Two are better than one: combining landscape genomics and common gardens for detecting local adaptation in forest trees

International audience; Predicting likely species responses to an alteration of their local environment is key to decision-making in resource management, ecosystem restoration and biodiversity conservation practice in the face of global human-induced habitat disturbance. This is especially true for forest trees which are a dominant life form on Earth and play a central role in supporting diverse communities and structuring a wide range of ecosystems. In Europe, it is expected that most forest tree species will not be able to migrate North fast enough to follow the estimated temperature isocline shift given current predictions for rapid climate warming. In this context, a topical question for forest genetics research is to quantify the ability for tree species to adapt locally to strongly altered environmental conditions (Kremer et al. 2012). Identifying environmental factors driving local adaptation is, however, a major challenge for evolutionary biology and ecology in general but is particularly difficult in trees given their large individual and population size and long generation time. Empirical evaluation of local adaptation in trees has traditionally relied on fastidious long-term common garden experiments (provenance trials) now supplemented by reference genome sequence analysis for a handful of economically valuable species. However, such resources have been lacking for most tree species despite their ecological importance in supporting whole ecosystems. In this issue of Molecular Ecology, De Kort et al. (2014) provide original and convincing empirical evidence of local adaptation to temperature in black alder, Alnus glutinosa L. Gaertn, a surprisingly understudied keystone species supporting riparian ecosystems. Here, De Kort et al. (2014) use an innovative empirical approach complementing state-of-the-art landscape genomics analysis of A. glutinosa populations sampled in natura across a regional climate gradient with phenotypic trait assessment in a common garden experiment (Fig. 1). By combining the two methods, De Kort et al. (2014) were able to detect unequivocal association between temperature and phenotypic traits such as leaf size as well as with genetic loci putatively under divergent selection for temperature. The research by De Kort et al. (2014) provides valuable insight into adaptive response to temperature variation for an ecologically important species and demonstrates the usefulness of an integrated approach for empirical evaluation of local adaptation in nonmodel species (Sork et al. 2013).