Université de Lyon, F-69000, Lyon, France.
CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622, Villeurbanne, France.
Ecology. 2018 Aug;99(8):1737-1747. doi: 10.1002/ecy.2376. Epub 2018 Jul 5.
The methods of direct gradient analysis and variation partitioning are the most widely used frameworks to evaluate the contributions of species sorting to metacommunity structure. In many cases, however, species are also driven by spatial processes that are independent of environmental heterogeneity (e.g., neutral dynamics). As such, spatial autocorrelation can occur independently in both species (due to limited dispersal) and the environmental data, leading to spurious correlations between species distributions and the spatialized (i.e., spatially autocorrelated) environment. In these cases, the method of variation partitioning may present high Type I error rates (i.e., reject the null hypothesis more often than the pre-established critical level) and inflated estimates regarding the environmental component that is used to estimate the importance of species sorting. In this paper, we (1) demonstrate that metacommunities driven by neutral dynamics (via limited dispersal) alone or in combination with species sorting leads to inflated estimates and Type I error rates when testing for the importance of species sorting; and (2) propose a general and flexible new variation partitioning procedure to adjust for spurious contributions due to spatial autocorrelation from the environmental fraction. We used simulated metacommunity data driven by pure neutral, pure species sorting, and mixed (i.e., neutral + species sorting dynamics) processes to evaluate the performances of our new methodological framework. We also demonstrate the utility of the proposed framework with an empirical plant dataset in which we show that half of the variation initially due to the environment by the standard variation partitioning framework was due to spurious correlations.
直接梯度分析和变分分解方法是评估物种分选对后生群落结构贡献的最广泛使用的框架。然而，在许多情况下，物种也受到与环境异质性无关的空间过程（例如中性动态）的驱动。因此，物种（由于扩散受限）和环境数据都可能独立地产生空间自相关，从而导致物种分布与空间化（即，空间自相关）环境之间产生虚假相关性。在这些情况下，变分分解方法可能会出现高的第一类错误率（即，比预先设定的临界水平更频繁地拒绝零假设），并且对用于估计物种分选重要性的环境分量的估计值过高。在本文中，我们 (1) 证明了仅由中性动态（通过有限的扩散）驱动或与物种分选相结合的后生群落会导致在测试物种分选的重要性时产生过高的估计值和第一类错误率；并且 (2) 提出了一种通用且灵活的新变分分解程序，以调整由于环境分量的空间自相关而产生的虚假贡献。我们使用由纯中性、纯物种分选和混合（即中性+物种分选动态）过程驱动的模拟后生群落数据来评估我们新的方法框架的性能。我们还使用包含植物数据的实证数据集来演示所提出框架的实用性，其中我们表明，标准变分分解框架最初归因于环境的一半变异是由于虚假相关性。
