Repository logo
 
Publication

Predicting gear used in a multi-gear coastal fleet

dc.contributor.authorLeitão, Pedro
dc.contributor.authorCampos, Aida
dc.contributor.authorCastro, Margarida
dc.date.accessioned2024-11-21T10:17:18Z
dc.date.available2024-11-21T10:17:18Z
dc.date.issued2025-01
dc.description.abstractKnowledge of the gear used in multi-gear fisheries is crucial for supporting fisheries management. Still, the high complexity and lack of data in the Portuguese multi-gear coastal fleet compromise this task. The present study developed a method to predict main fishing gear used in each fishing trip for the Portuguese multi-gear coastal fleet based on landing records (species caught, port, and month of landing). Landing records were used to predict gear (available for part of the fleet with electronic logbooks) using a machine learning model (random forest). This model was then applied to the remaining trips of the fleet, without electronic logbooks, to predict the gear used. A total of six gear types were considered: bivalve dredges, traps, gillnets, trammel nets, drifting longlines, and bottom longlines. The overall model prediction error was 14 %; bivalve dredges and longlines had the lowest errors, and trammel nets and gillnets were the highest. The study sheds new light on important aspects of the dynamics of this fleet, namely a decreasing trend in the use of longlines, poor electronic logbook coverage for some gear types, and greater diversity in the catches obtained with nets compared to other gear types.eng
dc.description.sponsorshipProject Mar 2020 16–01–04-FMP-0010–IPMA; 2022.11214.BDANA
dc.identifier.doi10.1016/j.fishres.2024.107199
dc.identifier.issn0165-7836
dc.identifier.urihttp://hdl.handle.net/10400.1/26313
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationCentre for Marine and Environmental Research
dc.relationAlgarve Centre for Marine Sciences
dc.relation2022.11214.BDANA
dc.relation.ispartofFisheries Research
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectMulti-gear fleet
dc.subjectFishing effort
dc.subjectMachine learning
dc.subjectRandom forest
dc.subjectGear prediction
dc.titlePredicting gear used in a multi-gear coastal fleeteng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre for Marine and Environmental Research
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0101%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04326%2F2020/PT
oaire.citation.startPage107199
oaire.citation.titleFisheries Research
oaire.citation.volume281
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCampos
person.familyNameCastro
person.givenNameAida
person.givenNameMargarida
person.identifier134652
person.identifier.ciencia-idFD1D-549D-6B28
person.identifier.ciencia-id971F-AF68-5799
person.identifier.orcid0000-0002-5972-6266
person.identifier.orcid0000-0002-7860-2074
person.identifier.ridA-3984-2012
person.identifier.ridN-1091-2013
person.identifier.scopus-author-id7202214975
person.identifier.scopus-author-id7402292554
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication8db52a03-420d-4974-bdc4-0a431577ac1c
relation.isAuthorOfPublication76e4cab8-f3b7-4b85-b830-06599975ed40
relation.isAuthorOfPublication.latestForDiscovery8db52a03-420d-4974-bdc4-0a431577ac1c
relation.isProjectOfPublication794d4c77-c731-471e-bc96-5a41dcd3d872
relation.isProjectOfPublicationfafa76a6-2cd2-4a6d-a3c9-772f34d3b91f
relation.isProjectOfPublication.latestForDiscovery794d4c77-c731-471e-bc96-5a41dcd3d872

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
1-s2.0-S0165783624002637-main.pdf
Size:
1.67 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
3.46 KB
Format:
Item-specific license agreed upon to submission
Description: