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莱州湾鱼类群落的关键种
引用本文:杨涛,单秀娟,金显仕,陈云龙,吴强,刘文辉.莱州湾鱼类群落的关键种[J].水产学报,2016,40(10):1613-1623.
作者姓名:杨涛  单秀娟  金显仕  陈云龙  吴强  刘文辉
作者单位:1. 中国水产科学研究院黄海水产研究所,农业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;上海海洋大学海洋科学学院,上海201306;2. 中国水产科学研究院黄海水产研究所,农业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;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266235;3. 中国水产科学研究院黄海水产研究所,农业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;4. 青岛市崂山区海洋与渔业局,山东青岛,266061
基金项目:国家重点基础研究发展计划(2015CB453303);鳌山科技创新计划(2015ASKJ02-05);基本科研业务费(20603022016003);山东省泰山学者专项
摘    要:关键种对群落结构稳定性起着决定作用,它的筛选对于整个生态系统的研究都具有重要的理论和实际意义。本研究基于2011年5月对莱州湾渔业底拖网数据,以摄食关系为基础构建了莱州湾鱼类群落种间相互作用关系网,运用网络分析法计算了该关系网的13种重要性指数及Key Player Problem参数(F、DF和DR)。根据13种指数的排序结果、聚类信息和3个Key Player Problem参数,对莱州湾鱼类群落的关键种进行了筛选。结果显示,13种重要性指数可划分为4个信息组:a(D、CC、IC、TI~1和TI~7)即基本信息组、b(D_(in)、H_(in)和K_t)即信息输入组、c(D_(out)、H_(out)和K_b)即信息输出组和d(BC和K)即信息控制组。细纹狮子鱼(D、D_(in)、BC、CC、IC、H_(in)、TI~1、TI~7、K、K_t、F、~DF和~DR)和六丝矛尾虾虎鱼(D_(out)、H_(out)和K_b)在莱州湾鱼类群落的网络分析结果中处于最高地位,密切联系着群落的其他种群,控制着群落的结构和能流,属于群落的关键种,其中细纹狮子鱼是关键捕食者,控制着群落中其他重要食物竞争者和捕食者密度,六丝矛尾虾虎鱼是关键被捕食者,通过维持捕食者的密度来限制其他被捕食者的密度。

关 键 词:关键种  食物网  网络分析法  莱州湾
收稿时间:2015/12/10 0:00:00
修稿时间:6/5/2016 12:00:00 AM

Keystone species of fish community in the Laizhou Bay
YANG Tao,SHAN Xiujuan,JIN Xianshi,CHEN Yunlong,WU Qiang and LIU Wenhui.Keystone species of fish community in the Laizhou Bay[J].Journal of Fisheries of China,2016,40(10):1613-1623.
Authors:YANG Tao  SHAN Xiujuan  JIN Xianshi  CHEN Yunlong  WU Qiang and LIU Wenhui
Institution:Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture;Shandong Provincial Key Laboratory of Fishery Resources and Eco-environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071;College of Marine Sciences, Shanghai Ocean University, Shanghai 201306,Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture;Shandong Provincial Key Laboratory of Fishery Resources and Eco-environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071;Function Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266235,Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture;Shandong Provincial Key Laboratory of Fishery Resources and Eco-environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071;Function Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266235,Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture;Shandong Provincial Key Laboratory of Fishery Resources and Eco-environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071,Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture;Shandong Provincial Key Laboratory of Fishery Resources and Eco-environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071;Function Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266235 and Ocean and Fishery Bureau of Laoshan District of Qingdao City, Qingdao 266061
Abstract:Keystone species play a decisive role in community structure and stability, and the key species study has both important theoretical and practical significance for the entire ecosystem. In this paper, we tried to screen out the fishery key species from the fish community in the Laizhou Bay based on the bottom trawl survey data in May, 2011. Firstly, we established a relationship network of the 24 fish species according to the predation relationships among fish populations. Then 13 network important indices were calculated using the Ucinet 6 and CoSBiLab Graph1.0 software. A hierarchical clustering was conducted to show the relationships between 13 network indices, and 3 Key Player Problem parameters were calculated using keyplayer1.44 programme. The result showed these 13 indices can be divided into 4 different information groups: a (D, CC, IC, TI1 and TI7), basic information group; b (Din, Hin and Kt), input information group; c (Dout, Hout and Kb), output information group; d (BC and K), control information group. The species were identified as the key species based on the information of 13 indices and 3 Key Player Problem parameters. Liparis tanakae (D, Din, BC, CC, IC, Hin, TI1, TI7, K, Kt, F, DF and DF) and Amblychaeturichthys hexanema (Dout, Hout and Kb) had the highest rank among the 24 study species, and closely tied to others in the fish community, controlled the structure and energy flow of the community, indicating that they were the key species in fish community in the Layzhou Bay. And L. tanakae was the key predator which could control the density of the predators and other competitors in fish community while A. hexanema was the key prey which could restrict the density of other prey species by maintaining the density of the predator.
Keywords:keystone species  food web  network analysis method  Laizhou Bay
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