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: | lansepi |
| Type: | Package |
| Version: | 0.0.4 |
| Date: | 2018-04-09 |
| 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 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). 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. The demonstration function is parameterise to roughly represent biotrophic foliar fungi of cereal crops, as typified by rusts of wheat (genus Puccinia).
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 {
library("landsepi")
## Run a demonstration
demo_landsepi()
## Run a simulation
simul_landsepi()
# }
Run the code above in your browser using DataLab