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The Use of Otolith Shape to Identify Stocks of Redlip Mullet, Liza haematocheilus




The Use of Otolith Shape to Identify Stocks of Redlip Mullet, Liza haematocheilus

Tao He1,2,3, Cheng-jing Chen4, Jian-guang Qin3, Yun Li1,2, Rong-hua Wu1,2 and Tian-xiang Gao4,*

1College of Animal Science and Technology, Southwest University, Chongqing 400715, P.R. China

2Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Key Laboratory of Aquatic Science of Chongqing 400715, P.R. China

3College of Science and Engineering, Flinders University, Adelaide 5001, Australia

4Fishery College, Zhejiang Ocean University, Zhoushan 316022, P.R. China


The aim of this study was to compare sagittal morphometric features between Liza haematocheilus stocks in the China coasts at the Ying Kou, Dong Ying, Qing Dao, Wen Zhou and Guang Zhou regions and test a complementary method to quantify the variation of sagittal shapes to discriminate L. haematocheilus stocks. The sagitta variation of five L. haematocheilus stocks was examined using nine shape indices (Roundness, Circularity, Form-factor, Rectangularity, Ellipticity, Radius ratio, Feret ratio, Aspect ratio and Surface density). Multiple comparisons on shape indices showed that there was no significant difference among the Ying Kou and Dong Ying stocks, possibly due to their similar living habitat in the Bohai Sea. As the distribution zones of these five stocks are much overlapped, principal component analysis for otolith shape is not possible to distinguish L. haematocheilus stocks. Based on the Fourier coefficient, Fisher’s discriminant analysis accurately classified 76.2% specimens into correct stocks whereas the discriminant function of shape indices only correctly identified 62.8% specimens into right stocks. As the sagittae in L. haematocheilus had an irregular round shape with many sharp notches, this study indicates that Fourier analysis is more suitable than shape indices to discriminate sagittae with irregular shapes. Our results demonstrate that as long as substantial intraspecific variations exist in sagittae shapes, geometric morphometrics for otolith shapes could be used as a complementary tool along with body morphology to distinguish L. haematocheilus stocks.

Article Information

Received 19 July 2018

Revised 03 September 2018

Accepted 19 September 2018

Available online 11 September 2020

Authors’ Contribution

TH, CC and TG designed the study. TH, CC and RW conducted the field work and analyzed the sample. TH, JQ, YL and TG drafted the manuscript.

Key words

Redlip mullet, Otolith morphology, Shape indices, Fourier analysis, Discrimination function.


* Corresponding author:

0030-9923/2020/0006-2265 $ 9.00/0

Copyright 2020 Zoological Society of Pakistan


Fish otoliths are composed of calcium carbonate crystals in a protein matrix. Calcium carbonate is usually deposited as aragonite on the saccule (sagittae) and utricle (lapillus) and as vaterite on the lagena (asteriscus) on different pieces of fish otolith (Oliveira et al., 1996; Falini et al., 2005). Among the components of fish otolith, the sagitta has the most remarkable feature as it shows a species-specific morphology but is less variable within a species (Campana, 2004). Morphological variation of sagittae can be influenced by age, genetic factors and environmental conditions (Vignon and Morat, 2010), and can also vary with growth rate, feeding history (Gagliano and McCormick, 2004) and habitat (Lombarte and Lleonart, 1993). Due to the influence of these complicated factors, the analysis of sagittal size and shape has become a useful tool for discriminating fish species and geographical stocks (Tuset et al., 2003).

Morphology of otolith shape has been widely used to distinguish phenotypic fish stocks as it is distinctly influenced by the environment and the habitats that fish live (Swain and Foote, 1999; Keating et al., 2014). Morphometry analysis can provide a quantitative description for shape and outline that can be statistically compared (Ibánez et al., 2017; Cheng et al., 2018). Many previous studies have used morphological characteristics to analyze and discriminate fish stocks or populations, especially using otolith morphology (Begg and Waldman, 1999; Adams et al., 2004).

The size-dependent variables of sagitta morphology include the anteroposterior length, maximal Feret’s diameter, dorsoventral width, distance from the center to margins, area of 2-dimensional (2-D) sagittal projection and otolith perimeter (Ponton, 2006). The variation of these variables can be expressed as shape indices to differentiate geometric morphometrics of otoliths (Tuset et al., 2003). The image-dependent analysis of sagitta shapes such as Fourier analysis plays a significant role in morphometric studies, and represents an accurate method for characterizing otolith outlines and capturing contour variation (Cadrin and Friedland, 1999). In recent years, Fourier analysis has been proved to be an efficient method for studying and describing contour shapes of an otolith (Cadrin and Friedland, 1999; Bani et al., 2013; Hüssy et al., 2016). This technique is useful for characterizing otolith morphometrics because: 1) the magnitude of contour variation associated with each harmonic measurement represents the contribution of a particular harmonic to the overall value of otolith morphometrics, and 2) the harmonics, when summed, can be used to recreate the otolith outline. The use of sagittal otolith morphology to identify different geographic stocks or populations has been reported in other fish species, such as silver hake Merluccius bilinearis, haddock Melanogrammus aeglefinus, king mackerel Scomberomorus cavalla, comber Serranus cabrilla , striped trumpeter Latris lineate, Atlantic molly Poecilia mexicana, roundnose grenadier Coryphaenoides rupestris, temperate seabass Lateolabrax japonicas; plainfin midshipman fish Porichthys notatus and white mullet Mugil curema (Bolles and Begg, 2000; Begg et al., 2001; DeVries et al., 2002; Tuset et al., 2003; Tracey et al., 2006; Schulz-Mirbach et al., 2010; Longmore et al., 2010; Fuji et al., 2014; Bose et al., 2017; Ibánez et al., 2017). Consequently, statistical analysis of otolith shape can be a powerful tool for fish stock management purposes (Hüssy et al., 2016), and has been already implemented in stock assessment (e.g., redlip mullet).

The redlip mullet Liza haematocheilus belongs to the family Mugilidae and is an important food fish distributed throughout China, Russia, Korea, and Europe (Li et al., 2006). This euryhaline estuarine-dependent fish, with a limited ability of migration, occurs in shallow coastal waters as well as estuarine freshwater regions (Gao et al., 2014). Because of good adaptability to an environment, fast growth rate, high productivity and favorable taste, L. haematocheilus has been an important species for marine aquaculture for decades (Meng et al., 2007). Han et al. (2013) compared the structure of population genetics among eight stocks of L. haematocheilus in north-western Pacific with amplified fragment length polymorphism markers, and found low genetic diversity between geographical populations. Gao et al. (2014) analyzed five populations from the coasts of China and Japan to investigate the genetic diversity and structure of redlip mullet based on sequence analysis of the mitochondrial DNA control region, examined its demographic history using neutrality tests and mismatch distribution analysis, and suggested this species was derived from a Pleistocene epoch population expansion. Recently, the analysis of cumulative otolith shape morphometric and microchemical of sagittal otoliths have been used to identify the presence of different mullet stocks in the southwestern region of the Atlantic Ocean (Fortunato et al., 2017).

However, to our best knowledge, there has been no study attempting to use morphometric indices analysis on L. haematocheilus otolith to differentiate fish stocks. As a complementary tool to differentiate redlip mullet stocks with body morphology, the morphometric analysis using multiple variables of the otolith could quantify the difference between otolith geometry. We hypothesize that the geometric morphometrics can detect the subtle difference between different stocks of L. haematocheilus, and offer a quantitative protocol to differentiate fish stocks in a fishing area. The aim of this study is to compare sagitta morphometric features between five locations of L. haematocheilus in China coast and test a complementary method to quantify the variation of sagitta shapes to discriminate L. haematocheilus stocks.


Table I.- Sample information of five L. haematocheilus stocks.



Sample No.

Total length (mm)

Body length (mm)

Ying Kou



257.2 ± 14.3

230.4 ± 12.8

Dong Ying



133.9 ± 9.8

115.8 ± 8.7

Qing Dao



242.3 ± 11.2

213.2 ± 10.9

Wen Zhou



98.0 ± 7.1

82.5 ± 6.5

Guang Zhou



332.9 ± 18.6

290.5 ± 17.5


Materials and methods

Sample collection

Fish were collected at Ying Kou (YK), Dong Ying (DY), Qing Dao (QD), Wen Zhou (WZ) and Guang Zhou (GZ), located along the south-east coast of China, from September 2009 to February 2011 (Fig. 1; Table I). The sampling gear is a trawl. The sample from each location, was captured by three times, which could keep the diversity of the same stock, and the sample were preserved with the formaldehyde solution.


Otoliths preparation

Both left and right sagittae were removed from each fish, and then cleaned with ultrasound (KQ3200) for 30 min to further remove tissue residuals and organics. Samples were then rinsed with Milli-Q water and left to dry overnight in a cupboard cabinet.

The left sagitta was examined and photographed using a Nikon SMZ8000 microscope equipped with a digital camera (Nikon SMZ1000). However, when the left sagitta was damaged, the right sagitta was used. If both otoliths were lost, the fish was discarded from the otolith shape analysis. Sagittae were positioned on a concave side before the image was taken. When the right otolith was used, the image was horizontally flipped using a standard image analysis technique to ensure that the rostrum was orientated to the right on the screen for each specimen.

Otolith shape analysis

The comparison of otolith shape was based on both the shape index analysis and Fourier analysis.

Shape index analysis

Using the image from each otolith, nine size parameters were calculated: Otolith weight (OW), area (A), perimeter (P), Feret length (FL), Feret width (FW), maximum radius (Rmax), minimum radius (Rmin), maximum Feret length (Fmax) and minimum Feret length (Fig. 2; Table II). Feret length and Feret width are the length and width of a box, which encloses the trace of the otolith (Tuset et al., 2003). All measurements were taken using the Image-Pro Plus 6.0 program.

Nine shape indices were obtained by calculating the above nine parameters in various ways and the equations are presented in Table II. Roundness and circularity provide information on the similarity of various features to a perfect circle, taking a minimum value of 1 and 12.57, respectively. The form-factor is the mean value to estimate the irregularity of surface area, taking the value of 1.0 when it is a perfect circle and <1.0 when it is an irregular shape. Rectangularity describes the variations of length and width with respect to the area with the value of 1.0 being a perfect square. Ellipticity indicates if the changes in the axes are proportional (Russ, 1990).


Table II.- Multiple comparisons for sagittal shape indices of five L. haematocheilus stocks (Mean ± S.E.). For each morphometric index, the locations with the different letter were significantly different (P<0.05).

Size parameter

Shape index


Ying Kou

Dong Ying

Qing Dao

Wen Zhou

Guang Zhou

Area (A)

Roundness (V01)=4A/πFL2






Perimeter (P)

Form-factor (V02)=4πA/P2






Feret length (FL)

Circularity (V03)=P2/A






Feret width (FW)

Rectangularity (V04)=A/(FL×FW)






Max radius (Rmax)

Ellipticity (V05)=(FL-FW)/(FL+FW)






Min radius (Rmin)

Radius ratio (V06)=Rmax/Rmin






Max Feret length (Fmax)

Feret ratio (V07)=Fmax/Fmin






Min Feret length (Fmin)

Aspect ratio (V08)=FL/FW






Otolith weight (OW)

Surface Density (V09) = OW/A

1.1×10-3± 0.2×10-3a

1.1×10-3± 0.3×10-3a

1.4×10-3± 0.4×10-3b

1.4×10-3± 0.2×10-3b

0.9×10-3± 0.3×10-3a


The radius ratio, Feret ratio and aspect ratio are the results of the division of the length by the width, and a larger value shows a more elongated shape (Ponton, 2006). Surface density indicates the thickness of otolith, and a higher value shows a thicker otolith.

Fourier analysis

The Fourier analysis decomposes the contour of an irregular 2-dimension image and forms a set of simpler components (harmonics) (Lestrel, 1997). Each successive harmonic can add more information on the property of shape morphometrics (Campana and Casselman, 1993). The Shape 1.3 software was used to extract the contours of the sagitta outline in preparation for Fourier analysis. The ChcViewer program generated a number of coordinates (x, y) best describing the outline shape of the sagitta. For each sagitta, 20 harmonics were generated using the CHC2NEF program. Each harmonic consisted of four coefficients resulting in 80 coefficients per otolith. The program standardizes the size and orientation, giving the first three coefficients with fixed values of A = 1, B = C = 0. Each individual was therefore represented by 77 unique coefficients (Longmore et al., 2010).

Principal component analysis

To reduce the dimensionality of the data, the principal component analysis was performed on both shape indices and Fourier coefficients by SPSS18.0. The significant principal components (PCs) were established according to the published methods (Duarte-Neto et al., 2008). The PC scores were used in the multivariate analysis between regions where fish were collected. Data were then tested for normality and homogeneity of variance using SPSS18.0. Any variables (shape indices/coefficients) that displayed a normal distribution and homogeneity of variance were tested for differences between sampling sites using univariate ANOVA (SPSS 18.0).

Discriminant function analysis

Fisher’s discriminant function was used because it combines two or more measurements to improve the power to discriminate species (Goldstein and Dillon, 1978). Discriminant function analysis was carried out to determine the proportion of individuals that could be correctly assigned to their capture site based on sagittal shape variables (shape indices/coefficients). These variables were then tested for univariate correlation with otolith length and also correlation between variables to prevent any multicollinearity between variables as correlated variables can result in the use of redundant variables and a false outcome (Graham, 2003). Analysis of covariance was used to examine the influence of otolith length on each shape variable using SPSS 18.0.



Shape index analysis

No significant differences were found (MANOVA, P = 0.05) between the shape indices of right and left otoliths. Nine shape indices were tested for significant differences among five locations using one-way ANOVA (P = 0.05, Table II). There was no significant difference between YK and GZ stocks, but the shape indices of the YK stock were significantly different from the DY stock in the V03 index, QD stock in the V05-V09 indices, and WZ stock in the V01 and V05-V09 indices. Eight shape indices were significantly different between the DY and QD stocks, except the V04 index. The DY stock was significantly different from the WZ stock in the V01 and V05-V09 indices, and GZ stock in the V02 and V03 indices. Seven indices were significantly different between QD and WZ stocks, except the V03 and V04 indices. The QD stock was different from the GZ stock in the V01 and V05-V09 indices. Seven indices were significantly different between the WZ and GZ stocks, except the V03 and V04 indices.


Principal component analysis showed that the first two PCs were relevant with eigenvalues of 4.83 and 2.20, respectively, according to the ‘broken stick model’ on sagittal contours (Jackson, 1993) and explained 78.06% of the overall variance (Supplementary Tables I, II). The first PC, accounting for 53.64% of the total variance, was mainly determined by four shape indices (V01, V05, V07 and V08). The second PC, accounting for 24.42% of the variance, was mostly determined by the V02, V03, V04 and V09 indices. In the plot of the first two PCs (Fig. 3), the distribution zones of five stocks are heavily overlapped.


Table III.- Loadings of first two PCs for nine shape indices of L. haematocheilus otoliths.
































Three shape indices (V02, V06 and V09) were screened by Fisher’s discriminant analysis to establish the five discriminant functions for each stock:

YK: Y1 = 360.81X1+74.01X2+17472.07X3–211.77

DY: Y2 = 376.37X1+73.22X2+16776.95X3–216.40

QD: Y3 = 360.17X1+79.95X2+20633.71X3–234.68

WZ: Y4 = 360.93X1+ 66.21X2+19953.11X3–193.00

GZ: Y5 = 353.26X1+73.64X2+14612.71X3–204.14

Where, X1 is V02, X2 is V06 and X3 is V09.

When the specimen was assigned, the three shape indices of each sagitta were substituted into the above five discriminant functions, and the corresponding maximum value (Y) indicated the attribution of each specimen. The results showed that the discriminant function analysis based on sagittal shape indices classified 62.8% of 161 individuals to the correct location (Table IV). Classification success was highest in the WZ stock with 90.9% of 31 individuals classified to their right sampling site. Classification success was 34.8% of 23 specimens in the YK stock, 51.8% of 55 individuals in the DY stock, 67.7% of 31 individuals in the QD stock, and 71.4% of 21 specimens in the GZ stock, respectively (Table IV).


Table IV.- Discriminant results of the otolith in five L. haematocheilus populations.


Ying Kou

Dong Ying

Qing Dao

Wen Zhou

Guang Zhou



Ying Kou







Dong Ying







Qing Dao







Wen Zhou







Guang Zhou







TDA (%)



IA, identification accuracy; TDA, toal discriminant accuracy.


Fourier analysis

The first 20 harmonics explained at least 99.99% of the otolith variation. PCA was applied to selected Elliptical Fourier Descriptors matrix of otolith contours. Out of 77 Fourier coefficients (FCs) tested, only the first fifteen PCs were significant as determined by their eigenvalues exceeding the threshold eigenvalue (>1) generated by the broken-stick model and their cumulative contribution ratio accounted for 90.58% of the overall variance (Supplementary Table II). In the plot of the first two PCs (Fig. 4), the distribution zones of five stocks are totally overlapped.


The shape variation (mean ± 2 SD; i.e. the square root of the eigenvalue of each component) was explained by the first 15 PCs (Supplementary Fig. S1). The principal characteristics mainly exhibited by the first four PCs (PC1 and PC2, the dorsal and ventral margins; PC3, the rostrum and posterior margin; PC4, the anterior-ventral margins). Therefore, the rostrum, and dorsal and ventral margins reflected the main variations between the five stocks.

In total, nine coefficients were chosen from 77 FCs by Fisher’s discriminant analysis, and then were used to establish five discriminant functions:

YK: Y1 = 685.41X1 – 62.46X2 + 285.71X3 – 131.70X4 – 9.32X5 + 11.13X6 +240.29X7 – 536.904X8 + 118.76X9– 168.15

DY: Y2 = 695.63X1 – 144.03X2 + 192.41X3 – 110.84X4 +204.98X5 – 91.88X6 +523.30X7 – 459.28X8 + 346.07X9 – 162.10

QD: Y3 = 650.85X1 – 58.92X2 + 332.78X3 – 344.74X4 + 37.49X5 - 105.40X6 +365.09X7 – 314.40X8 - 65.98X9– 152.51

WZ: Y4 = 765.19X1 – 179.42X2 + 167.63X3 – 170.10X4 + 66.33X5 – 376.36X6 + 374.4X7 – 517.36X8 + 399.67X9– 190.48

GZ: Y5 = 699.16X1 – 50.10X2 + 243.74X3 – 21.96X4 - 34.51X5 – 42.78X6 +124.70X7 – 820.19X8 + 641.30X9– 175.18

Where, X1 is FC01, X2 is FC06, X3 is FC09, X4 is FC17, X5 is FC21, X6 is FC25, X7 is FC33, X8 is FC43, and X9 is FC67.

When the sample was assigned, the Fourier coefficients of each sagitta were substituted into the five discriminant functions, and the corresponding maximum value (Y) indicated the attribution of the samples. The discriminant function analysis classified 76.2% of overall 161 individuals to the initial locations based on the sagitta FCs (Table V). Classification success was highest in DY location with 85.7% of 55 individuals classified to their sampling site. Classification success was 56.5% of 23 samples in the YK location, 67.7% of 31 individuals in the QD location, 84.8% of 31 individuals in the WZ location, and 71.4% of 21 samples in the GZ location, respectively.


Table V.- Discriminant results of five L. haematocheilus stocks based on Fourier analysis.


Ying Kou

Dong Ying

Qing Dao

Wen Zhou

Guang Zhou



Ying Kou







Dong Ying







Qing Dao







Wen Zhou







Guang Zhou







TDA (%)



IA, identification accuracy; TDA, toal discriminant accuracy.



Otolith morphology varies from a round shape in larval fish to an irregular shape in adult, and the shape indices represent the pattern of otolith growth in a bidimensional plane (Gauldie, 1988; Lagarde`re et al., 1995). Although the analysis of shape indices is complicated, it confirms that species identification can be made by the meristic characteristic of an otolith (Gaga, 1993; Tuset et al., 2003). In addition, the statistic analysis of otolith shapes is a more cost-effective and efficient method to differentiate fish stocks or populations than other methods such as the genetic marker discrimination technique, as recent divergence or secondary reproductive contact may result in no apparent difference in gene frequency between stocks (Begg and Waldman, 1999). In this study, the sagitta variation of five L. haematocheilus stocks was examined by using nine shape indices. Multiple comparisons of shape indices showed that there was no significant difference among the YK, DY and GZ locations. The possible explanation is that the YK and DY locations are distributed in a similar habitat in the Bohai Sea, resulting in the similar shape morphology of the otolith in these two stocks. Interestingly, although the GZ location is distributed in the South China Sea, which is far from the YK and DY locations, the shape indices of GZ location did not show significantly different from the YK and DY locations. Gao et al. (2014) tested the genetic diversity of five populations of L. haematocheilus, and the result showed that the nucleotide diversity of GZ and DY populations was similar, but was different from the QD and WZ stocks. So, YK, DY and GZ stocks could be classified as a group in this study.

The principal component analysis showed that the two PCs were mainly determined by eight shape indices out of the nine indices. As the distribution zones of the five stocks are overlapped on the plot, it is inferred that five stocks of L. haematocheilus are difficult to be discriminated by principal component analysis. Nevertheless, as Fisher’s discriminant analysis can create a classified function to differentiate individuals within a group (Camacho, 1995), it differentiated the five stocks with the accuracy of 62.8% in this study. The identification accuracy on the WZ stock was highest, reaching 90.9%. Fortunato et al. (2017) used otolith shape indices to indentify the stocks of Mugil liza (Valenciennes 1836) in the Southwestern Atlantic Ocean and obtained a lower identification accuracy of 52.75%. In the present study, there was a high chance of fake discrimination among YK, DY and GZ locations because no significant difference can be found using multiple comparisons of shape indices among the three stocks. The discriminant function in this study was based on three shape indices (Form-Factor, Radius ratio and Surface Density), which may not be sensitive enough to discriminate the redlip mullet stocks. Bani et al. (2013) reported that the form factor, circularity, rectangularity, and aspect ratio indices are more efficient than other indices to differentiate south Caspian gobies, suggesting that the type of shape indices should be carefully chosen to establish a sensitive function to discriminate fish stocks using the analysis of otolith shape indices.

Fourier coefficients of otoliths have been used to detect the differences among herring (Clupea harengus) stocks (Bird et al., 1986). The use of elliptic Fourier descriptors can highly discriminate different Atlantic salmon (Salmo salar) stocks (Pontual and Prouzet, 1987). In recent years, Fourier analysis has been proved to be an efficient method for studying and describing contour shapes of an otolith (Cadrin and Friedland, 1999; Bani et al., 2013; Hüssy et al., 2016). In the present study, as the first two Fourier coefficients accounted for 36.93% of the overall variance in the principal component analysis, the distribution zones of five stocks are overlapped in the plot, which is similar to the result from shape indices analysis. Consequently, principal component analysis for otolith shape could not efficiently distinguish L. haematocheilus stocks in this study.

Fisher’s discriminant analysis classified 76.2% of individuals into correct stocks based on Fourier coefficients, which is a better method than the discriminant function using shape indices with only 62.8% accuracy for stock identification. The one possible reason is the Fourier descriptors are a description of the outline and the morphometric indices are only some proxies of the outline. There is not the same level of precision. Another possible reason is that, the shape of L. haematocheilus sagittae is round with many sharp notches on the edge, resulting in irregular otolith shape. The shape index has higher classification accuracy on sagittae with a regular shape than with an irregular shape. Fourier analysis, on the other hand, can efficiently capture outline information for an otolith with complex and irregular shapes (Lestrel, 1997). Therefore, the Fourier analysis is more suitable to detect the variation of otoliths with irregular shapes like L. haematocheilus stocks.



Substantial intraspecific variations exist in sagitta shapes among L. haematocheilus stocks. Fisher’s discriminant analysis is able to distinguish L. haematocheilus stocks based on the morphology of sagitta shapes. Based on the Fourier coefficient, Fisher’s discriminant analysis accurately classified 76.2% specimens into correct stocks whereas the discriminant function of shape indices only correctly identified 62.8% specimens into right stocks. As the sagittae in L. haematocheilus had an irregular round shape with many sharp notches, this study indicates that Fourier analysis is more suitable than shape indices to discriminate sagittae with irregular shapes. In conclusion, morphometric analysis of otolith shapes could be used as a complementary tool along with body morphology to distinguish fish stocks.



This research is funded by National Natural Science Foundation of China (No. 41506158, 41776171), International Science and Technology Cooperation Program of China (No. 2015DFR30450), Public Science and Technology Research Funds Projects of Ocean (No. 201505025),Chongqing Research Program of Basic Research and Frontier Technology (No. CSTC2016JCYJA0327), Genetic Resources Conservation Grant (Fisheries) from Ministry of Agriculture of the P. R. China (No. 171721301354052181) and Ecological Fishery Industrial Technology System Project from Chongqing Municipal Agriculture Commission (Grant to YL). Fundamental Research Funds for the Central Universities (Grant Number: XDJK2016C016). Tao He is grateful for the support of the China Scholarship Council with visiting scholar programs to Flinders University. All procedures performed were complied with the ethical standards and guidelines of Zhejiang Ocean University of China.


Supplementary material

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Statement of conflict of interest

The authors have declared no conflict of interest.



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