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黑斑狗鱼形态性状对体重的影响效果分析
引用本文:霍堂斌,袁美云,张丽荣,姜作发.黑斑狗鱼形态性状对体重的影响效果分析[J].鲑鳟渔业,2011(2):26-30.
作者姓名:霍堂斌  袁美云  张丽荣  姜作发
作者单位:[1]中国水产科学研究院黑龙江水产研究所,农业部黑龙江流域渔业资源与环境重点野外科学观测试验站,黑龙江哈尔滨150070 [2]东北林业大学,黑龙江哈尔滨150040 [3]哈尔滨市农业科学院水产研究分院,黑龙江哈尔滨150078 [4]环境保护部环境规划院,北京100012
基金项目:公益性行业(农业)科研专项经费资助(200903048-06);环保部全国生物物种资源联合执法检查和调查项目资助(物种08-二-9);黑水研基本科研专项(2008HSYZX-ZH-11).
摘    要:选取0^+龄~4^+龄黑斑狗鱼332尾,对其体长、体高、体宽、尾柄长、尾柄高、头长、头高、头宽、吻长、眼间距、口裂宽、口裂长和体重等13个性状进行测量。采用多元分析法分析体重和形态形状的关系。结果表明,黑斑狗鱼除0^+龄头长与体重之间的相关系数未达到显著水平(P〉0.05)外,其它各形态性状与体重的相关系数均达到显著水平(P〈0.05)或极显著水平(P〈0.01);经通径分析,不同年龄阶段各形态性状对体重的通径系数存在一定的差异,但体长和头高对体重的通径系数均达到显著水平,而且体长对体重的直接作用均最大,其余各性状均是通过体长间接的影响体重;决定系数分析结果表明,体长对体重的决定系数最大,其它性状主要通过体长影响体重;通径分析结果与决定系数分析结果的变化趋势一致;所选形态性状与体重的相关指数R2〉0.85,说明所选性状是影响体重的主要性状。应用逐步多元回归分析,经偏回归系数的显著性检验,建立以体重为因变量(Y),各形态性状为自变量的多元回归方程,0^+龄~4^+龄黑斑狗鱼体重(Y)与形态性状参数的多元叵归方程分别为:LgY=-1.411+0.395LgX8+0.576LgX1+0.092LgX1n+0.538LgX7+0.35LgX6;LgY=-4.132+0.866LgX1+0.102LgX】2+0.083LgX7;LgY=一4.186+0.772LgX1+0.195LgX7;LgY=-2.542+0.372LgX1+0.185LgX3+0.256LgX8+0.23LgX7,;LgY=-4.585+0.816LgX1+0.178LgX7。经回归预测估计值与实际值间的差异不显著(P〉0.05),该方程可用于黑斑狗鱼实际生产中,为黑斑狗鱼选种提供了理论依据和理想的测量指标。

关 键 词:黑斑狗鱼  形态性状  相关分析  通径分析  多元回归方程

Mathematical Analysis of Effects of Morphometric Attributes on Body Weight for Esox reichrti Dyhowsk
Authors:HUO Tang-bin  YUAN Mei-yun  ZHANG Li-rong  JIANG Zuo-fa
Institution:1. Heilongjiang River Fisheries Research Institute, Key Field Scientific Observation Station of Fisheries Resoures and Enviroment in HeilongJiang River valley, Ministry of Agriculture, Harbin 150070, China;2.Northeast Forestry University, Harbin 150040,China; 3.Institute of Fisheries Research,Harbin Academy of Agricultural Sciences,Harbin 150070,China; 4.Chinese Academy for Environmental Planning,Beijing 100012, China)
Abstract:The relationship between morphometric attributes and body weight ofEsox reichrti Dyhowsk were analyzed on the basis of the measurement of 13 morphometric attributes form 332 E. reichrti in this study. Total length(X1), standard length(X2), head length(X3), snout length(X4),body width(X5), head width(X6), interorbital distance(XT), body depth(Xs), head depth(Xg), caudal peduncle depth(X10), caudal peduncle length (X11), oral fissure width (X10), oral fissure length (Xn)and body weight (Y) were measured. The correlation coefficients and path coefficients were calculated by correlation analysis, path analysis and multiple regression analysis. The results showed that all the correlation coefficients between each independent variable and dependent variable (body weight) were all at significant level (P〈0.05) or extremely significant level (P〈0.01) excepting for the correlation coefficients between head length and body weight of 0^+ years old. There were some differences on path coefficients of Esox reichrti from different years old. While the pathcoefficients of standard length and head depth to body weight all achieved very significant level (P〈0.01), among them standard length was the most predominant variable to affect body weight, and it was a key effective factor to body weight. The results of determinant coefficients analysis revealed that the determinant coefficients of standard length gave a predominant determinative effect, whereas the others exhibited a slight direct effect and significant indirect effect on the body weight via standard length. The diversification of determinant coefficients analysis was consistent with that of path analysis. The high value of multiple correlation index R2 〉0.85 between morphometric attributes and body weight suggested that the selected attributes were practical. The morphometric attributes were used to establish the multiple regression equations as LgY = -1.411 + 0.395Lg X8 + 0.576Lg X1 + 0.092Lg X10+ 0.538Lg X7+ 0.35Lg X6;LgY = -4.132 + 0.866Lg X1 + 0.102Lg X12 + 0.083Lg X7;LgY -4.186 + 0.772Lg Xj + 0,195Lg X7;LgY = -2.542 + 0.372Lg X1 + 0.185Lg X3 + 0.256Lg X5+ 0.23Lg X7;LgY =-4.585 + 0.816Lg X1 + 0.178Lg X7 for 0+ to 4+years old. This paper provided theoretical evidence and perfect measure target for breeding of E. reichrti in aquaculture.
Keywords:Esox reichrti Dyhowsk  morphometric attribute  correlation analysis  path analysis  multiple regression equation
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