淡水养殖花鲈形态性状与体质量的相关性及通径分析  

Correlation and Path Analysis of Morphological Traits and Body Weight of Lateolabrax maculatus in Freshwater Culture

作　　者：张凯强[1] 李刚[2] 王庆龙 张龙 陈志方 温海深[1] 李吉方[1] 徐扬涛 Zhang Kaiqiang;Li Gang;Wang Qinglong;Zhang Long;Chen Zhifang;Wen Haishen;Li Jifang;Xu Yangtao(Key Laboratory of Mariculture(Ocean University of China),Ministry of Education,Qingdao 266003,China;National Fisheries Technology Extension Center,Beijing 100125,China;Beijing Aquatic Wildlife Rescue and Conservation Center,Beijing 102100,China;Shuangying Aquatic Seedling Company Limited in Lijin Country,Dongying 355200,China)

机构地区：[1]海水养殖教育部重点实验室(中国海洋大学),山东青岛266003 [2]全国水产技术推广总站,北京100125 [3]北京市水生野生动植物救护中心,北京102100 [4]利津县双瀛水产苗种有限责任公司,山东东营355200

出　　处：《中国海洋大学学报（自然科学版）》2023年第9期58-68,共11页Periodical of Ocean University of China

基　　金：山东省自然科学基金项目(ZR2021QC071);国家重点研究发展计划项目(2020YFD0900204);现代农业产业技术体系专项(CARS-47)资助。

摘　　要：为明确淡水养殖花鲈(Lateolabrax maculatus)形态性状与体质量的关系,本研究测定了淡水养殖花鲈的体质量(Y)和吻长(X_(1))、眼间距(X_(2))、眼径长(X_(3))、头长(X_(4))、躯干长(X_(5))、尾柄长(X_(6))、头高(X_(7))、体高(X_(8))、尾柄高(X_(9))、体长(X_(10))、全长(X_(11))、体宽(X_(12))12个形态性状,通过相关分析、通径分析和回归分析方法研究了形态性状与体质量的相关关系。研究表明,除眼径长(X_(3))外,花鲈各形态性状与体质量呈极显著(P<0.01)或显著(P<0.05)的正相关关系;相关分析发现体高(X_(8))与体质量(Y)的相关系数最大(0.937),眼径长(X_(3))与体质量的相关系数最小(0.175)。通径分析发现,体高(X_(8))对体质量(Y)的直接作用最大(0.542),仅体高(X_(8))的直接作用大于间接作用(0.394),眼间距(X_(2))、尾柄长(X_(6))、体宽(X_(12))3个性状通过体高(X_(8))对体质量(Y)的间接作用均较大(0.293~0.436)。决定系数分析发现,体高(X_(8))对体质量(Y)的直接决定系数最大(0.294),远高于其他单个形态性状,体高(X_(8))和体宽(X_(12))的共同决定系数最大(0.219),4个主要形态性状对体质量的总决定系数为0.997。回归分析发现,形态性状(X)与体质量(Y)的多元线性回归方程为Y=16.291 X_(2)+9.297 X_(6)+30.411 X_(8)+18.498 X_(12)-166.44(R^(2)=0.943);模型拟合发现,眼间距(X_(2))最优拟合模型为二次函数,方程为Y=387.874 X-86.058 X^(2)-304.769,尾柄长(X_(6))、体高(X_(8))和体宽(X_(12))与体质量(Y)的最优模型均为幂函数,方程分别为Y=1.805 X^(2.52),Y=13.968 X^(1.828),Y=29.445 X^(1.039)。研究结果表明,对平均体质量为118 g的淡水养殖花鲈进行生长性状选育时,体高(X_(8))为主要选择性状,体宽(X_(12))、尾柄长(X_(6))和眼间距(X_(2))为辅助选择性状。To clarify the relationships between morphological traits and body weight of Lateolabrax maculatus,the body weight(Y)and 12 morphological traits were measured.The traits included snout length(X_(1)),interorbital space(X_(2)),eye diameter length(X_(3)),head length(X_(4)),trunk length(X_(5)),caudal peduncle length(X_(6)),head height(X_(7)),body height(X_(8)),caudal peduncle height(X_(9)),body length(X_(10)),total length(X_(11))and body width(X_(12)).Through correlation analysis,path analysis and regression analysis,the effects between morphological traits and body weight were studied.Except for the eye diameter length(X_(3)),there was an extremely significant(P<0.01)or significant(P<0.05)positive correlation between morphological traits and body weight of L.maculatus.The correlation coefficient between body height(X_(8))and body weight was the largest(0.937)while that between the eye diameter length(X_(3))and body weight was the smallest(0.175).Body height(X_(8))had the largest direct effect on body weight(0.542),and interorbital space(X_(2)),caudal peduncle length(X_(6))and body width(X_(12))had larger indirect effect varying between 0.293 and 0.436 on body weight through body height(X_(8)).The direct determinate coefficient of body height(X_(8))on body weight was the largest(0.294),the joint determinate coefficient of body height(X_(8))and body width(X_(12))was the largest(0.219),and the total determinate coefficient of the four main morphological traits on body weight was 0.997.The multiple linear regression equation of morphological traits and body weight was Y=16.291 X_(2)+9.297 X_(6)+30.411 X_(8)+18.498 X_(12)-166.44(R^(2)=0.943).The optimal fitting model for interorbital space(X_(2))was the quadratic function,and the equation is Y=387.874 X-86.058 X^(2)-304.769.The optimal models of caudal peduncle length(X_(6)),body height(X_(8))and body width(X_(12))on body weight were the power functions,and the equations were Y=1.805 X^(2.52),Y=13.968 X^(1.828),Y=29.445 X^(1.039).In summary,body height(X_(8))should be us

关 键 词：花鲈 体质量 形态性状 相关分析 通径分析 回归分析 

分 类 号：S917.4[农业科学—水产科学]