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Comparing GLM, GLMM, and GEE modeling approaches for catch rates of bycatch species: A case study of blue shark fisheries in the South Atlantic

2020-03Journal article Restricted access
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dc.contributor.authorCoelho, Rui
dc.contributor.authorInfante, Paulo
dc.contributor.authorSantos, Miguel N.
dc.date.accessioned2020-12-02T13:59:14Z
dc.date.available2021-03-01T01:30:22Z
dc.date.issued2020-03
dc.description.abstractModeling and understanding the catch rate dynamics of marine species is extremely important for fisheries management and conservation. For oceanic highly migratory species in particular, usually only fishery‐dependent data are available which have limitations in the assumption of independence and if often zero‐inflated and/or overdispersed. We tested different modeling approaches applied to the case study of blue shark in the South Atlantic, by using generalized linear models (GLMs), generalized linear mixed models (GLMMs), and generalized estimating equations (GEEs), as well as different error distributions to deal with the presence of zeros in the data. We used fractional polynomials to deal with non‐linearity in some of the explanatory variables. Operational (set level) data collected by onboard fishery observers, covering 762 longline sets (1,014,527 hooks) over a 9‐year period (2008–2016), were used. One of the most important variables affecting catch rates is leader material, with increasing catches when wire leaders are used. Spatial and seasonal variables are also important, with higher catch rates expected toward temperate southern waters and eastern longitudes, particularly between July and September. Environmental variables, especially SST, also affect catches. There were no major differences in the parameters estimated with GLMs, GLMMs, or GEEs; however, the use of GLMMs or GEEs should be more appropriate due to fishery dependence in the data. Comparing those two approaches, GLMMs seem to perform better in terms of goodness‐of‐fit and model validation.pt_PT
dc.description.sponsorshipFCT IF/00253/2014pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1111/fog.12462pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.1/14857
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherWileypt_PT
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/full/10.1111/fog.12462pt_PT
dc.subjectCatch-per-unit-effort (CPUE)pt_PT
dc.subjectGeneralized Linear Models (GLM)pt_PT
dc.subjectGeneralized Linear Mixed Models (GLMM)pt_PT
dc.subjectGeneralized Estimating Equations (GEE)pt_PT
dc.subjectLongline fisheriespt_PT
dc.subjectPelagic sharkspt_PT
dc.titleComparing GLM, GLMM, and GEE modeling approaches for catch rates of bycatch species: A case study of blue shark fisheries in the South Atlanticpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage184pt_PT
oaire.citation.issue2pt_PT
oaire.citation.startPage169pt_PT
oaire.citation.titleFisheries Oceanographypt_PT
oaire.citation.volume29pt_PT
person.familyNameCoelho
person.givenNameRui
person.identifier134638
person.identifier.ciencia-idDC1A-20B7-EAC0
person.identifier.orcid0000-0003-3813-5157
person.identifier.ridC-1163-2008
person.identifier.scopus-author-id56257111100
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication6366aa1b-1301-43a3-b2b0-1280311ac95f
relation.isAuthorOfPublication.latestForDiscovery6366aa1b-1301-43a3-b2b0-1280311ac95f

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