Natural mortality coefficient (M) was estimated from fish abundance (N) and catch (C) data using a Virtual Population Analysis (VPA) model. Monte Carlo simulations were used to evaluate the impact of different error d...Natural mortality coefficient (M) was estimated from fish abundance (N) and catch (C) data using a Virtual Population Analysis (VPA) model. Monte Carlo simulations were used to evaluate the impact of different error distributions for the simulated data on the estimates of M. Among the four error structures (normal, lognormal, Poisson and gamma), simulations of normally dis-tributed errors produced the most viable estimates for M, with the lowest relative estimation errors (REEs) and median mean absolute deviations (MADs) for the ratio of the true to the estimated Ms. In contrast, the lognormal distribution had the largest REE value. Errors with different coefficients of variation (CV) were added to N and C. In general, when CVs in the data were less than 10%, reliable estimates of M were obtained. For normal and lognormal distributions, the estimates of M were more sensitive to the CVs in N than in C; when only C had error the estimates were close to the true. For Poisson and gamma distributions, opposite results were obtained. For instance, the estimates were more sensitive to the CVs in C than in N, with the largest REE from the scenario of error only in C. Two scenarios of high and low fishing mortality coefficient (F) were generated, and the simulation results showed that the method performed better for the scenario with low F. This method was also applied to the published data for the anchovy (Engraulis japonicus) of the Yellow Sea. Viable estimates of M were obtained for young groups, which may be explained by the fact that the great uncertainties in N and C observed for older Yellow Sea anchovy introduced large variation in the corresponding estimates of M.展开更多
It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in dat...It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in data-poor situations through borrowing strength from prior information deduced from species with good-quality data or other known information. Because there is considerable uncertainty remaining in the stock assessment of albacore tuna（Thunnus alalunga） in the Indian Ocean due to the limited and low-quality data, we investigate the advantages of a Bayesian method in data-poor stock assessment by using Indian Ocean albacore stock assessment as an example. Eight Bayesian biomass dynamics models with different prior assumptions and catch data series were developed to assess the stock. The results show（1） the rationality of choice of catch data series and assumption of parameters could be enhanced by analyzing the posterior distribution of the parameters;（2） the reliability of the stock assessment could be improved by using demographic methods to construct a prior for the intrinsic rate of increase（r）. Because we can make use of more information to improve the rationality of parameter estimation and the reliability of the stock assessment compared with traditional statistical methods by incorporating any available knowledge into the informative priors and analyzing the posterior distribution based on Bayesian framework in data-poor situations, we suggest that the Bayesian method should be an alternative method to be applied in data-poor species stock assessment, such as Indian Ocean albacore.展开更多
为了解日本海和东海带鱼(Trichiurus japonicus)渔业资源状况,利用一种基于丰度指数的评估模型(Abundance maximum sustainable yields,AMSY)对2个海域的带鱼资源进行了评估。结果显示,2个海域的带鱼渔业在20世纪90年代至21世纪00年代...为了解日本海和东海带鱼(Trichiurus japonicus)渔业资源状况,利用一种基于丰度指数的评估模型(Abundance maximum sustainable yields,AMSY)对2个海域的带鱼资源进行了评估。结果显示,2个海域的带鱼渔业在20世纪90年代至21世纪00年代的大部分时间里相对资源量水平(B/BMSY)低于1,而相对捕捞死亡水平(F/FMSY)高于1,处于过度捕捞状态。AMSY模型里相对最大可持续产量(MSYq)和MSY水平下的捕捞死亡系数(FMSY)等参数评估结果对不同内禀增长率(r)先验分布范围比较敏感,而B/BMSY和F/FMSY等生物学参考点评估结果对特定年份的相对资源量水平(Bt/k)先验分布范围的下限设置比较敏感。AMSY模型仅需要单位捕捞努力量渔获量(Catch per unit effort,CPUE)数据、评估对象的r和特定年份的Bt/k水平先验信息,可以评估基于MSY的生物学参考点(B/BMSY和F/FMSY),适合无统计产量数据的海域内渔业资源评估。展开更多
文摘Natural mortality coefficient (M) was estimated from fish abundance (N) and catch (C) data using a Virtual Population Analysis (VPA) model. Monte Carlo simulations were used to evaluate the impact of different error distributions for the simulated data on the estimates of M. Among the four error structures (normal, lognormal, Poisson and gamma), simulations of normally dis-tributed errors produced the most viable estimates for M, with the lowest relative estimation errors (REEs) and median mean absolute deviations (MADs) for the ratio of the true to the estimated Ms. In contrast, the lognormal distribution had the largest REE value. Errors with different coefficients of variation (CV) were added to N and C. In general, when CVs in the data were less than 10%, reliable estimates of M were obtained. For normal and lognormal distributions, the estimates of M were more sensitive to the CVs in N than in C; when only C had error the estimates were close to the true. For Poisson and gamma distributions, opposite results were obtained. For instance, the estimates were more sensitive to the CVs in C than in N, with the largest REE from the scenario of error only in C. Two scenarios of high and low fishing mortality coefficient (F) were generated, and the simulation results showed that the method performed better for the scenario with low F. This method was also applied to the published data for the anchovy (Engraulis japonicus) of the Yellow Sea. Viable estimates of M were obtained for young groups, which may be explained by the fact that the great uncertainties in N and C observed for older Yellow Sea anchovy introduced large variation in the corresponding estimates of M.
基金The Innovation Program of Shanghai Municipal Education Commission under contract No.14ZZ147the Opening Project of Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources(Shanghai Ocean University),Ministry of Education under contract No.A1-0209-15-0503-1
文摘It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in data-poor situations through borrowing strength from prior information deduced from species with good-quality data or other known information. Because there is considerable uncertainty remaining in the stock assessment of albacore tuna（Thunnus alalunga） in the Indian Ocean due to the limited and low-quality data, we investigate the advantages of a Bayesian method in data-poor stock assessment by using Indian Ocean albacore stock assessment as an example. Eight Bayesian biomass dynamics models with different prior assumptions and catch data series were developed to assess the stock. The results show（1） the rationality of choice of catch data series and assumption of parameters could be enhanced by analyzing the posterior distribution of the parameters;（2） the reliability of the stock assessment could be improved by using demographic methods to construct a prior for the intrinsic rate of increase（r）. Because we can make use of more information to improve the rationality of parameter estimation and the reliability of the stock assessment compared with traditional statistical methods by incorporating any available knowledge into the informative priors and analyzing the posterior distribution based on Bayesian framework in data-poor situations, we suggest that the Bayesian method should be an alternative method to be applied in data-poor species stock assessment, such as Indian Ocean albacore.
文摘为了解日本海和东海带鱼(Trichiurus japonicus)渔业资源状况,利用一种基于丰度指数的评估模型(Abundance maximum sustainable yields,AMSY)对2个海域的带鱼资源进行了评估。结果显示,2个海域的带鱼渔业在20世纪90年代至21世纪00年代的大部分时间里相对资源量水平(B/BMSY)低于1,而相对捕捞死亡水平(F/FMSY)高于1,处于过度捕捞状态。AMSY模型里相对最大可持续产量(MSYq)和MSY水平下的捕捞死亡系数(FMSY)等参数评估结果对不同内禀增长率(r)先验分布范围比较敏感,而B/BMSY和F/FMSY等生物学参考点评估结果对特定年份的相对资源量水平(Bt/k)先验分布范围的下限设置比较敏感。AMSY模型仅需要单位捕捞努力量渔获量(Catch per unit effort,CPUE)数据、评估对象的r和特定年份的Bt/k水平先验信息,可以评估基于MSY的生物学参考点(B/BMSY和F/FMSY),适合无统计产量数据的海域内渔业资源评估。
