Energy determines broad pattern of plant distribution in Western Himalaya

Abstract

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.

Energy–water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperm species richness in China

“…A negative relationship between species richness and climatic seasonality has previously been reported (Gao & Liu, 2018;Kristiansen et al, 2011;Liu et al, 2017;Panda et al, 2017;Pandey et al, 2020;Shrestha et al, 2017), and our study supports the climate seasonality hypothesis. The MAT in China is between -25 and 25°C,…”

“…There are studies testing limited hypotheses (Lü, Cai, Yang, Wang, & Zeng, 2018;Lundholm, 2009;Osland et al, 2017;Panda, Behera, Roy, & Biradar, 2017), and multiple hypotheses (Gao & Liu, 2018;Kreft & Jetz, 2007;Liu et al, 2017;Shrestha et al, 2017;Su et al, 2020) to determine the spatial distribution of plant richness. A single variable or hypothesis limitedly explains the richness pattern, as multiple complex phenomena collectively determine the distribution pattern of species richness.…”

Climatic and vegetational drivers of insect beta diversity at the continental scale

“…There are many other candidate terrestrial variables both which are likely to impact insect community assembly, and that can viably be integrated, and therefore would be likely to increase explanatory power of GDMs. Examples include distance to coast (Bivand & Rundel, ), human footprint indicators (Venter et al, ), and measures of topographic heterogeneity between sites, which often better reflect spatial isolation of sites than geographic distance alone (Panda, Behera, Roy, & Biradar, ; Stein et al, ). Other sources might be the rapidly improving global plant diversity maps (Keil & Chase, ), as well as pesticide use records (Gibbs, Mackey, & Currie, ), species traits (McGill et al, ), and species interactions (Brooks et al, ).…”

“…There are many other candidate terrestrial variables both which are likely to impact insect community assembly, and that can viably be integrated, and therefore would be likely to increase explanatory power of GDMs. Examples include distance to coast (Bivand & Rundel, 2017), human footprint indicators (Venter et al, 2016), and measures of topographic heterogeneity between sites, which often better reflect spatial isolation of sites than geographic distance alone (Panda, Behera, Roy, & Biradar, 2017;Stein et al, 2015).…”

Modelling forest resilience in Hindu Kush Himalaya using geoinformation

“…Future projections of precipitation indicate a wetting trend with increases of 38 or 272 mm by 2100 under two scenarios (IPCC A2 and B1). Such climate diversity and possible future alteration estimated to modify the species richness pattern in this landscape where the impact of climate and topographic variability is identified as the major reasons (Panda et al 2017;Tripathi et al 2017).…”