Evaluating Bayesian Radiocarbon‐dated Event Count (REC) models for the study of long‐term human and environmental processes

W Christopher Carleton

doi:10.1002/jqs.3256

Abstract

ABSTRACTChronological uncertainty complicates attempts to use radiocarbon dates as proxies for processes such as human population growth/decline, forest fires and marine ingression. Established approaches involve turning databases of radiocarbon‐date densities into single summary proxies that cannot fully account for chronological uncertainty. Here, I use simulated data to explore an alternative Bayesian approach that instead models the data as what they are, namely radiocarbon‐dated event counts. The approach involves assessing possible event‐count sequences by sampling radiocarbon date densities and then applying a Markov Chain Monte Carlo method to estimate the parameters of an appropriate count‐based regression model. The regressions based on individual sampled sequences were placed in a multilevel framework, which allowed for the estimation of hyperparameters that account for chronological uncertainty in individual event times. Two processes were used to produce simulated data. One represented a simple monotonic change in event‐counts and the other was based on a real palaeoclimate proxy record. In both cases, the method produced estimates that had the correct sign and were consistently biased towards zero. These results indicate that the approach is widely applicable and could form the basis of a new class of quantitative models for use in exploring long‐term human and environmental processes.

Highlights

Radiocarbon‐dated event‐count (REC) sequences are often used as proxies for important past human and environmental processes
Spatio‐temporal variation in human population levels is thought to be related to variation in organic carbon deposition because certain human activities lead to concentrations of organic carbon in sediment and those activities occur more often when and where there are more people present
The models produced posterior parameter estimates that were consistent with the sign of true values and biased towards zero. This means that the model always indicated the correct direction of the known effect and, biased, was off in a consistent way. This means that REC models appear to be able to determine whether a real effect was positive or negative despite radiocarbon‐date uncertainty and that in general we can expect the magnitude of effect estimates to be lower than their true values

Summary

Introduction

Radiocarbon‐dated event‐count (REC) sequences are often used as proxies for important past human and environmental processes. Spatio‐temporal variation in human population levels is thought to be related to variation in organic carbon deposition because certain human activities lead to concentrations of organic carbon in sediment and those activities occur more often when and where there are more people present Activities such as agriculture, construction, plant and animal processing, fire‐use, and mortuary rituals all create localized concentrations of organic carbon—carbon deposition events. Radiocarbon dates contain uncertainty both from the measurement of carbon isotopes and from the calibration process that accounts for though‐time changes in the ratio of those isotopes in the environment (Taylor et al, 2014). These sources of uncertainty combine to produce calibrated radiocarbon dates with substantial, highly irregular temporal distributions (e.g. Fig. 1). Count‐based sequences of radiocarbon‐dated events are chronologically uncertain, which means that multiple potential sequences are always possible for any given sample of radiocarbon dates

Methods

Results

Discussion

Conclusion