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.

变异分割分析结合空间预测因子（莫兰特征向量图（Moran’s Eigenvector Maps, MEM））在生态学研究中被广泛用于检验仅由环境和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这些单独组分，但目前尚无专门方法用于检验空间-环境共享组分（Shared Space-Environment Fraction, SSEF）。尽管如此，SSEF被认为涵盖了群落构建的关键驱动因子——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影响效应）。因此，可靠检验该组分对于准确验证生态学数据中是否存在ISD至关重要。为填补这一方法空白，我们提出通过空间约束零模型（环面平移法与莫兰谱随机化法）来检验SSEF。我们基于两套真实环境数据集以及树木分布模拟数据，对所提方法的一类错误率与统计功效展开了评估。研究中模拟了10种具有不同聚集特性的树木分布，并在153个规则布设的20×20m样方中采样获取其多度数据。我们针对1000组模拟的树木分布数据检验了SSEF，这些数据要么与环境无关，要么经具有不同空间结构的环境变量过滤得到。所提方法的一类错误率控制良好（<0.05）。当多度数据经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，样方布设方案所能提供的空间分辨率限制了方法的功效。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ISD。本研究提供了可用于实现SSEF检验方法的R语言函数，并在教程中对其进行了详细说明。