From plant traits to plant communities: a statistical mechanistic approach to biodiversity.

We developed a quantitative method, analogous to those used in statistical mechanics, to predict how biodiversity will vary across environments, which plant species from a species pool will be found in which relative abundances in a given environment, and which plant traits determine community assembly. This provides a scaling from plant traits to ecological communities while bypassing the complications of population dynamics. Our method treats community development as a sorting process involving species that are ecologically equivalent except with respect to particular functional traits, which leads to a constrained random assembly of species; the relative abundance of each species adheres to a general exponential distribution as a function of its traits. Using data for eight functional traits of 30 herbaceous species and community-aggregated values of these traits in 12 sites along a 42-year chronosequence of secondary succession, we predicted 94% of the variance in the relative abundances.