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545 results on '"Estoup, Arnaud"'

1. How do invasion syndromes evolve? An experimental evolution approach using the ladybird Harmonia axyridis

Author

Foucaud, Julien, Hufbauer, Ruth A., Ravigné, Virginie, Olazcuaga, Laure, Loiseau, Anne, Ausset, Aurélien, Wang, Su, Zang, Lian-Sheng, Leménager, Nicolas, Tayeh, Ashraf, Weyna, Arthur, Gneux, Pauline, Bonnet, Elise, Dreuilhe, Vincent, Poutout, Bastien, Estoup, Arnaud, and Facon, Benoît

Subjects
Archaeology, CC1-960, Science
Abstract

Experiments comparing native to introduced populations or distinct introduced populations to each other show that phenotypic evolution is common and often involves a suit of interacting phenotypic traits. We define such sets of traits that evolve in concert and contribute to the success of invasive populations as an invasion syndrome. The invasive Harlequin ladybird Harmonia axyridis displays such an invasion syndrome with, for instance, females from invasive populations being larger and heavier than individuals from native populations, allocating more resources to reproduction, and spreading reproduction over a longer lifespan. Invasion syndromes could emerge due to selection acting jointly and directly on a multitude of traits, or due to selection on one or a few key traits that drive correlated indirect responses in other traits. Here, we investigated the degree to which the H. axyridis invasion syndrome would emerge in response to artificial selection on either female body mass or on age at first reproduction, two traits involved in their invasion syndrome. To further explore the interaction between environmental context and evolutionary change in molding the phenotypic response, we phenotyped the individuals from the selection experiments in two environments, one with abundant food resources and one with limited resources. The two artificial selection experiments show that the number of traits showing a correlated response depends upon the trait undergoing direct selection. Artificial selection on female body mass resulted in few correlated responses and hence poorly reproduced the invasion syndrome. In contrast, artificial selection on age at first reproduction resulted in more widespread phenotypic changes, which nevertheless corresponded only partly to the invasion syndrome. The artificial selection experiments also revealed a large impact of diet on the traits, with effects dependent on the trait considered and the selection regime. Overall, our results indicate that direct selection on multiple traits was likely necessary in the evolution of the H. axyridis invasion syndrome. Furthermore, they show the strength of using artificial selection to identify the traits that are correlated in different selective contexts, which represents a crucial first step in understanding the evolution of complex phenotypic patterns, including invasion syndromes.

Published
2021
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2. ABC random forests for Bayesian parameter inference

Author

Raynal, Louis, Marin, Jean-Michel, Pudlo, Pierre, Ribatet, Mathieu, Robert, Christian P., and Estoup, Arnaud

Subjects
Statistics - Methodology, Statistics - Computation, Statistics - Machine Learning
Abstract

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100036). Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector of informative statistics summarizing raw data. Furthermore, in almost all existing implementations, the tolerance level that separates acceptance from rejection of simulated parameter values needs to be calibrated. We propose to conduct likelihood-free Bayesian inferences about parameters with no prior selection of the relevant components of the summary statistics and bypassing the derivation of the associated tolerance level. The approach relies on the random forest methodology of Breiman (2001) applied in a (non parametric) regression setting. We advocate the derivation of a new random forest for each component of the parameter vector of interest. When compared with earlier ABC solutions, this method offers significant gains in terms of robustness to the choice of the summary statistics, does not depend on any type of tolerance level, and is a good trade-off in term of quality of point estimator precision and credible interval estimations for a given computing time. We illustrate the performance of our methodological proposal and compare it with earlier ABC methods on a Normal toy example and a population genetics example dealing with human population evolution. All methods designed here have been incorporated in the R package abcrf (version 1.7) available on CRAN., Comment: Main text: 24 pages, 6 figures Supplementary Information: 14 pages, 5 figures

Published
2016
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3. Deciphering the Routes of invasion of Drosophila suzukii by Means of ABC Random Forest

Author

Fraimout, Antoine, Debat, Vincent, Fellous, Simon, Hufbauer, Ruth A, Foucaud, Julien, Pudlo, Pierre, Marin, Jean-Michel, Price, Donald K, Cattel, Julien, Chen, Xiao, Deprá, Marindia, Duyck, Pierre François, Guedot, Christelle, Kenis, Marc, Kimura, Masahito T, Loeb, Gregory, Loiseau, Anne, Martinez-Sañudo, Isabel, Pascual, Marta, Richmond, Maxi Polihronakis, Shearer, Peter, Singh, Nadia, Tamura, Koichiro, Xuéreb, Anne, Zhang, Jinping, and Estoup, Arnaud

Subjects
Life on Land, Animals, Bayes Theorem, China, Computer Simulation, Drosophila, Genetic Variation, Genetics, Population, Genotype, Hawaii, Introduced Species, Japan, Microsatellite Repeats, Models, Genetic, North America, Phylogeography, Drosophila suzukii, invasion routes, random forest, approximate Bayesian computation, population genetics, Biochemistry and Cell Biology, Evolutionary Biology, Genetics
Abstract

Deciphering invasion routes from molecular data is crucial to understanding biological invasions, including identifying bottlenecks in population size and admixture among distinct populations. Here, we unravel the invasion routes of the invasive pest Drosophila suzukii using a multi-locus microsatellite dataset (25 loci on 23 worldwide sampling locations). To do this, we use approximate Bayesian computation (ABC), which has improved the reconstruction of invasion routes, but can be computationally expensive. We use our study to illustrate the use of a new, more efficient, ABC method, ABC random forest (ABC-RF) and compare it to a standard ABC method (ABC-LDA). We find that Japan emerges as the most probable source of the earliest recorded invasion into Hawaii. Southeast China and Hawaii together are the most probable sources of populations in western North America, which then in turn served as sources for those in eastern North America. European populations are genetically more homogeneous than North American populations, and their most probable source is northeast China, with evidence of limited gene flow from the eastern US as well. All introduced populations passed through bottlenecks, and analyses reveal five distinct admixture events. These findings can inform hypotheses concerning how this species evolved between different and independent source and invasive populations. Methodological comparisons indicate that ABC-RF and ABC-LDA show concordant results if ABC-LDA is based on a large number of simulated datasets but that ABC-RF out-performs ABC-LDA when using a comparable and more manageable number of simulated datasets, especially when analyzing complex introduction scenarios.

Published
2017

4. Building integrated plant health surveillance: A proactive research agenda for anticipating and mitigating disease and pest emergence

Author

Soubeyrand, Samuel, Estoup, Arnaud, Cruaud, Astrid, Malembic-Maher, S., Meynard, Charles, Ravigne, Virginie, Barbier, M., Barrès, Benoit, Berthier, Karine, Boitard, S., Dallot, Sylvie, Gaba, Sabrina, Grosdidier, Marie, Hannachi, Mourad, Jacques, Marie Agnès, Leclerc, M., Lucas, P., Martinetti, D., Mougel, Christophe, Robert, Corinne, Roques, Alain, Rossi, Jean-Pierre, Suffert, Frédéric, Abad, Pierre, Auger-Rozenberg, M.A., Ay, Jean-Sauveur, Bardin, Marc, Bernard, H., Bohan, David A., Candresse, Thierry, Castagnone-Sereno, Philippe, Danchin, Etienne G. J., Delmas, Chloé E. L., Ezanno, Pauline, Fabre, Frédéric, Facon, Benoît, Gabriel, E., Gaudin, Jacqueline, Gauffre, Bertrand, Gautier, Mathieu, Guinat, Claire, Lavigne, Claire, Lemaire, Olivier, Martinez, C., Michel, L., Moury, Benoit, Nam, Kiwoong, Nédellec, Claire, Ogliastro, Mylène, Papaïx, Julien, Parisey, N., Poggi, Sylvain, Radici, A., Rasplus, Jean-Yves, Reboud, Xavier, Robin, C., Roche, Mathieu, Rusch, Adrien, Sauvion, Nicolas, Streito, Jean-Claude, Verdin, Eric, Walker, Anne Sophie, Xuereb, Anne, Thébaud, Gaël, Morris, Cindy E., Soubeyrand, Samuel, Estoup, Arnaud, Cruaud, Astrid, Malembic-Maher, S., Meynard, Charles, Ravigne, Virginie, Barbier, M., Barrès, Benoit, Berthier, Karine, Boitard, S., Dallot, Sylvie, Gaba, Sabrina, Grosdidier, Marie, Hannachi, Mourad, Jacques, Marie Agnès, Leclerc, M., Lucas, P., Martinetti, D., Mougel, Christophe, Robert, Corinne, Roques, Alain, Rossi, Jean-Pierre, Suffert, Frédéric, Abad, Pierre, Auger-Rozenberg, M.A., Ay, Jean-Sauveur, Bardin, Marc, Bernard, H., Bohan, David A., Candresse, Thierry, Castagnone-Sereno, Philippe, Danchin, Etienne G. J., Delmas, Chloé E. L., Ezanno, Pauline, Fabre, Frédéric, Facon, Benoît, Gabriel, E., Gaudin, Jacqueline, Gauffre, Bertrand, Gautier, Mathieu, Guinat, Claire, Lavigne, Claire, Lemaire, Olivier, Martinez, C., Michel, L., Moury, Benoit, Nam, Kiwoong, Nédellec, Claire, Ogliastro, Mylène, Papaïx, Julien, Parisey, N., Poggi, Sylvain, Radici, A., Rasplus, Jean-Yves, Reboud, Xavier, Robin, C., Roche, Mathieu, Rusch, Adrien, Sauvion, Nicolas, Streito, Jean-Claude, Verdin, Eric, Walker, Anne Sophie, Xuereb, Anne, Thébaud, Gaël, and Morris, Cindy E.

Abstract

In an era marked by rapid global changes, the reinforcement and modernization of plant health surveillance systems have become imperative. Sixty-five scientists present here a research agenda for an enhanced and modernized plant health surveillance to anticipate and mitigate disease and pest emergence. Our approach integrates a wide range of scientific fields (from life, social, physical and engineering sciences) and identifies the key knowledge gaps, focusing on anticipation, risk assessment, early detection, and multi-actor collaboration. The research directions we propose are organized around four complementary thematic axes. The first axis is the anticipation of pest emergence, encompassing innovative forecasting, adaptive potential, and the effects of climatic and cropping system changes. The second axis addresses the use of versatile broad-spectrum surveillance tools, including molecular or imaging diagnostics supported by artificial intelligence, and monitoring generic matrices such as air and water. The third axis focuses on surveillance of known pests from new perspectives, i.e., using novel approaches to detect known species but also anticipating and detecting, within a species, the populations or genotypes that pose a higher risk. The fourth axis advocates the management of plant health as a commons through the establishment of multi-actor and cooperative surveillance systems for long-term data-driven alert systems and information dissemination. We stress the importance of integrating data and information from multiple sources through open science databases and metadata, alongside developing methods for interpolating and extrapolating incomplete data. Finally, we advocate an Integrated Health Surveillance approach in the One Health context, favoring tailored and versatile solutions to plant health problems and recognizing the interconnected risks to the health of plants, humans, animals and the environment, including food insecurity, pesticide residues, e

Published
2024

5. Likelihood-free Model Choice

Author

Marin, Jean-Michel, Pudlo, Pierre, Estoup, Arnaud, and Robert, Christian P.

Subjects
Statistics - Methodology, Statistics - Computation, Statistics - Machine Learning
Abstract

This document is an invited chapter covering the specificities of ABC model choice, intended for the incoming Handbook of ABC by Sisson, Fan, and Beaumont (2017). Beyond exposing the potential pitfalls of ABC based posterior probabilities, the review emphasizes mostly the solution proposed by Pudlo et al. (2016) on the use of random forests for aggregating summary statistics and and for estimating the posterior probability of the most likely model via a secondary random fores., Comment: 21 pages, 9 figures, 2 tables

Published
2015

6. Reliable ABC model choice via random forests

Author

Pudlo, Pierre, Marin, Jean-Michel, Estoup, Arnaud, Cornuet, Jean-Marie, Gautier, Mathieu, and Robert, Christian P.

Subjects
Statistics - Machine Learning, Quantitative Biology - Populations and Evolution, Statistics - Computation, Statistics - Methodology
Abstract

Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques. We propose a novel approach based on a machine learning tool named random forests to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with random forests and postponing the approximation of the posterior probability of the predicted MAP for a second stage also relying on random forests. Compared with earlier implementations of ABC model choice, the ABC random forest approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least fifty), and (iv) it includes an approximation of the posterior probability of the selected model. The call to random forests will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets. The proposed methodologies are implemented in the R package abcrf available on the CRAN., Comment: 39 pages, 15 figures, 6 tables

Published
2014

7. Inferring population history with DIYABC: a user-friendly approach to Approximate Bayesian Computation

Author

Cornuet, Jean-Marie, Santos, Filipe, Beaumont, Mark A., Robert, Christian P., Marin, Jean-Michel, Balding, David J., Guillemaud, Thomas, and Estoup, Arnaud

Subjects
Quantitative Biology - Populations and Evolution
Abstract

Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract this information (at least partially) but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIYABC) for inference based on Approximate Bayesian Computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and stepwise population size changes. DIYABC can be used to compare competing scenarios, estimate parameters for one or more scenarios, and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real data set, both with complex evolutionary scenarios, illustrates the main possibilities of DIYABC, Comment: submitted

Published
2008
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19. Rapid and transient evolution of local adaptation to seasonal host fruits in an invasive pest fly

Author

Olazcuaga, Laure, primary, Foucaud, Julien, additional, Deschamps, Candice, additional, Loiseau, Anne, additional, Claret, Jean-Loup, additional, Vedovato, Romain, additional, Guilhot, Robin, additional, Sévely, Cyril, additional, Gautier, Mathieu, additional, Hufbauer, Ruth A., additional, Rode, Nicolas O., additional, and Estoup, Arnaud, additional

Published
2022
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22. The harlequin ladybird, Harmonia axyridis: global perspectives on invasion history and ecology

Author

Roy, Helen E., Brown, Peter M. J., Adriaens, Tim, Berkvens, Nick, Borges, Isabel, Clusella-Trullas, Susana, Comont, Richard F., De Clercq, Patrick, Eschen, Rene, Estoup, Arnaud, Evans, Edward W., Facon, Benoit, Gardiner, Mary M., Gil, Artur, Grez, Audrey A., Guillemaud, Thomas, Haelewaters, Danny, Herz, Annette, Honek, Alois, Howe, Andy G., Hui, Cang, Hutchison, William D., Kenis, Marc, Koch, Robert L., Kulfan, Jan, Lawson Handley, Lori, Lombaert, Eric, Loomans, Antoon, Losey, John, Lukashuk, Alexander O., Maes, Dirk, Magro, Alexandra, Murray, Katie M., Martin, Gilles San, Martinkova, Zdenka, Minnaar, Ingrid A., Nedved, Oldřich, Orlova-Bienkowskaja, Marina J., Osawa, Naoya, Rabitsch, Wolfgang, Ravn, Hans Peter, Rondoni, Gabriele, Rorke, Steph L., Ryndevich, Sergey K., Saethre, May-Guri, Sloggett, John J., Soares, Antonio Onofre, Stals, Riaan, Tinsley, Matthew C., Vandereycken, Axel, van Wielink, Paul, Viglášová, Sandra, Zach, Peter, Zakharov, Ilya A., Zaviezo, Tania, and Zhao, Zihua

Published
2016
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24. Macroecological patterns of fruit infestation rates by the invasive fly Drosophila suzukii in the wild reservoir host plant Sambucus nigra

Author

Ulmer, Romain, primary, Couty, Aude, additional, Eslin, Patrice, additional, Catterou, Manuella, additional, Baliteau, Lucas, additional, Bonis, Anne, additional, Borowiec, Nicolas, additional, Colinet, Hervé, additional, Delbac, Lionel, additional, Dubois, Françoise, additional, Estoup, Arnaud, additional, Froissard, Joris, additional, Gallet‐Moron, Emilie, additional, Gard, Benjamin, additional, Georges, Romain, additional, Gibert, Patricia, additional, Le Goff, Isabelle, additional, Lemauviel‐Lavenant, Servane, additional, Loucougaray, Gregory, additional, Michelot‐Antalik, Alice, additional, Odoux, Jean‐Francois, additional, Pincebourde, Sylvain, additional, Rode, Nicolas O., additional, Thaon, Marcel, additional, Till‐Bottraud, Irène, additional, and Chabrerie, Olivier, additional

Published
2022
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28. Adaptation and correlated fitness responses over two time scales in Drosophila suzukii populations evolving in different environments

Author

Olazcuaga, Laure, Foucaud, Julien, Gautier, Mathieu, Deschamps, Candice, Loiseau, Anne, Leménager, Nicolas, Facon, Benoit, Ravigné, Virginie, Hufbauer, Ruth, Estoup, Arnaud, Rode, Nicolas, Centre de Biologie pour la Gestion des Populations (UMR CBGP), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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), Colorado State University [Fort Collins] (CSU), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Peuplements végétaux et bioagresseurs en milieu tropical (UMR PVBMT), Institut de Recherche pour le Développement (IRD)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de La Réunion (UR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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), Graduate Degree Program in Ecology, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Université de La Réunion (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), French Agence National pour la Recherche (ANR project SWING), French programme investissement d'avenir, and the LabEx CEMEB through the AAP 'invited scientist 2016', CeMEB LabEx/University of Montpellier (ANR-10-LABX-0004), INRAE-SPE starting grant, European Regional Development Fund (ERDF), Conseil Regional de La Reunion, European Union program FEFER FSE IEJ 2014-2020 (project CPADROL), INRAE scientific department SPE (AAP-SPE 2016), Occitanie region (France), National Science Foundation (DEB-0949619), USDA Agriculture and Food Research Initiative award (2014-67013-21594), Hatch project 1012868, French Agropolis Fondation (LabEx Agro-Montpellier) through the AAP ' International Mobility' (CfP 2015-02)., ANR-16-CE02-0015,SWING,Invasion mondiale de la drosophile à aile tachetée: Génétique, plasticité et potentiel évolutif(2016), ANR-10-LABX-0004,CeMEB,Mediterranean Center for Environment and Biodiversity(2010), ANR-10-LABX-0001,AGRO,Agricultural Sciences for sustainable Development(2010), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro

Subjects
fitness trade‐off, fitness landscape, Facteur écologique, Acclimatization, [SDV]Life Sciences [q-bio], fitness trade-off, Évolution, specialization, reciprocal transplant experiment, Animals, L50 - Physiologie et biochimie animales, experimental evolution, Adaptation physiologique, Adaptation, Ravageur des plantes, Adaptation, Physiological, Research Papers, H10 - Ravageurs des plantes, Culture Media, Physiologie animale, Drosophila, Research Paper
Abstract

The process of local adaptation involves differential changes in fitness over time across different environments. Although experimental evolution studies have extensively tested for patterns of local adaptation at a single time point, there is relatively little research that examines fitness more than once during the time course of adaptation. We allowed replicate populations of the fruit pest Drosophila suzukii to evolve in one of eight different fruit media. After five generations, populations with the highest initial levels of maladaptation had mostly gone extinct, whereas experimental populations evolving on cherry, strawberry and cranberry media had survived. We measured the fitness of each surviving population in each of the three fruit media after five and after 26 generations of evolution. After five generations, adaptation to each medium was associated with increased fitness in the two other media. This was also true after 26 generations, except when populations that evolved on cranberry medium developed on cherry medium. These results suggest that, in the theoretical framework of a fitness landscape, the fitness optima of cherry and cranberry media are the furthest apart. Our results show that studying how fitness changes across several environments and across multiple generations provides insights into the dynamics of local adaptation that would not be evident if fitness were analysed at a single point in time. By allowing a qualitative mapping of an experimental fitness landscape, our approach will improve our understanding of the ecological factors that drive the evolution of local adaptation in D. suzukii., Using fitness landscape theory to study adaptation and correlated fitness responses over two time scales in Drosophila suzukii populations evolving in different fruit environments.

Published
2021
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32. Prédiction génomique de l'adaptation de Drosophila suzukii à différentes plantes hôtes

Author

Boitard, Simon, Estoup, Arnaud, Gautier, Mathieu, Rode, Nicolas, Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, 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), and boitard, simon

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [SDV.GEN.GPO] Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2021

33. Macroecological Patterns of Fruit Infestation Rates by the Invasive Fly Drosophila suzukii in the Reservoir Host Plant Sambucus nigra

Author

Ulmer, Romain, Couty, Aude, Eslin, Patrice, Baliteau, Lucas, Bonis, Anne, Borowiec, Nicolas, Colinet, H, Delbac, Lionel, Estoup, Arnaud, Froissard, Joris, Gallet-Moron, Emilie, Gard, Benjamin, Georges, Romain, Gibert, Patricia, Le Goff, Isabelle, Lemauviel-Lavenant, Servanne, Loucougaray, Gregory, Michelot-Antalik, Alice, Odoux, Jean-François, Pincebourde, Sylvain, Rode, Nicolas, Thaon, Marcel, Till-Bottraud, Irène, Chabrerie, Olivier, Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Centre National de la Recherche Scientifique (CNRS)-Université de Picardie Jules Verne (UPJV), CPIE des Pays de l'Oise, Laboratoire de Géographie Physique et Environnementale (GEOLAB), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Université Clermont Auvergne (UCA)-Institut Sciences de l'Homme et de la Société (IR SHS UNILIM), Université de Limoges (UNILIM)-Université de Limoges (UNILIM)-Centre National de la Recherche Scientifique (CNRS), 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), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), 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), Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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 de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Ecophysiologie Végétale, Agronomie et Nutritions NCS (EVA), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire des EcoSystèmes et des Sociétés en Montagne (UR LESSEM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire Agronomie et Environnement (LAE), Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de recherche sur la biologie de l'insecte UMR7261 (IRBI), Université de Tours-Centre National de la Recherche Scientifique (CNRS), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Centre Permanent d'Initiatives pour l'Environnement (CPIE) des Pays de l’Oise (CPIE), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut Sciences de l'Homme et de la Société (IR SHS UNILIM), Université de Limoges (UNILIM)-Université de Limoges (UNILIM)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Université Nice Sophia Antipolis (1965 - 2019) (UNS), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, 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), Ctifl - Centre de Balandran (Ctifl - Centre de Balandran), Centre Technique Interprofessionnel des Fruits et Légumes (CTIFL), Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut Ecologie et Environnement (INEE), and Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.EE]Life Sciences [q-bio]/Ecology, environment, spotted wing drosophila, pest, biological invasion, [SDV.BID]Life Sciences [q-bio]/Biodiversity, wild plants, landscape, exotic species, macroclimate
Abstract

International audience; The invasive pest Drosophila suzukii (Matsumura, 1931) is a fruit fly native to Asia that infests a wide variety of cultivated and wild fruits, causing important damages on agricultural production. Wild plant species are major reservoirs for D. suzukii populations but their infestation rates varies greatly among geographical areas. This heterogeneity could partly be caused by a heterogeneity of environmental conditions acting across different spatial scales. This study aimed to disentangle the relative roles of macroclimatic, landscape and local factors that could affect the success of D. suzukii infestation of elderberry fruits (Sambucus nigra), a major and widespread host plant along climatic gradients. We collected elderberry fruits and measured vegetative and reproductive life traits of the plants in 215 sites distributed in 13 regions from North to South France during summer 2020. We counted the number of D. suzukii emerging from sampled fruits and tested for an effect of macroclimatic, landscape and local abiotic and biotic variables, as well as plant traits using linear mixed models with region as random factor. Latitude and mean maximum temperature had respectively the strongest positive and negative effects on mean infestation rates across regions (R = 0.761, p = 0.003 and R = −0.758, p = 0.004). Mixed models also showed that fruit infestation rate increased with the number of mature fruits within corymb and with forest cover in a 100 m radius around sampling sites and decreased with mean maximum temperature. The latitudinal and climatic clines in infestation rates suggests that D. suzukii population size might vary greatly among geographic regions. Our results also suggest that population sizes are larger in the presence of semi-natural habitats such as forest patches in the surrounding landscape. Our work contributes to enhancing our understanding of D. suzukii ecology, which is important to predict how infestation rates might change in the context of global climatic changes.

Published
2021
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38. Bringing ABC inference to the machine learning realm : AbcRanger, an optimized random forests library for ABC

Author

Collin, François-David, Estoup, Arnaud, Marin, Jean-Michel, Raynal, Louis, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, 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), Collin, François-David, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Random Forests, Model Choice, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Parameter Estimation, Approximate Bayesian Computation, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], C++, Python, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

Virtual conference; International audience; The AbcRanger library provides methodologies for model choice and parameter estimation based on fast and scalable Random Forests, tuned to handle large and/or high dimensional datasets. The library, initially intended for the population genetics ABC framework DIYABC, has been generalized to any ABC reference table generator. At first, computational issues were encountered with the reference ABC-Random Forest. Those issues have been diagnosed by us as friction between "strict" Machine Learning setup and ABC context, and this incited us to modify the C++ implementation of state-of-the-art random forests, ranger, to tailor it for ABC needs: potentially "deep" decision trees are not stored in memory anymore, but are processed by batches in parallel. We focused on memory and thread scalability, ease of use (minimal hyperparameter set). R and python interfaces are provided.

Published
2020

39. Changes in mating system and social structure of the ant Petalomymex phylax are associated with range expansion in Cameroon

Author

Dalecky, Ambroise, Debout, Gabriel, Estoup, Arnaud, McKey, Doyle B., and Kjellberg, Finn

Subjects
Courtship of animals -- Research, Ants -- Behavior, Social behavior in animals -- Research, Biological sciences
Abstract

Changes in mating system and social structure of the ant Petalomymex phylax from the coastal southern Cameroon were studied. The results suggested that the initial founders of new populations belong to monogynous to weakly polygynous phenotype.

Published
2007

40. Macroecological Patterns of Fruit Infestation Rates by the Invasive Fly <em>Drosophila suzukii</em> in the Reservoir Host Plant <em>Sambucus nigra</em> <sup>†</sup>

Author

Ulmer, Romain, primary, Couty, Aude, additional, Eslin, Patrice, additional, Baliteau, Lucas, additional, Bonis, Anne, additional, Borowiec, Nicolas, additional, Colinet, Hervé, additional, Delbac, Lionel, additional, Estoup, Arnaud, additional, Froissard, Joris, additional, Gallet-Moron, Emilie, additional, Gard, Benjamin, additional, Georges, Romain, additional, Gibert, Patricia, additional, Le Goff, Isabelle, additional, Lemauviel-Lavenant, Servanne, additional, Loucougaray, Gregory, additional, Michelot-Antalik, Alice, additional, Odoux, Jean-François, additional, Pincebourde, Sylvain, additional, Rode, Nicolas, additional, Thaon, Marcel, additional, Till-Bottraud, Irène, additional, and Chabrerie, Olivier, additional

Published
2021
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41. A spatial statistical model for landscape genetics

Author

Guillot, Gilles, Estoup, Arnaud, Mortier, Frederic, and Cosson, Jean Francois

Subjects
Genetic research, Monte Carlo method, Biological sciences
Abstract

Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual georeferenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., [F.sub.ST] < 0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites.

Published
2005

42. Clonal reproduction by males and females in the little fire ant

Author

Fournier, Denis, Estoup, Arnaud, Orivel, Jerome, Foucaud, Julien, Jourdan, Herve, Breton, Julien Le, and Keller, Laurent

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Denis Fournier (corresponding author) [1, 6, 7]; Arnaud Estoup (corresponding author) [1, 6]; Jérôme Orivel [2]; Julien Foucaud [1]; Hervé Jourdan [3]; Julien Le Breton [4]; Laurent Keller [5] [...]

Published
2005
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43. Bayesian analysis of an admixture model with mutations and arbitrarily linked markers

Author

Excoffier, Laurent, Estoup, Arnaud, and Cornuet, Jean-Marie

Subjects
Bayesian statistical decision theory -- Usage, Gene mutations -- Research, Genetic research, Biological sciences
Abstract

We introduce here a Bayesian analysis of a classical admixture model in which all parameters are simultaneously estimated. Our approach follows the approximate Bayesian computation (ABC) framework, relying on massive simulations and a rejection-regression algorithm. Although computationally intensive, this approach can easily deal with complex mutation models and partially linked loci, and it can be thoroughly validated without much additional computation cost. Compared to a recent maximum-likelihood (ML) method, the ABC approach leads to similarly accurate estimates of admixture proportions in the case of recent admixture events, but it is found superior when the admixture is more ancient. All other parameters of the admixture model such as the divergence time between parental populations, the admixture time, and the population sizes are also well estimated, unlike the ML method. The use of partially linked markers does not introduce any particular bias in the estimation of admixture, but ML confidence intervals are found too narrow if linkage is not specifically accounted for. The application of our method to an artificially admixed domestic bee population from northwest Italy suggests that the admixture occurred in the last 10-40 generations and that the parental Apis mellifera and A. ligustica populations were completely separated since the last glacial maximum.

Published
2005

44. Influence of spatial and temporal heterogeneities on the estimation of demographic parameters in a continuous population using individual microsatellite data

Author

Leblois, Raphael, Rousset, Francois, and Estoup, Arnaud

Subjects
Genetics -- Research, Biological sciences
Abstract

Drift and migration disequilibrium are very common in animal and plant populations. Yet their impact on methods of estimation of demographic parameters was rarely evaluated especially in complex realistic population models. The effect of such disequilibria on the estimation of demographic parameters depends on the population model, the statistics, and the genetic markers used. Here we considered the estimation of the product D[[sigma].sup.2] from individual microsatellite data, where D is the density of adults and [[sigma].sup.2] the average squared axial parent-offspring distance in a continuous population evolving under isolation by distance. A coalescence-based simulation algorithm was used to study the effect on D[[sigma].sup.2] estimation of temporal and spatial fluctuations of demographic parameters. Estimation of present-time D[[sigma].sup.2] values was found to be robust to temporal changes in dispersal, to density, reduction, and to spatial expansions with constant density, even for relatively recent changes (i.e., a few tens of generations ago). By contrast, density increase in the recent past gave D[[sigma].sup.2] estimations biased largely toward past demographic parameters values. The method was also robust to spatial heterogeneity in density and estimated local demographic parameters when the density is homogenous around the sampling area (e.g., on a surface that equals four times the sampling area). Hence, in the limit of the situations studied in this article, and with the exception of the case of density increase, temporal and spatial fluctuations of demographic parameters appear to have a limited influence on the estimation of local and present-time demographic parameters with the method studied.

Published
2004

46. f‐Statistics estimation and admixture graph construction with Pool‐Seq or allele count data using the R package poolfstat.

Author

Gautier, Mathieu, Vitalis, Renaud, Flori, Laurence, and Estoup, Arnaud

Subjects
HAPLOTYPES, ALLELES, DROSOPHILA suzukii, GENETIC variation, GENE frequency, ERROR rates
Abstract

By capturing various patterns of the structuring of genetic variation across populations, f‐statistics have proved highly effective for the inference of demographic history. Such statistics are defined as covariances of SNP allele frequency differences among sets of populations without requiring haplotype information and are hence particularly relevant for the analysis of pooled sequencing (Pool‐Seq) data. We here propose a reinterpretation of the F (and D) parameters in terms of probability of gene identity and derive from this unified definition unbiased estimators for both Pool‐Seq data and standard allele count data obtained from individual genotypes. We implemented these estimators in a new version of the R package poolfstat, which now includes a wide range of inference methods: (i) three‐population test of admixture; (ii) four‐population test of treeness; (iii) F4‐ratio estimation of admixture rates; and (iv) fitting, visualization and (semi‐automatic) construction of admixture graphs. A comprehensive evaluation of the methods implemented in poolfstat on both simulated Pool‐Seq (with various sequencing coverages and error rates) and allele count data confirmed the accuracy of these approaches, even for the most cost‐effective Pool‐Seq design involving relatively low sequencing coverages. We further analysed a real Pool‐Seq data made of 14 populations of the invasive species Drosophila suzukii, which allowed refining both the demographic history of native populations and the invasion routes followed by this emblematic pest. Our new package poolfstat provides the community with a user‐friendly and efficient all‐in‐one tool to unravel complex population genetic histories from large‐size Pool‐Seq or allele count SNP data. [ABSTRACT FROM AUTHOR]

Published
2022
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48. Inferring population history from microsatellite and enzyme data in serially introduced cane toads, Bufo marinus

Author

Estoup, Arnaud, Wilson, Ian J., Sullivan, Claire, Cornuet, Jean-Marie, and Moritz, Craig

Subjects
Caribbean Islands -- Natural history, Pacific Islands -- Natural history, Toads -- Research, Animal introduction -- Research, Animal populations -- Genetic aspects, Biological sciences
Abstract

Much progress has been made on inferring population history from molecular data. However, complex demographic scenarios have been considered rarely or have proved intractable. The serial introduction of the South-Central American cane toad Bufo marinus in various Caribbean and Pacific islands involves four major phases: a possible genetic admixture during the first introduction, a bottleneck associated with founding, a transitory population boom, and finally, a demographic stabilization. A large amount of historical and demographic information is available for those introductions and can be combined profitably with molecular data. We used a Bayesian approach to combine this information with microsatellite (10 loci) and enzyme (22 loci) data and used a rejection algorithm to simultaneously estimate the demographic parameters describing the four major phases of the introduction history. The general historical trends supported by microsatellites and enzymes were similar. However, there was a stronger support for a larger bottleneck at introductions for microsatellites than enzymes and for a more balanced genetic admixture for enzymes than for microsatellites. Very little information was obtained from either marker about the transitory population boom observed after each introduction. Possible explanations for differences in resolution of demographic events and discrepancies between results obtained with microsatellites and enzymes were explored. Limits of our model and method for the analysis of nonequilibrium populations were discussed.

Published
2001

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