引用本文:	赵旺,于刚,王江勇,严俊贤,杨蕊,吴开畅.7月龄方斑东风螺形态性状对体质量的通径分析[J].海洋科学,2017,41(11):82-88.

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7月龄方斑东风螺形态性状对体质量的通径分析
赵旺^1,2, 于刚^1,2, 王江勇¹, 严俊贤^1,2, 杨蕊^1,2, 吴开畅^1,2
1.中国水产科学研究院南海水产研究所, 热带水产研究开发中心;2.农业部南海渔业资源开发利用重点实验室

摘要:

为探明方斑东风螺(Babylonia areolata)形态性状对体质量的影响程度, 对7月龄方斑东风螺的体质量(Y)和11 个(X₁-X₁₁)形态性状进行了测量。结果显示, 7月龄方斑东风螺体质量与各形态性状间呈正相关, 相关系数均达到极显著水平(P<0.01); 通径分析发现壳宽(X₂)对体质量的直接作用最大(0.373),其次为体螺层高(X₇)、壳口高(X₃), 而次螺层宽(X₁₀)主要通过X₂、X₃及X₇间接影响体质量; 决定系数分析显示, 这4个性状对体质量的共同决定系数之和为0.852, 说明体质量主要受这些性状的影响; 经多元回归分析, 建立了7月龄方斑东风螺形态性状对体质量的回归方程。将回归方程中的X₂、X₃、X₇和X₁₀等4个形态性状与体质量分别进行模型拟合, 最优拟合模型分别为: Y=0.0034X₂^2.5128, Y=0.0034X₃^2.4349 , Y=0.0015X₇^2.5967, Y=?6.4964+1.3293X₁₀。

关键词: 方斑东风螺(Babylonia areolata) 形态性状体质量通径分析

DOI：10.11759/hykx20170714003

分类号:

基金项目:现代农业产业技术体系建设专项资金(CARS-49); 广东省渔港建设和渔业产业发展专项(海洋渔业科技推广方向-科技攻关与研发项目A201601B11); 三亚市农业科技创新项目(2015KJ05)

Path analysis of the effects of morphometric attributes on the body weight of 7-month-old Babylonia areolata

ZHAO Wang,YU Gang,WANG Jiang-yong,YAN Jun-xian,YANG Rui,WU Kai-chang

Abstract:

This study analyzed the relationship between 11 morphometric attributes (X₁–X₁₁) and the body weight (Y) of 7-month-old Babylonia areolata. The results showed highly significantly positive correlations between morphometric attributes and body weight (P<0.01). The path analysis revealed that X₂ displayed the highest positive direct effect (0.373) on body weight, followed by X₇ and X₃. X₁₀ showed an indirect effect on body weight through X₂, X₃, and X₇. The results of analysis of the determination coefficients revealed the total decision coefficient of four morphological attributes affecting body weight to be 0.852, indicating that these morphological attributes are the primary factors affecting body weight. Multiple regression equations established using multiple regression analysis revealed that all the morphometric attributes are the best models for the 7-month-old B. areolata, including Y=0.0034X₂^2.5128, Y=0.0034X₃^2.4349 , Y=0.0015X₇^2.5967, Y=?6.4964+1.3293X₁₀。

Key words: Babylonia areolata Morphometric attributes Body weight Path analysis