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

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...

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Autores principales: Rimbaud, Loup, Papaïx, Julien, Barrett, Luke G., Burdon, Jeremy J., Thrall, Peter H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231482/
https://www.ncbi.nlm.nih.gov/pubmed/30459830
http://dx.doi.org/10.1111/eva.12681
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author Rimbaud, Loup
Papaïx, Julien
Barrett, Luke G.
Burdon, Jeremy J.
Thrall, Peter H.
author_facet Rimbaud, Loup
Papaïx, Julien
Barrett, Luke G.
Burdon, Jeremy J.
Thrall, Peter H.
author_sort Rimbaud, Loup
collection PubMed
description 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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spelling pubmed-62314822018-11-20 Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance? Rimbaud, Loup Papaïx, Julien Barrett, Luke G. Burdon, Jeremy J. Thrall, Peter H. Evol Appl Original Articles 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. John Wiley and Sons Inc. 2018-09-17 /pmc/articles/PMC6231482/ /pubmed/30459830 http://dx.doi.org/10.1111/eva.12681 Text en © 2018 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Rimbaud, Loup
Papaïx, Julien
Barrett, Luke G.
Burdon, Jeremy J.
Thrall, Peter H.
Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance?
title Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance?
title_full Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance?
title_fullStr Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance?
title_full_unstemmed Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance?
title_short Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance?
title_sort mosaics, mixtures, rotations or pyramiding: what is the optimal strategy to deploy major gene resistance?
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231482/
https://www.ncbi.nlm.nih.gov/pubmed/30459830
http://dx.doi.org/10.1111/eva.12681
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