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Microgeographical adaptation occurs when the effects of directional selection persist despite gene flow. Traits and genetic loci under selection can then show adaptive divergence, against the backdrop of little differentiation at other traits or loci. How common such events are and how strong the selection is that underlies them remain open questions. Here, we discovered and analysed microgeographical patterns of genomic divergence in four European and Mediterranean conifers with widely differing life‐history traits and ecological requirements (Abies alba MIll., Cedrus atlantica [Endl.] Manetti, Pinus halepensis Mill. and Pinus pinaster Aiton) by screening pairs from geographically close forest stands sampled along steep ecological gradients. We inferred patterns of genomic divergence by applying a combination of divergence outlier detection methods, demographic modelling, Approximate Bayesian Computation inferences and genomic annotation to genomic data. Surprisingly for such small geographical scales, we showed that selection is strong in all species but generally affects different loci in each. A clear signature of selection was systematically detected on a fraction of the genome, of the order of 0.1%–1% of the loci depending on the species. The novel modelling method we designed for estimating selection coefficients showed that the microgeographical selection coefficient scaled by population size (Ns) was 2–30. Our results convincingly suggest that selection maintains within‐population diversity at microgeographical scales in spatially heterogeneous environments. Such genetic diversity is likely to be a major reservoir of adaptive potential, helping populations to adapt under fluctuating environmental conditions. [ABSTRACT FROM AUTHOR]

Abstract: Teasing apart the effects of natural selection and demography on current allele frequencies is challenging, due to both processes leaving a similar molecular footprint. In particular, when attempting to identify selection in species that have undergone a recent range expansion, the increase in genetic drift at the edges of range expansions (“allele surfing”) can be a confounding factor. To address this potential issue, we first assess the long‐range colonization history of the Aleppo pine across the Mediterranean Basin, using molecular markers. We then look for single nucleotide polymorphisms (SNPs) involved in local adaptation using: (a) environmental correlation methods ( bayenv2), focusing on bioclimatic variables important for the species’ adaptation (i.e., temperature, precipitation and water availability); and (b) FST‐related methods ( pcadapt). To assess the rate of false positives caused by the allele surfing effect, these results are compared with results from simulated SNP data that mimics the species’ past range expansions and the effect of genetic drift, but with no selection. We find that the Aleppo pine shows a previously unsuspected complex genetic structure across its range, as well as evidence of selection acting on SNPs involved with the response to bioclimatic variables such as drought. This study uses an original approach to disentangle the confounding effects of drift and selection in range margin populations. It also contributes to the increased evidence that plant populations are able to adapt to new environments despite the expected accumulation of deleterious mutations that takes place during long‐range colonizations. [ABSTRACT FROM AUTHOR]

Finding outlier loci underlying local adaptation is challenging and is best approached by suitable sampling design and rigorous method selection. In this study, we aimed to detect outlier loci (single nucleotide polymorphisms, SNPs) at the local scale by using Aleppo pine (Pinus halepensis), a drought resistant conifer that has colonized many habitats in the Mediterranean Basin, as the model species. We used a nested sampling approach that considered replicated altitudinal gradients for three contrasting sites. We genotyped samples at 294 SNPs located in genomic regions selected to maximize outlier detection. We then applied three different statistical methodologies—Two Bayesian outlier methods and one latent factor principal component method—To identify outlier loci. No SNP was an outlier for all three methods, while eight SNPs were detected by at least two methods and 17 were detected only by one method. From the intersection of outlier SNPs, only one presented an allelic frequency pattern associated with the elevational gradient across the three sites. In a context of multiple populations under similar selective pressures, our results underline the need for careful examination of outliers detected in genomic scans before considering them as candidates for convergent adaptation. [ABSTRACT FROM AUTHOR]