Department of Animal Ecology, Nicolaus Copernicus University, Gagarina 9, 87-100 Toruń, Poland.
Ecology. 2010 Nov;91(11):3384-97. doi: 10.1890/09-2157.1.
The influence of negative species interactions has dominated much of the literature on community assembly rules. Patterns of negative covariation among species are typically documented through null model analyses of binary presence/absence matrices in which rows designate species, columns designate sites, and the matrix entries indicate the presence (1) or absence (0) of a particular species in a particular site. However, the outcome of species interactions ultimately depends on population-level processes. Therefore, patterns of species segregation and aggregation might be more clearly expressed in abundance matrices, in which the matrix entries indicate the abundance or density of a species in a particular site. We conducted a series of benchmark tests to evaluate the performance of 14 candidate null model algorithms and six covariation metrics that can be used with abundance matrices. We first created a series of random test matrices by sampling a metacommunity from a lognormal species abundance distribution. We also created a series of structured matrices by altering the random matrices to incorporate patterns of pairwise species segregation and aggregation. We next screened each algorithm-index combination with the random and structured matrices to determine which tests had low Type I error rates and good power for detecting segregated and aggregated species distributions. In our benchmark tests, the best-performing null model does not constrain species richness, but assigns individuals to matrix cells proportional to the observed row and column marginal distributions until, for each row and column, total abundances are reached. Using this null model algorithm with a set of four covariance metrics, we tested for patterns of species segregation and aggregation in a collection of 149 empirical abundance matrices and 36 interaction matrices collated from published papers and posted data sets. More than 80% of the matrices were significantly segregated, which reinforces a previous meta-analysis of presence/absence matrices. However, using two of the metrics we detected a significant pattern of aggregation for plants and for the interaction matrices (which include plant-pollinator data sets). These results suggest that abundance matrices, analyzed with an appropriate null model, may be a powerful tool for quantifying patterns of species segregation and aggregation.
负种间相互作用的影响主导了群落组装规则的大部分文献。物种间负相关性的模式通常通过二元存在/缺失矩阵的零模型分析来记录，其中行指定物种，列指定地点，矩阵条目表示特定地点特定物种的存在（1）或不存在（0）。然而，种间相互作用的结果最终取决于种群水平的过程。因此，物种分离和聚集的模式可能在丰度矩阵中更清晰地表达，其中矩阵条目表示特定地点特定物种的丰度或密度。我们进行了一系列基准测试，以评估 14 种候选零模型算法和 6 种可以与丰度矩阵一起使用的协变度量的性能。我们首先通过从对数正态物种丰度分布中抽样元群落来创建一系列随机测试矩阵。我们还通过改变随机矩阵来创建一系列结构化矩阵，以纳入种间分离和聚集的模式。接下来，我们使用随机和结构化矩阵筛选每个算法-指数组合，以确定哪些测试具有较低的第一类错误率和检测分离和聚集物种分布的良好能力。在我们的基准测试中，表现最好的零模型不限制物种丰富度，但将个体分配到矩阵单元格中，与观察到的行和列边缘分布成正比，直到每行和每列达到总丰度。使用这种零模型算法和一组四个协变度量，我们在从已发表的论文和已发布的数据集中收集的 149 个经验丰度矩阵和 36 个相互作用矩阵中测试了物种分离和聚集的模式。超过 80%的矩阵显著分离，这强化了先前对存在/缺失矩阵的荟萃分析。然而，使用其中两个度量标准，我们检测到植物和相互作用矩阵（包括植物-传粉者数据集）存在显著的聚集模式。这些结果表明，使用适当的零模型分析的丰度矩阵可能是量化物种分离和聚集模式的有力工具。
