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    Accurate forecasts of species distributions in response to changing
climate is essential for proactive management and conservation
decision-making. However, species distribution models (SDMs) often have
limited capacity to produce robust forecasts under novel environmental
conditions, partly due to limitations in model training data.
Model-based approaches that leverage diverse types of data have advanced
over the last decade, yet their forecasting skill, especially during
episodic climatic events, remains uncertain. Here, we develop a suite of
SDMs for a commercially important fishery species, albacore tuna
(Thunnus alalunga), to evaluate forecast skill under marine heatwave
conditions. We compare models that use different methods to leverage
data sources (data pooling vs. joint likelihood) and to address spatial
dependence (environmental and spatial effects vs. environmental-only) to
assess their relative performance in predicting species distributions
under novel environmental conditions. Our results indicate model
performance declined across all model types as environmental novelty
increased as expected. However, joint-likelihood approaches were more
resilient to novel conditions, demonstrating greater predictive skill
and ecological realism than traditional SDMs. These results suggest that
ecological forecasts under novel environmental conditions are more
skillful with a model framework that accounts for unmeasured spatial and
temporal variability and uses model-based data integration to explicitly
leverage diverse data types. As access to diverse data sources continues
to increase, maximizing their utility will be key for delivering
accurate forecasts of species distributions and advancing proactive,
climate-ready management and conservation strategies.