Combining landscape and genetic graphs to address key issues in landscape genetics

• Published in: Landscape ecology Vol. 37; no. 9; pp. 2293 - 2309
• Main Authors: Savary, Paul, Foltête, Jean-Christophe, Moal, Hervé, Garnier, Stéphane
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2022; Springer Nature B.V; Springer Verlag
• Subjects: Biodiversity and Ecology; Biomedical and Life Sciences; Differentiation; Ecology; Environmental Management; Environmental Sciences; Gene flow; Genetic diversity; Genetic structure; Genetics; Graphs; Landscape Ecology; Landscape/Regional and Urban Planning; Life Sciences; Nature Conservation; Nodes; Population genetics; Research Article; Sustainable Development; Networks; Gene flow; Graph theory; Landscape genetics; Dispersal; Habitat connectivity
• ISSN: 0921-2973; 1572-9761
• DOI: 10.1007/s10980-022-01489-7

Context All the components of landscape and genetic structures can be associated with the nodes and links of landscape graphs and genetic graphs. Yet, these graphs have long been used separately despite the potential for their combined use in landscape genetics. Objectives First, comparing these graphs could be an effective way to disentangle the influence of intra-patch features from that of inter-patch connectivity on genetic structure or to assess whether intra-population genetic diversity and inter-population genetic differentiation are sensitive to the same landscape influences. Methods Moreover, because graph pruning determines which connections between nodes are considered in calculating neighbourhood-based metrics or graph-based distances, comparing the metrics or distances derived from differently pruned graphs can be an effective way to identify the scale of landscape effects or the scale at which both gene flow and drift determine genetic differentiation. Similarly, comparing node partitions in both types of graphs could strengthen the validity of barrier identifications. Results Second, beyond mere comparisons, integration of landscape and genetic graphs through gravity models can further enhance their joint use for theoretical and applied objectives alike. Conclusion We thus believe that future research could illustrate and enhance the relevance of these methods for a wider range of applications in landscape genetics.