Publication Date:
2014-01-01
Description:
Concerns about bycatch of protected species have become a dominant factor shaping fisheries management. However, efforts to mitigate bycatch are often hindered by a lack of data on the distributions of fishing effort and protected species. One approach to overcoming this problem has been to overlay the distribution of past fishing effort with known locations of protected species, often obtained through satellite telemetry and occurrence data, to identify potential bycatch hotspots. This approach, however, generates static bycatch risk maps, calling into question their ability to forecast into the future, particularly when dealing with spatiotemporally dynamic fisheries and highly migratory bycatch species. In this study, we use boosted regression trees to model the spatiotemporal distribution of fishing effort for two distinct fisheries in the North Pacific Ocean, the albacore (Thunnus alalunga) troll fishery and the California drift gillnet fishery that targets swordfish (Xiphias gladius). Our results suggest that it is possible to accurately predict fishing effort using
Print ISSN:
1051-0761
Electronic ISSN:
1939-5582
Topics:
Biology
Permalink