Search

Your search keyword '"Frédéric Hamelin"' showing total 151 results
Start Over Author "Frédéric Hamelin"
151 results on '"Frédéric Hamelin"'

8. Improving sustainable crop protection using population genetics concepts

Author

Méline Saubin, Clémentine Louet, Lydia Bousset, Frédéric Fabre, Pascal Frey, Isabelle Fudal, Frédéric Grognard, Frédéric Hamelin, Ludovic Mailleret, Solenn Stoeckel, Suzanne Touzeau, Benjamin Petre, Fabien Halkett, Interactions Arbres-Microorganismes (IAM), Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Santé et agroécologie du vignoble (UMR SAVE), Université de Bordeaux (UB)-Institut des Sciences de la Vigne et du Vin (ISVV)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), BIOlogie et GEstion des Risques en agriculture (BIOGER), Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Biological control of artificial ecosystems (BIOCORE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut Sophia Agrobiotech (ISA), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Côte d'Azur (UCA), ANR-18-CE32-0001,Clonix2D,Les conséquences génétiques de reproduction partiellement clonale dans les populations colonisant de nouveaux territoires(2018), Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers

Subjects
Plant immunity, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], Transient dynamics, Host-pathogen coevolution, [SDV]Life Sciences [q-bio], Genetics, Citation network, Genetic drift, Resistance durability, Ecology, Evolution, Behavior and Systematics, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

accepted; International audience; Growing genetically resistant plants allows pathogen populations to be controlled and reduces the use of pesticides. However, pathogens can quickly overcome such resistance. In this context, how can we achieve sustainable crop protection? This crucial question has remained largely unanswered despite decades of intense debate and research effort. In this study, we used a bibliographic analysis to show that the research field of resistance durability has evolved into three subfields: (i) ‘plant breeding’ (generating new genetic material), (ii) ‘molecular interactions’ (exploring the molecular dialogue governing plant–pathogen interactions) and (iii) ‘epidemiology and evolution’ (explaining and forecasting of pathogen population dynamics resulting from selection pressure(s) exerted by resistant plants). We argue that this triple split of the field impedes integrated research progress and ultimately compromises the sustainable management of genetic resistance. After identifying a gap among the three subfields, we argue that the theoretical framework of population genetics could bridge this gap. Indeed, population genetics formally explains the evolution of all heritable traits, and allows genetic changes to be tracked along with variation in population dynamics. This provides an integrated view of pathogen adaptation, in particular via evolutionary–epidemiological feedbacks. In this Opinion Note, we detail examples illustrating how such a framework can better inform best practices for developing and managing genetically resistant cultivars.

Published
2023
Full Text
View/download PDF

23. Taking advantage of pathogen diversity and immune priming to minimize disease prevalence in host mixtures: a model

Author

Didier Andrivon, Ludovic Mailleret, Florence Val, Frédéric Grognard, Frédéric Hamelin, Pauline Clin, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Biological control of artificial ecosystems (BIOCORE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Côte d'Azur (UCA), Institut Sophia Agrobiotech (ISA), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Côte d'Azur (UCA), Pauline Clin is supported by a Ph.D. fellowship from the INRAE 'Plant Health and the Environment' Division and the Council of Brittany Region. Frédéric Hamelin acknowledges funding from the INRAE 'Plant Health and the Environment' Division., Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université Nice Sophia Antipolis (... - 2019) (UNS)

Subjects
0106 biological sciences, Genotype, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], Plant Immunity, Virulence, Plant Science, Priming (agriculture), Biology, 01 natural sciences, polymorphism, induced resistance, 03 medical and health sciences, Immune system, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, Prevalence, priming, Pathogen, Plant Diseases, 030304 developmental biology, 2. Zero hunger, Genetics, 0303 health sciences, avirulent, Plant disease, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy, virulence, Cultivar mixtures, Host-Pathogen Interactions, gene-for-gene, systemic acquired resistance, Disease Susceptibility, Agronomy and Crop Science, Systemic acquired resistance, 010606 plant biology & botany
Abstract

International audience; Host mixtures are a promising method for agroecological plant disease control. Plant immunity is key to the success of host mixtures against polymorphic pathogen populations. This immunity results from priming-induced cross-protection, whereby plants able to resist infection by specific pathogen genotypes become more resistant to other pathogen genotypes. Strikingly, this phenomenon was absent from mathematical models aiming at designing host mixtures. We developed a model to specifically explore how priming affects the coexistence of two pathogen genotypes in host mixtures composed of two host genotypes and how it affects disease prevalence. The main effect of priming is to reduce the coexistence region in the parameter space (due to the cross-protection) and to generate a singular mixture of resistant and susceptible hosts corresponding to the maximal reduction disease prevalence (in absence of priming, a resistant pure stand is optimal). The epidemiological advantage of host mixtures over a resistant pure stand thus appears as a direct consequence of immune priming. We also showed that there is indirect cross-protection between host genotypes in a mixture. Moreover, the optimal mix prevents the emergence of a resistance-breaking pathogen genotype. Our results highlight the importance of considering immune priming to design optimal and sustainable host mixtures.

Published
2021
Full Text
View/download PDF

40. Metacommunity dynamics and the detection of species associations in co-occurrence analyses: why patch disturbance matters

Author

Calcagno, Frédéric Hamelin, and Nicholas James Cunniffe

Subjects
Metacommunity, Extinction, Disturbance (geology), Evolutionary biology, Covariate, Co-occurrence, Biology, Spurious relationship, Spatial heterogeneity, Environmental niche modelling
Abstract

Many statistical methods attempt to detect species associations — and so infer inter-specific interactions — from species co-occurrence patterns. Habitat heterogeneity and out-of-equilibrium colonization histories are well recognized as potentially causing species associations, even when interactions are absent. The potential for patch disturbance, a classical component of metacommunity dynamics, to also drive spurious species associations has however been over-looked. Using a new general metacommunity model, we derive mathematical predictions regarding how patch disturbance would affect the patterns of species 26 associations detected in null co-occurrence matrices. We also conduct numerical simulations to test our predictions and to compare the performance of several widespread statistical methods, including direct tests of pairwise independence, matrix permutation approaches and joint species distribution modelling. We show how classical metacommunity dynamics can produce statistical associations, both positive and negative, even when species do not interact, when there is no habitat heterogeneity, and at equilibrium. This occurs as soon as there is some rate of patch disturbance (i.e. simultaneous extinction of several species in a patch) and/or a finite life-span of patches, a common feature of a broad range of plant, animal or microbial systems. Patch disturbance can compromise species co-occurrence analyses and cause the artefactual detection of species associations if not taken into account. Including patch age (i.e. the time since the last patch disturbance event) as a covariate in a joint species distribution model can resolve the artefact. However, this requires additional data that often are not available in practice. We argue that the consequences of patch disturbance should not be underestimated when analysing species distribution patterns in metacommunity-like systems.

Published
2021
Full Text
View/download PDF

41. Optimal Control of Plant Disease Epidemics with Clean Seed Usage

Author

B Bowen, P Bernhard, Frédéric Hamelin, Vrushali A. Bokil, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Department of Mathematics [Corvallis, Oregon], Oregon State University (OSU), Biological control of artificial ecosystems (BIOCORE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Thomas Jefferson Fund, 2018, FACE Foundation, AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'océanographie de Villefranche (LOV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
0301 basic medicine, Mathematical optimization, General Mathematics, Immunology, Bio-economical model, Vitro-plants, Models, Biological, Zea mays, General Biochemistry, Genetics and Molecular Biology, Pontryagin's minimum principle, Plant Viruses, 03 medical and health sciences, 0302 clinical medicine, Maximum principle, Certified seeds, General Environmental Science, Mathematics, Simple (philosophy), Plant Diseases, 2. Zero hunger, Pharmacology, Tropical epidemiology, Virus-free seeds, General Neuroscience, Sowing, food and beverages, Subsidy, Africa, Eastern, Optimal control, Plant disease, Specific Pathogen-Free Organisms, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy, Static optimization, 030104 developmental biology, Computational Theory and Mathematics, 030220 oncology & carcinogenesis, Seeds, Vertical transmission, General Agricultural and Biological Sciences
Abstract

International audience; The distribution and use of pathogen-free planting material (“clean seeds”) is a promising method to control plant diseases in developing countries. We address the question of minimizing disease prevalence in plants through the optimal usage of clean seeds. We consider the simplest possible S–I model together with a simple economic criterion to be maximized. The static optimization problem shows a diversity of possible outcomes depending on economical and epidemiological parameters. We derive a simple condition showing to what extent subsidizing clean seeds relative to the epidemiological features of the disease may help eradicate or control the disease. Then we consider dynamic optimal control and Pontryagin’s maximum principle to study the optimal usage of clean seeds to control the disease. The dynamical results are comparable to the static ones and are even simpler in some sense. In particular, the condition on the critical subsidy rate that makes clean seed usage economically viable is unchanged from the static optimization case. We discuss how these results may apply to the control of maize lethal necrosis in East-Africa.

Published
2021
Full Text
View/download PDF

42. Identifiability and Observability in Epidemiological Models : A Primer

Author

Nik Cunniffe, Frédéric Hamelin, Abderrahman Iggidr, Alain Rapaport, Gauthier Sallet, Nik Cunniffe, Frédéric Hamelin, Abderrahman Iggidr, Alain Rapaport, and Gauthier Sallet

Subjects
Epidemiology--Mathematical models
Abstract

This book introduces the concepts of identifiability and observability in mathematical epidemiology, as well as those of observers'constructions. It first exposes and illustrates on several examples the mathematical definitions and properties of observability and identifiability. A chapter is dedicated to the well-known Kermack McKendrick model, for which the complete analysis of identifiability and observability is not available in the literature. Then, several techniques of observer constructions, in view of online estimation of state and parameters, are presented and deployed on several models. New developments relevant for applications in epidemiology are also given. Finally, practical considerations are discussed with data and numerical simulations related to models previously analysed in the book. The book will be appealing to epidemiological modellers and mathematicians working on models in epidemiology.This book contributes to Sustainable Development Goal 3 (SDG3): Good Health and Well Being.

Published
2024

43. Separate seasons of infection and reproduction can lead to multi-year population cycles

Author

Linda J. S. Allen, Frank M. Hilker, Frédéric Hamelin, T.A. Sun, Osnabrück University, Texas Tech University [Lubbock] (TTU), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Alexander von Humboldt-Stiftung, Niedersächsisches Ministerium für Wissenschaft und Kultur, National Science Foundation, Universität Osnabrück - Osnabrück University, Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
0301 basic medicine, Statistics and Probability, SI model, Difference equations, [SDV]Life Sciences [q-bio], Population Dynamics, Zoology, Biology, Models, Biological, General Biochemistry, Genetics and Molecular Biology, law.invention, 03 medical and health sciences, 0302 clinical medicine, law, Neimark-Sacker bifurcation, Quasi-periodic oscillation, General Immunology and Microbiology, Reproductive success, Host (biology), Reproduction, Applied Mathematics, Pathogen-driven outbreak, General Medicine, Host–pathogen dynamics, Fecundity, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy, Seasonal population dynamics, Fertility, 030104 developmental biology, Transmission (mechanics), Density dependence, Modeling and Simulation, Delayed density dependence, Population cycle, Seasons, General Agricultural and Biological Sciences, Epidemic model, 030217 neurology & neurosurgery
Abstract

International audience; Many host-pathogen systems are characterized by a temporal order of disease transmission and host reproduction. For example, this can be due to pathogens infecting certain life cycle stages of insect hosts; transmission occurring during the aggregation of migratory birds; or plant diseases spreading between planting seasons. We develop a simple discrete-time epidemic model with density-dependent transmission and disease affecting host fecundity and survival. The model shows sustained multi-annual cycles in host population abundance and disease prevalence, both in the presence and absence of density dependence in host reproduction, for large horizontal transmissibility, imperfect vertical transmission, high virulence, and high reproductive capability. The multi-annual cycles emerge as invariant curves in a Neimark-Sacker bifurcation. They are caused by a carry-over effect, because the reproductive fitness of an individual can be reduced by virulent effects due to infection in an earlier season. As the infection process is density-dependent but shows an effect only in a later season, this produces delayed density dependence typical for second-order oscillations. The temporal separation between the infection and reproduction season is crucial in driving the cycles; if these processes occur simultaneously as in differential equation models, there are no sustained oscillations. Our model highlights the destabilizing effects of inter-seasonal feedbacks and is one of the simplest epidemic models that can generate population cycles.

Published
2020
Full Text
View/download PDF

44. Modelling Vector Transmission and Epidemiology of Co-Infecting Plant Viruses

Author

Nik J. Cunniffe, Vrushali A. Bokil, Frédéric Hamelin, Linda J. S. Allen, Michael J. Jeger, Frank M. Hilker, Hamelin, Frédéric M. [0000-0003-2653-699X], Apollo - University of Cambridge Repository, Department of Mathematics and Statistics [Texas Tech], Texas Tech University [Lubbock] (TTU), Oregon State University (OSU), Department of Plant Sciences (Cambridge, UK), University of Cambridge [UK] (CAM), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute of Environmental Systems Research [Osnabrück], Osnabrück University, Centre for Environmental Policy, Imperial College London, Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Universität Osnabrück - Osnabrück University, and Hamelin, Frédéric M [0000-0003-2653-699X]

Subjects
0106 biological sciences, 0301 basic medicine, Disease status, vector transmission, Biology, Disease Vectors, 01 natural sciences, Models, Biological, Virus, Article, Plant Viruses, 03 medical and health sciences, co-infection, Virology, Plant virus, Animals, Agricultural crops, Virus classification, Plant Diseases, 2. Zero hunger, Coinfection, food and beverages, Plant community, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy, 030104 developmental biology, Infectious Diseases, Evolutionary biology, Vector (epidemiology), invasion reproduction number, Basic reproduction number, Algorithms, 010606 plant biology & botany, 0605 Microbiology
Abstract

International audience; Co-infection of plant hosts by two or more viruses is common in agricultural crops and natural plant communities. A variety of models have been used to investigate the dynamics of co-infection which track only the disease status of infected and co-infected plants, and which do not explicitly track the density of inoculative vectors. Much less attention has been paid to the role of vector transmission in co-infection, that is, acquisition and inoculation and their synergistic and antagonistic interactions. In this investigation, a general epidemiological model is formulated for one vector species and one plant species with potential co-infection in the host plant by two viruses. The basic reproduction number provides conditions for successful invasion of a single virus. We derive a new invasion threshold which provides conditions for successful invasion of a second virus. These two thresholds highlight some key epidemiological parameters important in vector transmission. To illustrate the flexibility of our model, we examine numerically two special cases of viral invasion. In the first case, one virus species depends on an autonomous virus for its successful transmission and in the second case, both viruses are unable to invade alone but can co-infect the host plant when prevalence is high.

Published
2019
Full Text
View/download PDF

45. Coinfections by noninteracting pathogens are not independent and require new tests of interaction

Author

Vrushali A. Bokil, Frédéric Hamelin, Frank H. Hilker, Michael Jeger, Carrie A. Manore, Alison G. Power, Linda J. S. Allen, Nik J. Cunniffee, Megan A. Rúa, Louis J. Gross, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Department of Mathematics and Statistics [Texas Tech], Texas Tech University [Lubbock] (TTU), Oregon State University (OSU), National Institute for Mathematical and Biological Synthesis (NIMBioS), The University of Tennessee [Knoxville], Institute of Environmental Systems Research [Osnabrück], Osnabrück University, Imperial College London, Theoretical Biology and Biophysics, Los Alamos National Laboratory (LANL), CORNELL UNIVERSITY ITHACA USA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), WRIGHT STATE UNIVERSITY DEPARTMENT OF BIOLOGICAL SCIENCES DAYTON USA, Department of Plant Sciences (Cambridge, UK), University of Cambridge [UK] (CAM), Cornell University [New York], Wright State University, DBI-1300426, National Science Foundation of Sri Lanka, Hamelin, Frédéric M [0000-0003-2653-699X], Power, Alison G [0000-0002-2442-0953], Rúa, Megan A [0000-0002-2883-2795], Cunniffe, Nik J [0000-0002-3533-8672], Apollo - University of Cambridge Repository, Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, and Universität Osnabrück - Osnabrück University

Subjects
0301 basic medicine, Plasmodium, Epidemics/statistics & numerical data, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, Plant Science, Pathogenesis, Pathology and Laboratory Medicine, 0302 clinical medicine, Models, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Medicine and Health Sciences, Prevalence, Biology (General), 11 Medical and Health Sciences, ComputingMilieux_MISCELLANEOUS, Statistical Data, Coinfection, General Neuroscience, Statistics, Infectious Diseases, Medical Microbiology, Viral Pathogens, Physical Sciences, Viruses, Host-Pathogen Interactions, epidemiology, Pathogens, General Agricultural and Biological Sciences, Research Article, QH301-705.5, Host-Pathogen Interactions/*physiology, Plant Pathogens, Computational biology, Biology, Positive correlation, Infections, Microbiology, Models, Biological, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 07 Agricultural and Veterinary Sciences, Parasite Groups, Parasitic Diseases, Animals, Humans, Epidemics, Microbial Pathogens, [SDV.BA.MVSA]Life Sciences [q-bio]/Animal biology/Veterinary medicine and animal Health, General Immunology and Microbiology, Organisms, Biology and Life Sciences, 06 Biological Sciences, Plant Pathology, Tropical Diseases, Biological, Malaria, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy, Infections/*epidemiology, 030104 developmental biology, Cross-Sectional Studies, Co-Infections, Parasitology, Apicomplexa, 030217 neurology & neurosurgery, Mathematics, Co infection, Developmental Biology
Abstract

If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts should be the product of the individual prevalences. Independence consequently underpins the wide range of methods for detecting pathogen interactions from cross-sectional survey data. However, the very simplest of epidemiological models challenge the underlying assumption of statistical independence. Even if pathogens do not interact, death of coinfected hosts causes net prevalences of individual pathogens to decrease simultaneously. The induced positive correlation between prevalences means the proportion of coinfected hosts is expected to be higher than multiplication would suggest. By modelling the dynamics of multiple noninteracting pathogens causing chronic infections, we develop a pair of novel tests of interaction that properly account for nonindependence between pathogens causing lifelong infection. Our tests allow us to reinterpret data from previous studies including pathogens of humans, plants, and animals. Our work demonstrates how methods to identify interactions between pathogens can be updated using simple epidemic models., If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts can be obtained by simply multiplying the individual prevalences. However, even simple epidemiological models show this to be untrue. This study develops new tests for interaction between pathogens that account for this surprising lack of statistical independence.

Published
2019
Full Text
View/download PDF

46. The ecological and evolutionary trajectory of oak powdery mildew in Europe

Author

Benoit Marçais, Marie-Laure Desprez-Loustau, Frédéric Hamelin, Biodiversité, Gènes & Communautés (BioGeCo), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Interactions Arbres-Microorganismes (IAM), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Université de Lorraine (UL)-Institut National de la Recherche Agronomique (INRA), and Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST

Subjects
0106 biological sciences, education.field_of_study, biology, Resistance (ecology), Host (biology), Ecology, Population, Context (language use), 15. Life on land, biology.organism_classification, 010603 evolutionary biology, 01 natural sciences, Life history theory, Pathosystem, 13. Climate action, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, education, Erysiphe, Powdery mildew, 010606 plant biology & botany
Abstract

International audience; Oak powdery mildew in Europe is an example of disease in a wild perennial plant which has displayed dramatic changes over the course of a century; from typical invasion dynamics after pathogen introduction into a new area, characterized by severe damage, to a new equilibrium with moderate damage. Several non-mutually exclusive hypotheses could account for this, including pathogen evolution towards lower virulence, a reciprocal increase in oak population resistance, and both environmental biotic (phyllosphere microbes) and abiotic (climate) factors. We show that understanding the pathosystem requires the accounting of both seasonality (i.e. succession of epidemic and inter-epidemic phases linked to availability of susceptible leaves) and the occurrence of a pathogen complex with several cryptic fungal species differing in their life history traits. Observational data suggests that the severity of annual epidemics is linked to inter-annual pathogen transmission, including winter survival and the infection success of the primary inoculum in spring. Climate-driven phenological synchrony between host and pathogen in spring thus appears to be a critical factor. Several cryptic Erysiphe species are associated with the disease and co-occur at multiple spatial scales from individual leaves to continent. A semi-discrete model combining a SIR model in the epidemic phase and a discrete-time model between years, based on a within season (intra-epidemic)-between season (inter-epidemic) transmission trade-off, adequately describes seasonality and the coexistence of pathogen species. We discuss the refinement of this model, through the introduction of age classes in the host population in particular, and other modelling approaches for the evolution of pathogen virulence and host resistance in a context of changing environment.

Published
2019
Full Text
View/download PDF

47. Assessing the effects of quantitative host resistance on the life-history traits of sporulating parasites with growing lesions

Author

Melen Leclerc, Frédéric Hamelin, Didier Andrivon, Julie A. J. Clément, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Plant Health and Environment Division of the Institut National de la Recherche Agronomique (INRA), UE, AGROCAMPUS OUEST-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Recherche Agronomique (INRA), and Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST

Subjects
0106 biological sciences, Phytophthora infestans, Biology, Models, Biological, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, Life history theory, 03 medical and health sciences, Life History Traits, Pathogen, Plant Diseases, Solanum tuberosum, 030304 developmental biology, General Environmental Science, Genetics, 0303 health sciences, Ecology, General Immunology and Microbiology, Resistance (ecology), General Medicine, aggressiveness, biology.organism_classification, Phenotype, lesion model, Spore, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy, sporulation dynamics, epidemiological modelling, Host-Pathogen Interactions, Evolutionary ecology, General Agricultural and Biological Sciences, potato late blight, Function (biology), 010606 plant biology & botany
Abstract

Assessing life-history traits of parasites on resistant hosts is crucial in evolutionary ecology. In the particular case of sporulating pathogens with growing lesions, phenotyping is difficult because one needs to disentangle properly pathogen spread from sporulation. By considering Phytophthora infestans on potato, we use mathematical modelling to tackle this issue and refine the assessment of pathogen response to quantitative host resistance. We elaborate a parsimonious leaf-scale model by convolving a lesion growth model and a sporulation function, after a latency period. This model is fitted to data obtained on two isolates inoculated on three cultivars with contrasted resistance level. Our results confirm a significant host–pathogen interaction on the various estimated traits, and a reduction of both pathogen spread and spore production, induced by host resistance. Most interestingly, we highlight that quantitative resistance also changes the sporulation function, the mode of which is significantly time-lagged. This alteration of the infectious period distribution on resistant hosts may have strong impacts on the dynamics of parasite populations, and should be considered when assessing the durability of disease control tactics based on plant resistance management. This inter-disciplinary work also supports the relevance of mechanistic models for analysing phenotypic data of plant–pathogen interactions.

Published
2019
Full Text
View/download PDF

49. How optimal foragers should respond to habitat changes? On the consequences of habitat conversion

Author

Vincent Calcagno, Frédéric Hamelin, Ludovic Mailleret, Frédéric Grognard, Institut Sophia Agrobiotech [Sophia Antipolis] (ISA), Institut National de la Recherche Agronomique (INRA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), AGROCAMPUS OUEST, Biological control of artificial ecosystems (BIOCORE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Côte d'Azur (UCA), Institut Sophia Agrobiotech (ISA), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Côte d'Azur (UCA), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Institut National de la Recherche Agronomique (INRA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), AGROCAMPUS OUEST-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Recherche Agronomique (INRA), Université Nice Sophia Antipolis (1965 - 2019) (UNS), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, 0303 health sciences, Ecology, Ecological Modeling, fungi, Marginal value theorem, Context (language use), 15. Life on land, Biology, 010603 evolutionary biology, 01 natural sciences, Regression, 010601 ecology, 03 medical and health sciences, [MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], Habitat, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, 030304 developmental biology
Abstract

A preprint peer-reviewed and recommended by Peer Community In Ecology: https://doi.org/10.1101/273557; The Marginal Value Theorem (MVT) provides a framework to predict how habitat modifications related to the distribution of resources over patches should impact the realized fitness of individuals and their optimal rate of movement (or patch residence times) across the habitat. Most MVT theory has focused on the consequences of changing the shape of the gain functions in some patches, describing for instance patch enrichment. However an alternative form of habitat modification is habitat conversion, whereby patches are converted from one existing type to another (e.g. closed habitat to open habitat). In such a case the set of gain functions existing in the habitat does not change, only their relative frequencies does. This has received comparatively very little attention in the context of the MVT. Here we analyze mathematically the consequences of habitat conversion under the MVT. We study how realized fitness and the average rate of movement should respond to changes in the frequency distribution of patch-types, and how they should covary. We further compare the response of optimal and non-plastic foragers. We find that the initial pattern of patch-exploitation in a habitat, characterized by the regression slope of patch yields over residence times, can help predict the qualitative responses of fitness and movement rate following habitat conversion. We also find that for some habitat conversion patterns, optimal and non-plastic foragers exhibit qualitatively different responses, and that adaptive foragers can have opposite responses in the early and late phases following habitat conversion. We suggest taking into account behavioral responses may help better understand the ecological consequences of habitat conversion.

Published
2018
Full Text
View/download PDF

50. Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya

Author

Michael J. Jeger, Karen A. Garrett, Vrushali A. Bokil, Alison G. Power, Nik J. Cunniffe, Carrie A. Manore, Megan A. Rúa, Cheryl J. Briggs, Frank M. Hilker, Frédéric Hamelin, Zhilan Feng, Linda J. S. Allen, Margaret G. Redinbaugh, Louis J. Gross, Osnabrück University, TexasTech University, Oregon State University (OSU), University of California [Santa Barbara] (UCSB), University of California, Purdue University, University of Tennessee, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Imperial College London, Los Alamos National Laboratory (LANL), Cornell University [New York], Ohio State University [Columbus] (OSU), Wright State University, University of Cambridge [UK] (CAM), Cunniffe, Nik [0000-0002-3533-8672], Apollo - University of Cambridge Repository, Universität Osnabrück - Osnabrück University, University of California [Santa Barbara] (UC Santa Barbara), University of California (UC), Purdue University [West Lafayette], and Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST

Subjects
CORN, 0106 biological sciences, 0301 basic medicine, Potyvirus, 1ST REPORT, Plant Science, 01 natural sciences, Roguing, Tombusviridae, CHLOROTIC-MOTTLE-VIRUS, SUB-SAHARAN AFRICA, 2. Zero hunger, Food security, Coinfection, food and beverages, Agriculture, Seeds, Life Sciences & Biomedicine, 0605 Microbiology, Plant Biology & Botany, 0607 Plant Biology, Biology, Insect Control, Zea mays, FOOD CROPS, SUGARCANE-MOSAIC-VIRUS, 03 medical and health sciences, Disease management (agriculture), medicine, Machlomovirus, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, DISEASE-CONTROL, Plant Diseases, PLANT-PATHOGENS, Science & Technology, business.industry, Plant Sciences, SEED-TRANSMISSION, MACHLOMOVIRUS, Crop rotation, Models, Theoretical, medicine.disease, biology.organism_classification, Kenya, Biotechnology, 030104 developmental biology, business, Agronomy and Crop Science, Cropping, 0703 Crop And Pasture Production, 010606 plant biology & botany
Abstract

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with large holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Published
2017
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results