Identifying the signal of environmental filtering and competition in invasion patterns - a contest of approaches from community ecology

摘要

list list-type="1" id="mee312257-list-0001"> In 1859, Darwin had already identified environmental constraints and competition with the native community as major drivers of invasion success. Since then, a toolbox of indices and statistical approaches has been developed and commonly applied to test for the relative importance of these drivers. This toolbox is largely based on community ecology theory with the underlying hypothesis that patterns of trait (or phylogenetic) similarities between invaders and native species permit to disentangle the signatures of competition and environmental filtering. However, so far the performance of the indices and statistical approaches has not been thoroughly evaluated, and there exists no study exploring the sensitivity of the different methods given common biases in field data. This severely hampers intercomparisons of invasion studies and ultimately prevents the elaboration of general conclusions. In this study, we developed a mechanistic community assembly model to simulate invasion patterns across a range of communities and tested the performance of four different indices aiming at disentangling environmental filtering vs. competition from these patterns. Furthermore, we evaluated the sensitivity of the statistical methods to biases in the data (resulting from non-equilibrium dynamics or observation errors). Our results indicated that the best performing index was mean distance to the native species (the average functional distance between the invader and all the species of the community), especially in heterogeneous landscapes. Further, we demonstrated that the detection of competition was more sensitive to the presence of biases in the data than the detection of environmental filtering. In conclusion, studying invasion mechanisms based on community patterns is possible when employing the appropriate statistical method, but it is highly sensitive to the quality of the data set used. doi origin="wiley" registered="yes">10.1111/(ISSN)2041-210X/doi