SignificanceThe relationship between human population, food production, and climate change is a pressing concern in need of high-resolution, long-term perspectives. Archaeological radiocarbon dates have increasingly been used to reconstruct past population dynamics, and Britain and Ireland provide both radiocarbon sampling densities and species-level sample identifications that are globally unrivalled. We use this evidence to demonstrate multiple instances of human population downturn over the Holocene that coincide with periodic episodes of reduced solar activity and climate reorganization as well as societal responses in terms of altered food-procurement strategies.
The last decade has seen the development of a range of new statistical and computational techniques for analysing large collections of radiocarbon (14C) dates, often but not exclusively to make inferences about human population change in the past. Here we introduce rcarbon, an open-source software package for the R statistical computing language which implements many of these techniques and looks to foster transparent future study of their strengths and weaknesses. In this paper, we review the key assumptions, limitations and potentials behind statistical analyses of summed probability distribution of 14C dates, including Monte-Carlo simulation-based tests, permutation tests, and spatial analyses. Supplementary material provides a fully reproducible analysis with further details not covered in the main paper.
We have compiled an extensive database of archaeological evidence for rice across Asia, including 400 sites from mainland East Asia, Southeast Asia and South Asia. This dataset is used to compare several models for the geographical origins of rice cultivation and infer the most likely region(s) for its origins and subsequent outward diffusion. The approach is based on regression modelling wherein goodness of fit is obtained from power law quantile regressions of the archaeologically inferred age versus a least-cost distance from the putative origin(s). The Fast Marching method is used to estimate the least-cost distances based on simple geographical features. The origin region that best fits the archaeobotanical data is also compared to other hypothetical geographical origins derived from the literature, including from genetics, archaeology and historical linguistics. The model that best fits all available archaeological evidence is a dual origin model with two centres for the cultivation and dispersal of rice focused on the Middle Yangtze and the Lower Yangtze valleys.
Raw counts of archaeological sites, estimates of changing settlement size and summed radiocarbon probability distributions have all become popular ways to investigate long-term regional trends in human population. Nevertheless, these three archaeological proxies have rarely been compared. This paper therefore explores the strengths and weaknesses of different kinds of archaeological evidence for population patterns, as well as how they address related issues such as taphonomic loss, chronological uncertainty and uneven sampling. Our overall substantive goal is to reconstruct demographic fluctuations in central Italy from the Late Mesolithic to the fall of the Roman Empire (7500 BC-AD 500), and with this in mind, we bring to bear an unusually detailed and extensive dataset of published central Italian archaeological surveys, consisting of some 10,971 occupation phases at 7,383 different sites. The comparative results demonstrate reassuring consistency in the suggested demographic patterns, and where such patterns diverge across different proxies (e.g. Late Bronze Age/Iron Age) they often do so in useful ways that suggest changes in population structure such as site nucleation or dispersal.
Summed probability distributions of radiocarbon dates are an increasingly popular means by which to reconstruct prehistoric population dynamics, enabling more thorough cross-regional comparison and more robust hypothesis testing, for example with regard to the impact of climate change on past human demography. Here we review another use of such summed distributions -to make spatially explicit inferences about geographic variation in prehistoric populations. We argue that most of the methods proposed so far have been strongly biased by spatially varying sampling intensity, and we therefore propose a spatial permutation test that is robust to such forms of bias and able to detect both positive and negative local deviations from pan-regional rates of change in radiocarbon date density. We test our method both on some simple, simulated population trajectories and also on a large real-world dataset, and show that we can draw useful conclusions about spatio-temporal variation in population across Neolithic Europe. Highlights• Spatial analyses of radiocarbon dates are reviewed.• A new method for detecting hot-spots and cold-spots in the temporal change of radiocarbon density is proposed. • The method is tested with simulated data and a case study from Neolithic Europe.• Results of the case study depict a front of sharp demographic growth linked to the expansion of farming. • The method is available as part of the R statistical package rcarbon.
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