Abstract
Many of the world's most important fisheries are experiencing illegal, unreported and unregulated (IUU) fishing, thereby undermining efforts to sustainably conserve and manage fish stocks. A major challenge to ending IUU fishing is improving our ability to identify whether a vessel is fishing illegally and where illegal fishing is likely to occur in the ocean. However, monitoring the oceans is costly, time‐consuming, and logistically challenging for maritime authorities to patrol. To address this problem, we use vessel tracking data and machine learning to predict whether a distant‐water fishing vessel is fishing within the Argentine exclusive economic zone (EEZ) on the Patagonian Shelf, one of the world's most productive regions for fisheries. We combine vessel location data with oceanographic seascapes—classes of oceanic areas based on oceanographic variables—and other remotely sensed oceanographic variables to train a series of machine learning models of varying levels of complexity. These models are able to predict whether a distant‐water fishing vessel is operating inside the EEZ with 69%–96% confidence, depending on the year and predictor variables used. These results offer a promising step towards pre‐empting illegal activities, rather than reacting to them forensically.