Environmental Microbiomics Research Center, School of Environmental Science and Engineering, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, 510006, China.
College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, 310036, China.
Environ Res. 2020 Aug;187:109666. doi: 10.1016/j.envres.2020.109666. Epub 2020 May 19.
The human activity introduces strong environmental stresses, and results in great spatiotemporal heterogeneity for the environment. Although the effects of environmental factors on the microbial diversity and succession have been widely studied, knowledge about how keystone taxa respond to environmental stresses remains poorly understood. We examined bacterial and archaeal communities from 45 wetland ponds covering a wide range of waters in Hangzhou. We found that shifts in bacterial and archaeal communities were strongly correlated with water pollution as indicated by the comprehensive water quality identification (CWQI). The SEGMENTED analysis suggested that there were non-linear responses of microbial communities and keystone taxa to the water pollution gradient. Moreover, these significant tipping points (e.g., CWQI > 4.0) would afford a warning line for urban wetland management. Notably, keystone taxa of bacterial communities could be used to successfully (~88.9% accuracy) predict water contamination levels. This study provides new insights into the potential for keystone bacterial taxa to predict water contamination.
人类活动引入了强烈的环境压力，导致环境在时空上呈现出巨大的异质性。尽管环境因素对微生物多样性和演替的影响已被广泛研究，但对于关键类群如何应对环境压力的知识仍知之甚少。我们研究了来自杭州 45 个湿地池塘的细菌和古菌群落，这些池塘涵盖了广泛的水域。我们发现，细菌和古菌群落的变化与综合水质识别（CWQI）所指示的水污染强烈相关。分段分析表明，微生物群落和关键类群对水污染梯度的反应是非线性的。此外，这些显著的转折点（例如，CWQI > 4.0）将为城市湿地管理提供一条预警线。值得注意的是，细菌群落的关键类群可以成功地（~88.9%的准确率）预测水污染水平。本研究为关键细菌类群预测水污染提供了新的见解。
