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Bolstering geometric morphometrics sample sizes with damaged and pathologic specimens: Is near enough good enough?
D Rex Mitchell1,2, Claire A Kirchhoff3, Siobhán B Cooke4,5
1Department of Anthropology, University of Arkansas, Fayetteville, AR, USA.
Journal of Anatomy
|January 9, 2021
Summary
Including damaged or pathological primate specimens in geometric morphometric studies enhances the assessment of normal shape variation. These specimens can strengthen statistical support for key biological predictors like sex and size.
Area of Science:
- Paleontology
- Biological Anthropology
- Comparative Anatomy
Background:
- Geometric morphometric studies often rely on museum skeletal specimens.
- Damaged or pathological specimens are typically excluded from shape analyses.
- Previous research on specimen inclusion has been limited to 2D data.
Purpose of the Study:
- To investigate the impact of including damaged/pathological specimens in 3D geometric morphometric analyses.
- To determine if these specimens strengthen or confound shape variation analyses.
- To assess the utility of damaged specimens for understanding intraspecific shape variation.
Main Methods:
- Collected 3D coordinate data from 100 crab-eating macaque crania and mandibles.
- Analyzed five datasets with varying degrees of specimen damage/pathology.
- Compared results from datasets including questionable specimens with those of high-quality specimens.
Main Results:
- Inclusion of damaged/pathological specimens increased variation linked to allometry, sexual dimorphism, and covariation.
- Analysis of only questionable specimens yielded consistent results for dominant shape aspects but affected minor components.
- Bolstered small datasets with damaged specimens adequately assessed major shape components, revealing finer scale differences.
Conclusions:
- Damaged/pathological specimens contribute normal, repeatable variation, emphasizing dominant shape predictors in larger datasets.
- Exclusion of these specimens may omit important demographic-specific shape variation.
- Inclusion is recommended for studies on dominant intraspecific shape variation, with caution for fine-scale analyses.

