Wu Linwei, Yang Yunfeng, Ning Daliang, Gao Qun, Yin Huaqun, Xiao Naija, Zhou Benjamin Y, Chen Si, He Qiang, Zhou Jizhong
Institute of Ecology, Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences Peking University Beijing China.
Institute for Environmental Genomics University of Oklahoma Norman OK USA.
mLife. 2023 Sep 12;2(3):239-252. doi: 10.1002/mlf2.12076. eCollection 2023 Sep.
Disentangling the assembly mechanisms controlling community composition, structure, distribution, functions, and dynamics is a central issue in ecology. Although various approaches have been proposed to examine community assembly mechanisms, quantitative characterization is challenging, particularly in microbial ecology. Here, we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer-resource model with a neutral model in stochastic differential equations. Using time-series data from anaerobic bioreactors that target microbial 16S rRNA genes, we tested the applicability of three ecological models: the consumer-resource model, the neutral model, and the combined model. Our results revealed that model performances varied substantially as a function of population abundance and/or process conditions. The combined model performed best for abundant taxa in the treatment bioreactors where process conditions were manipulated. In contrast, the neutral model showed the best performance for rare taxa. Our analysis further indicated that immigration rates decreased with taxa abundance and competitions between taxa were strongly correlated with phylogeny, but within a certain phylogenetic distance only. The determinism underlying taxa and community dynamics were quantitatively assessed, showing greater determinism in the treatment bioreactors that aligned with the subsequent abnormal system functioning. Given its mechanistic basis, the framework developed here is expected to be potentially applicable beyond microbial ecology.
解析控制群落组成、结构、分布、功能和动态的组装机制是生态学中的核心问题。尽管已经提出了各种方法来研究群落组装机制，但定量表征具有挑战性，尤其是在微生物生态学中。在这里，我们提出了一种新方法，通过将消费者 - 资源模型与随机微分方程中的中性模型相结合，来定量描述群落组装机制。利用针对微生物16S rRNA基因的厌氧生物反应器的时间序列数据，我们测试了三种生态模型的适用性：消费者 - 资源模型、中性模型和组合模型。我们的结果表明，模型性能随种群丰度和/或过程条件的变化而有很大差异。在处理条件受到控制的生物反应器中，组合模型对丰富的分类群表现最佳。相比之下，中性模型对稀有分类群表现出最佳性能。我们的分析进一步表明，迁入率随分类群丰度降低，分类群之间的竞争与系统发育密切相关，但仅在一定的系统发育距离内如此。定量评估了分类群和群落动态背后的确定性，结果表明在与随后的异常系统功能相关的处理生物反应器中，确定性更高。鉴于其机制基础，本文开发的框架有望在微生物生态学之外具有潜在的适用性。
