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Tracking and modeling global high seas fisheries

The Marine Geospatial Ecology Lab (MGEL) at Duke University became an official research partner with the Global Fishing Watch (GFW) partnership in 2016. Since the onset of their relationship, the two groups, in conjunction with researchers from other universities, have been engaged in a series of research and policy-oriented projects. Their join projects include exploring the role of new vessel tracking technologies and civil society partners in the monitoring, control and surveillance (MCS) of marine fisheries in areas beyond national jurisdiction [ABNJ, or high seas] or fishing effort estimates derived from automatic identification system (AIS) data to characterize the "environmental niche" of the global pelagic fishing fleet.


















The video shows the monthly redistribution of suitable longline fishing grounds in the high seas as the oceanographic conditions change. The movie is 24 frames, one for each of the 2015 and 2016 months we modeled.



Fleet Movement Models May Help Reduce Bycatch from Longline Fishing

The new models developed by Nereus researchers from Duke University and the University of British Columbia in conjunction with researchers at Dalhousie University may help reduce bycatch rates that threat non-target pelagic species by giving regulatory agencies a powerful new tool to predict the month-by-month movements of longline fishing fleets on the high seas. The predictions should help determine where and when the boats will enter waters where by-catch risks are greatest.

“By comparing our models with data showing where by-catch species are likely to be each month, ship captains, national agencies and regional fisheries management organizations can pinpoint potential hotspots they may want to temporarily avoid or place off-limits,” said Guillermo Ortuño Crespo, a Nereus Fellow and doctoral candidate at MGEL.

“This represents a movement away from a reactive approach to fisheries management — where we only know about problems as or after they occur — to a more proactive approach that helps us stay one step ahead of the game,” he said.

To devise the models, researchers collaborated with GFW to collect geospatial information from individual boats’ AIS. AIS data shows the movements and distribution of longline fishing fleets operating in the high seas in 2015 and 2016.

Then they statistically correlated each ship’s fishing efforts to 14 environmental variables — such as sea surface temperatures or distance to the nearest seamount — that influence a region’s seasonal suitability as a habitat for species targeted by longliners. This allowed them to create highly accurate models that predict where the fishing fleets will be each month of the year.

The new models track data for fleets from Japan, South Korea, Taiwan, China and Spain, which accounts for most of the longline fishing currently taking place in the open ocean beyond national jurisdictions. Future models could include fleets from other nations and offer expanded functionality that will allow regulatory agencies to view the data within a global context or break it down by individual nation, region or fleet.

CITATION: “The Environmental Niche of the Global High Seas Pelagic Longline Fleet,” Guillermo Ortuño Crespo, Daniel C. Dunn, Gabriel Reygondeau, Kristina Boerder, Boris Worm, William Cheung, Derek P. Tittsensor and Patrick N. Halpin; Science Advances, August 8, 2018. DOI: 10.1126/sciadv.aat3681

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