Guerre des sexes chez une fourmi : reproduction clonale des mâles et des reines

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Reliable ABC model choice via random forests

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 table

ABC random forests for Bayesian parameter inference

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 figure

Systèmes de reproduction et scénarios d'invasion chez la petite fourmi de feu, Wasmannia auropunctata

Cette thèse vise à améliorer notre connaissance des processus évolutifs et écologiques liés aux invasions biologiques au travers de l'étude de populations envahissantes et non envahissantes de la petite fourmi de feu, Wasmannia auropunctata. Cette espèce présente un polymorphisme du système de reproduction original. Dans les populations ancestrales, les reines et les mâles se reproduisent selon le mode de reproduction sexué classique des hyménoptères (haplo-diploïde). Dans d'autres populations, les reines sont parthénogénétiques et les mâles sont produits de manière clonale via les œufs pondus par la reine. Ces reines et ces mâles produisent néanmoins des ouvrières stériles sexuellement. Ce mode de reproduction clonal semble associé indirectement au succès d'invasion des populations. Dans un premier temps nous avons identifié les mécanismes sous-jacents au système de reproduction des populations clonales. Nos résultats montrent que les reines utilisent la parthénogenèse automictique associée à une réduction du taux de recombinaison pour la production de reines, l'androgenèse pour la production des mâles et la reproduction sexuée pour la production d'ouvrières stériles. La fixation des génomes parentaux dans les descendances successives permet la reproduction entre individus d'une même cohorte en évitant la dépression de consanguinité dans la descendance ouvrière. Nous avons ensuite montré que le changement de système de reproduction de la sexualité vers la clonalité est associé à un changement adaptatif permettant aux ouvrières des populations clonales de mieux tolérer les températures stressantes caractéristiques des localités envahies, comparativement aux ouvrières des populations sexuées ancestrales. Enfin, l'utilisation d'une approche multidisciplinaire couplant des modèles de distribution d'espèces, des analyses de génétique des populations et des expériences en laboratoire, nous a permis de montrer que les changements évolutifs clefs associés au succès d'invasion des populations, ont lieu dans des habitats marginaux de l'aire native, avant la dispersion vers des localités distantes caractérisées par des conditions environnementales similaires.The main goal of this thesis is to provide new insights on the evolutionary processes associated to biological invasions through the study of invasive and non-invasive populations of the little fire ant, W. auropunctata. This species is characterised by an eccentric breeding system polymorphism. In ancestral populations, queens and males reproduce following the classical sexual reproduction system of hymenopteran species (haplo-diploid). In some other populations, queens reproduce by parthenogenesis and the males are reproduced clonally through queens' eggs. These clonal queens and males nevertheless produce sterile workers sexually. Interestingly this clonal reproduction seems indirectly associated with the invasive success of populations. In this study, we first identified the mechanisms underlying the breeding system of clonal populations. Our results indicate that queens use automictic parthenogenesis associated with a drastic reduction of meiotic recombination rate, androgenesis and sexual reproduction for the production of queens, males, and sterile workers respectively. The fixation of parental genomes in the successive generations allows individuals from the same cohort to reproduce together avoiding inbreeding depression in their worker offspring. We also found that the change of breeding system from sexuality to clonality is associated with an adaptive change that allow workers from clonal populations to better tolerate the stressing temperatures of invaded areas better than workers from ancestral sexual populations. Finally, we used a developed mutlidisciplinary approach combining niche modelling, genetic analyses and laboratory experiments, and found that the above evolutionary changes occur within the native range in marginal habitats prior to long-distance dispersal events into localities that display similar environmental conditions.MONTPELLIER-SupAgro La Gaillarde (341722306) / SudocSudocFranceF

Likelihood-free model choice

Fan, and Beaumont (2017). Beyond exposing the potential pitfalls of ABC approximations to posterior probabilities, the review emphasizes mostly the solution proposed by [25] on the use of random forests for aggregating summary statistics and for estimating the posterior probability of the most likely model via a secondary random forest