Revealing the functional traits linked to hidden environmental factors in community assembly

Abstract

AbstractAimTo identify functional traits that best predict community assembly without knowing the underlying environmental drivers.MethodsWe propose a new method based on the correlation r(XY) between two matrices of potential community composition: the matrix X is fuzzy‐weighted by trait similarities of species, and the matrix Y is derived by Beals smoothing using the probabilities of species co‐occurrences. Since X is based on one or more traits, r(XY) measures how well the traits used for fuzzy‐weighting reflect the species co‐occurrence patterns in Y. We developed an optimisation algorithm to identify the traits maximising this correlation, together with an appropriate permutational test for significance. Using metacommunity data generated by a stochastic, individual‐based, spatially explicit model, we assessed the type I error and the power of our method across different simulation scenarios, varying environmental filtering parameters, number of traits and trait correlation structures. Then, we applied the method to real‐world community and trait data of dry calcareous grassland communities across Germany to identify, out of 49 traits, the combination of traits that maximised r(XY).ResultsThe method correctly identified the relevant traits involved in the assembly mechanisms of simulated communities, showing high power and accurate type I error. It proved to be robust against confounding aspects related to interactions between environmental factors, strength of limiting factors, and trait collinearity. In the grassland dataset, the method identified five traits that best explained community assembly. These traits reflect the size and the leaf economics spectrum, which are related to succession and resource supply, factors that may not be always measured in real‐world situations.ConclusionsOur method successfully identified the relevant traits mediating community assembly, therefore providing insights on the underlying environmental and biotic factors, even if these are hidden, unmeasured or not accessible at the spatial or temporal scale of the study.