Research Article |
Corresponding author: Tianyan Yang ( hellojelly1130@163.com ) Academic editor: Sanja Matić-Skoko
© 2024 Ziyan Zhu, Tianyan Yang, Sige Wang.
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Citation:
Zhu Z, Yang T, Wang S (2024) Relations between morphological traits and body weight of shortbelly eel, Dysomma anguillare (Actinopterygii: Anguilliformes: Synaphobranchidae), from coastal waters of Zhoushan, East China Sea, determined by multivariate analyses. Acta Ichthyologica et Piscatoria 54: 27-36. https://doi.org/10.3897/aiep.54.114014
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The shortbelly eel, Dysomma anguillare Barnard, 1923, is an essential component in the food chain of the marine ecosystem and plays an important role in nearshore fisheries and biodiversity in the East China Sea. In order to provide theoretical support for fishery resource assessment and sustainable utilization of D. anguillare, an important bycatch in the offshore area of China, the relations between morphological traits and body weight were investigated based on the measurement of 28 metric traits for the first time. The correlation analysis showed that 25 morphological traits were significantly (P < 0.05) correlated with the logarithm of body weight (lgX0), in which the correlation coefficient of the total length (X1) was the largest with the extremely high significance (P < 0.01). The optimum multiple regression equation of morphological traits was constructed after deleting redundant independent variables: lgX0 = 0.367 + 0.003X1 + 0.010X7 – 0.010X8 + 0.011X10 + 0.042X14 + 0.006X15 + 0.024X19 – 0.004X23. The total length (X1) had the highest positive direct relation with lgX0 (0.699), which was in accordance with the results of determinate coefficient analysis, while the indirect effect of body height (X7) through lower jaw length (X19) to lgX0 was the greatest. The gray correlation analysis indicated that body length (X2) and distance from snout to dorsal fin origin (X22) were the most closely related to body weight. The comprehensive comparison showed that X1, X2, and X22 should be used as the ideal morphometric traits for measuring the body weight of D. anguillare, and the conclusions obtained from this study will provide valuable references for fishery resource management of this commercial fish species.
body weight, Dysomma anguillare, gray relational analysis, morphological traits, path analysis
The shortbelly eel, Dysomma anguillare Barnard, 1923, belonging to the order Anguilliformes and family Synaphobranchidae, is a demersal fish species that is widely distributed in the tropical Indian and western Pacific oceans (
Body weight is an essential trait for growth and a direct reflection of production performance, which is correlated with various morphological traits. By analyzing the relations between the morphological traits and body weight of economic fishes, ichthyologists can provide reliable suggestions for population resource assessment and the optimum catchable size (
Recently, published studies have already demonstrated that the correlations between morphological traits and body weight can be clarified by multivariate analysis in many aquatic animals, such as fishes (
A total of 85 specimens of Dysomma anguillare were collected by trawling in the coastal waters of the Zhoushan Archipelago, East China Sea in October 2022. Frozen fish individuals were transported to the Fishery Ecology and Biodiversity Laboratory (FEBL) of the Zhejiang Ocean University of China for further analysis. The body weight (X0), was obtained by an electronic balance to the nearest 0.01 g, and the measurable characters were determined by a digital vernier caliper and a ruler with the accuracies of 0.01 mm and 1 mm, respectively. Twenty-seven measurable parameters are depicted in Fig.
Diagrams of morphometric measurements of Dysomma anguillare. (A) Lateral view (B) Dorsal view (C) Head view. Abbreviations: X1 = total length, X2 = body length, X3 = anal length, X4 = tail length, X5 = postanal length, X6 = trunk length, X7 = body height, X8 = head length, X9 = head breadth, X10 = body width, X11 = oral fissure height, X12 = oral fissure width, X13 = oral fissure length, X14 = eye diameter, X15 = head length after eye, X16 = interocular distance, X17 = snout length, X18 = upper jaw length, X19 = lower jaw length, X20 = distance from snout to anterior nostril, X21 = distance from snout to posterior nostril, X22 = distance from snout to dorsal fin origin, X23 = distance from snout to anal fin origin, X24 = distance from the anal fin origin to the anus, X25 = distance from the first aperture of lateral line to snout, X26 = distance from dorsal fin origin to anal fin origin, X27 = pectoral fin length.
Microsoft Excel 2019 was used for calculating the path coefficients and determination coefficients. Multivariate analyses such as correlation analysis, regression analysis, and path analysis are conducted to reveal the direct and indirect effects of morphological traits on body weight by using SPSS 26.0 software (
Kolmogorov–Smirnov test (K–S test) and Shapiro–Wilk test (S–W test) are two commonly used normal test methods, and are suitable for statistical analysis with large samples (n > 50) and small samples (n ≤ 50), respectively (
Correlation analysis can be performed through the software SPSS 26.0 after confirming the normal distribution of the data. It can be distinguished whether the two morphological traits are related to the analysis of the significance level (
The aim of multiple regression analysis is to find out the linear relation between body weight and the related morphological traits. After removing the morphological traits unrelated to the body weight based on correlation analysis, the effects of morphological traits on the body weight are studied by stepwise multiple regression analysis (
The path analysis reflects the effects of the independent variables on the dependent variable, and it can be divided into parts: the direct effects of each trait on body weight and the indirect effects of each trait on body weight through other traits (
PXiXj = rXiXj × PXj [1]
The coefficient of determination (CD) is a measure of how well a linear regression model fits the data and is calculated as the square of the correlation between the dependent variable and the predicted values from the regression model (
dXi = P2Xi [2]
dXiXj = 2 × rXiXj × PXi × PXj [3]
In the above three formulas, rXiXj means the correlation coefficient between morphological traits Xi and Xj. PXi and PXj mean the direct path coefficients of the morphological traits Xi and Xj on body weight, respectively.
The gray system theory (GST) was first proposed by a Chinese scholar, professor Julong Deng in 1982, and it has become an effective tool for studying the uncertainty of a small sample and limited information (
[4]
[5]
[6]
In the formula [4], X′i (K) is the dimensionless data, Xi (K) is the original data, is the mean value of Xi, and Si is the standard deviation of Xi. In the formula [5], Vi (K)is the absolute difference between Xi and X0 at a point K that denotes as Vi (K) = |X′0 (K) − X′i (K)|, ρ is the gray resolution coefficient (ρ = 0.5), as well as max Vi (K) and min Vi (K) represent the absolute values of the secondary maximum difference and the secondary minimum difference, respectively. In the formula [6], γi is the correlation degree between Xi and X0, and n is the sample size (n = 85).
Descriptive statistics of morphometric traits. The statistical data on 28 morphometric parameters of Dysomma anguillare are shown in Table
The descriptive statistics of phenotypic parameters for Dysomma anguillare from the East China Sea.
Trait | N | Value | Standard deviation (SD) | Coefficient of variation (CV) [%] | ||
---|---|---|---|---|---|---|
Min. | Max. | Mean | ||||
X 0 | 68 | 27.20 | 145.50 | 63.54 | 25.49 | 40.12 |
X 1 | 85 | 301.00 | 557.00 | 396.55 | 48.46 | 12.22 |
X 2 | 85 | 296.00 | 540.00 | 388.00 | 46.82 | 12.07 |
X 3 | 85 | 38.20 | 82.00 | 61.53 | 9.04 | 14.69 |
X 4 | 85 | 3.50 | 38.60 | 8.56 | 4.33 | 50.61 |
X 5 | 84 | 218.00 | 461.00 | 334.79 | 45.37 | 13.55 |
X 6 | 84 | 261.80 | 533.88 | 345.09 | 47.22 | 13.68 |
X 7 | 84 | 12.30 | 32.30 | 19.22 | 3.81 | 19.83 |
X 8 | 85 | 34.20 | 65.60 | 49.10 | 7.42 | 15.11 |
X 9 | 82 | 6.10 | 16.50 | 9.39 | 1.76 | 18.71 |
X 10 | 85 | 7.00 | 23.60 | 14.22 | 3.67 | 25.79 |
X 11 | 68 | 6.08 | 33.60 | 14.01 | 6.54 | 46.68 |
X 12 | 68 | 3.20 | 11.70 | 6.32 | 1.79 | 28.35 |
X 13 | 68 | 15.58 | 24.90 | 19.60 | 2.16 | 11.00 |
X 14 | 85 | 2.10 | 5.30 | 3.46 | 0.57 | 16.46 |
X 15 | 84 | 22.60 | 47.70 | 35.12 | 0.63 | 16.44 |
X 16 | 85 | 5.10 | 14.80 | 8.07 | 1.83 | 22.71 |
X 17 | 84 | 6.60 | 20.80 | 10.50 | 1.97 | 18.81 |
X 18 | 84 | 8.40 | 30.00 | 18.99 | 2.89 | 15.22 |
X 19 | 84 | 12.00 | 26.30 | 16.63 | 2.45 | 14.74 |
X 20 | 84 | 1.70 | 5.00 | 2.47 | 0.57 | 23.12 |
X 21 | 84 | 6.00 | 13.00 | 8.65 | 1.05 | 12.08 |
X 22 | 84 | 32.10 | 63.00 | 44.37 | 5.62 | 12.68 |
X 23 | 84 | 44.70 | 94.20 | 70.96 | 10.90 | 15.37 |
X 24 | 84 | 5.40 | 11.30 | 8.66 | 1.17 | 13.51 |
X 25 | 84 | 10.76 | 87.18 | 31.81 | 8.24 | 25.92 |
X 26 | 85 | 11.20 | 32.40 | 23.68 | 4.06 | 17.15 |
X 27 | 84 | 5.20 | 32.78 | 9.49 | 2.79 | 29.43 |
Normal distribution test and correlation analysis. In the K–S test and S–W test, the P-value lower than the significance level of 0.05 implies the null hypothesis is rejected and it is assumed that the data is non-normally distributed (
Multiple regression analysis. The results of stepwise multiple regression analysis are presented in Table
The regression model summary for Dysomma anguillare from the East China Sea.
Model | Correlation coefficient (R) | Coefficient of determination (R2) | Corrected coefficient of determination (Adjusted R2) | Standard error (SE) |
---|---|---|---|---|
1 | 0.865a | 0.748 | 0.744 | 0.083 |
2 | 0.900b | 0.810 | 0.804 | 0.073 |
3 | 0.919c | 0.844 | 0.836 | 0.067 |
4 | 0.927d | 0.860 | 0.851 | 0.064 |
5 | 0.942e | 0.888 | 0.878 | 0.058 |
6 | 0.950f | 0.903 | 0.893 | 0.054 |
7 | 0.955g | 0.912 | 0.901 | 0.052 |
8 | 0.959h | 0.920 | 0.909 | 0.050 |
The results of regression coefficient for Dysomma anguillare from the East China Sea.
Variable | Partial regression coefficient (B) | Standard error (SE) | Standardized regression coefficient (β) | T-statistics | P value | Variance inflation factor (VIF) |
---|---|---|---|---|---|---|
Constant | 0.367 | 0.064 | — | 5.758 | 0.000 | — |
X 1 | 0.003 | 0.000 | 0.699 | 6.267 | 0.000 | 8.698 |
X 7 | 0.010 | 0.003 | 0.194 | 3.314 | 0.002 | 2.394 |
X 8 | –0.010 | 0.002 | –0.418 | –4.409 | 0.000 | 6.288 |
X 10 | 0.011 | 0.002 | 0.227 | 4.722 | 0.000 | 1.613 |
X 14 | 0.042 | 0.014 | 0.149 | 3.054 | 0.003 | 1.661 |
X 15 | 0.006 | 0.003 | 0.199 | 2.356 | 0.022 | 4.981 |
X 19 | 0.024 | 0.006 | 0.303 | 4.140 | 0.000 | 3.747 |
X 23 | –0.004 | 0.001 | –0.226 | –2.596 | 0.012 | 5.320 |
Path analysis. The path analysis revealed the effects of the independent variable (Xi) on the dependent variable (lgX0). The results showed that the sum of each indirect relation to body weight was greater than that of the direct effects (Table
Effects of eight morphometric traits on body weight of Dysomma anguillare from the East China Sea.
Trait | Correlation coefficient (R) | Path coefficient (P) | Indirect path coefficient (IP) | Variance inflation factor (VIF) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Total | X 1 | X 7 | X 8 | X 10 | X 14 | X 15 | X 19 | X 23 | ||||
X 1 | 0.825 | 0.699 | 1.469 | 0.212 | 0.246 | 0.187 | 0.121 | 0.193 | 0.251 | 0.259 | 8.698 | |
X 7 | 0.771 | 0.194 | 2.474 | 0.489 | 0.392 | 0.376 | 0.380 | 0.372 | 0.554 | 0.400 | 2.394 | |
X 8 | 0.571 | –0.418 | 0.549 | 0.121 | 0.083 | 0.098 | 0.036 | 0.106 | 0.096 | 0.129 | 6.288 | |
X 10 | 0.553 | 0.227 | 0.712 | 0.140 | 0.122 | 0.149 | 0.046 | 0.113 | 0.135 | 0.147 | 1.613 | |
X 14 | 0.538 | 0.149 | 0.765 | 0.167 | 0.227 | 0.100 | 0.084 | 0.061 | 0.218 | 0.074 | 1.661 | |
X 15 | 0.384 | 0.199 | 0.642 | 0.123 | 0.103 | 0.138 | 0.097 | 0.028 | 0.113 | 0.161 | 4.981 | |
X 19 | 0.865 | 0.303 | 0.862 | 0.187 | 0.179 | 0.146 | 0.135 | 0.118 | 0.132 | 0.151 | 3.747 | |
X 23 | 0.579 | –0.226 | 0.749 | 0.170 | 0.114 | 0.173 | 0.129 | 0.035 | 0.166 | 0.132 | 5.320 |
Determination coefficient analysis. In this study, the sum of the determination coefficients was calculated to be 0.920 (Table
Determination coefficients of eight morphometric traits on body weight of Dysomma anguillare from the East China Sea.
Trait | X 1 | X 7 | X 8 | X 10 | X 14 | X 15 | X 19 | X 23 |
---|---|---|---|---|---|---|---|---|
X 1 | 0.489 | |||||||
X 7 | 0.190 | 0.038 | ||||||
X 8 | –0.475 | –0.091 | 0.175 | |||||
X 10 | 0.196 | 0.047 | –0.125 | 0.052 | ||||
X 14 | 0.083 | 0.031 | –0.030 | 0.014 | 0.022 | |||
X 15 | 0.177 | 0.041 | –0.119 | 0.045 | 0.009 | 0.040 | ||
X 19 | 0.350 | 0.093 | –0.164 | 0.082 | 0.047 | 0.071 | 0.092 | |
X 23 | –0.270 | –0.050 | 0.164 | –0.067 | –0.012 | –0.075 | –0.091 | 0.051 |
SDC | 0.920 | |||||||
RCD | 0.080 | |||||||
e | 0.392 |
Gray relational analysis. The mean values of gray relational coefficients between different morphological traits and body weight were different, ranging from 2.466 to 396.522 (Table
The gray relational coefficients and gray relational degrees of each trait of Dysomma anguillare from the East China Sea.
Traits | Gray relational coefficient | Gray correlation degree | Gray correlation order | ||
---|---|---|---|---|---|
Min. | Max. | Mean ± SD | |||
X 1 | 301.000 | 557.000 | 396.552 ± 48.460 | 0.918 | 9 |
X 2 | 296.000 | 540.000 | 388.004 ± 46.824 | 0.937 | 1 |
X 3 | 38.200 | 82.000 | 61.531 ± 9.039 | 0.893 | 23 |
X 4 | 3.500 | 38.600 | 8.560 ± 4.332 | 0.907 | 15 |
X 5 | 218.000 | 461.000 | 334.792 ± 45.375 | 0.936 | 4 |
X 6 | 261.800 | 533.880 | 345.091 ± 47.220 | 0.909 | 12 |
X 7 | 12.300 | 32.300 | 19.220 ± 3.810 | 0.925 | 6 |
X 8 | 34.200 | 65.600 | 49.099 ± 7.419 | 0.921 | 7 |
X 9 | 6.100 | 16.500 | 9.389 ± 1.756 | 0.908 | 13 |
X 10 | 7.000 | 23.600 | 14.225 ± 3.668 | 0.897 | 20 |
X 11 | 6.080 | 33.600 | 14.014 ± 6.541 | 0.840 | 27 |
X 12 | 3.200 | 11.700 | 6.323 ± 1.792 | 0.894 | 22 |
X 13 | 15.580 | 24.900 | 19.600 ± 2.155 | 0.883 | 25 |
X 14 | 2.100 | 5.300 | 3.461 ± 0.570 | 0.865 | 26 |
X 15 | 0.000 | 47.700 | 34.711 ± 6.890 | 0.900 | 19 |
X 16 | 5.100 | 14.800 | 8.070 ± 1.833 | 0.900 | 18 |
X 17 | 6.600 | 20.800 | 10.496 ± 1.974 | 0.920 | 8 |
X 18 | 8.400 | 30.000 | 18.994 ± 2.892 | 0.936 | 3 |
X 19 | 12.000 | 26.300 | 16.629 ± 2.452 | 0.894 | 21 |
X 20 | 1.700 | 5.000 | 2.466 ± 0.570 | 0.915 | 10 |
X 21 | 6.000 | 13.000 | 8.655 ± 1.045 | 0.901 | 17 |
X 22 | 32.100 | 63.000 | 44.370 ± 5.625 | 0.937 | 2 |
X 23 | 44.700 | 94.200 | 70.956 ± 10.903 | 0.925 | 5 |
X 24 | 5.400 | 11.300 | 8.661 ± 1.170 | 0.904 | 16 |
X 25 | 10.760 | 87.180 | 31.809 ± 8.244 | 0.908 | 14 |
X 26 | 11.200 | 32.400 | 23.679 ± 4.062 | 0.909 | 11 |
X 27 | 5.200 | 32.780 | 9.491 ± 2.794 | 0.888 | 24 |
The linear body measurements have been widely applied to evaluate body demission to an animal’s overall body size, and the prediction of body weight using morphometric features is very practical in aquaculture breeding programs and fishery management (
The correlation analysis results showed that 25 traits were positively correlated with the logarithm of body weight, except for the oral fissure height (X11) and distance from the first aperture of the lateral line to the snout (X25). The top 3 phenotypic correlation coefficients of morphological traits were lower jaw length (X19), total length(X1), and body height (X7), which were quite different from other bony fishes, such as Larimichthys polyactis (Bleeker, 1877) (see
Only using correlation coefficients can’t adequately explicate all aspects of the relations among all variables and will be misleading when investigating causal effects (
The conclusion of determinant coefficients analysis generally agreed with that of path analysis, with total length (X1) having the largest determinant relation with the body weight. The total coefficient of determination was higher than the critical value of 0.850, which manifested that 92% of the variation came from eight independent variables, and the selected morphological traits could reflect the variation of body weight to a large extent. The multiple regression equation constructed based on these 8 parameters reached a very significant level (P < 0.01), reconfirming the accuracy of prediction to the body weight through morphological traits aforesaid. Nevertheless, the correlation coefficients of the retained eight morphological traits disaccorded with their direct effects on body weight, indicating that correlation analysis could not bespeak the true relations among variables (
GST is proved to be useful for dealing with poor, incomplete, and uncertain information (
In the presently reported study, a multivariable statistic method including correlation analysis, regression analysis, path analysis, and gray correlation analysis was applied to evaluate the morphological influence on body weight for Dysomma anguillare in the coastal waters of China for the first time. Integrated research findings suggested that the three metric traits representing the longitudinal growth of the fish body could be regarded as suitable indicators for formulating mesh size and minimum landing allowable catch size during the fishery resource management of D. anguillare.
Certainly, the correlations between the morphological traits of fishes and their body weight were also related to growth stage, sex, habitat environment, nutritional condition as well as genetic regulation. Hence, we should comprehensively deliberate the influence of multiple factors and increase the sample size as much as possible to make the results more reliable.
We are grateful to Dr Wei Meng and Dr Rijin Jiang of Zhejiang Marine Fisheries Research Institute for sample collection. This research was funded by the Technology Planning Project of Zhoushan (No. 2022C41022), the Innovation and Entrepreneurship Training Program for College Students (No. 202310340056), and the Science and Technology Innovation Project of College Students in Zhejiang Province (No. 2023R411005).
Correlation analysis of morphological traits for Dysomma anguillare
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Variation analysis of multiple regression equations
Data type: docx