/CES2021

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CES2021: Bayesian inference of material culture phylogenies using continuous traits from the archaeological record

Ben Marwick^1^ , David N. Matzig^2^ , Felix Riede^2^

  1. Department of Anthropology, University of Washington, Seattle, USA
  2. Department of Archaeology and Heritage Studies, Aarhus University, Denmark

The twentieth century saw two modes of thinking spread through biological systematics: population thinking and tree thinking. Many archaeologists investigating ancient technologies have adopted the population thinking approach for archaeological systematics, as evident from the rise of work on quantifiable, attribute-based patterns of diversity in studies of past technological systems, much of it driven by application of geometric morphometrics. Yet, tree thinking, as one of the central concepts of phylogenetic biology, remains rare in the archaeological sciences. We review some of the obstacles that have impeded the uptake of this concept by archaeologists. Some of these are conceptual, but we identify one specific methodological obstacle: the prevailing use of discrete character traits in phylogenetic analysis in many fields. This is a major challenge for archaeological applications where standardised trait-analytical protocols are scarce. To address this challenge, we present a case study that demonstrates a Bayesian framework for inferring phylogenies using continuous traits derived from artefact shape coefficients obtained via outline based geometric morphometrics. We use a previously published sample of Late Neolithic/Early Bronze Age arrowheads from Northwestern Europe to demonstrate the efficacy and accessibility of our approach. We also sketch out the potential for phylogenetic comparative methods to address archaeological questions.

Contents of this repository

  • 📁 data: Data used in the analysis, from Matzig et al. 2021
  • 📁 figures: Plots and other illustrations generated by the scripts
  • 📁 outputs: text files output by RevBayes
  • 📁 scripts: R code files and RevBayes code files

Our presentation slides are online.

Dependencies

We used R v4.0.5 and RevBayes v1.1.1

Licenses

Text and figures : CC-BY-4.0

Code : See the DESCRIPTION file

Data : CC-0 attribution requested in reuse

Contributions

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