Combining single-gene-resistant and pyramided cultivars of perennial crops in agricultural landscapes compromises pyramiding benefits in most production situations.

While resistant cultivars are valuable in safeguarding crops against diseases, they can be rapidly overcome by pathogens. Numerous strategies have been proposed to delay pathogen adaptation (evolutionary control), while still ensuring effective protection (epidemiological control). For perennial crops, multiple resistance genes can be deployed 1) in the same cultivar (pyramiding strategy), in single-gene-resistant cultivars grown 2) in the same field (mixture strategy) or 3) in different fields (mosaic strategy), or 4) in hybrid strategies that combine the three previous options. In addition, the spatial scale at which resistant cultivars are deployed can affect the plant-pathogens interaction: small fields are thought to reduce pest density and disease transmission. Here we used the spatially-explicit stochastic model landsepi to compare the evolutionary and epidemiological control across spatial scales and deployment strategies relying on two major resistance genes. Our results, broadly focused on resistance to downy mildew of grapevine, show that the evolutionary control provided by the pyramiding strategy is at risk when single-gene-resistant cultivars are concurrently planted in the landscape (hybrid strategies), especially at low mutation probability. Moreover, the effectiveness of pyramiding compared to hybrid strategies is influenced by whether the adapted pathogen pays a fitness cost across all hosts or only for unnecessary virulence, particularly when the fitness cost is high rather than intermediate. Finally, field size did not affect model outputs for a wide range of mutation probabilities and associated fitness costs. The socio-economic policies favoring the adoption of optimal resistant management strategies are discussed.

Modelling interference between vectors of non-persistently transmitted plant viruses to identify effective control strategies.

Aphids are the primary vector of plant viruses. Transient aphids, which probe several plants per day, are considered to be the principal vectors of non-persistently transmitted (NPT) viruses. However, resident aphids, which can complete their life cycle on a single host and are affected by agronomic practices, can transmit NPT viruses as well. Moreover, they can interfere both directly and indirectly with transient aphids, eventually shaping plant disease dynamics. By means of an epidemiological model, originally accounting for ecological principles and agronomic practices, we explore the consequences of fertilization and irrigation, pesticide deployment and roguing of infected plants on the spread of viral diseases in crops. Our results indicate that the spread of NPT viruses can be i) both reduced or increased by fertilization and irrigation, depending on whether the interference is direct or indirect; ii) counter-intuitively increased by pesticide application and iii) reduced by roguing infected plants. We show that a better understanding of vectors' interactions would enhance our understanding of disease transmission, supporting the development of disease management strategies.

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.

Improving Management Strategies of Plant Diseases Using Sequential Sensitivity Analyses.

Improvement of management strategies of epidemics is often hampered by constraints on experiments at large spatiotemporal scales. A promising approach consists of modeling the biological epidemic process and human interventions, which both impact disease spread. However, few methods enable the simultaneous optimization of the numerous parameters of sophisticated control strategies. To do so, we propose a heuristic approach (i.e., a practical improvement method approximating an optimal solution) based on sequential sensitivity analyses. In addition, we use an economic improvement criterion based on the net present value, accounting for both the cost of the different control measures and the benefit generated by disease suppression. This work is motivated by sharka (caused by Plum pox virus), a vector-borne disease of prunus trees (especially apricot, peach, and plum), the management of which in orchards is mainly based on surveillance and tree removal. We identified the key parameters of a spatiotemporal model simulating sharka spread and control and approximated optimal values for these parameters. The results indicate that the current French management of sharka efficiently controls the disease, but it can be economically improved using alternative strategies that are identified and discussed. The general approach should help policy makers to design sustainable and cost-effective strategies for disease management.

Assessing the Mismatch Between Incubation and Latent Periods for Vector-Borne Diseases: The Case of Sharka.

The relative durations of the incubation period (the time between inoculation and symptom expression) and of the latent period (the time between inoculation and infectiousness of the host) are poorly documented for plant diseases. However, the extent of asynchrony between the ends of these two periods (i.e., their mismatch) can be a key determinant of the epidemic dynamics for many diseases and consequently it is of primary interest in the design of disease management strategies. In order to assess this mismatch, an experimental approach was developed and applied using sharka, a severe disease caused by Plum pox virus (PPV, genus Potyvirus, family Potyviridae) affecting trees belonging to the genus Prunus. Leaves of infected young peach trees were used individually as viral sources in aphid-mediated transmission tests carried out at different time points postinoculation in order to bracket symptom onset. By fitting a nonlinear logistic model to the obtained transmission rates, we demonstrated that the first symptoms appear on leaves 1 day before they rapidly become infectious. In addition, among symptomatic leaves, symptom intensity and transmission rate are positively correlated. These results strengthen the conclusion that, under our experimental conditions, incubation and latent periods of PPV infection are almost synchronous.

Frequency-dependent assistance as a way out of competitive exclusion between two strains of an emerging virus.

Biological invasions are the main causes of emerging viral diseases and they favour the co-occurrence of multiple species or strains in the same environment. Depending on the nature of the interaction, co-occurrence can lead to competitive exclusion or coexistence. The successive fortuitous introductions of two strains of Tomato yellow leaf curl virus (TYLCV-Mld and TYLCV-IL) in Réunion Island provided an ideal opportunity to study the invasion of, and competition between, these worldwide emerging pathogens. During a 7-year field survey, we observed a displacement of the resident TYLCV-Mld by the newcomer TYLCV-IL, with TYLCV-Mld remaining mostly in co-infected plants. To understand the factors associated with this partial displacement, biological traits related to fitness were measured. The better ecological aptitude of TYLCV-IL in single infections was demonstrated, which explains its rapid spread. However, we demonstrate that the relative fitness of virus strains can drastically change between single infections and co-infections. An epidemiological model parametrized with our experimental data predicts that the two strains will coexist in the long run through assistance by the fitter strain. This rare case of unilateral facilitation between two pathogens leads to frequency-dependent selection and maintenance of the less fit strain.

Effects of pathogen reproduction system on the evolutionary and epidemiological control provided by deployment strategies for two major resistance genes in agricultural landscapes.

Resistant cultivars are of value for protecting crops from disease, but can be rapidly overcome by pathogens. Several strategies have been proposed to delay pathogen adaptation (evolutionary control), while maintaining effective protection (epidemiological control). Resistance genes can be (i) combined in the same cultivar (pyramiding), (ii) deployed in different cultivars sown in the same field (mixtures) or in different fields (mosaics), or (iii) alternated over time (rotations). The outcomes of these strategies have been investigated principally in pathogens displaying pure clonal reproduction, but many pathogens have at least one sexual event in their annual life cycles. Sexual reproduction may promote the emergence of superpathogens adapted to all the resistance genes deployed. Here, we improved the spatially explicit stochastic model landsepi to include pathogen sexual reproduction, and we used the improved model to investigate the effect of sexual reproduction on evolutionary and epidemiological outcomes across deployment strategies for two major resistance genes. Sexual reproduction favours the establishment of a superpathogen when single mutant pathogens are present together at a sufficiently high frequency, as in mosaic and mixture strategies. However, sexual reproduction did not affect the strategy recommendations for a wide range of mutation probabilities, associated fitness costs, and landscape organisations.

Absence of Seed-Mediated Transmission of Cucumber Mosaic Virus in Espelette Pepper Crops despite Widespread and Recurrent Epidemics.

In the past decade, severe epidemics of cucumber mosaic virus (CMV) have caused significant damage to Espelette pepper crops. This virus threatens the production of Espelette pepper, which plays a significant role in the local economy and touristic attractiveness of the French Basque Country, located in southwestern France. In 2021 and 2022, CMV was detected via double-antibody sandwich enzyme-linked immunosorbent assays (DAS-ELISAs) in Gorria pepper seed lots harvested from naturally infected fields scattered throughout the entire Espelette pepper production area. These seed lots were used in greenhouse grow-out tests to determine whether CMV could be transmitted to seedlings from contaminated seeds, using visual symptom assessment, DAS-ELISAs, and reverse transcription-polymerase chain reaction (RT-PCR). Despite the widespread occurrence of CMV in seeds of field samples, the grow-out experiments on a total of over 5000 seedlings yielded no evidence of seed transmission of local CMV isolates in Gorria pepper. Therefore, rather than seeds from infected pepper plants, sources of CMV inoculum in Espelette are more likely to be alternative hosts present in and around pepper fields that can allow for the survival of CMV during the off-season. These results have important epidemiological implications and will guide the choice of effective measures to control current epidemics.

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.

Quantitative risk assessment relating to adventitious presence of allergens in food: a probabilistic model applied to peanut in chocolate.

Peanut allergy is a public health concern, owing to the high prevalence in France and the severity of the reactions. Despite peanut-containing product avoidance diets, a risk may exist due to the adventitious presence of peanut allergens in a wide range of food products. Peanut is not mentioned in their ingredients list, but precautionary labeling is often present. A method of quantifying the risk of allergic reactions following the consumption of such products is developed, taking the example of peanut in chocolate tablets. The occurrence of adventitious peanut proteins in chocolate and the dose-response relationship are estimated with a Bayesian approach using available published data. The consumption pattern is described by the French individual consumption survey INCA2. Risk simulations are performed using second-order Monte Carlo simulations, which separately propagates variability and uncertainty of the model input variables. Peanut allergens occur in approximately 36% of the chocolates, leading to a mean exposure level of 0.2 mg of peanut proteins per eating occasion. The estimated risk of reaction averages 0.57% per eating occasion for peanut-allergic adults. The 95% values of the risk stand between 0 and 3.61%, which illustrates the risk variability. The uncertainty, represented by the 95% credible intervals, is concentrated around these risk estimates. Children have similar results. The conclusion is that adventitious peanut allergens induce a risk of reaction for a part of the French peanut-allergic population. The method developed can be generalized to assess the risk due to the consumption of every foodstuff potentially contaminated by allergens.

Can Winged Aphid Abundance Be a Predictor of Cucurbit Aphid-Borne Yellows Virus Epidemics in Melon Crop?

Aphid-borne viruses are frequent yield-limiting pathogens in open field vegetable crops. In the absence of curative methods, virus control relies exclusively on measures limiting virus introduction and spread. The efficiency of control measures may greatly benefit from an accurate knowledge of epidemic drivers, in particular those linked with aphid vectors. Field experiments were conducted in southeastern France between 2010 and 2019 to investigate the relationship between the epidemics of cucurbit aphid-borne yellows virus (CABYV) and aphid vector abundance. Winged aphids visiting melon crops were sampled daily to assess the abundance of CABYV vectors (Aphis gossypii, Macrosiphum euphorbiae and Myzus persicae) and CABYV was monitored weekly by DAS-ELISA. Epidemic temporal progress curves were successfully described by logistic models. A systematic search for correlations was undertaken between virus variables including parameters µ (inflection point of the logistic curve) and γ (maximum incidence) and aphid variables computed by aggregating abundances on periods relative either to the planting date, or to the epidemic peak. The abundance of A. gossypii during the first two weeks after planting was found to be a good predictor of CABYV dynamics, suggesting that an early control of this aphid species could mitigate the onset and progress of CABYV epidemics in melon crops.

Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance?

Once deployed uniformly in the field, genetically controlled plant resistance is often quickly overcome by pathogens, resulting in dramatic losses. Several strategies have been proposed to constrain the evolutionary potential of pathogens and thus increase resistance durability. These strategies can be classified into four categories, depending on whether resistance sources are varied across time (rotations) or combined in space in the same cultivar (pyramiding), in different cultivars within a field (cultivar mixtures) or among fields (mosaics). Despite their potential to differentially affect both pathogen epidemiology and evolution, to date the four categories of deployment strategies have never been directly compared together within a single theoretical or experimental framework, with regard to efficiency (ability to reduce disease impact) and durability (ability to limit pathogen evolution and delay resistance breakdown). Here, we used a spatially explicit stochastic demogenetic model, implemented in the R package landsepi, to assess the epidemiological and evolutionary outcomes of these deployment strategies when two major resistance genes are present. We varied parameters related to pathogen evolutionary potential (mutation probability and associated fitness costs) and landscape organization (mostly the relative proportion of each cultivar in the landscape and levels of spatial or temporal aggregation). Our results, broadly focused on qualitative resistance to rust fungi of cereal crops, show that evolutionary and epidemiological control are not necessarily correlated and that no deployment strategy is universally optimal. Pyramiding two major genes offered the highest durability, but at high mutation probabilities, mosaics, mixtures and rotations can perform better in delaying the establishment of a universally infective superpathogen. All strategies offered the same short-term epidemiological control, whereas rotations provided the best long-term option, after all sources of resistance had broken down. This study also highlights the significant impact of landscape organization and pathogen evolutionary ability in considering the optimal design of a deployment strategy.

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.

Using sensitivity analysis to identify key factors for the propagation of a plant epidemic.

Identifying the key factors underlying the spread of a disease is an essential but challenging prerequisite to design management strategies. To tackle this issue, we propose an approach based on sensitivity analyses of a spatiotemporal stochastic model simulating the spread of a plant epidemic. This work is motivated by the spread of sharka, caused by plum pox virus, in a real landscape. We first carried out a broad-range sensitivity analysis, ignoring any prior information on six epidemiological parameters, to assess their intrinsic influence on model behaviour. A second analysis benefited from the available knowledge on sharka epidemiology and was thus restricted to more realistic values. The broad-range analysis revealed that the mean duration of the latent period is the most influential parameter of the model, whereas the sharka-specific analysis uncovered the strong impact of the connectivity of the first infected orchard. In addition to demonstrating the interest of sensitivity analyses for a stochastic model, this study highlights the impact of variation ranges of target parameters on the outcome of a sensitivity analysis. With regard to sharka management, our results suggest that sharka surveillance may benefit from paying closer attention to highly connected patches whose infection could trigger serious epidemics.

Rapid accumulation and low degradation: key parameters of Tomato yellow leaf curl virus persistence in its insect vector Bemisia tabaci.

Of worldwide economic importance, Tomato yellow leaf curl virus (TYLCV, Begomovirus) is responsible for one of the most devastating plant diseases in warm and temperate regions. The DNA begomoviruses (Geminiviridae) are transmitted by the whitefly species complex Bemisia tabaci. Although geminiviruses have long been described as circulative non-propagative viruses, observations such as long persistence of TYLCV in B. tabaci raised the question of their possible replication in the vector. We monitored two major TYLCV strains, Mild (Mld) and Israel (IL), in the invasive B. tabaci Middle East-Asia Minor 1 cryptic species, during and after the viral acquisition, within two timeframes (0-144 hours or 0-20 days). TYLCV DNA was quantified using real-time PCR, and the complementary DNA strand of TYLCV involved in viral replication was specifically quantified using anchored real-time PCR. The DNA of both TYLCV strains accumulated exponentially during acquisition but remained stable after viral acquisition had stopped. Neither replication nor vertical transmission were observed. In conclusion, our quantification of the viral loads and complementary strands of both Mld and IL strains of TYLCV in B. tabaci point to an efficient accumulation and preservation mechanism, rather than to a dynamic equilibrium between replication and degradation.

Sharka epidemiology and worldwide management strategies: learning lessons to optimize disease control in perennial plants.

Many plant epidemics that cause major economic losses cannot be controlled with pesticides. Among them, sharka epidemics severely affect prunus trees worldwide. Its causal agent, Plum pox virus (PPV; genus Potyvirus), has been classified as a quarantine pathogen in numerous countries. As a result, various management strategies have been implemented in different regions of the world, depending on the epidemiological context and on the objective (i.e., eradication, suppression, containment, or resilience). These strategies have exploited virus-free planting material, varietal improvement, surveillance and removal of trees in orchards, and statistical models. Variations on these management options lead to contrasted outcomes, from successful eradication to widespread presence of PPV in orchards. Here, we present management strategies in the light of sharka epidemiology to gain insights from this worldwide experience. Although focused on sharka, this review highlights more general levers and promising approaches to optimize disease control in perennial plants.