Abstract

Radiocarbon-date assemblages are commonly used as proxies for past human and environmental phenomena. Prominent examples of target phenomena include past population levels and sea level fluctuations. These processes are thought to have affected the amount of organic carbon deposited into the archaeological and/or palaeoenvironmental record. Time-series representing through-time fluctuations in the frequency of radiocarbon samples are, therefore, often used as proxies for such processes. However, there are critical problems with using radiocarbon “dates-as-data” in point-wise comparisons and these problems have gone largely underappreciated. The key problem is that the established proxies are easily misinterpreted. They conflate process variation and chronological uncertainty, which makes them unsuitable for point-wise comparisons aimed at identifying rates of change, comparing variables directly, or estimating parameters in regression models. Here we explore the interpretive and analytical problems in detail in an effort to raise awareness and promote skepticism about the use of the established proxies in point-wise comparisons. We also provide suggestions for future research and point to potential methodological alternatives that may improve the viability of dates-as-data approaches.

Highlights

  • Proxies based on aggregated radiocarbon-dates are popular in archaeological and palaeoenvironmental research

  • The paper reports a study of Holocene sea level changes along the North Sea coast with the help of “statistischen Auswertung von 14 C-Daten” (“statistical evaluation of 14 C data”) (Geyh, 1969)

  • Geyh argued that the amount of radiocarbon in different layers of sediment from around the North Sea could be used as a proxy for past through-time fluctuations in sea level

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Summary

Introduction

Proxies based on aggregated radiocarbon-dates are popular in archaeological and palaeoenvironmental research. Just like the SPDF, changes in the level of the proxy are a function of chronological uncertainty, not the number of radiocarbon samples dated to a particular time. This mixture of through-time variation in the number of dated events and chronological uncertainty is partly what makes the KDEa useful as a summary of chronological information for a large database of radiocarbon dates. It raises problems for interpreting specific fluctuations (point-wise differences) in the model. It is a summary of chronological information, not a simple reflection of through-time variation in event-counts

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