Based on the biological data of bigeye tuna measured from the longlining ground of the Central Atlantic Ocean from Jun.2001 to Oct.2001,this paper analyzed the bigeye tuna’s maturity stages of the gonad,feeding inten...Based on the biological data of bigeye tuna measured from the longlining ground of the Central Atlantic Ocean from Jun.2001 to Oct.2001,this paper analyzed the bigeye tuna’s maturity stages of the gonad,feeding intensity,species composition of prey,sex ratio,fork length distribution,relationships between fork length and dressed weight,fork length and round weight,round weight and dressed weight by statistic and regression methods.The results indicate: (1)Maturity at Ⅳ-Ⅵ of the gonad are dominant with the highest percentage of Ⅴ(30.11%).(2)The feeding intensity is mainly in the class 1,class 2 or class 3,totally 83.16%.(3)In the bigeye tuna’s species composition of prey,the percentage of miscellaneous fish or cephalopod is relatively high,38.05% or 30.48% respectively.Catch rate of bigeye tuna can be enhanced when cephalopod is used as the bait.(4)The male-female ratio is 2∶1.This pattern might result from elevated mortality of adult females.(5)The fork length distribution is suitable for the normal. The dominant fork length is 1.13-1.49m, 64.16%, with the mean value of 1.32m. (6)The relationship between fork length and dressed weight.(7)The relationship between fork length and round weight.If the fork length is the same,the round weight converted in this paper’s formula is a little lighter than the round weight converted in Parks’s formula concluded at 1981 of the longline bigeye tuna catch.It might be caused by the different sampling areas or sampling time.(8)The relationship between round weight and dressed weight.The round weight conversion factor in this paper is a little higher than the ICCAT.It might be caused by the different processing methods.ICCAT is recommended to adapt this paper’s conversion factor.展开更多
对渔船捕捞行为和捕捞强度空间高分辨率的估计可以作为海洋资源管理和生态脆弱性评估的重要信息。为识别远洋延绳钓渔船作业状态,该文基于2017年10-11月中西太平洋延绳钓渔船卫星船舶自动识别系统(automatic identification system,AIS...对渔船捕捞行为和捕捞强度空间高分辨率的估计可以作为海洋资源管理和生态脆弱性评估的重要信息。为识别远洋延绳钓渔船作业状态,该文基于2017年10-11月中西太平洋延绳钓渔船卫星船舶自动识别系统(automatic identification system,AIS)数据和捕捞日志数据,采用支持向量机(support vector machine,SVM)学习方法,构建了中国中西太平洋延绳钓渔船捕捞作业状态(捕捞/非捕捞)分类模型。通过计算模型分类准确率、精确率、敏感度和特异度来评价模型对渔船作业状态分类能力。结果表明,模型训练数据的准确率为95.24%(Kappa系数为0.9),验证数据的准确率为93.85%(Kappa系数为0.87)。采用构建好的模型识别2017年10月和11月中西太平洋延绳钓渔船共计125624条AIS记录数据,模型准确率在83.3%(Kappa系数为0.67)。2017年10、11月所有数据分类精确率为82.33%,灵敏度为88.32%,特异度为77.27%。渔船主要作业空间在168°E^173°E,12°S^18°S,有3个明显的作业强度较高区域。基于SVM模型和日志记录的捕捞强度信息在空间上相关性很高(r>0.98),SVM模型识别的渔船捕捞努力量空间分布特征和实际吻合。捕捞努力量与单位捕捞努力量渔获量(catch per unit of effort,CPUE)、渔获尾数、渔获质量和投钩数的相关系数分别是0.68、0.93、0.93和0.94。基于AIS信息挖掘的渔船空间捕捞努力量可用于渔业资源分析。展开更多
A survey was conducted in the equatorial area of Indian Ocean for a better understanding of the dynamics of hook depth distribution of pelagic longline fishery. We determined the relationship between hook depth and ve...A survey was conducted in the equatorial area of Indian Ocean for a better understanding of the dynamics of hook depth distribution of pelagic longline fishery. We determined the relationship between hook depth and vertical shear of current coefficieney, wind speed, hook position code, sine of wind angle, sine of angle of attack and weight of messenger weight. We identified the hook depth models by the analysis of covariance with a general linear model. The results showed that the wind effect on the hook depth can be ignored from October to November in the survey area; the surface current effect on the hook depth can be ignored; the equato- rial undercurrent is the key factor for the hook depth in Indian Ocean; and there is a negative correlation between the hook depth and vertical shear of current and angle of attack. It was also found that the deeper the hook was set, the higher hook depth shoaling was. The proposed model improves the accuracy of the prediction of hook depth, which can be used to estimate the vertical distribution of pelagic fish in water column.展开更多
文摘Based on the biological data of bigeye tuna measured from the longlining ground of the Central Atlantic Ocean from Jun.2001 to Oct.2001,this paper analyzed the bigeye tuna’s maturity stages of the gonad,feeding intensity,species composition of prey,sex ratio,fork length distribution,relationships between fork length and dressed weight,fork length and round weight,round weight and dressed weight by statistic and regression methods.The results indicate: (1)Maturity at Ⅳ-Ⅵ of the gonad are dominant with the highest percentage of Ⅴ(30.11%).(2)The feeding intensity is mainly in the class 1,class 2 or class 3,totally 83.16%.(3)In the bigeye tuna’s species composition of prey,the percentage of miscellaneous fish or cephalopod is relatively high,38.05% or 30.48% respectively.Catch rate of bigeye tuna can be enhanced when cephalopod is used as the bait.(4)The male-female ratio is 2∶1.This pattern might result from elevated mortality of adult females.(5)The fork length distribution is suitable for the normal. The dominant fork length is 1.13-1.49m, 64.16%, with the mean value of 1.32m. (6)The relationship between fork length and dressed weight.(7)The relationship between fork length and round weight.If the fork length is the same,the round weight converted in this paper’s formula is a little lighter than the round weight converted in Parks’s formula concluded at 1981 of the longline bigeye tuna catch.It might be caused by the different sampling areas or sampling time.(8)The relationship between round weight and dressed weight.The round weight conversion factor in this paper is a little higher than the ICCAT.It might be caused by the different processing methods.ICCAT is recommended to adapt this paper’s conversion factor.
文摘对渔船捕捞行为和捕捞强度空间高分辨率的估计可以作为海洋资源管理和生态脆弱性评估的重要信息。为识别远洋延绳钓渔船作业状态,该文基于2017年10-11月中西太平洋延绳钓渔船卫星船舶自动识别系统(automatic identification system,AIS)数据和捕捞日志数据,采用支持向量机(support vector machine,SVM)学习方法,构建了中国中西太平洋延绳钓渔船捕捞作业状态(捕捞/非捕捞)分类模型。通过计算模型分类准确率、精确率、敏感度和特异度来评价模型对渔船作业状态分类能力。结果表明,模型训练数据的准确率为95.24%(Kappa系数为0.9),验证数据的准确率为93.85%(Kappa系数为0.87)。采用构建好的模型识别2017年10月和11月中西太平洋延绳钓渔船共计125624条AIS记录数据,模型准确率在83.3%(Kappa系数为0.67)。2017年10、11月所有数据分类精确率为82.33%,灵敏度为88.32%,特异度为77.27%。渔船主要作业空间在168°E^173°E,12°S^18°S,有3个明显的作业强度较高区域。基于SVM模型和日志记录的捕捞强度信息在空间上相关性很高(r>0.98),SVM模型识别的渔船捕捞努力量空间分布特征和实际吻合。捕捞努力量与单位捕捞努力量渔获量(catch per unit of effort,CPUE)、渔获尾数、渔获质量和投钩数的相关系数分别是0.68、0.93、0.93和0.94。基于AIS信息挖掘的渔船空间捕捞努力量可用于渔业资源分析。
基金funded by Ministry of Agriculture of China under Project of Fishery Exploration in High Seasin 2006 (No. Z06-43)the National High Technology Research and Development Program of China (No. 2012AA092302)+1 种基金Specialized research fund for the doctoral program of higher education (No. 20113104110004)Shanghai Municipal Education Commission Innovation Project (No. 12ZZ168)
文摘A survey was conducted in the equatorial area of Indian Ocean for a better understanding of the dynamics of hook depth distribution of pelagic longline fishery. We determined the relationship between hook depth and vertical shear of current coefficieney, wind speed, hook position code, sine of wind angle, sine of angle of attack and weight of messenger weight. We identified the hook depth models by the analysis of covariance with a general linear model. The results showed that the wind effect on the hook depth can be ignored from October to November in the survey area; the surface current effect on the hook depth can be ignored; the equato- rial undercurrent is the key factor for the hook depth in Indian Ocean; and there is a negative correlation between the hook depth and vertical shear of current and angle of attack. It was also found that the deeper the hook was set, the higher hook depth shoaling was. The proposed model improves the accuracy of the prediction of hook depth, which can be used to estimate the vertical distribution of pelagic fish in water column.
