Abstract
(1) The rapid expansion of offshore wind energy raises concerns about potential impacts on marine wildlife, yet development often outpaces the capacity for ecological assessment. Data-limited species in offshore environments are particularly challenging to monitor, constraining the ability to evaluate potential risks and design evidence-based management strategies across large marine areas. (2) We present a novel, scalable framework based on ensemble machine learning methods, including shallow neural networks, to predict species distribution across extensive, data-limited marine systems. By combining multiple algorithms with high-resolution environmental predictors, the framework reduces model-specific bias, improves predictive reliability, and generates transparent, reproducible spatial predictions of species occurrence. This integrative approach demonstrates how heterogeneous and non-traditional data streams can be formally combined to inform applied ecological decisions. (3) We demonstrate the framework using Atlantic Sturgeon (Acipenser oxyrinchus), a long-lived, highly migratory species of conservation concern, by leveraging cooperative acoustic telemetry detections to predict distributions at ∼1 km² resolution across more than 620 000 km² of northwest Atlantic continental shelf waters. The approach explicitly accounts for dynamic habitat use and seasonal movements, providing a realistic representation of the species’ spatial ecology in areas of limited observational coverage. (4) In the context of expanding offshore development and other emerging ocean uses, map outputs from these models function as ecological triage tools, providing early, defensible predictions to prioritize monitoring and help to allocate survey resources across areas of varying predicted risk. Importantly, the approach avoids the longstanding tendency to interpret data gaps as evidence of negligible risk, supporting precautionary and adaptive decision-making to better inform management under constrained conservation resources. (5) Synthesis and applications. The framework is modular and broadly transferable, applicable to other taxa, regions, and regulatory contexts, and readily accommodates new data as monitoring coverage grows. Embedding ensemble-based predictions within existing regulatory processes enhances transparency, defensibility, and ecological realism of offshore impact assessments. By linking predictive models directly to monitoring and management decisions, this approach supports adaptive, evidence-based stewardship of marine resources in an era of rapid offshore development, increasing human ocean use, and escalating conservation pressures.