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Spatio-Temporal Fish Catch Assessments Using Fishing Vessel Trajectories and Coastal Fish Landing Data from around Jeju Island

Author

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  • Solomon Amoah Owiredu

    (College of Ocean Sciences, Jeju National University, Jeju 63243, Korea)

  • Kwang-Il Kim

    (College of Ocean Sciences, Jeju National University, Jeju 63243, Korea)

Abstract

While pressure on marine ecosystems leading to declines in global fish catches have been attributed to excessive fishing and to unregulated and unreported fishing, existing management practices have yet to fully address these declines. Estimation of spatial and temporal distribution of fisheries resources and the extent of fishing impacts on marine ecosystems using vessel trajectories has become central in recent studies. This study proposed the use of trajectories of 771 Korean coastal and offshore fishing vessels and one-year fish landing data to estimate variations in commercial fish species, vessel, and fishing gear activity distributions in the waters around Jeju island. A set of standards were applied to identify individual fishing tracks of major gears and uniformly distributed catch to fishing segments of trajectories to produce spatio-temporal distributions of catch, fishing activities, and vessel reliance on fishing grounds at a fine spatial scale. The method identified reference points that can inform management at local and regional scales. We discuss the opportunities of combining larger datasets collected over a longer period and applying predictive modeling techniques in making extensive assessments, including climate change impacts on fishing activities that can inform resource management and marine spatial planning.

Suggested Citation

  • Solomon Amoah Owiredu & Kwang-Il Kim, 2021. "Spatio-Temporal Fish Catch Assessments Using Fishing Vessel Trajectories and Coastal Fish Landing Data from around Jeju Island," Sustainability, MDPI, vol. 13(24), pages 1-18, December.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13841-:d:702725
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    References listed on IDEAS

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    1. Erico N de Souza & Kristina Boerder & Stan Matwin & Boris Worm, 2016. "Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-20, July.
    2. Fabrizio Natale & Maurizio Gibin & Alfredo Alessandrini & Michele Vespe & Anton Paulrud, 2015. "Mapping Fishing Effort through AIS Data," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    3. Campbell, Maria S. & Stehfest, Kilian M. & Votier, Stephen C. & Hall-Spencer, Jason M., 2014. "Mapping fisheries for marine spatial planning: Gear-specific vessel monitoring system (VMS), marine conservation and offshore renewable energy," Marine Policy, Elsevier, vol. 45(C), pages 293-300.
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