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.

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Aug. 9, 2019


Iliana Chollett, James Sanchirico, Larry Perruso, and Shay O'Farrell


Journal Article

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1 minute


Effective management of social-ecological systems (SESs) requires an understanding of human behavior. In many SESs, there are hundreds of agents or more interacting with governance and regulatory institutions, driving management outcomes through collective behavior. Agents in these systems often display consistent behavioral characteristics over time that can help reduce the dimensionality of SES data by enabling the assignment of types. Typologies of resource-user behavior both enrich our knowledge of user cultures and provide critical information for management. Here, we develop a data-driven framework to identify resource-user typologies in SESs with high-dimensional data. To demonstrate policy applications, we apply the framework to a tightly coupled SES, commercial fishing. We leverage large fisheries-dependent datasets that include mandatory vessel logbooks, observer datasets, and high-resolution geospatial vessel tracking technologies. We first quantify vessel and behavioral characteristics using data that encode fishers’ spatial decisions and behaviors. We then use clustering to classify these characteristics into discrete fishing behavioral types (FBTs), determining that 3 types emerge in our case study. Finally, we investigate how a series of disturbances applied selection pressure on these FBTs, causing the disproportionate loss of one group. Our framework not only provides an efficient and unbiased method for identifying FBTs in near real time, but it can also improve management outcomes by enabling ex ante investigation of the consequences of disturbances such as policy actions.


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