Elevational gradients of diversity for lizards and snakes in the Hengduan Mountains, China

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1 Biodiversity and Conservation (2007) 16: Ó Springer 2006 DOI /s Elevational gradients of diversity for lizards and snakes in the Hengduan Mountains, China CUIZHANG FU *, JINGXIAN WANG, ZHICHAO PU, SHENLI ZHANG, HUILI CHEN, BING ZHAO, JIAKUAN CHEN and JIHUA WU * Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, and Institute of Biodiversity Science, Fudan University, Shanghai , China; *Authors for correspondence ( cuizhangfu@yahoo.com or Jihuawu@fudan.edu.cn; phone: ; fax: ) Received 16 November 2004; accepted in revised form 10 October 2005 Key words: Climate, Elevation, Elevational gradient, Hengduan Mountains, Herpetological fauna, Lizard, Reptile, Snake, Species richness Abstract. Comparing elevational gradients across a wide spectrum of climatic zones offers an ideal system for testing hypotheses explaining the altitudinal gradients of biodiversity. We document elevational patterns of lizard and snake species richness, and explore how land area and climatic factors may affect species distributions of lizards and snakes. Our synthesis found 42 lizard species and 94 snake species known from the Hengduan Mountains. The lizards are distributed between 500 and 3500 m, and the snakes are distributed between 500 and 4320 m. The relationship between species richness and elevation for lizards and snakes is unimodal. Land area explains a significant amount of the variation in lizard and snake species richness. The cluster analysis reveals pronounced distinct assemblages for lizards and snakes to better reflect the vertical profiles of climate in the mountains. Climatic variables are strongly associated with lizard and snake richness along the elevational gradient. The data strongly implicate water availability as a key constraint on lizard species richness, and annual potential evapotranspiration is the best predictor of snake species richness along the elevational gradient in the Hengduan Mountains. Introduction Mountain biodiversity is not only a scientific theme of high interest, but also is perhaps the best indicator value of the integrity of mountain ecosystem (Ko rner 2002). As a consequence of physical heterogeneity of the mountains, mountain regions typically possess higher levels of biodiversity than plains, making it possible to conserve large amounts of diversity in relatively small areas (Lafon 2004). The Hengduan Mountains of China is also rich in plants and animals, and is referred to as one of the worlds hot spots of biodiversity (Boufford and Van Dyck 2000; Myers et al. 2000). Lizard and snake biodiversity of this area accounts for more than 33% of the total species number of lizards and snakes in China (see the discussion). As predictions of the loss of global biodiversity grow increasingly pessimistic, identifying the factors that determine species richness has become a hot topic (Willis and Whittaker 2002). Studies on different taxa along elevational

2 708 gradients reveal that there is large variation in species diversity patterns (Meyer and Thaler 1995; Brown and Lomolino 1998). Both hump-shaped and monotonically decreasing patterns of richness in relation to altitude are two common altitudinal richness patterns (see review in Rahbek 1995). Since Rahbek s review, some studies further confirm a unimodal pattern of richness occurring with altitude in plants (Kessler 2001; Vetaas and Grytnes 2002; Bhattarai et al. 2004), invertebrates (Samson et al. 1997; Pyrcz and Wojtusiak 2002; Sanders 2002), and mammals (Heaney 2001; Md. Nor 2001; Rickart 2001; Sa nchez-cordero 2001; Li et al. 2003a, b; McCain 2004), and others support monotonically decreasing patterns of richness occurring with altitude in plants (Tang and Ohsawa 1999; Odland and Birks 1999; Austrheim 2002), invertebrates (Lobo and Halffter 2000), fishes (Fu et al. 2004), birds (Patterson et al. 1998; Kattan and Franco 2004). For amphibians and reptiles, they generally show a monotonic decline in species richness with increasing altitude (Heatwole 1982; Heatwole and Taylor 1987), although opposite trends have been observed in particular habitats (Heyer 1967; Simbotwe 1985). Information is limited on altitudinal distributions of herpetological diversity in subtropical mountainous regions, China. Numerous hypotheses have been proposed to explain both a linear and humped relationship between species richness and altitude (Rahbek 1997; Kessler 2000; Brown 2001; Lomolino 2001; Grytnes and Vetaas 2002; Grytnes 2003). Climatic, biological, geographical and historical factors have been suggested as main causes of variation in species richness along elevational gradients (Rahbek 1995; Rosenzweig 1995; Brown 2001; Lomolino 2001; Whittaker et al. 2001). Area is a principal factor to affect species richness (Rosenzweig 1995; Whittaker et al. 2001). The elevational gradient of species richness is also intricately related to species-area relationships (Lomolino 2001). Some studies have reported that area of altitudinal belts explained a large proportion of the variation in species richness (e.g., Rahbek 1997; Sanders 2002; Bachman et al. 2004; Fu et al. 2004; Kattan and Franco 2004). Elevational patterns of diversity are also commonly explained by similar factors to the latitudinal gradient, such as water and energy (Currie 1991; Rohde 1992; O Brien 1993; Currie et al. 1998; Whittaker and Field 2000; Whittaker et al. 2001; Willig et al. 2003; Hawkins et al. 2003a). Lomolino (2001) pointed out that the many components of climate and local environments vary along the elevational gradients and ultimately create the variation in species richness. Several studies have found that climatic variables explained a large proportion of the variation in plant richness along the elevational gradients (Odland and Birks 1999; Bhattarai and Vetaas 2003; Bhattarai et al. 2004). Information is limited on relationships between animal richness and climatic variables along the elevational gradients. In this study, we document the elevational patterns of lizard and snake species richness, attempt to understand the elvational distributions of lizard and snake assemblages, and examine effects of land area and climatic factors on lizard and snake diversity along the elevational gradient in the Hengduan Mountains.

3 709 Materials and methods Study area The Hengduan Mountains (23 33 N, E) of China stretches across east of Tibet, west of Sichuan province and northwest of Yunnan Province (Figure 1). A majority of the mountains is a constitutional part of the Qinghai- Tibet Plateau. The total area of the mountains is about 0.5 million square kilometers, and altitudes range from about 500 to 7556 m above sea level (a.s.l.) (Zhang et al. 1997). The altitude of this area declines from northwest to southeast (Figure 1). Most parts of the area are characterized by a series of paralleled mountain ranges and rivers from south to north, and with a sharp altitudinal differentiation (Zhang et al. 1997; Zhao and Yang 1997). Taxonomic data sources A detailed survey on reptile fauna in the Hengduan Mountains was mainly carried out during the period of by the scientists from Chengdu Institute of Biology and Kunming Institute of Zoology, Chinese Academy of Sciences. According to this survey and accumulated information, a relatively Figure 1. The sketch map of the Hengduan Mountains in China.

4 710 detailed report on records of reptile fauna in the Mountains has been assembled by Er-mi Zhao, Datong Yang and their co-workers (Zhao and Yang 1997). In addition to these data, this study draws on data from Pan et al. (2002), Zhao and Huang (2003) and Li et al. (2003a, b). Our synthesis summarizes elevational records for 139 reptile species from the mountains, distributed among 2 orders, 14 families and 57 genera. There are only three turtle species in the mountains. Lizards and snakes are the main component of reptile fauna in the mountains, accounting for 30.2 and 67.6% of the total reptilian species. Therefore, we only analyze elevational distributions of lizards and snakes. In addition, two snake species Boiga cyanea and Plagiopholis unipostocularis were excluded from the analyses because of lacking information on their elevational distributions. Endemic species are defined according to their distribution being limited to the Hengduan Mountains. Climate and climatic variables The Hengduan Mountains is characterized by the Asian monsoon climate, with dry periods from November to April, and wet periods from May to October (Zhen 1989). The mountains include two climatic realms, the Qinghai-Tibet Plateau cold climatic realm (>3000 m a.s.l.) and the southeastern monsoon climatic realm (<3000 m a.s.l.) (Zhang 1989). Along the elevational gradients, the climatic zone of the mountains could be divided into the Tropical Zone (<1000 m a.s.l.), the Subtropical Zone (about m a.s.l.), the Warm Temperate Zone (about m a.s.l.), the Cold Temperate Zone (about m a.s.l.) and the Plateau Subfrigid Zone (>3400 m a.s.l.) (Zhang 1989; Zhang et al. 1997). In this study, we use climate data from 104 climate stations located from 500 m a.s.l to 4200 m a.s.l., with records covering These data were obtained from Climate Resource Database ( g03.asp) and Meteorological Administration of local or central government in China. Five climatic variables are included, selected because they have been shown to be important correlates of broad-scale richness gradients (see Hawkins et al. 2003a for details): (1) annual potential evapotranspiration (PET), (2) annual mean temperature (AMT), (3) mean daily temperature in the cold month (MINT), (4) annual mean rainfall and (5) annual actual evapotranspiration (AET). PET and AET are calculated using the following formula: PET = mean annual accumulated temperature for daily mean temperature over 10 C 0.16 (Liu 2000); AET=[Rain (1 exp( PET/Rain)) PET - TANH (Rain/PET)] 0.5 (Yang et al. 1994). Six 100-m elevation intervals lack climate stations include of m, m, m, m, m, and m. In these 100-m elevation zones, five climatic variables were interpolated from the mean value of the nearest adjacent upper and lower climatic station s record. Climatic trends along the elevational gradient are shown in Figure 2.

5 711 Figure 2. The relationships between elevation and climatic variables in the Hengduan Mountains: (a) potential evapotranspiration; (b) annual mean temperature; (c) mean daily temperature in the cold month; (d) annual precipitation; (e) actual evapotranspiration. Statistical analysis We divided the range of elevation into 100 m bands, and calculated the total number of species in each band to examine the relationship between species richness and elevation. A species was defined as present in every 100-m elevation band between its minimum and maximum elevation records. For example, a species with its elevation limit between 1060 and 1470 m is then present in the 1100, 1200, 1300, 1400 and 1500 m elevation bands (see Rahbek 1997; Patterson et al. 1998; Bhattarai et al. 2004; Fu et al. 2004).

6 712 An exact estimate of the area for each 100-m interval is not available, but area has been estimated for each 500-m interval by Zhang et al. (1997). The data were used to examine the influence of area on species richness pattern along the elevational gradient. We compared community composition among 100-m interval elevation bands to explore community composition pattern along the elevational gradient. An analysis of similarity measure was conducted using the Jaccard (1901) index. Pairwise similarities among all bands were computed to compose a similarity coefficient matrix. The un-weighted pair-group average agglomerative method was used in the cluster analysis based on the matrix (Dunn and Everitt 1982). Generalized linear models (GLMs) (McCullagh and Nelder 1989) were used to relate species richness with elevation or climatic variables. GLMs have been used to relate species richness to climatic variables along the elevational gradients (e.g., Bhattarai and Vetaas 2003; Grytnes 2003; Bhattarai et al. 2004). The first and second order polynomials were tested if there was or there was not a relationship between species richness and elevation or each climatic variable. The adequacy of the fitted statistics was confirmed by plotting standardized residuals against the fitted values, and with normal probability plots (Crawley 1993). The pattern of spatial autocorrelation in the richness data and its probable sources were evaluated by comparing the spatial autocorrelation in the original richness data with that of the residuals of the species-climate regression models, based on Moran s I coefficients (Diniz-Filho et al. 2003). We evaluated our models using F-tests, because they are more robust when there is over- or under-dispersion in the model (Hastie and Pregibon 1993). We used NTSYSpc (version 2.0) for the cluster analysis, S-plus (version 6.0) for the regression analysis and STATISTICA (version 6.0) for the graph representation. Results Fauna Our synthesis found 42 lizard species (6 families and 16 genera) and 94 snake species (6 families and 38 genera) known from the Hengduan Mountains. Among these, there are 8 lizard species and 6 snake species endemic to the mountains. The lizards are distributed between 500 and 3500 m. The most species-rich families are the Agamidae (5 genera and 16 species), the Scincidae (4 genera and 12 species), and the Gekkonidae (3 genera and 8 species). The snakes are distributed between 500 and 4320 m. The most species-rich families are the Colubridae (25 genera and 73 species) and the Viperidae (5 genera and 12 species). There are only one species, Gloydius strauchi (Viperidae) above 3200 m.

7 713 Patterns of diversity Lizard species richness and snake species richness along the elevational gradient in the Hengduan Mountains are strongly correlated (r = 0.97, p < 0.01). The relationship between species richness and elevation for lizards and snakes is unimodal, as indicated by a statistically significant second order term in generalized linear models (Table 1). For lizards, maximum richness is observed at the 1100-m band, and there are a significant monotonically increasing trend from 600 to 1100 m and a significant monotonically decreasing trend from 1100 to 3500 m. For snakes, maximum richness is observed at the 1300-m band, and above and below this elevation species richness significantly decreases (Figure 3). At a 500-m interval scale, lizard or snake species richness all peaks at m elevational zones. Area also exhibits a unimodal pattern, and peaks at m elevational zones (Figure 4). Maximun lizard or snake richness does not occur at the largest available area. However, the correlation between log (area) and species richness is still significant for lizards (r 2 = 0.55, p < 0.04) and snakes (r 2 = 0.58, p < 0.03). The cluster analysis reveals distinct assemblages along the elevational gradient for lizards and snakes (Figure 5). At similarity coefficient 0.53, lizard community could be divided into six assemblages, and snake community could be divided into five assemblages. Elevational boundaries of lizard assemblages are: (1) m; (2) m; (3) m; (4) m; (5) m; and (6) m. Those of snake assemblages are: (1) m; (2) m; and (3) m; (4) m; and (5) m. At similarity coefficient 0.23, lizard community or snake Table 1. Summary of regression statistics for the relationships between lizard or snake species richness and environmental variables along the elevation gradient. Lifeforms Environmental variables GLMs order d.f. % deviance Explained p (F) Lizards Elevation p < Potential evapotranspiration p < Annual mean temperature P < Mean daily temperature in the cold month p < Annual precipitation p < Actual evapotranspiration p < Snakes Elevation p < Potential evapotranspiration p < Annual mean temperature p < Mean daily temperature in the cold month p < Annual precipitation p < Actual evapotranspiration p < GLMs, generalized linear models; d.f., degree of freedom; p (F), probability in F-test. The number 1 and 2 indicates first order polynomials. The deviance explained indicates percentage of null deviance.

8 714 Figure 3. The relationship between species richness and elevation for lizards and snakes in the Hengduan Mountains. community could be divided into three assemblages. Elevational boundaries of lizard assemblages are: (1) m, (2) m, (3) m, and those of snake assemblages are (1) m, (2) m, (3) m. Species richness and climatic variables Lizard or snake species richness has a unimodal relationship with energy variables, potential evapotranspiration (PET), annual mean temperature (AMT) and mean daily temperature in the cold month (MINT) (Table 1). The richness trend along the three climatic variables is the same as elevation because the relationships between elevation and these variables are highly correlated (Figure 2a c). For lizard or snake species richness, optimum PET values are from 850 to 1100 mm Figure 6a), optimum AMT values are from 17 to 19 C (Figure 6b), and optimum MINT values are from 10 to 12 C (Figure 6c). Under or over the optimum values of the three climatic variables, lizard or snake species richness monotonically decreases. There is a log-linear relationship between lizard or snake species richness and mean annual rainfall or actual evapotranspiration (Figure 6d e and Table 1).

9 715 Figure 4. The relationship between species richness or area and elevation zone of 500-m intervals in the Hengduan Mountains. Lizard or snake species richness is autocorrelated along the elevational gradient Figure 7a). For lizards, there is strong spatial structure in the residuals when richness is regressed against the five climatic variables (Figure 7b f). For snakes, there is less spatial structure in the residuals when PET is used to as an explanatory variable in the generalized linear models Figure 7b), and there is no spatial structure at mid-large elevational distances when richness is regressed against AMT or MINT (Figure 7c d), and.there is strong spatial structure in the residuals when richness is regressed against mean annual rainfall or actual evapotranspiration Figure 7e f). Discussion Fauna In China, 162 lizard species and 209 snake species have been recorded, distributed among 17 families and 101 genera (Zhao et al. 2000). Of these, lizards and snakes in the Hengduan Mountain accounts for 25.9% of the

10 716

11 b 717 Figure 5. The classification of 100-m elevational intervals using Jaccard similarity measure for (a) lizard species and (b) snake species. The un-weighted pair-group average agglomerative method was used for the cluster analysis based on the similarity coefficient matrix. total lizard richness and 45.0% of the total snake richness in China. The unique physical conditions may make the mountains to be so rich in lizard and snake fauna. Further study is necessary to explain the high biodiversity of lizards and snakes in the mountains by compared to lizard and snake diversity in other regions, China. Figure 6. The relationships between lizard or snake richness and climate variables in the Hengduan Mountains: (a) potential evapotranspiration; (b) annual mean temperature; (c) mean daily temperature in the cold month; (d) annual precipitation; (e) actual evapotranspiration.

12 718 Figure 7. Spatial correlograms for (a) original species richness and residuals after each climatic variable is fitted: (b) potential evapotranspiration; (c) annual mean temperature; (d) mean daily temperature in the cold month; (e) annual precipitation; (f) actual evapotranspiration. Patterns of diversity A trend toward loss of herpetological diversity along tropical and subtropical altitudinal gradients has been documented for different study sites in the Andean mountains (Peafur and Duellman 1980; Lynch 1987; Cadle and Patton 1988; Duellman 1988; Luddecke 1997), the Philippines (Brown and Alcala 1961), Israel (Nathan and Werner 1999) and Costa Rica (Scott 1976; Fauth et al. 1989). This study do not show the monotonically decreasing altitudinal richness pattern, and show a diversity peak at low-to-middle elevations for

13 719 lizards and snakes in the Hengduan mountains. Rahbek (1995, 1997) pointed out that species richness generally declined monotonically from mid- to high elevation on local and region scales, and patterns of species richness below the median of complete elevation gradients varied considerably among taxa and geographical regions. Growing evidence also suggest that mid-elevational peaks in species richness for a wide variety of taxa are perhaps more general (Rahbek 1995; Brown 2001; Lomolino 2001). In this study, the maximum lizard or snake richness does not occur at the largest available area. However, area still explained 55% of variation in lizard species richness and 58% variation in snake species richness along the elevational gradient in the mountains. The influence of area in determining regional species richness in altitudinal ranges has also been shown for other taxa, such as plants (Bachman et al. 2004), ants (Sanders 2002), fishes (Fu et al. 2004), birds (Kattan and Franco 2004) and mammals (Rickart 2001). The cluster analysis of community similarity reveals pronounced distinct assemblages for lizards and snakes in the mountains (Figure 5). This pattern of species diversity better reflects the vertical profiles of climate in the Hengduan Mountains (see the materials and methods). The cluster analysis also reveals a high elevational species assemblage largely distinct from those of lower elevations for lizards or snakes in the mountains. Similar pattern was also observed in other studies (Patterson et al. 1998; Md. Nor 2001; Pyrcz and Wojtusiak 2002; Sanders et al. 2003; Fu et al. 2004). Species richness and climatic variables Broad-scale variation in taxonomic richness is strongly correlated with climate (Currie et al. 1998; Whittaker and Field 2000; Whittaker et al. 2001; Willig et al. 2003). Richness-climate relationships have been documented for plants and animals (see review in Hawkins et al. 2003a). Pianka (1986) reviewed the factors affecting the species richness of reptiles. Aside from the latitudinal effect, reptile species richness correlated strongly with the amount of sunshine received by an area, and also positively correlated with both rainfall and evapotranspiration. In this study, aside from the altitudinal effect, lizard or snake species richness are strongly affected by the climatic variables, i.e. PET, annual mean temperature, mean daily temperature in the cold month, mean annual rainfall and AET Figure 6 and Table 1). Energy The ambient energy hypothesis considers the input of solar energy to create a physical environment that affects organisms through their physiological responses to temperature (Willig et al. 2003). Reptiles, particularly lizards, are also commonly found to be most closely associated with energy measures

14 720 wherever they are studied. Because they are extreme solar ectotherms with a complex array of physiological and behavioral mechanisms for maintaining their body temperatures, it is believed that ambient temperature represents the most important climatic factors influencing reptiles (Heatwole 1976), a review supported by richness studies (Terent ev 1963; Pianka 1967; Scheibe 1987; Schall and Pianka 1978). In this study, energy variables, PET and annual mean temperature also explain significant variations in lizard and snake species richness (Table 1). The relationship between energy variables and lizard or snake richness is a unimodal Figure 6a b). The curvilinear relationships between energy variables and richness are commonly found in analyses focused on northern latitude (Hawkins et al. 2003a). In North America, vertebrate richness was also best described by PET based on a curvilinear regression (Currie 1991). In addition, the most common trend of temperature effects for vertebrate ecotherms is a tendency to improve performance at moderate temperatures and a decrease in performance as temperature departs from an optimum, producing asymmetric bell-shaped performance curves (Huey and Pianka 1982; Angilletta et al. 2002; Navas 2003). Effects of temperature on the physiological and behavioral performance of lizards and snakes maybe result in the curvilinear relationships between energy variables and richness in the mountains. In this study, optimum PET values for maximum richness of lizards or snakes are from 850 to 1100 mm Figure 6a). Similar optimum PET values were also observed for ferns and flowering plants along the elevational gradients in the adjacent region of the Hengduan Mountains, the Himalayas (Bhattarai and Vetaas 2003; Bhattarai et al. 2004). At high PET values (>1500 mm), species richness significantly deceasing with increasing PET values for plants has also reported in semi-arid Southern Africa (Hoffman et al. 1994). The von Humboldt s (1808) hypothesis explicitly argues that many organisms are limited at higher latitudes by their inability to withstand winter temperatures. Following the idea, an elevational equivalent of this hypothesis may be suggested that organisms could not survive at higher elevations. In this study, mean daily temperature in the cold month is strongly associated with lizard and snake richness patterns (Table 1), lizard species is limited at <3500 m a.s.l, and snake species is limited at <3200 m a.s.l except for one species, Gloydius strauchi (Viperidae). It may suggest that lizard or snake species richness is limited at higher elevations by their inability to withstand cold temperature. Water availability Based on the available evidence, it appears that energy is a strong predictor of animal diversity gradients in only a small part of the planet, and that over most of the earth the distribution of rainfall has a stronger influence on diversity gradients than temperature (Hawkins et al. 2003a). Several studies have

15 721 predicted that species richness would increase with increasing rainfall and primary productivity (Owen 1989; Rosenzweig 1992; Rosenzweig and Abramsky 1993; Rahbek, 1997). In the subtropical Hengduan Mountains, mean annual rainfall is strongly associated with lizard and snake richness patterns (Table 1). The unimodal relationship between mean annual rainfall and elevation (Figure 2d) contributes to the significant positive log-linear relationship between lizard or snake richness and mean annual rainfall (Table 1, Figure 6d). For lizard species diversity, Whitford and Creusere (1977) also reported that increased precipitation can result in increased lizard richness, but other studies reported that increased precipitation had a negative influence on lizard richness (Pianka 1971; Scheibe 1987; Owen 1989). For snake species diversity, the positive relationship between species richness and rainfall has been founded in Australia and USA (Schall and Pianka 1978; Pianka and Schall 1981; Owen 1989). The influence of rainfall in determining regional species richness in altitudinal ranges has also been shown for small mammals (Heaney 2001; Md. Nor 2001; Sa nchez-cordero 2001; Li 2003a, b). Water-energy Wright (1983) predicted that plant diversity would be limited by solar energy tempered by water availability. For animals, diversity would then be limited by the production of food items needed. This is referred to as the productivity hypothesis Hawkins et al. 2003b). AET, as a measure of water-energy balance, has been used to model plant productivity (Rosenzweig 1968; Lieth 1975). According to the productivity hypothesis, one should expect a linear increase of species richness with increasing AET. In this study, AET explains significant variations in lizard and snake richness (Table 1). Some studies have also reported that AET is the best predicator of animal richness patterns (Hawkins and Porter 2003; Hawkins et al. 2003b). However, vertebrate richness in North America depends on ambient energy rather than on AET (Currie 1991; Kerr et al. 1998). In general, AET is correlated to species richness, but not in all organisms and generally not as highly as is PET (Willig et al. 2003). In this study, it is also found that PET is the better predicator of species richness than AET (Table 1). Ecologists have become aware that spatial autocorrelation in richness data can inflate significance tests of predictor variables (Legendre et al. 2002; Diniz- Filho et al. 2003). If no spatial autocorrelation remains in a distance class, then the spatial pattern of species richness can be explained by the environmentally driven spatial pattern at distance. In contrast, remaining spatial autocorrelation at any distance class among the residuals indicates that the environmental model does not adequately describe the pattern in richness at that scale (Hawkins and Porter 2003). In this study, there is strong spatial structure in the residuals when lizard richness is regressed against the five climatic variables. It indicates that climatic factors are insufficient to account for lizard species

16 722 richness pattern along the elevational gradient. For snakes, fitting PET successfully removed almost all spatial autocorrelation, indicating that PET can account for the spatial pattern in snake richness very well. In summary, this study has provided some answers to the questions presented at the outset. (1) The Hengduan Mountains is one of the hot spots of lizard and snake biodiversity in China, (2) We can reject the argument that lizard and snake species richness has a monotonic increasing relationship along the elevational gradient and replaced this with an alternative unimodal hypothesis, (3) Land area explains a significant amount of the variation in lizard and snake species richness. (4) The cluster analysis reveals pronounced distinct assemblages for lizards and snakes to better reflect the vertical profiles of climate in the mountains. (5) Lizard or snake species richness is limited at higher elevations in the mountains, (6) climatic variables are strongly associated with lizard and snake richness along the elevational gradient. Clearly, the data strongly implicate water availability as a key constraint on lizard species richness, and PET is the best predictor of snake species richness along the mountains elevational gradient. Acknowledgements This study was financially supported by 211 Project (Project name: Biodiversity and Regional Eco-safety), and a Jun Zheng scholar student fund allocated to the second author. References Angilletta M.J., Niewiarowski P.H. and Navas C.A The evolution of thermal physiology in ectotherms. J. Therm. Biol. 27: Austrheim G Plant diversity patterns in semi-natural grasslands along an elevational gradient in southern Norway. Plant Ecol. 161: Bachman S., Baker W.J., Brummitt N., Dransfield J. and Moat J Elevational gradients, area and tropical island diversity: an example from the palms of New Guinea. Ecography 27: Bhattarai K.R. and Vetaas O.R Variation in plant species richness of different life forms along a subtropical elevational gradient in the Hymalyas, east Nepal. Global Ecol. Biogeogr. 12: Bhattarai K.R., Vetaas O.R. and Grytness J.A Fern species richness along a central Himalayan elevational gradient, Nepal. J. Biogeogr. 31: Boufford D.E. and Van Dyck P.P South-central China. In: Mittermeier R.A., Myers N. and Mittermeier C.G. (eds), Hotspots: Earth s Biologically Richest and Most Endangered Terrestrial Ecoregions. CEMEX, Mexico City, pp Brown J.H Mammals on mountainsides: elevational patterns of diversity. Global Ecol. Biogeogr. 10: Brown J.H. and Lomolino M.V Biogeography, 2nd edn. Sinauer, Sunderland. Brown W.C. and Alcala A.C Populations of amphibians and reptiles in the submontane and montane forest of Cuernos Negros, Philippine Islands. Ecology 42: Cadle J.E. and Patton J.L Distribution patterns of some amphibians, reptiles, and mammals of the Eastern Andean slope of Southern Peru. In: Heyer W.R. and Vanzolini P.E. (eds),

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