Traits differentially adapt plant species to particular conditions generating compositional shifts along environmental gradients. As a result, community-scale trait values show concomitant shifts, termed trait‒environment relationships. Trait‒environment relationships are often assessed by evaluating community-weighted mean (CWM) traits observed along environmental gradients. Regression-based approaches (CWMr) assume that local communities exhibit traits centered at a single optimum value and that traits do not covary meaningfully. Evidence suggests that the shape of trait‒abundance relationships can vary widely along environmental gradients—reflecting complex interactions—and traits are usually interrelated. We used a model that accounts for these factors to explore trait‒environment relationships in herbaceous forest plant communities in Wisconsin (USA). We built a generalized linear mixed model (GLMM) to analyze how abundances of 185 species distributed among 189 forested sites vary in response to four functional traits (vegetative height-VH, leaf size-LS, leaf mass per area-LMA, and leaf carbon content), six environmental variables describing overstory, soil, and climate conditions, and their interactions. The GLMM allowed us to assess the nature and relative strength of the resulting 24 trait‒environment relationships. We also compared results between GLMM and CWMr to explore how conclusions differ between approaches. The GLMM identified five significant trait‒environment relationships that together explain ~40% of variation in species abundances across sites. Temperature appeared as a key environmental driver, with warmer and more seasonal sites favoring taller plants. Soil texture and temperature seasonality affected LS and LMA; seasonality effects on LS and LMA were nonlinear, declining at more seasonal sites. Though often assumed for CWMr, only some traits under certain conditions had centered optimum trait‒abundance relationships. CWMr more liberally identified (13) trait‒environment relationships as significant but failed to detect the temperature-seasonality‒LMA relationship identified by the GLMM. Synthesis. Although GLMM represents a more methodologically complex approach than CWMr, it identified a reduced set of trait‒environment relationships still capable of accounting for the responses of forest understory herbs to environmental gradients. It also identified separate effects of mean and seasonal temperature on LMA that appear important in these forests, generating useful insights and supporting broader application of GLMM approach to understand trait‒environment relationships.

Please see main text and Suplementary Materials and Methods (Appendix S1).

The data consist of 35,965 cases wherein each case reflects the frequency of a species at a site (the number of 1 m2 quadrats out of the total number of quadrats sampled at each site). Each case also includes functional trait data for that species and environmental data for that site.