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| 工厂化养殖模式下星康吉鳗的形态分析及重要形态性状对体重和净体重的影响效应 |
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陈彦,晏科文,史宝,王成刚,赵新宇,马晓东
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1.中国水产科学研究院黄海水产研究所 海水养殖生物育种与可持续产出全国重点实验室 农业农村部海洋渔业与
可持续发展重点实验室 青岛海洋科技中心海洋渔业科学与食物产出过程功能实验室 山东 青岛 266071;2.大连海洋大学水产与生命学院 辽宁 大连 116023;3.海阳市黄海水产有限公司 山东 烟台 265100;4.中国水产科学研究院黄海水产研究所 海水养殖生物育种与可持续产出全国重点实验室 农业农村部海洋渔业与
可持续发展重点实验室 青岛海洋科技中心海洋渔业科学与食物产出过程功能实验室 山东 青岛 266072
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| 摘要: |
| 选取黄海沿岸3家养殖厂采集的90尾工厂化养殖模式下的星康吉鳗(Conger myriaster)作为研究对象，逐一测量其体重(BW)、净体重(NW)、全长(TL)、体长(BL)、体高(BH)、体宽(SW)、头宽(HW)等14项形态性状，进行形态性状间关联分析并构建形态性状度量构架图；采用相关性分析、通径分析、决定程度分析、多元回归分析和曲线拟合方法解析了星康吉鳗各形态性状对体重和净体重的影响效应。相关性分析显示，6项形态性状与体重的相关系数均达到极显著水平(P<0.01)，7项形态性状与净体重的相关系数均达到极显著水平(P<0.01)；经通径分析查明，体长、体高、体宽3项形态性状与体重、净体重的通径系数较大，分别为0.631、0.204、0.374和0.703、0.213、0.239；经决定程度分析发现，体长对体重、净体重的决定系数最大，分别为0.398和0.494；体长与体宽的组合对体重、净体重的决定系数最大，分别为0.162和0.116；经逐步多元回归分析，建立体长、体高、体宽与体重、净体重间的回归方程分别为BW=–642.699+1.086×BL+ 3.874×BH+7.917×SW和NW=–526.995+1.033×BL+3.438×BH+4.317×SW。进一步对多元回归方程中各形态性状与体重、净体重进行曲线拟合，得出体长、体高、体宽与体重的最佳拟合模型分别为对数函数、幂函数和对数函数，模型方程分别为BW=–3 542.357+608.061lnBL、BW=14.313BH0.941和BW=–1 028.49+ 416.452lnSW；体长、体高、体宽与净体重的最佳拟合模型分别为对数函数、线性函数和对数函数，模型方程分别为NW=–2 983.881+516.411lnBL、NW=–22.252+11.392BH和NW=–774.583+ 329.017lnSW。本研究揭示了工厂化养殖模式下，星康吉鳗体长为影响体重与净体重的核心形态性状，体高和体宽为影响体重与净体重的重要形态性状，可为后续星康吉鳗的种质特征鉴定、遗传育种和健康养殖提供基础理论参考。 |
| 关键词: 星康吉鳗 工厂化养殖模式 形态性状 体重 净体重 |
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| 基金项目:国家重点研发计划(2022YFD2400401)、山东省重点研发计划(2021LZGC028)、中国水产科学研究院黄海水产研究所基本科研业务费(20603022023023; 2020TD47)和财政部和农业农村部：现代农业产业技术体系(CARS-47)共同资助 |
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| Morphometric analysis and influence of important morphological traits on the body weight and net body weight of Conger myriaster in an industrialized culture model |
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CHEN Yan1,2,3,4, YAN Kewen1,2,3, SHI Bao1,2,3, WANG Chenggang5, ZHAO Xinyu1,2,3, MA Xiaodong1,2,3
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1.Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, State Key Laboratory of Mariculture Biobreeding and Sustainable Goods;2.Key Laboratory of Marine Fisheries and Sustainable Development,
Ministry of Agriculture and Rural Affairs;3.Laboratory for Marine Fisheries Science and Food Production Processes,
Qingdao Marine Science and Technology Center, Qingdao 266071, China;4.College of Fisheries and Life Science, Dalian Ocean University, Dalian 116023, China;5.Haiyang Yellow Sea Aquatic Product Co., Ltd., Yantai 265100, China
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| Abstract: |
| Conger myriaster has a high price on the international market and occupies an important position in aquatic product export in China, and it is widely distributed in the Yellow Sea, the Bohai Sea, the East China Sea, the coastal waters of Japan from southern Hokkaido to northern Okinawa, and the sea near the Korean Peninsula. The experimental aquaculture techniques of C. myriaster include pond culture, industrialized open-flowing water culture, and industrialized recirculating aquaculture. No report has been published on the morphological characteristics or the effects of various morphological traits on the body weight and net body weight of C. myriaster in an industrialized open-flowing water culture. In this study, C. myriaster was collected under this industrialized culture mode and assessed using traditional measurement methods, statistical analysis, and other means to systematically measure and analyze morphological traits. A total of 90 C. myriaster were collected equally from three aquaculture companies along the coast of the Yellow Sea. The body weight (BW), net body weight (NW), total length (TL), body length (BL), body height (BH), soma width (SW), head length (HL), head height (HH), head width (HW), proboscis length (PL), proboscis width (PW), eye diameter (ED), vertical eye diameter (VED), and distance between eyes (DE) were measured individually using an electronic balance, a ruler, and a Vernier caliper. We analyzed the relationships between morphological traits and constructed a truss network diagram of morphological traits. The effects of the morphological traits on BW and NW were studied by correlation analysis, path analysis, multiple regression analysis, and curve fitting. The correlation analysis showed that the correlations of BW with TL, BL, BH, SW, HH, and HW were extremely significant (P<0.01), with correlation coefficient values of 0.818, 0.829, 0.611, 0.697, 0.667, and 0.642, respectively. The correlations of NW with TL, BL, BH, SW, HH, HW, and VED were extremely significant (P<0.01), with correlation coefficients of 0.848, 0.857, 0.574, 0.591, 0.617, 0.564, and 0.519, respectively. Path analysis revealed that the path coefficients between BW and three morphological traits (namely BL, BH, and SW) were larger than those for other morphological traits, at 0.631, 0.204, and 0.374, respectively. The path coefficients between NW and these three morphological traits (BL, BH, and SW) were larger than those for other morphological traits, at 0.703, 0.213, and 0.239, respectively. Through determination coefficient analysis, it was observed that the determination coefficient for BL to BW was the largest, with a value of 0.398. The combination of SW and BL had the largest determination coefficient for BW, with a value of 0.162. This indicated that BL was a core morphological trait to determine the BW, and the SW was an important morphological trait to determine the BW. The determination coefficient of BL to NW was the largest at 0.494. The combination of SW and TL had the largest coefficient of determination for NW, with a value of 0.116. This showed that the BL was the core morphological trait to determine the NW, and SW was an important morphological trait to determine the NW. A stepwise regression method was used to establish a regression equation in which the three morphological traits were independent variables and the BW or NW was the dependent variable. The multiple regression equation of the important morphological traits for BW was BW = –642.699 + 1.086 × BL + 3.874 × BH + 7.917 × SW (P<0.01). The multiple regression equation of the important morphological traits for NW was NW = −526.995 + 1.033 × BL + 3.438 × BH + 4.317 × SW (P<0.01). Further curve fitting was performed for each morphological trait with BW and NW in the equation. The best fitting models of BL, BH, and SW for BW were the logarithmic function, logarithmic function, and power function, respectively, and the model equations were BW = −3,542.357 + 608.061lnBL (R2 = 0.929), BW = 14.313BH0.941 (R2 = 0.908), and BW = −1,028.49 + 416.452lnSW (R2 = 0.920), respectively. The best-fitting models of BL, BH, and SW for NW were the logarithmic, linear, and logarithmic functions, respectively. The model equations were NW = −2,983.881 + 516.411lnBL (R2 = 0.930), NW = −22.252 + 11.392BH (R2 = 0.914), and NW = −774.583 + 329.017lnSW (R2 = 0.921), respectively. In summary, BL was the core morphological trait affecting the BW and NW of C. myriaster in an industrialized culture model. BH and SW were important morphological traits affecting the BW and NW of C. myriaster in the industrialized culture model. These results provide a theoretical basis for the future identification of germplasm characteristics, genetic breeding, and healthy aquaculture of C. myriaster. |
| Key words: Conger myriaster Industrialized culture mode Morphological traits Body weight Net body weight |
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