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

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.

景观遗传学分析通常仅在单一空间尺度下开展。若要识别影响基因流的景观特征，考虑多尺度分析或为必要手段。本研究以林地驯鹿（Rangifer tarandus caribou）为研究对象，基于构建景观泰森多边形（Voronoi tessellation）的景观图新方法，在多空间尺度下分析其景观连通性。为阐明该方法的应用潜力，我们生成了5组抗性表面（resistance surfaces），用以阐释景观格局如何影响该种群分布范围内的基因流。我们采用现有景观数据的空间粒度（200米网格）对应的栅格（raster）数据对每组抗性表面进行检验；随后，利用本研究提出的方法，为每组抗性表面生成最多127种额外空间粒度。我们采用包含偏曼特尔检验（partial Mantel tests）的因果建模框架，将景观抗性的相关证据与距离隔离（isolation-by-distance）这一备择假设进行对比检验。结果显示，在507个被检验的空间粒度中，有89个粒度下的景观抗性对基因流的影响具有统计学显著性。研究发现，大型道路以及自然与人为干扰的累积效应可能是该种群遗传结构形成的驱动因素。若仅使用原始网格粒度的数据，则无法检测到景观抗性对基因流的影响。本研究结果表明，采用多空间粒度分析能够揭示单一粒度分析可能遗漏的景观对遗传结构的影响；同时提示，对于移动性极强的物种，采用粗化后的土地覆盖数据粒度开展分析或为适宜。我们还讨论了连通性粒度（grains of connectivity）及相关分析方法在各类生态系统中潜在的景观遗传学应用前景。