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.

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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.

Article impact statement: Graph-based connectivity models reflect genetic responses but the most complex methods are not necessarily leading to the best models.