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

Abstract

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 (F ST , D PS , 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 D PS or Euclidean genetic distances should be preferred over the F ST for landscape effect inference as they respond faster to landscape changes.

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

“…The relative genetic differentiation among populations was better explained by the spatial pattern of habitats when computed from pruned genetic graphs. The relevance of graph pruning for landscape genetic analyses has already been suggested by Wagner and Fortin (2013) for link-level analyses and evidenced by Arnaud (2003), Angelone et al (2011) and Savary et al (2021a), among others. Besides, Shirk and Cushman (2011) have highlighted the importance of considering the spatial distribution of populations for computing genetic diversity indices in a genetic neighbourhood including several populations.…”

“…ARH metrics are even more relevant for landscape genetics since the genetic structure of a set of populations can also be represented as a genetic graph in which nodes are sampled populations whereas links are weighted by genetic distances and represent substantial gene exchanges between populations (Dyer 2015;Greenbaum and Fefferman 2017;Savary et al 2021a). Their nodes can be weighted by local genetic diversity indices (node-level) as well as indices considering genetic differentiation with other populations (neighbourhood-level) (Koen et al 2016).…”

“…In the latter case, the topology of the population network can be taken into account through graph pruning, which removes certain links between populations. It makes it possible to consider gene exchanges at different spatial scales when computing these genetic differentiation indices (Savary et al 2021a). As evidenced by DiLeo and Wagner (2016), node-and neighbourhood-level approaches are the only landscape genetic approaches making it possible to study the relationships between (i) either genetic diversity or differentiation and (ii) either habitat amount or configuration.…”

Species–landscape interactions drive divergent population trajectories in four forest‐dependent Afromontane forest songbird species within a biodiversity hotspot in South Africa

“…To infer contemporary gene flow disruption, we compared pairwise F ST , estimated in ARLEQUIN 3.5, to the proportions of shared allele statistic D PS , calculated in MSA 4.0 (Dieringer & Schlötterer, 2003 ). Lag time to detection of new gene flow barriers is shorter when measuring D PS compared to F ST (Landguth et al, 2010 ; Robin et al, 2015 ; Savary et al, 2021 ), and so larger D PS : F ST ratios may suggest recent reductions in gene flow (Robin et al, 2015 ). We estimated regional contemporary effective population sizes (CN e ) for each species, using the LD model (for single sampling events) in NEESTIMATOR 2.1 (Do et al, 2014 ).…”

“…For computational efficiency, land‐cover resistances surfaces 1–4 were optimized three times per species, using least‐cost distances based on pairwise F ST . Given that D PS better responds to recent landscape change landscape change (Savary et al, 2021 ), we optimized the these resistance surfaces once using this genetic distance metric, as F ST results proved stable across replicates (Figure 4 ). The best‐supported thematic resolution for each species was used in subsequent analyses.…”