Using interpretable AI to integrate remote sensing and in situ data for forecasting marine species and climate-ready marine spatial planning

Rapid changes in ocean climate, circulation, and ecosystem structure are driving large-scale shifts in marine species distributions—challenging biodiversity conservation and the sustainability of fisheries worldwide. Anticipating these shifts is central to climate-ready ecosystem management, yet many existing species distribution modeling (SDM) approaches struggle to capture non-linear ecological responses, multi-scale environmental drivers, and emergent behaviors that arise under rapid climate change. To support proactive, climate-ready management, we developed an interpretable AI framework that integrates satellite remote sensing, high-resolution oceanographic reanalyses, and in situ data streams from a range of species spanning forage fishes to top predators. We use this framework to forecast the dynamic spatial and temporal (re)distribution of marine species in the Northwest Atlantic Ocean under variable oceanographic conditions. By combining multi-sensor observations with explainable machine learning methods, our approach provides both high predictive accuracy and transparent, ecologically grounded insights into the drivers of species movement. We compare the performance and usability of interpretable AI models with traditional statistical approaches. Whereas conventional SDMs typically assume smooth, stationary relationships between species occurrence and environmental predictors, interpretable AI models more flexibly capture threshold effects, interacting climate drivers, and regionally varying habitat relationships. We demonstrate how this system identifies climate-resilient habitats, reveals emerging hotspots of overlap between fisheries and vulnerable species, and dynamically informs spatial management measures such as time–area closures, protected area design, and climate sentinel site monitoring. Working directly with management agencies and protected area networks, we co-develop actionable metrics of ecosystem climate vulnerability and translate model outputs into operational decision support tools. This work illustrates a scalable pathway for uniting remote sensing, AI, and in situ observing networks to monitor biodiversity change and its drivers. By aligning technological advances with ecological understanding and management needs, our approach contributes directly to adaptive marine spatial planning and supports implementation of related domestic and global policy targets.