Spatiotemporal distribution of Holocene populations in North America

Native American geography shaped historical fire frequency in forests of eighteenth-century Pennsylvania, USA

Researchers have debated the relative importance of environmental versus Indigenous effects on past fire regimes in eastern North America. Tree-ring fire-scar records (FSRs) provide local-resolution physical evidence of past fire, but few studies have spatially correlated fire frequency from FSRs with environmental and anthropogenic variables. No study has compared FSR locations to Native American settlement features in the eastern United States. We assess whether FSRs in the eastern US are located near regions of past Native American settlement. We also assess relationships between distance to Native American settlement, environmental conditions, and fire frequency in central Pennsylvania (PA), US, using an "ensemble of small models" approach for low sample sizes. Regression models of fire frequency at 21 locations in central PA often selected distance-based proxies of Indigenous land use. Models with mean annual temperature and Native American variables as predictors explained > 70% of the variation in fire frequency. Alongside temperature and wind speed, "distance to nearest trail" and "mean distance to nearest town" were significant and important predictors. In 18th-century central PA, fires were more frequent near Indigenous trails and towns, and further south due to increasing temperature and pyrophilic vegetation. However, for the entire eastern US, FSRs are located far from past settlement, limiting their effectiveness in detecting fire patterns near population centers. Improving understanding of historical fire will require developing FSRs closer to past Native American settlement.

Crossing of the Hu line by Neolithic population in response to seesaw precipitation changes in China

How various peoples crossed geographical barriers, were affected by climate change and human-made technologies comprise some of the most interesting quandaries in the history of cultures. This paper considers the Hu line, which is a major boundary between population centres and different environments in China. The boundary became evident approximately 11,400 years ago; however, evidence suggests that people crossed through at 5200, 3800, and 2800 cal a BP, facilitating the increases of the trans-Eurasian exchange. The timings of the crossings correspond to the weakening of the East Asian summer monsoon that triggers seesaw changes of precipitation in western and eastern China. This analysis demonstrates that climate change on a millennial-to-centennial scale can have a profound influence on population distribution with long-term consequences.

p3k14c, a synthetic global database of archaeological radiocarbon dates

Archaeologists increasingly use large radiocarbon databases to model prehistoric human demography (also termed paleo-demography). Numerous independent projects, funded over the past decade, have assembled such databases from multiple regions of the world. These data provide unprecedented potential for comparative research on human population ecology and the evolution of social-ecological systems across the Earth. However, these databases have been developed using different sample selection criteria, which has resulted in interoperability issues for global-scale, comparative paleo-demographic research and integration with paleoclimate and paleoenvironmental data. We present a synthetic, global-scale archaeological radiocarbon database composed of 180,070 radiocarbon dates that have been cleaned according to a standardized sample selection criteria. This database increases the reusability of archaeological radiocarbon data and streamlines quality control assessments for various types of paleo-demographic research. As part of an assessment of data quality, we conduct two analyses of sampling bias in the global database at multiple scales. This database is ideal for paleo-demographic research focused on dates-as-data, bayesian modeling, or summed probability distribution methodologies.

A Bayesian approach for fitting and comparing demographic growth models of radiocarbon dates: A case study on the Jomon-Yayoi transition in Kyushu (Japan)

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.

Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument

Predictive models are central to both archaeological research and cultural resource management. Yet, archaeological applications of predictive models are often insufficient due to small training data sets, inadequate statistical techniques, and a lack of theoretical insight to explain the responses of past land use to predictor variables. Here we address these critiques and evaluate the predictive power of four statistical approaches widely used in ecological modeling-generalized linear models, generalized additive models, maximum entropy, and random forests-to predict the locations of Formative Period (2100-650 BP) archaeological sites in the Grand Staircase-Escalante National Monument. We assess each modeling approach using a threshold-independent measure, the area under the curve (AUC), and threshold-dependent measures, like the true skill statistic. We find that the majority of the modeling approaches struggle with archaeological datasets due to the frequent lack of true-absence locations, which violates model assumptions of generalized linear models, generalized additive models, and random forests, as well as measures of their predictive power (AUC). Maximum entropy is the only method tested here which is capable of utilizing pseudo-absence points (inferred absence data based on known presence data) and controlling for a non-representative sampling of the landscape, thus making maximum entropy the best modeling approach for common archaeological data when the goal is prediction. Regression-based approaches may be more applicable when prediction is not the goal, given their grounding in well-established statistical theory. Random forests, while the most powerful, is not applicable to archaeological data except in the rare case where true-absence data exist. Our results have significant implications for the application of predictive models by archaeologists for research and conservation purposes and highlight the importance of understanding model assumptions.

Using the Maximal Entropy Modeling Approach to Analyze the Evolution of Sedentary Agricultural Societies in Northeast China

The emergence of agriculture and the evolution of sedentary societies are among the most important processes in human history. However, although archeologists and social scientists have long been studying these processes, our understanding of them is still limited. This article focuses on the Fuxin area in present-day Liaoning province in Northeast China. A systematic archeological survey we conducted in Fuxin in recent years located sites from five successive stages of the evolution of agricultural sedentary society. We used the principles of Maximal Entropy to study changes in settlement patterns during a long-term local trajectory, from the incipient steps toward a sedentary agricultural way of life to the emergence of complex societies. Based on the detailed data collected in the field, we developed a geo-statistical model based on Maximal Entropy (MaxEnt) that characterizes the locational choices of societies during different periods. This combination of high-resolution information on the location and density of archeological remains, along with a maximal entropy-based statistical model, enabled us to chart the long-term trajectory of the interactions between human societies and their natural environment and to better understand the different stages of the transition to developed sedentary agricultural society.

Population growth as a driver of initial domestication in Eastern North America

The transition to agriculture is one of the most significant events in human prehistory; yet, explaining why people initially domesticated plants and animals remains a contentious research problem in archaeology. Two competing hypotheses dominate current debates. The first draws on niche construction theory to emphasize how intentional management of wild resources should lead to domestication regardless of Malthusian population-resource imbalances. The second relies on models from behavioural ecology (BE) to highlight how individuals should only exert selective pressure on wild resources during times of population-resource imbalance. We examine these hypotheses to explain the domestication event which occurred in Eastern North America approximately 5000 years ago. Using radiocarbon date density and site counts as proxies for human population, we find that populations increased significantly in the 1000 years prior to initial domestication. We therefore suggest that high populations prior to 5000 cal BP may have experienced competition for and possibly overexploitation of resources, altering the selective pressures on wild plants thereby producing domesticates. These findings support the BE hypothesis of domestication occurring in the context of population-resource imbalances. Such deficits, driven either by increased populations or decreased resource abundance, are predicted to characterize domestication events elsewhere.

Post-invasion demography of prehistoric humans in South America

As the last habitable continent colonized by humans, the site of multiple domestication hotspots, and the location of the largest Pleistocene megafaunal extinction, South America is central to human prehistory. Yet remarkably little is known about human population dynamics during colonization, subsequent expansions, and domestication. Here we reconstruct the spatiotemporal patterns of human population growth in South America using a newly aggregated database of 1,147 archaeological sites and 5,464 calibrated radiocarbon dates spanning fourteen thousand to two thousand years ago (ka). We demonstrate that, rather than a steady exponential expansion, the demographic history of South Americans is characterized by two distinct phases. First, humans spread rapidly throughout the continent, but remained at low population sizes for 8,000 years, including a 4,000-year period of 'boom-and-bust' oscillations with no net growth. Supplementation of hunting with domesticated crops and animals had a minimal impact on population carrying capacity. Only with widespread sedentism, beginning ~5 ka, did a second demographic phase begin, with evidence for exponential population growth in cultural hotspots, characteristic of the Neolithic transition worldwide. The unique extent of humanity's ability to modify its environment to markedly increase carrying capacity in South America is therefore an unexpectedly recent phenomenon.