Model-Based Clustering of World Craniometric Variation

Model-based clustering is applied to 2,504 crania of 28 populations of recent Homo sapiens using 57 cranial metric variates. This technique uses no a priori knowledge about the population affiliation of each skull. Model-based clustering varies the number and form of the clusters and selects a “good” model, showing a balance of data fit and parsimony, using the Bayes Information Criterion. Fourteen separate clusters were identified in the best run, each of which corresponds strongly to either one of the original populations, or to a racial group. It is shown that cranial variation can be used to infer ethno-racial affiliation. Recently, Rosenberg et al. (1) applied model-based clustering to 377 autosomal microsatellite loci in 1,056 individuals from 52 world populations. Using this approach, and without any prior information about the population affiliation of each individual, they were able to infer several clusters corresponding to the major continental regions, thus proving the geographical stratification of human genetic variation. In this article I apply model-based clustering to 2,504 crania from 28 populations based on 57 metric traits to investigate whether the geographic structure of human biodiversity can also be inferred by the methods of physical anthropology. Traditional works on human variation depended on ad-hoc racial typology (2, 3). The availability of abundant computing power and the development of multivariate statistics has made it possible to search for patterns in human variation, especially the skeletal variation of ancient populations in a systematic manner. Howells’ work (4, 5, 6) has been particularly important in this regard, demonstrating the power of the new quantitative approach. I revisit Howell's dataset, applying a powerful statistical technique which was not previously available, and has not -to my knowledgebeen applied to this material before.