Data from: Testing and interpreting the shared space-environment fraction in variation partitioning analyses of ecological data

Variation partitioning analyses combined with spatial predictors (Moran’s eigenvector maps, MEM) are commonly used in ecology to test the fractions of species abundance variation purely explained by environment and space. However, while these pure fractions can be tested using a classical residuals permutation procedure, no specific method has been developed to test the shared space-environment fraction (SSEF). Yet, the SSEF is expected to encompass a major driver of community assembly, that is, an induced spatial dependence effect (ISD; i.e. the reflection of a spatially structured habitat filter on a species distribution). A reliable test of this fraction is therefore crucial to properly test the presence of an ISD on ecological data. To bridge the gap, we propose to test the SSEF through spatially-constrained null models: torus-translations, and Moran spectral randomisations. We investigated the type I error rate and statistical power of our method based on two real environmental datasets and simulations of tree distributions. Ten types of tree distribution displaying contrasted aggregation properties were simulated, and their abundances were sampled in 153 regularly-distributed 20 × 20 m quadrats. The SSEF was tested for 1000 simulated tree distributions either unrelated to the environment, or filtered by environmental variables displaying contrasting spatial structures. The method proposed provided a correct type I error rate (< 0.05). The statistical power was high (> 0.9) when abundances were filtered by an environmental variable structured at broad scale. However, the spatial resolution allowed by the sampling design limited the power of the method when using a fine-scale filtering variable. This highlighted that an ISD can be properly detected providing that the spatial pattern of the filtering process is correctly captured by the sampling design of the study. An R function to apply the SSEF testing method is provided and detailed in a tutorial.

变异分区分析结合空间预测因子（莫兰特征向量图（Moran’s Eigenvector Maps, MEM））在生态学领域被广泛应用，用于检验仅由环境与空间单独解释的物种多度变异组分。然而，尽管可通过经典残差置换程序检验上述单独组分，但目前尚未开发出专门用于检验空间-环境共享组分（Shared Space-Environment Fraction, SSEF）的方法。尽管如此，空间-环境共享组分被认为涵盖了群落构建的核心驱动因子之一，即诱导空间依赖效应（Induced Spatial Dependence Effect, ISD）——也就是空间结构化生境过滤对物种分布的反射作用。因此，对该组分开展可靠检验，对于准确验证生态数据中是否存在诱导空间依赖效应至关重要。为填补这一研究空白，我们提出通过空间约束零模型——环面置换（torus-translations）与莫兰谱随机化（Moran spectral randomisations）——来检验空间-环境共享组分。我们基于两套真实环境数据集与树木分布模拟实验，评估了所提方法的I类错误率与统计功效。研究共模拟了10种具有差异化聚集特性的树木分布类型，并在153个规则布设的20×20米样方中对其多度进行采样。我们对1000组模拟得到的树木分布开展了空间-环境共享组分检验，这些模拟分布要么与环境无关，要么经具有不同空间结构的环境变量过滤得到。所提方法展现出了准确的I类错误率（<0.05）。当树木多度由大尺度结构化的环境变量过滤时，该方法的统计功效较高（>0.9）。但当使用细尺度过滤变量时，采样设计所能提供的空间分辨率限制了该方法的检验功效。这一结果表明，只要研究的采样设计能够准确捕捉过滤过程的空间格局，即可有效检测到诱导空间依赖效应。本文提供了可用于实施空间-环境共享组分检验方法的R语言函数，并在配套教程中进行了详细说明。