Jun Zuo, Siyu Yang, Hans-Peter Grossart, Peng Xiao, He Zhang, Rui Sun, Guoyou Li, Haoran Jiang, Qihang Zhao, Meng Jiao, Yao Cheng, Zeshuang Wang, Ruozhen Geng, Zengling Ma, Renhui Li
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引用次数: 0
Abstract
River ecosystems face escalating challenges due to altered flow regimes from human activities, such as urbanization with hydrological modifications. Understanding the role of microbial communities for ecosystems with changing flow regimes is still incomplete and remains at the frontier of aquatic microbial ecology. In particular, influences of riverine backward flow on the aquatic biota remain largely unknown. Therefore, we examined the impact of backward flow on the cyanobacterial, total prokaryotic, and eukaryotic communities in the Changdougang River, which naturally flows into Lake Taihu, through environmental DNA metabarcoding. We analyzed the differences in community diversity, assembly, and ecological network stability among groups under backward, weak, and forward flow direction conditions. Non-metric multidimensional scaling showed higher variations in communities of groups across flow direction conditions than seasonal groups. Variations in alpha and beta diversity showed that cyanobacterial and total prokaryotic communities experienced strong homogenization under backward flow conditions, whereas the ecological uniqueness of the eukaryotic community decreased. Assembly of the three flow-related communities was primarily governed by drift and dispersal limitation in stochastic processes. However, in the cyanobacterial community, homogeneous selection in deterministic processes increased from 22.79% to 42.86% under backward flow, aligning with trends observed in the checkerboard score (C-score). More importantly, the topological properties of ecological networks and the degree of average variation revealed higher stability in the cyanobacterial community compared to total prokaryotic and eukaryotic communities. Considering the variations in cohesion, the network stability in the cyanobacterial community decreased under backward flow. Our findings emphasize the distinct and sequentially diminishing responses of cyanobacterial, total prokaryotic, and eukaryotic communities to backward flowing rivers. This knowledge is crucial for maintaining ecological health of rivers, assessing the complex ecological impacts on hydrological engineering, and formulating sustainable water management strategies.
期刊介绍:
Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include:
•Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management;
•Urban hydrology including sewer systems, stormwater management, and green infrastructure;
•Drinking water treatment and distribution;
•Potable and non-potable water reuse;
•Sanitation, public health, and risk assessment;
•Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions;
•Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment;
•Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution;
•Environmental restoration, linked to surface water, groundwater and groundwater remediation;
•Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts;
•Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle;
•Socio-economic, policy, and regulations studies.