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Updated: Apr 6, 2026

Systematic Assessment of Mammalian Skull Specimens for Dental and Temporomandibular Joint Pathology
Published on: August 22, 2022
A multivariate approach to assess variation in tooth mineralization using free-lived and captive-raised chimpanzees
Julia C Boughner1, Jasmine Der1, Kevin L Kuykendall2
1Department of Anatomy and Cell Biology, University of Saskatchewan, Saskatoon, SK, Canada.
Objectives:
Understanding variation in dental development among primates is important to accurately characterize species-specific sequences and times of tooth formation. Conventional approaches that summarize dental development data (i.e., dental maturity score, DMS) inherently omit information about the full range of variation in raw scores; thus, classic bivariate analyses are limited for exploring patterns of variation in detail. Here we report a new multivariate approach to simultaneously assess all raw dental scores, for all teeth, among all individuals for all groups, thus retaining much greater detail about population-specific patterns of variation.
Methods:
We scored (0-12) permanent tooth mineralization using radiographs of mandibles of captive-raised known-age chimpanzees (Pan troglodytes; n = 114) and free-lived age-unknown conspecifics (n = 54). As a test of our method we also scored free-lived baboons (Papio anubis, n = 50) because of well-described contrasting patterns of permanent molar initiation between Papio and Pan. Using principal component analysis (PCA), we investigated how crypt, crown and root formation scores covaried with each other in all three genera, and with chronological age in captive chimpanzees.
Results:
PCA successfully captured additional detail about variation among raw scores. Also, compared to DMS, PC1 scores correlated equivalently well with known ages but had lower prediction error.
Conclusions:
We found different patterns of variation in scores between younger juvenile free and captive chimpanzees but saw no wholesale differences between groups. Pan and Papio showed different patterns of variation, further validating this multivariate approach to visualize, quantify and compare raw dental score datasets among primate species.
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