Volume 33 Issue 4
Jul.  2012
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CAI Bo, HUANG Yong, CHEN Yue-Ying, HU Jun-Hua, GUO Xian-Guang, WANG Yue-Zhao. Geographic patterns and ecological factors correlates of snake species richness in China. Zoological Research, 2012, 33(4): 343-354. doi: 10.3724/SP.J.1141.2012.04343
Citation: CAI Bo, HUANG Yong, CHEN Yue-Ying, HU Jun-Hua, GUO Xian-Guang, WANG Yue-Zhao. Geographic patterns and ecological factors correlates of snake species richness in China. Zoological Research, 2012, 33(4): 343-354. doi: 10.3724/SP.J.1141.2012.04343

Geographic patterns and ecological factors correlates of snake species richness in China

doi: 10.3724/SP.J.1141.2012.04343
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  • Author Bio:

    CAI Bo,E-mail: caibo.cd@gmail.com

  • Corresponding author: WANG Yue-Zhao
  • Received Date: 2012-03-20
  • Rev Recd Date: 2012-05-05
  • Publish Date: 2012-08-10
  • Understanding large-scale geographic patterns of species richness as well its underlying mechanisms are among the most significant objectives of macroecology and biogeography. The ecological hypothesis is one of the most accepted explanations of this mechanism. Here, we studied the geographic patterns of snakes and investigated the relationships between species richness and ecological factors in China at a spatial resolution of 100 km×100 km. We obtained the eigenvector-based spatial filters by Principal Coordinates Neighbor Matrices, and then analyzed ecological factors by multiple regression analysis. The results indicated several things: (1) species richness of snakes showed multi-peak patterns along both the latitudinal and longitudinal gradient. The areas of highest richness of snake are tropics and subtropical areas of Oriental realm in China while the areas of lowest richness are Qinghai-Tibet Plateau, the grasslands and deserts in northern China, Yangtze-Huai Plain, Two-lake Plain, and the Poyang-lake Plain; (2) results of multiple regression analysis explained a total of 56.5% variance in snake richness. Among ecological factors used to explore the species richness patterns, we found the best factors were the normalized difference vegetation index, precipitation in the coldest quarter and temperature annual range ; (3) our results indicated that the model based on the significant variables that (P<0.05) uses a combination of normalized difference vegetation index, precipitation of coldest quarterand temperature annual range is the most parsimonious model for explaining the mechanism of snake richness in China. This finding demonstrates that different ecological factors work together to affect the geographic distribution of snakes in China. Studying the mechanisms that underlie these geographic patterns are complex, so we must carefully consider the choice of impact-factors and the influence of human activities.
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