基于改进的SURF指数甄选海州湾鱼类群落的关键捕食者

doi:10.13287/j.1001-9332.202011.040

应用生态学报 ›› 2020, Vol. 31 ›› Issue (11): 3889-3894.doi: 10.13287/j.1001-9332.202011.040

基于改进的SURF指数甄选海州湾鱼类群落的关键捕食者

徐从军^1,3, 张崇良^1,3, 徐宾铎^1,3, 任一平^1,2,3, 薛莹^1,3*

¹中国海洋大学水产学院, 山东青岛 266003;
²青岛海洋科学与技术试点国家实验室, 海洋渔业科学与食物产出过程功能实验室, 山东青岛 266237;
³海州湾渔业生态系统教育部野外科学观测研究站, 山东青岛 266003

收稿日期:2020-03-16 接受日期:2020-08-24 出版日期:2020-11-15 发布日期:2021-06-10
通讯作者: * E-mail: xueying@ouc.edu.cn
作者简介:徐从军, 男, 1995年生, 硕士研究生。主要从事摄食生态学研究。E-mail: 1007264411@qq.com
基金资助:
国家自然科学基金项目(31772852)、山东省支持青岛海洋科学与技术试点国家实验室重大科技专项(2018SDKJ0501-2)和国家重点研发计划项目(2018YFD0900904)资助

Identification of keystone predators in Haizhou Bay food web based on the revised SURF index

XU Cong-jun^1,3, ZHANG Chong-liang^1,3, XU Bin-duo^1,3, REN Yi-ping^1,2,3, XUE Ying^1,3*

¹College of Fisheries, Ocean University of China, Qingdao 266003, Shandong, China;
²Pilot National Laboratory for Marine Science and Technology (Qingdao), Functional Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao 266237, Shandong, China;
³Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, Shandong, China

Received:2020-03-16 Accepted:2020-08-24 Online:2020-11-15 Published:2021-06-10
Contact: * E-mail: xueying@ouc.edu.cn
Supported by:
the National Natural Science Foundation of China (31772852), Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) (2018SDKJ0501-2), and National Key R&D Program of China (2018YFD0900904).

摘要/Abstract

摘要： 关键捕食者在生态系统中对饵料生物的数量波动、丰富度和空间分布等都起到重要的调控作用。本研究基于在海州湾进行的5个航次的渔业资源底拖网调查资料及胃含物分析数据,通过对传统SURF指数进行改进,甄选海州湾鱼类群落中的关键捕食者。结果表明: 星康吉鳗、长蛇鲻、大泷六线鱼、小眼绿鳍鱼和小黄鱼为海州湾鱼类群落中的关键捕食者,这5种鱼类不但具有较高的连接数,还是多种生物的主要捕食者,在物种连接中具有较强的聚集效应,它们的数量波动会对生态系统能量流动和食物网结构产生较大的影响。通过本方法对关键捕食者进行甄选,不仅考虑了物种间的摄食比例,还将捕捞量及物种的资源量作为重要影响因素,与传统方法相比,具有较大的改进,为关键捕食者的甄选提供了一种新的方法。本研究还发现,物种之间的强相互作用在维护食物网的结构与功能中起着重要作用,加强对关键捕食者的保护,有利于维持生物群落的稳定性和物种多样性,在实施基于生态系统的管理时,要优先保护这些关键物种。星康吉鳗和小黄鱼作为重要的经济鱼种,承受的捕捞压力较高,尤其需要加强保护和管理。

关键词: 关键捕食者, 摄食比例, SURF指数, 海州湾

Abstract: Keystone predator species play an important role in regulating the number, richness and distribution of prey species. Based on the bottom trawl surveys in Haizhou Bay, keystone predator species in fish communities were identified by the revised SURF (supportive role to fishery ecosystems) index. Conger myriaster, Saurida elongata, Hexagrammos otakii, Chelidonichthys spinosus, and Larimichthys polyactis were the keystone predators in the fish community. They had high number of nodes degree, and were also the main predators of many species. Further, they had strong ability of clustering. The fluctuations of their density would have great impacts on ecosystem energy flow and food web structure. The method not only considered the feeding ratio between species, but also took the amount of catch and abundance of species as important factors in the identification of keystone predators. Compared with the traditional method, this method had great improvement and provided a new one for the identification of key predators. Our results showed that the strong inter-specific interactions played an important role in maintaining the structure and function of food webs. Protection of key predators would benefit the stability of biological communities and species diversity. Therefore, ecosystem-based fisheries management (EBFM) should give priority to the protection of key species. The important economic fish species, C. myriaster and L. polyactis, should be protected with high priority as they suffered from higher fishing pressure.

Key words: keystone predator, food composition, SURF index, Haizhou Bay

徐从军, 张崇良, 徐宾铎, 任一平, 薛莹. 基于改进的SURF指数甄选海州湾鱼类群落的关键捕食者[J]. 应用生态学报, 2020, 31(11): 3889-3894.

XU Cong-jun, ZHANG Chong-liang, XU Bin-duo, REN Yi-ping, XUE Ying. Identification of keystone predators in Haizhou Bay food web based on the revised SURF index[J]. Chinese Journal of Applied Ecology, 2020, 31(11): 3889-3894.

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基于改进的SURF指数甄选海州湾鱼类群落的关键捕食者

Identification of keystone predators in Haizhou Bay food web based on the revised SURF index

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