殷兴俊, 花传祥, 朱清澄. 基于几何形态测量学的秋刀鱼雌雄个体形态差异分析和鉴别[J]. 南方水产科学. DOI: 10.12131/20240077
引用本文: 殷兴俊, 花传祥, 朱清澄. 基于几何形态测量学的秋刀鱼雌雄个体形态差异分析和鉴别[J]. 南方水产科学. DOI: 10.12131/20240077
YIN Xingjun, HUA Chuanxiang, ZHU Qingcheng. Analysis of morphological differences and discrimination between female and male Cololabis saira based on geometric morphometrics[J]. South China Fisheries Science. DOI: 10.12131/20240077
Citation: YIN Xingjun, HUA Chuanxiang, ZHU Qingcheng. Analysis of morphological differences and discrimination between female and male Cololabis saira based on geometric morphometrics[J]. South China Fisheries Science. DOI: 10.12131/20240077

基于几何形态测量学的秋刀鱼雌雄个体形态差异分析和鉴别

Analysis of morphological differences and discrimination between female and male Cololabis saira based on geometric morphometrics

  • 摘要: 秋刀鱼 (Cololabis saira) 广泛分布于西北太平洋海域,是中国远洋渔业的主要捕捞对象之一。为探究其雌、雄个体间的形态差异并有效区分其性别,根据2022年5—11月西北太平洋公海采集的150尾秋刀鱼样本的鱼体图片和生物学数据,首次采用几何形态测量学的地标点法对雌、雄秋刀鱼进行形态学研究,并通过判别分析构建了性别鉴定模型。其中样本分为2批,第1批107尾用于形态分析和鉴别模型的构建,第2批43尾用于检验模型的实际应用。结果表明,在相对扭曲主成分分析中,第1和第2主成分分别解释了总变异的63.43%和11.79%,降维效果和散点图区分效果均较好。其中I型和II型地标点的累积贡献率分别为57.94%和41.83%,说明在秋刀鱼雌、雄个体的形态区分上,I型和II型地标点的作用较大,III型地标点的作用较小。薄板样条分析结果显示,秋刀鱼雌、雄个体的形态差异主要表现在眼部、躯干前部和尾部。第1批107尾秋刀鱼的判别分析和交叉验证结果显示,判别正确率分别为91.6%和88.8%。将该模型用于第2批43尾秋刀鱼的性别鉴别时,判别正确率为88.45%。综上所述,几何形态测量学分析可以作为秋刀鱼形态学研究和性别鉴别的有效方法。

     

    Abstract: Pacific saury (Cololabis saira) is widely distributed in the Northwest Pacific Ocean and is one of the main targets of Chinese pelagic operations. To investigate the morphological differences between female and male individuals and differentiate the two sexes effectively, we applied geometric morphometrics for the first time on 150 Pacific saury samples which were collected from the Northwest Pacific high seas during May–November, 2022, so as to analyze their morphology and establish a sex differentiation model via discriminant analysis with fish images and biological data. The samples were divided into two batches: Batch 1 (107 individuals) was used for morphological analysis and model construction, while Batch 2 (43 individuals) was used to test the model's practical application. The results show that in the relative warp analysis, the first and second principal components explained 63.43% and 11.79% of the total variation, respectively, with good dimensionality reduction and scatter plot separation effects. Landmark I and II contributed cumulatively to 57.94% and 41.83% of the variance, respectively, indicating that these landmark types play a significant role in discrimination between the two sexes, whereas Landmark III had lesser influence. The results of the thin-plate spline analysis show that the morphological differences between them were mainly in the eyes, the front part of the trunk and the tail. The results of discriminant analysis and cross-validation of Batch 1 showed correct discrimination rates of 91.6% and 88.8%, respectively. When applying the model to Batch 2 for sex discrimination, the correct discrimination rate was 88.45%. Thus, geometric morphometrics analysis is an effective method for morphological studies and sex identification of C. saira.

     

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