Several statistical methods have been employed to study morphological divergences among wild populations of S. sarana collected from different geographic regimes. This is the first study on the population delineation of S. sarana using truss network analysis with geometric morphometrics. The results revealed that heterogeneity exists among examined populations of S. sarana procured from the specific sites of rivers (Ganga, Godavari, Narmada, and Mahanadi). Significant variations were detected for most of the analyses. The PCA loadings (truss analysis) of principal components revealed distinctness between populations. Though, there was a slight overlap found in the characters which were examined among the four groups. This separation was corroborated by DFA (truss analysis), showed significant morphological heterogeneity among populations, the level of differentiation between most of them as evidenced by a slight overlap of statistical data on derived plots.
Using geometric morphometrics, CVA plot obtained, have shown a slight level of overlaps among groups with a high percentage of correct classification suggesting differentiation among the examined populations. The PCA (geometric analysis) and DFA (geometric analysis) further confirmed the morphological heterogeneity among populations of S. sarana. The higher misclassification (DFA) observed for the Ganga with Mahanadi River and least with the Narmada. The biological variations of morphometric characters based on DFA are majorly associated with head morphology, covering lateral body lengths and caudal peduncle regions. Shape differences have been visualized with the deformation grids using geometric morphometrics. Geometric morphometry-based deformations grids (wireframes and relative warps) of average shapes between populations correspond to the high values of statistical distance between them and confirm the distinctness of populations in their immediate anatomical context.
Overall, the variations among the four groups in this study were largely owing to the dissimilarities of morphometric characters broadly associated to head, and body characteristics. However, the shape differences observed in this study presents little practical use in terms of discriminating fish populations in the field. The visualization of the body shape differences, associated with other groups of correlated morphological traits, allowed to obtain a clear diagnosis of fish morphology for each population (Viscosi and Cardini 2011; Orlofske and Baird 2014). Visualization tools might help to further study of the putative underlying mechanisms involved (Manacorda and Asurmendi 2018). The result of the present study is in line with other studies based on truss analysis (Dwivedi 2019; Khan et al. 2012; Mohaddasi et al. 2013a, Hanif et al. 2019) and geometric morphometrics (Mohaddasi et al. 2013b; Geladakis et al. 2018; Pérez-Quiñonez et al. 2018) which have shown the fish species to have a distinctive morphology.
The highest percentage of correct classification for the Narmada River population indicates greater distinction from the other populations which may be possibly due to west word flow of Narmada River compared to east word flow of other rivers. Overall, the selected populations were geographically isolated from each-others which could have hindered the movement of fish from intermingling with populations in other rivers. Therefore, the variability of morphological characters among populations possibly accredited due to separate geographical locations, the distance between the rivers, as well as the environmental variability of the river experienced by each population which leads to the local adaptations (Paugy and Lévêque 1999, Pardo 2002). The morphological variation could probably be coupled with the variation of feeding regimes and habitat circumstances (Langerhans et al. 2007; Sajina et al. 2011; Drinan et al. 2012; Khan et al. 2012; Lostrom et al. 2015; Jearranaiprepame 2017). Additionally, different reports indicate variations of the whole fish body are mainly due to fish inhibiting in different flow regimes (Jearranaiprepame 2017; Shukla and Bhat 2017).
Earlier efforts have been made to differentiate S. sarana populations using traditional morphometry (Siddik, et al. 2016). In the case of genetic studies, S. sarana, have only been carried out in Bangladesh from three geographically distant locations, analyses on RAPD revealed some degrees of genetic diversity among populations (Kabir et al. 2015). Furthermore, the overall accuracy of population differentiation in the present study is comparable to that found in other studies for closely-related Indian fishes. Findings reported by Mir et al. (2013) and Shukla and Bhat (2017) indicating that the results of the present study are in agreement with previous studies. Contrary, Das et al. (2013) reported low morphometric divergences (truss-based study) despite Cirrhinus mrigala populations were collected from isolated geographic locations.
Morphological differentiation can enable individuals to survive with existing environmental variability (Senay et al. 2015). Fishes are excellent model systems for studies on inter as well as intra-specific divergences to understand ecological correlates of morphological diversifications. Some factors were assumed to be controlling the differences observed such as plasticity owing to habitat dissimilarities or could be due to environment and genotype interactions. Earlier, it was assumed that the variation of morphometric characters was exclusively genetic, but recent studies have established its relation with environmental factors (Georga and Koumoundouros 2010; Nahar et al. 2015; Sharker et al. 2015) and role of epigenetics cannot be ruled out as suggested by many scientists, population differentiation associated with ecological factors have the main element as epigenetic (Felsenfeld 2014).
As mentioned above, intraspecific variability can have huge ecological effects (Fridley and Grime, 2010; Becks et al. 2010; Bolnick et al. 2011). Charles Darwin indicated that variations among individuals of species offer the raw materials for natural selection. All hereditary characters in the genotype are not expressed in the phenotype. Further, variation not attributable to genetic factors not necessarily is environmental. Interestingly, the environment is often made responsible for non-genetic variations in phenotypes but it could be because of meta-stable epigenetic regulation (Wong et al. 2005). Considering that morphological variations are raw materials, truss and geometric analysis techniques are the best approaches to delineate populations on the bases of morphological characters. Results from the present study show that geometric morphometrics can provide additional information for shape delineation between populations that might otherwise be unnoticed. Further, the use of both truss and geometric morphometrics can provide deeper insight into the pattern of shape variations. This study could not answer whether are the results of morphological plasticity, genetic difference, or interaction of either mechanisms or epigenetic related hence, to resolve this, additional studies such as common garden experiments and epigenetic and/or genetics studies can be performed. More precise results might be obtained if larger sample sizes with a greater geographical extent were available. Geometric morphometrics analyses that include other aspects of fish morphology could enhance the precision of results