Estimation of the Bluespot Mullet Valamugil seheli Differentiation using Multivariate Analyses for Morphometric Traits | ||
| Egyptian Journal of Aquatic Biology and Fisheries | ||
| Article 35, Volume 28, Issue 4, July and August 2024, Pages 573-593 PDF (1.36 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ejabf.2024.369164 | ||
| Author | ||
| Salem Almarri et al. | ||
| Abstract | ||
| In the current work, one hundred and fifty specimens of the bluespot mullet, Valamugil seheli, were randomly selected from the Egyptian lake Qarun, as well as the wetlands Manzalla and Burullus to differentiate them based on principal components analysis (PCA) and discriminant function analysis (DFA). In V. seheli populations, the PC1, PC2, and PC3 accounted for almost 77.87% of the observed morphometric variance. Of the variance, 43.2% was explained by the PC1 and 21.23% of the variance was explained by PC2. All populations of V. seheli had morphological data that allowed the three main groups to be distinguished from one another. All characters that were divided along PCI and PC2 showed significant population differences according to univariate analysis. Three distinct morphotypes were found to be significantly suggested by the step-wise discriminant function analysis of the body weight (BW), caudal peduncle depth (CPD), dorsal fin length (DFL), pectoral fin length (PecFL), maximum body height (MBH), and condition factor (CF) ratios. One of the two canonical functions explained 80.72% of the variance, whereas the other accounted for 100% of the variance. The Manzalla population, which scored negatively (< zero), was successfully separated by the first function, whereas the Burullus population, which scored positively (> zero), was successfully separated by the second. The current examination clearly distinguished between the blue-spot mullet populations using the morphometric analysis in a comparatively quick, cheap, and easily accessible method. | ||
| Keywords | ||
| Bluespot mullet; Valamugil seheli; Morphometric traits; Principal component analysis; Discriminant analysis | ||
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