Panda Rajendra M, Behera Mukunda Dev, Roy Partha S, Biradar Chandrashekhar
School of Water Resources Indian Institute of Technology Kharagpur West Bengal India.
Centre for Oceans, Rivers, Atmosphere and Land Sciences (CORAL) Indian Institute of Technology Kharagpur West Bengal India.
Ecol Evol. 2017 Nov 10;7(24):10850-10860. doi: 10.1002/ece3.3569. eCollection 2017 Dec.
Several factors describe the broad pattern of diversity in plant species distribution. We explore these determinants of species richness in Western Himalayas using high-resolution species data available for the area to energy, water, physiography and anthropogenic disturbance. The floral data involves 1279 species from 1178 spatial locations and 738 sample plots of a national database. We evaluated their correlation with 8-environmental variables, selected on the basis of correlation coefficients and principal component loadings, using both linear (structural equation model) and nonlinear (generalised additive model) techniques. There were 645 genera and 176 families including 815 herbs, 213 shrubs, 190 trees, and 61 lianas. The nonlinear model explained the maximum deviance of 67.4% and showed the dominant contribution of climate on species richness with a 59% share. Energy variables (potential evapotranspiration and temperature seasonality) explained the deviance better than did water variables (aridity index and precipitation of the driest quarter). Temperature seasonality had the maximum impact on the species richness. The structural equation model confirmed the results of the nonlinear model but less efficiently. The mutual influences of the climatic variables were found to affect the predictions of the model significantly. To our knowledge, the 67.4% deviance found in the species richness pattern is one of the highest values reported in mountain studies. Broadly, climate described by water-energy dynamics provides the best explanation for the species richness pattern. Both modeling approaches supported the same conclusion that energy is the best predictor of species richness. The dry and cold conditions of the region account for the dominant contribution of energy on species richness.
有几个因素描述了植物物种分布多样性的总体格局。我们利用该地区现有的高分辨率物种数据，探索喜马拉雅山西部物种丰富度的这些决定因素，这些因素涉及能量、水、地貌和人为干扰。花卉数据来自一个国家数据库的1178个空间位置的1279个物种和738个样地。我们使用线性（结构方程模型）和非线性（广义相加模型）技术，评估了它们与8个环境变量的相关性，这些变量是根据相关系数和主成分载荷选择的。共有645属176科，包括815种草本植物、213种灌木、190种乔木和61种藤本植物。非线性模型解释了67.4%的最大偏差，表明气候对物种丰富度的贡献最大，占比59%。能量变量（潜在蒸散和温度季节性）比水变量（干旱指数和最干旱季度降水量）能更好地解释偏差。温度季节性对物种丰富度的影响最大。结构方程模型证实了非线性模型的结果，但效率较低。发现气候变量的相互影响对模型预测有显著影响。据我们所知，在物种丰富度格局中发现的67.4%的偏差是山地研究中报告的最高值之一。总体而言，由水能动态描述的气候为物种丰富度格局提供了最好的解释。两种建模方法都支持相同的结论，即能量是物种丰富度的最佳预测因子。该地区的干燥和寒冷条件解释了能量对物种丰富度的主要贡献。
