Study Of The Surplus Production Models

With the continuous development of human society and the rapid growth of globalpopulation, there is a growing demand for aquatic products to feed the people. Fisheryplays an important role in the national economy and social development. However,the fishery resources in marine and inland waters are not infinite. Excessive fishingand deterioration of ecological environment have had negative impacts on the fisheryresources with declined yields. Therefore, in order to make a rational utilization offishery resources in China, we must get a clear understanding of our fishery resourcesand judge the utilization of the resource if they are under-exploited, or fully-exploitedor over-exploited. All of the work depends on the researchers on fishery resourcesassessment to evaluate the fishery biomass, and to forecast the fish populationdynamics, thus to provide a scientific basis for decision-making of fisherymanagement.As an important model in fish stock assessment, surplus production model has beenwidely used by many researchers for its simple form and less data requirement whichhas substantial development over the years. It has been used especially in some tunaresearch institutions (i.e. International Commission for the Conservation of AtlanticTunas (ICCAT)), and in some whales research institutions, such as the SoutheastPacific Fisheries International Committee (ICSEAF). There are usually two steps inbuilding an accurate and effective stock assessment model: first, selecting a suitableassessment model; second, estimating the parameters as accurate as possible in themodel. This thesis has focused on the surplus production model which includes theselection of surplus production models, parameter estimation methods and Bayesiandecision analysis. This work has answered some important questions in the field, haswidened the vision of research, and has enriched the theoretical basis of this type ofmodel, therefore has made useful contributions to the wider utilization of the model.Firstly, we used AIC (Akaike Information Criterion) and BIC (BayesianInformation Criterion) as a criterion of model selection. The simulated data are from three typical fisheries at four white noise levels with two operation models. In order toinvestigate the application of AIC and BIC in the selection of surplus productionmodels, eight surplus production models are used to evaluate the24fisheriessimulated data. Results show that for all of simulated data, AIC and BIC always selectthe correct model which simulates the data. Therefore, we may conclude that AIC andBIC are robust and effective in the selection of production models. However with theincreased white noise levels, the selection has became less effective. A case study ofHairtail (Trichiurus japonicus) fishery in the East China Sea showed that theobservation error Schaefer surplus production model may be the most appropriatemodel for this fishery.The third chapter studied the application of MA (Moving Average) in the surplusproduction models and provided a concise and effective method for the surplusproduction models. Results showed that MA can reduce the impact of the white noisein the data effectively and thus improve the accuracy of surplus production modelssignificantly.With the rapid development of computer technology, computer software packageswith non-equilibrium surplus production model are used widely today. The fourthchapter studied the application of software of CEDA (Catch and Effort Data Analysis)and ASPIC (A Surplus–Production Model Incorporate Covariates) on the Hairtail (Tjaponicus) fishery in the East China Sea, and discussed the characteristics of thesetwo softwares and conducted the assessment to the fishery. This work providedexamples of experiments for further development of these softwares. At the same time,we also have realized the application of computer software to the fishery in the EastChina Sea.Either by computer programming or applying software, the above three chapters allanalyzed the data with traditional parameter estimation methods, i.e. equilibriumestimation method, process error method and observation error method. However,when the existing fisheries data do not provide enough information for the parametersestimation of the production model, Bayesian statistics provides an effective solutionfor this problem. Therefore, chapter five took the Hairtail (T. japonicus) fishery in the East China Sea as an example and used Bayesian statistical method to estimate theparameters of Schaefer production model, and predicted the biomass and cumulatedcatch in several different schemes in the coming12years. Finally, risk analysis hasbeen made for the implementation of several management strategies, thus we proposeprecautious management measures for the fishery, so as to help for its scientificmanagement.So far, there is no publication on the application of AIC、BIC and MA in thesurplus production models in China, the study of surplus production models in Chinausually involved the estimation of the MSY and the validation of surplus productionwith environmental factors. There is also no record about using CEDA、ASPIC andBayesian statistics method to evaluate the Hairtail (T. japonicus) fishery in the EastChina Sea. Therefore with the work in this thesis, the author hopes to help the furtherdevelopment of surplus production models and to provide a useful contribution for thefishery assessment and management in our country.