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Crop-to-wild gene flow is a mechanism process widely documented, both in plants and animals. This can have positive or negative impacts on the evolution of admixed populations in natural environments, yet the phenomenon is still misunderstood in long-lived woody species, contrary to short-lived crops. Wild olive Olea europaea L. occurs in the same eco-geographical range as domesticated olive, i.e. the Mediterranean Basin (MB). Moreover, it is an allogamous and anemophilous species whose seeds are disseminated by birds, i.e. factors that drive gene flow between crops and their wild relatives. Here we investigated the genetic structure of western MB wild olive populations in natural environments assuming a homogenous gene pool with limited impact of cultivated alleles, as previously suggested. We used a target sequencing method based on annotated genes from the Farga reference genome to analyze 27 western MB olive tree populations sampled in natural environments in France, Spain and Morocco. We also target sequenced cultivated olive tree accessions from the Worldwide Olive Germplasm Bank of Marrakech and Porquerolles and from an eastern MB wild olive tree population. We combined PCA, sNMF, pairwise FST and TreeMix and clearly identified genuine wild olive trees throughout their natural distribution range along a north-south gradient including, for the first time, in southern France. However, contrary to our assumption, we highlighted more admixed than genuine wild olive trees. Our results raise questions regarding the admixed population evolution pattern in this environment, which might be facilitated by crop-to-wild gene flow.

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.

Genomic offset statistics predict the maladaptation of populations to rapid habitat alteration based on association of genotypes with environmental variation. Despite substantial evidence for empirical validity, genomic offset statistics have well-identified limitations, and lack a theory that would facilitate interpretations of predicted values. Here, we clarified the theoretical relationships between genomic offset statistics and unobserved fitness traits controlled by environmentally selected loci and proposed a geometric measure to predict fitness after rapid change in local environment. The predictions of our theory were verified in computer simulations and in empirical data on African pearl millet (Cenchrus americanus) obtained from a common garden experiment. Our results proposed a unified perspective on genomic offset statistics and provided a theoretical foundation necessary when considering their potential application in conservation management in the face of environmental change.

Humans have had a major influence on the dissemination of crops beyond their native range, thereby offering new hybridization opportunities. Characterizing admixed genomes with mosaic origins generates valuable insight into the adaptive history of crops and the impact on current varietal diversity. We applied the ELAI tool—an efficient local ancestry inference method based on a two-layer hidden Markov model to track segments of wild origin in cultivated accessions in the case of multiway admixtures. Source populations—which may actually be limited and partially admixed—must be generally specified when using such inference models. We thus developed a framework to identify local ancestry with admixed source populations. Using sequencing data for wild and cultivated Coffea canephora (commonly called Robusta), our approach was found to be highly efficient and accurate on simulated hybrids. Application of the method to assess elite Robusta varieties from Vietnam led to the identification of an accession derived from a likely backcross between two genetic groups from the Congo Basin and the western coastal region of Central Africa. Admixtures resulting from crop hybridization and diffusion could thus lead to the generation of elite high-yielding varieties. Our methods should be widely applicable to gain insight into the role of hybridization during plant and animal evolutionary history.

Pearl millet is among the top three-cereal production in one of the most climate vulnerable regions, sub-Saharan Africa. Its Sahelian origin makes it adapted to grow in poor sandy soils under low soil water regimes. Pearl millet is thus considered today as one of the most interesting crops to face the global warming. Flowering time, a trait highly correlated with latitude, is one of the key traits that could be modulated to face future global changes. West African pearl millet landraces, can be grouped into early- (EF) and late-flowering (LF) varieties, each flowering group playing a specific role in the functioning and resilience of Sahelian smallholders. The aim of this study was thus to detect genes linked to flowering but also linked to relevant traits within each flowering group. We thus investigated genomic and phenotypic diversity in 109 pearl millet landrace accessions, i.e., 66 early-flowering and 43 late-flowering, grown in the groundnut basin, the first area of rainfed agriculture in Senegal dominated by dry cereals (millet, maize, and sorghum) and legumes (groundnuts, cowpeas). We were able to confirm the role of PhyC gene in pearl millet flowering and identify several other genes that appear to be as much as important, such as FSR12 and HAC1. HAC1 and two other genes appear to be part of QTLs previously identified and deserve further investigation. At the same time, we were able to highlight a several genes and variants that could contribute to the improvement of pearl millet yield, especially since their impact was demonstrated across flowering cycles.

Understanding vulnerabilities of plant populations to climate change could help preserve their biodiversity and reveal new elite parents for future breeding programs. To this end, landscape genomics is a useful approach for assessing putative adaptations to future climatic conditions, especially in long-lived species such as trees. We conducted a population genomics study of 207 Coffea canephora trees from seven forests along different climate gradients in Uganda. For this, we sequenced 323 candidate genes involved in key metabolic and defense pathways in coffee. Seventy-one SNPs were found to be significantly associated with bioclimatic variables, and were thereby considered as putatively adaptive loci. These SNPs were linked to key candidate genes, including transcription factors, like DREB-like and MYB family genes controlling plant responses to abiotic stresses, as well as other genes of organoleptic interest, like the DXMT gene involved in caffeine biosynthesis and a putative pest repellent. These climate-associated genetic markers were used to compute genetic offsets, predicting population responses to future climatic conditions based on local climate change forecasts. Using these measures of maladaptation to future conditions, substantial levels of genetic differentiation between present and future diversity were estimated for all populations and scenarios considered. The populations from the forests Zoka and Budongo, in the northernmost zone of Uganda, appeared to have the lowest genetic offsets under all predicted climate change patterns, while populations from Kalangala and Mabira, in the Lake Victoria region, exhibited the highest genetic offsets. The potential of these findings in terms of ex-situ conservation strategies are discussed., Understanding vulnerabilities of plant populations to climate change could help preserve their biodiversity and reveal new elite parents for future breeding programs. To this end, landscape genomics is a useful approach for assessing putative adaptations to future climatic conditions, especially in long-lived species such as trees. We conducted a population genomics study of 207 Coffea canephora trees from seven forests along different climate gradients in Uganda. For this, we sequenced 323 candidate genes involved in key metabolic and defense pathways in coffee. Seventy-one SNPs were found to be significantly associated with bioclimatic variables, and were thereby considered as putatively adaptive loci. These SNPs were linked to key candidate genes, including transcription factors, like DREB-like and MYB family genes controlling plant responses to abiotic stresses, as well as other genes of organoleptic interest, like the DXMT gene involved in caffeine biosynthesis and a putative pest repellent. These climate-associated genetic markers were used to compute genetic offsets, predicting population responses to future climatic conditions based on local climate change forecasts. Using these measures of maladaptation to future conditions, substantial levels of genetic differentiation between present and future diversity were estimated for all populations and scenarios considered. The populations from the forests Zoka and Budongo, in the northernmost zone of Uganda, appeared to have the lowest genetic offsets under all predicted climate change patterns, while populations from Kalangala and Mabira, in the Lake Victoria region, exhibited the highest genetic offsets. The potential of these findings in terms of ex-situ conservation strategies are discussed.