Résumé: Aim To investigate biotic and abiotic correlates of reef-fish species richness across multiple spatial scales. Location Tropical reefs around the globe, including 485 sites in 109 sub-provinces spread across 14 biogeographic provinces. Time period Present. Major taxa studied 2,523 species of reef fish. Methods We compiled a database encompassing 13,050 visual transects. We used hierarchical linear Bayesian models to investigate whether fish body size, reef area, isolation, temperature, and anthropogenic impacts correlate with reef-fish species richness at each spatial scale (i.e., sites, sub-provinces, provinces). Richness was estimated using coverage-based rarefaction. We also tested whether species packing (i.e., transect-level species richness/m(2)) is correlated with province-level richness. Results Body size had the strongest effect on species richness across all three spatial scales. Reef area and temperature were both positively correlated with richness at all spatial scales. At the site scale only, richness decreased with reef isolation. Species richness was not correlated with proxies of human impacts. Species packing was correlated with species richness at the province level following a sub-linear power function. Province-level differences in species richness were also mirrored by patterns of body size distribution at the site scale. Species-rich provinces exhibited heterogeneous assemblages of small-bodied species with small range sizes, whereas species-poor provinces encompassed homogeneous assemblages composed by larger species with greater dispersal capacity. Main conclusions Our findings suggest that body size distribution, reef area and temperature are major predictors of species richness and accumulation across scales, consistent with recent theories linking home range to species-area relationships as well as metabolic effects on speciation rates. Based on our results, we hypothesize that in less diverse areas, species are larger and likely more dispersive, leading to larger range sizes and less turnover between sites. Our results indicate that changes in province-level (i.e., regional) richness should leave a tractable fingerprint in local assemblages, and that detailed studies on local-scale assemblage composition may be informative of responses occurring at larger scales.

Résumé: Identifying the main determinants of tropical marine biodiversity is essential for devising appropriate conservation measures mitigating the ongoing degradation of coral reef habitats. Based on a gridded distribution database and phylogenetic information, we compared the phylogenetic structure of assemblages for three tropical reef fish families (Labridae: wrasses, Pomacentridae: damselfishes and Chaetodontidae: butterflyfishes) using the net relatedness (NRI) and nearest taxon (NTI) indices. We then related these indices to contemporary and historical environmental conditions of coral reefs using spatial regression analyses. Higher levels of phylogenetic clustering were found for fish assemblages in the Indo-Australian Archipelago (IAA), and more particularly when considering the NTI index. The phylogenetic structure of the Pomacentridae, and to a lower extent of the Chaeotodontidae and Labridae, was primarily associated with the location of refugia during the Quaternary period. Phylogenetic clustering in the IAA may partly result from vicariance events associated with coral reef fragmentation during the glacial periods of the Quaternary. Variation in the patterns among fish families further suggest that dispersal abilities may have interacted with past habitat availability in shaping the phylogenetic structure of tropical reef fish assemblages.

Résumé: 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.