Data from: The trouble with isolation by distance

The genetic population structure of many species is characterised by a pattern of isolation by distance (IBD): due to limited dispersal, individuals that are geographically close tend to be genetically more similar than individuals that are far apart. Despite the ubiquity of IBD in nature, many commonly used statistical tests are based on a null model that is completely non-spatial, the Island model. Here, I argue that patterns of spatial autocorrelation deriving from IBD present a problem for such tests as it can severely bias their outcome. I use simulated data to illustrate this problem for two widely used types of tests: tests of hierarchical population structure and the detection of loci under selection. My results show that for both types of tests the presence of IBD can indeed lead to a large number of false positives. I therefore argue that all analyses in a study should take the spatial dependence in the data into account, unless it can be shown that there is no spatial autocorrelation in the allele frequency distribution that is under investigation. Thus, it is urgent to develop additional statistical approaches that are based on a spatially explicit null model instead of the non-spatial Island model.诸多物种的遗传种群结构均呈现距离隔离（Isolation by Distance, IBD）模式：由于扩散能力有限，地理距离较近的个体在遗传上往往比地理距离较远的个体更为相似。尽管距离隔离模式在自然界中普遍存在，但诸多主流统计检验均基于完全非空间的零模型——岛屿模型（Island model）。本文指出，由距离隔离产生的空间自相关（spatial autocorrelation）模式会对这类检验造成显著干扰，严重偏倚其检验结果。本文利用模拟数据，针对两类广泛应用的检验方法展开验证：分层种群结构（hierarchical population structure）检验与受选择位点（loci under selection）检测。研究结果表明，两类检验在距离隔离效应存在的情况下，均会产生大量假阳性（false positives）结果。据此，本文主张，除非能够证明所研究的等位基因频率（allele frequency）分布不存在空间自相关，否则所有分析均应考虑数据中的空间依赖性（spatial dependence）。因此，亟需开发基于空间显性零模型（spatially explicit null model）而非非空间岛屿模型的新型统计分析方法。