Positive feedbacks in deep-time transitions of human populations

Abrupt and rapid changes in human societies are among the most exciting population phenomena. Human populations tend to show rapid expansions from low to high population density along with increased social complexity in just a few generations. Such demographic transitions appear as a remarkable feature of Homo sapiens population dynamics, most likely fuelled by the ability to accumulate cultural/technological innovations that actively modify their environment. We are especially interested in establishing if the demographic transitions of pre-historic populations show the same dynamic signature of the Industrial Revolution transition (a positive relationship between population growth rates and size). Our results show that population growth patterns across different pre-historic societies were similar to those observed during the Industrial Revolution in developed western societies. These features, which appear to have been operating during most of our recent demographic history from hunter-gatherers to modern industrial societies, imply that the dynamics of cooperation underlay sudden population transitions in human societies. This article is part of the theme issue 'Evolution and sustainability: gathering the strands for an Anthropocene synthesis'.

An ABC Method for Whole-Genome Sequence Data: Inferring Paleolithic and Neolithic Human Expansions

Species generally undergo a complex demographic history consisting, in particular, of multiple changes in population size. Genome-wide sequencing data are potentially highly informative for reconstructing this demographic history. A crucial point is to extract the relevant information from these very large data sets. Here, we design an approach for inferring past demographic events from a moderate number of fully sequenced genomes. Our new approach uses Approximate Bayesian Computation, a simulation-based statistical framework that allows 1) identifying the best demographic scenario among several competing scenarios and 2) estimating the best-fitting parameters under the chosen scenario. Approximate Bayesian Computation relies on the computation of summary statistics. Using a cross-validation approach, we show that statistics such as the lengths of haplotypes shared between individuals, or the decay of linkage disequilibrium with distance, can be combined with classical statistics (e.g., heterozygosity and Tajima's D) to accurately infer complex demographic scenarios including bottlenecks and expansion periods. We also demonstrate the importance of simultaneously estimating the genotyping error rate. Applying our method on genome-wide human-sequence databases, we finally show that a model consisting in a bottleneck followed by a Paleolithic and a Neolithic expansion is the most relevant for Eurasian populations.