  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 United Nations'
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, however, most analyses to date focus on
anomalous spatial behaviors of vessels one at a time. To improve predictions,
we develop a method inspired by complex systems theory to monitor the anomalous
multi-scale behavior of whole fleets as they respond to nearby illegal
activities. Specifically, we analyze changes in the multiscale geospatial
organization of fishing fleets operating on the Patagonia Shelf, an important
fishing region with chronic exposure to illegal fishing. We show that legally
operating (and visible) vessels respond anomalously to nearby illegal
activities (by vessels that are difficult to detect). 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. Doing so will be a big step forward to achieving sustainable life
underwater.
