Pathogen population dynamics in agricultural landscapes: The Ddal modelling framework

Papaïx, Julien; Adamczyk-Chauvat, Katarzyna; Bouvier, Annie A.; Kiêu, Kiên; Touzeau, Suzanne; Lannou, Christian; Monod, Hervé

doi:10.1016/j.meegid.2014.01.022

Cited by 37 publications

(41 citation statements)

References 68 publications

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“…One of the main reasons for this is that most theoretical studies dealing with resistance durability consider only one susceptible and one resistant cultivar e.g . (van den Bosch & Gilligan, ; Skelsey et al ., ; Fabre et al ., , ; Lo Iacono et al ., ; Papaïx et al ., , ,b), making it impossible to compare strategies deploying more than one resistance gene. We extended the model proposed by Fabre et al .…”

Section: Discussionmentioning

confidence: 99%

Mosaics often outperform pyramids: insights from a model comparing strategies for the deployment of plant resistance genes against viruses in agricultural landscapes

Djidjou‐Demasse¹,

Moury²,

Fabre³

2017

New Phytologist

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The breakdown of plant virus resistance genes is a major issue in agriculture. We investigated whether a set of resistance genes would last longer when stacked into a single plant cultivar (pyramiding) or when deployed individually in regional mosaics (mosaic strategy). We modeled the genetic and epidemiological processes shaping the demogenetic dynamics of viruses under a multilocus gene-for-gene system, from the plant to landscape scales. The landscape consisted of many fields, was subject to seasonality, and of a reservoir hosting viruses year-round. Strategy performance depended principally on the fitness costs of adaptive mutations, epidemic intensity before resistance deployment and landscape connectivity. Mosaics were at least as good as pyramiding strategies in most production situations tested. Pyramiding strategies performed better only with slowly changing virus reservoir dynamics. Mosaics are more versatile than pyramiding strategies, and we found that deploying a mosaic of three to five resistance genes generally provided effective disease control, unless the epidemics were driven mostly by within-field infections. We considered the epidemiological and evolutionary mechanisms underlying the greater versatility of mosaics in our case study, with a view to providing breeders and growers with guidance as to the most appropriate deployment strategy.

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Section: Discussionmentioning

confidence: 99%

Mosaics often outperform pyramids: insights from a model comparing strategies for the deployment of plant resistance genes against viruses in agricultural landscapes

Djidjou‐Demasse¹,

Moury²,

Fabre³

2017

New Phytologist

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“…; Papaïx et al. ). Wild patches are then positioned on that agricultural landscape with spatial aggregation (random and clustered – Fig.…”

Section: Model and Methodsmentioning

confidence: 98%

Crop pathogen emergence and evolution in agro‐ecological landscapes

Papaïx

Burdon

Zhan

et al. 2015

Evolutionary Applications

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Remnant areas hosting natural vegetation in agricultural landscapes can impact the disease epidemiology and evolutionary dynamics of crop pathogens. However, the potential consequences for crop diseases of the composition, the spatial configuration and the persistence time of the agro-ecological interface – the area where crops and remnant vegetation are in contact – have been poorly studied. Here, we develop a demographic–genetic simulation model to study how the spatial and temporal distribution of remnant wild vegetation patches embedded in an agricultural landscape can drive the emergence of a crop pathogen and its subsequent specialization on the crop host. We found that landscape structures that promoted larger pathogen populations on the wild host facilitated the emergence of a crop pathogen, but such landscape structures also reduced the potential for the pathogen population to adapt to the crop. In addition, the evolutionary trajectory of the pathogen population was determined by interactions between the factors describing the landscape structure and those describing the pathogen life histories. Our study contributes to a better understanding of how the shift of land-use patterns in agricultural landscapes might influence crop diseases to provide predictive tools to evaluate management practices.

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“…(2018) for illustrations of landscape structures generated using a T‐tessellation algorithm, and see Papaïx et al. (2014) for details on the algorithm.aCrop cultivars are allocated using an algorithm based on latent Gaussian fields to control proportion and level of spatial aggregation of each cultivar; see Supporting information Figure S1 of the present article for illustrations, and see Rimbaud, Papaïx, Rey, Barrett et al. (2018) for details on the algorithm.…”

Section: Methodsmentioning

confidence: 99%

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

Rimbaud

Papaïx

Barrett

et al. 2018

Evolutionary Applications

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Once deployed uniformly in the field, genetically controlled plant resistance is often quickly overcome by pathogens, resulting in dramatic losses. Several strategies have been proposed to constrain the evolutionary potential of pathogens and thus increase resistance durability. These strategies can be classified into four categories, depending on whether resistance sources are varied across time (rotations) or combined in space in the same cultivar (pyramiding), in different cultivars within a field (cultivar mixtures) or among fields (mosaics). Despite their potential to differentially affect both pathogen epidemiology and evolution, to date the four categories of deployment strategies have never been directly compared together within a single theoretical or experimental framework, with regard to efficiency (ability to reduce disease impact) and durability (ability to limit pathogen evolution and delay resistance breakdown). Here, we used a spatially explicit stochastic demogenetic model, implemented in the R package landsepi, to assess the epidemiological and evolutionary outcomes of these deployment strategies when two major resistance genes are present. We varied parameters related to pathogen evolutionary potential (mutation probability and associated fitness costs) and landscape organization (mostly the relative proportion of each cultivar in the landscape and levels of spatial or temporal aggregation). Our results, broadly focused on qualitative resistance to rust fungi of cereal crops, show that evolutionary and epidemiological control are not necessarily correlated and that no deployment strategy is universally optimal. Pyramiding two major genes offered the highest durability, but at high mutation probabilities, mosaics, mixtures and rotations can perform better in delaying the establishment of a universally infective superpathogen. All strategies offered the same short‐term epidemiological control, whereas rotations provided the best long‐term option, after all sources of resistance had broken down. This study also highlights the significant impact of landscape organization and pathogen evolutionary ability in considering the optimal design of a deployment strategy.

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Pathogen population dynamics in agricultural landscapes: The Ddal modelling framework

Cited by 37 publications

References 68 publications

Mosaics often outperform pyramids: insights from a model comparing strategies for the deployment of plant resistance genes against viruses in agricultural landscapes

Mosaics often outperform pyramids: insights from a model comparing strategies for the deployment of plant resistance genes against viruses in agricultural landscapes

Crop pathogen emergence and evolution in agro‐ecological landscapes

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

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