Distance decay 2.0 : a global synthesis of taxonomic and functional turnover in ecological communities

Aim: Understanding the variation in community composition and species abundances (i.e., β-diversity) is at the heart of community ecology. A common approach to examine β-diversity is to evaluate directional variation in community composition by measuring the decay in the similarity among pairs of communities along spatial or environmental distance. We provide the first global synthesis of taxonomic and functional distance decay along spatial and environmental distance by analysing 148 datasets comprising different types of organisms and environments. Location: Global. Time period: 1990 to present. Major taxa studied: From diatoms to mammals. Method: We measured the strength of the decay using ranked Mantel tests (Mantel r) and the rate of distance decay as the slope of an exponential fit using generalized linear models. We used null models to test whether functional similarity decays faster or slower than expected given the taxonomic decay along the spatial and environmental distance. We also unveiled the factors driving the rate of decay across the datasets, including latitude, spatial extent, realm and organismal features. Results: Taxonomic distance decay was stronger than functional distance decay along both spatial and environmental distance. Functional distance decay was random given the taxonomic distance decay. The rate of taxonomic and functional spatial distance decay was fastest in the datasets from mid-latitudes. Overall, datasets covering larger spatial extents showed a lower rate of decay along spatial distance but a higher rate of decay along environmental distance. Marine ecosystems had the slowest rate of decay along environmental distances. Main conclusions: In general, taxonomic distance decay is a useful tool for biogeographical research because it reflects dispersal-related factors in addition to species responses to climatic and environmental variables. Moreover, functional distance decay might be a cost-effective option for investigating community changes in heterogeneous environments

A protocol for reproducible functional diversity analyses

The widespread use of species traits in basic and applied ecology, conservation and biogeography has led to an exponential increase in functional diversity analyses, with > 10 000 papers published in 2010-2020, and > 1800 papers only in 2021. This interest is reflected in the development of a multitude of theoretical and methodological frameworks for calculating functional diversity, making it challenging to navigate the myriads of options and to report detailed accounts of trait-based analyses. Therefore, the discipline of trait-based ecology would benefit from the existence of a general guideline for standard reporting and good practices for analyses. We devise an eight-step protocol to guide researchers in conducting and reporting functional diversity analyses, with the overarching goal of increasing reproducibility, transparency and comparability across studies. The protocol is based on: 1) identification of a research question; 2) a sampling scheme and a study design; 3-4) assemblage of data matrices; 5) data exploration and preprocessing; 6) functional diversity computation; 7) model fitting, evaluation and interpretation; and 8) data, metadata and code provision. Throughout the protocol, we provide information on how to best select research questions, study designs, trait data, compute functional diversity, interpret results and discuss ways to ensure reproducibility in reporting results. To facilitate the implementation of this template, we further develop an interactive web-based application (stepFD) in the form of a checklist workflow, detailing all the steps of the protocol and allowing the user to produce a final 'reproducibility report' to upload alongside the published paper. A thorough and transparent reporting of functional diversity analyses ensures that ecologists can incorporate others' findings into meta-analyses, the shared data can be integrated into larger databases for consensus analyses, and available code can be reused by other researchers. All these elements are key to pushing forward this vibrant and fast-growing field of research.Peer reviewe