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Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries

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Mendo_2019_RSOS_fishinggrounds_CC.pdf (737.1Kb)
Date
02/10/2019
Author
Mendo, Tania
Smout, Sophie Caroline
Photopoulou, Theoni
James, Mark
Funder
Scottish Government
Grant ID
SCO1434
Keywords
Fishing activities
Spatial distribution
Small-scale fishery
Gaussian mixture model
Hidden Markov model
QH301 Biology
SH Aquaculture. Fisheries. Angling
DAS
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Abstract
Recent technological developments facilitate the collection of location data from fishing vessels at an increasing rate. The development of low-cost electronic systems allows tracking of small-scale fishing vessels, a sector of fishing fleets typically characterized by many, relatively small vessels. The imminent production of large spatial datasets for this previously data-poor sector creates a challenge in terms of data analysis. Several methods have been used to infer the spatial distribution of fishing activities from positional data. Here, we compare five approaches using either vessel speed, or speed and turning angle, to infer fishing activity in the Scottish inshore fleet. We assess the performance of each approach using observational records of true vessel activity. Although results are similar across methods, a trip-based Gaussian mixture model provides the best overall performance and highest computational efficiency for our use-case, allowing accurate estimation of the spatial distribution of active fishing (97% of true area captured). When vessel movement data can be validated, we recommend assessing the performance of different methods. These results illustrate the feasibility of designing a monitoring system to efficiently generate information on fishing grounds, fishing intensity, or monitoring of compliance to regulations at a nationwide scale in near-real-time.
Citation
Mendo , T , Smout , S C , Photopoulou , T & James , M 2019 , ' Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries ' , Royal Society Open Science , vol. 6 , no. 10 , 191161 . https://doi.org/10.1098/rsos.191161
Publication
Royal Society Open Science
Status
Peer reviewed
DOI
https://doi.org/10.1098/rsos.191161
ISSN
2054-5703
Type
Journal article
Rights
Copyright © 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ , which permits unrestricted use, provided the original author and source are credited.
Description
This study (T.M., M.J. and S.S.) was funded by the European Maritime Fisheries Fund ‘Scottish Inshore Fisheries Integrated Data System' (grant reference no. SCO1434). T.P. was supported by a Newton International Fellowship, funded by the Royal Society (grant reference no. NF170682).
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/18605

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