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Catch-and-effort data are among the primary sources of information forassessing the status of terrestrial wildlife and fish. In fishery science,elaborate stock-assessment models are fitted to such data in order to estimatefish-population sizes and guide management decisions. Given the importance ofcatch-and-effort data, we scoured a comprehensive dataset pertaining toalbacore tuna (Thunnus alalunga) in the north Pacific ocean for novelecological information content about this commercially valuable species.Specifically, we used unsupervised learning based on finite mixture modellingto reveal that the north Pacific albacore-tuna stock can be divided into fourpseudo-cohorts ranging in age from approximately 3 to 12 years old. Wediscovered that smaller size pseudo-cohorts inhabit relatively high --subtropical to temperate -- latitudes, with hotspots off the coast of Japan.Larger size pseudo-cohorts inhabit lower -- tropical to subtropical --latitudes, with hotspots in the western and central north Pacific. Theseresults offer evidence that albacore tuna prefer different habitats dependingon their size and age, and point to long-term migratory routes for the speciesthat the current tagging technology is unlikely to capture in full. We discussthe implications of the results for data-driven modelling of albacore tuna inthe north Pacific, as well as the management of the north Pacific albacore-tunafishery.

Inferring the ecology of north-Pacific albacore tuna from catch-and-effort data