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Morphometric Variations among Isolated Populations of Catfish Sperata seenghala Sykes, 1839 in Gomati River of Tripura, India: A Truss Network based Analysis

S
Sukanta Banik1
D
Dipak Das2,*
0000-0001-9840-5898
1Department of Zoology, Tripura University, Suryamaninagar-799 022, Tripura, India.
2Department of Zoology, Ramkrishna Mahavidyalaya, Kailashahar-799 277, Tripura, India.

ABSTRACT

Background: Sperata seenghala Sykes, 1839 locally known as guchi aor is an economically important catfish inhabit in the Gomati River of Tripura, India and it is noticed that significantly reducing its population in its natural habitat for the last few decades. Therefore, proper identification of suitable stock is urgent need for artificial breeding purposes.

Methods: In this study, the morphometric variation of five S. seenghala populations of Gomati river were assessed with multivariate analyses, including principal component analysis (PCA) and cluster analysis (CA) to distinguish different populations.

Result: Of the total 26 transformed truss measurements, 5 exhibited significant (p<0.05) differences among populations for females and no such significant difference was observed for males. PCA performed on truss data accounted for a total variation of 91.01% and 98.08% by the first two principal components (PC1 and PC2) for male and female populations respectively. The UPGMA dendrogram showed that Sonamura population has the most morphologically different populations in comparison to other populations. The data support the existence of morphological variation across different study location. However, if these findings are supported by further molecular markers, this would be a strong indication of different stocks of this species in the Gomati river of Tripura. The results obtained may contribute to effective conservation and management practices of the studied fish S. seenghala in its natural habitat.

INTRODUCTION

Morphometric characters have been commonly used in fishery biology as powerful tools in the field of taxonomic studies for demarcation of diverse populations within species (Miller et al., 1988; Razzaq et al., 2015; Chaklader, 2019; Tripathy, 2020). Phenotypic variation according to environmental variability has been widely used by ichthyologists to differentiate between species and populations (Kovac and Copp, 1999; Simon et al., 2010; Brraich and Akhter, 2015). The body shape is not only determined with the genetics of a fish, it has also been influenced by environment conditions and its habitat (Guill et al., 2003; Kumari et al., 2020). Heterogeneity of the environment, where an animal lives, results in selective pressures that lead to some phenotypic differences among individuals of the same species (Kawecki and Ebert, 2004; Fusco and Minelli, 2010; Colihueque et al., 2017; Sobita and Basudha, 2017).
       
Morphometric variation between populations can provide a basis for population differentiation, which is an important tool for evaluating population structure and identifying discrete groups (Turan, 1999; Rawat et al., 2017; Kenthao and Jearranaiprepame, 2018; Murugesan et al., 2019; Pathak et al., 2024). However, the alternative system of morphometric measurements called the truss network system, constructed with the help of anatomical landmark points that enhance discrimination among groups, has been less explored for population structure analysis in the freshwater fish. The truss network system is considered superior to traditional morphometrics that use morphometric traits to represent the complete shape of fish, which has been recently used in the field of fish taxonomy and fisheries management and is gaining a momentum for stock assessment study in many parts of the world including India (Hamed and Hosein, 2013; Mir et al., 2013; Jearranaiprepame, 2017; Mahfuj et al., 2017; Basudha and Sushindrajit, 2018; Dwivedi, 2021; Chandran et al., 2022; Datta and Singh, 2023, Bhole et al., 2024). A number of studies have been conducted based on truss network system worldwide, but its application in fishery research of Tripura, a north eastern state of India is still lacking.
       
Sperata seenghala (Sykes, 1839), is one of the important catfish locally known as guchi aor and naturally distributed in India, Bangladesh, Afghanistan, Nepal, Pakistan, Myanmar (Talwar and Jhingran, 1991; Saini et al., 2008, Das and Banik, 2021). The genetic purity and population of many freshwater fish species including S. seenghala is deteriorating in the world wide in recent time due to inbreeding, over exploitation, pollution and other anthropogenic effects such as pesticides, agrochemicals etc. There is no stock structure information available for S. seenghala population in any part of the state Tripura of India. The present study is therefore designed to examine the morphometric variability of S. seenghala in their natural habitats of Gomati River, Tripura using truss network system and the findings may useful for proper wild stock identification for aquaculture practices and managements.

MATERIALS AND METHODS

A total of 250 adult samples of Sperata seenghala were studied from different sampling station viz. Jatanbari (n=50), Amarpur (n=50), Maharani(n=50), Kakraban (n=50) and Sonamura (n=50) comprising of 50% males and 50% females of Gomati River located in Tripura, North-eastern India. Fifty samples were targeted at each sampling station as suggested by Reist (1985) and following Turan et al., (2006) recommendation using at least 25 samples for morphological analysis. The sample size, sampling period, GPS coordination and sampling station map are presented in Table 1 and Fig 1.

Table 1: Details of collection of the Sperata seenghala from Gomati River in Tripura.



Fig 1: Location of sampling sites of Sperata seenghala populations in Gomati River of Tripura, India.


       
Twenty eight morphometric measurement including two conventional characters, total length and standard length were taken using a digital caliper to the nearest 0.1 mm and 26 derived distance characters were used for truss network based morphometric analysis constructed by interconnecting thirteen landmarks representing the basic shape of the fish (Table 2 and Fig 2) following the method of Colihueque et al. (2017); Jearranaiprepame (2017); Datta and Singh (2023). Visual assessment of genital organ and external sexual features was used to determine fish sex and whenever required fish sample were dissected to identify the sex by macroscopic examination of the gonads and gender was used as the class variables in the analysis of variances (ANOVA) to test for the significant differences in the morphometric characters if any, between the males and females. In respect of truss network measurements, the size-dependent variation was corrected by adapting an allometric method as suggested by Elliott et al., (1995).

Madj = M (Ls/Lo)b


Where,
M= The original measurements.
Madj= The size-adjusted measurement.
Lo= The standard length of the fish.
Ls= The overall mean of the standard length for all samples in each analysis and b is estimated for each character from the observed data as the slope of the regression of log M on log Lo using all fish from both groups.

Table 2: Morphometric measurements made for each sample of S.seenghala collected from the Gomati River of Tripura, India.



Fig 2: Location of 13 landmarks and scheme of the truss network used in S. seenghala morphometric analysis.


       
The results derived from the allometric method were confirmed by testing the significance of correlation between transformed variables and standard length (Turan, 1999).
       
The significant differences for each morphometric characters among the five populations were tested by univariate ANOVA using each character as a response variable. In this study, 26 size-corrected morphometric variables were subjected to multivariate analysis comprising principal component analysis (PCA) and cluster analysis (CA) in order to evaluate population wise morphometric variation among five study locations. Principal component analysis aids to lessen morphometric data, eliminate redundancy among the variables and to identify a number of influential variables for the discrimination of population. Cluster analysis by adopting the Euclidean distance as a measure of dissimilarity and using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) in the form of dendrogram was also performed as a complementary of PCA analysis to determine the phenotypic differences among the different populations. The Excel (Microsoft Office 2007), XLSTAT and Pact3 were used for statistical analyses.

RESULTS AND DISCUSSION

Table 3 and Table 4 shows the average values with standard deviation of TL,SL and 26 truss measurements analyzed for male and female populations respectively. The population of Jatanbari, Amarpur, Maharani, Kakraban and Sonamura were more or less similar in respect of mean size of total length and standard length for both sexes. One-way ANOVA test showed that incase of female 5 truss measurements out of 26 were found to be significantly (p< 0.05) different among populations whereas none was statistically significant difference in male. All correlation coefficients were estimated to know the degree of association between the traits for both male and female populations (Table 8 and Table 9). The ANOVA (p<0.05) revealed the most effective truss morphometric variation on sexual dimorphism was found in Maharani population followed by Sonamura (Table 5). In case of male, the PCA based on 26 truss measurements retained two components according to PA, explaining 91.01 % of the total variance. The first (PC1) and second (PC2) principal components accounted for 78.22 and 12.79 of the total variance, respectively (Table 6 and Fig 3). Thus, PC1 was the most important component contributing to separation among populations. These differences were primarily because of the strong loading of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10, 10-11, 11-12, 1-12, 2-11, 2-12, 2-13, 3-11, 3-13, 4-13, 5-11, 5-12, 6-10, 6-11, 11-13 and 12-13 characters.Almost 50% of these characters were involved in longitudinal body shape changes i.e. shape changes corresponding to the anterior-posterior body that reflect length changes. Strong loading of characters involved in body depth shape variation was also observed (Table 6). In the case of PC2, within the two characters that exhibited strong loadings, most were associated to body depth shape variation.

Table 3: Morphometric data of male for 28 characters of five Sperata seenghala populations from Gomati River of Tripura.



Table 4: Morphometric data of female for 28 characters of five Sperata seenghala populations from Gomati River of Tripura.



Table 5: Results of Anova for sexual dimorphism of truss morphometric characters of different sampling station.



Table 6: Component loadings of the first two principle components derived from PCA based on the correlation matrix of 26 truss measurements of Sperata seenghalain male populations from Gomati River of Tripura.



Fig 3: Results of the principal component showing the scree plot for S. seenghala based on morphometric measurements for different sexes in Gomati River of Tripura.


       
On the other hand in female, the PCA based on 26 truss measurements retained two components according to PA, explaining 98.08 % of the total variance. The first (PC1) and second (PC2) principal components accounted for 76.26 and 21.82 of the total variance, respectively (Table 7 and Fig 3). Thus, PC1 was the most important component contributing to separation among populations. These differences were primarily because of the strong loading of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10, 10-11, 11-12, 1-12, 2-11, 2-12, 2-13, 3-13, 4-13, 5-11, 5-12, 6-11, 11-13 and 12-13 characters. Most of these characters were involved in longitudinal body shape changes i,e. shape changes corresponding to the anterior-posterior body that reflect length changes. Strong loading of characters involved in body depth shape variation was also observed (Table 7). In the case of PC2, within the four characters that exhibited strong loadings, most were associated to body depth shape variation.

Table 7: Component loadings of the first two principle components derived from PCA based on the correlation matrix of 26 truss measurements of Sperata seenghalain female populations from Gomati River of Tripura.



Table 8: Correlations between variables and factors in male correlation matrix [Pearson (n)).



Table 9: Correlations between variables and factors in female correlation matrix [Pearson (n)].


       
Visual examination of plots of PC1 and PC2 scores revealed that the truss measurements were highly overlapped in male specimens among populations in compare with female populations (Fig 4 and 6). Also, plots of PC1 and PC2 scores revealed that morphological measurements in male were closely related among population of Jatanbari, Kakraban, Amarpur, Maharani  and in females Jatanbari, Amarpur and Kakraban populations (Fig 5, 6). The location wise scatter plot of PC1 and PC2 separated the Sonamura population from other populations in both sexes. A high degree of morphological homogeneity was observed between Amarpur and Maharani population in males (Fig 8) and Jatanbari and Amarpur populations in females (Fig 9).

Fig 4: Active variables of principal component analysis for different sexes.



Fig 5: Active observations of principal component analysis for different sexes.



Fig 6: Biplot of 26 morphometric characters of S. seenghala for different sexes in the five study sites of Gomati River of Tripura.



In case of male, the dendrogram derived from cluster analysis revealed the clear isolation of Sonamura population from other populations. In this analysis, the five populations resulted in two main distinct groups: the first group comprised only Sonamura population, while the second group included Maharani, Amarpur, Kakraban and Jatanbari populations. The strong morphometric similarity was observed between the populations of Sperata seenghala from Amarpur and Maharani when compared to other populations. On the other hand, in case of female populations, the dendrogram derived from cluster analysis revealed the distinct isolation of Sonamura population from other populations. In this analysis, the five populations resulted in two main groups: the first group comprised only Sonamura population, while the second group included Amarpur, Jatanbari, Kakraban and Maharani population. The strong morphometric similarity was observed between the populations from Jatanbari and Amarpur when compared to other populations (Fig 7).

Fig 7: The Dendrogram resulting from the cluster analysis (UPGMA-Euclidean distance index) based on morphometric characters among S. seenghala collected from five sampling stations of Gomati River of Tripura.



Fig 8: Scatterplot of 95% confidence ellipse on the first two principal components (PC1 on PC2) for the five groups of S. seenghala, based on morphometric characters in male.



Fig 9: Scatterplot of 95% confidence ellipse on the first two principal components (PC1 on PC2) for the five groups of S.seenghala, based on morphometric characters in female.


         
The results of the present study revealed that S.seenghala from different study area in the River Gomati of Tripura exhibited morphometric variability forming two morphological types in both sexes: Sonamura population was separated as one group from the Jatanbari, Amarpur, Maharani and  Kakraban populations, which together formed a separate group. Generally, teleost fishes show a greater degree of disparity within and between populations when compared to other vertebrates and are more prone to morphological variation induced by different environmental factors such as temperature, habitat condition, pH etc. (Wimberger, 1992). Our study results clearly demonstrate that there is significant phenotypic variation among the five studied populations, also between the sexes.
       
The scatterplot analysis of first two principal components of both male and female populations led to the identification of two phenotypically distinct local populations. The result derived from the PCA analysis in the study was further confirmed by the cluster analysis based on Euclidian square distance, which demonstrated that, among the five populations, four showed closely related and remaining one was clearly distinct.The intermingling relationship was highest in Amarpur and Maharani regions in males and Jatanbari and Amarpur regions in females which may be attributed to the similar geographical positions and climatic conditions of the study areas. The finding of the present study indicated that the population differentiation which resulted from different multivariate analyses in females was higher than in males. In the present study, the size effect had been removed successfully by allometric transformation and the significant differences between the populations are due to body shape variation when tested using ANOVA. Most of the morphometric measurements with strong loading irrespective of sex are related with longitudinal body shape changes i,e. shape changes corresponding to the anterior-posterior body that reflect length changes.
       
Our results are similar to those of Hamed and Hosein (2013); Mohaddasi et al. (2013); Vatandoust et al., (2015); Hanif et al. (2019); Muslimin et al. (2020). Understanding the origin of morphological differences between populations of S.seenghala is challenging because of fish morphology is a complex phenotype that is determined by genetics and environmental factors and the interaction between them. The different sampling stations are geographically isolated and might be environmental and water quality fluctuations in different sampling stations. This is why the dendrogram formed two clusters (Fig 7). The similar result has reported by Mahfuj et al. (2017) and Ethin et al. (2019).
       
Truss network measurements are a series of distances calculated between landmarks that form a regular pattern of connected quadrilaterals or cells across the body form that enhance discrimination between group, based on a systematic detection of body shape differences (Strauss and Bookstein, 1982; Rawat et al., 2017). This type of morphometric analysis has not yet explored in S.seenghala, in spite of its potential to facilitate interpretation of morphological variation through multivariate analysis, such as PCA. In our case, the truss-based system showed a high performance to distinguish S.seenghala populations based on morphological data and also to determine the specific body shape characters that contributed to such variations.

CONCLUSION

The present study showed that each sampling location represents an independent population. The long-term isolation of populations and interbreeding can lead to morphometric variations within populations and this morphometric variation can provide a basis for population differentiation. A detail study involving the molecular markers and environmental aspects may further confirm the present finding unambiguously. It is indispensable to select the genetically superior stocks with better features along with morphometric investigations, so the findings of the study are constructive as basic information of S.seenghala populations in relation to management practices, ex situ conservation and aquaculture practices as well.

ACKNOWLEDGEMENT

The suggestions and constructive comments of all those who helped to improve the final version of this manuscript are gratefully acknowledged. The authors are thankful to those fishermen who help to catch the specimens during sampling. We also wish to thank to Dr. Chiranjit Paul, Netaji Subash Mahavidyalaya,Tripura for his kind help in statistical analysis and Dr. Suman Das, Department of Geography, R. K. Mahavidyalaya, Tripura for help in sampling map representation.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study or preparation of the manuscript.

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    Full Research Article

    Morphometric Variations among Isolated Populations of Catfish Sperata seenghala Sykes, 1839 in Gomati River of Tripura, India: A Truss Network based Analysis

    S
    Sukanta Banik1
    D
    Dipak Das2,*
    0000-0001-9840-5898
    1Department of Zoology, Tripura University, Suryamaninagar-799 022, Tripura, India.
    2Department of Zoology, Ramkrishna Mahavidyalaya, Kailashahar-799 277, Tripura, India.

    ABSTRACT

    Background: Sperata seenghala Sykes, 1839 locally known as guchi aor is an economically important catfish inhabit in the Gomati River of Tripura, India and it is noticed that significantly reducing its population in its natural habitat for the last few decades. Therefore, proper identification of suitable stock is urgent need for artificial breeding purposes.

    Methods: In this study, the morphometric variation of five S. seenghala populations of Gomati river were assessed with multivariate analyses, including principal component analysis (PCA) and cluster analysis (CA) to distinguish different populations.

    Result: Of the total 26 transformed truss measurements, 5 exhibited significant (p<0.05) differences among populations for females and no such significant difference was observed for males. PCA performed on truss data accounted for a total variation of 91.01% and 98.08% by the first two principal components (PC1 and PC2) for male and female populations respectively. The UPGMA dendrogram showed that Sonamura population has the most morphologically different populations in comparison to other populations. The data support the existence of morphological variation across different study location. However, if these findings are supported by further molecular markers, this would be a strong indication of different stocks of this species in the Gomati river of Tripura. The results obtained may contribute to effective conservation and management practices of the studied fish S. seenghala in its natural habitat.

    INTRODUCTION

    Morphometric characters have been commonly used in fishery biology as powerful tools in the field of taxonomic studies for demarcation of diverse populations within species (Miller et al., 1988; Razzaq et al., 2015; Chaklader, 2019; Tripathy, 2020). Phenotypic variation according to environmental variability has been widely used by ichthyologists to differentiate between species and populations (Kovac and Copp, 1999; Simon et al., 2010; Brraich and Akhter, 2015). The body shape is not only determined with the genetics of a fish, it has also been influenced by environment conditions and its habitat (Guill et al., 2003; Kumari et al., 2020). Heterogeneity of the environment, where an animal lives, results in selective pressures that lead to some phenotypic differences among individuals of the same species (Kawecki and Ebert, 2004; Fusco and Minelli, 2010; Colihueque et al., 2017; Sobita and Basudha, 2017).
           
    Morphometric variation between populations can provide a basis for population differentiation, which is an important tool for evaluating population structure and identifying discrete groups (Turan, 1999; Rawat et al., 2017; Kenthao and Jearranaiprepame, 2018; Murugesan et al., 2019; Pathak et al., 2024). However, the alternative system of morphometric measurements called the truss network system, constructed with the help of anatomical landmark points that enhance discrimination among groups, has been less explored for population structure analysis in the freshwater fish. The truss network system is considered superior to traditional morphometrics that use morphometric traits to represent the complete shape of fish, which has been recently used in the field of fish taxonomy and fisheries management and is gaining a momentum for stock assessment study in many parts of the world including India (Hamed and Hosein, 2013; Mir et al., 2013; Jearranaiprepame, 2017; Mahfuj et al., 2017; Basudha and Sushindrajit, 2018; Dwivedi, 2021; Chandran et al., 2022; Datta and Singh, 2023, Bhole et al., 2024). A number of studies have been conducted based on truss network system worldwide, but its application in fishery research of Tripura, a north eastern state of India is still lacking.
           
    Sperata seenghala     (Sykes, 1839), is one of the important catfish locally known as guchi aor and naturally distributed in India, Bangladesh, Afghanistan, Nepal, Pakistan, Myanmar (Talwar and Jhingran, 1991; Saini et al., 2008, Das and Banik, 2021). The genetic purity and population of many freshwater fish species including S. seenghala is deteriorating in the world wide in recent time due to inbreeding, over exploitation, pollution and other anthropogenic effects such as pesticides, agrochemicals etc. There is no stock structure information available for S. seenghala population in any part of the state Tripura of India. The present study is therefore designed to examine the morphometric variability of S. seenghala in their natural habitats of Gomati River, Tripura using truss network system and the findings may useful for proper wild stock identification for aquaculture practices and managements.

    MATERIALS AND METHODS

    A total of 250 adult samples of Sperata seenghala were studied from different sampling station viz. Jatanbari (n=50), Amarpur (n=50), Maharani(n=50), Kakraban (n=50) and Sonamura (n=50) comprising of 50% males and 50% females of Gomati River located in Tripura, North-eastern India. Fifty samples were targeted at each sampling station as suggested by Reist (1985) and following Turan et al., (2006) recommendation using at least 25 samples for morphological analysis. The sample size, sampling period, GPS coordination and sampling station map are presented in Table 1 and Fig 1.

    Table 1: Details of collection of the Sperata seenghala from Gomati River in Tripura.



    Fig 1: Location of sampling sites of Sperata seenghala populations in Gomati River of Tripura, India.


           
    Twenty eight morphometric measurement including two conventional characters, total length and standard length were taken using a digital caliper to the nearest 0.1 mm and 26 derived distance characters were used for truss network based morphometric analysis constructed by interconnecting thirteen landmarks representing the basic shape of the fish (Table 2 and Fig 2) following the method of Colihueque et al. (2017); Jearranaiprepame (2017); Datta and Singh (2023). Visual assessment of genital organ and external sexual features was used to determine fish sex and whenever required fish sample were dissected to identify the sex by macroscopic examination of the gonads and gender was used as the class variables in the analysis of variances (ANOVA) to test for the significant differences in the morphometric characters if any, between the males and females. In respect of truss network measurements, the size-dependent variation was corrected by adapting an allometric method as suggested by Elliott et al., (1995).

    Madj = M (Ls/Lo)b


    Where,
    M= The original measurements.
    Madj= The size-adjusted measurement.
    Lo= The standard length of the fish.
    Ls= The overall mean of the standard length for all samples in each analysis and b is estimated for each character from the observed data as the slope of the regression of log M on log Lo using all fish from both groups.

    Table 2: Morphometric measurements made for each sample of S.seenghala collected from the Gomati River of Tripura, India.



    Fig 2: Location of 13 landmarks and scheme of the truss network used in S. seenghala morphometric analysis.


           
    The results derived from the allometric method were confirmed by testing the significance of correlation between transformed variables and standard length (Turan, 1999).
           
    The significant differences for each morphometric characters among the five populations were tested by univariate ANOVA using each character as a response variable. In this study, 26 size-corrected morphometric variables were subjected to multivariate analysis comprising principal component analysis (PCA) and cluster analysis (CA) in order to evaluate population wise morphometric variation among five study locations. Principal component analysis aids to lessen morphometric data, eliminate redundancy among the variables and to identify a number of influential variables for the discrimination of population. Cluster analysis by adopting the Euclidean distance as a measure of dissimilarity and using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) in the form of dendrogram was also performed as a complementary of PCA analysis to determine the phenotypic differences among the different populations. The Excel (Microsoft Office 2007), XLSTAT and Pact3 were used for statistical analyses.

    RESULTS AND DISCUSSION

    Table 3 and Table 4 shows the average values with standard deviation of TL,SL and 26 truss measurements analyzed for male and female populations respectively. The population of Jatanbari, Amarpur, Maharani, Kakraban and Sonamura were more or less similar in respect of mean size of total length and standard length for both sexes. One-way ANOVA test showed that incase of female 5 truss measurements out of 26 were found to be significantly (p< 0.05) different among populations whereas none was statistically significant difference in male. All correlation coefficients were estimated to know the degree of association between the traits for both male and female populations (Table 8 and Table 9). The ANOVA (p<0.05) revealed the most effective truss morphometric variation on sexual dimorphism was found in Maharani population followed by Sonamura (Table 5). In case of male, the PCA based on 26 truss measurements retained two components according to PA, explaining 91.01 % of the total variance. The first (PC1) and second (PC2) principal components accounted for 78.22 and 12.79 of the total variance, respectively (Table 6 and Fig 3). Thus, PC1 was the most important component contributing to separation among populations. These differences were primarily because of the strong loading of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10, 10-11, 11-12, 1-12, 2-11, 2-12, 2-13, 3-11, 3-13, 4-13, 5-11, 5-12, 6-10, 6-11, 11-13 and 12-13 characters.Almost 50% of these characters were involved in longitudinal body shape changes i.e. shape changes corresponding to the anterior-posterior body that reflect length changes. Strong loading of characters involved in body depth shape variation was also observed (Table 6). In the case of PC2, within the two characters that exhibited strong loadings, most were associated to body depth shape variation.

    Table 3: Morphometric data of male for 28 characters of five Sperata seenghala populations from Gomati River of Tripura.



    Table 4: Morphometric data of female for 28 characters of five Sperata seenghala populations from Gomati River of Tripura.



    Table 5: Results of Anova for sexual dimorphism of truss morphometric characters of different sampling station.



    Table 6: Component loadings of the first two principle components derived from PCA based on the correlation matrix of 26 truss measurements of Sperata seenghalain male populations from Gomati River of Tripura.



    Fig 3: Results of the principal component showing the scree plot for S. seenghala based on morphometric measurements for different sexes in Gomati River of Tripura.


           
    On the other hand in female, the PCA based on 26 truss measurements retained two components according to PA, explaining 98.08 % of the total variance. The first (PC1) and second (PC2) principal components accounted for 76.26 and 21.82 of the total variance, respectively (Table 7 and Fig 3). Thus, PC1 was the most important component contributing to separation among populations. These differences were primarily because of the strong loading of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10, 10-11, 11-12, 1-12, 2-11, 2-12, 2-13, 3-13, 4-13, 5-11, 5-12, 6-11, 11-13 and 12-13 characters. Most of these characters were involved in longitudinal body shape changes i,e. shape changes corresponding to the anterior-posterior body that reflect length changes. Strong loading of characters involved in body depth shape variation was also observed (Table 7). In the case of PC2, within the four characters that exhibited strong loadings, most were associated to body depth shape variation.

    Table 7: Component loadings of the first two principle components derived from PCA based on the correlation matrix of 26 truss measurements of Sperata seenghalain female populations from Gomati River of Tripura.



    Table 8: Correlations between variables and factors in male correlation matrix [Pearson (n)).



    Table 9: Correlations between variables and factors in female correlation matrix [Pearson (n)].


           
    Visual examination of plots of PC1 and PC2 scores revealed that the truss measurements were highly overlapped in male specimens among populations in compare with female populations (Fig 4 and 6). Also, plots of PC1 and PC2 scores revealed that morphological measurements in male were closely related among population of Jatanbari, Kakraban, Amarpur, Maharani  and in females Jatanbari, Amarpur and Kakraban populations (Fig 5, 6). The location wise scatter plot of PC1 and PC2 separated the Sonamura population from other populations in both sexes. A high degree of morphological homogeneity was observed between Amarpur and Maharani population in males (Fig 8) and Jatanbari and Amarpur populations in females (Fig 9).

    Fig 4: Active variables of principal component analysis for different sexes.



    Fig 5: Active observations of principal component analysis for different sexes.



    Fig 6: Biplot of 26 morphometric characters of S. seenghala for different sexes in the five study sites of Gomati River of Tripura.



    In case of male, the dendrogram derived from cluster analysis revealed the clear isolation of Sonamura population from other populations. In this analysis, the five populations resulted in two main distinct groups: the first group comprised only Sonamura population, while the second group included Maharani, Amarpur, Kakraban and Jatanbari populations. The strong morphometric similarity was observed between the populations of Sperata seenghala from Amarpur and Maharani when compared to other populations. On the other hand, in case of female populations, the dendrogram derived from cluster analysis revealed the distinct isolation of Sonamura population from other populations. In this analysis, the five populations resulted in two main groups: the first group comprised only Sonamura population, while the second group included Amarpur, Jatanbari, Kakraban and Maharani population. The strong morphometric similarity was observed between the populations from Jatanbari and Amarpur when compared to other populations (Fig 7).

    Fig 7: The Dendrogram resulting from the cluster analysis (UPGMA-Euclidean distance index) based on morphometric characters among S. seenghala collected from five sampling stations of Gomati River of Tripura.



    Fig 8: Scatterplot of 95% confidence ellipse on the first two principal components (PC1 on PC2) for the five groups of S. seenghala, based on morphometric characters in male.



    Fig 9: Scatterplot of 95% confidence ellipse on the first two principal components (PC1 on PC2) for the five groups of S.seenghala, based on morphometric characters in female.


             
    The results of the present study revealed that S.seenghala from different study area in the River Gomati of Tripura exhibited morphometric variability forming two morphological types in both sexes: Sonamura population was separated as one group from the Jatanbari, Amarpur, Maharani and  Kakraban populations, which together formed a separate group. Generally, teleost fishes show a greater degree of disparity within and between populations when compared to other vertebrates and are more prone to morphological variation induced by different environmental factors such as temperature, habitat condition, pH etc. (Wimberger, 1992). Our study results clearly demonstrate that there is significant phenotypic variation among the five studied populations, also between the sexes.
           
    The scatterplot analysis of first two principal components of both male and female populations led to the identification of two phenotypically distinct local populations. The result derived from the PCA analysis in the study was further confirmed by the cluster analysis based on Euclidian square distance, which demonstrated that, among the five populations, four showed closely related and remaining one was clearly distinct.The intermingling relationship was highest in Amarpur and Maharani regions in males and Jatanbari and Amarpur regions in females which may be attributed to the similar geographical positions and climatic conditions of the study areas. The finding of the present study indicated that the population differentiation which resulted from different multivariate analyses in females was higher than in males. In the present study, the size effect had been removed successfully by allometric transformation and the significant differences between the populations are due to body shape variation when tested using ANOVA. Most of the morphometric measurements with strong loading irrespective of sex are related with longitudinal body shape changes i,e. shape changes corresponding to the anterior-posterior body that reflect length changes.
           
    Our results are similar to those of Hamed and Hosein (2013); Mohaddasi et al. (2013); Vatandoust et al., (2015); Hanif et al. (2019); Muslimin et al. (2020). Understanding the origin of morphological differences between populations of S.seenghala is challenging because of fish morphology is a complex phenotype that is determined by genetics and environmental factors and the interaction between them. The different sampling stations are geographically isolated and might be environmental and water quality fluctuations in different sampling stations. This is why the dendrogram formed two clusters (Fig 7). The similar result has reported by Mahfuj et al. (2017) and Ethin et al. (2019).
           
    Truss network measurements are a series of distances calculated between landmarks that form a regular pattern of connected quadrilaterals or cells across the body form that enhance discrimination between group, based on a systematic detection of body shape differences (Strauss and Bookstein, 1982; Rawat et al., 2017). This type of morphometric analysis has not yet explored in S.seenghala, in spite of its potential to facilitate interpretation of morphological variation through multivariate analysis, such as PCA. In our case, the truss-based system showed a high performance to distinguish S.seenghala populations based on morphological data and also to determine the specific body shape characters that contributed to such variations.

    CONCLUSION

    The present study showed that each sampling location represents an independent population. The long-term isolation of populations and interbreeding can lead to morphometric variations within populations and this morphometric variation can provide a basis for population differentiation. A detail study involving the molecular markers and environmental aspects may further confirm the present finding unambiguously. It is indispensable to select the genetically superior stocks with better features along with morphometric investigations, so the findings of the study are constructive as basic information of S.seenghala populations in relation to management practices, ex situ conservation and aquaculture practices as well.

    ACKNOWLEDGEMENT

    The suggestions and constructive comments of all those who helped to improve the final version of this manuscript are gratefully acknowledged. The authors are thankful to those fishermen who help to catch the specimens during sampling. We also wish to thank to Dr. Chiranjit Paul, Netaji Subash Mahavidyalaya,Tripura for his kind help in statistical analysis and Dr. Suman Das, Department of Geography, R. K. Mahavidyalaya, Tripura for help in sampling map representation.

    CONFLICT OF INTEREST

    The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study or preparation of the manuscript.

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