Literature DB >> 34963701

Assessing the influence of the amount of reachable habitat on genetic structure using landscape and genetic graphs.

Paul Savary1,2,3, Jean-Christophe Foltête4, Maarten J van Strien5, Hervé Moal6, Gilles Vuidel4, Stéphane Garnier7.   

Abstract

Genetic structure, i.e. intra-population genetic diversity and inter-population genetic differentiation, is influenced by the amount and spatial configuration of habitat. Measuring the amount of reachable habitat (ARH) makes it possible to describe habitat patterns by considering intra-patch and inter-patch connectivity, dispersal capacities and matrix resistance. Complementary ARH metrics computed under various resistance scenarios are expected to reflect both drift and gene flow influence on genetic structure. Using an empirical genetic dataset concerning the large marsh grasshopper (Stethophyma grossum), we tested whether ARH metrics are good predictors of genetic structure. We further investigated (i) how the components of the ARH influence genetic structure and (ii) which resistance scenario best explains these relationships. We computed local genetic diversity and genetic differentiation indices in genetic graphs, and ARH metrics in the unified and flexible framework offered by landscape graphs, and we tested the relationships between these variables. ARH metrics were relevant predictors of the two components of genetic structure, providing an advantage over commonly used habitat metrics. Although allelic richness was significantly explained by three complementary ARH metrics in the best PLS regression model, private allelic richness and MIW indices were essentially related with the ARH measured outside the focal patch. Considering several matrix resistance scenarios was also key for explaining the different genetic responses. We thus call for further use of ARH metrics in landscape genetics to explain the influence of habitat patterns on the different components of genetic structure.
© 2021. The Author(s), under exclusive licence to The Genetics Society.

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Year:  2021        PMID: 34963701      PMCID: PMC8814055          DOI: 10.1038/s41437-021-00495-w

Source DB:  PubMed          Journal:  Heredity (Edinb)        ISSN: 0018-067X            Impact factor:   3.821


  32 in total

1.  Restoration of genetic variation lost - the genetic rescue hypothesis.

Authors:  P K. Ingvarsson
Journal:  Trends Ecol Evol       Date:  2001-02-01       Impact factor: 17.712

Review 2.  Landscape genetics: where are we now?

Authors:  Andrew Storfer; Melanie A Murphy; Stephen F Spear; Rolf Holderegger; Lisette P Waits
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3.  sGD: software for estimating spatially explicit indices of genetic diversity.

Authors:  A J Shirk; S A Cushman
Journal:  Mol Ecol Resour       Date:  2011-06-16       Impact factor: 7.090

4.  Quantifying population structure using the F-model.

Authors:  Oscar E Gaggiotti; Matthieu Foll
Journal:  Mol Ecol Resour       Date:  2010-05-13       Impact factor: 7.090

5.  ISOLATION BY DISTANCE IN EQUILIBRIUM AND NON-EQUILIBRIUM POPULATIONS.

Authors:  Montgomery Slatkin
Journal:  Evolution       Date:  1993-02       Impact factor: 3.694

6.  ESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE.

Authors:  B S Weir; C Clark Cockerham
Journal:  Evolution       Date:  1984-11       Impact factor: 3.694

7.  Landscape genetics as a tool for conservation planning: predicting the effects of landscape change on gene flow.

Authors:  Maarten J van Strien; Daniela Keller; Rolf Holderegger; Jaboury Ghazoul; Felix Kienast; Janine Bolliger
Journal:  Ecol Appl       Date:  2014-03       Impact factor: 4.657

8.  Node-based measures of connectivity in genetic networks.

Authors:  Erin L Koen; Jeff Bowman; Paul J Wilson
Journal:  Mol Ecol Resour       Date:  2015-05-12       Impact factor: 7.090

9.  Spatial scale affects landscape genetic analysis of a wetland grasshopper.

Authors:  Daniela Keller; Rolf Holderegger; Maarten J van Strien
Journal:  Mol Ecol       Date:  2013-03-04       Impact factor: 6.185

10.  Consequences of population topology for studying gene flow using link-based landscape genetic methods.

Authors:  Maarten J van Strien
Journal:  Ecol Evol       Date:  2017-06-02       Impact factor: 2.912

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