Description

Although originally domesticated in Mexico, the initial adoption and spread of maize (Zea mays) are key to understanding the forager-to-farmer transition in the North American Southwest. Fundamental to our understanding of this transition is chronology, especially related to the introduction, spread, and use of maize. Yet, the chronology of early maize typically focuses on single radiocarbon dates, and at times, has been controversial. This presentation shows how chronological modeling can be used to better understand early maize in the North American Southwest. Dr. Barkwill Love uses different statistical modeling techniques on over 800 maize radiocarbon dates from 150+ Archaic and early Formative sites to examine the initial introduction, pace of maize dispersal, and intensity of maize use. Bayesian chronological modeling is used to provide formal estimates for the initial introduction of maize. Tempo plots are constructed from the Bayesian models to provide a relative measure for the pace of maize dispersal. Kernel density estimation (KDE) models are used to examine the distribution of early maize to infer intensity of use. The results of the tempo plots and KDE models are then compared to different paleoenvironmental reconstruction datasets to explore the relationship between early maize dispersal, use, and climate change.