This thesis applies a new methodology to dietary reconstructions of a unique population excavated from Kellis 2, a Romano-Christian era (c.50-450 AD) cemetery located in the ancient city of Kellis, Dakhleh Oasis, Egypt. Previously, stable carbon and nitrogen isotope analyses were conducted on bulk hair keratin from 216 individuals to examine their dietary practices and health status. Although this research identified the presence of specific dietary choices in the community, the researchers were not able to determine what fraction of the diet was composed of those resources. This is the first such study to use a priori dietary reconstruction data in combination with the Bayesian mixing-model Food Reconstruction Using Isotopic Transfer Signals (FRUITS) to quantify the contributions various food groups make to a diet of age groups within this community. Combining paleodiet stable isotope analysis with FRUITS modeling provides an opportunity to predict and evaluate percentages of food groups consumed in previously identified dietary and social practices, such as weaning, and dietary dynamics between adult males and females and during aging. When C3 plants, C4 plants and protein sources were evaluated, FRUITS modeling confirmed that juvenile weaning began around 6 months of age at which time the addition of herbivore dairy and cereal grains were added to their diet. Younger aged adults (~15-35 years) ate a common omnivorous diet with no discernable differences between males and females, while elderly individuals (+50 years) appear to transition to a diet with greater emphasis on protein. The use of FRUITS modeling in this study has added greater clarity to previously identified food practices at Kellis 2 and demonstrated the applicability of this method on archaeological samples when investigating food group quantification and dynamics of diet resources within an ancient community.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1218 |
| Date | 01 January 2020 |
| Creators | Gomez, Melissa |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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