Inferring ecological selection from multidimensional community trait distributions along environmental gradients

Abstract

Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. While trait-based frameworks are often used to this end, correlations among traits and the assumption of unimodal trait-abundance distributions confound detection of the underlying processes. To overcome these caveats, here we quantify multidimensional trait distributions of communities (community trait niches), which we use to identify ecological selection types shaping communities along environmental gradients. We find that directional, stabilizing, and divergent selection all modify community trait niches in over 3600 boreal forest understory plant communities, and selection on a particular trait may change from one type to another over time. Our results provide novel and rare empirical evidence from a natural system for divergent selection. The results also show that while higher trait diversity (measured as trait niche volume) is generally associated with higher species richness, high resource availability may enable tighter niche packing. Jointly our approach provides a framework for identifying key traits under selection and facilitates the detection of processes underlying community dynamics.