Accounting for potential nonlinearity between catch and effort using meta-analysis and applying GLM and GLMM to fishing data from deployments of fixed and mobile gear

dc.contributor.authorAljafary, Michelle
dc.contributor.examiningcommitteeDavoren, Gail (Biological sciences) Muthukumarana, Saman (Statistics)en_US
dc.contributor.supervisorGillis, Darren (Biological sciences)en_US
dc.date.accessioned2016-04-12T19:44:19Z
dc.date.available2016-04-12T19:44:19Z
dc.date.issued2016
dc.degree.disciplineBiological Sciencesen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractMy thesis examines nonlinearity between catch and effort. I use a meta-analysis of published literature and generalized linear mixed-effects models (GLMM) on both fixed and mobile gear fisheries of Atlantic Canada. The meta-analysis examines the proportionality of catch to effort using the slope of the reduced major axis (RMA) log-log regression, which accounts for “errors-in-variables”. The GLMMs explored proportionality while accounting for variation among fishing vessels. Both analyses found evidence for disproportionality between catch and effort. Catch that increases disproportionally to effort could result from either facilitation or recruitment of effort into the fishery. Catch increases that are less than proportional are expected from competitive interactions among fishers or gear saturation. The GLMM also revealed that the level of aggregation (by set, trip, monthly, or annually) can affect the apparent proportionality between catch and effort. In general, catch and effort should not be considered to be proportional.en_US
dc.description.noteMay 2016en_US
dc.identifier.urihttp://hdl.handle.net/1993/31208
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectmeta-analysis, generalized linear mixed-effects model (GLMM), catch, effort, error-in-variables, proportionality, disproportionality, mixed effects, RMA, OLS, aggregation.en_US
dc.titleAccounting for potential nonlinearity between catch and effort using meta-analysis and applying GLM and GLMM to fishing data from deployments of fixed and mobile gearen_US
dc.typemaster thesisen_US
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