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.

中文翻译：

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海洋海景预测远洋渔船侵入专属经济区

世界上许多最重要的渔业正在经历非法、未报告和无管制 (IUU) 捕捞，从而破坏了可持续保护和管理鱼类种群的努力。结束 IUU 捕捞的一个主要挑战是提高我们识别船只是否在非法捕捞以及在海洋中可能发生非法捕捞的地方的能力。然而，海洋监测对于海事当局的巡逻来说成本高昂、耗时且在后勤方面具有挑战性。为了解决这个问题，我们使用船舶跟踪数据和机器学习来预测远洋渔船是否在巴塔哥尼亚大陆架的阿根廷专属经济区 (EEZ) 内捕鱼，巴塔哥尼亚大陆架是世界上渔业产量最高的地区之一。我们将船舶位置数据与海洋海景（基于海洋变量的海洋区域类别）和其他遥感海洋变量相结合，以训练一系列不同复杂程度的机器学习模型。这些模型能够以 69%–96% 的置信度预测远洋渔船是否在专属经济区内作业，这取决于所使用的年份和预测变量。这些结果为预防非法活动提供了有希望的一步，而不是对它们进行法医反应。取决于使用的年份和预测变量。这些结果为预防非法活动提供了有希望的一步，而不是对它们进行法医反应。取决于使用的年份和预测变量。这些结果为预防非法活动提供了有希望的一步，而不是对它们进行法医反应。