AI Article Synopsis
- The study introduces a new method called functional data geometric morphometrics (FDGM) to classify three shrew species from Peninsular Malaysia using 2D landmark data.
- FDGM transforms this data into continuous curves represented by linear combinations of basis functions, offering a comparison to traditional geometric morphometrics (GM).
- Results indicated that FDGM outperformed GM, with the dorsal view of the crania being the most effective for species classification, and various machine learning approaches were tested to enhance classification accuracy.
Article Abstract
This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11227550 | PMC |
| http://dx.doi.org/10.1038/s41598-024-66246-z | DOI Listing |
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