Abstract
Cirrhinus mrigala (Hamilton, 1822) is an Indian major carp originally inhabitant of the Ganga river system in Northern India. No information is available on morphometric variation in natural populations of C. mrigala from Ganga river basin. Therefore, to promote management optimization, this study was undertaken to evaluate the population structure on landmark-based morphometric variations of this commercially important species in Ganges. A total of 381 fish samples of C. mrigala were collected from 10 locations of 10 rivers belonging to Ganges basin. A 12-landmark truss network system was used to generate 66 morphometric variables were transformed (standard length was excluded from the final analysis retaining 65 variables). Transformed measurements were employed to univariate analysis of variance, principal component analysis (PCA) and discriminant function analysis (DFA). Univariate analysis of variance showed significant differences in all the 65 transformed morphometric characters studied. Eight principal components were extracted from 65 significant characters accounting for 94.1 % of the variation. Forward stepwise discriminant function analysis of the 65 significant variables produced eight discriminating variables. DFA extracted eight discriminating functions contributed overall to the variance. The first discriminant function (DF) accounted for 37.9 % while second DF accounted for 23.5% of the among-group variability. In DFA, 45.7 % original while 39.1 % of cross-validated grouped cases were correctly classified in the overall assignment of individuals, indicating low distinction among the stocks of all the species i.e. low isolation of the stocks for the morphometric measurements. Common ancestry in the prehistoric period and migration of individuals between the rivers could be the possible reason for the observed low level of morphometric differentiation among wild mrigal populations. In future, the application of molecular genetic markers along with morphometric studies would be an effective method to examine the genetic component of phenotypic relatedness between geographic regions and to facilitate the development of management recommendations.
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