Are absorptive root traits good predictors of ecosystem functioning? A test in a natural temperate forest.

Trait-based approaches provide a useful framework to predict ecosystem functions under intensifying global change. However, our current understanding of trait-functioning relationships mainly relies on aboveground traits. Belowground traits (e.g. absorptive root traits) are rarely studied although these traits are related to important plant functions. We analyzed four pairs of analogous leaf and absorptive root traits of woody plants in a temperate forest and examined how these traits are coordinated at the community-level, and to what extent the trait covariation depends on local-scale environmental conditions. We then quantified the contributions of leaf and absorptive root traits and the environmental conditions in determining two important forest ecosystem functions, aboveground carbon storage, and woody biomass productivity. The results showed that both morphological trait pairs and chemical trait pairs exhibited positive correlations at the community level. Absorptive root traits show a strong response to environmental conditions compared to leaf traits. We also found that absorptive root traits were better predictors of the two forest ecosystem functions than leaf traits and environmental conditions. Our study confirms the important role of belowground traits in modulating ecosystem functions and deepens our understanding of belowground responses to changing environmental conditions.

Unravelling Trait-Environment Relationships at Local and Regional Scales in Temperate Forests.

Understanding the trait-environment relationships has been a core ecological research topic in the face of global climate change. However, the strength of trait-environment relationships at the local and regional scales in temperate forests remains poorly known. In this study, we investigated the local and regional scale forest plots of the natural broad-leaved temperate forest in northeastern China, to assess what extent community-level trait composition depends on environmental drivers across spatial scales. We measured five key functional traits (leaf area, specific leaf area, leaf carbon content, leaf nitrogen content, and wood density) of woody plant, and quantified functional compositions of communities by calculating the "specific" community-weighted mean (CWM) traits. The sum of squares decomposition method was used to quantify the relative contribution of intraspecific trait variation to total trait variation among communities. Multiple linear regression model was then used to explore the community-level trait-environment relationships. We found that (i) intraspecific trait variation contributed considerably to total trait variation and decreased with the spatial scale from local to regional; (ii) functional composition was mainly affected by soil and topography factors at the local scale and climate factor at the regional scale, while explaining that variance of environment factors were decreased with increasing spatial scale; and (iii) the main environment driver of functional composition was varied depending on the traits and spatial scale. This work is one of the few multi-scale analyses to investigate the environmental drivers of community functional compositions. The extent of intraspecific trait variation and the strength of trait-environment relationship showed consistent trends with increasing spatial scale. Our findings demonstrate the influence of environmental filtering on both local- and regional-scale temperate forest communities, and contribute to a comprehensive understanding of trait-environment relationships across spatial scales.