" refers to cumulative alterations in chromosome numbers. For example, diploid (2n) chromosome number of an organism can be changed to

**Aneuploidy:** The word "aneuploidy" refers to non-cumulative alterations in chromosome numbers. For example, diploid (2n) chromosome number of an organism can be changed to nullisomy (2n-2), monosomy (2n-1) or trisomy (2n+1) form after these kind of

**Deletions:** Chromosomal deletions include losing of chromosomal pieces resulting

genome. **Insertions/Deletions:** The insertion term means addition of one or few bases into a

**…ATGGGCAAATATAGCATTCCATAAAAATATATA…**

mutations are also divide subcategories as base substitution and insertion/deletion [2,5,27,34].

pyrimidine base with a purine (A↔C, A↔T, G↔C or G↔T), then it is called as transversion.

**…ATGGGCAAATATAGCATTCCATAAAGATATATA…**

**…ATGGGCAAATATAGCATTCCATAAACATATATA…**

**…ATGGGCAAATATAGCATTCCATAAAAAATATA…**

**…ATGGGCAAATATAGCATTCCATAAAGAATATATA…**

be divided into two groups named as gene mutations and chromosome mutations [5,27].

to retrotransposons.

**4. Mutations** 

apparent and comprehensive evaluations [1-3,5,29-31,34].

**4.1. Classification of mutations** 

Figure 5. Base substitutions type of gene mutations

Fig. 6. Insertion/Deletions type of gene mutations

and numerical alterations in chromosomal mutations [5,9,27,34].

**Mutated Sequence**

cells. Euploidy and aneuploidy are two essential subgroups.

tetraploid (4n) form after these kind of mutations.

**4.1.2. Chromosome Mutations** 

**Figure 6.** Insertion/Deletions type of gene mutations

**4.1.2.1. Numerical Alterations** 

alterations in chromosomal mutations [5,9,27,34].

**4.1.2.2. Structural Alterations** 

translocations [5,9,27,72].

in gene losses from the genome.

mutations.

*4.1.2. Chromosome mutations*

are defined as removing of one or few bases from a genome.

**Mutated Sequence**

**Mutated Sequence**

**Mutated Sequence**

**4.1.1. Gene mutations** 

26 Current Progress in Biological Research

**Original Sequence**

**Figure 5.** Base substitutions type of gene mutations

from a genome.

**Original Sequence**

lysis of the host. However, lysogenic ones integrate their genomic materials into the host genome and directly cause genomic alterations [41]. For example, some retroviruses are common type of lysogenic viral elements and their effect mechanism is similar

The "Mutation" term was initially used by Hugo de Vries in 1905 to describe the phenotypic changes in evening-primrose plant (*Oenothera lamarckiana*). However, it commonly describes any sequential change in the genomic material of living organisms in the present day. Their various effects resulting in genotypic and phenotypic alterations that cause diseases, gaining or loss of advantageous or deleterious properties, attract the scientific attention on mutation focused investigations. In these researches, mutations are generally classified according to the effect mechanisms and size of effected genomic sequences to perform more

Effect size of mutations on genomes is one of the most widely-accepted criteria for classification. According to this, mutations can

Gene mutations are small-scale mutations that effect one or few bases in a genome. However, they can induce many important phenomenon depend on properties of effected genomic sequences. For example, a gene mutation in a protein coding region of genomic material can result in synthesis of a non-functional protein that mostly causes deleterious effects for the organism. Gene

**Base Substitutions:** They are also called as point mutations. These types of mutations are characterized by taking place of a different base instead of original one in the genome. When a purine base replaces with another purine or a pyrimidine base with another pyrimidine (A↔G or C↔T), it is called as transition. On the other hand, if a purine base replaces with a pyrimidine or a

**Insertions/Deletions:** The insertion term means addition of one or few bases into a genomic material. Contrary to this, deletions

**A transversion (A to C)**

**A single nucleotide (G) insertion**

**A single nucleotide (T) deletion**

**A transition (A to G)**

These types of mutations mainly cause alterations in chromosome numbers in the living cells. Euploidy and aneuploidy are two essential subgroups.

**Euploidy:** The word "euploidy" refers to cumulative alterations in chromosome numbers. For example, diploid (2n) chromosome number of an organism can be changed to tetraploid (4n) form after these kind of mutations.

**Aneuploidy:** The word "aneuploidy" refers to non-cumulative alterations in chromosome numbers. For example, diploid (2n) chromosome number of an organism can be changed to nullisomy (2n-2), monosomy (2n-1) or trisomy (2n+1) form after these kind of mutations.

#### *4.1.2.2. Structural alterations*

These types of mutations do not change chromosome numbers. However, their effects are mainly on chromosomal structure. According to their effect mechanisms, structural mutations are grouped in four subcategories including deletions, inversions, duplications and translo‐ cations [5,9,27,72].

**Deletions:** Chromosomal deletions include losing of chromosomal pieces resulting in gene losses from the genome.

**Inversions:** An inversion refers to a phenomenon in which a chromosome break following by 180° rotation and reattachment of the broken piece on the same chromosomal region. It does not cause gene losses, but results in an inverted genetic material.

**Duplications:** Duplication is a case having two or more copies of a chromosomal region.

**Translocations:** These types of alterations are arisen from non-homologues chromosomal piece exchanges.

**Figure 7.** Structural chromosome mutations

#### **5. Genome evolution**

The origin of life on the earth has always been an attractive subject for all human beings. The question about formation of the first active biomolecule is one of the most important perspec‐ tives in this subject, and has been heavily researched for many years. Initial studies referred to proteins as first biomolecules due to their catalytic activities that operates various reactions for maintaining of life. Although this view was confirmed for a long time, their lack of potential to carry genetic information was the major handicap. In 1982, the commonly accepted thought about the first biomolecule was drastically changed by Thomas Cech and co-workers who published a paper that demonstrate the single intron of the large ribosomal RNA of *Tetrahy‐ mena thermophila* has self-splicing activity *in vitro*. This was the first report about catalytic RNA molecules. A year later, Sydney Altman and co-workers pointed out that the RNA component of ribonuclease P (RNase P) from *Escherichia coli* is able to carry out processing of pre-tRNA in the absence of its protein subunit *in vitro*. These studies lead to formation of "RNA world" perspective in genome evolution, and both scientists were awarded by Nobel Prize in 1989. In the recent view, the RNA world term means that ribonucleic acids have both the informational function of DNA and the catalytic function of proteins at the same time [2,12,73-78]. According to this concept, various types of RNAs can be proposed as initial genomes evolved on the planet. Major RNA types and their characteristic properties are given in Table 1.

[15,24-26,63,65,68,85-90]. In his paper, Zhang [88] underlined the positive correlation between duplicated gene amount and evolutional status of an organism. Table 3 represents prevalence

**Type Features References**






responsible for regulation of gene expression














[2]

Genomic Rearrangements and Evolution http://dx.doi.org/10.5772/55456 29

[2]

[2]

[2]

[2]

[2]

[2]

[76]

[77]

[78]

[79]



living organisms




double strand molecule intracellular origin (nucleus)



of gene duplications in all three domains of life.

mRNA

rRNA

tRNA

snRNA

snoRNA

miRNA (MicroRNA)

siRNA

piRNA

gRNA (Guide RNA)

tmRNA

shRNA

stRNA

(Messenger RNA)

(Ribosomal RNA)

(Transfer RNA)

(Small Nuclear RNA)

(Small Nucleolar RNA)

(Short Interfering RNA)

(Piwi-interacting RNA)

(Transfer-messenger RNA)

(Small hairpin RNA)

(Small Temporal RNA)

**Table 1.** Major RNA types and their features

Although the first genome has a potential to be ribonucleic acid form, instability and limited life of RNA molecules may have forced evolution of a more complex genomic material called as deoxyribonucleic acid (DNA). In this stage, there are several gaps and unanswered questions. However, the most discussed scenario about formation of DNA based genomes from initial RNA molecules (protogenome) proposes a phenomenon that is catalyzed by a reverse transcriptase [2,78,84].

Contrary to the high stability property, evolutional changes are continuously occurring in DNA based genomes that result in development of valuable features for adaptation. These changes have been mainly dependent on external forces since the beginning of the life on the planet (approximately 3.5 billion years ago) [2]. Understanding of this evolutional dynamism in genomic materials requires recognizing definitions of several important terms given in Table 2, prepared according to Eugene V. Koonin (2005) who is senior investigator at National Central of Biotechnology Information (NCBI) and studies on empirical comparative and evolutionary genomics [8].

Up to this point, all mentioned events cause changes in size and construction of genomic materials acting as evolutional forces. The genomic size is referred as "C value". Although the genomic size may reduce via deletions, it has generally intended to increase when compared to the first genome of universal common ancestor (UCA). This expansion is controlled by rearrangement forces, especially duplications and mobile genetic elements. There are two fundamental hypotheses for why genome sizes vary. According to the "Selfish-DNA hypoth‐ esis": genome size expansion is due to insertion and proliferation selfish genetic elements such as retrotransposons, and "Bulk-DNA hypothesis": having more genetic bulk can be adaptive because genome size effects nuclear volume, cell size, cell division rate in turn effecting developmental rate and size at maturity, thus it results in organisms with larger body size have larger cell sizes, and organisms with larger cells generally have larger genomes [15,24-26,63,65,68,85-90]. In his paper, Zhang [88] underlined the positive correlation between duplicated gene amount and evolutional status of an organism. Table 3 represents prevalence of gene duplications in all three domains of life.


**Table 1.** Major RNA types and their features

**5. Genome evolution**

28 Current Progress in Biological Research

reverse transcriptase [2,78,84].

evolutionary genomics [8].

The origin of life on the earth has always been an attractive subject for all human beings. The question about formation of the first active biomolecule is one of the most important perspec‐ tives in this subject, and has been heavily researched for many years. Initial studies referred to proteins as first biomolecules due to their catalytic activities that operates various reactions for maintaining of life. Although this view was confirmed for a long time, their lack of potential to carry genetic information was the major handicap. In 1982, the commonly accepted thought about the first biomolecule was drastically changed by Thomas Cech and co-workers who published a paper that demonstrate the single intron of the large ribosomal RNA of *Tetrahy‐ mena thermophila* has self-splicing activity *in vitro*. This was the first report about catalytic RNA molecules. A year later, Sydney Altman and co-workers pointed out that the RNA component of ribonuclease P (RNase P) from *Escherichia coli* is able to carry out processing of pre-tRNA in the absence of its protein subunit *in vitro*. These studies lead to formation of "RNA world" perspective in genome evolution, and both scientists were awarded by Nobel Prize in 1989. In the recent view, the RNA world term means that ribonucleic acids have both the informational function of DNA and the catalytic function of proteins at the same time [2,12,73-78]. According to this concept, various types of RNAs can be proposed as initial genomes evolved on the

planet. Major RNA types and their characteristic properties are given in Table 1.

Although the first genome has a potential to be ribonucleic acid form, instability and limited life of RNA molecules may have forced evolution of a more complex genomic material called as deoxyribonucleic acid (DNA). In this stage, there are several gaps and unanswered questions. However, the most discussed scenario about formation of DNA based genomes from initial RNA molecules (protogenome) proposes a phenomenon that is catalyzed by a

Contrary to the high stability property, evolutional changes are continuously occurring in DNA based genomes that result in development of valuable features for adaptation. These changes have been mainly dependent on external forces since the beginning of the life on the planet (approximately 3.5 billion years ago) [2]. Understanding of this evolutional dynamism in genomic materials requires recognizing definitions of several important terms given in Table 2, prepared according to Eugene V. Koonin (2005) who is senior investigator at National Central of Biotechnology Information (NCBI) and studies on empirical comparative and

Up to this point, all mentioned events cause changes in size and construction of genomic materials acting as evolutional forces. The genomic size is referred as "C value". Although the genomic size may reduce via deletions, it has generally intended to increase when compared to the first genome of universal common ancestor (UCA). This expansion is controlled by rearrangement forces, especially duplications and mobile genetic elements. There are two fundamental hypotheses for why genome sizes vary. According to the "Selfish-DNA hypoth‐ esis": genome size expansion is due to insertion and proliferation selfish genetic elements such as retrotransposons, and "Bulk-DNA hypothesis": having more genetic bulk can be adaptive because genome size effects nuclear volume, cell size, cell division rate in turn effecting developmental rate and size at maturity, thus it results in organisms with larger body size have larger cell sizes, and organisms with larger cells generally have larger genomes


Besides, Xue et al. [91] laid emphasis on the roles of duplications in genomic size and compo‐ sitional changes in their studies via exploring the evolution of segmental gene duplication in haploid and diploid populations by analytical and simulation approaches. The result of this study highlighted that duplications do not only cause alterations in genome size but they are also result in many recombinational events that closely related to formation of variations that have value in rising evolutional forces. In another paper, Force et al. [92] focused on the DDC (duplication-degeneration-complementation) model for the alternative fates (nonfunctionali‐ zation, neofuctionalization and subfuctionalization) of duplicate genes, and underlined their

Genomic Rearrangements and Evolution http://dx.doi.org/10.5772/55456 31

Mobile genetic elements also affect genome size. For example, horizontal transfer of transpos‐ able elements plays a key role in genome evolution. In their "copy-and-paste" operation mechanisms, retrotransposons, as common examples of mobile genetic elements that may cause horizontal gene transfer, transpose via an RNA-intermediated process, and this increases genomic material size [26,93-94]. Furthermore, all advanced biology sources covering micro‐ bial genetic title mention the role of other types of mobile genetic elements including plasmids

On the other hand, reduction of genomic size in certain periods is an inevitable fact for genome evolution. In this manner, smaller genomes are more advantageous for selection than bigger ones due to their high replication potentials and metabolic inexpensiveness. Deletions can be given as the main force to diminish genomic size that causes gene losses [95-96]. In a recent paper, Pettersson and co-workers emphasized the role of deletions in regulation of genomic size and its coding density by using a mathematical model to

A genomic material may accept deletions and reduce its size up to reach minimal genome limits that have the smallest number of genetic elements sufficient to build a modern-type freeliving cellular organism. In addition, under some exceptional conditions, genomic materials of several endo-symbionts and co-symbionts carry much less genes than predicted minimal genome rates. For example, although *Pelagibacter ubique* (α-Proteobacteria) is known as a freeliving organism with the smallest genome (only 1308 Kb in size and potentially contains 1354 genes), endo-symbiont *Hodgkinia cicadicola* (α-Proteobacteria) has the smallest genome (only 144 Kb in size and potentially contains 188 genes) among known-living organisms [98-102]. According to Juhas and co-workers' study [102], the extremely small genomes of endosym‐ bionts usually encode only the most fundamental process, suggesting that some of their genes might have been transferred into the host cell genome. The endosymbiont *Wolbachia* strains that transfer ~1 Mb fragments of its genomic material to the host genome can be given as a

Contrary to the genomic material of *P. ubique* in which there is no pseudogenes, introns, transposons, or extrachromosomal elements, modern-type organism genomes need some or all of these differentiated genetic parts [97]. In this regard, genomic rearrangements have a critical potential via causing structural changes, especially new alleles and new regulatory regions in the genomes can be created by only mutations. There is a huge data giving infor‐ mation about the roles of mutations in evolution in the scientific literature

and viral genomes in formation of variations in genomic size and structure [41].

roles in genome evolution.

determine the evolutionary fate [97].

good example for this phenomenon [98-102].

**Table 2.** Homology: terms and definitions from Koonin 2005 [8].


a The most recent estimate is ~30000.

b Use of different computational methods or criteria results in slightly different estimates of the number of duplicated genes.

**Table 3.** Prevalence of gene duplications in all three domains of lifeb from Zhang 2003 [88].

Besides, Xue et al. [91] laid emphasis on the roles of duplications in genomic size and compo‐ sitional changes in their studies via exploring the evolution of segmental gene duplication in haploid and diploid populations by analytical and simulation approaches. The result of this study highlighted that duplications do not only cause alterations in genome size but they are also result in many recombinational events that closely related to formation of variations that have value in rising evolutional forces. In another paper, Force et al. [92] focused on the DDC (duplication-degeneration-complementation) model for the alternative fates (nonfunctionali‐ zation, neofuctionalization and subfuctionalization) of duplicate genes, and underlined their roles in genome evolution.

**Homologs Genes sharing a common origin**

30 Current Progress in Biological Research

event. **Paralogs Genes related by duplication**

meaning).

**Table 2.** Homology: terms and definitions from Koonin 2005 [8].

Inparalogs (Symparalogs)

Outparalogs (Alloparalogs)

**Bacteria**

**Archaea**

**Eukarya**

The most recent estimate is ~30000.

a

genes.

compared genomes.

genome comparisons.

Orthologs Genes originating from a single ancestral gene in the last common ancestor of the

Xenologs Homologous genes acquired via xenologous gene displacement (XGD) by one or

Co-orthologs Two or more genes in one lineage that are, collectively, orthologous to one or

Pseudoparalogs Homologous genes that come out as paralogs in a single-genome analysis but

inheritance and horizontal gene transfer.

*Mycoplasma pneumoniae* 677 298 (44) *Helicobacter pylori* 1590 266 (17) *Haemophilus influenzae* 1709 284 (17)

*Archaeoglobus fulgidus* 2436 719 (30)

*Saccharomyces cerevisiae* 6241 1858 (30) *Caenorhabditis elegans* 18424 8971 (49) *Drosophila melanogaster* 13601 5536 (41) *Arabidopsis thaliana* 25498 16574 (65) *Homo sapiens* 40580a 15343 (38)

**Table 3.** Prevalence of gene duplications in all three domains of lifeb from Zhang 2003 [88].

b Use of different computational methods or criteria results in slightly different estimates of the number of duplicated

both of the compared species but appearing to be orthologous in pairwise

more genes in another lineage due to a lineage-specific duplication(s). Members of a co-orthologous gene set are inparalogs relative to the respective speciation

Paralogs genes resulting from a lineage-specific duplication(s) subsequent to a given speciation event (defined only relative to a speciation event, no absolute

Paralogs genes resulting from a duplication(s) preceding a given speciation event

actually ended up in the given genome as a result of a combination of vertical

**Number of duplicate genes (% of duplicate genes)**

(defined only relative to a speciation event, no absolute meaning)

**Total number of genes**

Pseudoorthologs Genes that actually are paralogs but appeared to be orthologous due to differential, linage-specific gene loss.

> Mobile genetic elements also affect genome size. For example, horizontal transfer of transpos‐ able elements plays a key role in genome evolution. In their "copy-and-paste" operation mechanisms, retrotransposons, as common examples of mobile genetic elements that may cause horizontal gene transfer, transpose via an RNA-intermediated process, and this increases genomic material size [26,93-94]. Furthermore, all advanced biology sources covering micro‐ bial genetic title mention the role of other types of mobile genetic elements including plasmids and viral genomes in formation of variations in genomic size and structure [41].

> On the other hand, reduction of genomic size in certain periods is an inevitable fact for genome evolution. In this manner, smaller genomes are more advantageous for selection than bigger ones due to their high replication potentials and metabolic inexpensiveness. Deletions can be given as the main force to diminish genomic size that causes gene losses [95-96]. In a recent paper, Pettersson and co-workers emphasized the role of deletions in regulation of genomic size and its coding density by using a mathematical model to determine the evolutionary fate [97].

> A genomic material may accept deletions and reduce its size up to reach minimal genome limits that have the smallest number of genetic elements sufficient to build a modern-type freeliving cellular organism. In addition, under some exceptional conditions, genomic materials of several endo-symbionts and co-symbionts carry much less genes than predicted minimal genome rates. For example, although *Pelagibacter ubique* (α-Proteobacteria) is known as a freeliving organism with the smallest genome (only 1308 Kb in size and potentially contains 1354 genes), endo-symbiont *Hodgkinia cicadicola* (α-Proteobacteria) has the smallest genome (only 144 Kb in size and potentially contains 188 genes) among known-living organisms [98-102]. According to Juhas and co-workers' study [102], the extremely small genomes of endosym‐ bionts usually encode only the most fundamental process, suggesting that some of their genes might have been transferred into the host cell genome. The endosymbiont *Wolbachia* strains that transfer ~1 Mb fragments of its genomic material to the host genome can be given as a good example for this phenomenon [98-102].

> Contrary to the genomic material of *P. ubique* in which there is no pseudogenes, introns, transposons, or extrachromosomal elements, modern-type organism genomes need some or all of these differentiated genetic parts [97]. In this regard, genomic rearrangements have a critical potential via causing structural changes, especially new alleles and new regulatory regions in the genomes can be created by only mutations. There is a huge data giving infor‐ mation about the roles of mutations in evolution in the scientific literature

[1-3,5,8,9,11,12,29-33]. For instance, Halligan and Keightley [103] reviewed the relationship between mutagenesis and its role in genome evolution, and introduced mutational events as the ultimate source of genetic variation.

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6-7.

2732.

### **6. Conclusion**

Recent attention of evolutionary studies has shifted to genetics, molecular and cellular biology as a result of finding out principles of genetics and DNA is the main molecule responsible for inheritance. Thus, the popularity of genome-wide studies has increased. In this regard, genomic rearrangement mechanisms (recombinations, mutations or mobility of several genetic elements) are major research topics for evolution of genomes because any change in the DNA molecule of the organisms may cause a valuable process for evolution when it has inheritable potential.

Thus, aim of the present study was conducted to emphasize potential value of genomic rearrangements for evolution, and therefore, basic rearrangement mechanisms were explained in detail, and their evolutionary effects on genomes were briefly discussed via giving impor‐ tant samples in this chapter.

### **Acknowledgements**

The authors express their thanks to the Microbiology & Molecular Biology Research Team of Biology Department, Atatürk University. The author Mehmet Karadayı specially thanks to Biologist Alperen Tekin for his encouragement and support.

### **Author details**

Özlem Barış, Mehmet Karadayı, Derya Yanmış and Medine Güllüce

Biology Department of Atatürk University, Erzurum, Turkey

### **References**


[1-3,5,8,9,11,12,29-33]. For instance, Halligan and Keightley [103] reviewed the relationship between mutagenesis and its role in genome evolution, and introduced mutational events as

Recent attention of evolutionary studies has shifted to genetics, molecular and cellular biology as a result of finding out principles of genetics and DNA is the main molecule responsible for inheritance. Thus, the popularity of genome-wide studies has increased. In this regard, genomic rearrangement mechanisms (recombinations, mutations or mobility of several genetic elements) are major research topics for evolution of genomes because any change in the DNA molecule of the organisms may cause a valuable process for evolution when it has inheritable

Thus, aim of the present study was conducted to emphasize potential value of genomic rearrangements for evolution, and therefore, basic rearrangement mechanisms were explained in detail, and their evolutionary effects on genomes were briefly discussed via giving impor‐

The authors express their thanks to the Microbiology & Molecular Biology Research Team of Biology Department, Atatürk University. The author Mehmet Karadayı specially thanks to

[1] Watson JD and Berry A. DNA: The Secret of Life. New York: Alfred A. Knopf Inc.;

[2] Brown TA. Genomes 3 (3rd edition). New York: Garland Science; 2007.

Biologist Alperen Tekin for his encouragement and support.

Özlem Barış, Mehmet Karadayı, Derya Yanmış and Medine Güllüce

Biology Department of Atatürk University, Erzurum, Turkey

the ultimate source of genetic variation.

32 Current Progress in Biological Research

**6. Conclusion**

potential.

tant samples in this chapter.

**Acknowledgements**

**Author details**

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**Chapter 3**

**Contribution to the Moss Flora of**

Additional information is available at the end of the chapter

Serhat Ursavaş and Barbaros Çetin

http://dx.doi.org/10.5772/52937

**1. Introduction**

is still largely unknown.

**Kizildağ (Isparta) National Park in Turkey**

The Kızıl Mountain National Park chosen as the study area is in Dedegül Mountain range which is in the 122 important plant areas in Turkey [59]. As a reliable indication of its highly diversed flora. Although the National Park of Kızıl Mountain range was important plant area, was not studied for moss flora, up to now. So, we believed the necessity of studying the mosses of the Kızıl Mountain National Park in Turkey. It is located in a transitional zone of Mediterranean and continental climate. In accordance with its transitional location, Irano-

Studies on the bryophyte flora of Turkey were carried out firstly in the 18th century by Mül‐ ler [1829], Tchihatcheff [1860], Juratzka and Milde [1870], Wettstein [1889], Barbey [1890] and Schiffner [1896, 1897]. The available bryofloristic studies covering a number of localities in Turkey carried out by local and foreing botanists focus only on a small localized area. Es‐

Mosses are important components of forest ecosystems. They have important contributions on biological diversity providing wet habitats for much type living organisms. The study on mosses in Turkey are not extensive as in many other contries, thus the moss flora of Turkey

According to the grid system adopted by Henderson [30], the reserch area is between B7 and C12 squares. While the total number of new records for these square grids is 63, new taxa

To date, nearly studies have been deal with the bryophyte flora of southwest of Turkey. The new records belonging to the B7 mosses taxa were found out from the following literatures: Henderson and Muirhead [28], Henderson [27], Robinson and Godfrey [63], Walther [75],

and reproduction in any medium, provided the original work is properly cited.

© 2013 Ursavaş and Çetin; 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 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

records for B7 is 7, for C12 is 47, as well as both grid squares are 9, respectively.

Turanian and Mediterranean flora elements are dominant in the area (Figure 1).

pecially from late 20th century up to date, many studies were published.

**Chapter 3**
