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.

中文翻译：

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用遗传图分析景观对扩散网络和基因流的影响

图论方法在景观遗传分析中具有相关应用。当物种在离散的栖息地斑块中形成种群时，遗传图可用于 (a) 确定直接传播路径跟随繁殖体或 (b) 量化景观对多代基因流动的影响。然而，它们的构造参数的影响仍有待探索。使用模拟方法，我们使用几种修剪方法（地理距离阈值、拓扑约束、统计推断）和权重图链接的遗传距离（F _ST、D _PS）构建了遗传图, 欧几里得遗传距离）。然后，我们比较了这些不同图的能力，以 (a) 确定分散网络的精确拓扑结构和 (b) 从成本距离和遗传距离之间的关系推断景观对基因流的抵抗力。尽管并不总是很明确，但我们的结果表明，在大多数情况下，基于地理距离阈值的方法似乎可以更好地识别分散网络。更有趣的是，我们的研究表明，通过图形修剪（从而减少数据点的数量）对成对距离进行子选择可以违反直觉地导致景观对扩散的影响的更好的推断。最后，我们表明遗传距离，如D _PS或欧几里得遗传距离应该优于F_ST用于景观效果推断，因为它们对景观变化的响应速度更快。