Graduate School of Life Sciences, Tohoku University, 6-3 Aoba, Aramaki, Aoba-ku, Sendai 9808578 Japan.
Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, Mengla, Yunnan 666303 China.
Ecology. 2013 Dec;94(12):2873-85. doi: 10.1890/13-0269.1.
Numerous studies have revealed the existence of nonrandom trait distribution patterns as a sign of environmental filtering and/or biotic interactions in a community assembly process. A number of metrics with various algorithms have been used to detect these patterns without any clear guidelines. Although some studies have compared their statistical powers, the differences in performance among the metrics under the conditions close to actual studies are not clear. Therefore, the performances of five metrics of convergence and 16 metrics of divergence under alternative conditions were comparatively analyzed using a suite of simulated communities. We focused particularly on the robustness of the performances to conditions that are often uncertain and uncontrollable in actual studies; e.g., atypical trait distribution patterns stemming from the operation of multiple assembly mechanisms, a scaling of trait-function relationships, and a sufficiency of analyzed traits. Most tested metrics, for either convergence or divergence, had sufficient statistical power to distinguish nonrandom trait distribution patterns without uncertainty. However, the performances of the metrics were considerably influenced by both atypical trait distribution patterns and other uncertainties. Influences from these uncertainties varied among the metrics of different algorithms and their performances were often complementary. Therefore, under the uncertainties of an assembly process, the selection of appropriate metrics and the combined use of complementary metrics are critically important to reliably distinguish nonrandom patterns in a trait distribution. We provide a tentative list of recommended metrics for future studies.
大量研究表明，非随机性状分布模式的存在是群落组装过程中环境过滤和/或生物相互作用的标志。已经使用了许多具有不同算法的指标来检测这些模式，但没有明确的指导方针。尽管一些研究比较了它们的统计能力，但在接近实际研究的条件下，这些指标之间的性能差异并不清楚。因此，使用一套模拟群落比较分析了收敛性的 5 个度量和离散性的 16 个度量在替代条件下的性能。我们特别关注这些性能对实际研究中经常不确定和不可控的条件的稳健性；例如，由于多种组装机制的作用而产生的不典型性状分布模式、性状-功能关系的缩放以及分析性状的充分性。大多数测试的度量，无论是收敛性还是离散性，都有足够的统计能力来区分没有不确定性的非随机性状分布模式。然而，这些指标的性能受到非典型性状分布模式和其他不确定性的很大影响。不同算法的指标之间的不确定性影响不同，它们的性能往往是互补的。因此，在组装过程的不确定性下，选择适当的指标和使用互补指标的组合对于可靠地区分性状分布中的非随机模式至关重要。我们为未来的研究提供了一个暂定的推荐指标列表。
