Effects of Morphological Traits on Body Weight in 3-month Old Hemibarbus labeo
WEN Hao1, ZHANG Jian2
1. Liaoning Science and Technology Museum, Shengyang 110167, China;
2. Liaoning Key Laboratory of Aquatic Animal Infectious Diseases Control and Prevention, Liaoning Institute of Freshwater Fisheries Sciences, Liaoyang 111000, China
摘要为研究3月龄唇䱻形态性状对体质量的影响,运用相关分析、通径分析和回归分析,进行全长(x1)、体长(x2)、头长(x3)、吻长(x4)、眼径(x5)、眼后头长(x6)、头高(x7)、体高(x8)和尾柄高(x9)9项形态学指标与体质量(y)相关性分析。试验结果显示,唇䱻的体质量变异系数最大(21.59%)。各形态性状间的表型相关系数均达到极显著水平(P<0.01)。通径分析表明,全长、体长、眼径、头高、体高和尾柄高达显著水平,是影响体质量的主要形态性状,其通径系数分别为0.218、0.239、-0.075、0.106、0.339和0.211。主要形态性状对唇䱻体质量的决定系数中,体高最大(0.115);两两形态性状对其体质量的决定程度分析表明,体长和体高协同作用对体质量的影响最大(0.111)。3月龄唇䱻全长、体长、眼径、头高、体高和尾柄高对体质量的相关系数达到0.894,回归关系达到显著水平(P<0.05)。通过逐步回归分析,建立最优回归方程: y =-3.935+0.234x1+0.303x2-1.018x5+0.700x7+1.414x8+1.976x9。本研究确定3月龄唇䱻全长、体长、眼径、头高、体高和尾柄高6项形态学指标是影响其体质量的重要指标,为唇䱻的人工选育和形态学研究提供参考依据。
Abstract:A total of 213 3-month-old Hemibarbus labeo were sampled for measuring 10 morphological traits including total length(x1), body length(x2), head length(x3), snout length(x4), orbital diameter(x5), head length behind eye(x6), head depth(x7), body depth(x8), caudal peduncle depth (x9) and body weight(y). The results showed that the coefficient of variation of y(21.59%) was found to be the maximal value, higher than the others, with a normal distribution data of morphological traits. Correlation coefficients of morphological traits on body weight were all at very significant level(P<0.01). The path analysis revealed that the path coefficients of x1, x2, x5, x7, x8 and x9 were 0.218, 0.239, -0.075, 0.106, 0.339 and 0.211(P<0.05), with larger determination coefficients of x8 (0.115) than others. And the determination coefficient (0.111) of x2 and x8 to y was higher than others. The value of correlation index between morphological traits and body weight was 0.894 (r2),with significant regression relations (P<0.05),indicating that the six selected attributes (x1, x2, x5, x7, x8 and x9) were key morphological traits to the body weight and practical in the selective breeding of H. labeo. The morphological traits were used to establish the multiple regression equations was y =-3.935+0.234x1+0.303x2-1.018x5+0.700x7+1.414x8+1.976x9 (P<0.05).