Inferring ecological selection from multidimensional community trait distributions along environmental gradients.

Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. Ecological selection ubiquitously shapes communities by selecting for individuals with the most suitable trait combinations. Detecting selection types on key traits across environmental gradients and over time has the potential to reveal the underlying abiotic and biotic drivers of community dynamics. Here, we present a model-based predictive framework to quantify the multidimensional trait distributions of communities (community trait spaces), which we use to identify ecological selection types shaping communities along environmental gradients. We apply the framework to over 3600 boreal forest understory plant communities with results indicating that directional, stabilizing, and divergent selection all modify community trait distributions and that the selection type acting on individual traits may change over time. Our results provide novel and rare empirical evidence for divergent selection within a natural system. Our approach provides a framework for identifying key traits under selection and facilitates the detection of processes underlying community dynamics.