A spatio-temporal stochastic model to assess resistance deployment strategies against plant pathogens. The model is based on stochastic geometry for describing the landscape and the resistant hosts, a dispersal kernel for the dissemination of the pathogen, and a SEIR (Susceptible-Exposed-Infectious-Removed) architecture to simulate plant response to disease.
| Package: | landsepi |
| Type: | Package |
| Version: | 0.0.8 |
| Date: | 2019-10-04 |
| License: | GPL (>=2) |
The landsepi package implements a spatially explicit stochastic model able to assess the epidemiological and evolutionary outcomes of four major strategies to deploy plant resistance to pathogens. These strategies include the combination of several resistance sources across time (crop rotations) or space. The spatial scale of deployment can vary from multiple resistance sources occurring in a single cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). The simulated sources of resistance can consist of qualitative resistance (i.e. major genes) or quantitative resistance traits (including Adult Plant Resistant genes) against several components of pathogen aggressiveness: infection rate, latent period duration, propagule production rate, and infectious period duration. This model provides a useful tool to assess the performance of a wide range of deployment options, and helps investigate the effect of landscape, epidemiological and evolutionary parameters on the performance of a given strategy.
The simulation model is based on a SEIR (Susceptible-Exposed-Infectious-Removed) architecture to describe host response to disease. The lansdcape is represented by a set of polygons where the pathogen can disperse. Initially, the pathogen is not adapted to any source of resistance, and is only present on susceptible hosts. However, through mutation, it can evolve and may acquire infectivity genes (which leads to breakdown of major resistance genes) or increase aggressiveness (which leads to the erosion of the relevant quantitative resistance traits). Furthermore, loci may be re-assorted via sexual reproduction. However, evolution of a pathogen toward infectivity or increased aggressiveness on a resistant host may be penalised by a fitness cost on susceptible hosts. Consequently, pathogens carrying infectivity genes may have reduced infection rate (cost of infectivity) on susceptible hosts relative to pathogens that do not carry these genes. Similarly, a gain in pathogen aggressiveness on quantitatively resistant hosts is penalised by a decreased aggressiveness on susceptible hosts, leading to a trade-off.
The evolutionary outcome of a deployment strategy is assessed by measuring the time until the pathogen reaches the three steps to adapt to plant resistance:
(d1) first appearance of adapted mutants,
(d2) initial migration to resistant hosts and infection, and
(d3) broader establishment in the resistant host population (i.e. the point at which extinction becomes unlikely).
Epidemiological outcomes are evaluated using:
(e1) the Green Leaf Area (GLA) as a proxy for yield, and
(e2) the area under the disease progress curve (AUDPC) to measure disease severity.
The package includes five examples of landscape structures and a default parameterisation to represent plant pathogens as typified by rusts of cereal crops
(genus Puccinia, e.g. stripe rust, stem rust and leaf rust of wheat and barley). The main function of the package is simul_landsepi().
It can be parameterised to simulate various resistance deployment strategies using the provided landscapes and parameters for cereal rusts.
A set of graphics and a video showing epidemic maps can also be generated.
Future versions:
Future versions of the package will include in particular:
A more flexible parameterisation of pathogen life-history traits, in order to simulate other plant pathogens.
A more flexible parameterisation of deployment strategies, in order to simulate complex strategies combining several options (e.g. mosaic of pyramids) as well as the allocation of more than 3 different cultivars in the landscape.
Dependencies:
The package for compiling needs:
g++
libgsl2
libgsl-dev
gdal-bin
libgdal-dev
and the following R packages:
Rcpp
rgdal
sp
stats
Matrix
MASS
rgeos
maptools
fields
splancs
sf
In addition, to generate videos the package will need ffmpeg.
## When referencing the simulation model, please cite the following article:
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.
## When referencing the R package, please cite the following package:
Rimbaud L., Papa<U+00EF>x J. and Rey J.-F. (2018). landsepi: Landscape Epidemiology and Evolution. R package, url: https://cran.r-project.org/package=landsepi.
Useful links:
# NOT RUN {
library("landsepi")
## Run a demonstration (a 30-year simulation of a mosaic deployment strategy of two
## resistant cultivars in balanced proportions and high level of spatial aggregation)
demo_landsepi()
## Run a simulation with data included in the package (default parameterisation:
## 5-year simulation of a mosaic deployment strategy of two resistant cultivars
## in balanced proportions and high level of spatial aggregation)
simul_landsepi()
## (see ?simul_landsepi to help parameterise the function and simulate other scenarios)
# }
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