Drivers of Demographic Dynamics

Introduction

This project aims to reconstruct the human demography of SW Amazonia during the Holocene and to reveal the role of environmental and cultural changes in shaping it. Despite decades of research, the relative importance of environmental vs cultural factors in determining prehistoric population growth is still one of archaeology's greatest scientific challenges.

Challenges in Archaeological Research

Reaching an agreement about the drivers of demographic dynamics is very difficult because of the fragmentary, incomplete, and biased nature of archaeological records. DEMODRIVERS is designed to overcome this problem.

Project Overview

This five-year interdisciplinary project will investigate the patterns and drivers of human demographic dynamics by focusing on a regional case study, the Llanos de Moxos (LM) in the Bolivian Amazon, that has a potentially unparalleled explanatory power.

Unique Features of the Study Area

This region at the southwestern end of the Amazon hosts a recently discovered, almost complete archaeological record, made up of:

• 4700 sites
• Spanning 8300 years
• Distributed over 100,000 km²

This area is unique in the world, offering us the first opportunity to quantify how far demographic trends and patterns responded to:

1. Exogenous variables (climate, landscape)
2. Endogenous variables (culture, technology)

Methodology

This project will exploit the full potential of the LM archaeological record by taking an unconventional approach. The density of the occupation and its spatial and temporal boundaries will be measured by studying 150 stratigraphic cores extracted from 100 evenly distributed archaeological sites.

Interdisciplinary Approach

The approach is interdisciplinary, integrating:

• Conventional archaeology
• State-of-the-art geoarchaeology
• Biomarkers
• Paleoclimatology
• Palaeoecology
• Artificial intelligence

Expected Outcomes

The results of the project will provide a very thoroughly documented case study against which other models and reconstructions based on incomplete, fragmented, and often biased datasets can be compared. The results of the project have broad implications across several disciplines.