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
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
- Martin Hinz (martin.hinz@iaw.unibe.ch), Institute of
Archaeological Sciences & Oeschger Centre for Climate Change
Research, University of Bern
- Joe Roe (joe@joeroe.io), Institute of Archaeological Sciences,
University of Bern
- Julian Laabs (julian.laabs@ufg.uni-kiel.de), CRC 1266 - Scales of
Transformation, University of Kiel
- Caroline Heitz (caroline.heitz@iaw.unibe.ch), CRC 1266 - Scales of
Transformation, University of Kiel
- Jan Kolář (jan.kolar@ibot.cas.cz), Department of Vegetation
Ecology, Institute of Botany of the Czech Academy of Sciences &
Institute of Archaeology and Museology, Faculty of Arts, Masaryk
University
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.
- 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)
Prehistoric demography; Bayesian modelling; Multi-proxy; Settlement dynamics
The repository consists of:
- 📁 manuscript: R Markdown source document for manuscript.
- 📁 paper: A rendered version, of the submitted
manuscript as
paper_submission.docxandpaper_submission.pdf, suitable for reading (the code is replaced by figures and tables in this file). You also find (as an editors cut) those versions before we trimmed them down to the word count of the journal (paper_extended_version.docxandpaper_extended_version.pdfrespectively). - 📁 analysis: R Markdown source document for the actual analysis. Includes code to reproduce the figures and tables generated by the analysis. You can look at the Github-Markdown Version for a rendered version of the analytical run incl. figures.
- 📁 code: Functions used in the analysis.
- 📁 data: Data used in the analysis.
- 📁 figures: Plots and other illustrations
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.
Text, code and figures : CC-BY-4.0
Data : CC-0 attribution requested in reuse
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.