This article presents 6637 radiocarbon dates from archaeological sites in southernmost Sweden, from 9000 cal bc to the present. Based on summed probability distributions (SPDs) of the calibrated radiocarbon dates, the authors consider long-term trends in settlement and human population. Most dates are from the fertile and densely populated plains of south-western Scania, but coastal lowlands and forested uplands are also represented, allowing for a discussion of the relationship between central and peripheral areas. The authors distinguish between different types of archaeological contexts and features and between different types of dated material, so as to better understand the processes behind population and settlement change. They highlight three periods and phenomena revealed by the SPDs: a strong population increase at the onset of the Neolithic (4000–3700 cal bc), followed by a sharp decline; a steady and long-lasting expansion from the Early Bronze Age to the Roman Iron Age (1500 cal bc–cal ad 200); and a decrease in the Nordic Late Iron Age (seventh century ad), particularly in recently colonized upland areas. The SPDs presented provide a new framework for archaeology in southern Sweden and offer an empirical basis for discussion of long-term trends in settlement and population development.

The paper explores the emergence and development of arable farming in southeastern Norway by compiling and analyzing directly dated cereals from archaeological contexts. By using summed probability distributions of radiocarbon dates and Bayesian modeling, the paper presents the first comprehensive analysis of the directly dated evidence for farming in the region. The models provide a more precise temporal resolution to the development than hitherto presented. The results demonstrate that the introduction of arable farming to southeastern Norway was a long-term development including several steps. Three different stages are pointed out as important in the process of establishing arable farming: the Early and Middle Neolithic, the Late Neolithic, and the Early Iron Age.

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.

Over the last decade, archaeologists have turned to large radiocarbon (14C) data sets to infer prehistoric population size and change. An outstanding question concerns just how direct of an estimate 14C dates are for human populations. In this paper we propose that 14C dates are a better estimate of energy consumption, rather than an unmediated, proportional estimate of population size. We use a parametric model to describe the relationship between population size, economic complexity and energy consumption in human societies, and then parametrize the model using data from modern contexts. Our results suggest that energy consumption scales sub-linearly with population size, which means that the analysis of a large 14C time-series has the potential to misestimate rates of population change and absolute population size. Energy consumption is also an exponential function of economic complexity. Thus, the 14C record could change semi-independent of population as complexity grows or declines. Scaling models are an important tool for stimulating future research to tease apart the different effects of population and social complexity on energy consumption, and explain variation in the forms of 14C date time-series in different regions.

Population growth has been evoked both as a causal factor and consequence of the transition to agriculture. The use of radiocarbon (14C) dates as proxies for population allows for reevaluations of population as a variable in the transition to agriculture. In Korea, numerous rescue excavations during recent decades have offered a wealth of 14C data for this application. A summed probability distribution (SPD) of 14C dates is investigated to reconstruct population trends preceding and following adoptions of food production in prehistoric Korea. Important cultivars were introduced to Korea in two episodes: millets during the Chulmun Period (ca. 6000–1500 BCE) and rice during the Mumun Period (ca. 1500–300 BCE). The SPD suggests that while millet production had little impact on Chulmun populations, a prominent surge in population appears to have followed the introduction of rice. The case in prehistoric Korea demonstrates that the adoption of food production does not lead inevitably towards sustained population growth. Furthermore, the data suggest that the transition towards intensive agriculture need not occur under conditions of population pressure resulting from population growth. Rather, intensive rice farming in prehistoric Korea began during a period of population stagnation.