Fitness cost of resistance: impact on management

Fungicides are important tools for the management of fungal diseases in many crops. But eventually, most fungicides fail because the treated pathogen population evolves resistance to the fungicide. This chapter focuses on how our knowledge of fitness costs associated with resistance informs strategies of fungicide deployment that help to avoid or delay development of resistance. Many different fungicide deployment strategies should be considered that take into account fungal population genetics as well as the specific agroecosystem. Mono-applications will be replaced by strategies that use several fungicides with different modes of action. Modeling approaches will be needed to inform us regarding the optimum strategies to use under different circumstances. It is clear that fitness costs connected to mutations that encode fungicide resistance will need to be better measured and taken into account in order to design optimum fungicide deployment strategies. We discuss the importance of fitness costs in assessing the usefulness of fungicide mixtures that contain a high-risk fungicide together with a low-risk fungicide and the role of population dynamical mathematical models of plant–pathogen interaction. According to models, the fitness cost of resistance determines the outcome of competition between the sensitive and resistant pathogen strains. If fitness costs are absent, then the use of the high-risk fungicide in a mixture selects for resistance, and the fungicide eventually becomes nonfunctional. If there is a cost of resistance, then an optimal ratio of fungicides in the mixture can be found, at which selection for resistance is expected to vanish and the level of disease control can be optimized

Can high-risk fungicides be used in mixtures without selecting for fungicide resistance?

Fungicide mixtures produced by the agrochemical industry often contain low-risk fungicides, to which fungal pathogens are fully sensitive, together with high-risk fungicides known to be prone to fungicide resistance. Can these mixtures provide adequate disease control while minimizing the risk for the development of resistance? We present a population dynamics model to address this question. We found that the fitness cost of resistance is a crucial parameter to determine the outcome of competition between the sensitive and resistant pathogen strains and to assess the usefulness of a mixture. If fitness costs are absent, then the use of the high-risk fungicide in a mixture selects for resistance and the fungicide eventually becomes nonfunctional. If there is a cost of resistance, then an optimal ratio of fungicides in the mixture can be found, at which selection for resistance is expected to vanish and the level of disease control can be optimized

An improved method for measuring quantitative resistance to the wheat pathogen Zymoseptoria tritici using high-throughput automated image analysis

Zymoseptoria tritici causes Septoria tritici blotch (STB) on wheat. An improved method of quantifying STB symptoms was developed based on automated analysis of diseased leaf images made using a flatbed scanner. Naturally infected leaves (n = 949) sampled from fungicide-treated field plots comprising 39 wheat cultivars grown in Switzerland and 9 recombinant inbred lines (RIL) grown in Oregon were included in these analyses. Measures of quantitative resistance were percent leaf area covered by lesions, pycnidia size and gray value, and pycnidia density per leaf and lesion. These measures were obtained automatically with a batch-processing macro utilizing the image-processing software ImageJ. All phenotypes in both locations showed a continuous distribution, as expected for a quantitative trait. The trait distributions at both sites were largely overlapping even though the field and host environments were quite different. Cultivars and RILs could be assigned to two or more statistically different groups for each measured phenotype. Traditional visual assessments of field resistance were highly correlated with quantitative resistance measures based on image analysis for the Oregon RILs. These results show that automated image analysis provides a promising tool for assessing quantitative resistance to Z. tritici under field conditions

Mixed infections alter transmission potential in a fungal plant pathogen

Infections by more than one strain of a pathogen predominate under natural conditions. Mixed infections can have significant, though often unpredictable, consequences for overall virulence, pathogen transmission and evolution. However, effects of mixed infection on disease development in plants often remain unclear and the critical factors that determine the outcome of mixed infections remain unknown. The fungus Zymoseptoria tritici forms genetically diverse infections in wheat fields. Here, for a range of pathogen traits, we experimentally decompose the infection process to determine how the outcomes and consequences of mixed infections are mechanistically realized. Different strains of Z. tritici grow in close proximity and compete in the wheat apoplast, resulting in reductions in growth of individual strains and in pathogen reproduction. We observed different outcomes of competition at different stages of the infection. Overall, more virulent strains had higher competitive ability during host colonization, and less virulent strains had higher transmission potential. We showed that within‐host competition can have a major effect on infection dynamics and pathogen population structure in a pathogen and host genotype‐specific manner. Consequently, mixed infections likely have a major effect on the development of septoria tritici blotch epidemics and the evolution of virulence in Z. tritici

A polyetic modelling framework for plant disease emergence

Plant disease emergences have dramatically increased recently as a result of global changes, especially with respect to trade, host genetic uniformity, and climate change. A better understanding of the conditions and processes determining epidemic outbreaks caused by the emergence of a new pathogen, or pathogen strain, is needed to develop strategies and inform decisions to manage emerging diseases. A polyetic process-based model is developed to analyse conditions of disease emergence. This model simulates polycyclic epidemics during successive growing seasons, the yield losses they cause, and the pathogen survival between growing seasons. This framework considers an immigrant strain coming into a system where a resident strain is already established. Outcomes are formulated in terms of probability of emergence, time to emergence, and yield loss, resulting from deterministic and stochastic simulations. An analytical solution to determine a threshold for emergence is also derived. Analyses focus on the effects of two fitness parameters on emergence: the relative rate of reproduction (speed of epidemics), and the relative rate of mortality (decay of population between seasons). Analyses revealed that stochasticity is a critical feature of disease emergence. The simulations suggests that: (1) emergence may require a series of independent immigration events before a successful invasion takes place; (2) an explosion in the population size of the new pathogen (or strain) may be preceded by many successive growing seasons of cryptic presence following an immigration event, and; (3) survival between growing seasons is as important as reproduction during the growing season in determining disease emergence

Improved control of Septoria tritici blotch in durum wheat using cultivar mixtures

Mixtures of cultivars with contrasting levels of disease resistance are capable of suppressing infectious diseases in wheat, as demonstrated in numerous field experiments. Most studies focused on airborne pathogens in bread wheat, while splash-dispersed pathogens have received less attention, and no studies have been conducted in durum wheat. We conducted a two-year field experiment in Tunisia, a major durum wheat producer in the Mediterranean region, to evaluate the performance of cultivar mixtures in controlling the polycyclic, splash-dispersed disease Septoria tritici blotch (STB) in durum wheat. To measure STB severity, we used a novel, high-throughput method based on digital analysis of images captured from 3074 infected leaves collected from 42 and 40 experimental plots on the first and the second year, respectively. This method allowed us to quantify pathogen reproduction on wheat leaves and to acquire a large dataset that exceeds previous studies with respect to accuracy and statistical power. Our analyses show that introducing only 25% of a disease-resistant cultivar into a pure stand of a susceptible cultivar provides a substantial reduction of almost 50% in disease severity. However, adding a second resistant cultivar to the mixture did not further improve disease control, contrary to predictions of epidemiological theory. Susceptible cultivars can be agronomically superior to resistant cultivars or be better accepted by growers for other reasons. Hence, if mixtures with only a moderate proportion of the resistant cultivar provide similar degree of disease control as resistant pure stands, as our analysis indicates, such mixtures are more likely to be accepted by growers

Data on quantitative resistance of wheat to Septoria tritici blotch

Data consists of 10 columns, first row shows column names. Each subsequent row corresponds to data from an individual leaf. Column 1 - leaf index, column 2 - leaf label, column 3 - leaf area in mm2, column 4 - area covered by necrotic tissue in mm2, column 5 - percentage of leaf area covered by lesions (PLACL), column 6 - number of pycnidia on the leaf, column 7 - mean area of pycnidia on the leaf in mm2, column 8 - number of pycnidia per cm2 leaf, column 9 - number of pycnidia per cm2 lesion, column 10 - pycnidia grey value. Leaf label in column 2 uniquely identifies each leaf in the collection. It consists of three parts divided by underscore symbols "_". First part describes the time point of collection ("c1" - collection t1, 25 May 2016; "c3" - collection t2, 4 July, 2016). Second part is the sowing number that uniquely identifies the small wheat plot planted with a specific wheat cultivar. Third part is the index of a leaf within a specific plot. For example, leaf with the label "c1_sn133_7" comes from collection t1, sowing number 133, leaf index 7

Data on quantitative resistance of wheat to Septoria tritici blotch

Data consists of 10 columns, first row shows column names. Each subsequent row corresponds to data from an individual leaf. Column 1 - leaf index, column 2 - leaf label, column 3 - leaf area in mm2, column 4 - area covered by necrotic tissue in mm2, column 5 - percentage of leaf area covered by lesions (PLACL), column 6 - number of pycnidia on the leaf, column 7 - mean area of pycnidia on the leaf in mm2, column 8 - number of pycnidia per cm2 leaf, column 9 - number of pycnidia per cm2 lesion, column 10 - pycnidia grey value. Leaf label in column 2 uniquely identifies each leaf in the collection. It consists of three parts divided by underscore symbols "_". First part describes the time point of collection ("c1" - collection t1, 25 May 2016; "c3" - collection t2, 4 July, 2016). Second part is the sowing number that uniquely identifies the small wheat plot planted with a specific wheat cultivar. Third part is the index of a leaf within a specific plot. For example, leaf with the label "c1_sn133_7" comes from collection t1, sowing number 133, leaf index 7