Crop pathogen emergence and evolution in agro‐ecological landscapes

Papaïx, Julien; Burdon, Jeremy J.; Zhan, Jiasui; Thrall, Peter H.

doi:10.1111/eva.12251

Cited by 52 publications

(44 citation statements)

References 89 publications

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“…Over the course of months, S. solani evolved increased rates of infection on clover host species (Gilbert & Parker 2010). Pathogens of common crop species are expected to rapidly evolve in response to climate change and altered land use (Pangga, Hanan & Chakraborty 2011;Papaix et al 2015). The increasingly negative effects of such plant pathogens should impose selection on plant traits that confer resistance or tolerance of these pathogens.…”

Section: E V O L U T I O N O F M I C R O B E S M a Y A L T E R P L A mentioning

confidence: 99%

Eco‐evolutionary dynamics in plant–soil feedbacks

terHorst

Zee

2016

Functional Ecology

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Summary1. In the past decade, ecologists have begun to more fully appreciate the role of evolution in explaining contemporary ecological processes. Evolution is most likely to affect ecological patterns when selection pressure is particularly strong, or when the generation time of at least one interacting species is relatively short. 2. Interactions between plants and soil microbes are an excellent candidate for examining ecoevo interactions because interactions between organisms are tightly knit with the potential for species with relatively short generation times to impose strong selection on one another. Here, we examine the potential for eco-evolutionary dynamics in plant-soil feedbacks (PSFs). 3. Genetic variation in plant traits and subsequent evolution of those traits can affect traits and species composition of soil microbial communities. Soil microbial communities can, in turn, alter the evolutionary trajectory of plant traits. Further, the direction and magnitude of PSFs can affect the plant community, which may alter the selection on plant traits via intraand interspecific interactions. 4. Finally, we consider how eco-evolutionary feedbacks might enhance or mitigate the effects of PSFs in driving the structure of natural plant communities.

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Section: E V O L U T I O N O F M I C R O B E S M a Y A L T E R P L A mentioning

confidence: 99%

Eco‐evolutionary dynamics in plant–soil feedbacks

terHorst

Zee

2016

Functional Ecology

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“…The spatial scales also appeared crucial to understanding the evolution of plants and pathogens interactions and landscape influence on evolution of resistance efficiency has been investigated (Papaix et al. , ; Fabre et al. ).…”

Section: Discussionmentioning

confidence: 99%

Insight into the durability of plant resistance to aphids from a demo‐genetic study of Aphis gossypii in melon crops

Thomas

Vanlerberghe‐Masutti

Mistral

et al. 2016

Evolutionary Applications

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Resistance breakdown has been observed following the deployment of plant cultivars resistant to pests. Assessing the durability of a resistance requires long‐term experiments at least at a regional scale. We collected such data for melon resistance conferred by the Vat gene cluster to melon aphids. We examined landscape‐level populations of Aphis gossypii collected in 2004–2015, from melon‐producing regions with and without the deployment of Vat resistance and with different climates. We conducted demo‐genetic analyses of the aphid populations on Vat and non‐Vat plants during the cropping seasons. The Vat resistance decreased the density of aphid populations in all areas and changed the genetic structure and composition of these populations. Two bottlenecks were identified in the dynamics of adapted clones, due to the low levels of production of dispersal morphs and winter extinction. Our results suggest that (i) Vat resistance will not be durable in the Lesser Antilles, where no bottleneck affected the dynamics of adapted clones, (ii) Vat resistance will be durable in south‐west France, where both bottlenecks affected the dynamics of adapted clones and (iii) Vat resistance will be less durable in south‐east France, where only one of the two bottlenecks was observed.

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“…the period of time from initial deployment of a resistant cultivar to when resistance is considered to have been overcome) is a typical evolutionary target of resistance deployment strategies, but its computation in a model is not obvious. Proposed methods include targeting the point in time when the first adapted pathogens appear [29,30,39], when their prevalence [37,48,55,131] or frequency in pathogen population [39,51,52] exceeds a threshold, or when productivity of the resistant cultivar drops below an arbitrary threshold [45].…”

Section: Computation Of Epidemiological and Evolutionary Outputsmentioning

confidence: 99%

“…As a result, a comprehensive evaluation of different deployment schemes is complicated, and currently only feasible via pairwise comparisons [53,54]. The situation for quantitative resistance is similar, since often only one [28,34,41,42,55], two [36,37,56], or a combination [26,44,49] of pathogen aggressiveness components are targeted, although quantitative resistance can affect several life-history traits of the pathogen. As articulated above, this current gap in our ability to predict which strategy will maximise our ability to control disease epidemics as well as pathogen evolutionary potential (or indeed whether these goals are compatible) emphasises the need for models that can compare different deployment schemes within the same framework, using standardised assumptions.…”

Section: Introductionmentioning

confidence: 99%

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

Rimbaud¹,

Papaïx²,

Rey³

et al. 2018

Preprint

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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 landscapescale 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.not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/260836 doi: bioRxiv preprint first posted online Feb. 6, 2018; 2 Key words: demo-genetic modelling; deployment strategies; durable plant resistance; pathogen evolution; quantitative resistance; rust of wheat; spatially explicit stochastic modelling; spatiotemporal simulation model. Author summaryThere are many recent examples which demonstrate the evolutionary potential of plant pathogens to overcome the resistances deployed in agricultural landscapes to protect our crops. Increasingly, it is recognised that how resistance is deployed spatially and temporally can impact on rates of pathogen evolution and resistance breakdown. Such deployment strategies are mainly based on the combination of several sources of resistance ...

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Crop pathogen emergence and evolution in agro‐ecological landscapes

Cited by 52 publications

References 89 publications

Eco‐evolutionary dynamics in plant–soil feedbacks

Eco‐evolutionary dynamics in plant–soil feedbacks

Insight into the durability of plant resistance to aphids from a demo‐genetic study of Aphis gossypii in melon crops

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

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