A kernel integral method to remove biases in estimating trait turnover

Abstract

Abstract Trait diversity, including trait turnover, that differentiates the roles of species and communities according to their functions, is a fundamental component of biodiversity. Accurately capturing trait diversity is crucial to better understand and predict community assembly, as well as the consequences of global change on community resilience. Existing methods to compute trait turnover have limitations. Trait space approaches based on minimum convex polygons only consider species with extreme trait values. Tree‐based approaches using dendrograms consider all species but distort trait distance between species. More recent trait space methods using complex polytopes try to harmonise the advantages of both methods, but their current implementation has mathematical flaws. We propose a new kernel integral method (KIM) to compute trait turnover, based on the integration of kernel density estimators (KDEs) rather than using polytopes. We explore how this approach and the computational aspects of the KDE computation can influence the estimates of trait turnover. The novel method is compared with existing ones using justified theoretical expectations for a large number of simulations in which the number of species and the distribution of their traits is controlled for. The practical application of KIM is then demonstrated using data on plant species introduced to the Pacific Islands of French Polynesia. Analyses on simulated data show that KIM generates results better aligned with theoretical expectations than other methods and is less sensitive to the total number of species. Analyses for French Polynesia data also show that different methods can lead to different conclusions about trait turnover and that the choice of method should be carefully considered based on the research question. The mathematical properties of methods for computing trait turnover are crucial to consider because they can have important effects on the results, and therefore lead to different conclusions. The novel KIM method provided here generates values that better reflect the distribution of species in trait space than other methods. We therefore recommend using KIM in studies on trait turnover. In contrast, tree‐based approaches should be kept for phylogenetic diversity, as phylogenetic trees will then reflect the speciation process.