1. Climatic constraints on plant distributions are well known, but predicting community composition through knowledge of trait-based environmental filtering remains an important empirical challenge. Here, we evaluate the maximum entropy (MaxEnt) model of trait-based community assembly using forest communities occurring along a 12 °C gradient of mean annual temperature (MAT).We use independent cross-validation to evaluate model predictions from sites where trait constraints are predicted from environmental conditions. We also test whether orthogonal axes of trait variation can be used as predictors to improve model parsimony and explore MaxEnt forecasts of species distributions in a warmer climate.
2. Environmental factors explained between 31% and 74% of the community-weighted mean trait values, indicating moderate-to-strong selection of traits along the environmental gradients. A model with 10 traits explained 54% of the variation in observed relative abundances, which approached the upper limit of 57% given the available environmental information. Three orthogonal axes accounted for 81% of the trait variation among species, and environmental factors explained between 47% and 67% of the variation in these axes. However, the axes only explained 18% of the variation in relative abundances, suggesting that minor axes of functional variation may be important or that models with many traits may achieve good predictive capacity through over-fitting.
3. Trait–environment relationships formed the basis for predicting vegetation change in a future scenario where MAT was increased by 2.5 °C. The results suggested that up to 78% of Pinus ponderosa forest in Arizona may transition to dominance by Juniperus monosperma, but this forecast likely overestimates the rates of species migration.
4. Synthesis. MaxEnt is a mathematical translation of trait-based environmental filtering of the species pool and performs moderately well in predicting forest community structure using empirical trait–environment relationships. MaxEnt required many traits to achieve good fits, and three orthogonal axes of trait variation performed poorly as predictors of community structure. To be useful predictors, traits must vary strongly among species and community-weighted mean traits must vary predictably along environmental gradients.