Genetic structure is influenced by landscape features: empirical evidence from a roe deer population.
2006
Coulon, Aurélie | Guillot, Gilles | Cosson, Jean-Francois, J.-F. | Angibault, Jean-Marc | Aulagnier, Stephane | Cargnelutti, Bruno | Galan, Maxime | Hewison, Mark | Unité de recherche Comportement et Ecologie de la Faune Sauvage (CEFS) ; Institut National de la Recherche Agronomique (INRA) | Mathématiques et Informatique Appliquées (MIA-Paris) ; Ecole Nationale du Génie Rural, des Eaux et des Forêts (ENGREF)-Institut National de la Recherche Agronomique (INRA)-Institut National Agronomique Paris-Grignon (INA P-G) | Centre de Biologie pour la Gestion des Populations (UMR CBGP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [Occitanie])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
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Show more [+] Less [-]English. 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 this study, we investigated the genetic structuring of a roe deer population which recently recolonized a fragmented landscape. We sampled 1148 individuals from a 40 x 55-km area containing several putative barriers to deer movements, and hence to gene flow, namely a highway, rivers and several canals. In order to assess the effect of these landscape features on genetic structure, we implemented a spatial statistical model known as GENELAND which analyses genetic structure, explicitly taking into account the spatial nature of the problem. Two genetic units were inferred, exhibiting a very low level of differentiation (F-ST = 0.008). The location of their boundaries suggested that there are no absolute barriers in this study area, but that the combination of several landscape features with low permeability can lead to population differentiation. Our analysis hence suggests that the landscape has a significant influence on the structuring of the population under study. It also illustrates the use of GENELAND as a powerful method to infer population structure, even in situations of young populations exhibiting low genetic differentiation.
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