^{1}Shenzhen Base of South China Sea
Fisheries Research Institute, Chinese
Academy of Fishery Sciences, Shenzhen
518108, P.R. China

^{2}South China Sea Fisheries Research
Institute, Chinese Academy of Fishery
Sciences, Guangzhou 510300, P.R. China

^{3}Key Laboratory of South China Sea
Fishery Resources Exploitation and
Utilization, Ministry of Agriculture,
Guangzhou, 510300, P.R. China

- Corresponding Author:
- Jianhua Huang

South China Sea Fisheries Research Institute

Chinese Academy of Fishery Sciences

Guangzhou 510300, P.R. China

**Tel:**(979)845-5650

**E-mail:**[email protected]

**Received date:** July 31, 2017; **Accepted date:** August 22, 2017, 2017; **Published date:** August 29, 2017

**Citation: **Jiang S, Zhou F, Yang Q, Yang L,
Tao Li, et al. Correlation and Path
Coefficient Analyses ofthe Morphological
Characteristics and Body Weight of *Penaeus monodon* (Crustacea, Decapoda,
Penaeidae). Insights Aquac Cult Biotechnol
2017, 1:2.

The acute toxicity of glutaraldehyde, dibromohydantoin, methionine iodine and bleaching powder to juvenile Pinctada maxima was studied. The results showed that the order of toxicity was bleaching powder>dibromohydantoin>m ethionine iodine>glutaraldehyde and the median lethal concentrations (LC50) of above disinfectants were 13.05 mg/L, 27.49 mg/L, 53.20 mg/L and 67.36 mg/L, respectively in 24 h and the LC50 were 10.15 mg/L, 9.35 mg/L, 29.27 mg/L and 35.58 mg/L respectively in 48 h; and their SC (safe concentration) were 1.86 mg/L, 0.32 mg/L, 2.66 mg/L and 7.52 mg/L respectively. The research indicated that we can use glutaraldehyde to disinfect P. maxima under safe concentration, but should not use bleahing powder, and can use dibromohydantoin and methionine iodine carefully.

*Penaeus monodon*; Economic traits; Correlation analysis; Path analysis;
Multiple regression equation

*Penaeus monodon* is one of the world’s most commercially
important cultured penaeid shrimp. About two decades ago,
rapid development and expansion of prawn farming occurred
throughout much of southeast Asia [1,2]. Since then, China has
successfully used artificial breeding technology and directional
selection to improve *P.
monodon* yield [3-5]. Selective breeding
is essential to increase efficiency and profitability of production,
with body weight among the key selective breeding traits
that drives profitability in *P.
monodon* farming [6]. However,
determining body weight is frequently difficult due to measuring
locations, tools and environments, and thus morphological
indicators such as body length and carapace length are often
preferable and easier to obtain. Understanding the relationships between body weight and other morphological characteristics is
therefore vital for selection [7].

Path coefficient analysis can be used to determine selection criteria by measuring the direct influence of one variable on another and by separating the correlation coefficient into direct and indirect effects, such as seen in several aquatic organism studies [6,8]. For example, Wang et al. [9] used path analysis on three-month-old juvenile turbot (Psetta maxima) to obtain the body weight multiple regression equation with body length, height and thickness as independent variables. Harue et al. [10] used multiple correlation analysis on farmed Red Sea carp to estimate the impact of standard length and weight on body fat content. Zhan et al. [11] used correlation, path and multiple regression analyses to establish the body weight linear regression equation for body length, head length and tail height in Paralichthys olivaceus. Debowski et al. [12] used multiple regressions to study the relationship between body fat content and body length and weight of Salmo sala. Ahmed et al. [13] used multiple correlation analysis to analyze body length and weightrelated growth parameters of fish, whales and shellfish.

Detailed path coefficient and correlation analyses can
help identify the relationships between body weight and
morphological characteristics in *P.
monodon*. In particular, path
coefficient analysis can separate direct and indirect effects
and measure the relative importance of the potential causal
factors involved [14,15]. The objectives of the current study
were to: (a) determine the impact of morphological traits on
Body Weight (BW); (b) estimate correlation coefficients for
phenotypic characteristics between Body Length (BL), Carapace
Length (CL), Carapace Width (CW), Carapace Height (CH), pleon
segment lengths and BW; (c) evaluate the relative contribution of
each morphological characteristic on BW using path coefficient
analysis; and (d) establish the relationship between all measured
morphological characteristics and BW.

**Sampling and data collection**

We collected *P.
monodon* specimens from four different
populations (Indonesia, Thailand, Mozambique and China).
Sampling sites, sampling localities, geographical coordinates and
number of collected specimens are presented in **Table 1**. Nine
morphometric measurements were made on each specimen
(**Figure 1**): BW, BL, CL, CW, CH and first, second, third and sixth
pleon segment lengths (FSL, SSL, TSL and SISL, respectively). Body
features were measured with a digital vernier caliper (Mitutoyo
500-744, Japan, accurate to ± 0.01 mm) and BW was obtained
by using an electronic balance (Mettler Toledo 504, Switzerland,
accurate to ± 0.0001 g).

Populations | Locality | Latitude, Longitude | Number |
---|---|---|---|

Pop.1 | Banda Aceh, Republic of Indonesia | 05°30'N,94°40'E | 193 |

Pop.2 | Khanom, Kingdom of Thailand | 09°12'N,100°03'E | 130 |

Pop.3 | Mozambique Channel | 19°10'S,35°56'E | 85 |

Pop.4 | Sanya, China | 18°47'N,109°26'E | 62 |

**Table 1:** Populations, sampling localities, geographical coordinates and number of collected specimens.

**Data statistics**

Observed data of the nine morphometric measurements were used for Pearson’s correlation (simple correlation) and path coefficient analyses. For each specimen, Pearson’s correlation coefficient (r) was calculated between every two morphometrics to indicate the relationship between the two traits. Pearson’s correlation and path coefficient analyses were performed using SPSS (version 19.0, SPSS Inc. 2010) and Excel 2003 (Microsoft Corporation, Redmond, WA, USA). The general form of the equation was used as follows [8]:

Where, r_{xy} is the correlation coefficient of x_{i} to y_{i}, x_{i} is the flag value
of the independent variable, y_{i} is the flag value of the dependent
variable, P_{yxi} is the direct path coefficient of independent variable
x_{i} to dependent variable y_{i}, b_{xi} is the regression coefficient
of the independent variable, σ_{xi} is the standard deviation of
the independent variable, σ_{y} is the standard deviation of the
dependent variable, P_{ij} is the indirect path coefficient that trait
i generated through trait j, d_{i} is the direct coefficient of trait i
to dependent variable y, d_{ij} is the indirect coefficient of trait i to
dependent variable y through trait j, d is the total determination
coefficient of all traits, and R_{i} ^{2} is the total determination
coefficient of all traits to trait y.

The regression equation linear model of quality traits Y was as follows:

Where, Y is the dependent variable, b0 is a constant and bi is
the partial regression coefficient of independent variable x_{i} to
dependent variable Y.

The mean, standard deviation and maximum and minimum of
all *P.
monodon* measurements were recorded. The coefficient of
variation (CV%) was computed as: CV%=100 × SD/X, where SD is
the standard deviation and X is the mean of the morphometric
measurements of *P.
monodon*.

Descriptive statistics of the *P.
monodon* samples are presented
in **Table 2**. The coefficients of variation (CV) reflected the varying
degrees within the samples. The CVs estimated for morphological characteristics within the samples were different from each other,
with the CV estimated for BW the highest (42.98%), suggesting
that BW exhibited considerable variation, which is an advantage
for the selective breeding of *P.
monodon*.

Traits | Mean | SD | Range | CV (%) |
---|---|---|---|---|

BL | 209.92 | 29.87 | 240.50-261.20 | 14.23 |

CL | 66.40 | 13.15 | 45.57-86.31 | 19.80 |

CW | 34.03 | 6.90 | 22.47-44.51 | 20.29 |

CH | 38.51 | 7.34 | 25.56-50.42 | 19.06 |

FSL | 21.68 | 3.36 | 15.25-28.18 | 15.48 |

SSL | 18.96 | 2.47 | 13.27-24.71 | 13.01 |

TSL | 18.93 | 2.63 | 14.54-24.89 | 13.92 |

SISL | 28.33 | 3.55 | 21.81-34.94 | 12.54 |

BW | 149.22 | 64.13 | 56.24-261.19 | 42.98 |

SD: Standard Deviation; CV: Coefficient of Variation; BL: Body Length (mm); CL: Carapace Length (mm); CW: Carapace Width (mm); CH: Carapace Height (mm); FSL: First Pleon Segment Length (mm); SSL: Second Pleon Segment Length (mm); TSL: Third Pleon Segment Length (mm); SISL: Sixth Pleon Segment Length (mm); BW: Body Weight (g)

**Table 2:** Descriptive statistics of morphometric characteristics of *P.
monodon* specimens.

The correlations were highly significant for all variables (P<0.01, **Table 3**), indicating that the variables had important practical
significance for correlation analysis. Results showed that BW
was positively correlated with all other variables, and the order
of relevance was

Variable | BW | BL | CL | CW | CH | FSL | SSL | TSL | SISL |
---|---|---|---|---|---|---|---|---|---|

BW | 1.000 | ||||||||

BL | 0.985 | 1.000 | |||||||

CL | 0.962 | 0.964 | 1.000 | ||||||

CW | 0.973 | 0.973 | 0.954 | 1.000 | |||||

CH | 0.964 | 0.963 | 0.945 | 0.963 | 1.000 | ||||

FSL | 0.868 | 0.871 | 0.866 | 0.866 | 0.867 | 1.000 | |||

SSL | 0.776 | 0.779 | 0.733 | 0.763 | 0.761 | 0.631 | 1.000 | ||

TSL | 0.817 | 0.829 | 0.766 | 0.811 | 0.795 | 0.692 | 0.740 | 1.000 | |

SISL | 0.924 | 0.934 | 0.923 | 0.901 | 0.904 | 0.866 | 0.719 | 0.790 | 1.000 |

**Table 3:** Correlation coefficients between morphological characteristics
of *P. monodon*.

The significant correlation between BW and all other traits made
it difficult to assess the major factors associated with BW from
simple correlation analysis alone. Therefore, path coefficient
analyses between each trait and BW, including endogenous and dependent or exogenous and independent variables, were
performed to clarify the relationships. The results showed
that CL and CW were highly significant and significant to BW,
respectively (**Table 4**), suggesting that these two morphological
traits had strong direct effects on BW. The direct effect of BL on
BW was obviously greater than the indirect effect of BL on BW
through other traits. In addition, while the correlation coefficient
between CW and BW was very large, the direct impact on BW
was small. This indicated that the indirect effect of CW on BW
(via other traits) was much larger than its direct effect, suggesting
that CW affected BW through BL indirectly.

Trait | Correlation coefficient | Direct effect | Indirect effect | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|

BL | CL | CW | CH | FSL | SSL | TSL | SISL | Σ | |||

BL | 0.985** | 0.557** | 0.103 | 0.178 | 0.111 | -0.001 | 0.016 | 0.003 | 0.019 | 0.429 | |

CL | 0.962** | 0.107 | 0.537 | 0.175 | 0.109 | -0.001 | 0.015 | 0.003 | 0.018 | 0.856 | |

CW | 0.973** | 0.183* | 0.542 | 0.102 | 0.111 | -0.001 | 0.015 | 0.003 | 0.018 | 0.790 | |

CH | 0.964** | 0.115 | 0.536 | 0.101 | 0.176 | -0.001 | 0.015 | 0.003 | 0.018 | 0.849 | |

FSL | 0.868** | -0.001 | 0.485 | 0.093 | 0.158 | 0.100 | 0.013 | 0.003 | 0.017 | 0.869 | |

SSL | 0.776** | 0.020 | 0.434 | 0.078 | 0.140 | 0.088 | -0.001 | 0.003 | 0.014 | 0.756 | |

TSL | 0.817** | 0.004 | 0.462 | 0.082 | 0.148 | 0.091 | -0.001 | 0.015 | 0.016 | 0.813 | |

SISL | 0.924** | 0.020 | 0.520 | 0.099 | 0.165 | 0.104 | -0.001 | 0.014 | 0.003 | 0.905 |

* Significant at P<0.05, ** Significant at P<0.01

**Table 4:** Path analysis of the effects of various traits on body weight of *P. monodon*.

The determination coefficients of various traits on BW are shown
in **Table 5**. The direct determination coefficients of BL, CL, CW and
CH to BW were 31.0, 1.1, 3.3 and 1.3%, respectively. The indirect
determination coefficients of CL, CW and CH to BW through BL
were 5.7, 9.9 and 6.2%, respectively.

Trait | Direct determination coefficient | Indirect determination coefficient | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

BL |
CL |
CW |
CH |
FSL |
SSL |
TSL |
SISL |
∑ |
||

BL | 0.310 | 0.057 | 0.099 | 0.062 | 0.000 | 0.009 | 0.002 | 0.010 | 0.549 | |

CL | 0.011 | 0.019 | 0.012 | 0.000 | 0.002 | 0.000 | 0.002 | 0.046 | ||

CW | 0.033 | 0.020 | 0.000 | 0.003 | 0.001 | 0.003 | 0.060 | |||

CH | 0.013 | 0.000 | 0.002 | 0.000 | 0.002 | 0.017 | ||||

FSL | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |||||

SSL | 0.000 | 0.000 | 0.000 | 0.001 | ||||||

TSL | 0.000 | 0.000 | 0.000 | |||||||

SISL | 0.000 | 0.000 |

**Table 5:** Determination coefficients of various traits on body weight of *P. monodon*.

Based on the path analysis and multiple correlation analysis
results, multiple regression of the main morphological
characteristics was performed (**Table 6**). BW was regarded as
the dependent variable and the other traits as independent
variables. The partial regression coefficients of BL and CW to BW
were significant (P<0.05), and the multiple regression equation
was: BW=1.546BL+2.533CW-261.522.

Partial regression coefficient | Standard error | t | Significance | |
---|---|---|---|---|

Intercept | -254.377 | 12.379 | -20.548 | 0.000 |

BL | 1.196 | 0.187 | 6.394 | 0.000 |

CL | 0.523 | 0.299 | 1.746 | 0.084 |

CW | 1.697 | 0.671 | 2.528 | 0.013 |

CH | 1.008 | 0.526 | 1.916 | 0.058 |

FSL | -0.017 | 0.654 | -0.026 | 0.979 |

SSL | 0.519 | 0.631 | 0.822 | 0.413 |

TSL | 0.093 | 0.684 | 0.136 | 0.892 |

SISL | 0.353 | 0.853 | 0.414 | 0.680 |

Table 6 Partial regression coefficient test for various traits to body weight of *P. monodon*.

Predicted by the regression equation, no significant differences were found between the estimated and actual observed values. Thus, the above equation can be used conveniently and reliably in actual production.

Phenotype correlation coefficients are used to comprehensively
reflect relationships among variables. Since the influence of other
variables is not eliminated, the direct effect of the independent
variables on the dependent variables and their indirect effect
through other independent variables may act against each
other, and consequently phenotype correlation may be unable
to accurately explain the relationship among all variables.
Conversely, path coefficients are standardized partial regression
coefficients that can differentiate direct and indirect effects
without being influenced by other variables, and can therefore
accurately reflect the relative importance of the results [16,17].
Our study showed that all nine quantitative traits of *P.
monodon* showed positive correlations to each other, though the correlation
coefficients between BW and BL (0.985), BW and CW (0.973), BW
and CH (0.964) and BW and CL (0.962) were higher those between
BW and SSL (0.776), BW and TSL (0.817) and BW and FSL (0.868).
These results suggest that the four above mentioned variables
exhibited a stronger influence on BW compared with the other
traits, which is in accordance with the path coefficient results
reported by Li et al. [18] and Zhang et al. [19] for *Exopalaemon
carinicauda* and *Exopalaemon modestus*, respectively. However,
correlation analysis alone cannot determine whether the traits
with the largest correlation are the major factors that affect the
dependent variables. Therefore, we used path analysis to not only
reflect the relationship between variables, but also categorize
the relationships between characteristics into direct and indirect effects, and thus determine the major factors affecting the BW
of *P.
monodon*.

In this study, the direct determination coefficient of BL (0.310) was the highest among all traits, while the direct determination coefficients of CW, CH, CL, FSL, SSL, TSL and SISL (0.033, 0.013, 0.011, 0.000, 0.000, 0.000 and 0.000, respectively) were much lower, suggesting that BL was the most important determinant of BW, consistent with the results of Yang et al. [20]. The phenotypic correlation coefficients of BL, CL, CW and CH with BW were basically the same, but the path analysis results suggested that the direct determination coefficients of these four phenotypes to BW had obvious differences. Specifically, CL, CW and CH influenced BW through BL indirectly, which indicated that the correlation between the variables did not accurately reflect their real relationship.

The main independent variable influencing a dependent variable
can be determined when the sum of the multiple coefficient, or the sum of the single determination coefficient of variables to
the dependent variable, or the sum of the pairwise coefficient
are equal to or higher than 0.85. In this study, the summed
(total) determination coefficient of BL and CW to BW was
0.952, indicating that BL and CW were the main characteristics
that influenced BW. Hence, as far as *P.
monodon* is concerned,
morphological characteristics showed significant regression
correlation with BW and indicated that path coefficient analysis
was suitable for determining the real relationship between
morphological traits and BW, as observed in Zhang et al. and Li
et al. Due to the high genetic correlation between BL, CL, CW
and CH, it would be appropriate to select BW and BL followed
by CW as the main selection characteristics in the breeding of *P.
monodon*.

This research was supported by the Development of Biology Industry in Shenzhen (NYSW20140331010053); National 863 Program (2012AA10A409), China Agriculture Research System (CARS-47); Special Scientific Research Funds for Central Non-profit Institutes, South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences 2015TS10);Guangdong Oceanic and Fisheries Project of China (A201601A14、2017A0014)；Guangdong Province Science and Technology Project (2017A030303001).

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