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Sea-level rise (SLR) is predicted to elevate water depths above coral reefs and to increase coastal wave exposure as ecological degradation limits vertical reef growth, but projections lack data on interactions between local rates of reef growth and sea level rise. Here we calculate the vertical growth potential of more than 200 tropical western Atlantic and Indian Ocean reefs, and compare these against recent and projected rates of SLR under different Representative Concentration Pathway (RCP) scenarios. Although many reefs retain accretion rates close to recent SLR trends, few will have the capacity to track SLR projections under RCP4.5 scenarios without sustained ecological recovery, and under RCP8.5 scenarios most reefs are predicted to experience mean water depth increases of more than 0.5 m by 2100. Coral cover strongly predicts reef capacity to track SLR, but threshold cover levels that will be necessary to prevent submergence are well above those observed on most reefs. Urgent action is thus needed to mitigate climate, sea-level and future ecological changes in order to limit the magnitude of future reef submergence.

Micronutrient supply from global marine fisheries under climate change and overfishing Highlights d Micronutrient-dense catches are more vulnerable to climate change than fishing d Climate change threatens micronutrient fisheries yields in 40% of countries d Catches are nutrient dense but vulnerable where dietary intakes are most inadequate d Fisheries management can be optimized toward resilient and nutrient-dense species

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Climate-induced coral bleaching events are a leading threat to coral reef ecosystems and can result in coral-macroalgal regime shifts that are difficult to reverse. It is unclear how different factors causally influence regime shift or recovery trajectories after a bleaching event. Here, we use structural causal modeling (SCM) and its application of directed acyclic graphs (DAGs) to determine how key factors affect regime shift versus recovery potential across coral reefs in Seychelles, which were severely impacted by bleaching events in 1998 and 2016. Our causal models reveal additional causal drivers of regime shifts, including initial macroalgal cover, wave exposure, and branching coral cover.We also find that reduced depth and structural complexity and increased nutrients increase the likelihood of regime shifting. Further, we use a DAGinformed predictive model to show how recovering reefs are expected to change after a recent 2016 bleaching event, suggesting that three out of 12 recovering reefs are expected to regime shift given their predisturbance conditions. Collectively, our results provide the first causally grounded analysis of how different factors influence postbleaching regime shift versus recovery potential on coral reefs. More broadly, SCM stands apart from previous observational analysis and provides a strong framework for causal inference across other observational ecological studies.

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1The diversity of life on our planet has produced a remarkable variety of biological traits that 2 characterize different species. Such traits are widely employed instead of taxonomy to increase 3 our understanding of biodiversity and ecosystem functioning. However, for species' trophic 4 niches, one of the most critical aspects of organismal ecology, a paucity of empirical information 5 has led to inconsistent definitions of trophic guilds based on expert opinion. Using coral reef 6 fishes as a model, we show that experts often disagree on the assignment of trophic guilds for the 7 same species. Even when broad categories are assigned, 60% of the evaluated trait schemes 8 disagree on the attribution of trophic categories for at least 20% of the species. This 9 disagreement greatly hampers comparability across studies. Here, we introduce a quantitative, 10 unbiased, and fully reproducible framework to define species' trophic guilds based on empirical 11 data. First, we synthesize data from community-wide visual gut content analysis of tropical coral 12 reef fishes, resulting in trophic information from 13,961 individuals belonging to 615 reef fish 13 species across all ocean basins. We then use network analysis to cluster the resulting global 14 bipartite food web into distinct trophic guilds, resulting in eight trophic guilds, and employ a 15Bayesian phylogenetic model to predict trophic guilds based on phylogeny and maximum body 16 size. Our model achieved a misclassification error of 5%, indicating that our approach results in 17 a quantitative and reproducible trophic categorization scheme, which can be updated as new 18 information becomes available. Although our case study is for reef fishes, the most diverse 19 vertebrate consumer group, our approach can be applied to other organismal groups to advance 20 reproducibility in trait-based ecology. As such, our work provides an empirical and conceptual 21 advancement for trait-based ecology and a viable approach to monitor ecosystem functioning in 22 our changing world. 23