Meta-analytical approaches to the study of fish population dynamics.
Date
2002
Authors
Harley, Shelton James.
Journal Title
Journal ISSN
Volume Title
Publisher
Dalhousie University
Abstract
Description
The 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.
Thesis (Ph.D.)--Dalhousie University (Canada), 2002.
Thesis (Ph.D.)--Dalhousie University (Canada), 2002.
Keywords
Agriculture, Fisheries and Aquaculture.