Analysing landscape effects on dispersal networks and gene flow with genetic graphs

DOI10.5281/zenodo.4443958Zenodo4443958MaRDI QIDQ6681653

Dataset published at Zenodo repository.

Author name not available (Why is that?)

Publication date: 15 January 2021

Description

Graph-theoretic approaches have relevant applications in landscape genetic analyses. When species form populations in discrete habitat patches, genetic graphs can be used i) to identify direct dispersal paths followed by propagules or ii) to quantify landscape effects on multigenerational 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 (FST, DPS, Euclidean genetic distances). We then compared the capacity of these different graphs to i) identify the precise topology of the dispersal network and ii) 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 subselection 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 DPS or Euclidean genetic distances should be preferred over the FST for landscape effect inference as they respond faster to landscape changes.

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