Department of Archaeology, University of Cambridge, Cambridge, United Kingdom.
BioArCh, University of York, Wentworth Way, Heslington, York, United Kingdom.
PLoS One. 2021 May 19;16(5):e0251695. doi: 10.1371/journal.pone.0251695. eCollection 2021.
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.
越来越多的大组放射性碳年代数据集被用作推断过去人口动态的代理，特别是在过去几年中，开发了新的统计技术来克服这种数据带来的一些关键挑战。这些方法包括：1）基于蒙特卡罗模拟或标记置换的零假设显著性检验方法；2）非参数贝叶斯建模方法；3）在放射性碳年代的总和概率分布（SPD）上直接使用更传统的技术，如相关性、回归和基于 AIC 的模型比较。虽然这些解决方案提供的机会范围无疑很有吸引力，但它们通常不考虑校准效应或抽样误差引起的不确定性和偏差。在这里，我们介绍了一种新的贝叶斯方法和 nimbleCarbon，这是一个 R 包，提供了基于放射性碳年代时间频率数据的人口增长模型的拟合和比较。我们在一系列模拟场景中评估了所提出方法的稳健性，并说明了其在一个案例研究中的应用，该研究集中在公元前 1 千年史前日本引入水稻种植对人口的影响上。
