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- Model description
- Model analysis
- Supporting Information
The breakdown of genetic resistance by plant pathogens is a particularly spectacular case of disease emergence where new resistant genes can be impaired in a few years or months (for review, see McDonald & Linde (2002) for fungal pathogens and García-Arenal & McDonald (2003) for viruses). These types of emergence impact food production and are associated with environmental issues, as alternative control methods often rely on pesticides. Thus the promotion of durable resistance, defined by Johnson (1979) as resistance remaining effective in a cultivar for a long period of time during its widespread cultivation, is still an ongoing quest.
Resistance or susceptibility of plants to pathogens often results from a molecular relationship governed by a gene-for-gene interaction (Flor, 1971). For qualitative resistance genes (i.e. resistances that prevent any plant infection), the interaction between the resistance gene of the plant (with at least two allelic forms: ‘resistant’ and ‘susceptible’) and the avirulence gene of the pathogen (with at least two allelic forms: ‘wildtype’ and ‘resistance-breaking’ (RB)) determines the resistance or susceptibility of the plant. Ever since the work of Leonard (1977), the evolution of host resistance and pathogen pathogenicity (i.e. its ability to cause disease in a particular host) in gene-for-gene interactions has been the subject of much research highlighting how multiple locus interaction (e.g. Sasaki, 2000; Segarra, 2005; Tellier & Brown, 2007), genetic drift (e.g. Kirby & Burdon, 1997; Salathe et al., 2005), or spatial structuring of populations (e.g. Thrall & Burdon, 2002) impact the coevolution between plants and pathogens in natural conditions. A comprehensive review of the entire subject has recently been published by Brown & Tellier (2011). These works often do not apply to the management of resistance durability, as agricultural practices, by imposing the genetic composition and spatial distribution of fields, disrupt natural coevolution and drive the coevolution of crops and pathogens to instability (Sun & Yang, 1998, 1999).
Earlier research deriving durable strategies of resistance deployment stemmed from modelling approaches in population genetics where durability was assessed by the frequencies of the RB pathogen genotype (for a review, see Van den Bosch & Gilligan, 2003; Gilligan, 2008). Assuming that the RB genotype was pre-existing and disregarding the yield benefit provided by resistant crops, these works traditionally advise the introduction of resistance genes at a low cropping ratio (i.e. at low frequency) (Pink & Puddephat, 1999). Since that time, pathologists have widely recognized that considering the interactions occurring across scales between evolutionary and epidemiological processes greatly improves our understanding of disease emergence (Galvani, 2003; Day & Proulx, 2004; Jeger et al., 2006; Mideo et al., 2008). Van den Bosch & Gilligan (2003) were the first to propose a model linking population dynamics and population genetics to re-investigate the question of resistance durability. By introducing two new measures of durability, they showed that resistance durability can also be extended by high cropping ratios if the RB genotype is not pre-existing and that the additional yield provided by a resistant cultivar is only slightly dependent on the cropping ratio. These conclusions rely on two main assumptions: that no fitness cost is needed to overcome the resistance and that continuous planting and harvesting occur.
In the present study, we developed and analysed a model relaxing these two assumptions. Fitness costs associated with resistance breakdown, although not systematic, occur in many plant–pathogen interactions and especially for plant viruses (Sacristan & García-Arenal, 2008) where they are often high (Carrasco et al., 2007; Sanjuán, 2010; Fraile et al., 2011). Plant virus studies also indicate that one or two nucleotide substitutions in avirulence genes are often sufficient to break down resistance (Harrison, 2002; Lecoq et al., 2004; Kang et al., 2005). These two factors, fitness costs and number of mutations, along with the mutation rate, determine the equilibrium frequency of RB mutants in a virus population (Ribeiro et al., 1998). It corresponds to the mutation-selection balance. The seasonality of planting and harvesting activities is the rule in most agricultural systems and largely impacts epidemic dynamics as well as pathogen evolution (for a review based on modelling approaches, see Mailleret & Lemesle, 2009; Hamelin et al., 2011). Wild or weedy plant species that act as a ‘reservoir’ of inoculum by providing a ‘green bridge’ between the maturity of one crop and the sowing of the next are important for pathogen dynamics and evolution (Burdon & Thrall, 2008). Our model simulates the three steps of the breakdown of a qualitative resistance: at the scale of the cells of a susceptible host, mutations in the avirulence gene of a virus generate RB variants; at the host scale, the RB variants must be sufficiently competitive to invade their host and increase their frequency; and at the landscape scale, the RB variants should spread between hosts and fields to cause the breakdown of the resistance.
From an applied perspective, the analyses presented are designed to provide guidelines for farmers aiming to optimize the deployment of a resistant cultivar in a landscape over several years. To achieve this goal, we will answer the following questions. First, what is the relative efficiency of the farmers’ main leverages (choice of resistant cultivar, implementation of a cropping ratio, use of cultural practices, use of landscape planning policies) on the yield increase provided by the deployment of a resistant cultivar? Second, which cropping ratio maximizes the additional yield provided by the resistance? From a basic perspective, the analyses reveal the relative importance of epidemiological, genetic and evolutionary factors in pathogen emergence.