Upscaling Marine Fisheries Stock Assessment in the Red Sea

Estimating the productivity and distribution of fish communities across large marine areas remains one of the central challenges in fisheries science and reef ecology. Traditional diver-based surveys, while accurate, are constrained in depth, spatial coverage, and temporal frequency. This limits our ability to detect ecological change or assess stocks at the scales relevant to management. This visiting studentship is embedded in a broader effort to develop scalable, integrative approaches to reef fisheries assessment in the Red Sea, combining multiple methodological streams into spatially explicit models of fish productivity. These include underwater video systems for automated fish detection, species identification, and size estimation; remote sensing data capturing habitat structure, oceanographic conditions, and seascape context; and spatial ecology and data science frameworks for integrating these inputs into models that project fish biomass and productivity across reef systems. The student will contribute to one or more of these components depending on their background and interests, engaging with both the conceptual foundations of fisheries stock assessment and the practical tools used to conduct it. The Red Sea provides an exceptional study system: a semi-enclosed tropical sea with extensive, diverse, and productive reef systems facing a range of human pressures.

By the end of the internship, the student will have developed familiarity with at least one methodological component of large-scale fisheries assessment, whether in automated video analysis, remote sensing, or spatial modelling, and its conceptual underpinnings. Deliverables include a written methodological summary of the chosen component, a contribution to an ongoing data or analytical task, and a short internal presentation of progress and key takeaways. the assigned task.