Crown-of-thorns seastar (COTS) outbreaks are a major threat to coral reefs. Although the herbivorous juveniles and their switch to corallivory are key to seeding outbreaks, they remain a black box in our understanding of COTS. We investigated the impact of a delay in diet transition due to coral scarcity in cohorts reared on crustose coralline algae for 10 months and 6.5 years before being offered coral. Both cohorts achieved an asymptotic size (16-18 mm diameter) on algae and had similar exponential growth on coral. After 6.5 years of herbivory, COTS were competent coral predators. This trophic and growth plasticity results in a marked age-size disconnect adding unappreciated complexity to COTS boom-bust dynamics. The potential that herbivorous juveniles accumulate in the reef infrastructure to seed outbreaks when favourable conditions arise has implications for management of COTS populations.

Most marine organisms present an indirect lifecycle where a planktonic larval stage reaches competency before settling to the substrate and metamorphosing. Despite the critical importance of these early life history stages, little is known about how global change-related stressors, in particular ocean acidification (OA), affect marine larval settlement and metamorphosis. To date, 48 studies have investigated the effects of OA on larval settlement, focussing mostly on tropical corals (16), echinoderms (11) and fish (8). Most studies show negative effects of OA during settlement and post-settlement processes. For instance, reduced settlement is typically seen along natural pH gradients and in experimentally lowered pH treatments. This generally results in reduced settlement selectivity and metamorphosis and poorer post-settlement fitness. Carryover effects of OA exposure can also occur, with larval environmental history influencing early post-settlement performance. We conclude that OA may (1) alter larval supply for settlement by altering horizontal swimming behaviour or vertical migration; (2) directly influence settlement success through changes in the nature and distribution of suitable settlement substrates (e.g. biofilm, crustose coralline algae); and (3) mediate carryover effects at settlement by altering larval development or larval energy budgets. In contrast to fish larvae, there is little evidence for most invertebrate larvae that their perception of settlement cues is directly influenced by reduced pH. A summation of how OA affects the settlement and metamorphosis of marine invertebrates is timely, since altered settlement rates will influence the future distributions, abundances and ecology of marine benthic communities.

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Species distribution models (SDMs) have been increasingly used over the past decades to characterise the spatial distribution and the ecological niche of various taxa. Validating predicted species distribution is important, especially when producing broad-scale models (i.e. at continental or oceanic scale) based on limited and spatially aggregated presence-only records. In the present study, several model calibration methods are compared and guidelines are provided to perform relevant SDMs using a Southern Ocean marine species, the starfish Odontaster validus Koehler, 1906, as a case study. The effect of the spatial aggregation of presence-only records on modelling performance is evaluated and the relevance of a target-background sampling procedure to correct for this effect is assessed. The accuracy of model validation is estimated using k-fold random and spatial cross-validation procedures. Finally, we evaluate the relevance of the Multivariate Environmental Similarity Surface (MESS) index to identify areas in which SDMs accurately interpolate and conversely, areas in which models extrapolate outside the environmental range of occurrence records. Results show that the random cross-validation procedure (i.e. a widely applied method, for which training and test records are randomly selected in space) tends to overestimate model performance when applied to spatially aggregated datasets. Spatial cross-validation procedures can compensate for this over-estimation effect but different spatial cross-validation procedures must be tested for their ability to reduce over-fitting while providing relevant validation scores. Model predictions show that SDM generalisation is limited when working with aggregated datasets at broad spatial scale. The MESS index calculated in our case study show that over half of the predicted area is highly uncertain due to extrapolation. Our work provides methodological guidelines to generate accurate model assessments at broad spatial scale when using limited and aggregated presence-only datasets. We highlight the importance of taking into account the presence of spatial aggregation in species records and using non-random cross-validation procedures. Evaluating the best calibration procedures and correcting for spatial biases should be considered ahead the modelling exercise to improve modelling relevance.