不同空间位置的中西太平洋鲣资源变动趋势及预测  

Analysis of trends of skipjack tuna(Katsuwonus pelamis)resources in different spatial locations in the Central and Western Pacific

作　　者：王爽 许振琦 汪金涛[1,2,3,4,5] 雷林[1,2,3,4,5] 吕泽华 陈新军[1,2,3,4,5] 贺海平[6] 贾海滨 陈炯杰 WANG Shuang;XU Zhenqi;WANG Jintao;LEI Lin;LÜZehua;CHEN Xinjun;HE Haiping;JIA Haibin;CHEN Jiongjie(College of Marine Sciences,Shanghai Ocean University,Shanghai 201306,China;Key Laboratory of Oceanic Fisheries Exploration,Ministry of Agriculture and Rural Affairs,Shanghai Ocean University,Shanghai 201306,China;National Engineering Research Center for Oceanic Fisheries,Shanghai Ocean University,Shanghai 201306,China;Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources,Ministry of Education,Shanghai Ocean University,Shanghai 201306,China;Scientific Observing and Experimental Station of Oceanic Fishery Resources,Ministry of Agriculture and Rural Affairs,Shanghai Ocean University,Shanghai 201306,China;Dayangshijia(Zhejiang)Co.,Ltd.,Zhoushan 316000,China)

机构地区：[1]上海海洋大学海洋科学学院,上海201306 [2]上海海洋大学,农业农村部大洋渔业开发重点实验室,上海201306 [3]上海海洋大学,国家远洋渔业工程技术研究中心,上海201306 [4]上海海洋大学,大洋渔业资源可持续开发教育部重点实验室,上海201306 [5]上海海洋大学,农业农村部大洋渔业资源环境科学观测实验站,上海201306,上海201306 [6]大洋世家(浙江)股份公司,浙江舟山316000

出　　处：《水产学报》2025年第4期96-106,共11页Journal of Fisheries of China

基　　金：国家重点研发计划(2023YFD2401303)。

摘　　要：【目的】分析基于空间位置的中西太平洋鲣资源丰度变化趋势,并构建预测模型。【方法】本实验以5°×5°空间分辨率为1个研究网格,分别计算130°E~140°W、20°N~20°S海域内总共144个网格1990—2019年内的渔获量总和,选取渔获量前10个网格海区为分析对象(占研究海域内总渔获量的70%),利用动态因子分析法将10个网格的资源量时间序列变化趋势降维为2个时间序列变化趋势,计算载荷因子值,确定10个网格分别对应的2个时间序列趋势,利用相对重要分析法确定海表距平值、海表温度及混合层深度3个关键环境因子对鲣丰度2个趋势的贡献率,并分别构建2个时间序列趋势的SARIMA模型,预测中西太平洋鲣未来3年的资源丰度。【结果】中西太平洋鲣高产海区范围为5°S~5°N、145°~180°E;第一个时间序列变化趋势在空间上对应的海域是5°S~5°N和145°~160°E,第二个时间序列变化趋势在空间上对应的海域是0°~5°S和160°~180°E,两个共同趋势在空间上的分布是以160°E为分界线;两个时间序列趋势的季节性变化明显,上半年资源量高于下半年;近年来,第一时间序列趋势(空间分布上对应赤道太平洋西部)资源量不断减少,第二时间序列趋势(空间分布上对应赤道太平洋东部)资源量不断增加;海表面温度距平值对鲣资源量贡献率最大;针对两个共同趋势,SARIMA(9,1,0)(1,0,1)[12]和SARIMA(2,1,1)(1,0,1)的AIC和RMSE最小,分别为607.45和0.86、595.27和0.64,模型预测拟合度较好,预测精度较高。【结论】2020—2023年,赤道太平洋东部的鲣资源量的增长呈上升趋势,而赤道太平洋西部的鲣资源量将呈下降趋势。This research took 5°×5°spatial resolution as a research grid to analyze the trends in abundance of skipjack tuna(Katsuwonus pelamis)resources in the Central and Western Pacific Ocean based on spatial location and to construct a prediction model.Meanwhile,the total catch sum of 144 grids within 130°E-140°W and 20°N-20°S sea areas from 1990 to 2019 were calculated,the top ten grids sea areas for analysis(accounting for 70%of the total catch in the study sea area)were selected,and the dynamic factor analysis method to reduce the 10 grids were used.We used dynamic factor analysis to downscale the time series trends of the 10 grids into two time series trends,determined the two time series trends corresponding to each of the 10 grids according to the factor loadings,explored the contribution of three key environmental factors,namely sea surface spacing,sea surface temperature and mixed layer depth,to the two trends of K.pelamis abundance using relative importance analysis,and constructed SARIMA models for each of the two time series trends to predict the abundance of K.pelamis in the Central and Western.The SARIMA model was constructed to predict the resource abundance of K.pelamis in the Central and Western Pacific Ocean in the next three years.The range of the highly productive sea area of bonito in the Central and Western Pacific Ocean was 5°S-5°N,145°-180°E;the first time series trend spatially corresponded to the sea area of 5°S-5°N,145°-160°E and the second time series trend spatially corresponded to the sea area of 0°-5°S,160°-180E°.The distribution of the two common trends in space was with 160°E as the dividing line.The seasonal variation of the two time series trends was obvious,with higher resources in the first half of the year than in the second half of the year.In recent years,the first time series trend(spatially distributed corresponding to the western equatorial Pacific Ocean)had been decreasing and the second time series trend(spatially distributed corresponding to the eastern eq

关 键 词：鲣 动态因子分析 相对重要分析 SARIMA模型 中西太平洋 

分 类 号：S932.4[农业科学—渔业资源]