1. Assessing the influence of the amount of reachable habitat on genetic structure using landscape and genetic graphs
- Author
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Paul Savary, Jean-Christophe Foltête, Maarten J. van Strien, Hervé Moal, Gilles Vuidel, Stéphane Garnier, and Université de Bourgogne Franche-Comté, Théoriser et modéliser pour aménager (UMR 6049)
- Subjects
0106 biological sciences ,Gene Flow ,0303 health sciences ,graph theory ,Genetic Drift ,Genetic Variation ,landscape genetics ,Grasshoppers ,010603 evolutionary biology ,01 natural sciences ,Article ,[SDE.BE] Environmental Sciences/Biodiversity and Ecology ,03 medical and health sciences ,Genetics ,genetic structure ,Animals ,amount of reachable habitat ,Genetics (clinical) ,Ecosystem ,030304 developmental biology ,Microsatellite Repeats - 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.
- Published
- 2021