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dc.contributor.authorMendo, Tania
dc.contributor.authorSmout, Sophie Caroline
dc.contributor.authorPhotopoulou, Theoni
dc.contributor.authorJames, Mark
dc.date.accessioned2019-10-03T09:30:02Z
dc.date.available2019-10-03T09:30:02Z
dc.date.issued2019-10-02
dc.identifier.citationMendo , 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.191161en
dc.identifier.issn2054-5703
dc.identifier.otherPURE: 261213520
dc.identifier.otherPURE UUID: 23841e34-c4e9-41f3-8e1d-56150f261124
dc.identifier.otherORCID: /0000-0001-9616-9940/work/62668405
dc.identifier.otherORCID: /0000-0002-7182-1725/work/62668410
dc.identifier.otherScopus: 85074728267
dc.identifier.otherWOS: 000510431100050
dc.identifier.urihttp://hdl.handle.net/10023/18605
dc.descriptionThis 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).en
dc.description.abstractRecent 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.
dc.format.extent12
dc.language.isoeng
dc.relation.ispartofRoyal Society Open Scienceen
dc.rightsCopyright © 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.en
dc.subjectFishing activitiesen
dc.subjectSpatial distributionen
dc.subjectSmall-scale fisheryen
dc.subjectGaussian mixture modelen
dc.subjectHidden Markov modelen
dc.subjectQH301 Biologyen
dc.subjectSH Aquaculture. Fisheries. Anglingen
dc.subjectDASen
dc.subject.lccQH301en
dc.subject.lccSHen
dc.titleIdentifying fishing grounds from vessel tracks: model-based inference for small scale fisheriesen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Coastal Resources Management Groupen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews.Sea Mammal Research Uniten
dc.contributor.institutionUniversity of St Andrews.Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews.Marine Alliance for Science & Technology Scotlanden
dc.identifier.doihttps://doi.org/10.1098/rsos.191161
dc.description.statusPeer revieweden


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