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.

Florida’s recreational bonefish fishery generates substantial economic benefits to the region; however, the resource has never been adequately assessed to determine standard benchmarks for sustainability. The situation offered a unique opportunity to address unresolved issues in sustainability for a valuable recreational fishery that is almost exclusively catch and release. The goal of this dissertation was three-fold: (1) to develop a comprehensive framework for conducting rigorous stock assessments on recreational catch-and-release fisheries; (2) to apply these methods to the Florida bonefish fishery to compare results against internationally-recognized sustainable fishery benchmarks; and, (3) provide recommendations for longer-term assessment strategies and management efforts. Due to the dearth of available bonefish information, existing literature were synthesized and new quantitative data and models concerning bonefish demographics (i.e. growth, survivorship) and population dynamics were developed. Data for age-and-growth were collected with a focus on both small (< 100 mm FL) and large (>650 mm FL) bonefish which had been under-sampled in previous research. A two-stage growth model was developed that allowed predictions of size-at-age over the complete life history of the species. Evidence from multiple analyses suggested a single species of bonefish in the Florida fishery. A mail survey of bonefish captains (guides) acquired baseline statistics on the south Florida bonefish fishery. Fleet fishing effort is mostly concentrated in the northern Florida Keys (Biscayne Bay to Islamorada) and reflects to some extent bonefish spatial population abundance. The majority of the respondents indicated the stock had declined over the past few decades. A sized-based mortality estimator was used to determine mortality. Changes in current population size from 2003-2010 were determined from a visual survey. Historical stock size was inferred from a relative abundance index from standardized tournament catch rates. Annual trends of the mortality estimates implied a stable population that is not declining which contrasts with the index of abundance and visual survey results. Both the index of abundance and visual survey displayed overall declining trends in recent years. Bonefish movements were determined from anchor tag and acoustic telemetry. Anchor tagging data were analyzed to evaluate movements, stock size structure and mortality. Results revealed no significant relationships between distance moved and days at large or days at large and length at tagging; however, significant individual movements (>100 kilometers) were recorded. Use of acoustic telemetry showed frequent movements around the barrier islands, schooling behavior, and a possible spawning effect with movements to offshore reef habitats. Stock status was addressed with two different classes of assessment models: REEFS, a length-based model which estimated the stock as moderately exploited with the current fishing mortality rate less than the maximum sustainable yield fishing mortality rate; and a “catch-free” age-structured model which indicated a large stock decline over the past 40 years with the stock currently bordering an overfished benchmark. The age-structured model was assumed to be the most robust method because it incorporated the majority of the research data (age and growth, selectivity, mortality, visual survey, CPUE standardization, vessel effort). In conclusion, the stock’s productivity has been significantly reduced over the past 50 years due to fishing, but also degradation of key prey populations, habitats, and water quality, resulting in a current bonefish population that is bordering an overfished state. Recommendations are provided for improving future stock assessments and management approaches. The assessment framework and quantitative methods and models developed here are broadly applicable to bonefish stocks around the world.

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).

Thesis (M.S.)--Oregon State University, 2004. / Typescript (photocopy). Includes bibliographical references (leaves 53-57). Also available online.

Thesis (M.M.S.)--Memorial University of Newfoundland, 2001. / Restricted until June 2002. Bibliography: leaves 105-112.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references.

Thesis (Ph.D.)--Memorial University of Newfoundland, 2004. / Bibliography: leaves 181-197.