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Simulation of plant resistance deployment, using landscape structures provided with the package and a parameterisation of the model to represent pathogens as typified by rusts of cereals (e.g. stripe rust, stem rust , and leaf rust of wheat and barley). All parameters are optional. See details for explanations.
simul_landsepi(seed = 12345, nYears = 5, nTSpY = 120, idLan = 1,
propSR = 2/3, isolSR = 1, propRR = 1/2, isolRR = 1,
strat = "MO", Nhost = 3, pI0 = 5e-04, resistance1 = c(1, 0, 0, 0,
0, 0, 0, 0), resistance2 = c(0, 1, 0, 0, 0, 0, 0, 0),
costInfect = 0.75, costAggr = 0.75, taumut = 1e-07, probSex = 0,
MGeff = 1, QReff = 0.5, beta = 1, Naggr = 6, timeToQR_exp = 0,
timeToQR_var = 0, C0 = rep(0.1, Nhost), Cmax = rep(2, Nhost),
yield = cbind(H = rep(1/(120 * 1000), Nhost), L = rep(0, Nhost), I =
rep(0, Nhost), R = rep(0, Nhost)), purchPrice = rep(0.001, Nhost),
sellPrice = rep(0.2, Nhost), graphic = TRUE, video = FALSE)
an integer used as seed value (for random number generator).
an integer giving the number of simulated years.
an integer giving the number of time steps per year.
an integer giving the index of landscape structure (1 to 5).
proportion of fields where resistance is deployed: (RC)/(SC+RC) or (RC1+RC2)/(SC+RC1+RC2). Must be between 0 and 1.
an integer giving the spatial aggregation of fields where resistance is deployed (1=highly fragmented, 2=balanced, 3=highly aggregated).
when applicable (mixtures and mosaics only), relative proportion of the second resistant cultivar: (RC2)/(RC1+RC2). Must be between 0 and 1.
when applicable, an integer specifying the spatial (for mosaics: 1=highly fragmented, 2=balanced, 3=highly aggregated) or temporal (for rotations: 1=every year, 2=every two years, 3=every three years) aggregation of fields cultivated with the second resistant cultivar.
a character string specifying the deployment strategy ("MO"=mosaic, "MI"=mixture, "RO"=rotations, "PY"=pyramiding).
an integer giving the number of cultivars (1, 2 or 3).
initial probability of infection of the susceptible cultivar. Must be between 0 and 1.
a logical vector of size 8 giving the resistance formula of the 2nd cultivar (see details)
when applicable, a logical vector of size 8 giving the resistance formula of the 3rd cultivar (see details)
cost of infectivity paid by infective pathogens (i.e. adapted to plant cultivars carrying a major gene) on susceptible hosts. Must be between 0 and 1.
cost of aggressiveness paid by fully adapted pathogens (relative to plant cultivars carrying a quantitative resistance trait) on fully susceptible hosts. Must be between 0 and 1.
mutation probability: probability for a propagule to change its infectivity or its aggressiveness on a resistant cultivar carrying a major gene or a quantitative resistance trait. Must be above 0. If equal to 0, then the pathogen cannot evolve.
probability for an infection that its reproduction is sexual rather than clonal.
efficiency of major-gene resistance on the infection rate of non-adapted pathogens. Must be between 0 and 1.
efficiency of quantitative resistance on the target aggressiveness trait (infection rate, latent period duration, sporulation rate, or sporulation duration) of non-adapted pathogens. Must be between 0 and 1.
trade-off strength for pathogen adaptation to quantitative resistance (<1 for weak, =1 for linear, >1 for strong). Must be above 0.
an integer specifying the number of increments to completely adapt to quantitative resistance. Must be greater or equal 2.
average time to expression of quantitative resistance (to simulate Adult Plant Resistance).
variance of the time to expression of quantitative resistance (to simulate Adult Plant Resistance).
planting density of the different cultivars (in number of hosts per meter square).
carrying capacity of the different cultivars (in number of hosts per meter square).
a matrix of yield (weight units of product/individual/time-step) for each cultivar (rows) and each sanitary state (columns: H, L, I, R).
price of crop planting (in monetary units/planted individual)
selling price (in monetary unit/weight units of production)
a logical indicating if graphics must be generated (TRUE) or not (FALSE).
a logical indicating if a video must be generated (TRUE) or not (FALSE). Works only if graphic is TRUE as well.
A set of binary files is generated for every year of simulation and every compartment:
H: healthy hosts,
Hjuv: juvenile healthy hosts,
L: latently infected hosts,
I: infectious hosts,
R: removed hosts,
S: propagules.
Each file indicates for every time-step the number of individuals in each field, and when appropriate for each cultivar and pathotype) These binary files are used to generate a set of text files containing all outputs of the simulations (see details). A set of graphics and a video showing epidemic maps can also be generated.
The landscape structure is the physical structure of the area, defined as the spatial arrangement of fields.
Deployment strategies include the deployment of a susceptible cultivar (SC) and one (RC) or two (RC1 and RC2) resistant cultivars carrying up to four major resistance genes or up to four quantitative resistance traits (against infection rate, latent period, sporulation rate and sporulation duration of the pathogen). In addition, the different resistance sources can be combined in time (crop rotation: recurrent succession of cultivars in the same field), or space within a single cultivar (pyramiding), in different cultivars of the same field (mixtures) or in different fields (mosaics).
The genetic resistance carried by a plant cultivar is specified by a vector of size 8: the four first elements indicate whether the cultivar carries major resistance genes #1, #2, #3 and #4, respectively. The following four elements indicate whether the cultivar carried a quantitative resistance trait against the infection rate, the latent period duration, the sporulation rate, or the sporulation duration of the pathogen, respectively. For example, the formula c(1,0,0,0,0,1,0,0) indicates the presence of major gene #1 and a quantitative resistance which increases the duration of the latent period of the pathogen.
For a given major gene, several computations are performed:
(d1) time to first appearance of a pathogen mutant;
(d2) time to first true infection of a resistant host by such mutants; and
(d3) time when the number of infections of resistant hosts by these mutants reaches a threshold above which mutant pathogens are unlikely to go extinct.
pathogen adaptation to quantitative resistance is gradual, so the three measures described above are computed for every step towards complete erosion of resistance (i.e. nAgw-1 levels).
a simulation run is divided into three periods:
the initial short-term period when all resistance sources are at their highest potential;
a transitory period during which a given deployment strategy is only partially effective; and
a longer-term period when all the resistances have been overcome or completely eroded.
The epidemiological impact of pathogen spread is evaluated by two different measures:
Green Leaf Area (GLA): The GLA represents the average number of productive hosts per time step and per surface unit.
Area Under Disease Progress Curve (AUDPC): The AUDPC is the average proportion of diseased hosts relative to the carrying capacity and represents disease severity.
The GLA and AUDPC of every cultivar as well as the whole landscape are averaged across the whole simulation run, to measure the global epidemiological performance of a deployment strategy.
The average GLA and AUDPC of the susceptible cultivar is computed on whole cropping seasons from the beginning of the simulation until the end of the season preceding year before D1.
The average GLA and AUDPC of the susceptible cultivar is computed on whole seasons from the beginning of the season following year after D1 to the end of the season year before preceding D2.
The average GLA and AUDPC of the whole landscape is computed on whole seasons from the beginning of the year after D2 to the end of the simulation.
Rimbaud L., Papa<U+00EF>x 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 Computational Biology 14(4):e1006067.
# NOT RUN {
## Default parameterisation (5-year simulation of a mosaic deployment strategy of
## two resistant cultivars in balanced proportions and low level of spatial aggregation)
simul_landsepi()
## Mosaic of two major genes
simul_landsepi(seed=1, idLan=1, propSR=2/3, isolSR=3, propRR=1/2, isolRR=3, strat="MO", Nhost=3
, nYears=50, resistance1=c(1,0,0,0,0,0,0,0), resistance2=c(0,1,0,0,0,0,0,0)
, costInfect=0.5, taumut=1e-7)
## Mixture of two major genes
simul_landsepi(seed=1, idLan=1, propSR=2/3, isolSR=3, propRR=1/2, strat="MI", Nhost=3
, nYears=50, resistance1=c(1,0,0,0,0,0,0,0), resistance2=c(0,1,0,0,0,0,0,0)
, costInfect=0.5, taumut=1e-7)
## Rotations of two major genes
simul_landsepi(seed=1, idLan=1, propSR=2/3, isolSR=3, isolRR=1, strat="RO", Nhost=3
, nYears=50, resistance1=c(1,0,0,0,0,0,0,0), resistance2=c(0,1,0,0,0,0,0,0)
, costInfect=0.5, taumut=1e-7)
## Pyramiding of two major genes
simul_landsepi(seed=1, idLan=1, propSR=2/3, isolSR=3, strat="PY", Nhost=2
, nYears=50, resistance1=c(1,1,0,0,0,0,0,0), costInfect=0.5, taumut=1e-7)
## Combination of a major gene with a quantitative resistance against the latent period
simul_landsepi(seed=1, idLan=1, propSR=0.8, isolSR=1, strat="PY", Nhost=2
, nYears=50, resistance1=c(1,0,0,0,0,1,0,0)
, costInfect=0.5, costAggr=0.5, taumut=1e-7, MGeff=1.0, QReff=0.5, beta=1.0, Naggr=6)
# }
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