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引用本文:王开杰,徐永江,崔爱君,姜燕,王滨,柳学周.三种鰤属(Seriola)鱼类表型特征比较分析.海洋与湖沼,2023,54(1):214-224.
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三种鰤属(Seriola)鱼类表型特征比较分析
王开杰, 徐永江, 崔爱君, 姜燕, 王滨, 柳学周
中国水产科学研究院黄海水产研究所 青岛海洋科学与技术试点国家实验室深蓝渔业工程联合实验室 山东青岛 266071
摘要:
为全面了解鰤属鱼类表型性状的种间差异,描述了我国养殖的黄条鰤(Seriola lalandi)、高体鰤(Seriola dumerili)和五条鰤(Seriola quinqueradiata)的形态学特征,测量了群体的表型数据,通过单因素方差分析、通径分析、主成分分析、判别分析和聚类分析等多元统计方法,对3种鰤鱼共计190尾样品的形态种质特征和可量可数性状进行了比较研究。单因素方差分析显示,除叉长/体长(FL/SL)、体长/尾柄长SL/PL)、头长/眼间距(HL/ID)和下颌长/上颌长(LJL/UJL)外,其余9个指标均存在显著性差异(P<0.05)。通径分析显示,黄条鰤的体长(SL)、体高(BH)、尾柄长(PL)和尾柄高(PD)是主要影响其体质量的4个表型性状;高体鰤的体长(SL)、体高(BH)、眼间距(ID)、下颌长(LJL)和眼后头长(POL)是主要影响其体质量的5个表型性状;五条鰤的体高(BH)、下颌长(LJL)和全长(TL)是主要影响其体质量的3个表型性状。主成分分析构建了4个主成分,其中第一项主成分贡献率为34.974%,其他3个主成分的贡献率依次为23.897%、11.587%、9.489%,累积方差贡献率为79.947%。判别分析显示,3种鰤鱼的判别准确率P1P2均为100%,综合判别率为100%。聚类分析显示,黄条鰤和五条鰤聚为一个分支,之后再与高体鰤聚为一大支。研究结果可为建立鰤属鱼类种类鉴别、种质标准和开展遗传育种研究提供表型参数的判别依据。
关键词:  鰤鱼  表型特征  通径分析  多变量形态度量学  多元统计分析
DOI:10.11693/hyhz20220400110
分类号:Q789;Q954;S965
基金项目:国家重点研发计划项目,2022YFD2401100号,2019YFD0900901号;中国水产科学研究院基本科研业务费,2020TD47号;国家海洋水产种质资源库项目,2021-2025;财政部和农业农村部:国家现代农业产业技术体系资助,CARS-47号。
附件
COMPARATIVE ANALYSIS OF MORPHOLOGICAL CHARACTERISTICS OF THREE SERIOLA SPECIES
WANG Kai-Jie, XU Yong-Jiang, CUI Ai-Jun, JIANG Yan, WANG Bin, LIU Xue-Zhou
Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Joint Laboratory for Deep Blue Fishery Engineering of Pilot National Laboratory for Marine Science and Technology(Qingdao), Qingdao 266071, China
Abstract:
To reveal the interspecific differences of phenotypic characteristics among three Seriola species, the morphology of farmed Seriola lalandi, Seriola dumerili, and Seriola quinqueradiata in China was analyzed. The countable and morphometrical characteristics were measured and compared, and the relationship and difference were determined using multivariate statistical methods including one-way ANOVA, path analysis, principal component analysis, discriminant analysis and cluster analysis. A total of 190 experimental fish were used. Results of one-way ANOVA show that there were significant differences among all the morphometric indexes (P<0.05) except for fork length/soma length (FL/SL), soma length/caudal peduncle length (SL/PL) and head length/eye diameter (HL/ID), and mandibular length/maxillary length (LJL/UJL). In addition, four phenotypic traits (SL, body height (BH), caudal peduncle length (PL), and caudal peduncle height (PD)) are the main traits affecting body weight in S. lalandi as path analysis shown. Five phenotypic traits, soma length (SL, BH, eye diameter (ID), mandibular length (LJL) and head length behind the eyes (POL) in S. dumerili were the main traits affecting their body weight. Three phenotypic traits (BH, LJL, and total length (TL)) were the main traits affecting the body weight of S. quinqueradiata. Four principal components were constructed by principal component analysis, of which the first principal component contributed 34.974%, followed by the other three principal components (23.897%, 11.587%, 9.489%, respectively), and their cumulative variance contribution rate was 79.947%. The discriminant accuracy rate of P1 and P2 of Seriola fishes was 100%, and the comprehensive discriminant rate was 100.00% as revealed by discriminant analysis. S. lalandi and S. quinqueradiata were clustered into a branch, and then were clustered into a large branch with S. dumerili. The present results provide intuitive morphometric basis for the species identity, germplasm norm establishment, and genetic breeding of Seriola fishes.
Key words:  Seriola species  phenotypic characteristics  path analysis  multivariate morphometrics  multivariate statistical analysis
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