Forensic Tools for Species Identification of Skeletal Remains: Metrics, Statistics, and OsteoID

SIMPLE SUMMARY: Forensic anthropologists are commonly asked to determine whether bones are of human origin and, if not, to which species they belong. Current practice usually relies on visual assessments rather than quantitative analyses. This study aimed to test the utility of basic bone metrics in discriminating human from nonhuman elements and assigning faunal species. A database of more than 50,000 skeletal measurements was compiled from humans and 27 nonhuman species. Equations and classification trees were developed that can differentiate human from nonhuman species with upwards of 90% accuracy, even when the bone type is not first identified. Classification trees return accuracy rates greater than 98% for the human sample. These quantitative models provide statistical support to visual assessments and can be used for preliminary assessment of a bone’s forensic significance at a scene. The statistical models, however, could not classify species at acceptable rates. For species identification, a freely available web tool (OsteoID) was created from the study data, where users can filter photographs of potential bones/species using a few basic measurements and access 3D scans and additional resources to facilitate identification. OsteoID provides an important resource for forensic anthropologists lacking access to large comparative skeletal collections, as well as other disciplines where comparative osteological training is necessary. ABSTRACT: Although nonhuman remains constitute a significant portion of forensic anthropological casework, the potential use of bone metrics to assess the human origin and to classify species of skeletal remains has not been thoroughly investigated. This study aimed to assess the utility of quantitative methods in distinguishing human from nonhuman remains and present additional resources for species identification. Over 50,000 measurements were compiled from humans and 27 nonhuman (mostly North American) species. Decision trees developed from the long bone data can differentiate human from nonhuman remains with over 90% accuracy (>98% accuracy for the human sample), even if all long bones are pooled. Stepwise discriminant function results were slightly lower (>87.4% overall accuracy). The quantitative models can be used to support visual identifications or preliminarily assess forensic significance at scenes. For species classification, bone-specific discriminant functions returned accuracies between 77.7% and 89.1%, but classification results varied highly across species. From the study data, we developed a web tool, OsteoID, for users who can input measurements and be shown photographs of potential bones/species to aid in visual identification. OsteoID also includes supplementary images (e.g., 3D scans), creating an additional resource for forensic anthropologists and others involved in skeletal species identification and comparative osteology.