Thesis (M.S.)--Tennessee Technological University, 2009. / Title from title page screen (viewed on Jan. 22, 2010). Includes bibliographical references.

Thesis (Ph.D.) - James Cook University, 2007. / Typescript (photocopy) Bibliography: leaves 154-165.

Thesis (Ph.D.)--University of Washington, 1995. / Vita. Includes bibliographical references (leaves 100-111).

Thesis (M.S.)--University of Wisconsin--Stevens Point, 2002. / Includes bibliographical references (leaves 41-52).

Tam, Man Cheong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 254-262). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.x / Table of Contents --- p.xii / List of Tables --- p.xvii / List of Figures --- p.xviii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Coral reef fishes and their interactions with coral reef ecosystem --- p.1 / Chapter 1.2 --- Diversity and world distribution of coral reef fishes --- p.4 / Chapter 1.3 --- Fishery exploitation on reef fish communities --- p.5 / Chapter 1.4 --- Marine reserves and their effects on reef fish assemblages --- p.8 / Chapter 1.5 --- The Marine environment of Hong Kong --- p.19 / Chapter 1.6 --- Reef fishes and inshore fishery in Hong Kong --- p.20 / Chapter 1.7 --- Marine parks and marine reserves in Hong Kong --- p.23 / Chapter 1.8 --- Objectives --- p.25 / Chapter 1.9 --- Nature and structure of this thesis --- p.26 / Chapter Chapter 2 --- Spatial comparison of reef fish assemblages between fished and protected areas of Hong Kong --- p.29 / Chapter 2.1 --- Introduction --- p.29 / Chapter 2.2 --- Study Areas --- p.31 / Chapter 2.2.1 --- Tung Ping Chau Marine Park --- p.32 / Chapter 2.2.2 --- Hoi Ha Wan Marine Park --- p.33 / Chapter 2.2.3 --- Kat O Chau and Ngo Mei Chau --- p.34 / Chapter 2.3 --- Methodology --- p.34 / Chapter 2.3.1 --- Sampling of reef fish assemblages --- p.34 / Chapter 2.3.2 --- Sampling of habitat complexity --- p.36 / Chapter 2.4 --- Data Analysis --- p.37 / Chapter 2.5 --- Results --- p.41 / Chapter 2.5.1 --- "Seasonal trend in mean density, mean biomass and mean species richness" --- p.41 / Chapter 2.5.1.1 --- Seasonal trend in mean density --- p.41 / Chapter 2.5.1.2 --- Seasonal trend in mean biomass --- p.42 / Chapter 2.5.1.3 --- Seasonal trend in mean species richness --- p.42 / Chapter 2.5.2 --- Seasonal trend in fish assemblage structures --- p.43 / Chapter 2.5.2.1 --- A Ye Wan --- p.43 / Chapter 2.5.2.2 --- A Ma Wan --- p.44 / Chapter 2.5.2.3 --- Chau Mei --- p.46 / Chapter 2.5.2.4 --- Chau Tau --- p.47 / Chapter 2.5.2.5 --- Wu Pai --- p.48 / Chapter 2.5.2.6 --- Cheung Shek Tsui --- p.50 / Chapter 2.5.2.7 --- Coral Beach --- p.51 / Chapter 2.5.2.8 --- Moon Island --- p.52 / Chapter 2.5.3 --- Spatial comparison between protected and fished areas in each sampling season --- p.53 / Chapter 2.5.3.1 --- Comparison of mean fish density --- p.54 / Chapter 2.5.3.2 --- Comparison of mean fish species richness --- p.55 / Chapter 2.5.3.3 --- Comparison of mean fish biomass --- p.55 / Chapter 2.5.4 --- "Relationship of mean density, mean species richness and mean biomass with habitat complexity" --- p.55 / Chapter 2.5.4.1 --- Mean fish density versus habitat complexity --- p.56 / Chapter 2.5.4.2 --- Mean species richness of fish versus habitat complexity --- p.56 / Chapter 2.5.4.3 --- Mean fish biomass versus habitat complexity --- p.57 / Chapter 2.5.5 --- Spatial comparison of reef fish assemblage structures --- p.57 / Chapter 2.5.5.1 --- Summer 2002 --- p.57 / Chapter 2.5.5.2 --- Fall 2002 --- p.58 / Chapter 2.5.5.3 --- Winter 2003 --- p.59 / Chapter 2.5.5.4 --- Spring 2003 --- p.61 / Chapter 2.5.5.5 --- Summer 2003 --- p.62 / Chapter 2.5.5.6 --- Fall 2003 --- p.63 / Chapter 2.5.5.7 --- Winter 2004 --- p.65 / Chapter 2.5.5.8 --- Spring 2004 --- p.66 / Chapter 2.6 --- Discussion --- p.67 / Chapter 2.6.1 --- Seasonal changes in reef fish assemblages --- p.67 / Chapter 2.6.2. --- "Effects of protection and habitat complexity on density, biomass and species richness of fish assemblages" --- p.74 / Chapter 2.6.3 --- Effects of protection on fish assemblage structures --- p.78 / Chapter 2.6.4 --- Determination of best sampling seasons for monitoring of protection effects in protected areas --- p.83 / Chapter 2.7 --- Conclusion --- p.85 / Chapter Chapter 3 --- Temporal comparison of1 reef fish assemblages before and after protection in Tung Ping Chau Marine Park --- p.191 / Chapter 3.1 --- Introduction --- p.191 / Chapter 3.2 --- Study areas --- p.192 / Chapter 3.3 --- Methodology --- p.192 / Chapter 3.4 --- Data analysis --- p.193 / Chapter 3.5 --- Results --- p.195 / Chapter 3.5.1 --- Temporal comparison of mean density and species richness among years --- p.195 / Chapter 3.5.1.1 --- Temporal comparison of mean density among years --- p.195 / Chapter 3.5.1.2 --- Temporal comparison of mean species richness among years --- p.197 / Chapter 3.5.2 --- Temporal comparison of fish assemblages structures among years in A Ye Wan --- p.198 / Chapter 3.5.2.1 --- Summer --- p.198 / Chapter 3.5.2.2 --- Fall --- p.199 / Chapter 3.5.2.3 --- Winter --- p.200 / Chapter 3.5.2.4 --- Spring --- p.201 / Chapter 3.5.3 --- Temporal comparison of fish assemblages structures among years in A Ma Wan --- p.202 / Chapter 3.5.3.1 --- Summer --- p.202 / Chapter 3.5.3.2 --- Fall --- p.203 / Chapter 3.5.3.3 --- Winter --- p.204 / Chapter 3.5.3.4 --- Spring --- p.205 / Chapter 3.6 --- Discussion --- p.206 / Chapter 3.6.1 --- Effects of protection on mean density and species richness of fish assemblages --- p.206 / Chapter 3.6.2 --- Effects of protection on fish assemblage structures --- p.208 / Chapter 3.7 --- Conclusion --- p.210 / Chapter Chapter 4 --- Summary and Perspectives --- p.247 / Chapter 4.1. --- Major findings of the present study --- p.247 / Chapter 4.2. --- Significance and implications of the present study --- p.249 / Chapter 4.3 --- Perspectives for further study --- p.252 / References --- p.254 / Appendix --- p.263 / Appendix 1 --- p.263

Two dimensional stocks of fish can be assessed with methods that mimic the analysis of research survey data but that use commercial catch-effort data. This finite population approach has scarcely been used in fisheries science though it brings about very large sample sizes of local fish density with models of only moderate levels of complexity. The extracted information about the status of the stock can be interpreted as biomass indices. Statistical inference on finite populations has been the locus of a highly specialized branch of sampling-distribution inference, unique because observable variables are not considered as random variables. If statistical inference is defined as "the identification of distinct sets of plausible and implausible values for unobserved quantities using observations and probability theory" then it is shown that Godambe's paradox implies that the classical finite populations approach is inherently contradictory as a technique of statistical inference. The demonstration is facilitated by the introduction of an extended canonical form of an experiment of chance, that apart from the three components identified by Birnbaum, also contains the time at which the experiment is performed. Realization of the time random variable leaves the likelihood function as sole data-based mathematical tool for statistical inference, in contradiction with sampling-distribution inference and in agreement with direct-likelihood and Bayesian inference. A simple mathematical model is introduced for biomass indices in the spatial field defined by the fishing grounds. It contains three unknown parameters, the natural mortality rate, the probability of observing the stock in the area covered by the fishing grounds, and mean fish density in the sub-areas where the stock was present. A new theory for the estimation of mortality rates is introduced, using length frequency data, that is based on the population ecology analogue of Hamilton-Jacobi theory of classical mechanics. The family of equations require estimations of population growth, individual growth, and recruitment pattern. Well known or new techniques are used for estimating parameters of these processes. Among the new techniques, a likelihood-based geostatistical model to estimate fish density is proposed and is now in use in fisheries science (Roa-Ureta and Niklitschek, 2007, ICES Journal of Marine Science 64:1723-1734), as well as a new method to estimate individual growth parameters (Roa-Ureta, In Press, Journal of Agricultural, Biological, and Environmental Statistics). All inference is done only using likelihood functions and approximations to likelihood functions, as required by the Strong Likelihood principle and the direct-likelihood school of statistical inference. The statistical model for biomass indices is a hierarchical model with several sources of data, hyperparameters, and nuisance parameters. Even though the level of complexity is not low, a full Bayesian formulation is not necessary. Physical factors, mathematical manipulation, profile likelihoods and estimated likelihoods are used for the elimination of nuisance parameters. Marginal normal and multivariate normal likelihood functions, as well as the functional invariance property, are used for the hierarchical structure of estimation. In this manner most sources of information and uncertainty in the data are carried over up the hierarchy to the estimation of the biomass indices.

Thesis (M.Sc.)--Memorial University of Newfoundland, 2005. / Includes bibliographical references.

Thesis (Ph. D.)--Newcastle University, 2008. / Title from PDF title page (Newcastle University, viewed on Feb. 12, 2010). Includes bibliographical references (p. 244-258).

Thesis--Auburn University, 2009. / Abstract. Vita. Includes bibliographical references (p. 58-68).

Thesis (M.S.)--University of Wisconsin--Stevens Point, 2004. / Includes bibliographical references (leaves 93-104).