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Rice and millet arrived in Western Japan from Korea around 3,000 years ago and spread eastwards across the archipelago in the next 700 years. However, the extent to which agriculture transformed traditional Jōmon hunter-gatherer-fisher communities is debated. Central Japan is a key area of study as remodelling of radiocarbon dates shows a slowdown in the dispersal rate of rice agriculture in this area. Here, we examine and compare the use of pottery by Final Jōmon and Early to Middle Yayoi communities in the Tōkai and the Central Highland regions of central Japan, using lipid residue analysis. Although the identification of specific biomarkers for rice remains elusive, an increase in the ratio of E/H C18 APAAs with the arrival of rice and millet indicates a potential change in plant processing and consumption. We were also able to identify biomarkers for broomcorn millet (miliacin) in both Final Jōmon and Yayoi pottery. However, evidence for millet consumption is sparse and in all cases was likely mixed with wild hunted and foraged foods. We conclude therefore that, despite the introduction of rice and millet agriculture in central Japan, pre-existing diets and culinary habits of Jōmon hunter-gatherers remain important. [ABSTRACT FROM AUTHOR]

Large sets of radiocarbon dates are increasingly used as proxies for inferring past population dynamics and the last few years, in particular, saw an increase in the development of new statistical techniques to overcome some of the key challenges imposed by this kind of data. These include: 1) null hypothesis significance testing approaches based on Monte-Carlo simulations or mark permutations; 2) non-parametric Bayesian modelling approaches, and 3) the use of more traditional techniques such as correlation, regression, and AIC-based model comparison directly on the summed probability distribution of radiocarbon dates (SPD). While the range of opportunities offered by these solutions is unquestionably appealing, they often do not consider the uncertainty and the biases arising from calibration effects or sampling error. Here we introduce a novel Bayesian approach and nimbleCarbon, an R package that offers model fitting and comparison for population growth models based on the temporal frequency data of radiocarbon dates. We evaluate the robustness of the proposed approach on a range of simulated scenarios and illustrate its application on a case study focused on the demographic impact of the introduction of wet-rice farming in prehistoric Japan during the 1st millennium BCE. [ABSTRACT FROM AUTHOR]

This paper responds to a resurgence of interest in constructing long-term time proxies of human activity, especially but not limited to models of population change over the Pleistocene and/or Holocene. While very much agreeing with the need for this increased attention, we emphasize three important issues that can all be thought of as modifiable reporting unit problems: the impact of (i) archaeological periodization, (ii) uneven event durations and (iii) geographical nucleation-dispersal phenomena. Drawing inspiration from real-world examples from prehistoric Britain, Greece and Japan, we explore their consequences and possible mitigation via a reproducible set of tactical simulations. [ABSTRACT FROM AUTHOR]

Archaeological data provide a potential to investigate the diffusion of technological and cultural traits. However, much of this research agenda currently needs more formal quantitative methods to address small sample sizes and chronological uncertainty. This paper introduces a novel Bayesian framework for inferring the shape of diffusion curves using radiocarbon data associated with the presence/absence of a particular innovation. We developed two distinct approaches: 1) a hierarchical model that enables the fitting of an s-shaped diffusion curve whilst accounting for inter-site variations in the probability of sampling the innovation itself, and 2) a non-parametric model that can estimate the changing proportion of the innovation across user-defined time-blocks. The robustness of the two approaches was first tested against simulated datasets and then applied to investigate three case studies, the first pair on the diffusion of farming in prehistoric Japan and Britain and the third on cycles of changes in the burial practices of later prehistoric Britain. • Bayesian methods for modelling diffusion curves with radiocarbon dates are introduced. • Sample interdependence and non-sigmoid diffusion curves can be handled. • Robustness is tested on simulated datasets and archaeological case studies. • Code and data are available on a GitHub repository and archived on Zenodo. [ABSTRACT FROM AUTHOR]

Recent advances in the use of summed probability distribution (SPD) of calibrated 14C dates have opened new possibilities for studying prehistoric demography. The degree of correlation between climate change and population dynamics can now be accurately quantified, and divergences in the demographic history of distinct geographic areas can be statistically assessed. Here we contribute to this research agenda by reconstructing the prehistoric population change of Jomon hunter-gatherers between 7,000 and 3,000 cal BP. We collected 1,433 14C dates from three different regions in Eastern Japan (Kanto, Aomori and Hokkaido) and established that the observed fluctuations in the SPDs were statistically significant. We also introduced a new non-parametric permutation test for comparing multiple sets of SPDs that highlights point of divergences in the population history of different geographic regions. Our analyses indicate a general rise-and-fall pattern shared by the three regions but also some key regional differences during the 6th millennium cal BP. The results confirm some of the patterns suggested by previous archaeological studies based on house and site counts but offer statistical significance and an absolute chronological framework that will enable future studies aiming to establish potential correlation with climatic changes. [ABSTRACT FROM AUTHOR]

We introduce a new workflow for analysing archaeological frequency data associated with relative rather than absolute chronological time-stamps. Our approach takes into account multiple sources of uncertainty by combining Bayesian chronological models and Monte-Carlo simulation to sample possible calendar dates for each archaeological entity. We argue that when applied to settlement data, this combination of methods can bring new life to demographic proxies that are currently under-used due to their lack of chronological accuracy and precision, and provide grounds for further exploring the limits and the potential of the so-called "dates as data" approach based on the temporal frequency of radiocarbon dates. Here we employ this new workflow by re-examining a legacy dataset that has been used to describe a major population rise-and-fall that occurred in central Japan during the Jōmon period (16,000–2,800 cal BP), focusing on the temporal window between 8,000 and 3,000 cal BP. To achieve this goal we: 1) construct the first Bayesian model of forty-two Jōmon ceramic typology based cultural phases using a sample of 2,120 radiocarbon dates; 2) apply the proposed workflow on a dataset of 9,612 Jōmon pit-dwellings; and 3) compare the output to a Summed Probability Distribution (SPD) of 1,550 radiocarbon dates from the same region. Our results provide new estimates on the timing of major demographic fluctuations during the Jōmon period and reveal a generally good correlation between the two proxies, although with some notable discrepancies potentially related to changes in settlement pattern. • A new approach for characterising temporal frequency of archaeological data is proposed. • The first Bayesian chronological model of Jōmon phases is presented. • The fluctuations in the number of Jōmon dwellings in the western Kanto region is re-examined. • Demographic inference based on dwelling counts is compared against summed probability distribution of radiocarbon dates. [ABSTRACT FROM AUTHOR]