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An epi-evolutionary model to predict spore-producing pathogens adaptation to quantitative resistance in heterogeneous environments

View ORCID ProfileRamsès Djidjou-Demasse, Jean-Baptiste Burie, Arnaud Ducrot, Sébastien Lion, Quentin Richard, View ORCID ProfileFrédéric Fabre
doi: https://doi.org/10.1101/423467
Ramsès Djidjou-Demasse
aUniv. Montpellier, MIVEGEC, IRD, CNRS, Montpellier F-34394, France
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  • ORCID record for Ramsès Djidjou-Demasse
  • For correspondence: ramses.djidjoudemasse@ird.fr
Jean-Baptiste Burie
dUniv. Bordeaux, IMB, UMR 5251, Talence F-33400, France
eCNRS, IMB, UMR 5251, Talence F-33400, France
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Arnaud Ducrot
cUniv. Normandie, UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, 76600 Le Havre, France
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Sébastien Lion
bCEFE, CNRS, Univ. Montpellier, Univ. Montpellier 3 Paul-Valéry, EPHE, IRD, Montpellier F-34293, France
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Quentin Richard
aUniv. Montpellier, MIVEGEC, IRD, CNRS, Montpellier F-34394, France
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Frédéric Fabre
fINRAE, Bordeaux Sciences Agro, UMR SAVE, Villenave d’Ornon F-33882, France
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  • ORCID record for Frédéric Fabre
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Abstract

In contrast to the many theoretical studies on the adaptation of plant pathogens to qualitative resistances, few studies have investigated how quantitative resistance selects for increased pathogen aggressiveness. We formulate an integro-differential model with nonlocal effects of mutations to describe the evolutionary epidemiology dynamics of spore-producing pathogens in heterogeneous agricultural environments sharing a well-mixed pool of spores. Parasites reproduce clonally and each strain is characterized by pathogenicity traits corresponding to the epidemic process: (i) infection efficiency and (ii) sporulation curve (including the latent period, the total spore production and the shape of the sporulation curve). We first derive a general expression of the basic reproduction number Embedded Image for fungal pathogens in heterogeneous host environments. Next, by characterizing evolutionary attractors, we investigate how the choice of quantitative resistances altering pathogenicity traits impacts the evolutionary dynamics of the pathogen population both at equilibrium and during transient epidemiological dynamics. We show that evolutionary attractors of the model coincide with local maxima of the Embedded Image only for traits involved in the sporulation curve. Quantitative resistance impacting the sporulation curve will always select a monomorphic population while dimorphism can occur with resistance altering infection efficiency. We also highlight how the shape of the relationship between the latent period and the total number of spores produced during the infectious period, impacts resistance durability and how to take advantage of evolutionary dynamics to increase the durability of quantitative resistance. Our analyses can guide experimentations by providing testable hypotheses and help plant breeders to design breeding programs.

1 Introduction

Resistance to parasites, i.e. the capacity of a host to decrease its parasite development (Raberg et al., 2009), is a widespread defense mechanism in plants. Qualitative resistance usually confers disease immunity in such a way that parasites exhibit a discrete distribution of their disease phenotype (“cause disease” versus “do not cause disease”) (McDonald & Linde, 2002). Quantitative resistance leads to a reduction in disease severity (Poland et al., 2009; St. Clair, 2010) in such a way that parasites exhibit a continuous distribution of their quantitative pathogenicity (McDonald & Linde, 2002; St. Clair, 2010; Lannou, 2012). Quantitative pathogenicity, also termed aggressiveness, can be estimated in laboratory experiments through the measure of a small number of pathogenicity traits (Lannou, 2012) expressed during the basic steps of the host-pathogen interaction. Quantitative resistance has gained interest in plant breeding for pathogen control in low-input cropping systems, in particular due to their supposed higher durability compared to qualitative resistance (Niks et al., 2015). However plant pathogens also adapt to quantitative resistance (see Pilet-Nayel et al. (2017) for a review). The resulting gradual “erosion” of resistance efficiency (McDonald & Linde, 2002) corresponds, from the pathogen side, to a gradual increase in quantitative pathogenicity.

Few theoretical studies have investigated how the deployment of quantitative resistance in agrosystems impacts pathogen aggressiveness (Iacono et al., 2012; Bourget al., 2015; Rimbaud et al., 2018). These studies must address the fundamental short- and long-term objectives of sustainable management of plant diseases (Zhan et al., 2015; Rimbaud et al., 2018): the short-term goal focuses on the reduction of disease incidence, whereas the longer-term objective is to reduce the rate of evolution of new pathotypes. The evolutionary epidemiology analysis are well-suited for this purpose (Day & Proulx, 2004). Essentially inspired by quantitative genetics, it accounts for the interplay between epidemiological and evolutionary dynamics on the same time scale. As such it can be used to monitor the simultaneous dynamics of epidemics and evolution of any set of pathogen life-history trait of interest. It can also handle heterogeneous host populations resulting, for example, from differences in their genetic composition (Gandon & Day, 2007). This is typically the case of field mixtures, where several cultivars are cultivated in the same field, and of landscapes mosaics, with cultivars cultivated in different fields.

In this article, we follow this approach and study the evolutionary epidemiology of spore-producing pathogens in heterogeneous agricultural environments. Plant fungal pathogens (sensu lato, i.e. including Oomycetes) are typical spore-producing pathogens responsible for nearly one third of emerging plant diseases (Anderson et al., 2004). Here we use an integro-differential model where the pathogen traits are represented as a function of a N-dimensional phenotype. This model extends previous results of Djidjou-Demasse et al. (2017) to heterogeneous plant populations mixing cultivars with different quantitative resistances. First, we investigate how the deployment of quantitative resistances altering different pathogenicity traits impact the pathogen population structure at equilibrium. This question is addressed by characterizing the evolutionary attractors of the coupled epidemiological evolutionary dynamics. A particular emphasis will be put here on the differences between the cornerstone concepts of Embedded Image in epidemiology (Diekmann et al., 1990; van den Driessche & Watmough, 2008) and invasion fitness in evolution (Dieckmann, 2002; Diekmann et al., 2005; Geritz et al., 1998; Metz et al., 1996; Nowak & Sigmund, 2004). Secondly we investigate how the deployment of quantitative resistances impacts the transient behavior of the coupled epidemiological evolutionary dynamics.

2 An epi-evolutionary model structured by age of infection and strain

2.1 Host and pathogen populations

We consider an heterogeneous host population with Nc ≥ 2 host classes infected by a polymorphic spore-producing pathogen population. Here, host heterogeneity may refer to different host classes, typically plant cultivars, but more generally it can handle different host developmental stages, sexes, or habitats. The host population is further subdivided into two compartments: susceptible or healthy host tissue (S) and infectious tissue (I). In keeping with the biology of fungal pathogens, we do not track individual plants, but rather leaf area densities (leaf surface area per m2). The leaf surface is viewed as a set of individual patches corresponding to a restricted host surface area that can be colonized by a single pathogen individual. Thus only indirect competition between pathogen strains for a shared resource is considered. Spores produced by all infectious tissues are assumed to mix uniformly in the air. They constitute a well-mixed pool of spores landing on any host class according to the law of mass action. Thus the probability of contact between a spore and host k is proportional to the total susceptible leaf surface area of this host. The density of airborne pool of spores is denoted by A.

The parasite is assumed to reproduce clonally. Its population is made of a set of genetic variant termed thereafter strains. Each strain with phenotype x is characterised, on each host class, by pathogenicity traits that describe the basic steps of the disease infection cycle: (i) infection efficiency βk(x), i.e. probability that a spore deposited on a receptive host surface produces a lesion and (ii) sporulation curve rk(a, x). In line with the biology of plant fungi (van den Bosch et al., 1988; Sache et al., 1997; Kolnaar & Bosch, 2011; Segarra et al., 2001), a gamma sporulation curve defined by four parameters is assumed: (i) latent period Embedded Image, (ii) total number of spores produced pk(x) during the infectious period, (iii) number of repeated sporulation events occurring on an individual foliar lesion nk(x) and (iv) rate of sporulation events λk(x). Parameters nk and λk define the time shape of the sporulation curve by distributing the total amount of spores produced by a lesion (pk) over its average infectious period (nk/λk) (Figure 1A). Previous hypothesis lead to the following sporulation function: Embedded Image

Figure 1:
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Figure 1:

A Shapes of gamma sporulation function rk(a, x) with latent period Embedded Image and total spore production during the whole infectious period pk = 5.94 as a function of nk and λk. The average infectious period (nk/λk) is 24 for both functions. B Shape of the relationship between latency duration Embedded Image and total spore production pk as a function of α. The maximal spore production pmax = 5.94 is assumed to be obtained for the latency duration Embedded Image.

2.2 The model

We introduce a set of integro-differential equations modeling the epidemiological and the evolutionary dynamics of the host and pathogen populations just described. Table 1 lists the state variables and parameters of the model. The general formulation used encompasses several simpler models of the literature (Appendix C). A key feature of the model is to explicitly track both the age of infection and the pathogen strain. This leads to a non-local age-structured system of equations posed for time t > 0, age since infection a > 0 and phenotype x ∈ ℝN, Embedded Image

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Table 1:

Main notations, state variables and parameters of the model.

Sk(t) is the density of healthy tissue in host k at time t, ik(t, a, x) the density of tissue in host k that was infected at time t − a by a pathogen with phenotype x, and A(t, x) denotes the density of spores with phenotype x at time t. Susceptible hosts are produced at rate Λ > 0 and φk is the proportion of the host k at the disease-free equilibrium in the environment. With disease, susceptible hosts can become infected by airborne spores. The total force of infection on a host k is hk(t) = ∫ βk(y)A(t, y)dy. Airborne spores produced by infected hosts become unviable at rate δ > 0. Healthy hosts die at rate θ > 0 (regardless of their class) and infected hosts at rate θ + dk(a, x), where dk(a, x) is the disease-induced mortality. Hosts infected by strain y produce airborne spores with phenotype x at a rate m∊(x − y)rk(a, y), where rk(a, y) is the sporulation function, which depends on host class, age of infection and parasite phenotype and m∊(x y) is the probability of mutation from phenotype y to phenotype x. Thus mutations randomly displace strains into the phenotype space at each infection cycle (i.e. generation) according to the kernel mε. mε depends on a small parameter ε > 0 representing the mutation variance in the phenotypic space. To fix ideas, a multivariate Gaussian distribution m = N (0, Σ) leads to Embedded Image. The covariance terms Σ can allow us to take into account correlations between pathogen life-history traits (Gandon, 2004). Note that the kernel is not restricted to Gaussian distributions, provided it satisfies some properties such as positivity and symmetry (Appendix B).

3 Fitness function and evolutionary attractors

3.1 The fitness function

The basic reproduction number, usually denoted Embedded Image, is defined as the total number of infections arising from one newly infected individual introduced into a healthy (disease-free) host population (Diekmann et al., 1990; Anderson, 1991). The disease-free equilibrium density of susceptible hosts in class k is Embedded Image. In an environment with Nc host classes, a pathogen with phenotype x will spread if Embedded Image, with Embedded Image where the quantity Embedded Image is the basic reproduction number of a pathogen with phenotype x in the host k, and the fitness function Ψk is given by Embedded Image for all k ∈ {1, …, Nc}. The quantity Ψk(x) is the absolute fitness, or reproductive value, of a pathogen with phenotype x landing on host k (Appendix D). Note that Embedded Image is the probability that a lesion is alive at age a. Once multiplied by rk(a, x) and integrated over all infection age a it gives the total number of spores truly produced by a lesion during its life time while pk(x) is the number of spores potentially produced if the lesion stay alive during the whole infectious period.

Assuming that dk does not depend on the age of infection and a gamma sporulation function, we obtain the simpler expression Embedded Image

This equation traduces that (i) during its life time 1/δ, (ii) a propagule infects a leaf area at rate βk, (iii) this infected area survives the latent period with probability Embedded Image, (iv) achieves nk sporulation events with probability Embedded Image and (v) produces a total number of spores p during the infection period. If furthermore rk is a constant (i.e. rk(a, x) = pk(x) and dk(a, x) = dk(x) for all a), we recover the classical expression of Embedded Image for SIR models (Day, 2002) with Embedded Image

3.2 Evolutionary attractors

Embedded Image can be used to study the spread of a pathogen strain x in an uninfected host population. To study the spread of a new mutant strain in a host population already infected by a resident strain x, and to characterize pathogen’s evolutionary attractors among a large number of pathogen strains, we can use the adaptive dynamics methodology and calculate invasion fitness (Dieckmann, 2002; Diekmann et al., 2005; Geritz et al., 1998; Metz et al., 1996; Nowak & Sigmund, 2004).

Here, we work in generation time and use the lifetime reproductive success of a rare mutant as a fitness proxy. Once the pathogen with phenotype x spreads, let us assume that the population reaches a monomorphic endemic equilibrium denoted by Embedded Image. The calculations of Ex, the environmental feedback of the resident x, are detailed in Appendix D. A rare mutant with phenotype y will invade the resident population infected by the strain x if the invasion fitness fx(y) of y in the environment generated by x is such that Embedded Image where Embedded Image

Quantities Embedded Image and Embedded Image, given by equations (3.3) and (3.6), have a strong analogy. Both expressions are basic reproduction numbers that measure the weighted contribution of the pathogen to the subsequent generations, but while Embedded Image is calculated in the disease-free environment, Embedded Image is calculated in the environment Ex. Noteworthy Embedded Image takes the form of a sum of the mutant pathogen’s reproductive success in each host class as between-class transmission can be written as the product of host susceptibility times pathogen transmissibility. This is equivalent to the more general statement that pathogen propagules all pass through a common pool, as in our model in the compartment A (Rueffler & Metz (2013)). However, this property is not generally true as shown by Gandon (2004) in a two-class model.

In general, finding the evolutionary attractors of host-pathogen systems is mostly based on properties of the invasion fitness on an adaptive landscape (Diekmann et al., 2005; Geritz et al., 1997, 1998; Nowak & Sigmund, 2004). A Pairwise Invasibility Plot (PIP) must then be used to characterize evolutionary attractors. However, when infection efficiencies do not differ between host classes (i.e. βk = β, for all k), model (2.2) admits an optimisation principle (Mylius & Diekmann, 1995; Metz et al., 2008; Lion & Metz, 2018; Gyllenberg & Service, 2011). More precisely, the evolutionary attractors of the model coincide with the maxima of the Embedded Image (Appendix F).

4 Application to the durability of quantitative resistances impacting sporulation or infection efficiency

4.1 Case study: deployment of a quantitative resistance

We consider two habitats corresponding to a susceptible (S) and a resistant (R) cultivars. The S cultivar has been cultivated alone during a long time. As a result, it is infected by a monomorphic pathogen population A0. At t = 0, a fraction φ ∈ (0, 1) of the S cultivar is replaced by the R cultivar. Two scenarios were then considered: the quantitative resistance can either alter the total spore production (SP scenario) or the infection efficiency (IE scenario). To illustrate how these scenarios impact the coupled epidemiological and evolutionary dynamics we need to specify the fitness of a given pathogen strain x on each habitat. We used the following unnormalized Gaussian functions Embedded Image where −μ and μ are optimal phenotypes on S and R cultivars, ωmax is the optimal value of the pathogenicity trait on the S cultivar, and c ∈ [0, 1] quantified the cost of the pathogen adaptation on the R cultivar. The maximum points of Embedded Image and Embedded Image are given by μS = −μ and μR = μ and accordingly, we have ωS(μS) = ωmax and ωR(μR) = ωmax(1 − c). Furthermore, Embedded Image and Embedded Image define the selectivity of the S and R habitats. On the R cultivar it defines the spectrum of action of the quantitative resistance on different pathogen strains (Pilet-Nayel et al., 2017). Altogether, the parameters μ, c, Embedded Image and Embedded Image define the efficiency of the quantitative resistance.

A positive correlation has been sometimes observed for plant fungi between the duration of the latent period and fecundity (Pariaud et al., 2013). This relationship describes a phenotypic trade-off because short latent period and high sporulation probability represent fitness advantages. We introduced this relationship in the SP scenario assuming that the maximal spore production on each cultivar is obtained for the latency duration Embedded Image. The aggressiveness components pk(x) and Embedded Image (with k ∈ {S, R}) of a pathogen strain x are then linked as follow Embedded Image where α define the shape of the trade-off (Figure 1B). For example α = 2 is suggested by Pariaud et al. (2013).

Simulations were initiated with a Gaussian density A0 centered around the optimal phenotype on the S cultivar. We illustrated how contrasted values of μ (a proxy of resistance efficiency), φ (defining the deployment strategy) and α (shape of the trade-off between sporulation and latency) affect the evolutionary dynamics of the pathogen. We estimated two times to characterize these dynamics. We estimated the time Tshift of emergence of the adapted strains as as the time t from which A(t,μR)/ (A(t,μS) + A(t,μR)) is always ≥ 5%. Following (Rimbaud et al., 2018), we also estimated the durability of the R cultivar as the time Tinf when at least 5% of the leaf area density of the R cultivar is infected, i.e. time t from which ∫ ∫ iR(t, a, x)dadx/ SR(t) + ∫ ∫ iR(t, a, x)dadx is always ≥ 5%. In practice this is the time when the erosion of the quantitative resistance become detectable in the field. All model parameters and initial conditions used for the simulations are summarized in Table 2. Parameters for pathogenicity traits do not fit a particular pathogen species but rather a typical biotrohpic foliar fungal diseases such wheat rusts on susceptible cultivars. Following Rimbaud et al. (2018), we define an infection efficiency of 0.2 and the duration of the latent and infectious periods to 10 and 24 days, respectively. Finally the total spore production was set in order to obtain an Embedded Image of 30 in an environment with only the S cultivar (Mikaberidze et al., 2016).

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Table 2:

Initial conditions and parameters used for the simulations with two habitats (S and R cultivars). Two scenarios were considered: the R cultivar can either alter the Infection Efficiency of the pathogen (IE) or its total Spore Production (SP). Embedded Image stands for the unnormalized density function of the Gaussian distribution. References values given for each parameter are the one used in all simulations although stated otherwise. Values in bracket indicate the range of variation [minimum, maximum] used for the numerical exploration. Embedded Image is such that Embedded Image for φ = 0, leading to Embedded Image.

4.2 Effect of quantitative resistance impacting sporulation

When the quantitative resistance impacts total spore production, the Embedded Image optimization principle holds (Section 3.2): the population always becomes monomorphic around an evolutionary attractor characterized by the maximum of the global Embedded Image function. However the characteristics of the quantitative resistance (as determined among other by μ) and its deployment strategy (as determined by φ) impact the shape of Embedded Image (number of modes and their steepness) and the underlying transient dynamics (Figure 2).

Figure 2:
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Figure 2:

Evolutionary epidemiology dynamics when the resistance impacts the total spore production for three choices of μ and φ. A-C With μ = 0.055 and φ = 0.52, the fitness function is unimodal with a maximum located between μS and μR (panel A). At t = 0, the pathogen population is essentially concentrated around μS following the light blue Gaussian distribution A0. The dynamics of the density of infected tissue and the phenotypic composition of the pathogen population in the S and R cultivars are displayed in panels B and C, respectively. The dash-blue and black lines in panel C correspond to Tinf and Tshift respectively. D-F Same as A-C with μ = 0.095 and φ = 0.55. The fitness function is bimodal with a unique global maximum at μR but a local maximum also exists around μS. G-I Same as D-F with μ = 0.095 and φ = 0.53 which implies that the local maximum at μS is closer to μR compared to panels D-F. For all panels, α = 2, c = 0, σ = 5/6 and other parameters are set to their reference values (Table 2).

The choice of μ and φ can lead to a shape of Embedded Image with an unique maximum, located at μ*(μS < μ* < μR). The evolutionary attractor will then correspond to a generalist pathogen (Figure 2 A). The fast transient dynamics observed on simulations, with a pathogen population rapidly concentrating around μ* (Figure 2 B,C), corresponds to a fast erosion Tinf of the quantitative resistance (expressed here as a number of asexual reproduction cycle). The time to erosion is quickly followed by the time to emergence Tshift of the adapted strain in the pathogen population (Figure 2 C).

Other choice of μ and φ can lead to a shape of Embedded Image function with two local maxima. Embedded Image is maximized (globally) by a single phenotype around μR but a local maximum also exists around μS (Figure 2 D). The Embedded Image maximization principle indicates that μR is the evolutionary attractor. This phenotype is a specialist of the R cultivar. The pathogen population lives for a relatively long time around the initially dominant phenotype μS and then shifts by mutation on μR (Figure 2 E,F). These dynamics occur simultaneously on the S and R cultivars. The choice of the proportion of R cultivar strongly impact the duration of the transient dynamics. Values of φ bringing closer the fitness peaks (in the sense Embedded Image will increase the time of emergence Tshift of the evolutionary attractor (Figure 2 G-I). However, this longer emergence time do not corresponds to an increase durability of the R cultivar.

We further studied how the times to emergence and erosion depend on the deployment strategy φ (Figure 3) for the two values of μ used previously. Noticeably, the two times behave in opposite directions: while Tshift drastically decreases with φ, Tinf increases with φ. If both quantities converge toward close values for high proportions of R cultivar, the time to emergence Tshift is much longer than the time to erosion Tinf for most deployment strategies. This difference is particularly marked for small proportions of R cultivar for which significant infections of the R cultivar rapidly occur while the emergence of the adapted strain in the air-borne spore pool is much slower (Figure 3 A versus B). Moreover, we also studied how the shape of the trade-off between sporulation and latency α impact these times (Figure 3). Its effect, negligible for low values of φ, increases with the proportions of R cultivar deployed. In these configurations, a detailed knowledge of the shape of this trade-off is notably important for managing the durability of quantitative R characterized by weak efficiencies (in the sense of μ).

Figure 3:
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Figure 3:

Times to emergence and erosion as a function of the deployment strategies when the resistance impacts the total spore production. A Effect of the deployment strategy φ on the time of emergence of the adapted strains Tshift for two values of resistance efficiency μ and five shape of the trade-off between sporulation and latency α. Values of Tshift are right-censored for Tshift > 3000. B Same as A for the time when the erosion of the quantitative resistance become detectable in the field Tinf. For both panels, other parameters are set to their reference values (Table 2).

4.3 Effect of quantitative resistance impacting infection efficiency

As the optimization principle does not hold, the shape of Embedded Image function alone is not sufficient to characterize evolutionary attractors. PIP must be used instead (Figure S1 for an illustration). In sharp contrast with the SP scenario, two configurations - corresponding to monomorphic or dimorphic populations - are possible.

As previously, the choice of μ and φ can lead to the selection of a single generalist at equilibrium (Figure 4 A-C). But, other choices can lead to the selection of a pathogen population composed of two evolutionary attractors corresponding to specialists of each cultivars (Figure 4 panels D-F). Formally, dimorphism occurs if there exists two constants aS, aR > 0 defined by system (E.15), and typically quantifying weights of each strain μS and μR at the equilibrium (Appendix E). In practise, each cultivar is infected by a specific strain. At equilibrium, the pathogen population is composed of different proportions of the two evolutionary attractors μS and μR (Figure 4 E-F). We insist here that this is not the case for a quantitative resistance targeting sporulation. With such a resistance, the S and R cultivars are then simultaneously infected at a given time by the same pathogen strain (Figure 2 D-I).

Figure 4:
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Figure 4:

Evolutionary epidemiology dynamics when the resistance impacts the pathogen infection efficiency for two choices of μ and φ. A-C With μ = 0.055 and φ = 0.52, the PIP reveals a single evolutionary attractor μ* (panel A). The dynamics of the density of infected tissue and the phenotypic composition of the pathogen population in the S and R cultivars are displayed in panels B and C, respectively. The dash-blue and black lines in panel C correspond to Tinf and Tshift, respectively. D-F Same as A-C with μ = 0.055 and φ = 0.52. The PIP reveals two evolutionary attractors μS and μR. For all panels, α = 2, c = 0, σ = 5/6 and other parameters are set to their reference values (Table 2). In panels A and C, singular strategies μ* and μS are evolutionary stable (no nearby mutant can invade): in the PIP, the vertical line through μ* (panel A), μS (panels D) lies completely inside a region marked ‘−’, while μR is a branching point.

5 Discussion

This work follows an ongoing trend aiming to jointly model the epidemiological and evolutionary dynamics of host-parasite interactions. Our theoretical framework, motivated by fungal infections in plants, allows us to tackle the question of the durability of plant quantitative resistances altering specific pathogen life-history traits. Many problems and questions are reminiscent of the literature on the epidemiological and evolutionary consequences of vaccination strategies. For instance, quantitative resistance traits against pathogen infection rate, latent period and sporulation rate are analogous to partially effective (imperfect) vaccines with anti-infection, anti-growth or anti-transmission modes of action, respectively (Gandon et al., 2001). Similarly, the proportion of fields where a R cultivar is deployed is analogous to the vaccination coverage in the population (Gandon & Day, 2007).

Evolutionary outcomes with multimodal fitness functions

In line with early motivations for developing a theory in evolutionary epidemiology (Day & Proulx, 2004), we investigated both the short- and long-term epidemiological and evolutionary dynamics of the host-pathogen interaction. Although the short-term dynamics is investigated numerically, the long-term analysis is analytically tractable and allows us to predict the evolutionary outcome of pathogen evolution. In contrast with most studies in evolutionary epidemiology, the analysis proposed allows us to consider multimodal fitness functions, and to characterize evolutionary attractors at equilibrium through a detailed description of their shape (number of modes, steepness and any higher moments with even order). Similarly, our results are neither restricted to Gaussian mutation kernel m (see also Mirrahimi (2017)), provided that m is symmetric and positive (Appendix B), nor to rare mutations as in the classical adaptive dynamics approach. In our work, the SP scenario admits an optimisation principle and potential evolutionary attractors are located at the peaks Embedded Image. This is not always the case for the IE scenario (except for some special configurations). An important consequence of the existence of an optimisation principle is that evolutionary branching (i.e. a situation leading to pathogen diversification and to long term coexistence of different pathogen strategies) is impossible.

Embedded Image expression for fungal pathogens in heterogeneous host environment

Usually, the computation of Embedded Image is based on the spectral radius of the next generation operator (NGO) (Diekmann et al., 1990). The method was applied by van den Bosch et al. (2008) to calculate the Embedded Image for lesion forming foliar pathogens in a setting with only two cultivars and no effect of the age of infection a on sporulation rate and disease-induced mortality. Here, we follow the methodology based on the generation evolution operator (Inaba, 2012) to derive an expression for the basic reproduction number Embedded Image in heterogeneous host populations composed of Nc cultivars (Appendix D).

Lannou (2012) pointed out the need for Embedded Image expressions allowing to compare the fitness of competing pathogen strains with different latent periods. We provide such an expression of Embedded Image for the classical gamma sporulation curve proposed by Segarra et al. (2001) and observed for several plant fungi (van den Bosch et al., 1988; Sache et al., 1997; Kolnaar & Bosch, 2011; van den Bosch et al., 1988). This expression combine (i) the pathogenicity traits expressed at plant scale during the basic infection steps (infection efficiency βk(x), latent period Embedded Image and shape of the sporulation function pk(x), nk(x) and λk(x)) with (ii) the proportion of each cultivar k in the environment (φk). As these traits can be measured in the laboratory, Embedded Image bridges the gap between plant-scale and epidemiological studies, and between experimental and theoretical approaches. Embedded Image-based approach have been for example used to compare the fitness of a collection of isolates of potato light blight (Montarry et al., 2010), to highlight the competition exclusion principle for multi-strains within-host malaria infections (Djidjou-Demasse & Ducrot, 2013) and to predict the community field structure of Lyme disease pathogen from laboratory measures of the three transmission traits (Durand et al., 2017).

Embedded Image as a proxy of invasion fitness for pathogenicity traits involved in the sporulation curve

In the context of model (2.2) characterized by a common pool of well-mixed airborne pathogen propagules, the function Ψ(x), which is proportional to Embedded Image, is an exact fitness proxy for competing strains differing potentially their sporulation curve (including the latent period, the total spore production and the shape of the sporulation curve). The basic reproduction number Embedded Image can thus be used to investigate how basic choices made when deploying a new resistance (resistance factor, proportion cultivated) impact the emergence of specialist or generalist pathogen. With a unique local maximum of Embedded Image, a generalist pathogen will be selected while with two local maxima a specialist is selected. But, in any case, the pathogen population will become monomorphic at equilibrium after the deployment of a R cultivar impacting any pathogenicity traits involved in the sporulation curve (i.e. expressed after spore germination) as the optimization principle prevents evolutionary branching. This is typically the case in the SP scenario. However, more generally, a clear distinction between pathogen invasion fitness Embedded Image and epidemiological Embedded Image is necessary to properly discuss the adaptive evolution of pathogens (Lion & Metz, 2018). Even with a common pool of spore, the optimisation principle of Embedded Image does not hold when infection efficiencies differ between host classes. It follows that in our case study the deployment of a R cultivar impacting infection efficiency (IE scenario) can lead to the selection of either a monomorphic population with a generalist pathogen or a polymorphic population with two specialist pathogens (Figure 4). The occurrence of such evolutionary branching is of practical importance for management purposes as evidenced for example in the wheat rust fungal disease where disease prevalence varies with the frequencies of specialist genotypes in the rust population (Papaïx et al., 2011).

Our results also highlight that a detailed knowledge of within-host pathogenicity trait correlations is required to manage resistance durability. Only few studies taking explicitly into account evolutionary principles compared how resistant cultivars targeting different aggressiveness components of fungal pathogens impact durability (Iacono et al., 2012; Bourget al., 2015; Rimbaud et al., 2018). These studies assumed that aggressiveness components are mutually independent while correlations have been sometimes identified between latent period and propagule production (Lannou, 2012; Pariaud et al., 2013). We show that such correlations can impact durability when quantitative resistance targets traits involved in the sporulation curve. In the SP scenario, the shape of this correlation finely interacts with the detailed choices of the quantitative resistance factors and their deployment strategies to determine resistance durability (Figure 3 B). Therefore, any guidelines aiming to help plant breeders to adequately choose resistance QTLs in breeding programs should consider these potential correlations. A detailed numerical exploration of the model relying notably on global and local sensitivity analysis could also help to develop such frameworks both for the questions of breeders (”which resistance factors integrated in breeding scheme ?”) and growers (”how to best deploy the existing cultivar portfolio to lower pesticide use ?”). In particular the relative effects of the main components of resistance efficiency (parameters μ, c, Embedded Image and Embedded Image) and their interactions with φ deserve to be studied in details. Similarly, our results illustrate that the time to emergence of the adapted strain, which corresponds to the duration of the transient dynamic, does not necessarily inform on the time to erosion of a quantitative resistance, in particular when small proportions of R cultivar are deployed (Figure 3). An in-depth analysis of this relationship in a large number of possible states of nature will be interesting.

Notes on some model assumptions

The model assumes an infinitely large pathogen population. Demographic stochasticity is thus ignored while it can impact evolutionary dynamics (e.g., lower probabilities of emergence and fixation of beneficial mutations, reduction of standing genetic variation (Kimura, 1962)). In particular, genetic drift is more likely to impact the maintenance of a neutral polymorphism rather than of a protected polymorphism where selection favors coexistence of different genotypes against invasions by mutant strategies (Geritz et al., 1998). The effect of genetic drift depends on the stability properties of the model considered. As our model has a unique globally stable equilibrium, genetic drift is likely to play a much lesser role than with models characterized by unstable equilibrium. Moreover large Ne in the range ≃ 103 - 3.104 have been reported at field scale for several species of wind-dispersed, spore-producing plant pathogens (Ali et al., 2016; Zhan et al., 2001; Walker et al., 2017), suggesting a weak effect of genetic drift for their evolution.

The model also assumes a unique pool of well mixed propagules. Thus spore dispersal disregard the location of healthy and infected hosts. This assumption, which ensure the one-dimensional environmental feedback loop of the model, is more likely when the extent of the field or landscape considered is not too large with respect to the dispersal function of airborne propagules. Airborne fungal spores often disperse over substantial distances with mean dispersal distance in the range 102 to 103 meters and, in most case, fat-tail dispersal kernels associated to substantial long-distance dispersal events (Fabre et al. (2020) for a review). Recently, a spatially-implicit framework embedded into integro-differential equations was used to describe the eco-evolutionary dynamics of a phenotypically structured population subject to mutation, selection and migration between two habitats (Mirrahimi, 2017). With the same goal Rimbaud et al. (2018) used a spatially-explicit framework with n habitats embedded into a SEIR stochastic model. It would be interesting to draw on these examples and extend our approach to a spatially explicit environment. Indeed, when dispersal decreases with distance, large homogeneous habitats promote diversification while smaller habitats, favoring migration between distinct patches, hamper diversification (Débarre & Gandon, 2010; Haller et al., 2013; Papaïx et al., 2013). Managing the resulting population polymorphisms, either for conservation purpose in order to preserve the adpative potential of endangered species or for disease control purpose in order to hamper pest and pathogen adaptation should become a priority (Vale, 2013).

Funding

RDD received support from the Conseil Interprofessionnel du Vin de Bordeaux (CIVB) under the CIVB project ‘Recherche, experimentation, etudes et outils’, and from the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills/AgreenSkills+ fellowship under grant agreement number FP7-609398. QR received support from the cluster SysNum of Bordeaux University.

Code availability

The MATLAB codes used to simulate the model and generate the main figures have been deposited in Dataverse at https://doi.org/10.15454/WAEIMA

Authors’ contributions

RDD, AD, JBB and FF planned and designed the research. RDD, AD, JBB and FF conducted analytic tools and numerical experiments. SL and QR critically revised the manuscript and helped draft the manuscript. All authors gave final approval for publication and agree to be held accountable for the work performed therein.

A Supplementary figure

Figure S1:
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Figure S1:

An example of configuration where a polymorphic pathogen population is selected at equilibrium (panels A, D, E) while a single local maximum exists for Embedded Image (panel B) as confirmed by its gradient (panel C). Parameters values are φ = 0.64, σS = 0.06, σ = 4/6, μS = −μR = −0.0579, c = 0, α = 2 and ε = 0.01/5. Other parameters are set to their reference values.

B Properties of the mutation kernel mε

The kernel function mε arising in model (2.2) should satisfy the following properties:

(H1) The function mε is almost everywhere strictly positive on ℝN and should be normalised such that, Embedded Image

This last condition expresses that all interactions generated on the phenotypic space of pathogens necessarily end up somewhere on that space.

(H2) Its variation should only depend on the distance separating the points between which the interactions are evaluated (i.e. mε(x) = mε(−x), for all x ∈ ℝN).

(H3) It is highly concentrated and decays rather fast at infinity in the sens that mε(x) = ε−N m(x/ε) and Embedded Image Embedded Image.

C Some special cases of the general model (2.2)

By omitting the age structure, we re-write model (2.2) as follows Embedded Image wherein we take into account the (host and strain-specific) duration of the sporulation period, denoted by lk(x).

Furthermore, if we assume that there are no “interactions” in the phenotypic space of pathogens, i.e. without mutations: ε → 0, then the simplified model (C.1) rewrites Embedded Image

D The fitness function

In this appendix we explain how to compute the fitness function. To that aim, by formally taking the limit ε → 0 into (2.2), this system becomes Embedded Image

Let us assume that system (D.3) reaches a monomorphic epidemiological equilibrium Embedded Image for some trait z, before a new mutation with trait value, say, y occurs. Note that Ez is the environmental feedback of the resident z. We introduce a small perturbation in (D.3) in the phenotype trait y, so that the evolution of the system reads as follows: Embedded Image and Embedded Image and the small perturbations for the infection, jk and B, are governed by the linearized system of equations around Ez. This reads as Embedded Image

In order to study the evolution of this perturbation we will derive a renewal equation on bz(t, y), the density of newly produced spores at time t with phenotype y in the resident population with phenotype x. This term is more precisely defined by Embedded Image

It then follows from the jk-equation of the linear system (D.4), that Embedded Image while Embedded Image

As a consequence, bz(t, y) satisfies the following renewal equation: Embedded Image wherein we have set Embedded Image

Then (D.5) can be rewritten as Embedded Image where Bz(a, y) is the expected number of new infections produced per unit time, in a resident host population with phenotype z, by an individual which was infected a units of time ago with the phenotype y, given by Embedded Image

Due to the above formulation, it follows from classical adaptive dynamics (Diekmann et al., 2005; Geritz et al., 1997; Metz et al., 1996) that the spore numbers, Embedded Image, of a rare mutant strategy, y, in the resident z-population is given by Embedded Image wherein Embedded Image. Then, the invasion fitness fz (y) of a mutant strategy y in the resident z-population is given by Embedded Image

Note that when the environmental feedback Ez is reduced to the disease-free environment, then Embedded Image re-writes as Embedded Image. And the epidemiological basic reproduction number of the pathogen with the phenotype y is calculated as Embedded Image

Once the pathogen has spread and reached the monomorphic equilibrium, then the endemic feedback environment Ez becomes Embedded Image where Az > 0 is the unique solution of the following equation (only defined when Embedded Image): Embedded Image

E Dimorphic or monomorphic equilibrium

To simplify the presentation, we consider system (2.2) with Nc = 2 corresponding to S and R cultivars. Denote by (S0, i0(·), A0) the endemic equilibrium of system (2.2) as ε → 0 and when only S is cultivated (i.e. when the proportion φ of R is zero). From results in Djidjou-Demasse et al. (2017) we have Embedded Image

Now, let (SS, SR, iS(·), iR(·), A) be an equilibrium of system (2.2) when a proportion φ > 0 of R is cultivated. Next recall that, for k ∈ {S, R}, Embedded Image so that A(·) becomes a solution of the nonlinear equation: Embedded Image

Using this equation we heuristically explore conditions yielding to dimorphic or monomorphic equilibrium.

Quantitative resistance impacting infection efficiencies βS and βR (IE scenario)

We formally assume that the population of spores writes Embedded Image, and we plug this ansatz into equation (E.10) above. This yields, for any x, Embedded Image

Letting ε → 0 and recalling that mε(x) ≈ δ0(x), one obtains Embedded Image that is Embedded Image

As a consequence, for the equilibrium to be dimorphic, namely aR > 0 and aS > 0, it is necessary that there exist aR > 0 and aS > 0 satisfying the following system of equations: Embedded Image

We set Embedded Image

Recall that with the IE scenario we have rS = rR and dS = dR such that the fitness function Embedded Image takes the form Ψk = c0βk for k = S, R (where c0 is the same positive funtional for S and R). Doing that, the above system rewrites Embedded Image wherein Embedded Image and c0 = c0(μS) = c0(μR). With the IE scenario (i.e. with trade-off on infection efficiency βk), we reasonably have βR(μR) > βR(μS) and βS(μS) > βS(μR). Therefore, Embedded Image Embedded Image. Then, solving system (E.12) for (X, Y) yields to Embedded Image

Since Embedded Image the above system rewrites Embedded Image

Coming back to the definition of X = X(aS, aR) and Y = Y (aS, aR) provided by (E.11), we then find Embedded Image with Embedded Image. Because Embedded Image, it comes that for the equilibrium to be dimorphic it is necessary that Embedded Image

This heuristic condition (E.14) is necessary (but not sufficient) for system (2.2) (here with Nc = 2) to admit an endemic dimorphic equilibrium. The situation with a technical assumption on disjoint supports of βk, is rigorously studied in Burie al. (2019).

But here, in order to go slightly further in our analysis, we assume a strong trade-off on infection efficiency, namely Embedded Image

We deduce that the above system of equation roughly simplifies into Embedded Image

Hence the proportions of each phenotype, μS and μR, can be calculated as Embedded Image provided the following threshold conditions in this strong trade-off framework Embedded Image

Quantitative resistance impacting total sporulation production pS and pR (SP scenario)

In this case, using the same argument as in Djidjou-Demasse et al. (2017) we can prove that the spore population is monomorphic at equilibrium such that Embedded Image; with a* > 0, providing that we are not in a strict symmetric configuration of the fitness function. Moreover, with a strong trade-off on sporulation rate, it is well known that μ* ∈ {μS, μR}. Then, applying the same arguments as in the previous section lead to Embedded Image

Again with ε → 0, it comes Embedded Image with Embedded Image.

F Embedded Image as the fitness proxy

By Equations (D.6) and (D.7), it comes Embedded Image

Using Equation (D.8) defining the resident equilibrium, i.e. Embedded Image, (F.16) becomes Embedded Image

When infection efficiencies do not differ between host classes (i.e. βk = β(x), for every k and every x). Then (F.17) gives Embedded Image and then Embedded Image

Acknowledgements

Authors thank Ludek Berec, Eva Kisdi and Loup Rimbaud for comments and suggestions to improve the manuscript.

References

  1. ↵
    Anderson, P.K., Cunningham, A.A., Patel, N.G., Morales, F.J., Epstein, P.R., and Daszak, P. 2004. Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends Ecol. Evol. 19:535–544.
    OpenUrlCrossRefPubMedWeb of Science
  2. ↵
    Ali, S., Soubeyrand, S., Gladieux, P., Giraud, T., Leconte, M., Gautier, A. Mboup, M., Chen, W., Vallavieille-Pope, C,. and Enjalbert, J. 2016. Cloncase: Estimation of sex frequency and effective population size by clonemate resampling in partially clonal organisms. Mol. Ecol. Resour. 16:45–861.
    OpenUrl
  3. ↵
    Anderson, R. M. 1991. Populations and infectious diseases: ecology or epidemiology? J. Anim. Ecol. 60:1–50.
    OpenUrlCrossRefWeb of Science
  4. Bourget, R., Chaumont, L., Durel, C-E., and Sapoukhina, N. 2008. Sustainable deployment of QTLs conferring quantitative resistance to crops: first lessons from a stochastic model. New Phytol. 206(3):1163–1171
    OpenUrl
  5. ↵
    Burie, J. B., Ducrot, A., Griette, Q., and Richard, Q. 2019. Concentration estimates in a multi-host epidemiological model structured by phenotypic traits. arXiv:1910.09385.
  6. ↵
    Day, T. 2002. Virulence evolution via host exploitation and toxin production in spore-producing pathogens. Ecol. Lett. 5(4):471–476.
    OpenUrlCrossRefWeb of Science
  7. Day, T., and Proulx, S. R. 2000. A general theory for the evolutionary dynamics of virulence. Am. Nat. 163(4):e40–63.
    OpenUrl
  8. ↵
    Débarre, F., and Gandon, S. 2010. Evolution of specialization in a spatially continuous environment. J. Evol. Biol. 23(5):1090–1099.
    OpenUrlCrossRefPubMed
  9. ↵
    Diekmann, O., Heesterbeek, J. A. P., and Metz, J. A. 1990. On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. J. Math. Biol. 28(4):365–382.
    OpenUrlCrossRefPubMedWeb of Science
  10. ↵
    Diekmann, O., Jabin, P.E., Mischler, S., and Perthame, B. 2005. The dynamics of adaptation: an illuminating example and a Hamilton-Jacobi approach. Theor. Popul. Biol. 67:257–271.
    OpenUrlCrossRefPubMed
  11. ↵
    1. Dieckmann, U.,
    2. Metz, J.A.J.,
    3. Sabelis, M.W., and
    4. Sigmund, K.
    Dieckmann, U. 2002. Adaptive dynamics of pathogen-host interactions. Pages 39–59 in: Adaptive dynamics of infectious diseases: In pursuit of virulence management. Dieckmann, U., Metz, J.A.J., Sabelis, M.W., and Sigmund, K., eds. Cambridge University Press.
  12. ↵
    Djidjou Demasse, R., and Ducrot, A. 2013. An age-structured within-host model for multistrain malaria infections. SIAM J. Appl. Math. 73:572–593.
    OpenUrl
  13. ↵
    Djidjou-Demasse, R., Ducrot, A., and Fabre, F. 2017. Steady state concentration for a phenotypic structured problem modeling the evolutionary epidemiology of spore producing pathogens. Math. Models Methods Appl. Sci. 27:385–426.
    OpenUrl
  14. ↵
    Durand, J., Jacquet, M., Rais, O., Gern, L., and Voordouw, M. J. 2017. Fitness estimates from experimental infections predict the long-term strain structure of a vector-borne pathogen in the field. Sci. Rep. 7(1):1851.
    OpenUrlCrossRef
  15. ↵
    1. Scott, P.,
    2. Strange, R.N.,
    3. Korsten, L., and
    4. Gullino, M.L.
    Fabre, F., Coville, J., and Cunniffe, N. J. 2020. Optimising reactive disease management using spatially explicit models at the landscape scale. In: Plant Diseases and Food Security in the 21st Century. Scott, P., Strange, R.N., Korsten, L., and Gullino, M.L., eds. Springer International Publishing.
  16. ↵
    Gandon, S., Mackinnon, M.J., Nee, S., and Read, A.F. 2001. Imperfect vaccines and the evolution of pathogen virulence. Nature 414(6865):751–756.
    OpenUrlCrossRefPubMedWeb of Science
  17. ↵
    Gandon, S. 2004. Evolution of multihost parasites. Evolution, 58(3):455–469.
    OpenUrlCrossRefPubMedWeb of Science
  18. ↵
    Gandon, S., and Day, T. 2007. The evolutionary epidemiology of vaccination. J. R. Soc. Interface 4(16):803–817.
    OpenUrlCrossRefPubMed
  19. ↵
    Geritz, S.A., Metz, J.A., Kisdi, É., and Meszéna, G. 1997. Dynamics of adaptation and evolutionary branching. Phys. Rev. Lett. 78(10):2024–2027.
    OpenUrlCrossRefWeb of Science
  20. ↵
    Geritz, S.A.H., Kisdi, É., Meszéna, G., and Metz, J.A.J. 1998. Evolutionarily singular strategies and the adaptive growth and branching of the evolutionary tree. Evol. Ecol. 12:35–57.
    OpenUrl
  21. ↵
    Gyllenberg, M., and Service, R. 2011. Necessary and sufficient conditions for the existence of an optimisation principle in evolution. J. Math. Biol. 62(3):359–369.
    OpenUrlCrossRefPubMed
  22. ↵
    Haller, B.C., Mazzuccco, R., and Dieckmann, U. 2013. Evolutionary branching in complex landscape. Am. Nat. 184(4):e127–141.
    OpenUrl
  23. ↵
    Iacono, G.L., van den Bosch, F., and Paveley, N. 2012. The evolution of plant pathogens in response to host resistance: factors affecting the gain from deployment of qualitative and quantitative resistance. J. Theor. Biol. 304:152–163.
    OpenUrlCrossRefPubMed
  24. ↵
    Inaba, H. 2012. On a new perspective of the basic reproduction number in heterogeneous environments. J. Math. Biol. 65(2):309–348.
    OpenUrlCrossRefPubMed
  25. Kennelly, M. M., Gadoury, D. M., Wilcox, W. F., Magarey, P. A., and Seem, R. C. 2007. Primary infection, lesion productivity, and survival of sporangia in the grapevine downy mildew pathogen Plasmopara viticola. Phytopathology, 97(4):512–522.
    OpenUrlPubMed
  26. ↵
    Kimura, M. 1962. On the probability of fixation of mutant genes in a population. Genetics 47:713–719.
    OpenUrlFREE Full Text
  27. Kolnaar, R.W., and Bosch, F.V.D. 2001. Effect of temperature on epidemiological parameters of Puccinia lagenophorae. Plant Pathol. 50:363–370.
    OpenUrl
  28. ↵
    Lannou, C. 2012. Variation and selection of quantitative traits in plant pathogens. Annu. Rev. Phytopathol. 50:319–338.
    OpenUrlCrossRefPubMedWeb of Science
  29. ↵
    Lion, S., and Metz, J. A. J. 2018. Beyond R0 maximisation: on pathogen evolution and environmental dimensions. Trends Ecol. Evol. 33:75–90.
    OpenUrl
  30. ↵
    McDonald, B. A., and Linde, C. 2002. Pathogen population genetics, evolutionary potential, and durable resistance. Annu. Rev. Phytopathol. 40(1):349–379.
    OpenUrlCrossRefPubMedWeb of Science
  31. ↵
    1. van Strien, S.J., and
    2. Verduyn Lunel, S.M.
    Metz, J.A.J., Geritz, S.A.H., Meszéna, G., Jacobs, F.J.A., and van Heerwaarden, J.S. 1996. Adaptive dynamics, a geometrical study of the consequences of nearly faithful reproduction. Pages 183–231 in: Stochastic and spatial Structures of Dynamical Systems. van Strien, S.J., and Verduyn Lunel, S.M., eds. North-Holland, Amsterdam.
  32. ↵
    Metz, J. A. J., Mylius, S. D., and Diekmann, O. 2008. When does evolution optimise? Evol. Ecol. Res. 10:629–654
    OpenUrl
  33. ↵
    Mikaberidze, A., Mundt, C. C., and Bonhoeffer, S. 2016. Invasiveness of plant pathogens depends on the spatial scale of host distribution. Ecol. Appl. 26:1238–1248.
    OpenUrl
  34. ↵
    Montarry, J., Hamelin, F.M., Glais, I., Corbière, R., and Andrivon, D. 2010. Fitness costs associated with unnecessary virulence factors and life history traits: evolutionary insights from the potato late blight pathogen Phytophthora infestans. BMC Evol. Biol. 10:1–9.
    OpenUrlCrossRefPubMed
  35. ↵
    Mirrahimi, S. 2017. A Hamilton–Jacobi approach to characterize the evolutionary equilibria in heterogeneous environments. Math. Models Methods Appl. Sci. 27(13):2425–2460.
    OpenUrlCrossRef
  36. ↵
    Mylius, S. D., and Diekmann, O. 1995. On evolutionarily stable life histories, optimization and the need to be specific about density dependence. Oikos, 74:218–224.
    OpenUrlCrossRefWeb of Science
  37. ↵
    Niks, R. E., Qi, X., and Marcel, T.C. 2015. Quantitative resistance to biotrophic filamentous plant pathogens: concepts, misconceptions, and mechanisms. Annu. Rev. Phytopathol. 53:444–470.
    OpenUrl
  38. ↵
    Nowak, M. A., and Sigmund, K. 2004. Evolutionary dynamics of biological games. Science 303(5659):793–799.
    OpenUrlAbstract/FREE Full Text
  39. ↵
    Pariaud, B., van den Berg, F., van den Bosch F., Powers, S.J., Kaltz, O., and Lannou, C. 2013. Shared influence of pathogen and host genetics on a trade-off between latent period and spore production capacity in the wheat pathogen, Puccinia triticina. Evol. Appl. 6:303–312.
    OpenUrl
  40. ↵
    Papaïx, J., Goyeau, H, Du Cheyron, P., Monod, H., and Lannou, C. 2011. Dynamics of adaptation in spatially heterogeneous metapopulations. New Phytol. 191(4):1095–1107.
    OpenUrlCrossRefPubMedWeb of Science
  41. ↵
    Papaïx, J., David, O., Lannou, C., and Monod, H. 2013. Dynamics of Adaptation in Spatially Heterogeneous metapopulations. PLoS ONE 8(2):e54697.
    OpenUrl
  42. ↵
    Poland, J. A., Balint-Kurti, P. J., Wisser, R. J., Pratt, R. C., and Nelson, R. J. 2009. Shades of gray: the world of quantitative disease resistance. Trends Plant Sci. 14:21–29.
    OpenUrlCrossRefPubMedWeb of Science
  43. ↵
    Pilet-Nayel, M.L., Moury, B., Caffier, V., Montarry, J., Kerlan, M.C., Fournet, S., Durel, C.E., and Delourme, R. 2017. Quantitative resistance to plant pathogens in pyramiding strategies for durable crop protection. Front. Plant Sci. 8:21–29.
    OpenUrlCrossRefPubMed
  44. ↵
    Raberg, L., Graham, A. L., and Read, A. F. 2009. Decomposing health: tolerance and resistance to parasites in animals. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 364:37–49.
    OpenUrlCrossRefPubMed
  45. ↵
    Rimbaud, L., Papaïx, J., Rey, J. F., Barrett, L. G., and Thrall, P.H. 2018. Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens. PLoS Comput. Biol. 14(4):e1006067.
    OpenUrl
  46. ↵
    Rueffler, C., and Metz, J. A. 2013. Necessary and sufficient conditions for R0 to be a sum of contributions of fertility loops. J. Math. Biol. 66(4-5):1099–1122.
    OpenUrl
  47. ↵
    Sache, I. 1997. Effect of density and age of lesions on sporulation capacity and infection efficiency in wheat leaf rust (Puccinia recondita f.sp. tritici). Plant Pathol. 4:581–589.
    OpenUrl
  48. ↵
    Segarra, J., Jeger, M. J., and van den Bosch, F. 2001. Epidemic dynamics and patterns of plant diseases. Phytopathology 91(10):1001–1010.
    OpenUrlCrossRefPubMedWeb of Science
  49. ↵
    St Clair, D. A. 2010. Quantitative disease resistance and quantitative resistance loci in breeding. Annu. Rev. Phytopathol. 48:247–268.
    OpenUrlCrossRefPubMedWeb of Science
  50. ↵
    1. Brauer F.,
    2. van den Driessche P., and
    3. Wu J.
    van den Driessche, P., and Watmough, J. 2008. Further notes on the basic reproduction number. Pages 159–178 in: Mathematical epidemiology. Lecture Notes in Mathematics, vol 1945. Brauer F., van den Driessche P., and Wu J., eds. Springer, Berlin, Heidelberg.
    OpenUrl
  51. ↵
    van den Bosch, F., Frinking, H.D., Metz, J.A.J., and Zadoks, J.C. 1988. Focus expansion in plant disease. III: two experimental examples. Phytpathology 78:919–925.
    OpenUrl
  52. ↵
    van den Bosch, F., McRoberts, N., van den Berg, F., and Madden, L. V. 2008. The basic reproduction number of plant pathogens: matrix approaches to complex dynamics. Phytopathology 98(2):239–249.
    OpenUrlCrossRefPubMed
  53. ↵
    Vale, P. F. 2013. Killing them softly: managing pathogen polymorphism and virulence in spatially variable environments. Trends Parasitol. 29(9):417–422.
    OpenUrl
  54. ↵
    Walker, A.S., Ravigne, V., Rieux, A., Ali, S., Carpentier, F., and Fournier, E. 2017. Fungal adaptation to contemporary fungicide applications: the case of Botrytis cinerea populations from Champagne vineyards (France). Mol. Ecol. 26:1919–1935.
    OpenUrlCrossRef
  55. ↵
    Zhan, J., Mundt, C.C., and McDonald B.A. 2001. Using restriction fragment length polymorphisms to assess temporal variation and estimate the number of ascospores that initiate epidemics in field populations of Mycosphaerella graminicola. Phytopathology 91:1011–1017.
    OpenUrlCrossRefPubMed
  56. ↵
    Zhan, J., Thrall, P. H., Papaïx, J., Xie, L., and Burdon, J. J. 2015. Playing on a pathogen’s weakness: using evolution to guide sustainable plant disease control strategies. Annu. Rev. Phytopathol. 53:19–43.
    OpenUrlCrossRefPubMed
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An epi-evolutionary model to predict spore-producing pathogens adaptation to quantitative resistance in heterogeneous environments
Ramsès Djidjou-Demasse, Jean-Baptiste Burie, Arnaud Ducrot, Sébastien Lion, Quentin Richard, Frédéric Fabre
bioRxiv 423467; doi: https://doi.org/10.1101/423467
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An epi-evolutionary model to predict spore-producing pathogens adaptation to quantitative resistance in heterogeneous environments
Ramsès Djidjou-Demasse, Jean-Baptiste Burie, Arnaud Ducrot, Sébastien Lion, Quentin Richard, Frédéric Fabre
bioRxiv 423467; doi: https://doi.org/10.1101/423467

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