渔业数据失真对两种非平衡剩余产量模型评估结果的影响比较
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摘要
为了研究渔业数据失真对两种非平衡剩余产量模型评估结果的影响,以南大西洋长鳍金枪鱼渔业产量和单位捕捞努力量渔获量(CPUE)数据作为基础数据,加入5种不同程度[变异系数(CV)=1%、5%、10%、20%和30%]的随机误差,模拟了(1)无数据失真,(2)仅产量数据失真,(3)仅CPUE数据失真,(4)产量和CPUE数据均失真等4种情况。利用基于ASPIC的非平衡剩余产量模型(ASM)和基于贝叶斯状态空间建模方法的非平衡剩余产量模型(BSM)分别评估了最大可持续产量(MSY)、B_(MSY)、F_(MSY)、B_(2011)/B_(MSY)、F2011/F_(MSY)等5种生物学参考点和管理指标。结果显示,在无数据失真情况下,ASM和BSM评估的MSY分别为2.866×10~4 t和2.836×10~4 t,B_(2011)/B_(MSY)分别为1.366和1.324,F2011/F_(MSY)分别为0.627和0.667,均相差不大,表明该渔业目前状态良好,ASM得到了较大的B_(MSY)(31.48×10~4 t)和较小的F_(MSY)(0.091);数据失真对ASM评估的B_(MSY)和F_(MSY)分别产生了严重的过低估计和过高估计,且CPUE数据失真产生的影响要比产量数据失真大;随着随机误差的增大,BSM评估的生物学参考点和管理指标的绝对百分比偏差有增大趋势;与ASM相比,BSM能够更好地处理渔业数据中存在的随机误差,除了MSY以外,BSM评估的生物学参考点和管理指标绝对百分比偏差均要比ASM的评估结果低,尤其是B_(MSY)和F_(MSY)。因此,在使用存在较大随机误差的渔业数据进行资源评估时,BSM具有一定的优势。
Marine fisheries provide a major source of food and livelihoods for people worldwide. Fishery management plays an important role in achieving sustainable fisheries. Catch per unit effort(CPUE) data from either fishery independent or-dependent surveys are the most informative for variations in population size over time, meanwhile catches from the fishery-dependent survey are also required to assess fishing. If these data are inaccurate, the statistical analyses would be biased, leading to mismanagement of fishery resources. However, systematic distortions appeared in world fisheries catch trends. Moreover, due to lack of fishery scientific investigation, CPUE data were mainly from commercial fishing, and influenced by spatial-temporal factors, environmental factors and also spatial autocorrelation problem. Therefore, it is important to understand the impacts of distorted fishery data on stock assessments. This study used catch and CPUE data of the albacore(Thunnus alalunga) fishery in the South Atlantic. Simulations were conducted to estimate biological reference points(BRPs), i.e., maximum sustainable yield(MSY), B_(MSY), F_(MSY), B_(2011)/B_(MSY), and F2011/F_(MSY) using non-equilibrium surplus production models based on ASPIC(ASM) and Bayesian state-space modelling(BSM). Simulations were conducted under the following scenarios: both catch and CPUE data are accurate; only catch data is misreporting; only CPUE data is misreporting,and both catch and CPUE data are misreporting. Five levels(coefficient of variation, CV=1%, 5%, 10%, 20%, and30%) of stochastic errors were superimposed on catch and CPUE data. The estimated MSYs were 2.866×10~4 t and2.836×10~4 t, B_(2011)/B_(MSY) were 1.366 and 1.324, F2011/F_(MSY) were 0.627 and 0.667 by ASM and BSM, respectively,for the first scenario. Larger B_(MSY)(31.48×10~4 t) and smaller F_(MSY)(0.091) were obtained by ASM. These results indicate that this fishery was in a good condition in 2011. Overestimate B_(MSY) and underestimate F_(MSY) were obtained using distorted catch and CPUE data by ASM, and distorted CPUE data made more impact than distorted catch data. Absolute percentage bias of estimated BRPs by BSM had a tendency to increase with the stochastic error increasing, and smaller than those by ASM, especially B_(MSY) and F_(MSY). BSM can deal with the stochastic errors better than ASM. Therefore, BSM is suggested to be applied in fishery stock assessment when the fishery data include stochastic error.
Marine fisheries provide a major source of food and livelihoods for people worldwide. Fishery management plays an important role in achieving sustainable fisheries. Catch per unit effort(CPUE) data from either fishery independent or-dependent surveys are the most informative for variations in population size over time, meanwhile catches from the fishery-dependent survey are also required to assess fishing. If these data are inaccurate, the statistical analyses would be biased, leading to mismanagement of fishery resources. However, systematic distortions appeared in world fisheries catch trends. Moreover, due to lack of fishery scientific investigation, CPUE data were mainly from commercial fishing, and influenced by spatial-temporal factors, environmental factors and also spatial autocorrelation problem. Therefore, it is important to understand the impacts of distorted fishery data on stock assessments. This study used catch and CPUE data of the albacore(Thunnus alalunga) fishery in the South Atlantic. Simulations were conducted to estimate biological reference points(BRPs), i.e., maximum sustainable yield(MSY), B_(MSY), F_(MSY), B_(2011)/B_(MSY), and F2011/F_(MSY) using non-equilibrium surplus production models based on ASPIC(ASM) and Bayesian state-space modelling(BSM). Simulations were conducted under the following scenarios: both catch and CPUE data are accurate; only catch data is misreporting; only CPUE data is misreporting,and both catch and CPUE data are misreporting. Five levels(coefficient of variation, CV=1%, 5%, 10%, 20%, and30%) of stochastic errors were superimposed on catch and CPUE data. The estimated MSYs were 2.866×10~4 t and2.836×10~4 t, B_(2011)/B_(MSY) were 1.366 and 1.324, F2011/F_(MSY) were 0.627 and 0.667 by ASM and BSM, respectively,for the first scenario. Larger B_(MSY)(31.48×10~4 t) and smaller F_(MSY)(0.091) were obtained by ASM. These results indicate that this fishery was in a good condition in 2011. Overestimate B_(MSY) and underestimate F_(MSY) were obtained using distorted catch and CPUE data by ASM, and distorted CPUE data made more impact than distorted catch data. Absolute percentage bias of estimated BRPs by BSM had a tendency to increase with the stochastic error increasing, and smaller than those by ASM, especially B_(MSY) and F_(MSY). BSM can deal with the stochastic errors better than ASM. Therefore, BSM is suggested to be applied in fishery stock assessment when the fishery data include stochastic error.
引文
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[19]李九奇,叶昌臣,王文波,等.基于Bayes方法的东海小黄鱼资源评析[J].上海海洋大学学报,2011,20(6):873-882.Li J Q,Ye C C,Wang W B,et al.A stock assessment of small yellow croaker by Bayes-based Pella-Tomlinson model in the East China Sea[J].Journal of Shanghai Ocean University,2011,20(6):873-882(in Chinese).
[20]李纲,陈新军,官文江.基于贝叶斯方法的东、黄海鲐资源评估及管理策略风险分析[J].水产学报,2010,34(5):740-750.Li G,Chen X J,Guan W J.Stock assessment and risk analysis of management strategies for Scomber japonicus in the East China Sea and Yellow Sea using a Bayesian approach[J].Journal of Fisheries of China,2010,34(5):740-750(in Chinese).
[21]ICCAT.Report of the 2013 ICCAT north and south Atlantic albacore data preparatory meeting[R].Madrid:International Commission for the Conservation of Atlantic Tunas,2013:1-68.
[22]Zhang K,Liu Q,Kalhoro M A.Application of a delaydifference model for the stock assessment of southern Atlantic albacore(Thunnus alalunga)[J].Journal of Ocean University of China,2015,14(3):557-563.
[23]Prager M H.A suite of extensions to a nonequilibrium surplus-production model[J].Fishery Bulletin,1994,92:374-389.
[24]Fletcher R I.On the restructuring of the Pella-Tomlinson system[J].Fishery Bulletin,1978,76(3):515-521.
[25]张魁,刘群,廖宝超.基于贝叶斯方法的南大西洋长鳍金枪鱼渔业的风险评估与管理[J].中国海洋大学学报,2015,45(6):51-56.Zhang K,Liu Q,Liao B C.Risk assessment and management for the southern Atlantic albacore(Thunnus alalunga)fishery based on Bayes method[J].Periodical of Ocean University of China,2015,45(6):51-56(in Chinese).
[26]Meyer R,Millar R B.BUGS in Bayesian stock assessments[J].Canadian Journal of Fisheries and Aquatic Sciences,1999,56(6):1078-1087.
[27]Zhang Y Y,Chen Y,Wilson C.Developing and evaluat-ing harvest control rules with different biological reference points for the American lobster(Homarus americanus)fishery in the Gulf of Maine[J].ICES Journal of Marine Science,2011,68(7):1511-1524.
[28]徐海龙,陈新军,陈勇,等.死亡因素对放流明对虾群体资源变动及生物学参考点影响的量化评估[J].水产学报,2016,40(5):721-730.Xu H L,Chen X J,Chen Y,et al.Impacts of mortality factors and uncertainty on the population and biological reference points in stock Chinese shrimp fishery[J].Journal of Fisheries of China,2016,40(5):721-730(in Chinese).
[29]曹少鹏,刘群.把不确定性引入生物学参考点F0.1和Fmax的估计以评估东海带鱼渔业资源[J].南方水产,2007,3(2):42-48.Cao S P,Liu Q.Stock assessment of the hairtail(Trichiurus haumela)fishery in the East China Sea by incorporating uncertainty into the estimation of the biological reference points F0.1 and Fmax[J].South China Fisheries Science,2007,3(2):42-48(in Chinese).
[30]Lee L K,Yeh S Y.Assessment of south Atlantic albacore resource based on 1959-2005 catch and effort statistics from ICCAT[J].ICCAT Collective Volume of Sci-entific Papers,2008,62(3):870-883.
[31]ICCAT.Report of the 2011 ICCAT south Atlantic and Mediterranean albacore stock assessment sessions[J].ICCAT Collective Volume of Scientific Papers,2012,68(2):387-491.
[32]Jiao Y,Cortés E,Andrews K,et al.Poor-data and datapoor species stock assessment using a Bayesian hierarchical approach[J].Ecological Applications,2011,21(7):2691-2708.
[33]Maunder M N,Punt A E.A review of integrated analysis in fisheries stock assessment[J].Fisheries Research,2013,142:61-74.
[34]Rudd M B,Branch T A.Does unreported catch lead to overfishing?[J].Fish and Fisheries,2017,18(2):313-323.
[35]官文江,田思泉,王学昉,等.CPUE标准化方法与模型选择的回顾与展望[J].中国水产科学,2014,21(4):852-862.Guan W J,Tian S Q,Wang X F,et al.A review of methods and model selection for standardizing CPUE[J].Journal of Fishery Sciences of China,2014,21(4):852-862(in Chinese).
[2]Pauly D,Zeller D.Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining[J].Nature Communications,2016,7:10244.
[3]Kleisner K,Zeller D,Froese R,et al.Using global catch data for inferences on the world’s marine fisheries[J].Fish and Fisheries,2013,14(3):293-311.
[4]Costello C,Ovando D,Hilborn R,et al.Status and solutions for the world's unassessed fisheries[J].Science,2012,338(6106):517-520.
[5]Watson R,Pauly D.Systematic distortions in world fisheries catch trends[J].Nature,2001,414(6863):534-536.
[6]Reeves S A,Pastoors M A.Evaluating the science behind the management advice for North Sea cod[J].ICES Journal of Marine Science,2007,64(4):671-678.
[7]Pauly D,Zeller D.Comments on FAOs State of World Fisheries and Aquaculture(SOFIA 2016)[J].Marine Policy,2017,77:176-181.
[8]Horwood J,O'Brien C,Darby C.North Sea cod recovery?[J].ICES Journal of Marine Science,2006,63(6):961-968.
[9]Diamond B,Beukers-Stewart B D.Fisheries discards in the North Sea:Waste of resources or a necessary evil?[J].Reviews in Fisheries Science,2011,19(3):231-245.
[10]唐议,邹伟红,胡振明.基于统计数据的中国海洋渔业资源利用状况及管理分析[J].资源科学,2009,31(6):1061-1068.Tang Y,Zou W H,Hu Z M.An analysis of utilization status and management of marine fisheries resources inChina based on statistics data[J].Resources Science,2009,31(6):1061-1068(in Chinese).
[11]Wang Y,Liu Q.Application of CEDA and ASPIC computer packages to the hairtail(Trichiurus japonicus)fishery in the East China Sea[J].Chinese Journal of Oceanology and Limnology,2013,31(1):92-96.
[12]张魁,陈作志.应用贝叶斯状态空间建模对东海带鱼的资源评估[J].中国水产科学,2015,22(5):1015-1026.Zhang K,Chen Z Z.Using Bayesian state-space modelling to assess Trichiurus japonicus stock in the East China Sea[J].Journal of Fishery Sciences of China,2015,22(5):1015-1026(in Chinese).
[13]李九奇,聂小杰,叶昌臣,等.基于Bayes方法的渤海渔业资源动态评析[J].自然资源学报,2012,27(4):643-649.Li J Q,Nie X J,Ye C C,et al.A stock assessment of Bohai Sea by Bayes-based Pella-Tomlinson model[J].Journal of Natural Resources,2012,27(4):643-649(in Chinese).
[14]刘尊雷,严利平,袁兴伟,等.基于多源数据的东海小黄鱼资源评估与管理[J].中国水产科学,2013,20(5):1039-1049.Liu Z L,Yan L P,Yuan X W,et al.Stock assessment of small yellow croaker in the East China Sea based on multi-source data[J].Journal of Fishery Sciences of China,2013,20(5):1039-1049(in Chinese).
[15]Wang Y B,Zheng J,Wang Z.Impacts of distorted fishery statistical data on assessments of three surplus production models[J].Chinese Journal of Oceanology and Limnology,2011,29(2):270-276.
[16]童玉和,陈新军,田思泉,等.渔业管理中生物学参考点的理论及其应用[J].水产学报,2010,34(7):1040-1050.Tong Y H,Chen X J,Tian S Q,et al.Theory and application of biological reference points in fisheries management[J].Journal of Fisheries of China,2010,34(7):1040-1050(in Chinese).
[17]ISSF.Status of the world fisheries for tuna[R].ISSF Technical Report 2017-02.Washington,DC:International Seafood Sustainability Foundation,2017:62-64.
[18]徐洁,官文江,陈新军.基于空间相关性的西北太平洋柔鱼CPUE标准化研究[J].水产学报,2015,39(5):754-760.Xu J,Guan W J,Chen X J.A study of incorporating spa-tial autocorrelation into CPUE standardization with an application to Ommastrephes bartramii in the northwest Pacific Ocean[J].Journal of Fisheries of China,2015,39(5):754-760(in Chinese).
[19]李九奇,叶昌臣,王文波,等.基于Bayes方法的东海小黄鱼资源评析[J].上海海洋大学学报,2011,20(6):873-882.Li J Q,Ye C C,Wang W B,et al.A stock assessment of small yellow croaker by Bayes-based Pella-Tomlinson model in the East China Sea[J].Journal of Shanghai Ocean University,2011,20(6):873-882(in Chinese).
[20]李纲,陈新军,官文江.基于贝叶斯方法的东、黄海鲐资源评估及管理策略风险分析[J].水产学报,2010,34(5):740-750.Li G,Chen X J,Guan W J.Stock assessment and risk analysis of management strategies for Scomber japonicus in the East China Sea and Yellow Sea using a Bayesian approach[J].Journal of Fisheries of China,2010,34(5):740-750(in Chinese).
[21]ICCAT.Report of the 2013 ICCAT north and south Atlantic albacore data preparatory meeting[R].Madrid:International Commission for the Conservation of Atlantic Tunas,2013:1-68.
[22]Zhang K,Liu Q,Kalhoro M A.Application of a delaydifference model for the stock assessment of southern Atlantic albacore(Thunnus alalunga)[J].Journal of Ocean University of China,2015,14(3):557-563.
[23]Prager M H.A suite of extensions to a nonequilibrium surplus-production model[J].Fishery Bulletin,1994,92:374-389.
[24]Fletcher R I.On the restructuring of the Pella-Tomlinson system[J].Fishery Bulletin,1978,76(3):515-521.
[25]张魁,刘群,廖宝超.基于贝叶斯方法的南大西洋长鳍金枪鱼渔业的风险评估与管理[J].中国海洋大学学报,2015,45(6):51-56.Zhang K,Liu Q,Liao B C.Risk assessment and management for the southern Atlantic albacore(Thunnus alalunga)fishery based on Bayes method[J].Periodical of Ocean University of China,2015,45(6):51-56(in Chinese).
[26]Meyer R,Millar R B.BUGS in Bayesian stock assessments[J].Canadian Journal of Fisheries and Aquatic Sciences,1999,56(6):1078-1087.
[27]Zhang Y Y,Chen Y,Wilson C.Developing and evaluat-ing harvest control rules with different biological reference points for the American lobster(Homarus americanus)fishery in the Gulf of Maine[J].ICES Journal of Marine Science,2011,68(7):1511-1524.
[28]徐海龙,陈新军,陈勇,等.死亡因素对放流明对虾群体资源变动及生物学参考点影响的量化评估[J].水产学报,2016,40(5):721-730.Xu H L,Chen X J,Chen Y,et al.Impacts of mortality factors and uncertainty on the population and biological reference points in stock Chinese shrimp fishery[J].Journal of Fisheries of China,2016,40(5):721-730(in Chinese).
[29]曹少鹏,刘群.把不确定性引入生物学参考点F0.1和Fmax的估计以评估东海带鱼渔业资源[J].南方水产,2007,3(2):42-48.Cao S P,Liu Q.Stock assessment of the hairtail(Trichiurus haumela)fishery in the East China Sea by incorporating uncertainty into the estimation of the biological reference points F0.1 and Fmax[J].South China Fisheries Science,2007,3(2):42-48(in Chinese).
[30]Lee L K,Yeh S Y.Assessment of south Atlantic albacore resource based on 1959-2005 catch and effort statistics from ICCAT[J].ICCAT Collective Volume of Sci-entific Papers,2008,62(3):870-883.
[31]ICCAT.Report of the 2011 ICCAT south Atlantic and Mediterranean albacore stock assessment sessions[J].ICCAT Collective Volume of Scientific Papers,2012,68(2):387-491.
[32]Jiao Y,Cortés E,Andrews K,et al.Poor-data and datapoor species stock assessment using a Bayesian hierarchical approach[J].Ecological Applications,2011,21(7):2691-2708.
[33]Maunder M N,Punt A E.A review of integrated analysis in fisheries stock assessment[J].Fisheries Research,2013,142:61-74.
[34]Rudd M B,Branch T A.Does unreported catch lead to overfishing?[J].Fish and Fisheries,2017,18(2):313-323.
[35]官文江,田思泉,王学昉,等.CPUE标准化方法与模型选择的回顾与展望[J].中国水产科学,2014,21(4):852-862.Guan W J,Tian S Q,Wang X F,et al.A review of methods and model selection for standardizing CPUE[J].Journal of Fishery Sciences of China,2014,21(4):852-862(in Chinese).