Illegal fishing is prevalent throughout the world and heavily impacts the health of our oceans, the sustainability and profitability of fisheries, and even acts to destabilize geopolitical relations. To achieve the UN’s Sustainable Development Goal of "Life Below Water", our ability to detect and predict illegal fishing must improve. Recent advances have been made through the use of vessel location data, NASA nighttime lights data and estimates of the state of oceanic biophysical variables; however most analyses to date use these data to identify anomalous spatial behaviors of vessels retrospectively. To improve predictions, we use methods from complex systems and information theory together with remotely sensed data, to analyze the anomalous multi-scale behavior of whole fleets as they respond to nearby illegal activities. Specifically, we develop several algorithms that detect anomalous multi-scale spatial behaviors in fleets as they respond to nearby illegal activities.
Results/Conclusions
Our initial analysis focuses on the multi-scale geospatial organization of fishing fleets operating on the Patagonia Shelf, an important fishing region with chronic exposure to illegal fishing. For several historical instances of illegal fishing, we show that our spatial anomaly index can be used to detect illegal activities. Indeed, precursor behaviors are identified, suggesting a path towards pre-empting illegal activities. This approach offers a promising step towards a global system for detecting, predicting and deterring illegal activities at sea in near real-time, based on complex systems analysis of remotely sensed information. Doing so will be a big step forward to achieving sustainable life under water.