A three-dimensional functional data geometric morphometrics approach for exploring shape variation

Aneesha Balachandran Pillay,Sophie Dabo‐Niang,Dharini Pathmanathan

Research Square (Research Square)(2023)

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摘要
Abstract This research introduces a new method for analysing shape variation for 3D landmark coordinate data, called functional data geometric morphometrics (FDGM). FDGM uses functional data analysis (FDA) to treat landmark coordinates as continuous curves or functions. This allows for a more exhaustive description and analysis of shape variation compared to geometric morphometrics (GM), which treats landmark coordinates as discrete points. A simulation study was conducted to demonstrate the general effectiveness of FDGM compared to the GM. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to both the landmark coordinates and the functional form of the landmark coordinates. The analyses favoured FDGM. The reconstruction error for FDGM was smaller when smoothed data was considered in generating the data. FDGM and GM were then applied to distinguish dietary categories of kangaroos (omnivores, mixed feeders, browser, and grazer) using landmarks obtained from crania of 41 kangaroo extant species. The results demonstrate that FDGM is a powerful method for analysing shape variation in 3D landmark coordinate data. FDGM can substantially enhance the domain of morphometrics, providing a valuable resource for driving future progress within this realm.
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关键词
geometric morphometrics approach,shape,variation,three-dimensional
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