COLLEGE PARK, Maryland, 31 August 2016 (UMD) – Following the arrival of early agricultural crops from southwest Asia, ancient European societies experienced a series of population booms followed by a collapse that historical scientists are still working to explain. New research from the University of Maryland published in the Proceedings of the National Academy of Sciences (PNAS) uncovers indicators—called early warning signals—that foretold of this dramatic shift in population long before it happened.
Led by Sean Downey, PhD, an assistant professor in the Department of Anthropology, the UMD research team analyzed a catalogue of radiocarbon dates from the European Neolithic period (Stone Age), which began roughly 8,000 years ago. In 2013, it was Downey and colleagues who first discovered the boom-bust cycle in ancient Europe, but the researchers next wanted to determine whether statistical patterns could be detected that preceded the population decline.
“To our knowledge, this study is the first to find early warning signals of major demographic shifts among human populations,” Downey said. “You need long time sequences to show these collapses or shifts are coming. And although we have seen studies showing this in biology and ecology, nobody’s ever shown it for humans, mainly because the data requirements are very high.”
Understanding why ancient populations experience rapid growth or decline is monumentally important to the health of modern societies, Downey suggests, in order to prevent the past from repeating itself. In this instance, the invention of human agriculture served as the catalyst for major population changes that led to an eventual collapse. Downey explains that there is relevance to contemporary debates over whether modern technological developments can continue to outpace rapidly increasing population growth.
“Our population structure is being perturbed by our behavior,” Downey said. “Technology may not necessarily buffer us from all the consequences of rapid population growth. In fact, it may have been innovations in agricultural technology that triggered the kinds of instability we’ve seen during the European Neolithic Period.”
Downey is hopeful the statistical framework developed in this research will provide a way to analyze complex dynamics in human populations and ultimately help the emerging field of sustainability science monitor and prevent catastrophic consequences of societal shifts.
“You have to look at these issues from an evolutionary time frame and we simply don’t have the data to be able to do that except from archaeology,” Downey said. “The historical sciences contain information that can help improve the resilience of modern society.”
Downey’s research team included W. Randall Haas, Jr., a post-doctoral associate in the UMD Department of Anthropology, and Stephen J. Shennan, Professor of Theoretical Archaeology for the Institute of Archaeology at University College London.
Sara Gavin, 301-405-1733
Sean Downey, 301-405-1423
Ecosystems on the verge of major reorganization—regime shift—may exhibit declining resilience, which can be detected using a collection of generic statistical tests known as early warning signals (EWSs). This study explores whether EWSs anticipated human population collapse during the European Neolithic. It analyzes recent reconstructions of European Neolithic (8–4 kya) population trends that reveal regime shifts from a period of rapid growth following the introduction of agriculture to a period of instability and collapse. We find statistical support for EWSs in advance of population collapse. Seven of nine regional datasets exhibit increasing autocorrelation and variance leading up to collapse, suggesting that these societies began to recover from perturbation more slowly as resilience declined. We derive EWS statistics from a prehistoric population proxy based on summed archaeological radiocarbon date probability densities. We use simulation to validate our methods and show that sampling biases, atmospheric effects, radiocarbon calibration error, and taphonomic processes are unlikely to explain the observed EWS patterns. The implications of these results for understanding the dynamics of Neolithic ecosystems are discussed, and we present a general framework for analyzing societal regime shifts using EWS at large spatial and temporal scales. We suggest that our findings are consistent with an adaptive cycling model that highlights both the vulnerability and resilience of early European populations. We close by discussing the implications of the detection of EWS in human systems for archaeology and sustainability science.
This study explores whether archaeologically detectable declines in resilience precede the onset of large-scale human population collapses. Our case study is the European Neolithic: a period that began approximately 9,000 years ago when the introduction of agricultural technologies initiated phases of rapid population growth that were in many cases followed by demographic instability and dramatic collapse. Our study finds evidence that statistical signatures of decreasing resilience can be detected long before population decline begins. To our knowledge, this study is the first to find early warning signals of demographic regime shift among human populations. The results suggest that archaeological information can potentially be used to monitor social and ecological vulnerability in human societies at large spatial and temporal scales.