Analysing landscape effects on dispersal networks and gene flow with genetic graphs. (2nd February 2021)

• Record Type: Journal Article
• Authors: Savary, Paul; Foltête, Jean‐Christophe; Moal, Hervé; Vuidel, Gilles; Garnier, Stéphane
• Is Part Of: Molecular ecology resources. Volume 21:Number 4(2021)
• Page Start: 1167
• Page End: 1185
• Publication Date: 2021-02-02
• Subjects: dispersal -- ecological connectivity -- graph theory -- landscape genetics -- simulation; Molecular ecology -- Periodicals; 572.8
• DOI: 10.1111/1755-0998.13333
• Languages: English
• ISSNs: 1755-098X

Abstract

Abstract: Graph‐theoretic approaches have relevant applications in landscape genetic analyses. When species form populations in discrete habitat patches, genetic graphs can be used (a) to identify direct dispersal paths followed by propagules or (b) to quantify landscape effects on multi‐generational gene flow. However, the influence of their construction parameters remains to be explored. Using a simulation approach, we constructed genetic graphs using several pruning methods (geographical distance thresholds, topological constraints, statistical inference) and genetic distances to weight graph links ( F ST, D PS, Euclidean genetic distances). We then compared the capacity of these different graphs to (a) identify the precise topology of the dispersal network and (b) to infer landscape resistance to gene flow from the relationship between cost‐distances and genetic distances. Although not always clear‐cut, our results showed that methods based on geographical distance thresholds seem to better identify dispersal networks in most cases. More interestingly, our study demonstrates that a sub‐selection of pairwise distances through graph pruning (thereby reducing the number of data points) can counter‐intuitively lead to improved inferences of landscape effects on dispersal. Finally, we showed that genetic distances such as the D PS or Euclidean genetic distances should be preferred over the F ST for landscape effect inference as they respond faster to landscape changes.