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Interpreting Past Human Diets Using Stable Isotope Mixing Models—Best Practices for Data Acquisition

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Abstract

Using stable isotope mixing models (SIMMs) to quantify past diets is becoming increasingly common in archaeology. This study highlights important field-specific difficulties encountered by archaeologists in reconstructing palaeodiets using SIMMs. Focusing on the data acquisition stage, we discuss several issues that could confound dietary quantification if not accounted for. These issues are categorized under several broad categories: diagenesis, intra-individual variability, representativeness of both the consumers and sources, and other commonly encountered field-specific problems. We summarize these issues with a flow chart to help archaeologists to select the most appropriate samples for dietary reconstruction using SIMMs, thereby decreasing the probability that the outputs of the SIMM are inaccurate. We conclude by discussing the ways in which SIMMs may not be appropriate for all archaeological contexts, highlighting those areas that are likely to be the most problematic for end users.

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Data Availability

All simulated data are provided in the Supplementary files.

Code Availability

All codes used in this study are provided in the Supplementary files.

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Acknowledgement

The authors wish to thank the anonymous reviewers for their helpful comments.

Funding

CC is supported by the Agence Nationale de la Recherche (Project-ANR-17-CE27-0023 “NEOGENRE”). PS is supported by the Canada Research Chairs program.

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  1. EA—Eco-anthropologie (UMR 7206), Muséum National d’Histoire Naturelle, CNRS, Université Paris Diderot, Paris, France

    Christina Cheung

  2. Aix Marseille Univ, CNRS, Minist Culture, LAMPEA, Aix-en-Provence, France

    Christina Cheung

  3. Department of Anthropology, Trent University, 1600 West Bank Drive, Peterborough, Ontario, K9L 0G2, Canada

    Paul Szpak

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Cheung, C., Szpak, P. Interpreting Past Human Diets Using Stable Isotope Mixing Models—Best Practices for Data Acquisition. J Archaeol Method Theory 29, 138–161 (2022). https://doi.org/10.1007/s10816-021-09514-w

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