Quantifying Atlantic halibut (Hippoglossus hippoglossus) migratory diversity and stock mixing across the Northwest Atlantic and implications for fisheries management

Understanding spatial population structure and movement dynamics is essential for the sustainable management of widely distributed marine species, particularly those that support high-value fisheries. This thesis investigates the migratory diversity and stock mixing of Atlantic halibut (Hippoglossus hippoglossus) in the Northwest Atlantic. Here, I used pop-up satellite archival tags (PSATs) deployed on the Scotian Shelf and Southern Grand Banks between 2012 and 2020 to reconstruct individual movement tracks with a Hidden Markov geolocation model and identify distinct migratory strategies and putative spawning locations. I identified five distinct migratory strategies: shelf residency, slope residency, shelf-channel migration, shelf-slope migration, and dispersal. In addition, I compiled a large collaborative tagging database across Canada, France, and the USA, integrating 187 electronic and 18,795 conventional tagging records. These data were used to quantify seasonal mixing within and among Northwest Atlantic Fisheries Organization (NAFO) divisions and across national boundaries. While most fish displayed regional fidelity, movements across international and stock boundaries were observed, particularly in the Cabot Strait and on the southern Scotian Shelf. These findings highlight how Atlantic halibut in the Northwest Atlantic exhibit a high degree of migratory diversity and connectivity with adjacent jurisdictions, underscoring the limitations of static, politically defined stock units. Effective management of this transboundary species will require not only the incorporation of fine-scale movement data into assessment frameworks, which is now facilitated by seasonal mixing rate estimates from this study, but also strong collaboration among regional and national authorities. By combining electronic and conventional tagging data from multiple countries and organizations, this thesis provides a model for cooperative research and integrated data-sharing, an essential foundation for sustainable, ecosystem-based fisheries management across borders.