Discussion
Approaches for leveraging diverse data in SDMs have advanced over the last decade, showing great potential to enhance our understanding of species distributions and improving the accuracy of predictive models (Fletcher et al. 2019, Isaac et al. 2020, Braun et al. 2023b). Digital and technological advancements have greatly expanded data availability which has increased efforts to mobilize more diverse data types, opening new opportunities to leverage multiple data sources for developing robust SDMs (Isaac et al. 2020). Our exploration of modelling approaches builds on these advances by considering how to maintain skillful SDM predictions under novel environmental conditions, pointing to promising paths to characterize and predict the impact of changing ocean ecosystems. Our findings affirm previous research that demonstrates that SDM performance declines as environmental novelty increases (Muhling et al. 2020, Brodie et al. 2022, Allyn et al. 2024). However, our results also highlight how spatially explicit joint likelihood approaches maintained greater predictive skill and ecological realism under novel environmental conditions than traditional spatially implicit data pooling models. These results emphasize the importance of model-based data integration as a tool to leverage multiple data sources to make robust predictions under novel conditions. The approaches can support marine conservation and management applications under uncertain and variable future conditions.