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Mapping Fishing Effort through AIS Data

Overview of attention for article published in PLOS ONE, June 2015
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Title
Mapping Fishing Effort through AIS Data
Published in
PLOS ONE, June 2015
DOI 10.1371/journal.pone.0130746
Pubmed ID
Authors

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.

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Geographical breakdown

Country Count As %
Germany 1 <1%
Italy 1 <1%
Australia 1 <1%
Brazil 1 <1%
United Kingdom 1 <1%
Unknown 276 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 57 20%
Student > Master 46 16%
Student > Ph. D. Student 36 13%
Student > Bachelor 28 10%
Other 9 3%
Other 35 12%
Unknown 70 25%
Readers by discipline Count As %
Environmental Science 56 20%
Agricultural and Biological Sciences 53 19%
Computer Science 23 8%
Engineering 18 6%
Earth and Planetary Sciences 13 5%
Other 34 12%
Unknown 84 30%