Data from: Multi-level patterns in population genetics: variogram series detects a hidden isolation-by- distance- dominated structure of Scandinavian brown bears Ursus arctos

1. Large-scale pattern-oriented approaches are useful to understand the multi-level processes that shape the genetic structure of a population. Matching the scales of patterns and putative processes is both a key to success and a challenge. 2. We have developed a simple statistical approach, based on variogram analysis, that identifies multiple spatial scales where the population pattern, in this case genetic structure, have highest expression (i.e. the spatial scales at which the strength of patterning of isolation-by-distance (IBD) residual variance reached maximum) from empirical data and, thus, at which scales it should be studied relative to the underlying processes. The approach is applicable to any spatially explicit pairwise data, including genetic, morphological or ecological distance or similarity of individuals, populations and ecosystems. To exemplify possible applications of this approach, we analysed microsatellite genotypes of 1,530 brown bears from Sweden and Norway. 3. The variogram approach identified two scales at which population structure was strongest, thus indicating two different scale-dependent processes: home-rangerelated processes at scales <35 km, and subpopulation division at scales >98 km. On the basis of this, we performed a scale-explicit analysis of genetic structure using DResD analysis and compared the results with those obtained by the Bayesian clustering implemented in structure. 4. We found that the genetic cluster identified in central Scandinavia by Structure is caused by IBD, with distinct gene flow barriers to the south and north. We discuss possible applications and research perspectives to further develop the approach.

1. 面向模式的大规模分析方法，有助于解析塑造种群遗传结构的多级过程。匹配模式尺度与假定过程尺度，既是研究成功的关键，亦是核心挑战。
2. 本研究基于变异函数（variogram）分析，开发了一种简易统计方法，可从实测数据中识别出种群模式（此处特指遗传结构）表达最强的多个空间尺度——即距离隔离（isolation-by-distance, IBD）残差方差的模式强度达到峰值的空间尺度，进而明确适配底层过程的适宜研究尺度。该方法可应用于所有空间显式成对数据，涵盖个体、种群与生态系统的遗传、形态或生态距离及相似性数据。为展示该方法的潜在应用场景，我们对瑞典与挪威共计1530头棕熊的微卫星基因型进行了分析。
3. 基于变异函数的方法识别出两个种群结构最强的空间尺度，由此揭示两类不同的尺度依赖过程：尺度小于35km时为与家域相关的过程，尺度大于98km时则为亚种群分化过程。基于此发现，我们采用DResD分析开展了遗传结构的尺度显式分析，并将结果与Structure软件实现的贝叶斯聚类所得结果进行了对比。
4. 研究发现，Structure软件在斯堪的纳维亚中部识别出的遗传簇由距离隔离（IBD）所致，其南北两侧存在显著的基因流屏障。最后，我们讨论了该方法的潜在应用方向与进一步研发的研究前景。