Data integration improves species distribution forecasts under novel ocean conditions

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 versus joint‐likelihood) and to address spatial dependence (environmental and spatial effects versus 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.