Data from: Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses

Direct gradient analyses in spatial genetics provide unique opportunities to describe the inherent complexity of genetic variation in wildlife species and are the object of many methodological developments. However, multicollinearity among explanatory variables are a systemic issue in multivariate regression analyses and are likely to cause serious difficulties in properly interpreting results of direct gradient analyses, with the risk of erroneous conclusions, misdirected research and inefficient or counter-productive conservation measures. Using simulated datasets along with linear and logistic regressions on distance matrices, we illustrate how commonality analysis (CA), a detailed variance partitioning procedure that was recently introduced in the field of ecology, can be used to deal with non-independence among spatial predictors. By decomposing model fit indices into unique and common (or shared) variance components, CA allows identifying the location and magnitude of multicollinearity, revealing spurious correlations and thus thoroughly improving the interpretation of multivariate regressions. Despite a few inherent limitations, especially in the case of resistance model optimisation, this review highlights the great potential of CA to account for complex multicollinearity patterns in spatial genetics and identifies future applications and lines of research. We strongly urge spatial geneticists to systematically investigate commonalities when performing direct gradient analyses.

空间遗传学中的直接梯度分析为刻画野生生物物种遗传变异的内在复杂性提供了独特契机，同时也是诸多方法学研究进展的核心对象。然而，解释变量间的多重共线性是多元回归分析中的系统性问题，极易对直接梯度分析结果的正确解读造成严重阻碍，进而带来错误结论、误导研究方向，以及低效甚至适得其反的保护措施等风险。本研究借助模拟数据集，结合基于距离矩阵的线性与逻辑回归分析，演示了共性分析（Commonality Analysis, CA）——一种近年在生态学领域提出的精细化方差分解方法——如何用于处理空间预测变量间的非独立性问题。通过将模型拟合指标拆解为独特方差与共同（或共享）方差分量，共性分析能够识别多重共线性的位置与强度，揭示伪相关关系，从而全面优化多元回归的解读效果。尽管共性分析存在若干固有局限，尤其是在抗性模型优化场景下，本综述仍凸显了其在解析空间遗传学中复杂多重共线性模式方面的巨大潜力，并指明了未来的应用方向与研究路径。我们强烈呼吁空间遗传学家在开展直接梯度分析时，系统性地对变量间的共性进行探究。