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应用生态学报 ›› 2024, Vol. 35 ›› Issue (11): 3165-3173.doi: 10.13287/j.1001-9332.202411.029

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基于耳石形态判别舟山海域条石鲷自然群体和养殖群体

王嘉浩, 朱凯, 徐开达*, 王好学, 陈睿毅, 曾佳颖   

  1. 浙江省海洋水产研究所/农业农村部重点渔场渔业资源环境科学观测实验站/浙江省海洋渔业资源可持续利用技术研究重点实验室, 浙江舟山 316021
  • 收稿日期:2024-04-30 修回日期:2024-08-26 出版日期:2024-11-18 发布日期:2025-05-18
  • 通讯作者: *E-mail: xkd1981@163.com
  • 作者简介:王嘉浩, 男, 1998年生, 硕士研究生。主要从事渔业资源研究。E-mail: wjh1187969399wjh@126.com
  • 基金资助:
    国家重点研发计划项目(2019YFD0901204,2019YFD0901205)、浙江省重点研发计划项目(2019C02056)、浙江省公益技术应用研究项目(LGN21C190005)和浙江省渔业资源调查专项(HYS-CZ-202405)

Discrimination of natural and cultured Oplegnathus fasciatus populations in Zhoushan sea area based on otolith morphology.

WANG Jiahao, ZHU Kai, XU Kaida*, WANG Haoxue, CHEN Ruiyi, ZENG Jiaying   

  1. Zhejiang Marine Fisheries Research Institute/Ministry of Agriculture and Rural Affairs Scientific Observation and Experimental Station of Fishery Resources of Key Fishing Grounds/Zhejiang Province Key Laboratory of Sustainable Utilization of Technology Research for Fisheries Resources, Zhoushan 316021, Zhejiang, China
  • Received:2024-04-30 Revised:2024-08-26 Online:2024-11-18 Published:2025-05-18

摘要: 为有效判别条石鲷自然群体和养殖群体,本研究基于174尾随机样品(自然群体100尾、养殖群体74尾),利用传统统计分析和神经网络方法对自然和养殖两个群体耳石的6个形状指标和21个框架指标进行研究。结果表明: 形状指标中椭圆率、圆度和幅形比在两个群体间差异显著,框架指标中有12个指标差异显著。判别结果表明,传统统计分析和神经网络方法基于形状指标的判别正确率分别为57.5%和81.4%,基于框架指标的判别正确率分别为69.5%和85.4%。研究表明,与传统统计分析方法相比,基于耳石形态的神经网络判别方法更能有效区分条石鲷自然群体和养殖群体。

关键词: 条石鲷, 群体判别, 耳石, 逐步判别分析, 神经网络

Abstract: To effectively distinguish natural and cultured populations of Oplegnathus fasciatus, we used both traditional statistical analysis and neural network methods to compare six shape indices and twenty-one truss indices from otoliths of 174 randomly selected specimens (100 from a natural population and 74 from a cultured population). The results showed that among the six shape indices, ellipticity, roundness, and aspect ratio exhibited significant differences between the two populations. Twelve out of the twenty-one truss indices displayed significant differences. Results of discriminant analysis indicated that traditional statistical analysis and neural network methods achieved correct discrimination rates of 57.5% and 81.4% for shape indices, while for truss indices were 69.5% and 85.4%, respectively. These findings indicated that neural network technique is more effectively than traditional statistical method to distinguish natural and cultured populations of O. fasciatus.

Key words: Oplegnathus fasciatus, population discrimination, otolith, stepwise discriminant analysis, neural network