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Classifying fishing behavioral diversity using high-frequency movement data

Effective fisheries management is needed to rebuild overfished stocks and prevent future overfishing, and doing so requires an understanding of fishers’ behavior. We offer an approach where “big data” routinely collected by many fisheries agencies can be used in a data-driven framework to classify fishers into discrete behavioral types, refining the métier concept and facilitating the inclusion of behavioral information into near-real-time fisheries management.

Journal Article by Iliana Chollett, James Sanchirico, Larry Perruso, and Shay O'Farrell — 1 minute read — Aug. 9, 2019

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Authors

Iliana Chollett

James N. Sanchirico

University Fellow

Larry Perruso

Shay O'Farrell