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Jauslin, Raphaël

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Jauslin, Raphaël
Affiliation principale
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Ancien.ne collaborateur.trice
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https://libra.unine.ch/handle/123456789/30755

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  • Publication
    Accès libre
    Spatially balanced sampling, stratification and statistical matching
    (2023)
    Jauslin, Raphaël 
    ;
    Tillé, Yves 
    Dans cette thèse, nous nous intéressons à trois champs de la théorie de l'échantillonnage. Ces trois champs sont l'échantillonnage spatial, la stratification et finalement l'appariement statistique. Après un premier chapitre qui rappelle les notions principales de la théorie de l'échantillonnage, la thèse est constituée de deux parties qui contiennent chacune deux chapitres. La première partie concerne l'échantillonnage spatial. Dans le secteur de l'environnement en particulier, il est important de sélectionner un échantillon bien étalé. Les populations que nous étudions sont souvent auto-corrélées, c'est-à-dire que deux unités proches l'une de l'autre partagent les mêmes caractéristiques et ne devraient pas être sélectionnées dans le même échantillon. Dans le second chapitre, nous proposons une méthode qui permet de sélectionner un échantillon très bien étalé. Le troisième chapitre propose une méthode pour sélectionner un échantillon à la fois étalé sur des coordonnées géographiques et équilibré sur des variables auxiliaires. Cette méthode possède la particularité d'être séquentielle, ce qui offre un champ d'application plus large, notamment dans les très grands ensembles de données. La deuxième partie de la thèse aborde la stratification et l'appariement statistique. Dans une enquête, on améliore presque toujours l'estimateur si on sépare la population en sous-groupes lorsque cette information est disponible. Ces sous-groupes peuvent être grands ou petits selon les caractéristiques des variables qui les conditionnent. Le quatrième chapitre propose un algorithme pour tirer un échantillon équilibré dans des populations fortement stratifiées. Finalement, le cinquième chapitre parle de l'appariement statistique qui consiste à fusionner deux enquêtes. Nous utilisons le problème du transport optimal pour combiner les deux enquêtes en une pseudo-population qui permet de tirer des conclusions sur des variables mesurées uniquement dans chacune des enquêtes respectives.
    Abstract
    In this thesis, we are interested in three fields of sampling theory. These three fields are spatial sampling, stratification and finally statistical matching. After the first chapter recapitulating the main notions of sampling theory, the thesis comprises two parts, each containing two chapters. The first part deals with spatial sampling. Particularly in the environmental sector, it is important to select a well-spread sample. The populations we study are often auto-correlated, i.e. two units close to each other share the same characteristics and should not be selected in the same sample. In the second chapter, we propose a method to select a very well-spread sample. The third chapter proposes a method to select a sample that is both spread on geographical coordinates and balanced on auxiliary variables. This method has the particularity of being sequential, which offers a wider scope of application, especially in very large datasets. The second part of the thesis discusses stratification and statistical matching. In a survey, the estimator is almost always improved by separating the population into subgroups when this information is available. These subgroups can be large or small depending on the characteristics of the variables that condition them. The fourth chapter proposes an algorithm for drawing a balanced sample in highly stratified populations. Finally, the fifth chapter discusses statistical matching, which consists in merging two surveys. We use the optimal transport problem to combine the two surveys into a pseudo-population that allows conclusions to be drawn on variables measured only in each of the respective surveys.
  • Publication
    Accès libre
    Global distribution modelling of a conspicuous Gondwanian soil protist reveals latitudinal dispersal limitation and range contraction in response to climate warming
    (2023)
    Estelle P. Bruni 
    ;
    Olivia Rusconi 
    ;
    Olivier Broennimann
    ;
    Antoine Adde
    ;
    Jauslin, Raphaël 
    ;
    Valentyna Krashevska
    ;
    Anush Kosakyan 
    ;
    Eric Armynot du Châtelet
    ;
    João P. B. Alcino
    ;
    Louis Beyens
    ;
    Quentin Blandenier 
    ;
    Anatoly Bobrov
    ;
    Luciana Burdman
    ;
    Clément Duckert 
    ;
    Leonardo D. Fernández 
    ;
    Maria Beatriz Gomes e Souza
    ;
    Thierry J. Heger
    ;
    Isabelle Koenig 
    ;
    Daniel J. G. Lahr
    ;
    Michelle McKeown
    ;
    Ralf Meisterfeld
    ;
    David Singer 
    ;
    Eckhard Voelcker
    ;
    Janet Wilmshurst
    ;
    Sebastien Wohlhauser
    ;
    David M. Wilkinson
    ;
    Antoine Guisan
    ;
    Edward A. D. Mitchell
    AbstractAimThe diversity and distribution of soil microorganisms and their potential for long‐distance dispersal (LDD) are poorly documented, making the threats posed by climate change difficult to assess. If microorganisms do not disperse globally, regional endemism may develop and extinction may occur due to environmental changes. Here, we addressed this question using the testate amoeba Apodera vas, a morphologically conspicuous model soil microorganism in microbial biogeography, commonly found in peatlands and forests mainly of former Gondwana. We first documented its distribution. We next assessed whether its distribution could be explained by dispersal (i.e. matching its climatic niche) or vicariance (i.e. palaeogeography), based on the magnitude of potential range expansions or contractions in response to past and on‐going climatic changes. Last, we wanted to assess the likelihood of cryptic diversity and its potential threat from climate and land‐use changes (e.g. due to limited LDD).LocationDocumented records: Southern Hemisphere and intertropical zone; modelling: Global.MethodsWe first built an updated global distribution map of A. vas using 401 validated georeferenced records. We next used these data to develop a climatic niche model to predict its past (LGM, i.e. 21 ± 3 ka BP; PMIP3 IPSL‐CM5A‐LR), present and future (IPSL‐CMP6A‐LR predictions for 2071–2100, SSP3 and 5) potential distributions in responses to climate, by relating the species occurrences to climatic and topographic predictors. We then used these predictions to test our hypotheses (dispersal/vicariance, cryptic diversity, future threat from LDD limitation).ResultsOur models show that favourable climatic conditions for A. vas currently exist in the British Isles, an especially well‐studied region for testate amoebae where this species has never been found. This demonstrates a lack of interhemispheric LDD, congruent with the palaeogeography (vicariance) hypothesis. Longitudinal LDD is, however, confirmed by the presence of A. vas in isolated and geologically young peri‐Antarctic islands. Potential distribution maps for past, current and future climates show favourable climatic conditions existing on parts of all southern continents, with shifts to higher land from LGM to current in the tropics and a strong range contraction from current to future (global warming IPSL‐CM6A‐LR scenario for 2071–2100, SSP3.70 and SSP5.85) with favourable conditions developing on the Antarctic Peninsula.Main ConclusionsThis study illustrates the value of climate niche models for research on microbial diversity and biogeography, along with exploring the role played by historical factors and dispersal limitation in shaping microbial biogeography. We assess the discrepancy between latitudinal and longitudinal LDD for A. vas, which is possibly due to contrast in wind patterns and/or likelihood of transport by birds. Our models also suggest that climate change may lead to regional extinction of terrestrial microscopic organisms, thus illustrating the pertinence of including microorganisms in biodiversity conservation research and actions.
  • Publication
    Accès libre
    An Efficient Approach for Statistical Matching of Survey Data Trough Calibration, Optimal Transport and Balanced Sampling
    (2022)
    Jauslin, Raphaël 
    ;
    Tillé, Yves 
    Statistical matching aims to integrate two statistical sources. These sources can be two samples or a sample and the entire population. If two samples have been selected from the same population and information has been collected on different variables of interest, then it is interesting to match the two surveys to analyse, for example, contingency tables or correlations. In this paper, we propose an efficient method for matching two samples that may each contain a weighting scheme. The method matches the records of the two sources. Several variants are proposed in order to create a directly usable file integrating data from both information sources.
  • Publication
    Accès libre
    Enhanced cube implementation for highly stratified population
    (2021)
    Jauslin, Raphaël 
    ;
    Eustache, Esther 
    ;
    Tillé, Yves 
    A balanced sampling design should always be the adopted strategy if auxiliary information is available. In addition, integrating a stratified structure of the population in the sampling process can considerably reduce the variance of the estimators. We propose here a new method to handle the selection of a balanced sample in a highly stratified population. The method improves substantially the commonly used sampling designs and reduces the time-consuming problem that could arise if inclusion probabilities within strata do not sum to an integer.
  • Publication
    Restriction temporaire
    Spatial Spread Sampling Using Weakly Associated Vectors
    (2020-8-11)
    Jauslin, Raphaël 
    ;
    Tillé, Yves 
    Geographical data are generally autocorrelated. In this case, it is preferable to select spread units. In this paper, we propose a new method for selecting well-spread samples from a finite spatial population with equal or unequal inclusion probabilities. The proposed method is based on the definition of a spatial structure by using a stratification matrix. Our method exactly satisfies given inclusion probabilities and provides samples that are very well spread. A set of simulations shows that our method outperforms other existing methods such as the generalized random tessellation stratified or the local pivotal method. Analysis of the variance on a real dataset shows that our method is more accurate than these two. Furthermore, a variance estimator is proposed.