not-yet-known not-yet-known not-yet-known unknown 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.

Despite the rapid development and application of species distribution models (SDMs) to predict species responses to climate-driven ecosystem changes, we have a limited understanding of model predictive performance under novel environmental conditions. We aimed to address this gap using a simulation experiment to evaluate how novel ecosystem conditions and species movement influence SDM predictability. We leveraged observed sea surface temperature responses in the California Current and Northeast U.S. Shelf large marine ecosystems (LMEs) and prescribed species-response curves to simulate the distribution of a resident but mobile ectotherm, and a seasonally migrating ectotherm in each LME. For each LME and species archetype, we fitted boosted regression tree SDMs using data from 1985-2004 and then predicted the monthly probability of presence from 2005-2020 and calculated the environmental novelty of prediction month conditions. Generally, climate-driven ocean warming resulted in increasing environmental novelty over time, though patterns varied seasonally as warming caused novel conditions to increase over time in the summer and fall and decrease in the winter and spring as novel, cool conditions became more rare. Overall, predictive performance declined as novelty increased and occurred before prediction conditions became distinguishable from observation conditions. There were also unexpected increases in performance under novel environmental conditions when these novel conditions occurred at optimum species-response curve temperatures. These results highlight that environmental novelty may not always pose prediction challenges and will depend on where novel conditions map onto species-response curves. As SDM applications expand, there will be an ongoing need to maximize data quantity and quality to more fully characterize a species’ fundamental niche, explore environmental novelty relative to species-response curves, and continue to improve methods for quantifying and communicating model uncertainty. These efforts will open opportunities for model improvement and support stakeholders’ capacity to understand and integrate predictions into decision-making processes.