Data integration improves species distribution forecasts under novel ocean conditions

Accurate forecasts of species distributions in response to a changing
climate are 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.