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中国农学通报

中国农学通报 ›› 2024, Vol. 40 ›› Issue (2): 152-158.doi: 10.11924/j.issn.1000-6850.casb2023-0014

• 水产·渔业 • 上一篇    下一篇

基于通径分析和灰色关联分析舟山近海中国鱚形态性状对体质量的影响

蒋小姿(), 江文静, 张倩雅, 肖梦, 杨天燕()   

  1. 浙江海洋大学水产学院，浙江舟山 316022
  • 收稿日期:2022-12-18 修回日期:2023-04-12 出版日期:2024-01-10 发布日期:2024-01-10
  • 通讯作者:
    杨天燕，女，1982年出生，湖北武汉人，副教授，博士，研究方向：渔业资源学。通信地址：316022 浙江省舟山市定海区临城街道海大南路1号 浙江海洋大学，E-mail：。
  • 作者简介:

    蒋小姿，女，2000年出生，江西上饶人，研究方向：海洋资源与环境。通信地址：316022 浙江省舟山市定海区临城街道海大南路1号 浙江海洋大学，E-mail：。

  • 基金资助:
    国家自然科学基金项目“基于群体基因组学的中国鱚遗传结构和环境适应性研究”(32100405); 国家级大学生创新创业训练计划项目“浙江海域前肛鳗形态学和遗传学研究”(202210340023); 舟山市科技计划项目“东海重要海洋鱼类种质资源保护技术研究”(2022C41022)

Effects of Morphological Traits on Body Weight of Sillago Sinica in Zhoushan Offshore Based on Path Analysis and Grey Relational Analysis

JIANG Xiaozi(), JIANG Wenjing, ZHANG Qianya, XIAO Meng, YANG Tianyan()   

  1. Fishery College of Zhejiang Ocean University, Zhoushan, Zhejiang 316022
  • Received:2022-12-18 Revised:2023-04-12 Published-:2024-01-10 Online:2024-01-10

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摘要：

为了研究中国鱚(Sillago sinica)形态参数与体质量的关系，随机选取107尾舟山近海分布的中国鱚为研究对象，测量其体质量(X0)和11个形态性状：吻长(X1)、头长(X2)、体长(X3)、全长(X4)、尾柄长(X5)、尾柄高(X6)、体厚(X7)、眼径(X8)、眼间隔(X9)、眼后头长(X10)、体高(X11)，并进行了相关性分析、多元回归分析、通径分析和灰色关联分析。测定的形态指标和体质量的相关性都达到了极显著水平(P<0.01)，其中体长与体质量的相关性最大；剔除存在严重共线性的形态指标后，分别设置与体质量显著线性相关的体长和尾柄高为自变量、并将体质量设为因变量，构建了最优多元线性方程为Y=-87.734+6.174X3+24.398X6；通径分析结果显示，对体质量直接作用最大的为体长，间接作用最大的为尾柄高；灰色关联分析结果表明，与体质量的关联度排序靠前的5个形态参数分别为体长、全长、尾柄高、体高和头长。综合上述研究表明，中国鱚渔业资源的开发管理过程中应以体长为优先衡量指标、以尾柄高为辅助指标来制定最佳可捕规格和网目尺寸。

关键词: 中国鱚, 形态性状, 体质量, 通径分析, 灰色关联分析

Abstract:

In order to study the relationships between morphological traits and body weight of Sillago sinica, 107 samples in the offshore of Zhoushan were randomly selected, and their body weight (X0) and 11 morphological traits including snout length (X1), head length (X2), body length (X3), total length (X4), caudal peduncle length (X5), caudal peduncle height (X6), body thickness (X7), eye diameter (X8), interorbital diameter (X9), head length behind eyes (X10) and body height (X11) were measured and analyzed by correlation analysis, multiple regression analysis, path analysis and grey relational analysis. The correlation analysis results showed that all the morphological traits were significantly correlated with body weight (P<0.01), and the correlation coefficient between body length and body weight was the greatest of all. After eliminating the severe collinearity effects, two indexes (body length and caudal peduncle height) that significantly linearly correlated with body weight were considered as the independent variables, and body weight was regarded as the dependent variable. The optimal multiple linear regression equation was finally constructed as: Y=-87.734+6.174X3+24.398X6. The path analysis results revealed that body length had the largest direct effect on body weight, while caudal peduncle height had the largest indirect effect on it. The top five grey relational degrees between each morphological trait and body weight were body length, total length, caudal peduncle height, body height, and head length. Comprehensive findings indicated that during the process of Sillago sinica fishery resource development and management, body length and caudal peduncle height should be separately used as the priority measure index and auxiliary one to formulate the optimal catchable size and mesh size.

Key words: Sillago sinica, morphological traits, body weight, path analysis, grey relational analysis