Distinguishing the signatures of local environmental filtering and regional trait range limits in the study of trait–environment relationships

Abstract

Understanding the imprint of environmental filtering on community assembly along environmental gradients is a key objective of trait‐gradient analyses. Depending on local constraints, this filtering generally entails that species departing from an optimum trait value have lower abundances in the community. The community‐weighted mean (CWM) and variance (CWV) of trait values are then expected to depict the optimum and intensity of filtering, respectively. However, the trait distribution within the regional species pool and its limits can also affect local CWM and CWV values apart from the effect of environmental filtering. The regional trait range limits are more likely to be reached in communities at the extremes of environmental gradients. Analogous to the mid‐domain effect in biogeography, decreasing CWV values in extreme environments can then represent the influence of regional trait range limits rather than stronger filtering in the local environment. We name this effect the ‘trait‐gradient boundary effect’ (TGBE). First, we use a community assembly framework to build simulated communities along a gradient from a species pool and environmental filtering with either constant or varying intensity while accounting for immigration processes. We demonstrate the significant influence of TGBE, in parallel to environmental filtering, on CWM and CWV at the extremes of the environmental gradient. We provide a statistical tool based on Approximate Bayesian Computation to decipher the respective influence of local environmental filtering and regional trait range limits. Second, as a case study, we reanalyze the functional composition of alpine plant communities distributed along a gradient of snow cover duration. We show that leaf trait convergence found in communities at the extremes of the gradient reflect an influence of trait range limits rather than stronger environmental filtering. These findings challenge correlative trait–environment relationships and call for more explicitly identifying the mechanisms responsible of trait convergence/divergence along environmental gradients.

Extinction–immigration dynamics lag behind environmental filtering in shaping the composition of tropical dry forests within a changing landscape

“…Extensive process‐based simulation of community dynamics has proved helpful to disentangle the respective influences of stochastic and trait‐based assembly processes on diversity patterns (Jabot et al , Zurell et al , Munoz et al , Denelle et al ). Indeed, community functional composition should reflect the imprint of environmental filtering (McGill et al ), while taxonomic diversity should better capture the influence of stochastic immigration–extinction dynamics (Munoz et al ).…”

Biennial Mowing Maintains the Biomass and Functional Diversity of Semi-Arid Grassland

“…Therefore, Roel et al [22] suggested keeping an unmowed refuge zone within the grassland to increase species diversity in grasslands. Based on theoretical and empirical evidence, Doležal et al [23] argued that seasonal mowing shifts community composition, which may reduce competitive interactions and promote the coexistence of dominant and subordinate species.Plant functional traits not only determine how plants respond to environmental factors [24], but also represent important indicators of ecological strategies that affect the attributes of plant communities and ecosystems. However, knowledge of the effects of overuse on grassland plant traits and plant community stability in the context of farm management remains scarce.…”

“…Therefore, in this study, we focus on the responses of functional traits to mowing [1,16].Varying the functional traits of plant communities along an environmental disturbance gradient is expected to reflect changes in the optimal values. Because the variation of performance around an optimal value translates into a variation in species abundances related to trait values, the mean values of the traits in communities known as the community-weighted mean (CWM) are used to reflect the local optimal trait values [24].Previous research [25] indicated that leaf traits were primarily selected in the best models for predicting biomass. The CWM of leaf area is the most important predictor [26] with an expected positive relationship [25].…”

“…Plant functional traits not only determine how plants respond to environmental factors [24], but also represent important indicators of ecological strategies that affect the attributes of plant communities and ecosystems. However, knowledge of the effects of overuse on grassland plant traits and plant community stability in the context of farm management remains scarce.…”

“…Varying the functional traits of plant communities along an environmental disturbance gradient is expected to reflect changes in the optimal values. Because the variation of performance around an optimal value translates into a variation in species abundances related to trait values, the mean values of the traits in communities known as the community-weighted mean (CWM) are used to reflect the local optimal trait values [24].…”