Literature DB >> 16629819

Genetic structure is influenced by landscape features: empirical evidence from a roe deer population.

A Coulon1, G Guillot, J-F Cosson, J M A Angibault, S Aulagnier, B Cargnelutti, M Galan, A J M Hewison.   

Abstract

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.

Mesh:

Year:  2006        PMID: 16629819     DOI: 10.1111/j.1365-294X.2006.02861.x

Source DB:  PubMed          Journal:  Mol Ecol        ISSN: 0962-1083            Impact factor:   6.185


  55 in total

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Authors:  Tianhua He; Byron B Lamont; Siegfried L Krauss; Neal J Enright
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3.  A longitudinal genetic survey identifies temporal shifts in the population structure of Dutch house sparrows.

Authors:  L Cousseau; M Husemann; R Foppen; C Vangestel; L Lens
Journal:  Heredity (Edinb)       Date:  2016-06-08       Impact factor: 3.821

4.  Joint inference of population assignment and demographic history.

Authors:  Sang Chul Choi; Jody Hey
Journal:  Genetics       Date:  2011-07-20       Impact factor: 4.562

5.  Diversity of Frankia populations in root nodules of geographically isolated Arizona alder trees in central Arizona (United States).

Authors:  Allana K Welsh; Jeffrey O Dawson; Gerald J Gottfried; Dittmar Hahn
Journal:  Appl Environ Microbiol       Date:  2009-09-04       Impact factor: 4.792

6.  The link between behavioural type and natal dispersal propensity reveals a dispersal syndrome in a large herbivore.

Authors:  L Debeffe; N Morellet; N Bonnot; J M Gaillard; B Cargnelutti; H Verheyden-Tixier; C Vanpé; A Coulon; J Clobert; R Bon; A J M Hewison
Journal:  Proc Biol Sci       Date:  2014-09-07       Impact factor: 5.349

7.  A new large-scale index (AcED) for assessing traffic noise disturbance on wildlife: stress response in a roe deer (Capreolus capreolus) population.

Authors:  Carlos Iglesias-Merchan; Fernando Horcajada-Sánchez; Luis Diaz-Balteiro; Gema Escribano-Ávila; Carlos Lara-Romero; Emilio Virgós; Aimara Planillo; Isabel Barja
Journal:  Environ Monit Assess       Date:  2018-03-02       Impact factor: 2.513

8.  Assessing the genetic landscape of a contact zone: the case of European hare in northeastern Greece.

Authors:  Aglaia Antoniou; Antonios Magoulas; Petros Platis; Georgios Kotoulas
Journal:  Genetica       Date:  2013-02-05       Impact factor: 1.082

9.  Where and when does a ring start and end? Testing the ring-species hypothesis in a species complex of Australian parrots.

Authors:  Leo Joseph; Gaynor Dolman; Stephen Donnellan; Kathleen M Saint; Mathew L Berg; Andrew T D Bennett
Journal:  Proc Biol Sci       Date:  2008-11-07       Impact factor: 5.349

10.  Landscape features influence gene flow as measured by cost-distance and genetic analyses: a case study for giant pandas in the Daxiangling and Xiaoxiangling Mountains.

Authors:  Lifeng Zhu; Xiangjiang Zhan; Tao Meng; Shanning Zhang; Fuwen Wei
Journal:  BMC Genet       Date:  2010-07-23       Impact factor: 2.797

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