Oversimplified models underestimate the role of local environmental filtering.

Aims: Fitting community assembly via trait selection (CATS) models is a useful method of estimating the relative role of environmental selection in community assembly. In the simplest version of CATS models, only linear and additive trait–environment relationships are supposed. This paper explores the consequences of neglecting non‐linearity and trait interactions by analyzing simulated and field data. Location: Northern Hungary, Central Europe. Methods: Starting with Gaussian response curves, we discussed how more and more complex species–environment relationships can be modeled by CATS regression. Using simulated data, we calculated the bias unsatisfying linearity and additivity assumptions introduce in the estimated role of environmental filtering in community assembly. Field data collected in the forest understorey vegetation were used for illustrating the magnitude of the underestimation in a real data set. Results: A non‐linear trait–environment relationship appeared even in the simplest, but realistic simulation (i.e., a Gaussian response curve). Neglecting both non‐linearity and trait interactions resulted in considerable underestimation of the strength of the trait–environment correlation. Moreover, this underestimation was stronger in the middle part of the environmental gradients, leading to a spurious pattern. Conclusion: At least, a second‐order polynomial regression model (including interactions among traits) should be fitted to avoid the underestimation of the strength of environmental filtering. [ABSTRACT FROM AUTHOR]