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
通过模型分析环境变量对延绳钓大眼金枪鱼渔获率的影响,评估适宜垂直活动空间对大西洋大眼金枪鱼延绳钓渔获率的作用。首先采用回归分析检验环境变量对延绳钓渔获率(由单位捕捞努力渔获量(catch per unit fishing effort,CPUE)表示)的...通过模型分析环境变量对延绳钓大眼金枪鱼渔获率的影响,评估适宜垂直活动空间对大西洋大眼金枪鱼延绳钓渔获率的作用。首先采用回归分析检验环境变量对延绳钓渔获率(由单位捕捞努力渔获量(catch per unit fishing effort,CPUE)表示)的影响显著性,结合时空变量,采用GAM(generalized additive model)模型分析各变量对大眼金枪鱼CPUE非线性作用。模型结果表明,环境因子和时空变量对热带大西洋延绳钓大眼金枪鱼渔获率空间分布影响明显。大西洋大眼金枪鱼延绳钓的高渔获率月份出现在夏季和冬季,空间上在赤道以北和30?~50?W。12℃等温线深度对大眼金枪鱼延绳钓渔获率的影响表现为抛物线形状,高渔获率出现在深度较浅的250 m水层,随着12℃等温线深度的增加,大眼金枪鱼延绳钓渔获率降低。温跃层下界深度和深度差对大眼金枪鱼延绳钓渔获率的影响都是穹顶状。随着温跃层下界深度值和深度差由小变大至200 m,延绳钓渔获率递增;温跃层下界深度和深度差超过200 m后,延绳钓渔获率变小。温跃层下界深度和深度差对大眼金枪鱼延绳钓渔获率影响显著的水层分别是200 m和50 m。研究结果显示,12℃等温线深度和温跃层对热带大西洋延绳钓大眼金枪鱼渔获率影响是交叉的,在大眼金枪鱼适宜垂直活动水层受限到和延绳钓作业深度相同时,延绳钓渔获率最高;在适宜垂直活动空间过深或者过浅时,延绳钓渔获率都变小,但可以通过改变作业方式提高渔获率。采用延绳钓CPUE进行渔场和资源评估要考虑金枪鱼适宜垂直活动空间。展开更多
Understanding the potential vertical distribution of bigeye tuna(Thunnus obesus) is necessary to understand the catch rate fluctuations and the stock assessment of bigeye tuna. To characterize the potential vertical d...Understanding the potential vertical distribution of bigeye tuna(Thunnus obesus) is necessary to understand the catch rate fluctuations and the stock assessment of bigeye tuna. To characterize the potential vertical distribution of this fish while foraging and determine the influences of the distribution on longline efficiency in the tropical Atlantic Ocean, the catch per unit effort(CPUE) data were compiled from the International Commission for the Conservation of Atlantic Tunas and the Argo buoy data were downloaded from the Argo data center. The raw Argo buoy data were processed by data mining methods. The CPUE was standardized by support vector machine before analysis. We assumed the depths with the upper and lower limits of the optimum water temperatures of 15℃ and 9℃ as the preferred swimming depth, while the lower limit of the temperature(12℃) associated with the highest hooking rate as the preferred foraging depth(D12) of bigeye tuna during the daytime in the Atlantic Ocean. The preferred swimming depth and foraging depth range in the daytime were assessed by plotting the isobath based on Argo buoy data. The preferred swimming depth and vertical structure of the water column were identified to investigate the spatial effects on the CPUE by using a generalized additive model(GAM). The empirical cumulative distribution function was used to assess the relationship between the spatial distribution of CPUE and the depth of 12℃ isolines and thermocline. The results indicate that 1) the preferred swimming depth of bigeye tuna in the tropical Atlantic is from 100 m to 400 m and displays spatial variation;2) the preferred foraging depth of bigeye tuna is between 190 and 300 m and below the thermocline;3) the number of CPUEs peaks at a relative depth of 30 –50 m(difference between the 12℃ isolines and the lower boundary of the thermocline);and 4) most CPUEs are within the lower depth boundary of the thermocline levels(LDBT) which is from 160 m to 230 m. GAM analysis indicates that the general relationship between the nominal CPUE and LDBT is characterized by a dome shape and peaks at approximately 190 m. The oceanographic features influence the habitat of tropical pelagic fish and fisheries. Argo buoy data can be an important tool to describe the habitat of oceanic fish. Our results provide new insights into how oceanographic features influence the habitat of tropical pelagic fish and fisheries and how fisheries exploit these fish using a new tool(Argo profile data).展开更多
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supported by the National Natural Science Foundation of China (No.41606138)the Special Funds of Basic Research of Central Public Welfare Institute (Nos.2019T09, 2016Z01-02)+1 种基金the National Key Research and Development Project of China (No.2019YFD 0901405)the Fund of Key Laboratory of Open-Sea Fishery Development, Ministry of Agriculture, P.R.China (No.LOF2018-01)。
文摘Understanding the potential vertical distribution of bigeye tuna(Thunnus obesus) is necessary to understand the catch rate fluctuations and the stock assessment of bigeye tuna. To characterize the potential vertical distribution of this fish while foraging and determine the influences of the distribution on longline efficiency in the tropical Atlantic Ocean, the catch per unit effort(CPUE) data were compiled from the International Commission for the Conservation of Atlantic Tunas and the Argo buoy data were downloaded from the Argo data center. The raw Argo buoy data were processed by data mining methods. The CPUE was standardized by support vector machine before analysis. We assumed the depths with the upper and lower limits of the optimum water temperatures of 15℃ and 9℃ as the preferred swimming depth, while the lower limit of the temperature(12℃) associated with the highest hooking rate as the preferred foraging depth(D12) of bigeye tuna during the daytime in the Atlantic Ocean. The preferred swimming depth and foraging depth range in the daytime were assessed by plotting the isobath based on Argo buoy data. The preferred swimming depth and vertical structure of the water column were identified to investigate the spatial effects on the CPUE by using a generalized additive model(GAM). The empirical cumulative distribution function was used to assess the relationship between the spatial distribution of CPUE and the depth of 12℃ isolines and thermocline. The results indicate that 1) the preferred swimming depth of bigeye tuna in the tropical Atlantic is from 100 m to 400 m and displays spatial variation;2) the preferred foraging depth of bigeye tuna is between 190 and 300 m and below the thermocline;3) the number of CPUEs peaks at a relative depth of 30 –50 m(difference between the 12℃ isolines and the lower boundary of the thermocline);and 4) most CPUEs are within the lower depth boundary of the thermocline levels(LDBT) which is from 160 m to 230 m. GAM analysis indicates that the general relationship between the nominal CPUE and LDBT is characterized by a dome shape and peaks at approximately 190 m. The oceanographic features influence the habitat of tropical pelagic fish and fisheries. Argo buoy data can be an important tool to describe the habitat of oceanic fish. Our results provide new insights into how oceanographic features influence the habitat of tropical pelagic fish and fisheries and how fisheries exploit these fish using a new tool(Argo profile data).
