Trait-based modelling of temperate forests in Western Himalaya, India

Singh, S. (2021). Trait-based modelling of temperate forests in Western Himalaya, India. IIASA YSSP Report. Laxenburg, Austria: IIASA

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Abstract

A central goal of ecology is to understand how ecological communities assemble and how species assemblages determine ecosystem functioning (reflected in ’effect traits’) in response to environmental factors (reflected in ’response traits’). In this study, we aim to characterize the spatial variability of major plant functional traits along an environmental gradient in temperate forests of western Himalaya, and to assess the extent to which this variability differs between broad-leaved and conifer tree species. Further, to analyze the effect of traits on vegetation dynamics mechanistically, we calibrated the Plant and Plant-FATE vegetation models. Four functional traits were selected to incorporate information on both the leaf and the wood economic spectra: maximum tree height, wood density, leaf mass per area and leaf phosphorous content. We found that environmental filtering shapes local species composition and associated functional characteristics in the region – particularly, elevation, light intensity, and soil texture are the most important determinants of vegetation dynamics which affect plant functional trait variation and determine vegetation carbon storage in the temperate forests of Western Himalaya. Moreover, most of the plant functional trait variability was determined at the level of individual plants, thus suggesting higher trait variation within species than between species. To further investigate how these environmental drivers affect the size distribution of trees, we calibrated trait-based eco-evolutionary vegetation models (Plant and Plant-FATE) capable of predicting species-specific size distributions. We found that predicted height and DBH distributions of trees successfully match with field observations, suggesting that inclusion of the mechanistic factors driving intraspecific trait variation is crucial for linking environmental variation to changes in community assemblage processes and emergent ecosystem functioning. Our approach has the potential to improve the prediction of ecosystem services under future climate change scenarios for the Himalayan region.

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