A Statistical model for an aquatic stock estimation with application to a fish population.
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AuthorKones, Julius Kipyegon
MetadataShow full item record
AbstractFish stocks are renewable resources if correctly handled. If all mature fish are caught before spawning, then there will be no recruitment. The problems that relate to fish management vary enormously depending on the objective. The basic purpose of fish stock assessment is to provide advice on the optimum exploitation level of aquatic living resources such as fish. The problem is to find the appropriate middle ground where it is possible to obtain good catches for a long time. This can only be achieved through estimation of the size and productivity of fish stocks, yet it is rarely possible to count the abundance of an aquatic species. Mathematical models are used to relate the measurements to the stock size while statistical techniques are used to estimate the unknown parameters. The study aims at developing an ecologically sustainable statistical model that can be applied to describe the dynamics of aquatic population through their life stages. The developed model will be applied to tilapia fish population in a tropical environment with specific application to Kenyan waters. More specifically, the study will use empirical data to describe the population dynamics of tilapia fish and model it appropriately, estimate the parameters of the derived model and make recommendations that can be adopted for proper planning of fish breeding and harvesting. The theory of matrix modeling forms the basis of the study. The Markov Chain Monte Carlo (MCMC) technique and Bayesian approach shall be applied to derive the parameters of the modeled population. Bayesian estimation makes use of prior information regarding the population under study, a property that has been lauded as the only coherent statistical methodology for updating knowledge using the information contained in the data: This property enables the posterior from one analysis to be used as an induced prior in a subsequent analysis, thereby building and exploiting an accumulated base of knowledge. The study will make use of primary and secondary data to be obtained from fisheries research institutions locally and abroad for comparative purposes. Elasticity analysis shall be incorporated in the model to indicate which parameters and ages/sizes are contributing the most to population growth rate, and which parameters have the most influence on growth rate.
Publisher or UniversityUniversity of Nairobi