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dc.contributor.authorHarley, Shelton James.en_US
dc.date.accessioned2014-10-21T12:38:04Z
dc.date.available2002
dc.date.issued2002en_US
dc.identifier.otherAAINQ75725en_US
dc.identifier.urihttp://hdl.handle.net/10222/55877
dc.descriptionThe aim of this thesis is to reduce uncertainties in our understanding of fish population dynamics by: (a) developing statistical methods that allow information to be shared or combined; and (b) applying these methods to real fisheries problems. Meta-analysis is a statistical framework for making inferences by combining results across studies or experiments. In this thesis I demonstrated that meta-analysis is particularly useful for studies of fisheries population dynamics because: (1) there is often considerable observation error in fisheries data that creates difficulties in making useful inferences; and (2) there are potentially relevant data for many fish populations or years. I showed, using data for many populations, that catch-per-unit-effort abundance indices generally underestimate the declines in the population being fished---a finding critical to fisheries stock assessment. I developed a generalized framework to test hypotheses relating to the regulation of fish abundance during the larval and juvenile stages---thus allowing for future meta-analyses of this problem. I extended the traditional meta-analytical methods to combine estimates of the relationship between the size of a fish and its catchability to trawl surveys---this allowed for predictions to be made for species where no data were available. Using a method from the field of econometrics, I developed an approach to integrate random effects into a complex nonlinear model---this allowed for improved modelling and interpretation of million dollar acoustic surveys of a New Zealand fish population. I demonstrated how inferences from meta-analysis can be used in the assessment of stocks with little information (e.g., developing fisheries) and to obtain reliable estimates required for management decisions. To conclude, I provided a discussion of how the further extension of methods and fisheries databases would be critical to the future of meta-analysis, and highlighted important directions for future research.en_US
dc.descriptionThesis (Ph.D.)--Dalhousie University (Canada), 2002.en_US
dc.languageengen_US
dc.publisherDalhousie Universityen_US
dc.publisheren_US
dc.subjectAgriculture, Fisheries and Aquaculture.en_US
dc.titleMeta-analytical approaches to the study of fish population dynamics.en_US
dc.typetexten_US
dc.contributor.degreePh.D.en_US
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