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.