Search

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. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Location Time Period Major Taxa Studied Methods Results Main Conclusions We map global patterns of taxonomic and functional change between past (pre‐human impacts) and present (after anthropogenic extinctions and introductions) in large oceanic island bird assemblages and investigate if these patterns can be explained by island characteristics and anthropogenic factors.Sixty‐four oceanic islands (>100 km2).Late Holocene.Terrestrial and freshwater bird species.We compiled information on extinct, extant native and introduced bird species for all islands and used a probabilistic hypervolume approach to build a multi‐dimensional trait space and calculate several functional diversity metrics before and after extinctions and introductions. We identified which islands are global hotspots of human‐induced transformation by mapping multiple facets of biotic change and investigated intrinsic island characteristics and anthropogenic factors as drivers for these observed patterns.The Hawaiian and Mascarene islands stand out as hotspots of taxonomic and functional change, but all islands changed taxonomically and functionally, mostly gaining species but losing functional richness. Taxonomic and functional changes vary across islands but are often consistent within the same archipelago. Island isolation and surface can explain some of the observed variations, but anthropogenic factors, namely human occupation, also shaped both taxonomic and functional changes. Islands with higher human pressure, as well as larger islands with high elevation ranges, tended to have greater losses in functional richness.Most biodiversity change assessments are still largely based exclusively on taxonomic diversity, which is particularly worrying in the case of oceanic islands given that the magnitude of functional diversity change is often considerably larger. We call for comprehensive assessments of changes in both taxonomic and functional diversity across oceanic islands in order to better understand the drivers of these changes and, in turn, predict future trends. [ABSTRACT FROM AUTHOR]