Complex trait‒environment relationships underlie the structure of forest plant communities

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.