ArticlePloS one2021
A Bayesian approach for fitting and comparing demographic growth models of radiocarbon dates: A case study on the Jomon-Yayoi transition in Kyushu (Japan).
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Divergent demographic responses to the Kikai-Akahoya volcanic catastrophe in mid-Holocene Japan.Science advances · 2026Article
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- Demographic interactions between the last hunter-gatherers and the first farmers.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Assessing geometric microliths as cultural markers through an analysis of shape variation and projectile performance.Scientific reports · 2025Article
- Towards understanding human-environment feedback loops: the Atacama Desert case.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Ambiguous landscapes: A framework for assessing robustness and uncertainties in archaeological point pattern analysis.PloS one · 2024Article
- Uncovering the Holocene roots of contemporary disease-scapes: bringing archaeology into One Health.Proceedings. Biological sciences · 2023Review
- Bayesian analyses of direct radiocarbon dates reveal geographic variations in the rate of rice farming dispersal in prehistoric Japan.Science advances · 2022Article
- Bayesian analyses question the role of climate in Chulmun demography.Scientific reports · 2021Article
- Approximate Bayesian Computation of radiocarbon and paleoenvironmental record shows population resilience on Rapa Nui (Easter Island).Nature communications · 2021Article
- The spread of agriculture in Iberia through Approximate Bayesian Computation and Neolithic projectile tools.PloS one · 2021Article
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2 authors.
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Abstract
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.
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