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Classifying fishing behavioral diversity using high-frequency movement data.

Shay O'Farrell1, Iliana Chollett2, James N Sanchirico3,4

  • 1Department of Environmental Science and Policy, University of California, Davis, CA 95616; shay.ofarrell.ac@gmail.com.

Proceedings of the National Academy of Sciences of the United States of America
|August 11, 2019
PubMed
Summary
This summary is machine-generated.

Understanding human behavior is key to managing social-ecological systems (SESs). This study identifies distinct fishing behavioral types (FBTs) using data-driven methods, revealing how disturbances impact these groups for better fisheries management.

Keywords:
fisherieshuman mobilitymovement ecologynatural resource managementresilience

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Area of Science:

  • Ecological economics
  • Social-ecological systems analysis
  • Fisheries science

Background:

  • Effective management of social-ecological systems (SESs) hinges on understanding human behavior and collective action.
  • Resource-user typologies offer critical insights into user cultures and inform management strategies.
  • High-dimensional data in SESs necessitates robust frameworks for identifying behavioral patterns.

Purpose of the Study:

  • To develop and demonstrate a data-driven framework for identifying resource-user typologies in high-dimensional SES data.
  • To apply this framework to commercial fishing to classify distinct fishing behavioral types (FBTs).
  • To investigate the impact of disturbances on identified FBTs and inform policy applications.

Main Methods:

  • Quantified vessel and behavioral characteristics using fisheries-dependent datasets (vessel logbooks, observer data, vessel tracking).
  • Employed clustering techniques to classify behavioral characteristics into discrete fishing behavioral types (FBTs).
  • Analyzed the effects of disturbances on FBTs to understand selection pressures and group loss.

Main Results:

  • Identified three distinct fishing behavioral types (FBTs) within the commercial fishing case study.
  • Demonstrated that disturbances applied selection pressure, leading to the disproportionate loss of one FBT.
  • Validated the framework's ability to efficiently and unbiasedly identify FBTs in near real-time.

Conclusions:

  • The developed framework provides an efficient method for identifying FBTs in SESs with high-dimensional data.
  • This approach can improve fisheries management by enabling ex-ante assessments of policy impacts and disturbances.
  • Understanding and classifying FBTs is crucial for adaptive management of complex social-ecological systems.