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脊尾白虾形态性状对体重的相关性及通径分析
李洋1,2, 刘萍1, 李健1, 高保全1
1.农业部海洋渔业可持续发展重点实验室 中国水产科学研究院黄海水产研究所,青岛 266071;2.大连海洋大学,116023
摘要:
随机选取莱州湾、海洲湾和象山湾3个采样地点各75只共计225只脊尾白虾对其 11项形态性状进行测量。采用相关分析和通径分析的方法,计算以形态性状为自变量对体重做依变量的相关系数、通径系数、决定系数及相关指数,定量的分析形态性状对体重的影响效果。结果表明,脊尾白虾的10个形态性状与体重的相关系数达到极显著水平(P<0.01)。全长、体长、头胸甲长、尾节长、头胸甲高、头胸甲宽、腹节1宽对体重的通经系数达到了显著水平,是直接影响体重的重要指标。所选形态性状与体重的复相关指数为R2=0.931,则表明其为影响体重的主要自变量指标;多元回归分析建立全长X1、体长X2、头胸甲长X3、尾节长X5、头胸甲高X6、头胸甲宽X7、腹节1宽X9对体重Y的回归方程:Y=0.007X1+0.043X2+0.044X3+0.044X5+0.073X6+0.111X7+0.057X9-3.838。
关键词:  脊尾白虾  形态性状  相关分析  通径分析  多元回归方程
DOI:
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基金项目:国家863高技术研究发展计划项目(2012AA10A409)和中国水产科学研究院基本科研业务费(20603022012021)
Correlation and path analysis of morphometric traits on body weight for Exopalamon carincauda
Abstract:
The relationships between 11 morphological characters and body weight of Exopalamon carincauda from three areas, including Haizhou Bay, Xiangshan Bay and Laizhou Bay were investigated. Morphological traits were used as independent variables and body weight was used as the dependent variable in correlation and path analysis.It was found that the correlation coefficients between all the 11 morphological characters and body weight were statistically significant (P<0.01). The path analysis revealed a convincible relationship between the independent variables and the dependent variable. The path coefficients (Pi) of full length, body length, carapace length,telson length,carapace width,1st abdominal segment width to the body weight were all statistically significant,and were crucial to determine the body weight of E. carincauda. Judging from the result of multiple correlation coefficient (R2 = 0.931), the main variables have been selected. Multiple regression equation of X1,X2,X3,X5,X6,X7,X9 and Y was set up as: Y=0.007X1 +0.043X2 +0.044X3 +0.044X5+0.073X6 +0.111X7 +0.057X9 -3.838. This study provided basic data for E. carincauda biology and ideal measuring indicators for its extension as a model species for mariculture.
Key words:  Exopalamon carincauda  Morphometric traits  Correlation analysis  Path analysis  Multiple regression equation