Reconstructing the demographic history of populations is a central issue in evolutionary biology. Using likelihood-based methods coupled with Monte Carlo simulations, it is now possible to reconstruct past changes in population size from genetic data. Using simulated data sets under various demographic scenarios, we evaluate the statistical performance of Msvar, a full-likelihood Bayesian method that infers past demographic change from microsatellite data. Our simulation tests show that Msvar is very efficient at detecting population declines and expansions, provided the event is neither too weak nor too recent. We further show that Msvar outperforms two moment-based methods (the M-ratio test and Bottleneck) for detecting population size changes, whatever the time and the severity of the event. The same trend emerges from a compilation of empirical studies. The latest version of Msvar provides estimates of the current and the ancestral population size and the time since the population started changing in size. We show that, in the absence of prior knowledge, Msvar provides little information on the mutation rate, which results in biased estimates and/or wide credibility intervals for each of the demographic parameters. However, scaling the population size parameters with the mutation rate and scaling the time with current population size, as coalescent theory requires, significantly improves the quality of the estimates for contraction but not for expansion scenarios. Finally, our results suggest that Msvar is robust to moderate departures from a strict stepwise mutation model.

SummaryIn order to understand the successful spread of Ambrosia artemisiifolia in France, the variability of colonised habitat by this species was studied at 48 locations, from its central to peripheral area of distribution. Each site was characterised by a vegetation survey, a description of the A. artemisiifolia population and a soil analysis. Differences in the number of species, Shannon diversity index, evenness index and plant life form spectra were compared among the sites. A total of 276 species occurring along with A. artemisiifolia was observed. Therophytes and hemicryptophytes represented more than 80% of all the species. The two most frequent species occurring along with A. artemisiifolia were Chenopodium album and Polygonum aviculare. Multivariate analysis of vegetation surveys showed that A. artemisiifolia has a wide ecological tolerance. It colonises a large range of disturbed habitats differing in terms of vegetation cover, species composition and type of soil. The present study highlights the potential of A. artemisiifolia for invading spring crops and all semi‐natural or disturbed open areas. The success of its ongoing invasion can be explained by both its generalist character and the existence of vacant ecological niches, which are poorly occupied by the French native flora.

Rapid adaptive evolution has been advocated as a mechanism that promotes invasion. Demonstrating adaptive evolution in invasive species requires rigorous analysis of phenotypic shifts driven by selection. Here, we document selection-driven evolution of Phyla canescens, an Argentine weed, in two invaded regions (Australia and France). Invasive populations possessed similar or higher diversity than native populations, and displayed mixed lineages from different sources, suggesting that genetic bottlenecks in both countries might have been alleviated by multiple introductions. Compared to native populations, Australian populations displayed more investment in sexual reproduction, whereas French populations possessed enhanced vegetative reproduction and growth. We partitioned evolutionary forces (selection vs. stochastic events) using two independent methods. Results of both analyses suggest that the pattern of molecular and phenotypic variability among regions was consistent with selection-driven evolution, rather than stochastic events. Our findings indicate that selection has shaped the evolution of P. canescens in two different invaded regions.