Doctoral Dissertation Research: Dental Analysis of Classic Period Population Variability in the Maya Region

Texas A&M Research Foundation, College Station TX

Investigators

Abstract

This research will examine biological variability among the Classic period Maya using dental metric and nonmetric data from ancient human skeletal remains. Analyses of biological distance and variability within physical anthropology can be classified as model-free and model-bound. In model-free approaches, low levels of biological variability are treated as evidence for high levels of related ancestry and gene flow. In contrast, model-bound approaches take into account the affects of genetic drift and thus provide a more satisfying model of ancient population dynamics. In this study, both model-free and model-bound approaches will be utilized. One of the major benefits of applying model-bound analyses to the study of the Classic Maya is the availability of large skeletal samples sizes from archaeological sites with well-known population estimates and well-controlled chronologies; factors that have been difficult to control in previous model-bound analyses. This study will address problems both specific to Maya archaeology and will produce a large data set on Pre-Columbian Central American dental morphology, furthering our understanding of ancient biological variation in the Americas. In this study of ancient Maya biological variation, Scherer will test the hypothesis that biological distance between Classic period Maya site populations is correlated with geographic distances between sites. This hypothesis rests on the assumptions that migration between Maya urban centers was relatively minor and that increases in population size were largely due to natural population growth. Current archaeological evidence, however, suggests that these assumptions are not correct. Recent studies of Maya demography have demonstrated that numerous sites exhibit fluctuations in population size during the Classic period. Migration, including movement across major archaeological regions, may be one explanation for these episodes of growth and decline. Further, the Maya historical record describes numerous migration events during the Classic period, including inter-site marriages, the placement of foreign lords at subjugated sites, and the founding of new sites by splintering dynastic lineages. What is lacking, however, is an understanding of ancient Maya population history from a biological perspective. This research will fill that void by providing the first comprehensive study of biological variation in the Maya lowlands. If this research determines that populations were not stable, as posited in the first hypothesis, it will be necessary to evaluate what segments of ancient Maya society were mobile. Thus, Scherer will test a second hypothesis that when gene flow occurred during the Classic period, it was primarily at the elite level of society. The data for each site will be partitioned into elite and non-elite segments based on burial context and mortuary variability. If elite migration was more substantial than non-elite migration, we should expect greater biological differentiation between sites for commoners than for the elite. As an additional way to examine population stability during the Classic period, the samples will be divided by time period to examine the relative degree of genetic continuity through time for each site. In order to test these hypotheses, Scherer will collect dental metric and nonmetric data from 15 sites representing seven of the archaeological zones in the Maya region. Statistical analyses will employ both model-free and model-bound approaches. Model-free statistics will include Mahalanobis D2 (metrics) and the mean measure of divergence (nonmetrics). Model-bound analyses will follow recent statistical modifications of the R matrix model of migration for metric and nonmetric data. This award will have the broader impact of contributing to the professional development of the student and providing him with an international experience.