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

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.

Keywords: comparative osteology; forensic anthropology; forensic significance; human osteology; medicolegal death investigation; nonhuman; skeletal morphology.

Figure 1

Decision tree developed to classify human (Hum) versus nonhuman (Non) elements from a pooled-bone sample (i.e., all long bones pooled). Working from the top of the tree, the variable listed at each level would be measured, and based on the provided sectioning point, the user would move down the tree to the next level. This process would continue until arriving at a terminal node where classification would be assigned. Terminal nodes are outlined in red. Group classification is highlighted in yellow and bolded at each node. Percentages and counts of bones classified to each group in the training and testing samples are presented, as well as the total percentage of the sample represented in that node. Overall correct classification for the test sample is 91.0% (99.6% for human and 75.0% for nonhuman elements). This decision tree corresponds with the first line in Table 4.