Multi-seasonal modelling of plant-nematode interactions reveals efficient plant resistance deployment strategies

Revolutionizing nematode management to achieve global food security goals - An overview

Nematodes are soil-dwelling organisms that inflict substantial damage to crops, resulting in significant declines in agricultural productivity. Consequently, they are recognized as one of the primary contributors to global crop damage, with profound implications for food security. Nematology research assumes a pivotal role in tackling this issue and safeguarding food security. The pursuit of nematology research focused on mitigating nematode-induced crop damage and promoting sustainable agriculture represents a fundamental strategy for enhancing food security. Investment in nematology research is crucial to advance food security objectives by identifying and managing nematode species, developing novel technologies, comprehending nematode ecology, and strengthening the capabilities of researchers and farmers. This endeavor constitutes an indispensable step toward addressing one of the most pressing challenges in achieving global food security and promoting sustainable agricultural practices. Primarily, research endeavors facilitate the identification of nematode species responsible for crop damage, leading to the development of effective management strategies. These strategies encompass the utilization of resistant crop varieties, implementation of cultural practices, biological control, and chemical interventions. Secondly, research efforts contribute to the development of innovative technologies aimed at managing nematode populations, such as gene editing techniques that confer resistance to nematode infestations in crops. Additionally, the exploration of beneficial microbes, such as certain fungi and bacteria, as potential biocontrol agents against nematodes, holds promise. The study of nematode ecology represents a foundational research domain that fosters a deeper comprehension of nematode biology and ecological interactions. This knowledge is instrumental in devising precise and efficacious management strategies.

The proportion of resistant hosts in mixtures should be biased towards the resistance with the lowest breaking cost

Current agricultural practices facilitate emergence and spread of plant diseases through the wide use of monocultures. Host mixtures are a promising alternative for sustainable plant disease control. Their effectiveness can be partly explained by priming-induced cross-protection among plants. Priming occurs when plants are challenged with non-infective pathogen genotypes, resulting in increased resistance to subsequent infections by infective pathogen genotypes. We developed an epidemiological model to explore how mixing two distinct resistant varieties can reduce disease prevalence. We considered a pathogen population composed of three genotypes infecting either one or both varieties. We found that host mixtures should not contain an equal proportion of resistant plants, but a biased ratio (e.g. 80 : 20) to minimize disease prevalence. Counter-intuitively, the optimal ratio of resistant varieties should contain a lower proportion of the costliest resistance for the pathogen to break. This benefit is amplified by priming. This strategy also prevents the invasion of pathogens breaking all resistances.

Exploiting Plant-Phytonematode Interactions to Upgrade Safe and Effective Nematode Control

Plant-parasitic nematodes (PPNs) bring about substantial losses of economic crops globally. With the environmental and health issues facing the use of chemical nematicides, research efforts should focus on providing economically effective and safe control methods. The sound exploitation of plant-PPN interactions is fundamental to such efforts. Initially, proper sampling and extraction techniques should be followed to avoid misleading nematode data. Recent evolutions in plant-PPN interactions can make use of diverse non-molecular and molecular approaches to boost plant defenses. Therefore, PPN control and increasing crop yields through single, sequential, dual-purpose, and simultaneous applications of agricultural inputs, including biocontrol agents, should be seriously attempted, especially within IPM schemes. The use of biologicals would ideally be facilitated by production practices to solve related issues. The full investment of such interactions should employ new views of interdisciplinary specialties in the relevant modern disciplines to optimize the PPN management. Having an accurate grasp of the related molecular events will help in developing tools for PPN control. Nonetheless, the currently investigated molecular plant-PPN interactions favoring plant responses, e.g., resistance genes, RNA interference, marker-assisted selection, proteinase inhibitors, chemo-disruptive peptides, and plant-incorporated protectants, are key factors to expanding reliable management. They may be applied on broader scales for a substantial improvement in crop yields.

Improving sustainable crop protection using population genetics concepts

Growing genetically resistant plants allows pathogen populations to be controlled and reduces the use of pesticides. However, pathogens can quickly overcome such resistance. In this context, how can we achieve sustainable crop protection? This crucial question has remained largely unanswered despite decades of intense debate and research effort. In this study, we used a bibliographic analysis to show that the research field of resistance durability has evolved into three subfields: (i) 'plant breeding' (generating new genetic material), (ii) 'molecular interactions' (exploring the molecular dialogue governing plant-pathogen interactions) and (iii) 'epidemiology and evolution' (explaining and forecasting of pathogen population dynamics resulting from selection pressure(s) exerted by resistant plants). We argue that this triple split of the field impedes integrated research progress and ultimately compromises the sustainable management of genetic resistance. After identifying a gap among the three subfields, we argue that the theoretical framework of population genetics could bridge this gap. Indeed, population genetics formally explains the evolution of all heritable traits, and allows genetic changes to be tracked along with variation in population dynamics. This provides an integrated view of pathogen adaptation, in particular via evolutionary-epidemiological feedbacks. In this Opinion Note, we detail examples illustrating how such a framework can better inform best practices for developing and managing genetically resistant cultivars.

Host mixtures for plant disease control: benefits from pathogen selection and immune priming

Multiline and cultivar mixtures are highly effective methods for agroecological plant disease control. Priming‐induced cross protection, occurring when plants are challenged by avirulent pathogen genotypes and resulting in increased resistance to subsequent infection by virulent ones, is one critical key to their lasting performance against polymorphic pathogen populations. Strikingly, this mechanism was until recently absent from mathematical models aiming at designing optimal host mixtures. We developed an epidemiological model to explore the effect of host mixtures composed of variable numbers of single‐resistance cultivars on the equilibrium prevalence of the disease caused by pathogen populations polymorphic for virulence complexity. This model shows that a relatively large amount of resistance genes must be deployed to achieve low disease prevalence, as pathogen competition in mixtures tends to select for intermediate virulence complexity. By contrast, priming significantly reduces the number of plant genotypes needed to drop disease prevalence below an acceptable threshold. Given the limited availability of resistance genes in cultivars, this mechanism of plant immunity should be assessed when designing host mixtures.

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.

Taking Advantage of Pathogen Diversity and Immune Priming to Minimize Disease Prevalence in Host Mixtures: A Model

Host mixtures are a promising method for agroecological plant disease control. Plant immunity is key to the success of host mixtures against polymorphic pathogen populations. This immunity results from priming-induced cross-protection, whereby plants able to resist infection by specific pathogen genotypes become more resistant to other pathogen genotypes. Strikingly, this phenomenon was absent from mathematical models aiming at designing host mixtures. We developed a model to specifically explore how priming affects the coexistence of two pathogen genotypes in host mixtures composed of two host genotypes and how it affects disease prevalence. The main effect of priming is to reduce the coexistence region in the parameter space (due to the cross-protection) and to generate a singular mixture of resistant and susceptible hosts corresponding to the maximal reduction disease prevalence (in absence of priming, a resistant pure stand is optimal). The epidemiological advantage of host mixtures over a resistant pure stand thus appears as a direct consequence of immune priming. We also showed that there is indirect cross-protection between host genotypes in a mixture. Moreover, the optimal mix prevents the emergence of a resistance-breaking pathogen genotype. Our results highlight the importance of considering immune priming to design optimal and sustainable host mixtures.

Epidemiological and evolutionary consequences of periodicity in treatment coverage

Host heterogeneity is a key driver of host-pathogen dynamics. In particular, the use of treatments against infectious diseases creates variation in quality among hosts, which can have both epidemiological and evolutionary consequences. We present a general theoretical model to highlight the consequences of different imperfect treatments on pathogen prevalence and evolution. These treatments differ in their action on host and pathogen traits. In contrast with previous studies, we assume that treatment coverage can vary in time, as in seasonal or pulsed treatment strategies. We show that periodic treatment strategies can limit both disease spread and virulence evolution, depending on the type of treatment. We also introduce a new method to analytically calculate the selection gradient in periodic environments, which allows our predictions to be interpreted using the concept of reproductive value, and can be applied more generally to analyse eco-evolutionary dynamics in class-structured populations and fluctuating environments.