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
实际种群分析法（virtual population analysis,VPA）是开展渔业资源评估最有效的技术之一,一般以世代为基础开展评估。基于实际渔业存在渔汛期、休渔期等特点,本研究运用分期评估的概念对传统实际种群分析进行了扩展,即分期种群分析法...实际种群分析法（virtual population analysis,VPA）是开展渔业资源评估最有效的技术之一,一般以世代为基础开展评估。基于实际渔业存在渔汛期、休渔期等特点,本研究运用分期评估的概念对传统实际种群分析进行了扩展,即分期种群分析法,并根据不同时期的捕捞死亡特征,评估与分析了4种不同分期情景对评估结果的影响。模拟研究表明,由于分期不当造成评估结果的误差为6％～33％。文中一并给出了开展分期实际种群分析法对资料收集和参数评估的要求。该方法克服了传统实际种群分析法中没有全面分期产生的误差,使其扩展至适合于评估全年捕捞死亡率不稳定或非连续性渔业种群,评估结果也更接近于评估种群的真实值。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.
文摘实际种群分析法（virtual population analysis,VPA）是开展渔业资源评估最有效的技术之一,一般以世代为基础开展评估。基于实际渔业存在渔汛期、休渔期等特点,本研究运用分期评估的概念对传统实际种群分析进行了扩展,即分期种群分析法,并根据不同时期的捕捞死亡特征,评估与分析了4种不同分期情景对评估结果的影响。模拟研究表明,由于分期不当造成评估结果的误差为6％～33％。文中一并给出了开展分期实际种群分析法对资料收集和参数评估的要求。该方法克服了传统实际种群分析法中没有全面分期产生的误差,使其扩展至适合于评估全年捕捞死亡率不稳定或非连续性渔业种群,评估结果也更接近于评估种群的真实值。
