Zhao Jianshu, Brandt Genevieve, Gronniger Jessica L, Wang Zhao, Li Jiaqian, Hunt Dana E, Rodriguez-R Luis M, Hatt Janet K, Konstantinidis Konstantinos T
Center for Bioinformatics and Computational Genomics, Georgia Institute of Technology, Atlanta, GA 30332, United States.
School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, United States.
ISME J. 2025 Jan 2;19(1). doi: 10.1093/ismejo/wraf129.
Understanding how populations respond to disturbances represents a major goal for microbial ecology. While several hypotheses have been advanced to explain microbial community compositional changes in response to disturbance, appropriate data to test these hypotheses is scarce, due to the challenges in delineating rare vs. abundant taxa and generalists vs. specialists, a prerequisite for testing the theories. Here, we operationally define these two key concepts by employing the patterns of coverage of a (target) genome by a metagenome to identify rare populations, and by borrowing the proportional similarity index from macroecology to identify generalists. We applied these concepts to time-series (field) metagenomes from the Piver's Island Coastal Observatory to establish that coastal microbial communities are resilient to major perturbations such as tropical cyclones and (uncommon) cold or warm temperature events, in part due to the response of rare populations. Therefore, these results provide support for the insurance hypothesis [i.e. the rare biosphere has the buffering capacity to mitigate the effects of disturbance]. Additionally, generalists appear to contribute proportionally more than specialists to community adaptation to perturbations like warming, supporting the disturbance-specialization hypothesis [i.e. disturbance favors generalists]. Several of these findings were also observed in replicated laboratory mesocosms that aimed to simulate disturbances such as a rain-driven washout of microbial cells and a labile organic matter release from a phytoplankton bloom. Taken together, our results advance understanding of the mechanisms governing microbial population dynamics under changing environmental conditions and have implications for ecosystem modeling.
了解种群如何应对干扰是微生物生态学的一个主要目标。虽然已经提出了几种假说来解释微生物群落组成对干扰的变化,但由于在区分稀有与丰富分类群以及广适种与特化种方面存在挑战(这是检验这些理论的先决条件),用于检验这些假说的适当数据很少。在这里,我们通过利用宏基因组对(目标)基因组的覆盖模式来识别稀有种群,并借鉴宏观生态学中的比例相似性指数来识别广适种,从而在操作上定义这两个关键概念。我们将这些概念应用于皮弗岛海岸观测站的时间序列(实地)宏基因组,以确定沿海微生物群落对热带气旋和(罕见的)寒冷或温暖温度事件等主要扰动具有恢复力,部分原因是稀有种群的响应。因此,这些结果为保险假说[即稀有生物圈具有减轻干扰影响的缓冲能力]提供了支持。此外,广适种在群落适应诸如变暖等扰动方面的贡献似乎比特化种更大,这支持了干扰特化假说[即干扰有利于广适种]。在旨在模拟干扰(如降雨驱动的微生物细胞冲刷和浮游植物大量繁殖导致的不稳定有机物释放)的重复实验室中宇宙实验中也观察到了其中一些发现。综上所述,我们的结果推进了对不断变化的环境条件下微生物种群动态调控机制的理解,并对生态系统建模具有启示意义。