Accounting for underlying complexities identifies simple hierarchy of
trait‒environment relationships in Wisconsin forest understory
communities
Abstract
Adaptive relationships between traits and the environment are often
inferred from observational data by regressing community-weighted mean
(CWM) traits on environmental gradients. However, trait‒environment
relationships are better understood as the outcome of trait‒abundance
and environment‒abundance relationships, and the interaction between
traits and the environment. Accounting for this functional structure and
for interrelationships among traits should improve our ability to
accurately describe general trait‒environment relationships. Using
forest understory communities in Wisconsin, we applied a generalized
mixed model (GLMM) incorporating this structure. We identified a simple
hierarchy of trait‒environment relationships dominated by a strong
positive effect of mean temperature on plant height. Compared to the
traditional CWM approach, the GLMM was more conservative in identifying
significant trait‒environment relationships, and also detected important
relationships that CWM regressions overlooked. This work highlights the
need to consider the complexity underlying trait‒environment
relationships in future analyses