Generate epidemiological and evolutionary outputs from model simulations.
HLIRdynamics(pathRES, graphOn, times, landscape, hostP, epiP, evolP,
th_break = 50000, nMapPY = 0)a character string indicating the path of the repository where outputs will be generated.
a logical indicating if graphics of the outputs must be generated (1) or not (0).
a list of simulation parameters (number of years, number of time-steps per year).
a shapefile containing the agricultural landscape (can be generated through function AgriLand).
a list of host parameters (number of cultivars, growth rate of the susceptible cultivar, reproduction rate of the susceptible cultivar, growth rate of resistant cultivars, reproduction rate of resistant cultivars, death rate, number of possible resistance sources (8) , resistance formula, parameters of the sigmoid invasion function: kappa, sigma and s).
a list of pathogen parameters (probability to survive the off-season, infection rate , reproduction rate, average latent period duration, variance of the latent period, average infectious period duration , variance of the infectious period duration, parameters of the sigmoid contamination function: kappa, sigma, s).
a list of evolution parameters (cost of infectivity, cost of aggressiveness, mutation rate, efficiency of major resistance genes, efficiency of quantitative resistance, trade-off strength, number of increments of quantitative resistance erosion, adaptation formula).
an integer giving the threshold (number of infections) above which mutant pathogen are unlikely to go extinct, used to characterise resistance breakdown.
an integer specifying the number of epidemic maps per year to generate.
A set of text files containing all outputs of the simulations (see details). A set of graphics and epidemic maps can also be generated.
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. (in press). Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens. PLoS Computational Biology.
# NOT RUN {
demo_landsepi()
# }
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