A kernel integral method to remove biases in estimating trait turnover

Abstract

O_LITrait 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 have mathematical flaws. C_LIO_LIWe 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 difference and the computational aspects of the KDE computation can influence the estimates of trait turnover. We compare our novel method to existing ones using justified theoretical expectations for a large number of simulations in which we control the number of species and the distribution of their traits. We illustrate the practical application of KIM using plant species introduced to the Pacific Islands of French Polynesia. C_LIO_LIAnalyses 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. C_LIO_LIMathematical aspects for computing trait turnover are crucial as they can have important effects on the results and therefore lead to different conclusions. Our novel kernel integral method generates values that better reflect the distribution of species in the trait space than other existing methods. We therefore recommend using KIM in future studies on trait turnover. In contrast, tree-based approaches should be kept for phylogenetic diversity, as phylogenetic trees will then reflect the constrained speciation process. C_LI