Despite the rapid development and application of species distribution models (SDMs) to predict species responses to climate-driven ecosystem changes, we have a limited understanding of model predictive performance under novel environmental conditions. We aimed to address this gap using a simulation experiment to evaluate how novel ecosystem conditions and species movement influence SDM predictability. We leveraged observed sea surface temperature responses in the California Current and Northeast U.S. Shelf large marine ecosystems (LMEs) and prescribed species-response curves to simulate the distribution of a resident but mobile ectotherm, and a seasonally migrating ectotherm in each LME. For each LME and species archetype, we fitted boosted regression tree SDMs using data from 1985-2004 and then predicted the monthly probability of presence from 2005-2020 and calculated the environmental novelty of prediction month conditions. Generally, climate-driven ocean warming resulted in increasing environmental novelty over time, though patterns varied seasonally as warming caused novel conditions to increase over time in the summer and fall and decrease in the winter and spring as novel, cool conditions became more rare. Overall, predictive performance declined as novelty increased and occurred before prediction conditions became distinguishable from observation conditions. There were also unexpected increases in performance under novel environmental conditions when these novel conditions occurred at optimum species-response curve temperatures. These results highlight that environmental novelty may not always pose prediction challenges and will depend on where novel conditions map onto species-response curves. As SDM applications expand, there will be an ongoing need to maximize data quantity and quality to more fully characterize a species’ fundamental niche, explore environmental novelty relative to species-response curves, and continue to improve methods for quantifying and communicating model uncertainty. These efforts will open opportunities for model improvement and support stakeholders’ capacity to understand and integrate predictions into decision-making processes.

Designing appropriate management plans requires knowledge of both the dispersal ability and what has shaped the current distribution of the species under consideration. Here we investigated the evolutionary history of the endangered grey reef shark (Carcharhinus amblyrhynchos) across its range by sequencing thousands of RAD-seq loci in 173 individuals in the Indo-Pacific (IP) . We first bring evidence of the occurrence of a range expansion (RE) originating close to the Indo-Australian Archipelago (IAA) where two stepping-stone waves (east and westward) colonized almost the entire IP. Coalescent modeling additionally highlighted a homogenous connectivity (Nm~10 per generation) throughout the range, and an isolation by distance model suggested the absence of barriers to dispersal despite the affinity of C. amblyrhynchos to coral reefs. This coincides with long-distance swims previously recorded, suggesting that the strong genetic structure at the IP scale (FST ~ 0.56 between its ends) is the consequence of its broad current distribution and organization in a large number of demes. Our results strongly suggest that management plans for the grey reef shark should be designed on a range-wide rather than a local scale due to its continuous genetic structure. We further contrasted these results with those obtained previously for the sympatric but strictly lagoon-associated Carcharhinus melanopterus, known for its restricted dispersal ability. C. melanopterus exhibits similar RE dynamic, but is characterized by stronger genetic structure and a non-homogeneous connectivity largely dependent on local coral reefs availability. This sheds new light on shark evolution, emphasizing the roles of IAA as source of biodiversity and of life history traits in shaping the extent of genetic structure and diversity.