Testing how populations are locally adapted and predicting their response to their future environment is of key importance in view of climate change. Landscape genomics is a powerful approach to investigate genes and environmental factors involved in local adaptation. In a pooled amplicon sequencing approach of 94 genes in 71 populations, we tested whether >3500 single nucleotide polymorphisms (SNPs) in the three most common oak species in Switzerland (Quercus petraea, Q. pubescens, Q. robur) show an association with abiotic factors related to local topography, historical climate and soil characteristics. In the analysis including all species, the most frequently associated environmental factors were those best describing the habitats of the species. In the species-specific analyses, the most important environmental factors and associated SNPs greatly differed among species. However, we identified one SNP and seven genes that were associated with the same environmental factor across all species. We finally used regressions of allele frequencies of the most strongly associated SNPs along environmental gradients to predict the risk of nonadaptedness (RONA), which represents the average change in allele frequency at climate-associated loci theoretically required to match future climatic conditions. RONA is considerable for some populations and species (up to 48% in single populations) and strongly differs among species. Given the long generation time of oaks, some of the required allele frequency changes might not be realistic to achieve based on standing genetic variation. Hence, future adaptedness requires gene flow or planting of individuals carrying beneficial alleles from habitats currently matching future climatic conditions.

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In the alpine landscape studied, genetic structure occurred on small spatial scales as expected for alpine plants. However, gene flow also covered large distances. This makes it plausible for alpine plants to spread beneficial alleles at least via pollen across landscapes at a short time scale. Thus, gene flow potentially facilitates rapid adaptation in A. alpina likely to be required under ongoing climate change.

Numerous landscape genomic studies have identified single-nucleotide polymorphisms (SNPs) and genes potentially involved in local adaptation. Rarely, it has been explicitly evaluated whether these environmental associations also hold true beyond the populations studied. We tested whether putatively adaptive SNPs in Arabidopsis halleri (Brassicaceae), characterized in a previous study investigating local adaptation to a highly heterogeneous environment, show the same environmental associations in an independent, geographically enlarged set of 18 populations. We analysed new SNP data of 444 plants with the same methodology (partial Mantel tests, PMTs) as in the original study and additionally with a latent factor mixed model (LFMM) approach. Of the 74 candidate SNPs, 41% (PMTs) and 51% (LFMM) were associated with environmental factors in the independent data set. However, only 5% (PMTs) and 15% (LFMM) of the associations showed the same environment-allele relationships as in the original study. In total, we found 11 genes (31%) containing the same association in the original and independent data set. These can be considered prime candidate genes for environmental adaptation at a broader geographical scale. Our results suggest that selection pressures in highly heterogeneous alpine environments vary locally and signatures of selection are likely to be population-specific. Thus, genotype-by-environment interactions underlying adaptation are more heterogeneous and complex than is often assumed, which might represent a problem when testing for adaptation at specific loci.

& Key message We show that joint multivariate analyses of leaf morphological characters and molecular-genetic markers improve the taxonomic assignment in hybridizing European white oaks. However, model-based approaches using genetic data alone represent straightforward alternatives to laborious, detailed morphological assessments.