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Mapping Fishing Effort through AIS Data

Author

Listed:
  • Fabrizio Natale
  • Maurizio Gibin
  • Alfredo Alessandrini
  • Michele Vespe
  • Anton Paulrud

Abstract

Several research initiatives have been undertaken to map fishing effort at high spatial resolution using the Vessel Monitoring System (VMS). An alternative to the VMS is represented by the Automatic Identification System (AIS), which in the EU became compulsory in May 2014 for all fishing vessels of length above 15 meters. The aim of this paper is to assess the uptake of the AIS in the EU fishing fleet and the feasibility of producing a map of fishing effort with high spatial and temporal resolution at European scale. After analysing a large AIS dataset for the period January-August 2014 and covering most of the EU waters, we show that AIS was adopted by around 75% of EU fishing vessels above 15 meters of length. Using the Swedish fleet as a case study, we developed a method to identify fishing activity based on the analysis of individual vessels’ speed profiles and produce a high resolution map of fishing effort based on AIS data. The method was validated using detailed logbook data and proved to be sufficiently accurate and computationally efficient to identify fishing grounds and effort in the case of trawlers, which represent the largest portion of the EU fishing fleet above 15 meters of length. Issues still to be addressed before extending the exercise to the entire EU fleet are the assessment of coverage levels of the AIS data for all EU waters and the identification of fishing activity in the case of vessels other than trawlers.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0130746
    DOI: 10.1371/journal.pone.0130746
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    References listed on IDEAS

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    1. Natale, Fabrizio & Carvalho, Natacha & Harrop, Michael & Guillen, Jordi & Frangoudes, Katia, 2013. "Identifying fisheries dependent communities in EU coastal areas," Marine Policy, Elsevier, vol. 42(C), pages 245-252.
    2. Tommaso Russo & Lorenzo D'Andrea & Antonio Parisi & Stefano Cataudella, 2014. "VMSbase: An R-Package for VMS and Logbook Data Management and Analysis in Fisheries Ecology," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-18, June.
    3. Fabrizio Natale & Natacha Carvalho & Michael Harrop & Jordi Guillen & Katia Frangoudes, 2013. "Identifying fisheries dependent communities in EU coastal areas," Post-Print hal-00835566, HAL.
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    Cited by:

    1. Wolfert, Sjaak & Ge, Lan & Verdouw, Cor & Bogaardt, Marc-Jeroen, 2017. "Big Data in Smart Farming – A review," Agricultural Systems, Elsevier, vol. 153(C), pages 69-80.
    2. 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.
    3. Steven Holmes & Fabrizio Natale & Maurizio Gibin & Jordi Guillen & Alfredo Alessandrini & Michele Vespe & Giacomo Chato Osio, 2020. "Where did the vessels go? An analysis of the EU fishing fleet gravitation between home ports, fishing grounds, landing ports and markets," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-13, May.
    4. Brandon Kuczenski & Camila Vargas Poulsen & Eric L. Gilman & Michael Musyl & Bri Winkler & Roland Geyer, 2022. "A model for the intensity of fishing gear," Journal of Industrial Ecology, Yale University, vol. 26(6), pages 1847-1857, December.
    5. 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.

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