  Catch-and-effort data are among the primary sources of information for
assessing the status of terrestrial wildlife and fish. In fishery science,
elaborate stock-assessment models are fitted to such data in order to estimate
fish-population sizes and guide management decisions. Given the importance of
catch-and-effort data, we scoured a comprehensive dataset pertaining to
albacore tuna (Thunnus alalunga) in the north Pacific ocean for novel
ecological information content about this commercially valuable species.
Specifically, we used unsupervised learning based on finite mixture modelling
to reveal that the north Pacific albacore-tuna stock can be divided into four
pseudo-cohorts ranging in age from approximately 3 to 12 years old. We
discovered 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. These
results offer evidence that albacore tuna prefer different habitats depending
on their size and age, and point to long-term migratory routes for the species
that the current tagging technology is unlikely to capture in full. We discuss
the implications of the results for data-driven modelling of albacore tuna in
the north Pacific, as well as the management of the north Pacific albacore-tuna
fishery.

Populations and Evolution;Quantitative Methods