Evolution in Archaeology

This review begins with a brief outline of the key concepts of Darwinian archaeology. Its history is then summarized, beginning with its emergence as a significant theoretical focus within the discipline in the early 1980s; its main present-day currents are then presented, citing examples of recent work. The developments in archaeology are part of broader trends in anthropology and psychology and are characterized by the same theoretical disagreements. There are two distinct research traditions: one centered on cultural transmission and dual inheritance theory and the other on human behavioral ecology. The development of specifically archaeological methodologies within these two traditions for testing evolutionary hypotheses relating to diachronic questions using archaeological data is discussed. Finally, this review suggests that the greatest challenge for the future lies in finding ways of using archaeological data to address current major debates in evolutionary social science as a whole concerning, for example, the emergence of largescale cooperation

David Clarke’s Analytical Archaeology at 50

David Clarke’s Analytical Archaeology (1968) has been seen as a pivotal work that emerged when new ideas and approaches were transforming archaeology as a discipline. However, the authors contend that some of its key ideas have only been picked up on and given closer consideration in more recent years. At the 50th anniversary of its publication, the authors outline the central ideas discussed in Analytical Archaeology and evaluate its role in ongoing discussions. They conclude that in the light of recent work, which can demonstrably be seen as revisiting some of its central themes, there is much to be gained in contemporary archaeological discussion by revisiting Clarke’s book

Transition to farming more likely for small, conservative groups with property rights, but increased productivity is not essential

Theories for the origins of agriculture are still debated, with a range of different explanations offered. Computational models can be used to test these theories and explore new hypotheses; Bowles and Choi [Bowles S, Choi J-K (2013) Proc Natl Acad Sci USA 110(22):8830–8835] have developed one such model. Their model shows the coevolution of farming and farming-friendly property rights, and by including climate variability, replicates the timings for the emergence of these events seen in the archaeological record. Because the processes modeled occurred a long time ago, it can be difficult to justify exact parameter values; hence, we propose a fitting to idealized outcomes (FIO) method to explore the model’s parameter space in more detail. We have replicated the model of Bowles and Choi, and used the FIO method to identify complexities and interactions of the model previously unidentified. Our results indicate that the key parameters for the emergence of farming are group structuring, group size, conservatism, and farming-friendly property rights (lending further support to Bowles and Choi’s original proposal). We also find that although advantageous, it is not essential that farming productivity be greater than foraging productivity for farming to emerge. In addition, we highlight how model behaviors can be missed when gauging parameter sensitivity via a fix-all-but-one variation approach

European Neolithic societies showed early warning signals of population collapse

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

Understanding cumulative cultural evolution

This is the final version of the article. Available from the publisher via the DOI in this record.No abstract availabl

A generative inference framework for analysing patterns of cultural change in sparse population data with evidence for fashion trends in LBK culture

Cultural change can be quantified by temporal changes in frequency of different cultural artefacts and it is a central question to identify what underlying cultural transmission processes could have caused the observed frequency changes. Observed changes, however, often describe the dynamics in samples of the population of artefacts, whereas transmission processes act on the whole population. Here we develop a modelling framework aimed at addressing this inference problem. To do so, we firstly generate population structures from which the observed sample could have been drawn randomly and then determine theoretical samples at a later time t2 produced under the assumption that changes in frequencies are caused by a specific transmission process. Thereby we also account for the potential effect of time-averaging processes in the generation of the observed sample. Subsequent statistical comparisons (e.g. using Bayesian inference) of the theoretical and observed samples at t2 can establish which processes could have produced the observed frequency data. In this way, we infer underlying transmission processes directly from available data without any equilibrium assumption. We apply this framework to a dataset describing pottery from settlements of some of the first farmers in Europe (the LBK culture) and conclude that the observed frequency dynamic of different types of decorated pottery is consistent with age-dependent selection, a preference for 'young' pottery types which is potentially indicative of fashion trends