Can entropy maximization use functional traits to explain species abundances? A comprehensive evaluation

Entropy maximization (EM) is a method that can link functional traits and community composition by predicting relative abundances of each species in a community using limited trait information. We developed a complementary suite of tests to examine the strengths and limitations of EM and the community-aggregated traits (CATs; i.e., weighted averages) on which it depends that can be applied to virtually any plant community data set. We show that suites of CATs can be used to differentiate communities and that EM can address the classic problem of characterizing ecological niches by quantifying constraints (CATs) on complex trait relationships in local communities. EM outperformed null models and comparable regression models in communities with different levels of dominance, diversity, and trait similarity. EM predicted well the abundance of the dominant species that drive community-level traits; it typically identified rarer species as such, although it struggled to predict the abundances of the rarest species in some cases. Predictions were sensitive to choice of traits, were substantially improved by using informative priors based on null models, and were robust to variation in trait measurement due to intraspecific variability or measurement error. We demonstrate how similarity in species' traits confounds predictions and provide guidelines for applying EM.