生物学杂志

生物学杂志 ›› 2023, Vol. 40 ›› Issue (5): 54-.doi: 10.3969/j.issn.2095-1736.2023.05.054

• 研究报告 • 上一篇    下一篇

南海中南部金带细鲹与长体圆鲹矢耳石外型比较分析

李伟畅1,2， 朱国平1,4,5， 王雪辉2,6,7， 林龙山3， 李　渊3， 杜飞雁2   

  1. 1. 上海海洋大学 海洋科学学院， 上海 201306;
    2. 中国水产科学研究院南海水产研究所， 广州 510300;
    3. 自然资源部第三海洋研究所海洋生物与生态实验室， 厦门 361005;
    4. 上海海洋大学 极地研究中心， 上海 201306;
    5. 大洋渔业资源可持续开发教育部重点实验室极地海洋生态系统研究室， 上海 201306;
    6. 农业农村部海洋牧场重点实验室， 广州 510300;
    7. 国家数字渔业(海洋牧场)创新分中心， 广州 510300
  • 出版日期:2023-10-18 发布日期:2023-10-17
  • 通讯作者: 王雪辉，博士，副研究员，研究方向为渔业资源，E-mail:wxhscs@163.com
  • 作者简介:李伟畅，硕士研究生，研究方向为海洋渔业生物学，E-mail:weichang2022@126.com
  • 基金资助:
    海南省自然科学基金项目(422MS156)； 国家科技基础资源调查专项(2017FY201405，2018FY100105)； 全球变化与海气相互作用专项(GASI-02-SCS-YDsum)； 中国水产科学研究院南海水产研究所中央级公益性科研院所基本科研业务费专项（2021SD14）

Comparative analysis on otolith morphology of Selaroides leptolepis and Decapterus macrosoma in the southcentral South China Sea

LI Weichang1,2, ZHU Guoping1,4,5, WANG Xuehui2,6,7, LIN Longshan3, LI Yuan3, DU Feiyan2   

  1. 1. College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China; 2. South China Sea Fisheries
    Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510300, China; 3. Third Institute of
    Oceanography, Ministry of Natural Resources, Xiamen 361005, China; 4. Center for Polar Research, Shanghai Ocean
    University, Shanghai 201306, China; 5. Polar Marine Ecosystem Laboratory, Ministry of Education Key Laboratory of
    Sustainable Exploitation of Oceanic Fisheries Resources, Shanghai 201306, China; 6. Key Laboratory of Marine
    Ranching, Ministry of Agriculture and Rural Affairs, Guangzhou 510300, China; 7. National Digital Fisheries
    (Marine Ranching) Innovation Sub-Center, Guangzhou 510300, China
  • Online:2023-10-18 Published:2023-10-17

PDF (PC)

40

摘要： 为了解鲹科鱼类矢耳石的形态特征和研究不同机器学习算法对南海鲹科鱼类矢耳石的种群分类效果，根据南海中南部水域采集的金带细鲹(Selaroides leptolepis)及长体圆鲹(Decapterus macrosoma)样本，对其耳石进行4种基础形态参数测量后转换为6种形态指标，并由两种耳石中提取出44个椭圆傅里叶描述子系数对其进行主成分分析，使用线性判别分析、随机森林、K-最近邻、支持向量机这4种不同的机器学习分类模型对其进行判别。结果表明，两种鲹科鱼的耳石参数和叉长均有显著性差异，金带细鲹和长体圆鲹耳石的长、高、面积、周长与叉长均呈幂函数关系。由形态指标分析可知，金带细鲹较长体圆鲹耳石环率更低，即更趋近于圆，更为规则,两者各形态指标间均存在显著性差异。主成分分析显示，第1和第2主成分分别解释了总变异的20.1%和13.3%，可对其进行较好的区分。4种分类模型中，随进森林的判别正确率最高为100%，支持向量机的判别正确率最低为93.3%。研究结果表明，机器学习算法对南海鲹科鱼类矢耳石具有较好的判别效果，且傅里叶分析更加直观清晰和准确。

关键词: 耳石形态, 金带细鲹, 长体圆鲹, 机器学习, 判别分析, 南海

Abstract: In order to understand the morphological characteristics of sagittal otoliths of fish species in Carangidae and to study the effectiveness of different machine learning algorithms on the population classification of Carangidae in the South China Sea, otolith samples of Selaroides leptolepis and Decapterus macrosoma were collected from the South China Sea. The otoliths were measured with 4 basic morphological parameters and transformed into 6 morphological indexes, and the differences in otolith morphology were compared between two species, forty four elliptic Fourier descriptor coefficients were extracted from the otoliths for principal component analysis (PCA), and four different machine learning classification models, namely, linear discriminant analysis (LDA), random forest(RF), K-nearest neighbor(KNN), and support vector machines(SVM) were used to discriminate them. The result showed that the otolith parameters and body length of the two species were significantly different. The length, height, area, perimeter, and body length of the two species were all power functions. From the analysis of morphological indexes, the otoliths of S. leptolepis had a lower ring rate described as more round and more regular, there were significant differences in morphological indexes between the two groups. PCA showed that the first and second principal component accounted for 20.1% and 13.3% of the total variation, respectively. Among the four classification models, the highest correct rate of RF was 100%, and the lowest was 93.3% for SVM.

Key words: otolith morphology, Selaroides leptolepis, Decapterus macrosoma, machine learning, discriminant analysis, South China Sea

中图分类号: