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Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering the full trajectory compared to few time samples. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetics changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

With the availability of high quality full genome polymorphism (SNPs) data, it becomes feasible to study the past demographic and selective history of populations in exquisite detail. However, such inferences still suffer from a lack of statistical resolution for recent, e.g. bottlenecks, events, and/or for populations with small nucleotide diversity. Additional heritable (epi)genetic markers, such as indels, transposable elements, microsatellites or cytosine methylation, may provide further, yet untapped, information on the recent past population history. We extend the Sequential Markovian Coalescent (SMC) framework to jointly use SNPs and other hyper-mutable markers. We are able to 1) improve the accuracy of demographic inference in recent times, 2) uncover past demographic events hidden to SNP-based inference methods, and 3) infer the hyper-mutable marker mutation rates under a finite site model. As a proof of principle, we focus on demo-graphic inference inA. thalianausing DNA methylation diversity data from 10 European natural accessions. We demonstrate that segregat-ing Single Methylated Polymorphisms (SMPs) satisfy the modelling assumptions of the SMC framework, while Differentially Methylated Regions (DMRs) are not suitable as their length exceeds that of the genomic distance between two recombination events. Combining SNPs and SMPs while accounting for site- and region-level epimutation processes, we provide new estimates of the glacial age bottleneck and post glacial population expansion of the EuropeanA. thalianapopulation. Our SMC framework readily accounts for a wide range of heritable genomic markers, thus paving the way for next generation inference of evolutionary history by combining information from several genetic and epigenetic markers.

Drought stress is a key factor limiting plant growth and the colonization of arid habitats by plants. Here, we study the evolution of gene expression response to drought stress in a wild tomato,Solanum chilensenaturally occurring around the Atacama Desert in South America. We conduct a transcriptome analysis of plants under standard and drought experimental conditions to understand the evolution of drought-response gene networks. We identify two main regulatory networks corresponding to two typical drought-responsive strategies: cell cycle and fundamental metabolic processes. We estimate the age of the genes in these networks and the age of the gene expression network, revealing that the metabolic network has a younger origin and more variable transcriptome than the cell-cycle network. Combining with analyses of population genetics, we found that a higher proportion of the metabolic network genes show signatures of recent positive selection underlying recent adaptation withinS. chilense,while the cell-cycle network appears of ancient origin and is more conserved. For both networks, however, we find that genes showing older age of selective sweeps are the more connected in the network. Adaptation to southern arid habitats over the last 50,000 years occurred inS. chilenseby adaptive changes core genes with substantial network rewiring and subsequently by smaller changes at peripheral genes.

1AbstractThe demography and genetic structure of a population are closely linked. The study of this interplay is crucial, especially for organisms with frequent demographic fluctuations such as pathogen species responsible for emerging diseases. The joint study of demographic variations and genetic evolution of a pathogen population can be achieved by coupling epidemiology and population genetics. This framework is particularly suited to the study of genetic signatures of strong and rapid selective events, such as pathogen adaptation to plant genetic resistances. We have limited knowledge of how these events shape the genetic evolution of pathogens and what would be the typical genetic signatures of strong demographic and selective events on the pathogen population. In this study, we model pathogen population dynamics and genetic evolution at neutral loci to draw a typology of scenarios of eco-evolutionary dynamics. Using a clustering method dedicated to time-series variations, we identify three main scenarios: 1) small variations in the pathogen population size and small changes in genetic structure, 2) a strong founder event on the resistant host that in turn leads to a genetic structure on the susceptible host, and 3) evolutionary rescue that results in a strong founder event on the resistant host, preceded by a bottleneck on the susceptible host. Beyond the selective event itself, the demographic trajectory imposes specific changes in the genetic structure of the pathogen population. Most of these genetic changes are transient, with a signature of resistance overcoming that vanishes in a few years only. Considering time series is therefore of utmost importance for accurate tracking of the evolutionary trajectories of pathogen populations.

The reproductive mechanism of a species is a key driver of genome evolution. The standard Wright-Fisher model for the reproduction of individuals in a population assumes that each individual produces a number of offspring negligible compared to the total population size. Yet many species of plants, invertebrates, prokaryotes or fish exhibit neutrally skewed offspring distribution or strong selection events yielding few individuals to produce a number of offspring of up to the same magnitude as the population size. As a result, the genealogy of a sample is characterized by multiple individuals (more than two) coalescing simultaneously to the same common ancestor. The current methods developed to detect such multiple merger events do not account for complex demographic scenarios or recombination, and require large sample sizes. We tackle these limitations by developing two novel and different approaches to infer multiple merger events from sequence data or the ancestral recombination graph (ARG): a sequentially Markovian coalescent (SMβC) and a graph neural network (GNNcoal). We first give proof of the accuracy of our methods to estimate the multiple merger parameter and past demographic history using simulated data under theβ-coalescent model. Secondly, we show that our approaches can also recover the effect of positive selective sweeps along the genome. Finally, we are able to distinguish skewed offspring distribution from selection while simultaneously inferring the past variation of population size. Our findings stress the aptitude of neural networks to leverage information from the ARG for inference but also the urgent need for more accurate ARG inference approaches.

Positive selection is the driving force underpinning local adaptation and leaves footprints of selective sweeps on the underlying major genes. Quantifying the timing of selection and revealing the genetic bases of adaptation in plant species occurring in steep and varying environmental gradients are crucial to predict a species' ability to colonize new niches. We use whole-genome sequence data from six populations across three different habitats of the wild tomato species Solanum chilense to infer the past demographic history and search for genes under strong positive selection. We then correlate current and past climatic projections with the demographic history, allele frequencies, the age of selection events and distribution shifts. Several selective sweeps occur at regulatory networks involved in root-hair development in low altitude and response to photoperiod and vernalization in high-altitude populations. These sweeps appear to occur in a concerted fashion in a given regulatory gene network at particular periods of substantial climatic change. Using a unique combination of genome scans and modelling of past climatic data, we quantify the timing of selection at genes likely underpinning local adaptation to semiarid habitats.

Seed banking (or dormancy) is a widespread bet-hedging strategy, generating a form of population overlap, which decreases the magnitude of genetic drift. The methodological complexity of integrating this trait implies it is ignored when developing tools to detect selective sweeps. But, as dormancy lengthens the ancestral recombination graph (ARG), increasing times to fixation, it can change the genomic signatures of selection. To detect genes under positive selection in seed banking species it is important to 1) determine whether the efficacy of selection is affected, and 2) predict the patterns of nucleotide diversity at and around positively selected alleles. We present the first tree sequence-based simulation program integrating a weak seed bank to examine the dynamics and genomic footprints of beneficial alleles in a finite population. We find that seed banking does not affect the probability of fixation and confirm expectations of increased times to fixation. We also confirm earlier findings that, for strong selection, the times to fixation are not scaled by the inbreeding effective population size in the presence of seed banks, but are shorter than would be expected. As seed banking increases the effective recombination rate, footprints of sweeps appear narrower around the selected sites and due to the scaling of the ARG are detectable for longer periods of time. The developed simulation tool can be used to predict the footprints of selection and draw statistical inference of past evolutionary events in plants, invertebrates, or fungi with seed banks.

Contemporary gene flow, when resumed after a period of isolation, can have crucial consequences for endangered species, as it can both increase the supply of adaptive alleles and erode local adaptation. Determining the history of gene flow and thus the importance of contemporary hybridization, however, is notoriously difficult. Here, we focus on two endangered plant species, Arabis nemorensis and A. sagittata, which hybridize naturally in a sympatric population located on the banks of the Rhine. Using reduced genome sequencing, we determined the phylogeography of the two taxa but report only a unique sympatric population. Molecular variation in chloroplast DNA indicated that A. sagittata is the principal receiver of gene flow. Applying classical D-statistics and its derivatives to whole-genome data of 35 accessions, we detect gene flow not only in the sympatric population but also among allopatric populations. Using an Approximate Bayesian computation approach, we identify the model that best describes the history of gene flow between these taxa. This model shows that low levels of gene flow have persisted long after speciation. Around 10 000 years ago, gene flow stopped and a period of complete isolation began. Eventually, a hotspot of contemporary hybridization was formed in the unique sympatric population. Occasional sympatry may have helped protect these lineages from extinction in spite of their extremely low diversity.

Ramularia leaf spot is becoming an ever-increasing problem in main barley-growing regions since the 1980s, causing up to 70% yield loss in extreme cases. Yet, the causal agent Ramularia collo-cygni remains poorly studied. The diversity of the pathogen in the field thus far remains unknown. Furthermore, it is unknown to what extent the pathogen has a sexual reproductive cycle. The teleomorph of R. collo-cygni has not been observed. To study the genetic diversity of R. collo-cygni and get more insights in its biology, we sequenced the genomes of 19 R. collo-cygni isolates from multiple geographic locations and diverse hosts. Nucleotide polymorphism analyses of all isolates shows that R. collo-cygni is genetically diverse worldwide, with little geographic or host specific differentiation. Next, we used two different methods to detect signals of recombination in our sample set. Both methods find putative recombination events, which indicate that sexual reproduction happens or has happened in the global R. collo-cygni population. Lastly, we used these data on recombination to perform historic population size analyses. These suggest that the effective population size of R. collo-cygni decreased during the domestication of barley and subsequently grew with the rise of agriculture. Our findings deepen our understanding of R. collo-cygni biology and can help us to understand the current epidemic. We discuss how our findings support possible global spread through seed transfer, and we highlight how recombination, clonal spreading, and lack of host specificity could amplify global epidemics of this increasingly important disease and suggest specific approaches to combat the pathogen.

Coevolutionary interactions, from the delicate co-dependency in mutualistic interactions to the antagonistic relationship of hosts and parasites, are a ubiquitous driver of adaptation. Surprisingly, little is known about the genomic processes underlying coevolution in an ecological context. However, species comprise genetically differentiated populations that interact with temporally variable abiotic and biotic environments. We discuss the recent advances in coevolutionary theory and genomics as well as shortcomings, to identify coevolving genes that take into account this spatial and temporal variability of coevolution, and propose a practical guide to understand the dynamic of coevolution using an ecological genomics lens.

Summary Positive Darwinian selection is the driving force underpinning local (in space) and temporal (in time) adaptation, and leaves footprints of selective sweeps at the underlying major genes. These two adaptive processes are classically considered independently, so that most genomic selection scans uncover only recent sweeps underpinning spatial adaptation. However, understanding if these adaptive processes are intermingled and share common genetic bases is crucial to predict a species’ evolutionary potential, for example in response to changing environmental conditions. We use whole genome data from six populations across three different habitats of the wild tomato species Solanum chilense, to 1) infer the past demographic history, and 2) search for genes under strong positive selection. We then correlate the demographic history, allele frequencies in space and time, the age of selection events and the reconstructed historical ecological distribution of the species over five main climatic periods spanning 150,000 years. We find evidence for several selective sweeps targeting regulatory networks involved in root hair development in low altitude, and response to photoperiod and vernalization in high altitude populations. These sweeps occur in a concerted fashion in a given regulatory gene network and only at particular time periods, thereby underpinning temporal local adaptation. These genes under positive selection provide subsequently the basis for spatial local adaptation to novel habitats when new ecological niches become available. Our results reveal the importance of jointly studying spatial and temporal adaptations in species during habitat colonization.

International audience; Host-parasite coevolution is ubiquitous, shaping genetic and phenotypic diversity and the evolutionary trajectory of interacting species. With the advances of high throughput sequencing technologies applicable to model and non-model organisms alike, it is now feasible to study in greater detail (a) the genetic underpinnings of coevolution, (b) the speed and type of dynamics at coevolving loci, and (c) the genomic consequences of coevolution. This review focuses on three recently developed approaches that leverage information from host and parasite full genome data simultaneously to pinpoint coevolving loci and draw inference on the coevolutionary history. First, co-genome-wide association study (co-GWAS) methods allow pinpointing the loci underlying host-parasite interactions. These methods focus on detecting associations between genetic variants and the outcome of experimental infection tests or on correlations between genomes of naturally infected hosts and their infecting parasites. Second, extensions to population genomics methods can detect genes under coevolution and infer the coevolutionary history, such as fitness costs. Third, correlations between host and parasite population size in time are indicative of coevolution, and polymorphism levels across independent spatially distributed populations of hosts and parasites can reveal coevolutionary loci and infer coevolutionary history. We describe the principles of these three approaches and discuss their advantages and limitations based on coevolutionary theory. We present recommendations for their application to various host (prokaryotes, fungi, plants, and animals) and parasite (viruses, bacteria, fungi, and macroparasites) species. We conclude by pointing out methodological and theoretical gaps to be filled to extract maximum information from full genome data and thereby to shed light on the molecular underpinnings of coevolution.

Several methods based on the sequentially Markovian coalescent (SMC) make use of full genome sequence data from samples to infer population demographic history including past changes in population size, admixture, migration events and population structure. More recently, the original theoretical framework has been extended to allow the simultaneous estimation of population size changes along with other life history traits such as selfing or seed banking. The latter developments enhance the applicability of SMC methods to nonmodel species. Although convergence proofs have been given using simulated data in a few specific cases, an in-depth investigation of the limitations of SMC methods is lacking. In order to explore such limits, we first develop a tool inferring the best case convergence of SMC methods assuming the true underlying coalescent genealogies are known. This tool can be used to quantify the amount and type of information that can be confidently retrieved from given data sets prior to the analysis of the real data. Second, we assess the inference accuracy when the assumptions of SMC approaches are violated due to departures from the model, namely the presence of transposable elements, variable recombination and mutation rates along the sequence, and SNP calling errors. Third, we deliver a new interpretation of SMC methods by highlighting the importance of the transition matrix, which we argue can be used as a set of summary statistics in other statistical inference methods, uncoupling the SMC from hidden Markov models (HMMs). We finally offer recommendations to better apply SMC methods and build adequate data sets under budget constraints.

Population genetics models typically consider a fixed population size and a unique selection coefficient. However, population dynamics inherently generate fluctuations in numbers of individuals and selection acts on various components of the individuals’ fitness. In plant species with seed banks, the size of both the above- and below-ground compartments induce fluctuations depending on seed production and the state of the seed bank. We investigate if this fluctuation has consequences on (1) the rate of genetic drift, and (2) the efficacy of selection. We consider four variants of two-allele Moran-type models defined by combinations of presence and absence of fluctuations in the population size in above-ground and seed bank compartments. Time scale analysis and dimension reduction methods allow us to reduce the corresponding Fokker–Planck equations to one-dimensional diffusion approximations of a Moran model. We first show that if the fluctuations of above-ground population size classically affect the rate of genetic drift, fluctuations of below-ground population size reduce the diversity storage effect of the seed bank. Second, we consider that selection can act on four different components of the plant fitness: plant or seed death rate, seed production or seed germination. Our striking result is that the efficacy of selection for seed death rate or germination rate is reduced by fluctuations in the seed bank size, whereas selection occurring on plant death rate or seed production is not affected. We derive the expected site-frequency spectrum reflecting this heterogeneity in selection efficacy between genes underpinning different plant fitness components. Our results highlight the importance to consider the effect of ecological noise to predict the impact of seed banks on neutral and selective evolution.

Background Coevolution is a selective process of reciprocal adaptation in hosts and parasites or in mutualistic symbionts. Classic population genetics theory predicts the signatures of selection at the interacting loci of both species, but not the neutral genome-wide polymorphism patterns. To bridge this gap, we build an eco-evolutionary model, where neutral genomic changes over time are driven by a single selected locus in hosts and parasites via a simple biallelic gene-for-gene or matching-allele interaction. This coevolutionary process may lead to cyclic changes in the sizes of the interacting populations. Results We investigate if and when these changes can be observed in the site frequency spectrum of neutral polymorphisms from host and parasite full genome data. We show that changes of the host population size are too smooth to be observable in its polymorphism pattern over the course of time. Conversely, the parasite population may undergo a series of strong bottlenecks occurring on a slower relative time scale, which may lead to observable changes in a time series sample. We also extend our results to cases with 1) several parasites per host accelerating relative time, and 2) multiple parasite generations per host generation slowing down rescaled time. Conclusions Our results show that time series sampling of host and parasite populations with full genome data are crucial to understand if and how coevolution occurs. This model provides therefore a framework to interpret and draw inference from genome-wide polymorphism data of interacting species.

Seed (egg) banking is a common bet-hedging strategy maximizing the fitness of organisms facing environmental unpredictability by the delayed emergence of offspring. Yet, this condition often requires fast and drastic stochastic shifts between good and bad years. We hypothesize that the host seed banking strategy can evolve in response to coevolution with parasites because the coevolutionary cycles promote a gradually changing environment over longer times than seed persistence. We study the evolution of host germination fraction as a quantitative trait using both pairwise competition and multiple mutant competition methods, while the germination locus can be genetically linked or unlinked with the host locus under coevolution. In a gene-for-gene model of coevolution, hosts evolve a seed bank strategy under unstable coevolutionary cycles promoted by moderate to high costs of resistance or strong disease severity. Moreover, when assuming genetic linkage between coevolving and germination loci, the resistant genotype always evolves seed banking in contrast to susceptible hosts. Under a matching-allele interaction, both hosts' genotypes exhibit the same seed banking strategy irrespective of the genetic linkage between loci. We suggest host-parasite coevolution as an additional hypothesis for the evolution of seed banking as a temporal bet-hedging strategy.

Several methods based on the Sequential Markovian coalescence (SMC) have been developed that make use of genome sequence data to uncover population demographic history, which is of interest in its own right and is a key requirement to generate a null model for selection tests. While these methods can be applied to all possible kind of species, the underlying assumptions are sexual reproduction in each generation and non-overlapping generations. However, in many plants, invertebrates, fungi and other taxa, those assumptions are often violated due to different ecological and life history traits, such as self-fertilization or long term dormant structures (seed or egg-banking). We develop a novel SMC-based method to infer 1) the rates/parameters of dormancy and of self-fertilization, and 2) the populations’ past demographic history. Using simulated data sets, we demonstrate the accuracy of our method for a wide range of demographic scenarios and for sequence lengths from one to 30 Mb using four sampled genomes. Finally, we apply our method to a Swedish and a German population of Arabidopsis thaliana demonstrating a selfing rate of ca. 0.87 and the absence of any detectable seed-bank. In contrast, we show that the water flea Daphnia pulex exhibits a long lived egg-bank of three to 18 generations. In conclusion, we here present a novel method to infer accurate demographies and life-history traits for species with selfing and/or seed/egg-banks. Finally, we provide recommendations for the use of SMC-based methods for non-model organisms, highlighting the importance of the per site and the effective ratios of recombination over mutation., Author summary With the rapid advancement of sequencing technologies it has become feasible to use genome sequence data from several individuals per population/species to learn about the past history of populations, such as changes in population size, timing of colonization of a given habitat/continent or past migration events. However, all statistical methods to date rely on a mathematical model built upon characteristics of hominid species. Namely, this model ignores specific ecological traits common to many plant, invertebrate, or fungal species such as self-fertilization and dormancy. The latter is defined as the ability of plant seeds or invertebrates to remain dormant for long periods of time in the soil or sediments. Here we develop a new statistical method which uses several whole genome sequence data per population/species to simultaneously infer 1) the rates of dormancy and of self-fertilization, and 2) past demographic history. We demonstrate the accuracy of our method using extensive simulations. We also apply our method to Arabidopsis thaliana confirming a high rate of selfing and the absence of long term seed dormancy, and to Daphnia Pulex demonstrating a long lived egg bank. We discuss the wide applicability of our method to draw accurate inference of past evolution for non-hominid species.

Achieving high intraspecific genetic diversity is a critical goal in ecological restoration as it increases the adaptive potential and long-term resilience of populations. Thus, we investigated genetic diversity within and between pristine sites in a fossil floodplain and compared it to sites restored by hay-transfer between 1997 and 2014. RAD-seq genotyping revealed that the stenoecious flood-plain species Arabis nemorensis is co-occurring with individuals that, based on ploidy, ITS-sequencing and morphology, probably belong to the close relative Arabis sagittata, which has a documented preference for dry calcareous grasslands but has not been reported in floodplain meadows. We show that hay-transfer maintains genetic diversity for both species. Additionally, in A. sagittata, transfer from multiple genetically isolated pristine sites resulted in restored sites with increased diversity and admixed local genotypes. In A. nemorensis, transfer did not create novel admixture dynamics because genetic diversity between pristine sites was less differentiated. Thus, the effects of hay-transfer on genetic diversity also depend on the genetic makeup of the donor communities of each species, especially when local material is mixed. Our results demonstrate the efficiency of hay-transfer for habitat restoration and emphasize the importance of pre-restoration characterization of micro-geographic patterns of intraspecific diversity of the community to guarantee that restoration practices reach their goal, i.e. maximize the adaptive potential of the entire restored plant community. Overlooking these patterns may alter the balance between species in the community. Additionally, our comparison of summary statistics obtained from de novo and reference-based RAD-seq pipelines shows that the genomic impact of restoration can be reliably monitored in species lacking prior genomic knowledge.

Uncovering the genes governing host-parasite coevolutionary interactions is of importance for disease management. The increasing availability of host and parasite full genome-data allows for cross-species genome-wide association studies based on the genomic data of in-fected hosts and their associated parasite strains sampled from natural populations (i.e. natural co-GWAs). Such studies focus on searching for cross-species allelic associations between pairs of host and parasite SNPs. We aim to quantify the power of natural co-GWAs to pinpoint loci under coevolution with respect to the intrinsic complexities of coevolutionary systems, such as the genetic specificity of the interaction and the temporal allele frequency changes resulting from the interaction. Therefore, we develop the cross-species association (CSA) and the cross-species prevalence (CSP) indices, the latter additionally incorporating genomic data from uninfected hosts. To provide an assessment of the statistical power of these indices, we analytically derive their genome-wide False Discovery Rates (FDR) based on the neutral site-frequency spectrum of the host and the parasite population. Using two coevolutionary models, we investigate under which parameter regimes these indices pin-point the coevolving loci. Under trench warfare dynamics, CSA and CSP are very accurate in pinpointing the loci under coevolution, while under arms race dynamics the power is limited especially for gene-for-gene interactions. Furthermore, we reveal that the combination of both indices across time samples is an indicator for the specificity of the interaction. Our results provide novel insights into the power and biological interpretation of natural cross-species association studies.

Ramularia leaf spot is becoming an ever-increasing problem in main barley-growing regions since the 1980s, causing up to 70% yield loss in extreme cases. Yet, the causal agent

Nucleotide binding site, leucine-rich repeat receptors (NLRs) are canonical resistance (R) genes in plants, fungi and animals, functioning as central (helper) and peripheral (sensor) genes in a signalling network. We investigate NLR evolution during the colonization of novel habitats in a model tomato species, Solanum chilense. We used R-gene enrichment sequencing to obtain polymorphism data at NLRs of 140 plants sampled across 14 populations covering the whole species range. We inferred the past demographic history of habitat colonization by resequencing whole genomes from three S. chilense plants from three key populations and performing approximate Bayesian computation using data from the 14 populations. Using these parameters, we simulated the genetic differentiation statistics distribution expected under neutral NLR evolution and identified small subsets of outlier NLRs exhibiting signatures of selection across populations. NLRs under selection between habitats are more often helper genes, whereas those showing signatures of adaptation in single populations are more often sensor-NLRs. Thus, centrality in the NLR network does not constrain NLR evolvability, and new mutations in central genes in the network are key for R-gene adaptation during colonization of different habitats.

Despite the wealth of empirical and theoretical studies, the origin and maintenance of cooperation is still an evolutionary riddle. In this context, ecological life-history traits which affect the efficiency of selection may play a role despite being often ignored. We consider here species such as bacteria, fungi, invertebrates and plants which exhibit resting stages in the form of a quiescent state or a seed bank. When quiescent, individuals are inactive and reproduce upon activation, while under seed bank parents produce offspring remaining dormant for different amount of time. We assume weak frequency-dependent selection modeled using game-theory and the prisoner's dilemma (cooperation/defect) as payoff matrix. The cooperators and defectors are allowed to evolve different quiescence or dormancy times. By means of singular perturbation theory we reduce the model to a one-dimensional equation resembling the well known replicator equation, in which the gain functions are scaled with lumped parameters reflecting the time scale of the resting state of the cooperators and defectors. If both time scales are identical cooperation cannot persist in a homogeneous population. If, however, the time scale of the cooperator is distinctively different from that of the defector, cooperation may become a locally asymptotically stable strategy. Interestingly enough, in the seed bank case the cooperator needs to become active faster than the defector, while in the quiescent case the cooperator has to be slower. We use adaptive dynamics to identify situations where cooperation may evolve and form a convergent stable ESS. We conclude by highlighting the relevance of these results for many non-model species and the maintenance of cooperation in microbial, invertebrate or plant populations.

BackgroundWild tomato species, likeSolanum chilense, are important germplasm resources for enhanced biotic and abiotic stress resistance in tomato breeding. In addition,S. chilenseserves as a model system to study adaptation of plants to drought and to investigate the evolution of seed banks. However to date, the absence of a well annotated reference genome in this compulsory outcrossing, very diverse species limits in-depth studies on the genes involved.FindingsWe generated ∼134 Gb of DNA and 157 Gb of RNA sequence data ofS chilense, which yielded a draft genome with an estimated length of 914 Mb in total encoding 25,885 high-confidence (hc) predicted gene models, which show homology to known protein-coding genes of other tomato species. Approximately 71% (18,290) of the hc gene models are additionally supported by RNA-seq data derived from leaf tissue samples. A benchmarking with Universal Single-Copy Orthologs (BUSCO) analysis of predicted gene models retrieved 93.3% BUSCO genes, which is in the current range of high-quality genomes for non-inbred plants. To further verify the genome annotation completeness and accuracy, we manually inspected the NLR resistance gene family and assessed its assembly quality. We revealed the existence of unique gene families of NLRs toS. chilense. Comparative genomics analyses ofS. chilense, cultivated tomatoS. lycopersicumand its wild relativeS. pennelliirevealed similar levels of highly syntenic gene clusters between the three species.ConclusionsWe generated the first genome and transcriptome sequence assembly for the wild tomato speciesSolanum chilenseand demonstrated its value in comparative genomics analyses. We make these genomes available for the scientific community as an important resource for studies on adaptation to biotic and abiotic stress inSolanaceae, on evolution of self-incompatibility, and for tomato breeding.

Wild tomato species, like Solanum chilense, are important germplasm resources for enhanced biotic and abiotic stress resistance in tomato breeding. S. chilense also serves as a model to study adaptation of plants to drought and the evolution of seed banks. The absence of a well-annotated reference genome in this compulsory outcrossing, very diverse species limits in-depth studies on the genes involved. We generated ∼134 Gb of DNA and 157 Gb of RNA sequence data for S chilense, which yielded a draft genome with an estimated length of 914 Mb, encoding 25,885 high-confidence predicted gene models, which show homology to known protein-coding genes of other tomato species. Approximately 71% of these gene models are supported by RNA-seq data derived from leaf tissue samples. Benchmarking with Universal Single-Copy Orthologs (BUSCO) analysis of predicted gene models retrieved 93.3% of BUSCO genes. To further verify the genome annotation completeness and accuracy, we manually inspected the NLR resistance gene family and assessed its assembly quality. We find subfamilies of NLRs unique to S. chilense. Synteny analysis suggests significant degree of the gene order conservation between the S. chilense, S. lycopersicum and S. pennellii genomes. We generated the first genome and transcriptome sequence assemblies for the wild tomato species Solanum chilense and demonstrated their value in comparative genomics analyses. These data are an important resource for studies on adaptation to biotic and abiotic stress in Solanaceae, on evolution of self-incompatibility and for tomato breeding.

[This corrects the article DOI: 10.1371/journal.pgen.1008698.].

To meet the current challenges in human food production, improved understanding of the genetic diversity of crop species that maximizes the selection efficacy in breeding programs is needed. The present study offers new insights into the diversity, genetic structure and demographic history of cultivated rye (Secale cereale L.). We genotyped 620 individuals from 14 global rye populations with a different end use (grain or forage) at 32 genome-wide simple sequence repeat markers. We reveal the relationships among these populations, their sizes and the timing of domestication events using population genetics and model-based inference with approximate Bayesian computation. Our main results demonstrate (i) a high within-population variation and genetic diversity, (ii) an unexpected absence of reduction in diversity with an increasing improvement level and (iii) patterns suggestive of multiple domestication events. We suggest that the main drivers of diversification of winter rye are the end use of rye in two early regions of cultivation: rye forage in the Mediterranean area and grain in northeast Europe. The lower diversity and stronger differentiation of eastern European populations were most likely due to more intensive cultivation and breeding of rye in this region, in contrast to the Mediterranean region where it was considered a secondary crop or even a weed. We discuss the relevance of our results for the management of gene bank resources and the pitfalls of inference methods applied to crop domestication due to violation of model assumptions and model complexity.

Despite major progress in dissecting the molecular pathways that control DNA methylation patterns in plants, little is known about the mechanisms that shape plant methylomes over evolutionary time. Drawing on recent intra- and interspecific epigenomic studies, we show that methylome evolution over long timescales is largely a byproduct of genomic changes. By contrast, methylome evolution over short timescales appears to be driven mainly by spontaneous epimutational events. We argue that novel methods based on analyses of the methylation site frequency spectrum (mSFS) of natural populations can provide deeper insights into the evolutionary forces that act at each timescale.

first posted online on May 16, 2018; International audience; Filamentous pathogens, including fungi and oomycetes, pose major threats to global food security. Crop pathogens cause damage by secreting effectors that manipulate the host to the pathogen’s advantage. Genes encoding such effectors are among the most rapidly evolving genes in pathogen genomes. Here, we review how the major characteristics of the emergence, function, and regulation of effector genes are tightly linked to the genomic compartments where these genes are located in pathogen genomes. The presence of repetitive elements in these compartments is associated with elevated rates of point mutations and sequence rearrangements with a major impact on effector diversification. The expression of many effectors converges on an epigenetic control mediated by the presence of repetitive elements. Population genomics analyses showed that rapidly evolving pathogens show high rates of turnover at effector loci and display a mosaic in effector presence-absence polymorphism among strains. We conclude that effective pathogen containment strategies require a thorough understanding of the effector genome biology and the pathogen’s potential for rapid adaptation.

Seed (egg) banking is a common bet-hedging strategy maximizing the fitness of organisms facing environmental unpredictability by the delayed emergence of offspring. Yet, this condition often requires fast and drastic stochastic shifts between good and bad years. We hypothesize that the host seed banking strategy can evolve in response to coevolution with parasites because the coevolutionary cycles promote a gradually changing environment over longer times than seed persistence. We study the evolution of host germination fraction as a quantitative trait using both pairwise competition and multiple mutant competition methods, while the germination locus can be genetically linked or unlinked with the host locus under coevolution. In a gene-for-gene model of coevolution, hosts evolve a seed bank strategy under unstable coevolutionary cycles promoted by moderate to high costs of resistance or strong disease severity. Moreover, when assuming genetic linkage between coevolving and germination loci, the resistant genotype always evolves seed banking in contrast to susceptible hosts. Under a matching-allele interaction, both hosts’ genotypes exhibit the same seed banking strategy irrespective of the genetic linkage between loci. We suggest host-parasite coevolution as an additional hypothesis for the evolution of seed banking as a temporal bet-hedging strategy.

An axiom of modern evolutionary theory is that intra-species genetic diversity determines the adaptive potential of any species. This diversity results from the interaction between three factors: the effective population size, natural selection and the rate of recombination. All three factors are influenced by the occurrence of long dormant stages (seed banking or seed persistence), which is an evolutionary bet hedging strategy and a key characteristic of many angiosperms, but also bacteria, fungi or invertebrates. Perhaps surprisingly, this ecological trait has so far been almost ignored in evolutionary genomics. Seed banking is expected to have a fundamental influence on neutral and selective evolutionary processes, and is therefore a key factor to comprehend angiosperm genomic evolution. Theoretical modeling aims to predict the effect of seed banking on patterns of nucleotide diversity. We first adapt for seed banks the two classical mathematical frameworks of population genetics: (1) the backward in time process of the Kingman n-coalescent, and (2) the forward in time diffusion approach. This allows us to derive population genetics quantities and statistics that can be obtained from DNA sequence data. Second, we generate new predictions on neutral diversity and past demographic inference for single and multiple populations under seed banks. Third, we compute the expected effect of seed banks on unlinked (genome wide selection) and linked (gene level selection) sites. Finally, we conclude by suggesting three hypotheses, which can be tested by contrasting polymorphism data in seed banking and non-seed banking species.

After colonization population sizes may vary across the species range depending on environmental conditions and following colonizations. An interesting question is whether local adaptation occurs more frequently in large ancestral populations or in small derived populations. A higher number of new mutations and a lower effect of genetic drift should favor selection in large populations, whereas small derived populations may require an initial local adaptation event to facilitate the colonization of new habitats. Wild tomatoes are native to a broad range of different habitats characterized by variable abiotic conditions in South America, and represent an ideal system to study this interplay between demography and natural selection. Population genetic analyses and statistical inference of past demography were conducted on pooled-sequencing data from 30 genes (8,080 single nucleotide polymorphisms) from an extensive sampling of 23 Solanum chilense populations over Chile and Peru. We reveal first a north-south colonization associated with relaxed purifying selection in the south as shown by a decrease of genetic variation and an increasing proportion of nonsynonymous polymorphism from north to south, and population substructure with at least four genetic groups. Second, we uncover a dual picture of adaptation consisting of 1) a decreasing proportion of adaptive amino acid substitutions from north to south suggesting that adaptation is favored in large populations, whereas 2) signatures of local adaptation predominantly occur in the smaller populations from the marginal ranges in the south.

Ramularia collo-cygni is the biotic factor responsible for the disease Ramularia leaf spot (RLS) of barley (Hordeum vulgare). Despite having been described over 100 years ago and being considered a minor disease in some countries, the fungus is attracting interest in the scientific community as a result of the increasing number of recorded economically damaging disease epidemics. New reports of disease spread and fungal identification using molecular diagnostics have helped redefine RLS as a global disease. This review describes recent developments in our understanding of the biology and epidemiology of the fungus, outlines advances made in the field of the genetics of both the fungus and host, and summarizes the control strategies currently available.

Population genetic research has transformed from the study of genetic structures into a much wider and highly multidisciplinary research field. In this contribution, we outline the limitations of classic population genetic data and highlight the potential of combining molecular data with additional environmental, ecological and biological data sets in multidisciplinary approaches. The combination of data sets from various fields allows a more comprehensive understanding of extrinsic and intrinsic evolutionary processes affecting populations such as the distinction between genetic drift and natural selection. The integration of population size estimates and demographic dynamics as well as individual behaviour (embracing aspects like dispersal behaviour) allows for testing of adaptations to local environments and understanding time-lags frequently observed in molecular data. Modelling approaches are integrated at increasing rates into population genetic research to identify potential barriers and corridors. In our review, we therefore highlight multiple synergistic effects when combining population genetic data with morphological, ecological, behavioural data and modelling. We outline strengths and limitations and indicate future possibilities and challenges. © 2015 The Linnean Society of London, Biological Journal of the Linnean Society, 2015, 115, 1–12.