Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America.
Plant Biology, University of Illinois, Urbana-Champaign, Illinois, United States of America.
PLoS Comput Biol. 2019 Jan 22;15(1):e1006688. doi: 10.1371/journal.pcbi.1006688. eCollection 2019 Jan.
Patterns of trait distribution among competing species can potentially reveal the processes that allow them to coexist. It has been recently proposed that competition may drive the spontaneous emergence of niches comprising clusters of similar species, in contrast with the dominant paradigm of greater-than-chance species differences. However, current clustering theory relies largely on heuristic rather than mechanistic models. Furthermore, studies of models incorporating demographic stochasticity and immigration, two key players in community assembly, did not observe clusters. Here we demonstrate clustering under partitioning of resources, partitioning of environmental gradients, and a competition-colonization tradeoff. We show that clusters are robust to demographic stochasticity, and can persist under immigration. While immigration may sustain clusters that are otherwise transient, too much dilutes the pattern. In order to detect and quantify clusters in nature, we introduce and validate metrics which have no free parameters nor require arbitrary trait binning, and weigh species by their abundances rather than relying on a presence-absence count. By generalizing beyond the circumstances where clusters have been observed, our study contributes to establishing them as an update to classical trait patterning theory.
竞争物种之间的特征分布模式可能揭示了使它们能够共存的过程。最近有人提出,竞争可能会促使形成由相似物种集群组成的生态位,这与物种差异大于偶然的主导范式形成对比。然而,目前的聚类理论在很大程度上依赖于启发式而不是机械模型。此外,对包含群落组装两个关键因素——种群动态和移民的模型的研究并没有观察到聚类。在这里,我们展示了在资源划分、环境梯度划分和竞争-定居权衡下的聚类。我们表明,聚类对种群动态具有鲁棒性,并且可以在移民的情况下持续存在。虽然移民可能维持那些本来是短暂的聚类,但过多的移民会使模式变得模糊。为了在自然界中检测和量化聚类,我们引入并验证了没有自由参数也不需要任意特征分组的度量标准,并且根据物种的丰度对其进行加权,而不是依赖存在-缺失计数。通过超越已经观察到聚类的情况进行推广,我们的研究有助于将聚类确立为对经典特征模式理论的更新。