An EEZ-wide dynamic management and monitoring approach for a pelagic tuna fishery

Dr Megan Cimino1, Mark Anderson2, Travis Schramek2, Sophia  Merrifield2, Eric  Terrill2

1UC Santa Cruz/NOAA SWFSC, Pacific Grove, United States, 2Scripps Institution of Oceanography, San Diego, USA

Fisheries monitoring and management face many challenges in the wake of climate change, overfishing and illegal activates, which can lead to altered fish distributions and depleted stocks. For small island nations, management challenges are compounded by loosely understood fish habitat associations, and difficulties associated with monitoring and surveilling vast exclusive economic zones (EEZ). In response to resource concerns, the Pacific island of Palau, a leader and model in ocean conservation, established a National Marine Sanctuary that closes 80% of their EEZ to fishing in 2020. To evaluate the effectiveness of their proposed domestic fishing zone and to develop a system for predicting fishing activity to aid in monitoring and surveillance, we investigated the relationship between tuna longline fishing locations and oceanographic variables, vessel flag, and climate indices over a 6-year period (2011-2016). We found a strong dependence of fishing locations on oceanographic conditions and a zonal partition of the EEZ between the two dominate flag states. During the 2015 El Niño, there was an average 40% drop in suitable habitat and fishing effort, highlighting the tight link between ocean conditions, fish, and fishers. The high-resolution fishing forecasting approach demonstrated here could be utilized for dynamic ocean management and increased ocean security.


Megan received her BS in 2009 from Cal Poly San Luis Obispo and her PhD in oceanography in 2016 from the University of Delaware. She was a postdoctoral scholar at Scripps Institution of Oceanography from 2016 to 2018 and is currently a project scientist at UC Santa Cruz. Broadly,

Megan’s research utilizes many remote sensing technologies (for example, robots, satellites, and bio-logging) coupled with biological observations to understand relationships between animals and their habitat.

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