Using principal component analysis to identify the component affecting skull weight of Japanese Quail
2025
israa Abd Alsada
Principal Component Analysis (PCA) is a powerful statistical tool used to reduce the complexity of large datasets while preserving significant variations. In this study, PCA was applied to explore the morphological traits of Japanese quails (Coturnix japonica), specifically focusing on skull measurements to identify key components affecting skull weight. A total of 112 quails (64 males and 48 females) were measured for various skull features, which were then analyzed through PCA. The analysis extracted three principal components for both sexes, explaining 52.76% of the variance in males and 56.52% in females. Key features such as Cerebellar Prominentia and Paraoccipital Process were identified as significant contributors to skull morphology. PCA was correspondingly applied to the measurements of male and female Japanese Quails' skulls, in order to identify those components which may explain most of the variation in skull weight. In this respect, simplification of data by PCA may indicate which morphological features supply most to the observed variation in skull weight and, therefore, provide interesting insights into the avian skull morphology. The goal of this research will be helpful in the laying of clear understanding regarding the anatomical features highly influential for skull structure, and of high importance to evolutionary biology, studies of veterinary importance, and poultry breeding programs. These findings highlight the applicability of PCA in anatomical studies and provide a deeper understanding of avian skull morphology.
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