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

Style, Function and Cultural Transmission

Recent evolutionary approaches to the understanding of lithic variability take us back to long-standing issues in lithic studies to do with the claimed contrast between style and function and the Binford-Bordes debate of the 1960s concerning the factors that affect inter-assemblage variation. In fact, the style and function contrast is an unhelpful one, not least when considering the question of convergence. Taking the definition of style as ‘a way of doing’, all functions are carried out in locally specific ways that have a transmission history, although the extent to which the history of the attributes relevant to the function have been subject to random drift and innovation patterns, as opposed to selection, will vary. Moreover, in a subtractive technology like lithics the extent to which a transmission signal will be visible in an attribute like the angle of a cutting edge is unclear. The contrasting view is that, in the case of lithics, functional requirements will always call into existence the technical innovations to satisfy them, which in any case are not that difficult to find. The paper addresses these and related issues with reference to previous work by Shennan and colleagues on the use of material culture to identify within and between group variation, the extent to which isolation-by-distance in space and time can account for the similarities and differences between assemblages, and the role of phylogenetic methods

Reporting errors, incidence and risk factors for postpartum haemorrhage and progression to severe PPH:a prospective observational study

OBJECTIVE: To quantify reporting errors, measure incidence of postpartum haemorrhage (PPH) and define risk factors for PPH (≥500 ml) and progression to severe PPH (≥1500 ml). DESIGN: Prospective observational study. SETTING: Two UK maternity services. POPULATION: Women giving birth between 1 August 2008 and 31 July 2009 (n = 10 213). METHODS: Weighted sampling with sequential adjustment by multivariate analysis. MAIN OUTCOME MEASURES: Incidence and risk factors for PPH and progression to severe PPH. RESULTS: Errors in transcribing blood volume were frequent (14%) with evidence of threshold preference and avoidance. The incidences of PPH ≥500, ≥1500 and ≥2500 ml were 33.7% (95% CI 31.2–36.2), 3.9% (95% CI 3.3–4.6) and 0.8% (95% CI 0.6–1.0). New independent risk factors predicting PPH ≥ 500 ml included Black African ethnicity (adjusted odds ratio [aOR] 1.77, 95% CI 1.31–2.39) and assisted conception (aOR 2.93, 95% CI 1.30–6.59). Modelling demonstrated how prepregnancy- and pregnancy-acquired factors may be mediated through intrapartum events, including caesarean section, elective (aOR 24.4, 95% CI 5.53–108.00) or emergency (aOR 40.5, 95% CI 16.30–101.00), and retained placenta (aOR 21.3, 95% CI 8.31–54.7). New risk factors were identified for progression to severe PPH, including index of multiple deprivation (education, skills and training) (aOR 1.75, 95% CI 1.11–2.74), multiparity without caesarean section (aOR 1.65, 95% CI 1.20–2.28) and administration of steroids for fetal reasons (aOR 2.00, 95% CI 1.24–3.22). CONCLUSIONS: Sequential, interacting, traditional and new risk factors explain the highest rates of PPH and severe PPH reported to date