Trade-offs between bycatch and target catches in static versus dynamic fishery closures.

While there have been recent improvements in reducing bycatch in many fisheries, bycatch remains a threat for numerous species around the globe. Static spatial and temporal closures are used in many places as a tool to reduce bycatch. However, their effectiveness in achieving this goal is uncertain, particularly for highly mobile species. We evaluated evidence for the effects of temporal, static, and dynamic area closures on the bycatch and target catch of 15 fisheries around the world. Assuming perfect knowledge of where the catch and bycatch occurs and a closure of 30% of the fishing area, we found that dynamic area closures could reduce bycatch by an average of 57% without sacrificing catch of target species, compared to 16% reductions in bycatch achievable by static closures. The degree of bycatch reduction achievable for a certain quantity of target catch was related to the correlation in space and time between target and bycatch species. If the correlation was high, it was harder to find an area to reduce bycatch without sacrificing catch of target species. If the goal of spatial closures is to reduce bycatch, our results suggest that dynamic management provides substantially better outcomes than classic static marine area closures. The use of dynamic ocean management might be difficult to implement and enforce in many regions. Nevertheless, dynamic approaches will be increasingly valuable as climate change drives species and fisheries into new habitats or extended ranges, altering species-fishery interactions and underscoring the need for more responsive and flexible regulatory mechanisms.

Fishery catch records support machine learning-based prediction of illegal fishing off US West Coast.

Illegal, unreported, and unregulated (IUU) fishing is a major problem worldwide, often made more challenging by a lack of at-sea and shoreside monitoring of commercial fishery catches. Off the US West Coast, as in many places, a primary concern for enforcement and management is whether vessels are illegally fishing in locations where they are not permitted to fish. We explored the use of supervised machine learning analysis in a partially observed fishery to identify potentially illicit behaviors when vessels did not have observers on board. We built classification models (random forest and gradient boosting ensemble tree estimators) using labeled data from nearly 10,000 fishing trips for which we had landing records (i.e., catch data) and observer data. We identified a set of variables related to catch (e.g., catch weights and species) and delivery port that could predict, with 97% accuracy, whether vessels fished in state versus federal waters. Notably, our model performances were robust to inter-annual variability in the fishery environments during recent anomalously warm years. We applied these models to nearly 60,000 unobserved landing records and identified more than 500 instances in which vessels may have illegally fished in federal waters. This project was developed at the request of fisheries enforcement investigators, and now an automated system analyzes all new unobserved landings records to identify those in need of additional investigation for potential violations. Similar approaches informed by the spatial preferences of species landed may support monitoring and enforcement efforts in any number of partially observed, or even totally unobserved, fisheries globally.