Abstract
Iran is usually considered as a bridge between Oriental and African zoogeographical region, and also the 20th global biodiversity hotspot. Herpetofauna of the Iranian Plateau has a high diversity compared to other areas in the region and has always been interesting for herpetologists in terms of biogeography, ecology and zoogeography. In this study, distribution maps of 215 terrestrial reptilian species (of which 50 were endemic to Iran) were digitized and the species richness patterns were correlated with 13 environmental factors using spatial analyses methods. Our results showed that the hotspot regions for all reptilian species are concentrated on south and southwest of Iran. This result is consistent with the Irano-Anatolian biodiversity hotspot. Based on spatial analyses, species richness in the area is affected by seven environmental variables which are associated with temperature and probably interpreted as the most important factor on reptile richness in Iran.
Introduction
Biologists have long discussed the ecological and evolutionary mechanisms underlying large scale spatial patterns in species richness (Chown and Gaston, 2000). Reptiles generally have narrower distributional ranges than other vertebrates such as birds and mammals (Anderson, 1984; Anderson and Marcus, 1992). Providing that reptiles could be potentially interesting for hotspot richness studies because small-ranged species are often spatially concentrated in regions with high species richness. Reid (1998) defined the term hotspot as any geographical area high in species richness, level of endemism, number of rare or threatened species, and intensity of threat. Here, the term ‘hotspot’ describes the geographic peaks of species richness and endemism for Iranian reptiles. When we compare our patterns with other studies, the term hotspot will also be used in a more general sense for areas with high scores on any measure (following Aliabadian et al., 2007).
Iran is a vast land which lies between latitude 25 to 40 degrees north of the Equator and between longitude 44 to 63 degrees east of Greenwich Meridian. Iran has an arid climate in which most of the relatively scant annual precipitation falls from October through April. In most of the country, yearly precipitation averages 25 centimeters or fewer, except in the higher mountain valleys of the Zagros and the Caspian coastal plain. There are two main high mountains range in this region: Elburz that runs from northwest to northeast and Zagros that ranges from northwest to southeast of Iran (Fisher, 1968). Iran is considered as a bridge between Oriental and African zoogeographical regions and also as the 20th global hotspot, but its species richness yet to be studied comprehensively (Mittermeier et al., 2004). The herpetofauna of the Iranian plateau has particularly high diversity for the region, and has always been interesting for herpetologists in terms of high diversity in the region; biogeography, ecology and zoogeography.
This fauna has been studied since 1865 when De Flippi (1865) published a list of herpetofauna of Iran, and since then many researchers have studied different aspects of the Iranian herpetofauna (e.g. Darvesky et al., 1978; Nilson et al., 1978; Latifi, 1991; Anderson, 1999; Firouz, 2005; Rastegar-Pouyani et al., 2008). Among recent studies Steven Clement Anderson is one of the researchers who have studied herpetofauna of Iran between 1963 and 1999 (Anderson, 1966, 1999). He did a comprehensive study on zoogeography and distribution of the Iranian reptiles especially lizards. But no one has presented a species richness map and distribution patterns of hotspot of lizards or any other reptiles in Iran. Sindaco and Jeremčenko (2008) presented the distribution maps of western Palearctic reptiles, in which they have found a few hotspots for Palearctic zoogeographical region, including southwest of Iran. In other group of vertebrate there seems to have more studies. Roselaar et al. (2007) digitized the distribution of 3036 phylogenetic species of the Palearctic songbirds, and showed several hotspot regions, mostly located in mountainous areas and Elburz mountain range was presented as one of hotspots in their richness map. In another study (Aliabadian et al., 2008), patterns of the distribution of 2401 breeding taxa of songbirds in the Palearctic region were analyzed. Patterns of species richness in small ranged endemics reveal hotspots particularly in south of the Caspian Sea, and the eastern parts of the Mediterranean.
According to Hawkins et al. (2003), current climate drives broad-scale species richness gradients of both plants and animals based on robust evidence. The spatial gradient of reptile richness corresponds to energy and environmental stability gradients significantly including: temperature, precipitation, and normalized difference vegetation index (NDVI). Reptile’s diversity increases with increasing annual mean temperature, precipitation, NDVI, and decreases with increasing variation in temperature and precipitation (Luo et al., 2012). Reptiles are ectotherms and completely depend completely on ambient warmth to raise their body temperature and thus become active (Whittaker et al., 2007; Buckley et al., 2008; Luo et al., 2012).
The aim of this study is to describe the range-size frequency distribution and patterns of species richness of reptiles in Iran, based on digitized distribution maps of all available data for the Iranian reptiles. We also correlated these patterns with environmental variables using several techniques, in an attempt to understand which factors drive species richness across all species and across endemic species.
Materials and methods
Species and environmental data
The raw data on species distribution of reptiles were taken from several published sources, including: snakes – Latifi (1991); lizards – Anderson (1963, 1974, 1999); turtles, crocodiles, amphisbaenians and lizards – Sindaco and Jeremčenko (2008) and Leviton et al. (1992); and updated them with adding new species and new distribution records (e.g. Anderson and Leviton, 1966; Nilson and Andrén, 1978; Moravec, 1994; Rastegar-Pouyani and Rastegar-Pouyani, 2005; Ananjeva et al., 2006; Bostanchi et al., 2006; Rastegar-Pouyani et al., 2006; Venchi and Sindaco, 2006; Mozaffari and Parham, 2007; Nazarov and Rajabizadeh, 2007; Nilson and Rastegar-Pouyani, 2007; Rastegar-Pouyani et al., 2008; Fahimi, Papenfuss and Anderson, 2009; Gholamhosseini et al., 2009; Nazarov, Ananjeva and Radjabizadeh, 2009; Fathinia and Rastegar-Pouyani, 2010; Rajabizadeh et al., 2010; Ahmadzadeh et al., 2011; Farhadi Qomi et al., 2011; Fathinia et al., 2011; Gholamifard, 2011; Karamiani and Rastegar-Pouyani, 2011; Kazemi et al., 2011; Moradi and Shafiei, 2011; Mozaffari, Ahmadzadeh and Parham, 2011; Torki, 2011; Torki, Manthey and Barts, 2011; Torki et al., 2011; Rajabizadeh, Nilson and Kami, 2011; Nazarov, Bondarenko and Radjabizadeh, 2012; Rajabizadeh et al., 2012; Heidari et al., 2013; Krause et al., 2013; Uetz and Hallermann, 2013). The list includes species recognized on the basis of biological, evolutionary and morphological species concepts (Uetz and Hallermann, 2013).
The distribution maps (extents of occurrence) of 215 terrestrial reptilian species were digitized and optimized for the Iranian boundaries using WORLDMAP v. 4.1 (Williams, 2000). The geographic distributions were plotted on a one-degree longitude equal area map (grid-cell area: 4062 km2) of Iran following Aliabadian et al. (2007). Endemic reptiles of Iran have been defined based on literature reviews in Iran.
The hotspot and richness map were correlated with 13 environmental variables (table 2): 11 climatic variables describing temperature, precipitation, seasonality, altitude, all with 30-arc-seconds resolution. All climatic information was extracted from the Worldclim data set (http://www.worldclim.org/); normalized difference vegetation index (NDVI) is considered as an index of primary productivity that has been shown to be correlated with green leaf biomass and green leaf area index (http://daac.ornl.gov/mapserver.shtml). Land-cover diversity representing number of vegetation type was extracted from Global Land cover facility. The data is in resolutions of 1 km that cover the entire Earth. The data captured years are between 1981 till 1994. The data is available in AVHRR global land cover product (http://glcfapp.glcf.umd.edu:8080/esdi/).
Statistical analyses
All statistical analyses were performed in the Spatial Analysis in Macroecology (SAM) software, v. 4.0 (Rangel et al., 2006; www.ecoevol.ufg.br/sam). According to Legendre (1993), the property of random variables taking values, at pairs of locations a certain distance apart, that are more similar (positive autocorrelation) or less similar (negative autocorrelation) than expected for randomly associated pairs of observations. Moran’s I coefficient is one of the most commonly used descriptors of spatial autocorrelation. Moran’s I can be calculated for individual distance classes (e.g. from 0 to 300 km, 300 to 600 km), producing a plot known as a spatial correlogram (Diniz-Filho et al., 2003, 2008; Rangel et al., 2010). A spatial correlogram was used to describe the spatial pattern of reptile richness, based on Moran’s I in 10 geographic distances classes. For a simple spatial modelling, SAM provides tools for ordinary least squares (OLS) regression, one of them is partial regression analysis and using up to a 6th order polynomial expansion of geographical coordinates (Rangel et al., 2006). Spatial autocorrelation in the original data and in the residuals of ordinary least-squares multiple regression (OLS) model was assessed using Moran’s I coefficients estimated for 10 distance classes. Since Moran’s I coefficients of residual regression showed spatial autocorrelation, another analytical techniques were used to account for this autocorrelation.
Frequency of range size of the Iranian reptiles. Horizontal and vertical axes are referred to range size and percentage of species respectively.
Citation: Amphibia-Reptilia 35, 2 (2014) ; 10.1163/15685381-00002946
Several authors (Borcard and Legendre, 2002; Griffith, 2003) proposed that eigenvector-based spatial filters could be a more simple solution to the autocorrelation problem. The basic idea is to extract eigenvectors of a connectivity (binary) or truncated geographical distance matrix among spatial units (e.g. cells in a grid), and use these eigenvectors, which describe the spatial structure of the region under study at different scales, as additional predictors of the response variable. This way, any remaining spatial structures in regression residuals would be taken into account, and so these models would not be affected by the problem of spatial autocorrelation (Diniz-Filho et al., 1998; Diniz-Filho and Bini, 2005). Among the techniques available today for spatial regression, one of the most flexible and statistically powerful techniques is spatial eigenvector mapping (SEVM). Eigenvector mapping is a unique method that expresses space as a set of eigenvectors, which provides additional ways to select eigenvectors, including the minimization of Moran’s I in model residuals (Diniz-Filho and Bini, 2005; Rangel et al., 2010). The next step includes the selection of the eigenvectors that should enter as predictors in the model. Following Griffith (2003), strategies of selected eigenvectors included: (i) maximization of the regression multiple correlation coefficient (R2); (ii) minimization of residual spatial autocorrelation; and (iii) a significant correlation between response variable and each selected eigenvector. Here, we followed Diniz-Filho et al. (2008)’s method, where eigenvectors were selected based on minimum Moran’s I coefficient (>−0.01). Furthermore, ordinary least-squares multiple regression (OLS) was used to analyze simultaneously the eigenvectors resulting from SEVM and 13 predictors, removing the residual spatial autocorrelation in an OLS without SEVM. Here, we calculated partial regression coefficients of environmental variables after including the important spatial filters into the regression model for correction of variables effects and removing the influence of spatial autocorrelation. Furthermore, we obtained results of two models: the OLS model and the full model (OLS + filters).
Species richness map of reptiles in Iran. This figure is published in colour in the online version.
Citation: Amphibia-Reptilia 35, 2 (2014) ; 10.1163/15685381-00002946
Results
Reptile species richness and hotspot region in Iran
The range size of 215 terrestrial reptilian species of Iran varied from 1 to 379 grid cells (fig. 1). The median range size was 15.5 grid cells (62 961 km2). Based on Böhm et al. (2013), maximum median range size for reptiles is 110 175 km2 whereas our results showed smaller range. Therefore, this means half of species occupied small-ranged. The range size of all the Iranian reptiles’ species and number of taxa are presented in fig. 1.
Analysis of the geographic distribution of species richness for the full dataset showed three hotspot regions that included: southwest of Iran in Khuzestan plain with an average of 56 taxa, east of Iran with an average of 45 taxa and Azerbaijan region in north and northwest of Iran with an average of 35 taxa across cells in this region (fig. 2).
Species richness map of endemic reptiles in Iran. This figure is published in colour in the online version.
Citation: Amphibia-Reptilia 35, 2 (2014) ; 10.1163/15685381-00002946
Hotspots of endemic species richness were concentrated in southwest Iran (fig. 3). The range size of the endemic species comprised between 1 to 99 grid cells. Almost one fifth of species were considered to be endemic (following Gholamifard, 2011): 50 species in 20 genera and seven families (table 1); although the trend of describing new species is continuing in Iran. The reptilian families with the most endemic species are ranked respectively: The Gekkonidae, with 17 (34% of all endemic species in Iran), Lacertidae with 11 species (22%), Phyllodactylidae with eight species (16%), Viperidae with five species (10%), Scincidae with four species (8%), Colubridae with four species (8%), and Typhlopidae with only one endemic species (2%). The genera with the most endemic species were: Cyrtopodion and Asaccus with eight endemic species (each of which with 16% of all endemic species in Iran) containing the most endemic species, followed by Eremias with five (10%), Ophiomorus and Mediodactylus with four endemic species (8%). 15 genera have less than three endemic species each.
Number of species in Iran (endemic and non-endemic), and percentage of endemic species.
Effect of ecogeographical factors on reptile species richness in Iran
The spatial correlogram showed decreasing positive autocorrelation coefficients up to 600 km (Moran’s I = −0.2) and followed by a continuous decrease in Moran’s I coefficients up to 700 km (Moran’s I = −0.23). After this distance, autocorrelation is no longer significant. At the distance of 1800, Moran’s I coefficient has a highly significant negative autocorrelation coefficient (Moran’s I = −0.36) (fig. 4). The residuals of OLS regression had short-distance positive autocorrelation (fig. 5). This indicates that broad scale spatial patterns in species richness were fully explained by environmental factors, but that short-distance variation still remains unexplained. As a result, another analytical strategy is necessary to avoid biased estimation of results.
Spatial correlogram of reptiles species richness in Iran, established for 10 geographic distance classes.
Citation: Amphibia-Reptilia 35, 2 (2014) ; 10.1163/15685381-00002946
Spatial correlogram of residual of OLS, established for 10 geographic distance classes.
Citation: Amphibia-Reptilia 35, 2 (2014) ; 10.1163/15685381-00002946
Standardized partial coefficients (SPC) of a multiple regression model including 13 predictors and three spatial filters of SEVM to model spatial patterns of reptile’s species richness in Iran.
The ordinary least-squares regression model (OLS), using the 13 environmental predictors, explained 19.2% of variation in reptile richness (, ), with large standard regression coefficient for annual mean temperature. Full model adding SEVM comprised 21% overall variation of reptiles species richness, including both environmental drivers and spatial eigenvectors (, , ). This indicated 21% of the variation in species richness could be explained by the combined effects of geographical structure and environmental predictors. Among significant variables, annual mean temperature was the strongest correlate of reptile species richness (table 2). The patterns of relationship between predictors and species richness were strong, linear and positive. Species richness increased with isothermality, maximum temperature of warmest month, mean temperature driest quarter but species richness was linear and mediocre related to annual mean temperature, minimum temperature of coldest month, mean temperature of wettest quarter (fig. 6).
Relationships between environmental variables and reptile richness in Iran. Predictions are shown for (a) annual mean temperature; (b) mean temperature driest quarter; (c) isothermality; (d) maximum temperature of warmest month; (e) minimum temperature of coldest month; (f) mean temperature of wettest quarter.
Citation: Amphibia-Reptilia 35, 2 (2014) ; 10.1163/15685381-00002946
Discussion
Based on Mittermeier et al. (2004) and Myers et al. (2000), north and west of Iran are considered as a part of the Irano-Anatolian biodiversity hotspot, so-called 20th global hotspot region. Mittermeier et al. (2004) defined hotspot by their exceptional species endemism and extensive loss of habitat, the 34 identified hotspots harbor the entire ranges of at least 42% of terrestrial vertebrate species and at least 50% of known plant species within extant habitat that covers only 2.3% of the earth’s land surface. Our results showed Khuzestan province (southwest of Iran) as one of the richest hotspots of Iranian reptiles. This finding is in congruent with a recent study by Sindaco and Jeremčenko (2008). In which south-west of Iran has been identified as one of the hotspots species richness for western Palearctic reptiles. Anderson 1968 also referred to the faunal connection of Iran with the Arabian Peninsula through the Mesopotamian Plain at the head of the Persian Gulf.
On the one hand, the position of Iran as a corridor between African and Oriental zoogeographical regions is a strong reason for its importance for biodiversity and species richness. On the other hand, the presence of the Zagros Mountain from northwest to almost southeast of Iran could act as a geographic barrier for speciation, isolation and the creation of species distribution patterns. In recent studies the major role of the Zagros Mountains in the speciation of some genera has been identified and truly may be called a world hotspot for the studied genera (e.g. Asaccus and Tropiocolotes, Gholamifard, 2011). In addition, the Zagros Mountains separate the central Iranian Plateau from the Mesopotamian plain.
The other hotspot, east of Iran encompassed Sistan-Bluchistan, Khorasan-e-Razavi and Khorasna-e-Jonobi Provinces. The southern part of these regions is considered as a region for connection of the Iranian herpetofauna with the oriental region (Anderson, 1968). Northwest Iran also formed a hotspot region in our species richness pattern, which is congruent with the Irano-Anatolian hotspot region (Mittermeier et al., 2004). Based on present research, the hotspots of endemic species is located in the southwest and west of Iran which is completely consistent with the Irano-Anatolian biodiversity hotspot and also very close to the hotspots identified for total species richness in this study.
Ficetola et al. (2013) suggested east and southwest of Iran as reptile richness hotspots. They have concluded that Irano-Anatolian region and the Iranian Plateau probably predict higher species richness than the currently known values. They emphasized that richness was highest in area characterized by warm and low annual actual evapotranspiration (AET) and this is completely in congruence with climatic condition in defined area.
The influence of environmental factors in the species richness pattern was moderate to weak (21%), the most explanatory factor was temperature, probably because of the temperature-dependency of reptiles as ectotherms (Whittaker et al., 2007; Buckley et al., 2008; Luo et al., 2012). Furthermore, the results indicated that species richness had linear and positive association with annual mean temperature, minimum temperature of coldest month, mean temperature of wettest quarter particularly isothermality, maximum temperature of warmest month, mean temperature driest quarter. Based on previous studies, the spatial gradient, diversity and activity of reptiles corresponds to energy and environmental stability gradients significantly especially temperature, precipitation and NDVI factors (Rodríguez et al., 2005; Whittaker et al., 2007). Reptiles need solar energy to survive and be active for catching food, reproduction and growing. Another study also reported mean annual temperatures as a string correlate of reptile species richness (Diniz-Filho et al., 2008). Poweny et al. (2010) suggested that lizard species richness increases with temperature and is partly predicted by temperature in line with the available energy hypothesis and the peak in richness in dry conditions supports the hypothesis that lizard diversification is highest in hot and arid environments.
The full model explained 21% of the variation in reptiles species richness based on 13 environmental and three eigenvectors variables. The explained variation was lower than expected, probably because other factors particular to the climatic conditions of Iran have influenced species richness patterns, such as the desert climate and also microclimatic conditions. As indicated above, low annual actual evapotranspiration (AET) is an important factor for explaining species richness and could form the basis for future study. This is the first macroecology study on reptile species richness in Iran and complementary studies focusing on larger geographical area and more ecological factors are needed to evaluate correlates of reptile richness. Moreover, it must be taken into account that sampling in such studies is often patchy and biased. It can be effective on geographical distribution of sampling or sampling effort of reptiles in Iran and might be influenced on species richness patterns. Therefore, more comprehensive sampling is needed in further studies.
Acknowledgements
We appreciate Dr Roberto Sindaco for sending us a copy of his book (turtles, crocodiles, amphisbaenians and lizards); Dr Diniz-Filho for helping us in statistical analyses with SAM and for providing helpful comments on the preliminary draft of the manuscript. We are also grateful to Seyed Mahdi Kazemi, Peter Utez for providing distribution information of Iranian reptiles; Dr Omid Mirshamsi for his helpful structure for using environmental data.
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Footnotes
Associate Editor: José Brito.
