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Reconstruction of prehistoric pottery use from fatty acid carbon isotope signatures using Bayesian inference

Fernandes, R.; Eley, Y.; Brabec, M.; Lucquin, A.; Millard, A.; Craig, O.

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Authors

R. Fernandes

Y. Eley

M. Brabec

A. Lucquin

O. Craig



Abstract

Carbon isotope measurements of individual fatty acids (C16:0 and C18:0) recovered from archaeological pottery vessels are widely used in archaeology to investigate past culinary and economic practices. Typically, such isotope measurements are matched with reference to food sources for straightforward source identification, or simple linear models are used to investigate mixing of contents. However, in cases where multiple food sources were processed in the same vessel, these approaches result in equivocal solutions. To address this issue, we tested the use of a Bayesian mixing model to determine the proportional contribution of different food sources to a series of different mixed food compositions, using data generated both by simulation and by experiment. The model was then applied to previously published fatty acid isotope datasets from pottery from two prehistoric sites: Durrington Walls, near Stonehenge in southern Britain and Neustadt in northern Germany. We show that the Bayesian approach to the reconstruction of pottery use offers a reliable probabilistic interpretation of source contributions although the analysis also highlights the relatively low precision achievable in quantifying pottery contents from datasets of this nature. We suggest that, with some refinement, the approach outlined should become standard practice in organic residue analysis, and also has potential application to a wide range of geological and geochemical investigations.

Citation

Fernandes, R., Eley, Y., Brabec, M., Lucquin, A., Millard, A., & Craig, O. (2018). Reconstruction of prehistoric pottery use from fatty acid carbon isotope signatures using Bayesian inference. Organic Geochemistry, 117, 31-42. https://doi.org/10.1016/j.orggeochem.2017.11.014

Journal Article Type Article
Acceptance Date Nov 30, 2017
Online Publication Date Dec 7, 2017
Publication Date Mar 1, 2018
Deposit Date Nov 30, 2017
Publicly Available Date Dec 7, 2018
Journal Organic Geochemistry
Print ISSN 0146-6380
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 117
Pages 31-42
DOI https://doi.org/10.1016/j.orggeochem.2017.11.014

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