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Dive into the research topics where Julien Papaïx is active.

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Featured researches published by Julien Papaïx.


New Phytologist | 2011

Influence of cultivated landscape composition on variety resistance: an assessment based on wheat leaf rust epidemics

Julien Papaïx; Henriette Goyeau; Philippe Du Cheyron; Hervé Monod; Christian Lannou

In plant pathology, the idea of designing variety management strategies at the scale of cultivated landscapes is gaining more and more attention. This requires the identification of effects that take place at large scales on host and pathogen populations. Here, we show how the landscape varietal composition influences the resistance level (as measured in the field) of the most grown wheat varieties by altering the structure of the pathogen populations. For this purpose, we jointly analysed three large datasets describing the wheat leaf rust pathosystem (Puccinia triticina/Triticum aestivum) at the country scale of France with a Bayesian hierarchical model. We showed that among all compatible pathotypes, some were preferentially associated with a variety, that the pathotype frequencies on a variety were affected by the landscape varietal composition, and that the observed resistance level of a variety was linked to the frequency of the most aggressive pathotypes among all compatible pathotypes. This data exploration establishes a link between the observed resistance level of a variety and landscape composition at the national scale. It illustrates that the quantitative aspects of the host-pathogen relationship have to be considered in addition to the major resistance/virulence factors in landscape epidemiology approaches.


PLOS ONE | 2013

Dynamics of adaptation in spatially heterogeneous metapopulations.

Julien Papaïx; Olivier David; Christian Lannou; Hervé Monod

The selection pressure experienced by organisms often varies across the species range. It is hence crucial to characterise the link between environmental spatial heterogeneity and the adaptive dynamics of species or populations. We address this issue by studying the phenotypic evolution of a spatial metapopulation using an adaptive dynamics approach. The singular strategy is found to be the mean of the optimal phenotypes in each habitat with larger weights for habitats present in large and well connected patches. The presence of spatial clusters of habitats in the metapopulation is found to facilitate specialisation and to increase both the level of adaptation and the evolutionary speed of the population when dispersal is limited. By showing that spatial structures are crucial in determining the specialisation level and the evolutionary speed of a population, our results give insight into the influence of spatial heterogeneity on the niche breadth of species.


Journal of Evolutionary Biology | 2010

Combining capture–recapture data and pedigree information to assess heritability of demographic parameters in the wild

Julien Papaïx; Sarah Cubaynes; Mathieu Buoro; Anne Charmantier; Philippe Perret; Olivier Gimenez

Quantitative genetic analyses have been increasingly used to estimate the genetic basis of life‐history traits in natural populations. Imperfect detection of individuals is inherent to studies that monitor populations in the wild, yet it is seldom accounted for by quantitative genetic studies, perhaps leading to flawed inference. To facilitate the inclusion of imperfect detection of individuals in such studies, we develop a method to estimate additive genetic variance and assess heritability for binary traits such as survival, using capture–recapture (CR) data. Our approach combines mixed‐effects CR models with a threshold model to incorporate discrete data in a standard ‘animal model’ approach. We employ Markov chain Monte Carlo sampling in a Bayesian framework to estimate model parameters. We illustrate our approach using data from a wild population of blue tits (Cyanistes caeruleus) and present the first estimate of heritability of adult survival in the wild. In agreement with the prediction that selection should deplete additive genetic variance in fitness, we found that survival had low heritability. Because the detection process is incorporated, capture–recapture animal models (CRAM) provide unbiased quantitative genetics analyses of longitudinal data collected in the wild.


Infection, Genetics and Evolution | 2014

Pathogen population dynamics in agricultural landscapes: the Ddal modelling framework.

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

Modelling processes that occur at the landscape scale is gaining more and more attention from theoretical ecologists to agricultural managers. Most of the approaches found in the literature lack applicability for managers or, on the opposite, lack a sound theoretical basis. Based on the metapopulation concept, we propose here a modelling approach for landscape epidemiology that takes advantage of theoretical results developed in the metapopulation context while considering realistic landscapes structures. A landscape simulator makes it possible to represent both the field pattern and the spatial distribution of crops. The pathogen population dynamics are then described through a matrix population model both stage- and space-structured. In addition to a classical invasion analysis we present a stochastic simulation experiment and provide a complete framework for performing a sensitivity analysis integrating the landscape as an input factor. We illustrate our approach using an example to evaluate whether the agricultural landscape composition and structure may prevent and mitigate the development of an epidemic. Although designed for a fungal foliar disease, our modelling approach is easily adaptable to other organisms.


Evolutionary Applications | 2015

Crop pathogen emergence and evolution in agro-ecological landscapes

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

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.


PLOS Computational Biology | 2014

Evolution of Pathogen Specialisation in a Host Metapopulation: Joint Effects of Host and Pathogen Dispersal

Julien Papaïx; Jeremy J. Burdon; Christian Lannou; Peter H. Thrall

Metapopulation processes are important determinants of epidemiological and evolutionary dynamics in host-pathogen systems, and are therefore central to explaining observed patterns of disease or genetic diversity. In particular, the spatial scale of interactions between pathogens and their hosts is of primary importance because migration rates of one species can affect both spatial and temporal heterogeneity of selection on the other. In this study we developed a stochastic and discrete time simulation model to specifically examine the joint effects of host and pathogen dispersal on the evolution of pathogen specialisation in a spatially explicit metapopulation. We consider a plant-pathogen system in which the host metapopulation is composed of two plant genotypes. The pathogen is dispersed by air-borne spores on the host metapopulation. The pathogen population is characterised by a single life-history trait under selection, the infection efficacy. We found that restricted host dispersal can lead to high amount of pathogen diversity and that the extent of pathogen specialisation varied according to the spatial scale of host-pathogen dispersal. We also discuss the role of population asynchrony in determining pathogen evolutionary outcomes.


Phytopathology | 2016

Addressing the Challenges of Pathogen Evolution on the World’s Arable Crops

Jeremy J. Burdon; Jiasui Zhan; Luke G. Barrett; Julien Papaïx; Peter H. Thrall

Advances in genomic and molecular technologies coupled with an increasing understanding of the fine structure of many resistance and infectivity genes, have opened up a new era of hope in controlling the many plant pathogens that continue to be a major source of loss in arable crops. Some new approaches are under consideration including the use of nonhost resistance and the targeting of critical developmental constraints. However, the major thrust of these genomic and molecular approaches is to enhance the identification of resistance genes, to increase their ease of manipulation through marker and gene editing technologies and to lock a range of resistance genes together in simply manipulable resistance gene cassettes. All these approaches essentially continue a strategy that assumes the ability to construct genetic-based resistance barriers that are insurmountable to target pathogens. Here we show how the recent advances in knowledge and marker technologies can be used to generate more durable disease resistance strategies that are based on broad evolutionary principles aimed at presenting pathogens with a shifting, landscape of fluctuating directional selection.


Methods in Ecology and Evolution | 2016

Mapping Averaged Pairwise Information (MAPI): a new exploratory tool to uncover spatial structure.

Sylvain Piry; Marie Pierre Chapuis; Bertrand Gauffre; Julien Papaïx; Astrid Cruaud; Karine Berthier

1. Visualisation of spatial networks based on pairwise metrics such as (dis)similarity coefficients provides direct information on spatial organisation of biological syste ms. However, for large networks, graphical representations are often unreadable as nodes (samples), and edges (links between sa mples) strongly overl ap. We present a new method, MAPI, allowing translation from sp atial networks to variation surfaces. 2. MAPI relies on (i) a spatial network in which samples are linked by ellipses and (ii) a grid of hexagonal cells encompassing the study area. Pairwise metric values are attributed to ellipses and averaged within the cells they intersect. The resulting surf ace of variation can be displayed as a colour map in Geographical Information System (GIS), along with other relevant layers, such as land cover. The method also allows the identification of significant discontinuities in grid cell values through a nonparametric randomisation procedure. 3. The interest of MAPI is here demonstrated in the field of spatial and landsc ape genetics. Using simulated test data sets, as well as observed data from three biological models, we show that MAPI is (i) relatively insensi tive to confound ing effects resulting fro m isolation by distance (i.e. over-structuring), (ii) efficient in detecting barriers when they are not too permeable to gene flow and, (iii) useful to explore relationships between spatial genetic patterns and landscape features. 4. MAPI is freely provided as a PostgreSQL/PostGIS data base extension allowing easy interaction with GIS or the R software and other programming languages. Although developed for spatial and landscape genetics, the method can also be useful to visualise spatial organisation from other kinds of data from which pairwise metrics can be computed.


Evolutionary Applications | 2018

Differential impact of landscape-scale strategies for crop cultivar deployment on disease dynamics, resistance durability and long-term evolutionary control

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

A multitude of resistance deployment strategies have been proposed to tackle the evolutionary potential of pathogens to overcome plant resistance. In particular, many landscape‐based strategies rely on the deployment of resistant and susceptible cultivars in an agricultural landscape as a mosaic. However, the design of such strategies is not easy as strategies targeting epidemiological or evolutionary outcomes may not be the same. Using a stochastic spatially explicit model, we studied the impact of landscape organization (as defined by the proportion of fields cultivated with a resistant cultivar and their spatial aggregation) and key pathogen life‐history traits on three measures of disease control. Our results show that short‐term epidemiological dynamics are optimized when landscapes are planted with a high proportion of the resistant cultivar in low aggregation. Importantly, the exact opposite situation is optimal for resistance durability. Finally, well‐mixed landscapes (balanced proportions with low aggregation) are optimal for long‐term evolutionary equilibrium (defined here as the level of long‐term pathogen adaptation). This work offers a perspective on the potential for contrasting effects of landscape organization on different goals of disease management and highlights the role of pathogen life history.


PLOS Computational Biology | 2018

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

Loup Rimbaud; Julien Papaïx; Jean-François Rey; Luke G. Barrett; Peter H. Thrall

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.

Collaboration


Dive into the Julien Papaïx's collaboration.

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Christian Lannou

Institut national de la recherche agronomique

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Hervé Monod

Institut national de la recherche agronomique

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Peter H. Thrall

Commonwealth Scientific and Industrial Research Organisation

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Jeremy J. Burdon

Institut national de la recherche agronomique

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Henriette Goyeau

Institut national de la recherche agronomique

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Jiasui Zhan

Fujian Agriculture and Forestry University

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Loup Rimbaud

Commonwealth Scientific and Industrial Research Organisation

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Luke G. Barrett

Commonwealth Scientific and Industrial Research Organisation

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Olivier Gimenez

Centre national de la recherche scientifique

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