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Research compendium for ‘Bayesian inference of prehistoric population dynamics from multiple proxies: a case study from the North of the Swiss Alps’

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MartinHinz/bayesian.demographic.reconstruction.2022

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bayesian.demographic.reconstruction.2022

Binder DOI

This repository contains the data and code for our paper:

Hinz, M., Roe, J., Laabs, J., Heitz, C., Kolar, J. (2022). Bayesian inference of prehistoric population dynamics from multiple proxies: a case study from the North of the Swiss Alps. Name of journal/book https://doi.org/xxx/xxx

Our pre-print is online here:

Hinz, M., Roe, J., Laabs, J., Heitz, C., Kolar, J. (2022). Bayesian inference of prehistoric population dynamics from multiple proxies: a case study from the North of the Swiss Alps. Name of journal/book, Accessed 30 May 2022. Online at https://doi.org/xxx/xxx

How to cite

Please cite this compendium as:

Hinz, M., Roe, J., Laabs, J., Heitz, C., Kolar, J., (2022). Compendium of R code and data for Bayesian inference of prehistoric population dynamics from multiple proxies: a case study from the North of the Swiss Alps. Accessed 30 May 2022. Online at https://doi.org/10.5281/zenodo.6594498

Authors

  • Martin Hinz ([email protected]), Institute of Archaeological Sciences & Oeschger Centre for Climate Change Research, University of Bern ORCiD
  • Joe Roe ([email protected]), Institute of Archaeological Sciences, University of Bern ORCiD
  • Julian Laabs ([email protected]), CRC 1266 - Scales of Transformation, University of Kiel ORCiD
  • Caroline Heitz ([email protected]), CRC 1266 - Scales of Transformation, University of Kiel ORCiD
  • Jan Kolář ([email protected]), Department of Vegetation Ecology, Institute of Botany of the Czech Academy of Sciences & Institute of Archaeology and Museology, Faculty of Arts, Masaryk University ORCiD

Abstract

Robust estimates of population are essential to the study of human–environment relations and socio-ecological dynamics in the past. Population size and density can directly inform reconstructions of prehistoric group size, social organisation, economic constraints, exchange, and political and social institutions. In this pilot study, we present an approach that we believe can be usefully transferred to other regions, as well as refined and extended to greatly advance our understanding of prehistoric demography.

Here, we present a Bayesian hierarchical model that uses Poisson regression and state-space representation to produce absolute estimates of past population size and density. Using the area North of the main ridge of the Swiss Alps in prehistoric times (6000–1000 BCE) as a case study, we show that combining multiple proxies (site counts, radiocarbon dates, dendrochronological dates, and landscape openness) produces a more robust reconstruction of population dynamics than any single proxy alone. The model’s estimates of the credibility of its prediction, and the relative weight it affords to individual proxies through time, give further insights into the relative reliability of the evidence currently available for paleodemographic research. Our prediction of population development of the case study area accords well with the current understanding in the wider literature, but provides a more precise and higher-resolution estimate that is less sensitive to spurious fluctuations in the proxy data than existing approaches, especially the popular summed probability distribution of radiocarbon dates.

The archaeological record provides several potential proxies of human population dynamics, but individually they are inaccurate, biased, and sparse in their spatial and temporal coverage. Similarly, current methods for estimating past population dynamics are often simplistic: they work on limited spatial scales, tend to rely ona single proxy, and are rarely able to infer population size or density in absolute terms. In contemporary demography, it is becoming increasingly common to use Bayesian statistics to estimate population trends and project them into the future. The Bayesian approach is popular because offers the possibility of combining heterogenous data, and at the same time qualifying the uncertainty and credibility attached to forecasts. These same characteristics make it well-suited to applications to archaeological data in paleodemographic studies.

Highlights

  • Bayesian modelling can integrate multiple, heterogeneous population proxies from the archaeological record
  • Our initial model produces more robust, high-resolution estimates of past population dynamics than previous, single-proxy approaches
  • We provide absolute estimates of population size and density on the area north of the Swiss Alpes in prehistoric times (6000–1000 BCE)

Keywords

Prehistoric demography; Bayesian modelling; Multi-proxy; Settlement dynamics

Contents

The repository consists of:

How to run in your browser or download and run locally

This research compendium has been developed using the statistical programming language R. To work with the compendium, you will need installed on your computer the R software itself and optionally RStudio Desktop.

To perform the actual analysis, we recommend a powerful computer with a multi-core processor, ideally with the Linux operating system, which has at least 64GB RAM memory. In addition, a hard disk space of at least 5GB should be reserved for the process.

Licenses

Text, code and figures : CC-BY-4.0

Data : CC-0 attribution requested in reuse

Contributions

We welcome contributions from everyone. Before you get started, please see our contributor guidelines. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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Research compendium for ‘Bayesian inference of prehistoric population dynamics from multiple proxies: a case study from the North of the Swiss Alps’

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