Abstract
OBJECTIVES: This study provides a proof-of-concept for incorporating evolutionary theory into forensic anthropology practice. Specifically, we test whether innominate measurements used in the DSP 2 sex-estimation method reflect known patterns of morphological integration and whether variable redundancy can be reduced without compromising classification accuracy. MATERIALS AND METHODS: Innominate measurements were obtained from published datasets totaling 3045 individuals. Principal component analysis (PCA) was used to identify clusters of measurements. Relative standard deviation of eigenvalues was used to assess the degree of morphological integration. Posterior probabilities of sex classification were computed using one variable per cluster (cluster-based approach) and compared to a randomly selected four-variable approach, consistent with the minimum recommended by the original study. Simulations were used to generate posterior distributions of accuracy and the percentage of samples reaching a decision threshold. RESULTS: Three distinct clusters of innominate measurements were identified, broadly corresponding to known modules of the innominate. The degree of morphological integration was higher within clusters than in the full measurement set or nonintegrated matrices. The cluster-based classification approach showed comparable accuracy (mean = 96.38%) to the randomized approach (mean = 95.64%) despite using only three variables. While fewer individuals were assigned a sex under the cluster-based method, the results demonstrated higher consistency. DISCUSSION: Results suggest that accounting for morphological integration can streamline sex estimation by reducing variable redundancy without compromising accuracy. This study demonstrates how evolutionary principles can improve the theoretical foundation of forensic anthropology methods and offers a framework for future method development grounded in evolutionary theory.