Best practice for osteological sexing in forensics and bioarchaeology: The utility of combining metric and morphological traits from different anatomical regions

Abstract

This paper aims to systematically investigate the value of combining traits from different anatomical regions in osteological sexing by contrasting the utility of single traits and established scores with those of ensembles of traits from single or multiple anatomical regions, allowing metric and morphological traits to be combined. The utility was defined as the fraction of the population for whom we could reach a posterior probability above 95% of being male or female. A total of 675 adult individuals from the sixth to eighth century AD cemetery of Mannheim Bösfeld, Germany, were assessed, and 27 postcranial metric traits and 41 morphological traits from the pelvis, mandible, and cranium were used. In addition, 13 metric and 3 morphological scores were considered. Linear discriminant analysis (LDA) was used to construct rules and cross validation to determine accuracy and utility. These parameters were determined for single traits and scores, trait groups defined by anatomical regions and/or previously considered in the literature, and ensembles of traits defined by selecting several promising traits from different anatomical regions. Accuracy of single traits ranged from 0.76 to 0.94, with scores even reaching 0.97, but utility remained around 0.2–0.4 for metric traits and up to 0.6 for morphological traits. Only scores and ensembles combining traits from different anatomical regions reached a utility above 0.7; that is, sex could be estimated in more than 70% of the individuals with a posterior probability above 95%. When selecting a limited number of traits for systematic sexing in a human skeletal series, it is advisable to select traits from different anatomical regions to obtain a reasonably reliable result in as many individuals as possible. Large scale investigations covering all relevant anatomical regions and involving a wide range of populations are required for more precise recommendations.