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Many of the world's most important fisheries are experiencing increases inillegal fishing, undermining efforts to sustainably conserve and manage fishstocks. A major challenge to ending illegal, unreported, and unregulated (IUU)fishing is improving our ability to identify whether a vessel is fishingillegally and where illegal fishing is likely to occur in the ocean. However,monitoring the oceans is costly, time-consuming, and logistically challengingfor maritime authorities to patrol. To address this problem, we use vesseltracking data and machine learning to predict illegal fishing on the PatagonianShelf, one of the world's most productive regions for fisheries. Specifically,we focus on Chinese fishing vessels, which have consistently fished illegallyin this region. We combine vessel location data with oceanographic seascapes --classes of oceanic areas based on oceanographic variables -- as well as otherremotely sensed oceanographic variables to train a series of machine learningmodels of varying levels of complexity. These models are able to predictwhether a Chinese vessel is operating illegally with 69-96% confidence,depending on the year and predictor variables used. These results offer apromising step towards preempting illegal activities, rather than reacting tothem forensically.

Predicting Illegal Fishing on the Patagonia Shelf from Oceanographic Seascapes