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<p>We examined the phenotypic variation in <i>Schizopyge niger</i> across three locations in the Kashmir Valley—via the Jhelum River, Dal Lake and Manasbal Lake. Geometric morphometrics assessed body shape differences among these populations. <i>S. niger</i> holds economic importance in the Kashmir Valley, and our study aimed to distinguish it based on shape and size variations. A total of 91 <i>S. niger</i> individuals were collected for analysis. The data obtained from 11 homologous landmarks were analyzed using Generalized Procrustes Analysis (GPA), Principal Component Analysis (PCA), and Canonical Variate Analysis (CVA). The Procrustes ANOVA conducted on shape data (<i>n</i> = 91) reveals notable shape differences among individuals (<i>p</i> &lt; 0.05). Principal Component Analysis (PCA) extracted 18 components, with the first two accounting for 51.33% of total shape variation. Canonical Variates Analysis (CVA) further distinguished populations, with CV1 and CV2 explaining 72.78% and 27.21% of variance, respectively. Significant differences in shape, particularly in body depth, head, and fin regions, were observed between the populations. Environmental factors or changes in feeding habits may be the cause of these variations. Cross-validated discriminant analysis resulted in a high classification accuracy of 98.6%.</p>

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Geometric Morphometry Analysis of Body Form Variability in Schizopyge niger from Kashmir Himalayas

  • 期刊论文

  • 发表日期：

    2026年4月3日

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  • Sobiya Gul,
  • Ifrah Rashid

摘要

We examined the phenotypic variation in Schizopyge niger across three locations in the Kashmir Valley—via the Jhelum River, Dal Lake and Manasbal Lake. Geometric morphometrics assessed body shape differences among these populations. S. niger holds economic importance in the Kashmir Valley, and our study aimed to distinguish it based on shape and size variations. A total of 91 S. niger individuals were collected for analysis. The data obtained from 11 homologous landmarks were analyzed using Generalized Procrustes Analysis (GPA), Principal Component Analysis (PCA), and Canonical Variate Analysis (CVA). The Procrustes ANOVA conducted on shape data (n = 91) reveals notable shape differences among individuals (p < 0.05). Principal Component Analysis (PCA) extracted 18 components, with the first two accounting for 51.33% of total shape variation. Canonical Variates Analysis (CVA) further distinguished populations, with CV1 and CV2 explaining 72.78% and 27.21% of variance, respectively. Significant differences in shape, particularly in body depth, head, and fin regions, were observed between the populations. Environmental factors or changes in feeding habits may be the cause of these variations. Cross-validated discriminant analysis resulted in a high classification accuracy of 98.6%.