Reconstructing regional population fluctuations in the European Neolithic using radiocarbon dates: a new case-study using an improved method

Abstract

a b s t r a c tIn a previous study we presented a new method that used summed probability distributions (SPD) of radiocarbon dates as a proxy for population levels, and Monte-Carlo simulation to test the significance of the observed fluctuations in the context of uncertainty in the calibration curve and archaeological sampling. The method allowed us to identify periods of significant short-term population change, caveated with the fact that around 5% of these periods were false positives. In this study we present an improvement to the method by applying a criterion to remove these false positives from both the simulated and observed distributions, resulting in a substantial improvement to both its sensitivity and specificity. We also demonstrate that the method is extremely robust in the face of small sample sizes. Finally we apply this improved method to radiocarbon datasets from 12 European regions, covering the period 8000e4000 BP. As in our previous study, the results reveal a boom-bust pattern for most regions, with population levels rising rapidly after the local arrival of farming, followed by a crash to levels much lower than the peak. The prevalence of this phenomenon, combined with the dissimilarity and lack of synchronicity in the general shapes of the regional SPDs, supports the hypothesis of endogenous causes.

A long hard road… Reviewing the evidence for environmental change and population history in the eastern Adriatic and western Balkans during the Late Pleistocene and Early Holocene

“…For this purpose, we have resorted to summed calibrated date probability distributions (hereafter SCDPDs), using a revised version of the method originally devised by Timpson and colleagues (i.e. comparison of summed data with a null model, and statistically significant differences between both being considered as potentially meaningful; Timpson et al, 2014;see Vander Linden et al, submitted). This method assumes that any regional radiocarbon record reflects the magnitude of past activities, so that fluctuating densities of radiocarbon dates are informative of the intensity of past human settlement, including past demography.…”

On Applications of Space–Time Modelling with Open-Source 14C Age Calibration

“…To test whether the Gaussian KDE can resolve the Black Death Bevent^under a similar set of assumptions, the historic demographic data were used to simulate various numbers of calendar dates whose frequency distribution in time was randomly sampled from the demographic data supplied by Contreras and Meadows (2014) for the period AD 1000 to 1700. These were Bback-calibrated^and re-calibrated (Timpson et al 2014) and used to build a Bbootstrapped^Gaussian KDE based on the Monte Carlo process described previously (for the R code, see the supplementary materials). For this exercise, the radiocarbon dates were simulated several times, and subsequent runs of Monte Carlo sampling and kernel density estimation were undertaken on each (727) to render them directly comparable with the summed probability.…”

“…In this exercise, a hypothetical model was defined describing declining activity spanning AD 400 and 650 followed firstly by steady activity levels, then a short increase in activity. This was used to Bbackcalibrate^a set of simulated 727 radiocarbon Bdates^that could be subjected to both forms of analysis (following Timpson et al 2014). The resulting KDE (Fig.…”

“…Further, in prehistoric contexts, where by definition no historical data exists to construct null models, the only things available are theoretical constraints informed by human population growth rates (e.g. Downey et al 2014;Timpson et al 2014) and expected taphonomic loss (Surovell et al 2009). A profitable way forward is to formulate many different kinds of models and see which one best fits the archaeological data or which ones are refuted as not realistically matching the evidence.…”

“…These studies build on pioneering continental-scale studies also published in recent years (e.g. Hinz et al 2012;Peros et al 2010;Shennan et al 2013;Timpson et al 2014;Wang et al 2014;Williams 2013). The current high level of interest in this kind of work doubtless has many causes, including (a) the fact that available archaeological data have now reached a critical mass, allowing the work to proceed (b) hyper-familiarity by archaeologists with computers and the internet, which now pervade virtually every aspect of modern life, (c) nextgeneration ancient DNA work which has invigorated interest in demography and population history and (d) a broader shift towards Bdigital humanities^, positioning archaeology at the interface between scientific research and BBig Data^(for an extended discussion, see Kristiansen 2014).…”