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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/m2) 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. [ABSTRACT FROM AUTHOR]

Reef fish assessments were undertaken in 17 Pacific islands to describe the status of finfish resources in 63 villages where fishing is mainly artisanal. Surveys were performed by recording the number and size of edible fish species and benthic composition. Fishing impact was described through six proxies representing level of catch, alternative incomes, degree of commercial catch and country economic development derived from a simultaneous socioeconomic assessment. The relative importance of broadly defined habitat (geographical location, island and reef type, substrate composition) and fishing impact in controlling the distribution of fish trophic groups, families and species was measured through multivariate analysis. The extreme faunistic diversity was shown by the large variation in fish density (difference of up to an order of magnitude) and fish biomass (displaying a 20-fold difference across the region). Herbivores were dominant in the eastern part of the region, at what we classified as complex islands and at islands with small lagoon and at coastal reefs, while carnivores were dominant at oceanic islands and atolls and at outer reefs. Specific habitat associations were shown for Scaridae, Acanthuridae, Siganidae, Balistidae, Lethrinidae, Lutjanidae and Serranidae. Relative importance and size decrease of several fish families (Mullidae, Scaridae, Lutjanidae and Serranidae) were related to high fishing impact. Acanthuridae and Lethrinidae appeared to have a role as opportunistic groups in impacted sites. The relative impact from fishing and habitat on fishes accounted for, respectively, 20 and 30% of variance, demonstrating the effect of human impacts even at such large scale and taking into account only limited fishing impact variables. [ABSTRACT FROM AUTHOR]

Six cohorts of the silver-stripe round herring Spratelloides gracilis, a fast-growing and short-lived tropical clupeid, were collected as juveniles and then as adults during austral summers from November to February in 1998–1999 and 1999–2000, using light traps in the Dampier Archipelago, Western Australia. Otolith analysis allowed backcalculation of size and growth rate at age to examine the relative influences of selective mortality and water temperature on early growth. Negative size-selective mortality and growth-selective mortality between the juvenile and the adult stages was found only in the cohort that was the smallest and slowest growing in the period immediately following hatching. Selective mortality preferentially removed members of this cohort that were smaller from age 0 to 15 days, and slower growing from 0 to 10 days, resulting in an elevation of size at age to, or even above, that of cohorts that had not undergone this process. Size and growth rate at 5 day age intervals in the first 20 days after hatching differed among cohorts within and between summers and were strongly and positively correlated ( r2= 0·61–0·83) with water temperature. [ABSTRACT FROM AUTHOR]

Reliable and efficient techniques are urgently needed to monitor elasmobranch populations that face increasing threats worldwide. Aerial video-surveys provide precise and verifiable observations for the rapid assessment of species distribution and abundance in coral reefs, but the manual processing of videos is a major bottleneck for timely conservation applications. In this study, we applied deep learning for the automated detection and mapping of vulnerable eagle rays from aerial videos. A light aircraft dedicated to touristic flights allowed us to collect 42 h of aerial video footage over a shallow coral lagoon in New Caledonia (Southwest Pacific). We extracted the videos at a rate of one image per second before annotating them, yielding 314 images with eagle rays. We then trained a convolutional neural network with 80% of the eagle ray images and evaluated its accuracy on the remaining 20% (independent data sets). Our deep learning model detected 92% of the annotated eagle rays in a diversity of habitats and acquisition conditions across the studied coral lagoon. Our study offers a potential breakthrough for the monitoring of ray populations in coral reef ecosystems by providing a fast and accurate alternative to the manual processing of aerial videos. Our deep learning approach can be extended to the detection of other elasmobranchs and applied to systematic aerial surveys to not only detect individuals but also estimate species density in coral reef habitats. • Efficient techniques are needed to monitor vulnerable elasmobranchs in space and time. • Deep learning applied to images is a powerful tool for automated wildlife monitoring. • Our deep learning model successfully detected 92% of eagle rays on images. • This study is a step forward for ray monitoring in coral reef ecosystems. [ABSTRACT FROM AUTHOR]