A Review of Ecological Determinants of Territory With Invertebrate Species

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Environmental Effects on Vertebrate Species Richness: Testing the Energy, Ecology Stability and Habitat Heterogeneity Hypotheses

• Zhenhua Luo,
• Songhua Tang,
• Chunwang Li,
• Hongxia Fang,
• Huijian Hu,
• Ji Yang,
• Jingjing Ding,
• Zhigang Jiang

x

• Published: April 18, 2012
• https://doi.org/x.1371/periodical.pone.0035514

Figures

Abstract

Background

Explaining species richness patterns is a central issue in biogeography and macroecology. Several hypotheses have been proposed to explain the mechanisms driving biodiversity patterns, but the causes of species richness gradients remain unclear. In this study, we aimed to explain the impacts of energy, environmental stability, and habitat heterogeneity factors on variation of vertebrate species richness (VSR), based on the VSR design in Red china, so every bit to exam the energy hypothesis, the environmental stability hypothesis, and the habitat heterogeneity hypothesis.

Methodology/Principal Findings

A dataset was compiled containing the distributions of two,665 vertebrate species and eleven ecogeographic predictive variables in China. We grouped these variables into categories of free energy, environmental stability, and habitat heterogeneity and transformed the data into 100×100 km quadrat systems. To examination the three hypotheses, AIC-based model selection was carried out betwixt VSR and the variables in each grouping and correlation analyses were conducted. At that place was a decreasing VSR gradient from the southeast to the northwest of China. Our results showed that energy explained 67.vi% of the VSR variation, with the almanac mean temperature as the main factor, which was followed by annual precipitation and NDVI. Environmental stability factors explained 69.1% of the VSR variation and both temperature annual range and precipitation seasonality had important contributions. Past contrast, habitat heterogeneity variables explained only 26.three% of the VSR variation. Significantly positive correlations were detected among VSR, annual mean temperature, annual precipitation, and NDVI, whereas the relationship of VSR and temperature annual range was strongly negative. In addition, other variables showed moderate or cryptic relations to VSR.

Conclusions/Significance

The energy hypothesis and the ecology stability hypothesis were supported, whereas picayune support was found for the habitat heterogeneity hypothesis.

Commendation: Luo Z, Tang S, Li C, Fang H, Hu H, Yang J, et al. (2012) Environmental Effects on Vertebrate Species Richness: Testing the Energy, Environmental Stability and Habitat Heterogeneity Hypotheses. PLoS ONE 7(4): e35514. https://doi.org/10.1371/journal.pone.0035514

Editor: Ethan P. White, Utah State University, U.s.a. of America

Received: December 23, 2011; Accepted: March 16, 2012; Published: April xviii, 2012

Copyright: © 2012 Luo et al. This is an open-admission article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted employ, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This research was funded by the Scientific discipline and Technology Supporting Projection of Ministry of Science and Technology of People's Republic of China (2008BAC39B04) and the Key Program of Knowledge Innovation Program of Chinese Academy of Sciences (KSCX2-EW-Z-4). The funders had no office in study design, data collection and analysis, decision to publish, or grooming of the manuscript.

Competing interests: The authors have declared that no competing interests be.

Introduction

The variability of spatial patterns of species richness and its underlying mechanisms at big scales are hot debates in macroecology and biogeography [ane]–[three]. Research on plants, invertebrates, fish, amphibians, reptiles, birds and mammals has been conducted at global, regional, and local scales, to document species richness patterns and explore the impacts of biotic and abiotic biogeographical factors [4]–[10], such equally the environs and habitat [11]–[16]. These effects are obvious, but intense debates still be regarding the underpinning machinery, while comprehensive explanations of the source of species richness variation remain controversial [17], [18]. Therefore, more than detailed studies are needed to support these arguments [xix], [20]. Over the years, more than 30 competing hypotheses have been proposed [1], [18]–[22]. Among these hypotheses, the energy hypothesis, the environmental stability hypothesis and the habitat heterogeneity hypothesis are the about oftentimes mentioned. Several investigations have provided evidence supporting those hypotheses, but a clear cause-issue relationship has not yet been establish [6].

The energy hypothesis posits that college productivity, ambience energy and water-energy dynamics issue in higher species diversity [16], [23]–[26]. Areas with college solar radiation and precipitation have higher chief production and they promote thermoregulation, growth, reproduction, differentiation, and the evolution of species, thus leading to higher biomass, larger population sizes, lower extinction rates, and ultimately more than co-occurring species, i.eastward., higher biodiversity [26]–[29]. The environmental stability hypothesis proposes that species are expected to have broader environmental tolerances if they are to survive with greater ecology variation, which will atomic number 82 to an extension of the range for each species, a reduction of the number of co-existing species in an area, i.e., a decrease in species richness. In dissimilarity, a stable environment could advance species specialization and ecological niche diversification, which will increase the environmental chapters for species richness [30]–[33]. The habitat heterogeneity hypothesis claims that topographical and spatial variation, such as tiptop range, mural, or vegetation variability, could produce mosaics and gradients for critical resources affecting co-existing species, thereby leading to higher biodiversity [34]–[36].

Ideally, studies of species richness patterns should cover large areas at a macro-scale, considering misleading results may occur if studies are performed under weather of partial coverage [six]. With its vast territory, wide latitudinal range, complex terrain and diverse climate, China is i of the top twelve mega-biodiversity countries in the world [37]. Furthermore, biodiversity surveys have been conducted nation-wide in China over recent decades. This offers a perfect opportunity to study the impacts of biogeographical factors on species richness gradient. However, examination of patterns of total vertebrate species richness in this region has been limited [38], [39]. In this study, we compiled the distributions of vertebrate species and produced a dataset of predictive variables to: (1) examine the relationships among vertebrate species richness (VSR) and the factors of energy, environment stability, and habitat heterogeneity; and (2) test the free energy hypothesis, the surroundings stability hypothesis, and the habitat heterogeneity hypothesis with VSR.

Methods

This report was conducted across the mainland and the 2 main islands (Taiwan and Hainan) of Mainland china, at latitudes ranging from xviii°N to 54°N and longitudes ranging from 73°Eastward to 135°E (Effigy 1).

Effigy 1. Vertebrate species richness design of Cathay at the calibration of 100×100 km.

The species richness was calculated past overlaying the distributions of mammals, birds, reptiles and amphibians. The colour gradient represented vertebrate species richness.

https://doi.org/10.1371/journal.pone.0035514.g001

Information organization

Nosotros compiled an exhaustive database containing the distributions of 2665 vertebrate species, including 625 mammals, 1330 birds, 402 reptiles, and 298 amphibians, based on Fei (1999), MacKinnon et al. (2000), Ji and Wen (2002), Baillie et al. (2004), Sheng et al. (2005), Pan et al. (2007) and the Vertebrate Species Information Database of our own research group [xl]–[46]. Nosotros excluded marine and aquatic species, whose geographical ranges are distinct from terrestrial animals. Any species that were subject field to taxonomic disputes or that lacked comprehensive distributional information were besides removed from the overall data set. As a result, a total of 365 species (110 mammals, 135 birds, 54 reptiles, and 66 amphibians) were excluded, leaving 2290 terrestrial species for the assay. We digitized the range maps at a scale of 1×1 km and updated them past adding new distribution records (recorded after the original publications) of these species, which were collected from comprehensive published papers, faunistic atlases, nature reserve biodiversity survey reports, documents of museum collections, and field survey records from our laboratory over the by seventeen years [47], [48]. We then overlaid all the range maps to calculate the VSR for each filigree cell.

We used eleven ecogeographic variables (all at a scale of 1×one km), which were classified into three categories: (i) Energy: annual hateful temperature (ANMT, °C), annual precipitation (ANPR, mm), and normalized difference vegetation index (NDVI); (ii) environment stability: temperature seasonality (TS, °C), temperature annual range (TEMR, °C), and atmospheric precipitation seasonality (PRS, mm); and (iii) habitat heterogeneity: altitude range (ATR, m), slope (SLP, °), aspect (ASP), land cover diversity (Landcover), and vegetation type diversity (Vegetation type). ANMT, TS, TEMR, ANPR, and PRS were obtained from WorldClim 1.iv at http://world wide web.worldclim.org/ [49]. ATR, SLP, and ASP were calculated using a 1-km² digital elevation model (DEM) obtained from http://srtm.csi.cgiar.org/ [12], [50]. Land cover information was derived from Global Landcover 2000 at http://ies.jrc.ec.europa.european union/global-land-encompass-2000 [51]. We calculated the annual mean NDVI in 1998 by averaging the monthly NDVI layers from http://www.data.ac.cn/ [52]. The vegetation type data was obtained from the China Vegetation Database [53].

Analyses based on range map information represent species coexistence and distributions at some relatively coarse scales, considering species practise not occur everywhere within their geographical ranges [54]–[56]. Thus, our one×1 km range map based VSR values may be not actually realistic. To overcome this trouble, we re-sampled the VSR and all the rasters of predictive variables into a 100×100 km resolution, following Ding et al. (2006) [6]. We counted the numbers of species, land comprehend, and vegetation types in each 100×100 km grid cell and used them as the variables of vertebrate species richness (VSR), land cover diversity (Landcover), and vegetation type variety (Vegetation type). As to ATR, we extracted the departure betwixt the maximum and minimum altitudes in each 100×100 km grid jail cell. We conducted the mean distance of each 100×100 km filigree cell based on the ane-km² DEM, and calculated ASP and SLP for each grid. ASP was classified according to (form: label (value range)): North: one (337.5° (−22.5°)–22.5°), Northeast: two (22.5°–67.five°), East: 3 (67.5°–112.5°), Southeast: 4 (112.5°–157.5°), S: v (157.five°–202.5°), Southwest: 6 (202.5°–247.5°), West: 7 (247.5°–292.5°) and Northeast: eight (292.v°–337.v°(−22.five°)) (0° was defined as the direction of North). The rest of the variables and the VSR were re-sampled by averaging procedures for each of the 100×100 km grid jail cell.

Statistical analyses

To test the energy hypothesis, the environmental stability hypothesis, and the habitat heterogeneity hypothesis, nosotros generated the best-fit predictive models between the VSR and each of the iii variable groups based on an information theoretic approach [57], [58]. For each variable group, nosotros used generalized linear models (GLMs) to found a set of candidate models that including all the possible combinations of variables, and used Akaike'southward information criterion (AIC) to compare these candidate models by ranking them with ΔAICc [57]. We chose the model with ΔAICc = 0 every bit the best-fit model and the relative likelihood of each candidate model was assessed by Akaike weight (AICw) [57]. We calculated the R ⁱⁱ value of the GLM to assess the explanatory power of the all-time-fit model to the VSR. In guild to evaluate the relative importance of the predictive variables in each grouping, we followed Burnham and Anderson (2002) to sum the AICwdue south over all models that included a given variable [57], [58]. Furthermore, correlation analyses were performed to identify the relationships of the predictive variables in the best-fit models and the VSR. We used linear and 2d-order polynomial models and nosotros selected the model with college explanatory power (R ²) for each variable.

To determine if spatial autocorrelation was important for our models, we followed the method of Diniz-Filho et al. (2003) to calculate the Moran'southward I coefficients for the VSR and the residuals of the best-fit GLMs for variable groups at x altitude classes (100, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 km) in SAM 4.0 [59]. Our statistical analyses were performed using SPSS Version 13.0 and SAS Version 9.ane. All the analyses were considered significant at P<0.05. The spatial analyses were conducted with ESRI ArcGIS 9.2, and all coordinates were transferred into WGS 1984 UTM Zone 50N.

Results

Spatial pattern of vertebrate species richness

At the calibration of 100×100 km grid size, the VSRs were 137 to 956 with an boilerplate of 398.2±140.1 (mean ± SD)(Figure 1). More often than not, the VSR decreased from southeast to northwest in People's republic of china (Effigy 1). Simply 12.9% of the 100×100 km grids contained more than 500 species. These high VSR grids were mainly located in the southwestern areas, tropics, and sub-tropics of the country, which contained several hot-spots, including the Hengduan Mountains, the Xishuangbanna region of Yunnan Province, the southeastern and southern coasts, Hainan, and Taiwan (Figure 1). The grids containing 200–500 species were mainly full-bodied in the vast eastern and northeastern plains of the country, which accounted for 49.2% of the full of grid cells. The remaining grid cells (37.9% of the total) had VSRs of <200 and they were mainly located in the northwestern areas and Qinghai-Tibetan Plateau (Effigy i).

Spatial autocorrelation

The spatial correlogram for the VSR showed a potent spatial construction, as decreasing highly positive autocorrelation coefficients were detected up to 400 km (Moran's I>0.2) (Figure 2). At the distance classes from 500 to 1000 km, the Moran's I coefficients were between −0.2 and 0.2 (Effigy two). The spatial correlograms for the residuals of the best-fit models of energy, environmental stability, and habitat heterogeneity variable groups showed that their Moran's I values were all close to zero (Figure 2). We considered that our models successfully eliminated most spatial autocorrelation in the species richness data.

Figure 2. Spatial correlograms (Moran'southward I coefficients) for the VSR and the residuals of the all-time-fit GLMs of energy, ecology stability, and habitat heterogeneity variable groups.

X distance classes (100, 200, 300, 400, 500, 600, 700, 800, 900, and g km) were included. The dotted lines represented the Moran's I values of 0.2 and −0.2.

https://doi.org/10.1371/journal.pone.0035514.g002

Testing the species richness hypotheses

The results of AIC-based model choice showed that the best-fit model between VSR and energy variables included all the predictive factors in this group, which explained 67.half dozen% of VSR variation (AICw = 0.636, F = 221.439, P<0.001, R ² = 0.676; Tabular array ane). ANMT was the most pregnant variable for the VSR, with a sum of AICws of 0.950 (Table 2). The side by side most important variable was ANPR, with a sum of AICws equal to 0.887, followed by NDVI (sum of AICws = 0.799) (Table ii). The best-fit model for environmental stability factors included two variables (TEMR and PRS) and explained 69.1% of the variation of VSR (AICw = 0.685, F = 344.429, P<0.0001, R ^two = 0.691; Table 1), where TEMR and PRS had summed AICws of 0.989 and 0.961 respectively, irrespective of the effect of TS (sum of AICws = 0.315) (Table ii). ATR (sum of AICwsouth = 0.821), Vegetation type (sum of AICws = 0.729), and ASP (sum of AICwdue south = 0.723) were included in the best-fit model of habitat heterogeneity variables (Tabular array ii), which explained simply 26.3% of the variation of VSR (AICw = 0.789, F = 42.512, P = 0.017, R ² = 0.246; Table 1). In add-on, SLP and Landcover were excluded with AICw sums of 0.431 and 0.279 (Table 2).

Correlation analyses indicated that VSR was strongly, positively, and nonlinearly related to ANMT (P<0.0001, R ² = 0.667; Effigy iii(a)) and ANPR (P<0.0001, R ² = 0.504; Figure 3(b)). A significant, positive, and linear correlation was detected between vertebrate species richness and NDVI (P = 0.013, R ² = 0.483; Figure 3(c)). The relationship between VSR and TMPR was potent and linear, with species number decreasing as TMPR increased (P<0.0001, R ² = 0.687; Figure 3(d)). VSR had a moderate and nonlinear relationship with PRS (P<0.0001, R ^two = 0.262; Figure three(e)), while ATR (P<0.0001, R ² = 0.224; Figure 3(f)) and Vegetation blazon (P<0.0001, R ² = 0.110; Figure 3(chiliad)) were positively and negatively related to VSR, respectively. In add-on, ASP (P = 0.07, R ² = 0.023; Figure three(h)) explained limited variation and it was non significantly associated with vertebrate species richness (R ²<0.1).

Figure 3. Relationships between vertebrate species richness and the variables included in the all-time-fit model that testing the three species richness hypotheses.

Linear and 2nd-lodge polynomial models were used and only the models with higher explanatory powers (R ²) were showed hither. ASP was defined as (class: characterization (value range)) North: 1 (337.5° (−22.5°) −22.5°), Northeast: 2 (22.5°–67.5°), Due east: 3 (67.5°–112.5°), Southeast: iv (112.5°–157.5°), South: 5 (157.5°–202.5°), Southwest: 6 (202.5°–247.5°), Due west: seven (247.5°–292.5°) and Northeast: 8 (292.5°–337.5°(−22.five°)) (0° was defined equally the direction of north).

https://doi.org/x.1371/journal.pone.0035514.g003

Discussion

The energy hypothesis

Energy is essential for the survival of animals, and the dynamics of its availability may induce changes in the species richness gradient compared with their initial condition [26], [sixty]. The energy supply also determines the environmental chapters of species variety [61]. Thus, higher energy levels support more species, considering it maintains more individuals of each species and avoids extinction [6], [24], [25]. In related studies of plants and animals, the energy hypothesis was considered the critical machinery for the species richness spatial pattern [26], [62]–[67]. If this hypothesis is true, the species richness should positively and monotonically correlate with the mean conditions of temperature, atmospheric precipitation or chief productivity [half-dozen].

This study supported the energy hypothesis, considering all three energy-related factors (ANPR, ANMT, and NDVI) had high values of relative importance and, when combined these variables, explained more than than threescore% of VSR variation. The robust, positive relationships between ANPR, ANMT, and NDVI with VSR indicated that higher ambient energy and a greater water supply could sustain more species. VSR decreased with increasing latitude in China, and the highest VSR was located between twenty°N to 30°North, which had the highest temperature and precipitation. Similar conclusions were reached in the analyses of vertebrates in the whole Americas [26], [29]. Enquiry on collywobbles, birds, reptiles, and plants has too supported this hypothesis [68]–[71]. NDVI is considered as an index of primary productivity in ecosystem, and college NDVI implies higher energy input. Our results showed a significant tendency of increase in vertebrate species number with increasing NDVI, which indicates low latitudinal areas, peculiarly tropical regions, support higher VSR. Ding et al. (2006) reported a similarly positive relationship between bird species diversity and NDVI in East Asia, especially on the mainland, and regarded energy (chief productivity) as the all-time factor in explaining species richness. This suggests that the energy hypothesis may play an important role in patterns of biodiversity [6].

The ecology stability hypothesis

The environmental stability hypothesis suggests that stable environmental weather condition tin can increase species diversity, considering they narrow the niche widths, increase the number of ecological niches, and promote specialization in species [31], [32]. If this hypothesis holds truthful, areas with less variation in climate (e.k. low latitudes rather than high latitude areas) should contain larger numbers of species. Our results supported this hypothesis, considering TEMR and PRS accounted for well-nigh 70% of the VSR variation and both variables showed >0.95 relative importance. The strongly negative relationship of TEMR and species richness indicated that lower temperature variation could allow more species to co-be and information technology significantly increases species diversity in an surface area. Meanwhile, a general decreasing tendency of VSR was emerged with rising PRS, although their correlation was moderate and nonlinear. Thus, the hot and moisture tropics and sub-tropics in Communist china have lower temperature and precipitation variation and they contain more vertebrate species. This hypothesis was also supported by Lin et al. (2009), who concluded that TEMR was i of the main contributory factors to mammalian biodiversity in Prc [20]. Similar results were besides reported by Qian et al. (2009), Altamirano et al. (2010), and Carrara and Vazquez (2010) for birds and mammals in the Americas and woody plants in temperate Andean forests [26], [72], [73].

The habitat heterogeneity hypothesis

The habitat heterogeneity hypothesis suggests that college diversity in topographical and spatial habitat structures could allow finer subdivisions of the limiting resources, produce diverse and sufficient ecological niches, promote greater specialization and greater co-existence of species, thereby increasing species richness and community composition [34], [35]. Topographic heterogeneity was found to account for a loftier proportion of the species richness pattern at the macro-scale [6], [72], [73]. Spatial heterogeneity was shown to have an important office in shaping the species richness slope at finer-scales in previous studies of birds and mammals [17], [35], [74]–[77], which could be considered as variations of landscape and vegetation [sixteen], [24], [36], [62], [78].

In our results, the predictive variables for habitat heterogeneity (ATR, Vegetation type, and ASP) explained simply ane fourth of the VSR variation, where ATR and Vegetation blazon had subequal importance and followed by SLP. The results indicated that higher altitude ranges could moderately increase species richness, whereas Vegetation type and SLP had ambiguous or even slightly negative correlations with VSR. This was mayhap a consequence of the scale-dependent effects of habitat heterogeneity [73], [77]. Topography heterogeneity might generate a specific upshot on biodiversity at the national scale, merely the influence of spatial heterogeneity was secondary. Similarly, Lin et al. (2009) took the number of ecosystems in an area as a parameter of habitat heterogeneity and ended that it was a central cistron affecting mammalian biodiversity variation in People's republic of china [twenty].

Decision

The species richness of vertebrates shows a decreasing blueprint from the southeast to the northwest in China. Several regions in the southwestern mountainous areas, tropics/sub-tropics along the southeastern/southern coasts, Taiwan, and Hainan are with abundant vertebrate species and should be received more attention in the national biodiversity conservation system. The spatial gradient of vertebrate species richness corresponds to free energy and environmental stability gradients significantly, in which mean atmospheric condition and variations of temperature, precipitation, and NDVI play of import roles. The free energy hypothesis and the surround stability hypothesis are supported by the data from this country, every bit vertebrate diversity increases with the increases of almanac mean temperature, atmospheric precipitation, and NDVI, whereas decreases with the increases of variations in temperature and precipitation. By contrast, the habitat heterogeneity hypothesis is non well supported, every bit topographic, land cover, and vegetation heterogeneities just account for express variation in species richness in this expanse, and barely positive correlations or even ambiguous relationships are detected betwixt biodiversity and these habitat heterogeneity factors. In general, our study provides a cursory analysis on biodiversity gradient and its mechanisms in Communist china, and could produce a basic reference in establishing of biodiversity conservation strategies and nature reserves.

Acknowledgments

Nosotros give thanks Yang C. for the help with statistical analyses. We thank Ping X. and Hu J. for providing valuable advice on the early typhoon of the newspaper.

Author Contributions

Conceived and designed the experiments: ZL ZJ. Performed the experiments: ZL ST CL HF HH JY JD ZJ. Analyzed the data: ZL ZJ. Wrote the paper: ZL ZJ. Contributed to the writing of the last version of the manuscript: ZL ST CL HF HH JY JD ZJ.

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