Modeling the Landscape Ranging Ecology of Clovis Groups: A Spatial Analysis of Lithic Raw Material Transport in the Great Lakes Region

Summary

This is an abstract from the "Machine-Learning Approaches to Studying Ancient Human-Environmental Interactions" session, at the 90th annual meeting of the Society for American Archaeology.

Fluted-point technology, like Clovis, is associated with some of the earliest modern-human dispersals across the Americas. Forager groups utilizing this technology emerged and proliferated in North America approximately 13,000 years ago. Archaeologists generally agree that Clovis groups dispersed their technology broadly from west to east, with recent research suggesting northern-bound routes across the ice-free corridor into Alaska. However, more localized patterns of landscape-ranging behavior still need to be better understood. To fill this knowledge gap, our study analyzes the spatial distribution of lithic raw material discard patterns observed in a robust database of Clovis locations around the Great Lakes region relative to their respective source locations. To understand the observed patterns, we simulated multiple permutations of lithic raw material movement, including acquisition and discard behaviors of Clovis groups, employing a simple Agent-Based Model (ABM). Our objective was to generate expected patterns of raw material spatial distribution, providing multiple testable hypotheses about the ranging ecology of Clovis technology. We then compare the observed raw material distributions with the simulation results using a multinomial model. The study’s resulting quantitative parameter estimates allow us to make future testable inferences about how Clovis groups might have utilized the landscape surrounding the Great Lakes region.

This Resource is Part of the Following Collections

• Society for American Archaeology 90th Annual Meeting, Denver, CO (2025)   •
• Machine-Learning Approaches to Studying Ancient Human-Environmental Interactions

Keywords

Record Identifiers

Abstract Id(s): 53332