Gilles Guillot

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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(More)
UNLABELLED We introduce a new algorithm to account for the presence of null alleles in inferences of populations clusters from individual multilocus genetic data. We show by simulations that the presence of null alleles can affect the accuracy of inferences if not properly accounted for and that our algorithm improve signficantly their accuracy. (More)
We introduce a new Bayesian clustering algorithm for studying population structure using individually geo-referenced multilocus data sets. The algorithm is based on the concept of hidden Markov random field, which models the spatial dependencies at the cluster membership level. We argue that (i) a Markov chain Monte Carlo procedure can implement the(More)
The joint analysis of spatial and genetic data is rapidly becoming the norm in population genetics. More and more studies explicitly describe and quantify the spatial organization of genetic variation and try to relate it to underlying ecological processes. As it has become increasingly difficult to keep abreast with the latest methodological developments,(More)
The delimitation of population units is of primary importance in population management and conservation biology. Moreover, when coupled with landscape data, the description of population genetic structure can provide valuable knowledge about the permeability of landscape features, which is often difficult to assess by direct methods (e.g. telemetry). In(More)
MOTIVATION This article considers the problem of estimating population genetic subdivision from multilocus genotype data. A model is considered to make use of genotypes and possibly of spatial coordinates of sampled individuals. A particular attention is paid to the case of low genetic differentiation with the help of a previously described Bayesian(More)
Many models for inference of population genetic parameters are based on the assumption that the data set at hand consists of groups displaying within-group Hardy-Weinberg equilibrium at individual loci and linkage equilibrium between loci. This assumption is commonly violated by the presence of within-group spatial structure arising from nonrandom mating of(More)
1. Informatics and Mathematical Modelling Department, Technical University of Denmark, Copenhagen. Richard Petersens Plads, Bygning 305, 2800 Lyngby, Denmark. 2. Institut des Sciences de l’Évolution (UM2-CNRS-IRD), Université Montpellier 2, Place Eugène Bataillon, CC 065, Montpellier cedex 34095, France. Running title: Dismantling the Mantel tests. ∗(More)
Recognition of evolutionary units (species, populations) requires integrating several kinds of data, such as genetic or phenotypic markers or spatial information in order to get a comprehensive view concerning the differentiation of the units. We propose a statistical model with a double original advantage: (i) it incorporates information about the spatial(More)