landsepi (version 1.2.5)

model_landsepi: Model for Landscape Epidemiology & Evolution

Description

Stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution of a plant pathogen in a heterogeneous landscape.

Usage

model_landsepi(
  time_param,
  area_vector,
  rotation_matrix,
  croptypes_cultivars_prop,
  dispersal,
  inits,
  seed,
  cultivars_param,
  basic_patho_param,
  genes_param,
  treatment_param
)

Value

A set of binary files is generated for every year of simulation and every compartment:

• H: healthy hosts,

• Hjuv: juvenile healthy hosts (for host reproduction),

• L: latently infected hosts,

• I: infectious hosts,

• R: removed hosts,

• P: propagules.

Each file indicates for every time-step the number of individuals in each field, and when appropriate for each host and pathogen genotypes).

Arguments

time_param

list of simulation parameters:

• Nyears = number cropping seasons,

• nTSpY = number of time-steps per cropping season.

area_vector

a vector containing areas of polygons (i.e. fields), in surface units.

rotation_matrix

a matrix containing for each field (rows) and year (columns, named "year_1", "year_2", etc.), the index of the cultivated croptype. Importantly, the matrix must contain 1 more column than the real number of simulated years.

croptypes_cultivars_prop

a matrix with three columns named 'croptypeID' for croptype index, 'cultivarID' for cultivar index and 'proportion' for the proportion of the cultivar within the croptype.

dispersal

list of dispersal parameters:

• disp_patho_clonal = vectorised dispersal matrix of the pathogen (clonal propagules),

• disp_patho_sex = vectorised dispersal matrix of the pathogen (sexual propagules),

• disp_host = vectorised dispersal matrix of the host.

inits

list of initial conditions:

• pI0 = initial probability for the first host (whose index is 0) to be infectious (i.e. state I) at t=0.

seed

seed (for random number generation).

cultivars_param

list of parameters associated with each host genotype (i.e. cultivars) when cultivated in pure crops:

• initial_density = vector of host densities (per surface unit) at the beginning of the cropping season,

• max_density = vector of maximum host densities (per surface unit) at the end of the cropping season,

• growth rate = vector of host growth rates,

• reproduction rate = vector of host reproduction rates,

• relative_yield_H = Yield of H individuals relative to H individuals (100%)

• relative_yield_L = Yield of L individuals relative to H individuals

• relative_yield_I = Yield of I individuals relative to H individuals

• relative_yield_R = Yield of R individuals relative to H individuals

• sigmoid_kappa_host = kappa parameter for the sigmoid invasion function (for host dispersal),

• sigmoid_sigma_host = sigma parameter for the sigmoid invasion function (for host dispersal),

• sigmoid_plateau_host = plateau parameter for the sigmoid invasion function (for host dispersal),

• cultivars_genes_list = a list containing, for each host genotype, the indices of carried resistance genes,

basic_patho_param

list of i. pathogen aggressiveness parameters on a susceptible host for a pathogen genotype not adapted to resistance and ii. sexual reproduction parameters:

• infection_rate = maximal expected infection rate of a propagule on a healthy host,

• propagule_prod_rate = maximal expected reproduction_rate of an infectious host per timestep,

• latent_period_mean = minimal expected duration of the latent period,

• latent_period_var = variance of the latent period duration,

• infectious_period_mean = maximal expected duration of the infectious period,

• infectious_period_var = variance of the infectious period duration,

• survival_prob = probability for a propagule to survive the off-season,

• repro_sex_prob = probability for an infectious host to reproduce via sex rather than via cloning,

• sigmoid_kappa = kappa parameter of the sigmoid contamination function,

• sigmoid_sigma = sigma parameter of the sigmoid contamination function,

• sigmoid_plateau = plateau parameter of the sigmoid contamination function,

• sex_propagule_viability_limit = maximum number of cropping seasons up to which a sexual propagule is viable

• sex_propagule_release_mean = average number of cropping seasons after which a sexual propagule is released.

• clonal_propagule_gradual_release = whether or not clonal propagules surviving the bottleneck are gradually released along the following cropping season.

genes_param

list of parameters associated with each resistance gene and with the evolution of each corresponding pathogenicity gene:

• target_trait = vector of aggressiveness components (IR, LAT, IP, or PR) targeted by resistance genes,

• efficiency = vector of resistance gene efficiencies (percentage of reduction of the targeted aggressiveness component: IR, 1/LAT, IP and PR),

• time_to_activ_mean = vector of expected delays to resistance activation (for APRs),

• time_to_activ_var = vector of variances of the delay to resistance activation (for APRs),

• mutation_prob = vector of mutation probabilities for pathogenicity genes (each of them corresponding to a resistance gene),

• Nlevels_aggressiveness = vector of number of adaptation levels related to each resistance gene (i.e. 1 + number of required mutations for a pathogenicity gene to fully adapt to the corresponding resistance gene),

• fitness_cost = vector of fitness penalties paid by pathogen genotypes fully adapted to the considered resistance genes on hosts that do not carry this gene,

• tradeoff_strength = vector of strengths of the trade-off relationships between the level of aggressiveness on hosts that do and do not carry the resistance genes.

treatment_param

list of parameters related to pesticide treatments:

• treatment_degradation_rate = degradation rate (per time step) of chemical concentration,

• treatment_efficiency = maximal efficiency of chemical treatments (i.e. fractional reduction of pathogen infection rate at the time of application),

• treatment_timesteps = vector of time-steps corresponding to treatment application dates,

• treatment_cultivars = vector of indices of the cultivars that receive treatments,

• treatment_cost = cost of a single treatment application (monetary units/ha)

Details

See ?landsepi for details on the model and assumptions. Briefly, the model is stochastic, spatially explicit (the basic spatial unit is an individual field), based on a SEIR (‘susceptible-exposed-infectious-removed’, renamed HLIR for 'healthy-latent-infectious-removed' to avoid confusions with 'susceptible host') structure with a discrete time step. It simulates the spread and evolution (via mutation, recombination through sexual reproduction, selection and drift) of a pathogen in a heterogeneous cropping landscape, across cropping seasons split by host harvests which impose potential bottlenecks to the pathogen. A wide array of resistance deployment strategies (possibly including chemical treatments) can be simulated.

References

Rimbaud L., Papaïx J., Rey J.-F., Barrett L. G. and Thrall P. H. (2018). Assessing the durability andefficiency of landscape-based strategies to deploy plant resistance to pathogens. PLoS Computational Biology 14(4):e1006067.

Examples

if (FALSE) {
#### Spatially-implicit simulation with 2 patches (S + R) during 3 years ####

## Simulation parameters
time_param <- list(Nyears=3, nTSpY=120)
Npoly=2
Npatho=2
area <- c(100000, 100000)
cultivars <- as.list(rbind(loadCultivar(name="Susceptible", type="growingHost")
, loadCultivar(name="Resistant", type="growingHost")))
names(cultivars)[names(cultivars)=="cultivarName"] <- "name"
cultivars <- c(cultivars, list(sigmoid_kappa_host=0.002, sigmoid_sigma_host=1.001,
 sigmoid_plateau_host=1, cultivars_genes_list=list(numeric(0),0)))
rotation <- data.frame(year_1=c(0,1), year_2=c(0,1), year_3=c(0,1), year_4=c(0,1))
croptypes_cultivars_prop <- data.frame(croptypeID=c(0,1), cultivarID=c(0,1), proportion=c(1,1))
genes <- as.list(loadGene(name="MG", type="majorGene"))
    
## run simulation
model_landsepi(seed=1,
               time_param = time_param,
               basic_patho_param = loadPathogen(disease = "rust"),
               inits = list(pI0=0.01), area_vector = area,
               dispersal = list(disp_patho_clonal=c(0.99,0.01,0.01,0.99),
               disp_patho_sex=c(1,0,0,1),
               disp_host=c(1,0,0,1)),
               rotation_matrix = as.matrix(rotation),
               croptypes_cultivars_prop = as.matrix(croptypes_cultivars_prop),
               cultivars_param = cultivars, genes_param = genes)  

## Compute outputs
eco_param <- list(yield_perHa = cbind(H = as.numeric(cultivars$yield_H),
             L = as.numeric(cultivars$yield_L),
             I = as.numeric(cultivars$yield_I),
             R = as.numeric(cultivars$yield_R)),
             planting_cost_perHa = as.numeric(cultivars$planting_cost),
             market_value = as.numeric(cultivars$market_value))
            
evol_res <- evol_output(, time_param, Npoly, cultivars, genes)
epid_output(, time_param, Npatho, area, rotation
, croptypes_cultivars_prop, cultivars, eco_param)




#### 1-year simulation of a rust epidemic in pure susceptible crop in a single 1-km2 patch ####
## Simulation and pathogen parameters
time_param <- list(Nyears=1, nTSpY=120)
area <- c(1E6)
basic_patho_param = loadPathogen(disease = "rust")
## croptypes, cultivars and genes
rotation <- data.frame(year_1=c(0), year_2=c(0))
croptypes_cultivars_prop <- data.frame(croptypeID=c(0), cultivarID=c(0), proportion=c(1))
cultivars <- as.list(rbind(loadCultivar(name="Susceptible", type="growingHost")))
names(cultivars)[names(cultivars)=="cultivarName"] <- "name"
yield0 <- cultivars$yield_H + as.numeric(cultivars$yield_H==0)
cultivars <- c(cultivars, list(relative_yield_H = as.numeric(cultivars$yield_H / yield0)
    , relative_yield_L = as.numeric(cultivars$yield_L / yield0)
    , relative_yield_I = as.numeric(cultivars$yield_I / yield0)
    , relative_yield_R = as.numeric(cultivars$yield_R / yield0)
    , sigmoid_kappa_host=0.002, sigmoid_sigma_host=1.001, sigmoid_plateau_host=1
    , cultivars_genes_list=list(numeric(0))))
genes <-   list(geneName = character(0) , fitness_cost = numeric(0)
    , mutation_prob = numeric(0)
    , efficiency = numeric(0) , tradeoff_strength = numeric(0)
    , Nlevels_aggressiveness = numeric(0)
    , time_to_activ_mean = numeric(0) , time_to_activ_var = numeric(0)
    , target_trait = character(0)
    , recombination_sd = numeric(0))
treatment=list(treatment_degradation_rate=0.1
    , treatment_efficiency=0
    , treatment_timesteps=logical(0)
    , treatment_cultivars=logical(0)
    , treatment_cost=0)

## run simulation
model_landsepi(seed=1, time_param = time_param
    , basic_patho_param = basic_patho_param
    , inits = list(pI0=5E-4), area_vector = area
    , dispersal = list(disp_patho_clonal=c(1), disp_patho_sex=c(1), disp_host=c(1))
    , rotation_matrix = as.matrix(rotation)
    , treatment_param = treatment
    , croptypes_cultivars_prop = as.matrix(croptypes_cultivars_prop)
    , cultivars_param = cultivars,  genes_param = genes)
}