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中国沿海长蛸群体形态性状的差异
引用本文:高晓蕾,许然,张志新,郑小东.中国沿海长蛸群体形态性状的差异[J].水产学报,2019,43(7):1593-1602.
作者姓名:高晓蕾  许然  张志新  郑小东
作者单位:中国海洋大学, 海洋生物多样性与进化研究所, 山东 青岛 266003,中国海洋大学, 海洋生物多样性与进化研究所, 山东 青岛 266003;中国海洋大学, 海水养殖教育部重点实验室, 山东 青岛 266003,山东威海市荣成市海洋与渔业局, 山东 威海 264300,中国海洋大学, 海洋生物多样性与进化研究所, 山东 青岛 266003;中国海洋大学, 海水养殖教育部重点实验室, 山东 青岛 266003
基金项目:国家自然科学基金(31672257);中央高校基本科研业务费专项(201822022)
摘    要:中国海域跨度大,生态环境差别显著,栖息在不同地理位置的长蛸群体在形态特征上存在差异。实验采用主成分分析、判别分析、单因素方差分析及逐步判别分析的多元分析方法对中国沿海长蛸11个自然群体的14个形态指标进行了综合分析,并建立了群体判别函数。结果显示,长蛸腕式为1234,腕间膜式为ABCDE。主成分分析构建了2个主成分,累计贡献率达66.067%。主成分1主要包括胴宽/体长、头宽/体长、漏斗后部长/体长3个参数,累计贡献率达36.979%;主成分2主要包括体质量/体长、胴背长/体长、漏斗前部长/体长3个参数,累计贡献率达29.088%。基于主成分散点图可以清晰区分澎湖群体与其他群体。依据判别函数,群体综合判别准确率为60.40%。单因素方差分析显示,腕吸盘数目在澎湖群体与其他群体之间存在显著差异。聚类分析显示,11个群体中北方5个群体与宜兰群体先聚为一支,再与嵊泗、南麂岛、连江3个南方群体聚成一支,之后与澎湖群体聚成一支,泉港群体单独聚成一支。研究表明,北方5个群体间形态相似度较大,且与南方群体差异显著;澎湖和泉港群体与其他群体之间存在较大的形态差异。中国沿海11个长蛸群体在形态上存在一定地域差异性,形态多样性较高。

关 键 词:长蛸  形态多样性  群体差异  环境差异  多元分析
收稿时间:2018/11/2 0:00:00
修稿时间:2019/4/13 0:00:00

Morphological variation analysis of Octopus minor inthe coastal waters of China
GAO Xiaolei,XU Ran,ZHANG Zhixin and ZHENG Xiaodong.Morphological variation analysis of Octopus minor inthe coastal waters of China[J].Journal of Fisheries of China,2019,43(7):1593-1602.
Authors:GAO Xiaolei  XU Ran  ZHANG Zhixin and ZHENG Xiaodong
Institution:Institute of Evolution and Marine Biodiversity, Ocean University of China, Qingdao 266003, China,Institute of Evolution and Marine Biodiversity, Ocean University of China, Qingdao 266003, China;Key Laboratory of Mariculture of Ocean University of China, Ministry of Education, Qingdao 266003, China,Ocean and Fisheries Bureau of Rongcheng, Weihai 264300, China and Institute of Evolution and Marine Biodiversity, Ocean University of China, Qingdao 266003, China;Key Laboratory of Mariculture of Ocean University of China, Ministry of Education, Qingdao 266003, China
Abstract:The wide coverage of the north and south seas of China makes the ecological environment geographically different. Octopus minor inhabiting different sea areas thus formed corresponding apparent differences in morphology. A multi-variate analysis method including the principal component analysis, discriminant analysis, One-Way ANOVA and stepwise discriminant analysis was used to comprehensively analyze the 14 morphological indexes of 11 natural O. minor populations along the coasts of China and a population discriminant function was established. The results showed that the arm formula of O. minor was 1 > 2 > 3 > 4 and the web formula was A > B > C > D > E. Principal component analysis showed two principal components were constructed with a cumulative contribution rate of 66.067%. The first principal component included the MW/TL, HW/TL and FFL/TL, with their contribution rate of 36.979%, while the second principal component contained the TWt/TL, ML/TL and FL/TL with the contributory rate of 29.088%. Importantly, the Penghu group can be clearly distinguished based on the scatter plots of principal component analysis. The integrative discrimination accuracy was 60.400% based on the discriminant functions. One-Way analysis of variance (ANOVA) implied that there are significant differences between the number of suckers in the arms of Penghu and the other populations. The cluster analysis revealed that five northern populations and the Yilan population grouped together at first, then clustered with Shengsi, Nanjidao and Lianjiang, then finally clustered with Penghu, while Quangang formed into a separated group. All of those indicated that five northern populations showed high morphological similarity while it was not the case for the southern populations. And there are significant variations between the northern and southern populations. Especially Penghu and Quangang populations exhibited/showed relatively large differences with the others. In summary, eleven O. minor populations in the coastal Chinese sea exhibited significant morphological divergence, and the morphological diversity was relatively high.
Keywords:Octopus minor  morphological diversity  population variations  geographic difference  multi-variate analysis
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