Species richness is a strong driver of forest biomass along broad bioclimatic gradients in the Himalayas

Forest biomass is an important component of terrestrial carbon pools. However, how climate, biodiversity, and structural attributes co-determine spatiotemporal variation in forest biomass remains not well known. We aimed to shed light on these drivers of forest biomass by measuring diversity and structural attributes of tree species in 400-m(2) plots located every 100 m along a 4200-m elevational gradient in the eastern Himalayas. We applied structural equation models to test how climate, species richness, structural attributes, and their interactions influence forest biomass. Importantly, species richness was a stronger driver of biomass than environmental and structural attributes such as annual air temperature or stem density. Integrating the availability of energy and the demand for water, potential evapotranspiration was more strongly correlated with biomass than water availability, likely due to the strong influence of the Indian summer monsoon. Thus, interactions between climate and tree community composition ultimately control how much carbon is stored in woody biomass across bioclimatic gradients. This fundamental understanding will support predictive efforts of the forest carbon sink in this hydroclimatically important region and help preserving regional forests as a potent natural solution for climate change mitigation.

Automated summary

This study investigates the effects of climate, species richness, and structural attributes on forest biomass by measuring tree diversity and structural attributes. The results show that species richness has a stronger influence on biomass than environmental and structural attributes, and potential evapotranspiration is more strongly correlated with biomass than water availability, especially in the presence of the Indian summer monsoon. These findings have important implications for predicting forest carbon sinks and mitigating climate change in hydroclimatically significant regions.