Schmitz Désirée A, Wechsler Tobias, Mignot Ingrid, Kümmerli Rolf
Department of Quantitative Biomedicine, University of Zurich, 8057 Zurich, Switzerland.
Department of Microbiology, Harvard Medical School, Boston, MA 02115, United States.
ISME Commun. 2024 Mar 27;4(1):ycae045. doi: 10.1093/ismeco/ycae045. eCollection 2024 Jan.
How to derive principles of community dynamics and stability is a central question in microbial ecology. Bottom-up experiments, in which a small number of bacterial species are mixed, have become popular to address it. However, experimental setups are typically limited because co-culture experiments are labor-intensive and species are difficult to distinguish. Here, we use a four-species bacterial community to show that information from monoculture growth and inhibitory effects induced by secreted compounds can be combined to predict the competitive rank order in the community. Specifically, integrative monoculture growth parameters allow building a preliminary competitive rank order, which is then adjusted using inhibitory effects from supernatant assays. While our procedure worked for two different media, we observed differences in species rank orders between media. We then parameterized computer simulations with our empirical data to show that higher order species interactions largely follow the dynamics predicted from pairwise interactions with one important exception. The impact of inhibitory compounds was reduced in higher order communities because their negative effects were spread across multiple target species. Altogether, we formulated three simple rules of how monoculture growth and supernatant assay data can be combined to establish a competitive species rank order in an experimental four-species community.
如何推导群落动态和稳定性的原理是微生物生态学中的一个核心问题。自下而上的实验，即将少数细菌物种混合在一起的实验，已成为解决这一问题的常用方法。然而，实验设置通常受到限制，因为共培养实验劳动强度大，且物种难以区分。在这里，我们使用一个四种细菌的群落来表明，来自单培养生长的信息和由分泌化合物诱导的抑制作用可以结合起来，以预测群落中的竞争等级顺序。具体来说，综合单培养生长参数可以构建一个初步的竞争等级顺序，然后使用来自上清液测定的抑制作用进行调整。虽然我们的方法在两种不同的培养基上都有效，但我们观察到不同培养基之间物种等级顺序存在差异。然后，我们用经验数据对计算机模拟进行参数化，以表明高阶物种相互作用在很大程度上遵循从成对相互作用预测的动态，但有一个重要例外。在高阶群落中，抑制性化合物的影响减弱，因为它们的负面影响分散在多个目标物种上。总之，我们制定了三条简单的规则，说明如何将单培养生长和上清液测定数据结合起来，以在一个实验性的四种群落中建立竞争物种等级顺序。
