Water–energy, climate, and habitat heterogeneity mutually drives spatial pattern of tree species richness in the Indian Western Himalaya

Abstract

Analyzing plant species richness across a broad geographic gradient is critical for understanding the patterns and processes of biodiversity. In view of this, a species richness map was developed by stacking the ranges of 51 tree species along an elevational gradient in the Western Himalaya using stacked species distribution models (SSDMs). Among modeling algorithms available in SSDMs, random forest and artificial neural networks exhibited the best performance (r = 0.81, p < 0.001). The predicted tree species richness distribution pattern revealed a mid-elevation peak at around 2,000 m asl, which is in concordance with the observed richness pattern (R2 = 0.94, p < 0.001). Additionally, structural equation models (SEMs) were used to confirm the key factors that influence tree richness. The results based on SEMs confirm that the elevational pattern of predicted tree species richness is explained by mutual effects of water–energy availability, climate, and habitat heterogeneity. This study also validates that the impact of moisture on tree species richness coincides geographically with climate factors. The results have revealed that water–energy-related variables are likely to impact the species richness directly at higher elevations, whereas the effect is more likely to be tied to moisture at lower elevations. SSDMs provide a good tool to predict a species richness pattern and could help in the conservation and management of high biodiverse areas at different spatial scales. However, more investigation is needed to validate the SSDMs in other parts of the Himalayan region to provide a comprehensive synoptic perspective of Himalayan biodiversity at a larger scale.