Crop pathogen emergence and evolution in agro-ecological landscapes

Alternative host shapes transmission and life-history trait correlations in a multi-host plant pathogen

Most pathogens are generalists capable of infecting multiple host species or strains. Trade-offs in performance among different hosts are expected to limit the evolution of generalism. Despite the commonness of generalism, the variation in infectivity, transmission, and trade-offs in performance among host species have rarely been studied in the wild. To understand the ecological and evolutionary drivers of multi-host pathogen infectivity and transmission potential, I studied disease severity, transmission dynamics, and infectivity variation of downy mildew pathogen Peronospora sparsa on its three host plants Rubus arcticus, R. chamaemorus, and R. saxatilis. In a survey of 20 wild and cultivated sites of the three host species, disease severity varied by host species and by host population size but not among wild and cultivated sites. To understand how alternative host presence and plant diversity affect transmission of the pathogen, I conducted a transmission experiment. In this experiment, alternative host abundance and plant diversity together modified P. sparsa transmission to trap plants. To understand how resistance to P. sparsa varies among host species and genotypes, I conducted an inoculation experiment using 10 P. sparsa strains from different locations and 20 genotypes of the three host species. Significant variation in infectivity was found among host genotypes but not among host species. When trade-offs for infectivity were tested, high infectivity in one host species correlated with high infectivity in another host species. However, when pathogen transmission-related life-history correlations were tested, a positive correlation was found in R. arcticus but not in R. saxatilis. The results suggest that host resistance may shape pathogen life-history evolution with epidemiological consequences in a multi-host pathogen.

Genetic Evidence for Colletotrichum gloeosporioides Transmission Between the Invasive Plant Ageratina adenophora and Co-occurring Neighbor Plants

To understand the disease-mediated invasion of exotic plants and the potential risk of disease transmission in local ecosystems, it is necessary to characterize population genetic structure and spatio-temporal dynamics of fungal community associated with both invasive and co-occurring plants. In this study, multiple genes were used to characterize the genetic diversity of 165 strains of Colletotrichum gloeosporioides species complex (CGSC) isolated from healthy leaves and symptomatic leaves of invasive plant Ageratina adenophora, as well as symptomatic leaves of its neighbor plants from eleven geographic sites in China. The data showed that these CGSC strains had a high genetic diversity in each geographic site (all Hd > 0.67 and Pi > 0.01). Haplotype diversity and nucleotide diversity varied greatly in individual gene locus: gs had the highest haplotype diversity (Hd = 0.8972), gapdh had the highest nucleotide diversity (Pi = 0.0705), and ITS had the lowest nucleotide diversity (Pi = 0.0074). Haplotypes were not clustered by geographic site, invasive age, or isolation source. AMOVA revealed that the genetic variation was mainly from within-populations, regardless of geographic or isolation origin. Both AMOVA and neutrality tests indicated these CGSC strains occurred gene exchange among geographic populations but did not experience population expansion along with A. adenophora invasion progress. Our data indicated that A. adenophora primarily accumulated these CGSC fungi in the introduced range, suggesting a high frequency of CGSC transmission between A. adenophora and co-occurring neighbor plants. This study is valuable for understanding the disease-mediated plant invasion and the potential risk of disease transmission driven by exotic plants in local ecosystems.

Plant disease risk is modified by multiple global change drivers

Plant diseases are strongly influenced by host biodiversity, spatial structure, and abiotic conditions. All of these are undergoing rapid change, as the climate is warming, habitats are being lost, and nitrogen deposition is changing nutrient dynamics of ecosystems with ensuing consequences for biodiversity. Here, I review examples of plant-pathogen associations to demonstrate how our ability to understand, model and predict disease dynamics is becoming increasingly difficult, as both plant and pathogen populations and communities are undergoing extensive change. The extent of this change is influenced via both direct and combined effects of global change drivers, and especially the latter are still poorly understood. Change at one trophic level is expected to drive change also at the other, and hence feedback loops between plants and their pathogens are expected to drive changes in disease risk both through ecological as well as evolutionary mechanisms. Many of the examples discussed here demonstrate an increase in disease risk as a result of ongoing change, suggesting that unless we successfully mitigate global environmental change, plant disease is going to become an increasingly heavy burden on our societies with far-reaching consequences for food security and functioning of ecosystems.

Direct and indirect viral associations predict coexistence in wild plant virus communities

Viruses are a vastly underestimated component of biodiversity that occur as diverse communities across hierarchical scales from the landscape level to individual hosts. The integration of community ecology with disease biology is a powerful, novel approach that can yield unprecedented insights into the abiotic and biotic drivers of pathogen community assembly. Here, we sampled wild plant populations to characterize and analyze the diversity and co-occurrence structure of within-host virus communities and their predictors. Our results show that these virus communities are characterized by diverse, non-random coinfections. Using a novel graphical network modeling framework, we demonstrate how environmental heterogeneity influences the network of virus taxa and how the virus co-occurrence patterns can be attributed to non-random, direct statistical virus-virus associations. Moreover, we show that environmental heterogeneity changed virus association networks, especially through their indirect effects. Our results highlight a previously underestimated mechanism of how environmental variability can influence disease risks by changing associations between viruses that are conditional on their environment.

Models of Plant Resistance Deployment

Owing to their evolutionary potential, plant pathogens are able to rapidly adapt to genetically controlled plant resistance, often resulting in resistance breakdown and major epidemics in agricultural crops. Various deployment strategies have been proposed to improve resistance management. Globally, these rely on careful selection of resistance sources and their combination at various spatiotemporal scales (e.g., via gene pyramiding, crop rotations and mixtures, landscape mosaics). However, testing and optimizing these strategies using controlled experiments at large spatiotemporal scales are logistically challenging. Mathematical models provide an alternative investigative tool, and many have been developed to explore resistance deployment strategies under various contexts. This review analyzes 69 modeling studies in light of specific model structures (e.g., demographic or demogenetic, spatial or not), underlying assumptions (e.g., whether preadapted pathogens are present before resistance deployment), and evaluation criteria (e.g., resistance durability, disease control, cost-effectiveness). It highlights major research findings and discusses challenges for future modeling efforts.

Agricultural land use disrupts biodiversity mediation of virus infections in wild plant populations

Human alteration of natural habitats may change the processes governing species interactions in wild communities. Wild populations are increasingly impacted by agricultural intensification, yet it is unknown whether this alters biodiversity mediation of disease dynamics. We investigated the association between plant diversity (species richness, diversity) and infection risk (virus richness, prevalence) in populations of Plantago lanceolata in natural landscapes as well as those occurring at the edges of cultivated fields. Altogether, 27 P. lanceolata populations were surveyed for population characteristics and sampled for PCR detection of five recently characterized viruses. We find that plant species richness and diversity correlated negatively with virus infection prevalence. Virus species richness declined with increasing plant diversity and richness in natural populations while in agricultural edge populations species richness was moderately higher, and not associated with plant richness. This difference was not explained by changes in host richness between these two habitats, suggesting potential pathogen spill-over and increased transmission of viruses across the agro-ecological interface. Host population connectivity significantly decreased virus infection prevalence. We conclude that human use of landscapes may change the ecological laws by which natural communities are formed with far reaching implications for ecosystem functioning and disease.

Eco-Epidemiological Uncertainties of Emerging Plant Diseases: The Challenge of Predicting Xylella fastidiosa Dynamics in Novel Environments

In order to prevent and control the emergence of biosecurity threats such as vector-borne diseases of plants, it is vital to understand drivers of entry, establishment, and spatiotemporal spread, as well as the form, timing, and effectiveness of disease management strategies. An inherent challenge for policy in combatting emerging disease is the uncertainty associated with intervention planning in areas not yet affected, based on models and data from current outbreaks. Following the recent high-profile emergence of the bacterium Xylella fastidiosa in a number of European countries, we review the most pertinent epidemiological uncertainties concerning the dynamics of this bacterium in novel environments. To reduce the considerable ecological and socio-economic impacts of these outbreaks, eco-epidemiological research in a broader range of environmental conditions needs to be conducted and used to inform policy to enhance disease risk assessment, and support successful policy-making decisions. By characterizing infection pathways, we can highlight the uncertainties that surround our knowledge of this disease, drawing attention to how these are amplified when trying to predict and manage outbreaks in currently unaffected locations. To help guide future research and decision-making processes, we invited experts in different fields of plant pathology to identify data to prioritize when developing pest risk assessments. Our analysis revealed that epidemiological uncertainty is mainly driven by the large variety of hosts, vectors, and bacterial strains, leading to a range of different epidemiological characteristics further magnified by novel environmental conditions. These results offer new insights on how eco-epidemiological analyses can enhance understanding of plant disease spread and support management recommendations.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Quantifying the sharing of foliar fungal pathogens by the invasive plant Ageratina adenophora and its neighbours

Local pathogens can accumulate as asymptomatic endophytes, making it difficult to detect the impacts of invasive species as propagators of disease in the invaded range. We used the invasive plant Ageratina adenophora to assess such accumulation. We intensively collected foliar fungal endophytes and leaf spot pathogens of A. adenophora and co-occurring neighbours and performed an inoculation experiment to evaluate their pathogenicity and host range. Ageratina adenophora harboured diverse necrotrophic pathogens; its communities of endophytes and leaf spot pathogens were different in composition and shared only a small number of fungal species. In the pathogen communities of local plant hosts, 21% of the operational taxonomic units (OTUs), representing 50% of strains, also occurred as leaf spot pathogens and/or endophytes of A. adenophora. The local pathogen community was more similar to the endophytes than to the pathogens of A. adenophora. The inoculation experiment showed that local pathogens could infect A. adenophora leaves asymptomatically and that local plant hosts were susceptible to both A. adenophora endophytes and pathogens. Ageratina adenophora is a highly competent host for local pathogens, and its asymptomatic latent pathogens are fungi primarily shared with local neighbours. This poses challenges for understanding the long-term ecological consequences of plant invasion.

Skirmish or war: the emergence of agricultural plant pathogens

Understanding the ecological and evolutionary processes underlying the emergence of infectious disease is critically important in guiding prevention, management and breeding strategies. Novel pathogen lineages may arise within agricultural environments, wild hosts or from non-host associated disease reservoirs. Although the source of most disease outbreaks remains unknown, environmental and zoonotic origins are frequently identified in mammalian pathosystems and expanded sampling of plant pathosystems reveals important links with wild populations. This review describes key ecological and evolutionary processes underlying disease emergence, with particular emphasis on shifts from wild reservoirs to cultivated hosts and genetic mechanisms driving host adaption subsequent to emergence.

Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases

Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of learning the structure of epidemiological links with a pseudo-evolutionary model applied to training data obtained from contact tracing, for example, and using this initial stage to infer links for the whole dataset. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions, it is sufficiently parsimonious to allow the handling of big datasets in the future, and it is versatile enough to be applied to very different contexts from animal, human and plant epidemiology.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

Eco-evolutionary agriculture: Host-pathogen dynamics in crop rotations

Since its origins, thousands of years ago, agriculture has been challenged by the presence of evolving plant pathogens. Temporal rotations of host and non-host crops have helped farmers to control epidemics among other utilities, but further efforts for strategy assessment are needed. Here, we present a methodology for developing crop rotation strategies optimal for control of pathogens informed by numerical simulations of eco-evolutionary dynamics in one field. This approach can integrate agronomic criteria used in crop rotations—soil quality and cash yield—and the analysis of pathogen evolution in systems where hosts are artificially selected. Our analysis shows which rotation patterns perform better in maximising crop yield when an unspecified infection occurs, with yield being dependent on both soil quality and the strength of the epidemic. Importantly, the use of non-host crops, which both improve soil quality and control the epidemic results in similar rational rotation strategies for diverse agronomic and infection conditions. We test the repeatability of the best rotation patterns over multiple decades, an essential end-user goal. Our results provide sustainable strategies for optimal resource investment for increased food production and lead to further insights into the minimisation of pesticide use in a society demanding ever more efficient agriculture.

The invention of agriculture is a major evolutionary transition in the social evolution of the human race. Transforming the lifestyle from nomadic to sedentary, agriculture provided humankind with the stability necessary to make rapid advancements. However, agriculture, as we know it, is now in danger. While agriculture is a grand artificial selection experiment, it is in a constant battle with the brute force of natural selection, generating highly infectious plant pathogens. Traditional techniques such as slash-burn techniques are not sustainable for feeding the ever-increasing population. Crop rotation, on the other hand, has been developed over thousands of years as a sustainable method. We provide a computational model of how crop rotations can be used to tackle pathogen infection and what properties of rotation patterns make them sustainable in the long run. We hope that this study, together with other sustainable methods such as minimal pesticide use and biocontrol, can make agriculture more efficient.

Complete genome sequences of Streptomyces spp. isolated from disease-suppressive soils

Background

Bacteria within the genus Streptomyces remain a major source of new natural product discovery and as soil inoculants in agriculture where they promote plant growth and protect from disease. Recently, Streptomyces spp. have been implicated as important members of naturally disease-suppressive soils. To shine more light on the ecology and evolution of disease-suppressive microbial communities, we have sequenced the genome of three Streptomyces strains isolated from disease-suppressive soils and compared them to previously sequenced isolates. Strains selected for sequencing had previously showed strong phenotypes in competition or signaling assays.

Results

Here we present the de novo sequencing of three strains of the genus Streptomyces isolated from disease-suppressive soils to produce high-quality complete genomes. Streptomyces sp. GS93–23, Streptomyces sp. 3211–3, and Streptomyces sp. S3–4 were found to have linear chromosomes of 8.24 Mb, 8.23 Mb, and greater than 7.5 Mb, respectively. In addition, two of the strains were found to have large, linear plasmids. Each strain harbors between 26 and 38 natural product biosynthetic gene clusters, on par with previously sequenced Streptomyces spp. We compared these newly sequenced genomes with those of previously sequenced organisms. We see substantial natural product biosynthetic diversity between closely related strains, with the gain/loss of episomal DNA elements being a primary driver of genome evolution.

Conclusions

Long read sequencing data facilitates large contig assembly for high-GC Streptomyces genomes. While the sample number is too small for a definitive conclusion, we do not see evidence that disease suppressive soil isolates are particularly privileged in terms of numbers of biosynthetic gene clusters. The strong sequence similarity between GS93–23 and previously isolated Streptomyces lydicus suggests that species recruitment may contribute to the evolution of disease-suppressive microbial communities.

Diverse and variable virus communities in wild plant populations revealed by metagenomic tools

Wild plant populations may harbour a myriad of unknown viruses. As the majority of research efforts have targeted economically important plant species, the diversity and prevalence of viruses in the wild has remained largely unknown. However, the recent shift towards metagenomics-based sequencing methodologies, especially those targeting small RNAs, is finally enabling virus discovery from wild hosts. Understanding this diversity of potentially pathogenic microbes in the wild can offer insights into the components of natural biodiversity that promotes long-term coexistence between hosts and parasites in nature, and help predict when and where risks of disease emergence are highest. Here, we used small RNA deep sequencing to identify viruses in Plantago lanceolata populations, and to understand the variation in their prevalence and distribution across the Åland Islands, South-West Finland. By subsequent design of PCR primers, we screened the five most common viruses from two sets of P. lanceolata plants: 164 plants collected from 12 populations irrespective of symptoms, and 90 plants collected from five populations showing conspicuous viral symptoms. In addition to the previously reported species Plantago lanceolata latent virus (PlLV), we found four potentially novel virus species belonging to Caulimovirus, Betapartitivirus, Enamovirus, and Closterovirus genera. Our results show that virus prevalence and diversity varied among the sampled host populations. In six of the virus infected populations only a single virus species was detected, while five of the populations supported between two to five of the studied virus species. In 20% of the infected plants, viruses occurred as coinfections. When the relationship between conspicuous viral symptoms and virus infection was investigated, we found that plants showing symptoms were usually infected (84%), but virus infections were also detected from asymptomatic plants (44%). Jointly, these results reveal a diverse virus community with newly developed tools and protocols that offer exciting opportunities for future studies on the eco-evolutionary dynamics of viruses infecting plants in the wild.

Differential impact of landscape-scale strategies for crop cultivar deployment on disease dynamics, resistance durability and long-term evolutionary control

A multitude of resistance deployment strategies have been proposed to tackle the evolutionary potential of pathogens to overcome plant resistance. In particular, many landscape-based strategies rely on the deployment of resistant and susceptible cultivars in an agricultural landscape as a mosaic. However, the design of such strategies is not easy as strategies targeting epidemiological or evolutionary outcomes may not be the same. Using a stochastic spatially explicit model, we studied the impact of landscape organization (as defined by the proportion of fields cultivated with a resistant cultivar and their spatial aggregation) and key pathogen life-history traits on three measures of disease control. Our results show that short-term epidemiological dynamics are optimized when landscapes are planted with a high proportion of the resistant cultivar in low aggregation. Importantly, the exact opposite situation is optimal for resistance durability. Finally, well-mixed landscapes (balanced proportions with low aggregation) are optimal for long-term evolutionary equilibrium (defined here as the level of long-term pathogen adaptation). This work offers a perspective on the potential for contrasting effects of landscape organization on different goals of disease management and highlights the role of pathogen life history.

Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens

Genetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs quickly after resistance is deployed, resulting in significant crop losses and a continuing need to develop new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolution of pathogen populations and thus increase genetic resistance durability. These strategies mainly rely on varying different combinations of resistance sources across time (crop rotations) and space. The spatial scale of deployment can vary from multiple resistance sources occurring in a single cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). However, experimental comparison of the efficiency (i.e. ability to reduce disease impact) and durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of landscape-scale deployment strategies presents major logistical challenges. Therefore, we developed a spatially explicit stochastic model able to assess the epidemiological and evolutionary outcomes of the four major deployment options described above, including both qualitative resistance (i.e. major genes) and quantitative resistance traits against several components of pathogen aggressiveness: infection rate, latent period duration, propagule production rate, and infectious period duration. This model, implemented in the R package landsepi, provides a new and useful tool to assess the performance of a wide range of deployment options, and helps investigate the effect of landscape, epidemiological and evolutionary parameters. This article describes the model and its parameterisation for rust diseases of cereal crops, caused by fungi of the genus Puccinia. To illustrate the model, we use it to assess the epidemiological and evolutionary potential of the combination of a major gene and different traits of quantitative resistance. The comparison of the four major deployment strategies described above will be the objective of future studies.

There are many recent examples which demonstrate the evolutionary potential of plant pathogens to overcome the resistances deployed in agricultural landscapes to protect our crops. Increasingly, it is recognised that how resistance is deployed spatially and temporally can impact on rates of pathogen evolution and resistance breakdown. Such deployment strategies are mainly based on the combination of several sources of resistance at different spatiotemporal scales. However, comparison of these strategies in a predictive sense is not an easy task, owing to the logistical difficulties associated with experiments involving the spread of a pathogen at large spatio-temporal scales. Moreover, both the durability of a strategy and the epidemiological protection it provides to crops must be assessed since these evaluation criteria are not necessarily correlated. Surprisingly, no current simulation model allows a thorough comparison of the different options. Here we describe a spatio-temporal model able to simulate a wide range of deployment strategies and resistance sources. This model, implemented in the R package landsepi, facilitates assessment of both epidemiological and evolutionary outcomes across simulated scenarios. In this work, the model is used to investigate the combination of different sources of resistance against fungal diseases such as rusts of cereal crops.

Reciprocal Hosts' Responses to Powdery Mildew Isolates Originating from Domesticated Wheats and Their Wild Progenitor

The biotroph wheat powdery mildew, Blumeria graminis (DC.) E.O. Speer, f. sp. tritici Em. Marchal (Bgt), has undergone long and dynamic co-evolution with its hosts. In the last 10,000 years, processes involved in plant evolution under domestication, altered host-population structure. Recently both virulence and genomic profiling separated Bgt into two groups based on their origin from domestic host and from wild emmer wheat. While most studies focused on the Bgt pathogen, there is significant knowledge gaps in the role of wheat host diversity in this specification. This study aimed to fill this gap by exploring qualitatively and also quantitatively the disease response of diverse host panel to powdery mildew [105 domesticated wheat genotypes (Triticum turgidum ssp. dicoccum, T. turgidum ssp. durum, and T. aestivum) and 241 accessions of its direct progenitor, wild emmer wheat (T. turgidum ssp. dicoccoides)]. A set of eight Bgt isolates, originally collected from domesticated and wild wheat was used for screening this wheat collection. The isolates from domesticated wheat elicited susceptible to moderate plant responses on domesticated wheat lines and high resistance on wild genotypes (51.7% of the tested lines were resistant). Isolates from wild emmer elicited reciprocal disease responses: high resistance of domesticated germplasm and high susceptibility of the wild material (their original host). Analysis of variance of the quantitative phenotypic responses showed a significant Isolates × Host species interaction [P(F) < 0.0001] and further supported these findings. Furthermore, analysis of the range of disease severity values showed that when the group of host genotypes was inoculated with Bgt isolate from the reciprocal host, coefficient of variation was significantly higher than when inoculated with its own isolates. This trend was attributed to the role of major resistance genes in the latter scenario (high proportion of complete resistance). By testing the association between disease severity and geographical distance from the source of inoculum, we have found higher susceptibility in wild emmer close to the source. Both qualitative and quantitative assays showed a reciprocal resistance pattern in the wheat host and are well aligned with the recent findings of significant differentiation into wild-emmer and domesticated-wheat populations in the pathogen.

Mosaics often outperform pyramids: insights from a model comparing strategies for the deployment of plant resistance genes against viruses in agricultural landscapes

The breakdown of plant virus resistance genes is a major issue in agriculture. We investigated whether a set of resistance genes would last longer when stacked into a single plant cultivar (pyramiding) or when deployed individually in regional mosaics (mosaic strategy). We modeled the genetic and epidemiological processes shaping the demogenetic dynamics of viruses under a multilocus gene-for-gene system, from the plant to landscape scales. The landscape consisted of many fields, was subject to seasonality, and of a reservoir hosting viruses year-round. Strategy performance depended principally on the fitness costs of adaptive mutations, epidemic intensity before resistance deployment and landscape connectivity. Mosaics were at least as good as pyramiding strategies in most production situations tested. Pyramiding strategies performed better only with slowly changing virus reservoir dynamics. Mosaics are more versatile than pyramiding strategies, and we found that deploying a mosaic of three to five resistance genes generally provided effective disease control, unless the epidemics were driven mostly by within-field infections. We considered the epidemiological and evolutionary mechanisms underlying the greater versatility of mosaics in our case study, with a view to providing breeders and growers with guidance as to the most appropriate deployment strategy.

Addressing the Challenges of Pathogen Evolution on the World's Arable Crops

Advances in genomic and molecular technologies coupled with an increasing understanding of the fine structure of many resistance and infectivity genes, have opened up a new era of hope in controlling the many plant pathogens that continue to be a major source of loss in arable crops. Some new approaches are under consideration including the use of nonhost resistance and the targeting of critical developmental constraints. However, the major thrust of these genomic and molecular approaches is to enhance the identification of resistance genes, to increase their ease of manipulation through marker and gene editing technologies and to lock a range of resistance genes together in simply manipulable resistance gene cassettes. All these approaches essentially continue a strategy that assumes the ability to construct genetic-based resistance barriers that are insurmountable to target pathogens. Here we show how the recent advances in knowledge and marker technologies can be used to generate more durable disease resistance strategies that are based on broad evolutionary principles aimed at presenting pathogens with a shifting, landscape of fluctuating directional selection.

Insight into the durability of plant resistance to aphids from a demo-genetic study of Aphis gossypii in melon crops

Resistance breakdown has been observed following the deployment of plant cultivars resistant to pests. Assessing the durability of a resistance requires long-term experiments at least at a regional scale. We collected such data for melon resistance conferred by the Vat gene cluster to melon aphids. We examined landscape-level populations of Aphis gossypii collected in 2004-2015, from melon-producing regions with and without the deployment of Vat resistance and with different climates. We conducted demo-genetic analyses of the aphid populations on Vat and non-Vat plants during the cropping seasons. The Vat resistance decreased the density of aphid populations in all areas and changed the genetic structure and composition of these populations. Two bottlenecks were identified in the dynamics of adapted clones, due to the low levels of production of dispersal morphs and winter extinction. Our results suggest that (i) Vat resistance will not be durable in the Lesser Antilles, where no bottleneck affected the dynamics of adapted clones, (ii) Vat resistance will be durable in south-west France, where both bottlenecks affected the dynamics of adapted clones and (iii) Vat resistance will be less durable in south-east France, where only one of the two bottlenecks was observed.

Epidemiological and Evolutionary Outcomes in Gene-for-Gene and Matching Allele Models

Gene-for-gene (GFG) and matching-allele (MA) models are qualitatively different paradigms for describing the outcome of genetic interactions between hosts and pathogens. The GFG paradigm was largely built on the foundations of Flor's early work on the flax-flax rust interaction and is based on the concept of genetic recognition leading to incompatible disease outcomes, typical of host immune recognition. In contrast, the MA model is based on the assumption that genetic recognition leads to compatible interactions, which can result when pathogens require specific host factors to cause infection. Results from classical MA and GFG models have led to important predictions regarding various coevolutionary phenomena, including the role of fitness costs associated with resistance and infectivity, the distribution of resistance genes in wild populations, patterns of local adaptation and the evolution and maintenance of sexual reproduction. Empirical evidence (which we review briefly here), particularly from recent molecular advances in understanding of the mechanisms that determine the outcome of host-pathogen encounters, suggests considerable variation in specific details of the functioning of interactions between hosts and pathogens, which may contain elements of both models. In this regard, GFG and MA scenarios likely represent endpoints of a continuum of potentially more complex interactions that occur in nature. Increasingly, this has been recognized in theoretical studies of coevolutionary processes in plant host-pathogen and animal host-parasite associations (e.g., departures from strict GFG/MA assumptions, diploid genetics, multi-step infection processes). However, few studies have explored how different genetic assumptions about host resistance and pathogen infectivity might impact on disease epidemiology or pathogen persistence within and among populations. Here, we use spatially explicit simulations of the basic MA and GFG scenarios to highlight qualitative differences between these scenarios with regard to patterns of disease and impacts on host demography. Given that such impacts drive evolutionary trajectories, future theoretical advances that aim to capture more complex genetic scenarios should explicitly address the interaction between epidemiology and different models of host-pathogen interaction genetics.