A spatial statistical model for landscape genetics. Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. This approach is of primary importance in population management and conservation biology because it provides valuable knowledge about landscape connectivity. 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 research project, we first clarify what is conceptually involved in the spatial modelling of genetic data. Then we describe a Bayesian model that allows inference of the location of such genetic discontinuities from individual georeferenced multilocus genotypes, without a priori knowledge on the number of populational units and their limits. In this method, the global set of sampled individuals is modelled as a spatial mixture of panmictic populations, and the spatial organization of populations is modelled through coloured 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 migrants between populations. The performance of the method was evaluated through the analysis of simulated data sets. Results showed good performances for standard microsatellite data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (FST < 0.05). The method was then applied to two real data sets on large mammals with contrasted differentiation levels. The first application, to wolverines (Gulo gulo) sampled in the North-western United States, showed the ability of the method to detect populations consistent with landscape structures known to slow down dispersal movements of that species, and to locate putative migrants in a context of rather high genetic differentiation (FST from 0.08 to 0.17). The second application, to roe deer (Capreolus capreolus) in South-western France, illustrate the ability of the method to infer genetic discontinuities coherent with landscape structures (highways, canals) in a situation of very low genetic differentiation (FST =0.008). A computer program named GENELAND is freely available at http://www.inapg.inra.fr/ens_rech/mathinfo/personnel/guillot/Geneland.html. A mailing list for users is managed by one of us (G. Guillot).
|Title of host publication||Les Actes du BRG|
|Publisher||Bureau des Ressources Génétiques|
|Publication status||Published - 2006|
- Bayesian computations
- Gene flow
- Landscape connectivity
- Landscape genetics