Data from: Grains of connectivity: analysis at multiple spatial scales in landscape genetics

• Main Authors: Galpern, Paul, Manseau, Micheline, Wilson, Paul
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2012
• Subjects: Landscape resistance; Gene flow; Patch-based landscape graphs; Voronoi tessellation; Landscape change; Canada; Rangifer tarandus
• Online Access: http://hdl.handle.net/10255/dryad.39692; https://doi.org/10.5061/dryad.r3j5d

Landscape genetic analyses are typically conducted at one spatial scale. Considering multiple scales may be essential for identifying landscape features influencing gene flow. We examined landscape connectivity for woodland caribou (Rangifer tarandus caribou) at multiple spatial scales using a new approach based on landscape graphs that creates a Voronoi tessellation of the landscape. To illustrate the potential of the method, we generated five resistance surfaces to explain how landscape pattern may influence gene flow across the range of this population. We tested each resistance surface using a raster at the spatial grain of available landscape data (200 m grid squares). We then used our method to produce up to 127 additional grains for each resistance surface. We applied a causal modelling framework with partial Mantel tests, where evidence of landscape resistance is tested against an alternative hypothesis of isolation-by-distance, and found statistically significant support for landscape resistance to gene flow in 89 of the 507 spatial grains examined. We found evidence that major roads as well as the cumulative effects of natural and anthropogenic disturbance may be contributing to the genetic structure. Using only the original grid surface yielded no evidence for landscape resistance to gene flow. Our results show that using multiple spatial grains can reveal landscape influences on genetic structure that may be overlooked with a single grain, and suggest that coarsening the grain of landcover data may be appropriate for highly-mobile species. We discuss how grains of connectivity and related analyses have potential landscape genetic applications in a broad range of systems.