剩余产量模型的研究
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摘要
随着人类社会的不断发展，全球人口数量的迅速增长，人们对于水产品的需求量日益增加,渔业在国民经济和社会发展中的地位愈来愈重要。然而海洋和内陆水域中的渔业资源并不是“取之不尽，用之不竭”的。过度的渔业捕捞和渔业生态环境的恶化使渔业资源日益衰竭，渔获产量持续下降。因此，为了合理利用渔业资源，实现我国渔业资源的可持续发展，就必须摸清渔业资源的具体状况，准确判断对资源的利用是开发不足、已充分利用或是利用过度。这些工作都有赖于渔业资源评估工作者对渔业资源做出评估，对渔业资源的数量动态进行预报，从而为渔业决策和渔业管理提供科学依据。
剩余产量模型作为渔业资源评估领域中重要的资源评估模型之一，由于其简单易懂，所需数据量较少，一直为许多资源评估研究者所使用，因此得到了长足的发展。它是许多渔业主要的资源评估工具，特别是在一些金枪鱼研究机构中使用较多(例如国际大西洋金枪鱼保护组织（ICCAT）,以及一些长须鲸的研究部门，例如东南太平洋渔业国际委员会（ICSEAF）。在渔业资源评估研究工作中，一个准确而又有效的评估模型的建立通常需要两个步骤：一是选择出一个适合的模型表达形式；二是尽可能准确地估算出模型中的各个参数。本论文主要以剩余产量模型为主要研究目标，研究了关于剩余产量模型的选择、参数的估计方法以及贝叶斯决策分析等问题。解答了关于剩余产量模型研究领域的若干问题，拓宽了剩余产量模型的研究视野，丰富了该类模型的理论基础，为剩余产量模型的更广泛应用提供了有益的贡献。
本文首先采用AIC（Akaike Information Criterion）与BIC（Bayesian InformationCriterion）作为模型选择标准，以两类不同的剩余产量模型为操作模型，在四种白色噪音水平下模拟了三种典型渔业的数据。然后利用模拟的24种渔业类型的观测数据，分别用八种剩余产量模型对其进行评估，讨论了AIC和BIC在剩余产量模型选择中的应用。研究结果显示，对于所有模拟数据，AIC与BIC都准确地选择了最初产生它的“正确”的模型。所以AIC与BIC在剩余产量模型选择中是稳健有效。但是随着噪音水平的升高，这种效力将会减弱。以东海带鱼(Trichiurus japonicus)渔业为例，选择结果表明观测误差法的Schaefer剩余产量模型可以作为东海带鱼渔业资源评估最合适的模型。
本文第三章研究了MA（Moving Average）在剩余产量模型中的应用，为剩余产量模型的有效利用提供了一种简洁的方法。研究结果表明MA可以有效缓解白色噪音对数据的影响从而显著地提高剩余产量模型的评估效果。
随着现今计算机科技的快速发展，编入非平衡剩余产量模型的计算机软件得到了系统的应用。本文第四章以CEDA(Catch and Effort DataAnalysis)和ASPIC(A Surplus-Production Model Incorporate Covariates)两种计算机软件在东海带鱼渔业中的应用为主要内容，论述了两种软件的特点和对东海带鱼的评估。从而为这两种软件的进一步发展提供了实验范例。同时，也实现了应用计算机软件对东海带鱼渔业的资源评估。
以上三章无论是通过计算机编程还是应用相关软件进行直接估算，都是应用传统的参数估计方法对数据进行分析，即平衡估计方法、过程误差法和观测误差法等方法。然而在目前的渔业资源评估中，当已有的渔业数据不能够提供足够的信息来对剩余产量模型的参数进行估算时，贝叶斯数理统计方法为这一问题提供了一个有效的解决方法。于是本文第五章同样以东海带鱼渔业为例，采用贝叶斯统计方法对Schaefer剩余产量模型的参数进行估计，并预测了在几种不同的捕捞方案下东海带鱼资源量和累计渔获量在未来十二年的变化情况。并对几种管理策略的实施情况进行了风险分析，从而提出了东海带鱼渔业预防性的管理措施，以期为科学管理东海带鱼资源提供理论依据。
到目前为止，国内并没有关于AIC、BIC和MA在剩余产量模型中应用的文章，国内学者对剩余产量模型的研究一般都是基于对MSY最大可持续产量的估计以及对一些加入环境因子的剩余产量模型的验证。亦未见通过CEDA、ASPIC以及贝叶斯数理统计方法对东海带鱼渔业进行评估的记载。所以笔者拟通过本论文的研究，希望能为剩余产量模型的进一步发展提供理论基础，同时也为我国渔业资源的评估和管理提供一个有益的贡献。
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.
剩余产量模型作为渔业资源评估领域中重要的资源评估模型之一，由于其简单易懂，所需数据量较少，一直为许多资源评估研究者所使用，因此得到了长足的发展。它是许多渔业主要的资源评估工具，特别是在一些金枪鱼研究机构中使用较多(例如国际大西洋金枪鱼保护组织（ICCAT）,以及一些长须鲸的研究部门，例如东南太平洋渔业国际委员会（ICSEAF）。在渔业资源评估研究工作中，一个准确而又有效的评估模型的建立通常需要两个步骤：一是选择出一个适合的模型表达形式；二是尽可能准确地估算出模型中的各个参数。本论文主要以剩余产量模型为主要研究目标，研究了关于剩余产量模型的选择、参数的估计方法以及贝叶斯决策分析等问题。解答了关于剩余产量模型研究领域的若干问题，拓宽了剩余产量模型的研究视野，丰富了该类模型的理论基础，为剩余产量模型的更广泛应用提供了有益的贡献。
本文首先采用AIC（Akaike Information Criterion）与BIC（Bayesian InformationCriterion）作为模型选择标准，以两类不同的剩余产量模型为操作模型，在四种白色噪音水平下模拟了三种典型渔业的数据。然后利用模拟的24种渔业类型的观测数据，分别用八种剩余产量模型对其进行评估，讨论了AIC和BIC在剩余产量模型选择中的应用。研究结果显示，对于所有模拟数据，AIC与BIC都准确地选择了最初产生它的“正确”的模型。所以AIC与BIC在剩余产量模型选择中是稳健有效。但是随着噪音水平的升高，这种效力将会减弱。以东海带鱼(Trichiurus japonicus)渔业为例，选择结果表明观测误差法的Schaefer剩余产量模型可以作为东海带鱼渔业资源评估最合适的模型。
本文第三章研究了MA（Moving Average）在剩余产量模型中的应用，为剩余产量模型的有效利用提供了一种简洁的方法。研究结果表明MA可以有效缓解白色噪音对数据的影响从而显著地提高剩余产量模型的评估效果。
随着现今计算机科技的快速发展，编入非平衡剩余产量模型的计算机软件得到了系统的应用。本文第四章以CEDA(Catch and Effort DataAnalysis)和ASPIC(A Surplus-Production Model Incorporate Covariates)两种计算机软件在东海带鱼渔业中的应用为主要内容，论述了两种软件的特点和对东海带鱼的评估。从而为这两种软件的进一步发展提供了实验范例。同时，也实现了应用计算机软件对东海带鱼渔业的资源评估。
以上三章无论是通过计算机编程还是应用相关软件进行直接估算，都是应用传统的参数估计方法对数据进行分析，即平衡估计方法、过程误差法和观测误差法等方法。然而在目前的渔业资源评估中，当已有的渔业数据不能够提供足够的信息来对剩余产量模型的参数进行估算时，贝叶斯数理统计方法为这一问题提供了一个有效的解决方法。于是本文第五章同样以东海带鱼渔业为例，采用贝叶斯统计方法对Schaefer剩余产量模型的参数进行估计，并预测了在几种不同的捕捞方案下东海带鱼资源量和累计渔获量在未来十二年的变化情况。并对几种管理策略的实施情况进行了风险分析，从而提出了东海带鱼渔业预防性的管理措施，以期为科学管理东海带鱼资源提供理论依据。
到目前为止，国内并没有关于AIC、BIC和MA在剩余产量模型中应用的文章，国内学者对剩余产量模型的研究一般都是基于对MSY最大可持续产量的估计以及对一些加入环境因子的剩余产量模型的验证。亦未见通过CEDA、ASPIC以及贝叶斯数理统计方法对东海带鱼渔业进行评估的记载。所以笔者拟通过本论文的研究，希望能为剩余产量模型的进一步发展提供理论基础，同时也为我国渔业资源的评估和管理提供一个有益的贡献。
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.
引文
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