Ecological Indicators (Nov 2021)
Importance and benefit of incorporating the responses of species mean trait values in trait-based community assembly
Abstract
Species mean trait values are the common currency of current trait-based community ecology, in which species are approximated by a set of constant mean trait values and are assumed to be passively selected by abiotic and biotic filters according to these values. However, species mean trait values can respond to both species interactions and the environmental context. Do such responses in traits subsequently alter the effect of filters on communities? Can we benefit by incorporating the responses of species mean trait values in trait-based community assembly? We tested the responses of mean values of four key functional traits to reduced light and interspecific competition in a two-year common garden experiment, using 1,584 seedlings of 11 tree species. We quantified the changes in interspecific trait differences, community-weighted means (CWM), and functional diversity (FD) and compared these to their corresponding changes under the assumption of constant mean trait values. Species mean values of most examined traits underwent substantial changes (mean = 18 ± 3%) to shading and interspecific competition treatments, but the direction and extent of these responses varied among traits and species, resulting in significant changes in interspecific trait differences, CWM and FD. Considering these trait responses largely improved our ability to identify these effects of low light and interspecific competition on CWM and FD. Further, we showed that strong abiotic filters could promote trait divergence if trait responses were considered, but that this pattern would be obscured if species mean trait values were considered as constant. Consequently, incorporating the responses of species mean trait values, e.g. using multiple mean values for a trait of a species in a heterogeneous environment, into the current trait-based community ecology offers us a more accurate, sensitive, and time-saving way to identify the underlying ecological mechanisms.
