| 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. |
