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Intrageneric taxonomic distinction based on morphological variation in the macaque (Macaca) skeleton.

Brittany Kenyon-FlattNoreen von Cramon-Taubadel
Published in: Anatomical record (Hoboken, N.J. : 2007) (2023)
Taxonomic classification is important for understanding the natural world, yet current methods for species assessment often focus on craniodental morphology rather than the entire skeleton. Moreover, it is currently unknown how much variation could, or should, exist intragenerically. Here, we tested whether taxonomy can be accurately predicted based on patterns of morphological variation in macaques (H 1 ) and whether postcranial bones reflect subgeneric macaque taxonomy similarly, or better, than the cranium (H 2 ). Data included 3D scans of cranial and postcranial bones for eight macaque species (Macaca arctoides, Macaca fascicularis, Macaca fuscata, Macaca mulatta, Macaca nemestrina, Macaca nigra, Macaca radiata, and Macaca sylvanus). Fixed anatomical and semilandmarks were applied to scans of eight skeletal elements (crania = 45; mandible = 31; scapula = 66; humerus = 38; radius = 33; os coxa = 28; femur = 40; tibia = 40). For each skeletal element, regression analyses were performed to minimize the effects of sexual dimorphism. Between-groups principal components analysis was used to visualize the major patterns of among-species morphological variation, while the strength of correct taxon classification was measured with discriminant function analysis. Results suggested accepting the alternate hypothesis that different macaque species can be distinguished morphologically. Both cranial and many postcranial elements appeared to possess a taxonomic signal, and the limb bones-especially the upper limb-are reported to be more useful for taxonomic assessment than previously realized. Theoretically, certain behaviors and/or ecogeographical factors, as well as phylogeny, influenced skeletal morphology in macaques, likely contributing to taxonomic distinctions among different species.
Keyphrases
  • computed tomography
  • upper limb
  • machine learning
  • genetic diversity
  • magnetic resonance
  • mental health
  • body composition
  • big data
  • artificial intelligence