Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens

Abstract

Genetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs quickly after resistance is deployed, resulting in significant crop losses and a continuing need to develop new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolution of pathogen populations and thus increase genetic resistance durability. These strategies mainly rely on varying different combinations of resistance sources across time (crop rotations) and 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). However, experimental comparison of the efficiency (i.e. ability to reduce disease impact) and durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of landscape-scale deployment strategies presents major logistical challenges. Therefore, we developed a spatially explicit stochastic model able to assess the epidemiological and evolutionary outcomes of the four major deployment options described above, including both qualitative resistance (i.e. major genes) and quantitative resistance traits against several components of pathogen aggressiveness: infection rate, latent period duration, propagule production rate, and infectious period duration. This model, implemented in the R package landsepi, provides a new and useful tool to assess the performance of a wide range of deployment options, and helps investigate the effect of landscape, epidemiological and evolutionary parameters. This article describes the model and its parameterisation for rust diseases of cereal crops, caused by fungi of the genus Puccinia. To illustrate the model, we use it to assess the epidemiological and evolutionary potential of the combination of a major gene and different traits of quantitative resistance. The comparison of the four major deployment strategies described above will be the objective of future studies.

Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance?

“…In the current study, all computations of AUDPC (see list of model outputs in Table 3 and details in Rimbaud, Papaïx, Rey, Barrett et al., 2018) were expressed relative to AUDPC 0 ; hence, they might vary from 0% (i.e., no disease) to 100% (i.e., same disease severity as in a fully susceptible landscape).…”

“…(2018) for illustrations of landscape structures generated using a T‐tessellation algorithm, and see Papaïx et al. (2014) for details on the algorithm.…”

“… Notes See Supporting information Text S1 in (Rimbaud, Papaïx, Rey, Barrett et al., 2018) for calibration details. Epidemic processes associated with some of the parameters are illustrated in Figure 1.…”

ALeptosphaeria maculansset of isolates characterised on all available differentials and used as control to identify virulence frequencies in a current French population

“…Modelling studies (Lô-Pelzer et al 2010) and field experiments provide knowledge on the interplay between the acreages on which genes are deployed, the combination of genes in varieties, the intensity of inoculum sources and the spatial organization of crops in the landscape. There is currently no agreement on the best way to deploy available resistance genes, either individually or stacked in varieties (Rimbaud et al 2018). On the one hand, diversifying selection has been proposed to play on pathogen's weakness, such as stabilizing evolutionary dynamics (Zhan et al, 2015).…”

“…Transmission between fields can be predicted from spore dispersal (Marcroft et al, 2004;Bousset et al, 2015). Thus, spatially explicit models can be used to study and ultimately design combinations of landscapes, varietal choice and tillage practices promoting resistance durability against phoma stem canker (Lô-Pelzer et al, 2010;Rimbaud et al 2018). As to the practicalities of spatial organization of the landscape, lessons have to be learned from the available case studies.…”