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Taxonomic nestedness, the degree to which the taxonomic composition of species-poor assemblages represents a subset of richer sites, commonly occurs in habitat fragments and islands differing in size and isolation from a source pool. However, species are not ecologically equivalent and the extent to which nestedness is observed in terms of functional trait composition of assemblages still remains poorly known. Here, using an extensive database on the functional traits and the distributions of 6316 tropical reef fish species across 169 sites, we assessed the levels of taxonomical vs functional nestedness of reef fish assemblages at the global scale. Functional nestedness was considerably more common than taxonomic nestedness, and generally associated with geographical isolation, where nested subsets are gradually more isolated from surrounding reef areas and from the center of biodiversity. Because a nested pattern in functional composition implies that certain combinations of traits may be represented by few species, we identified these groups of low redundancy that include large herbivore-detritivores and omnivores, small piscivores, and macro-algal herbivores. The identified patterns of nestedness may be an outcome of the interaction between species dispersal capabilities, resource requirements, and gradients of isolation among habitats. The importance of isolation in generating the observed pattern of functional nestedness within biogeographic regions may indicate that disturbance in depauperate and isolated sites can have disproportionate effects on the functional structure of their reef fish assemblages.

In the marine realm, the tropics host an extraordinary diversity of taxa but the drivers underlying the global distribution of marine organisms are still under scrutiny and we still lack an accurate global predictive model. Using a spatial database for 6336 tropical reef fishes, we attempted to predict species richness according to geometric, biogeographical and environmental explanatory variables. In particular, we aimed to evaluate and disentangle the predictive performances of temperature, habitat area, connectivity, mid-domain effect and biogeographical region on reef fish species richness. We used boosted regression trees, a flexible machine-learning technique, to build our predictive model and structural equation modeling to test for potential 'mediation effects' among predictors. Our model proved to be accurate, explaining 80% of the total deviance in fish richness using a cross-validated procedure. Coral reef area and biogeographical region were the primary predictors of reef fish species richness, followed by coast length, connectivity, mid-domain effect and sea surface temperature, with interactions between the region and other predictors. Important indirect effects of water temperature on reef fish richness, mediated by coral reef area, were also identified. The relationship between environmental predictors and species richness varied markedly among biogeographical regions. Our analysis revealed that a few easily accessible variables can accurately predict reef fish species richness. They also highlight concerns regarding ongoing environmental declines, with region-specific responses to variation in environmental conditions predicting a variable response to anthropogenic impacts.