The study of evolutionary relationships among human populations is fundamental to inferring processes that determine their structure and history. Among the different data types used to infer such relationships, molecular data, particularly nuclear and mitochondrial DNA, are preferred because of their high heritability and the low probability of changes during development. However, although the reliability of relatedness patterns based on other traits is discussed, except in unusual circumstances most prehistoric populations remain within the domain of morphological study. Therefore the primary goal of this study is to test the reliability of relatedness patterns constructed on the basis of craniometric data on a regional scale. In particular, we analyze samples from populations belonging to the Chaco, Pampa, and Patagonia regions of South America for which craniometric and molecular data are available. We compare a strongly supported relatedness pattern based on molecular data with the results obtained through landmark-based and semilandmark-based facial data. The matrices based on Euclidean distance for morphometric data and DA distances for molecular data were used to perform principal coordinates analyses and to obtain reticulograms. Finally, a principal components analysis of all individuals was performed with morphometric data. The results indicate that ordination analyses yield slightly different results depending on the morphometric data used. However, the reticulograms obtained with both landmark-based and semilandmark-based data allow the separation of the Chubut samples from the Chaco samples, with the Pampa sample in between the others; this pattern is congruent with molecular-based analyses. As a consequence, our results indicate that facial morphometric data allow the inference of the structure and history of the prehistoric populations for the studied region.
Perez, S. Ivan; Bernal, Valeria; and Gonzalez, Paula N.
"Evolutionary Relationships Among Prehistoric Human
Populations: An Evaluation of Relatedness Patterns Based on
Facial Morphometric Data Using Molecular Data,"
1, Article 3.
Available at: https://digitalcommons.wayne.edu/humbiol/vol79/iss1/3