Land-Mark based morphometric and meristic variations in two congeneric hilsha population, Tenualosa ilisha and Tenualosa toli from Bangladesh water bodies
DOI:
https://doi.org/10.3329/ajmbr.v6i2.48072Keywords:
land-mark; morphometrics; truss-network; population structure; Tenualosa spp.Abstract
The morphometric characters are effectively used for the better differentiation among the fish population and sustainable management. The appraisal of the natural population stock and morphological variation within and between two hilsha species (Tenualosa ilisha and Tenualosa toli) from three different habitat (Coastal, riverine and marine) of Bangladesh, were investigated by applying the land mark based morphometric and meristic variation measurement methods. All data were adjusted and Univariate ANOVA, where discriminant function analysis (DFA) and principal component analysis (PCA) exhibited the divergences in eight morphometric measurements and eight truss network measurements among the three stocks of T. ilisha. The 1st DFA accounted for 89.8% & 87.4% and the second DFA resolved 10.2% and 12.6%, respectively in morphometric characteristics variation among the group studied. Scattered plotting from PCA and dendogram from cluster analysis (CA) revealed that, the river habitants were morphologically different from the coastal and marine population. Twelve of fifteen morphometric measurements and thirteen of fourteen truss network measurements showed significant differences between T. ilisha and T. toil with significant variation in meristic characters. PCA revealed 89.23% and 88.29% in case of morphometric and truss measurement respectively confirmed high degree of variations in morphological characteristics between two species. Overall, our results based on morphometrics with truss measurements together provide useful information about the morphological differentiation which will be helpful for sustainable exploration and effective management for these two species.
Asian J. Med. Biol. Res. June 2020, 6(2): 265-282
Downloads
24
31