Despite the increasing number of genomic tools, identifying the genetics underlying adaptive complex traits remains challenging in the model species Arabidopsis thaliana. This is due, at least in part, to the lack of data on the geographical scale of adaptive phenotypic variation. The aims of this study were (i) to tease apart the historical roles of adaptive and nonselective processes in shaping phenological variation in A. thaliana in France and (ii) to gain insights into the spatial scale of adaptive variation by identifying the putative selective agents responsible for this selection. Forty-nine natural stands from four climatically contrasted French regions were characterized (i) phenologically for six traits, (ii) genetically using 135 SNP markers and (iii) ecologically for 42 variables. Up to 63% of phenological variation could be explained by neutral genetic diversity. The remaining phenological variation displayed stronger associations with ecological variation within regions than among regions, suggesting the importance of local selective agents in shaping adaptive phenological variation. Although climatic conditions have often been suggested as the main selective agents acting on phenology in A. thaliana, both edaphic conditions and interspecific competition appear to be strong selective agents in some regions. In a first attempt to identify the genetics of phenological variation at different geographical scales, we phenotyped worldwide accessions and local polymorphic populations from the French RegMap in a genome-wide association (GWA) mapping study. The genomic regions associated with phenological variation depended upon the geographical scale considered, stressing the need to account for the scale of adaptive phenotypic variation when choosing accession panels for GWAS.

Ecological Niche Models (ENMs) are increasingly used by ecologists to project species potential future distribution. However, the application of such models may be challenging, and some caveats have already been identified. While studies have generally shown that projections may be sensitive to the ENM applied or the emission scenario, to name just a few, the sensitivity of ENM-based scenarios to General Circulation Models (GCMs) has been often underappreciated. Here, using a multi-GCM and multi-emission scenario approach, we evaluated the variability in projected distributions under future climate conditions. We modeled the ecological realized niche (sensu Hutchinson) and predicted the baseline distribution of species with contrasting spatial patterns and representative of two major functional groups of European trees: the dwarf birch and the sweet chestnut. Their future distributions were then projected onto future climatic conditions derived from seven GCMs and four emissions scenarios using the new Representative Concentration Pathways (RCPs) developed for the Intergovernmental Panel on Climate Change (IPCC) AR5 report. Uncertainties arising from GCMs and those resulting from emissions scenarios were quantified and compared. Our study reveals that scenarios of future species distribution exhibit broad differences, depending not only on emissions scenarios but also on GCMs. We found that the between-GCM variability was greater than the between-RCP variability for the next decades and both types of variability reached a similar level at the end of this century. Our result highlights that a combined multi-GCM and multi-RCP approach is needed to better consider potential trajectories and uncertainties in future species distributions. In all cases, between-GCM variability increases with the level of warming, and if nothing is done to alleviate global warming, future species spatial distribution may become more and more difficult to anticipate. When future species spatial distributions are examined, we propose to use a large number of GCMs and RCPs to better anticipate potential trajectories and quantify uncertainties.

Dormancy in sea beet could be interpreted as a way to limit summer germination and spread germination over the first autumn and spring or following autumns. This highly heritable trait has the potential to evolve in the relatively near future because of climate change.