Climate‐associated genetic variation in Fagus sylvatica and potential responses to climate change in the French Alps. (20th March 2020) - Capblancq, Thibaut; Morin, Xavier; Gueguen, Maya; Renaud, Julien; Lobreaux, Stéphane; Bazin, Eric
Abstract: Local adaptation patterns have been found in many plants and animals, highlighting the genetic heterogeneity of species along their range of distribution. In the next decades, global warming is predicted to induce a change in the selective pressures that drive this adaptive variation, forcing a reshuffling of the underlying adaptive allele distributions. For species with low dispersion capacity and long generation time such as trees, the rapidity of the change could impede the migration of beneficial alleles and lower their capacity to track the changing environment. Identifying the main selective pressures driving the adaptive genetic variation is thus necessary when investigating species capacity to respond to global warming. In this study, we investigate the adaptive landscape of Fagus sylvatica along a gradient of populations in the French Alps. Using a double‐digest restriction‐site‐associated DNA (ddRAD) sequencing approach, we identified 7, 000 SNPs from 570 individuals across 36 different sites. A redundancy analysis (RDA)‐derived method allowed us to identify several SNPs that were strongly associated with climatic gradients; moreover, we defined the primary selective gradients along the natural populations of F. sylvatica in the Alps. Strong effects of elevation and humidity, which contrast north‐western and south‐eastern site, were found and were believed to be important drivers of genetic adaptation. Finally, simulations of future genetic landscapes that used these findings allowed identifying populations at risk for F. sylvatica in the Alps, which could be helpful for future management plans. Abstract : Spatial representation, across the current range of F. sylvatica, of (A) genetic offset between current and future climatic conditions, (B) distances from populations currently showing a genetic adaptive component equivalent to the future adaptive needs (geographic offset) and (C) Population Adaptive Index (PAI) and levels of adaptive standing genetic variation (SGV) in the sampled populations.