基于地统计二阶广义线性混合模型的黄海冬季小黄鱼时空分布和资源量指数估算  

Spatiotemporal distribution and the estimated abundance indices of Larimichthys polyactis in winter in the Yellow Sea based on geostatistical delta-generalized linear mixed models

作　　者：韩青鹏 单秀娟[1,3] 万荣 关丽莎[1] 金显仕 陈云龙[1] 吴强[1] HAN Qingpeng;SHAN Xiujuan;WAN Rong;GUAN Lisha;JIN Xianshi;CHEN Yunlong;WU Qiang(Key Laboratory of Sustainable Development of Marine Fisheries,Ministry of Agriculture and Rural Affairs,Shandong Provincial Key Laboratory of Fishery Resources and Ecological Environment,Yellow Sea Fisheries Research Institute,Chinese Academy of Fishery Sciences,Qingdao 266071,China;Function Laboratory for Marine Fisheries Science and Food Production Processes,Qingdao National Laboratory for Marine Science and Technology,Qingdao 266237,China;College of Fisheries,Ocean University of China,Qingdao 266003,China;College of Marine Sciences,Shanghai Ocean University,Shanghai 201306,China)

机构地区：[1]中国水产科学研究院黄海水产研究所,农业农村部海洋渔业可持续发展重点实验室,山东省渔业资源与生态环境重点实验室,山东青岛266071 [2]青岛海洋科学与技术国家实验室,海洋渔业科学与食物产出过程功能实验室,山东青岛266237 [3]中国海洋大学水产学院,山东青岛266003 [4]上海海洋大学海洋科学学院,上海201306

出　　处：《水产学报》2019年第7期1603-1614,共12页Journal of Fisheries of China

基　　金：国家重点研发计划(2017YFE0104400);国家重点基础研究发展计划(2015CB453303);国家自然科学基金(31872692);山东省泰山学者专项;青岛海洋科学与技术国家实验室鳌山人才培养计划(2017ASTCP-ES07)~~

摘　　要：使用地统计二阶广义线性混合模型(geostatistical delta-GLMM)分析了2001—2011和2015—2017年黄、渤海小黄鱼越冬群体在黄海中部、南部的空间分布,并用geostatistical delta-GLMM、基于普通克里格插值法和基于站位调查设计的扫海面积法分别估计了小黄鱼资源量指数,对geostatistical delta-GLMM相较基于普通克里格插值法和基于站位调查设计的性能进行了比较研究。结果显示,在2001和2002年,黄海越冬场主要存在北部(36°00′~37°37.5′N,123°15′~124°15′E)、中部(33°75′~36°00′N,123°15′~124°75′E)和东南部(32°00′~33°75′N,124°00′~125°15′E)3个生物量高密度区,其中中部区密度最高。从2003年开始,小黄鱼的生物量密度开始下降,北部和东南部高密度区下降程度高于中部高密度区;至2016—2017年高密度区变得不明显。冬季小黄鱼总资源量指数与小黄鱼的年产量、渔船功率变化趋势相反,呈下降趋势,且大部分年份站位数在37站以上,站位范围覆盖了本实验区域,可排除采样站位因素,这说明小黄鱼资源仍面临过度捕捞,种群处于衰退状态。研究表明,地统计二阶广义线性混合模型估计的2001—2017年冬季黄海中部、南部小黄鱼的总资源量指数相对扫海面积法和普通克里格法的估计值精确度更高。Larimichthys polyactis is an important economic bottom fish in China. Understanding the spatial and temporal distribution and abundance index of L. polyactis contributes to the scientific management of L. polyactis resources. In this study, the Geostatistical delta-generalized linear mixed models (geostatistical delta-GLMM) was used to analyze the spatial distribution of the wintering population of the Yellow Sea and Bohai Sea L. polyactis from 2001 to 2011 and from 2015 to 2017 in the central and southern Yellow Sea. The biomass index of L. polyactis was estimated by geostatistical delta-GLMM, ordinary Kriging interpolation-based method and designbased swept area method, respectively. The results showed that there were mainly three high-density areas of biomass areas in the overwintering ground of the Yellow Sea in 2001 and 2002, namely, the north (36°00′- 37°37.5′N, 123°15′-124°15′E), the central (33°75′-36°00′N, 123°15′-124°75′E) and the southeast (32°00′-33°75′N, 124°00′-125°15′E), and the central region had the highest density. Since 2003, the biomass density of L. polyactis has declined, and the density core area in the north and southeast has declined more than the central density core area. In winter, the total biomass index of L. polyactis is contrary to the change trend of the annual yield of L. polyactis and fishing boat power, showing a downward trend. In most years, the number of stations is more than 37, and the range of stations covers the study area. The factors of sampling stations can be excluded, which indicates that the resources of L. polyactis are still facing overfishing and the population is in a declining state. By 2016-2017, the density core area became less obvious. The geostatistical delta-GLMM estimated the total biomass index of the L. polyactis in the winter of 2001-2017 is more accurate than that estimated by the swept area method and ordinary Kriging interpolation-based method.

关 键 词：小黄鱼 总资源量指数 时空分布 地统计二阶广义线性混合模型 黄海 

分 类 号：S931.4[农业科学—渔业资源]