Eutrophication increases the similarity of cyanobacterial community features in lakes and reservoirs

IF 12.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Jun Zuo , Peng Xiao , Jani Heino , Fengjiao Tan , Janne Soininen , Huihuang Chen , Jun Yang
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Abstract

Eutrophication of inland waters is a mostly anthropogenic phenomenon impacting aquatic biodiversity worldwide, and might change biotic community structure and ecosystem functions. However, little is known about the patterns of cyanobacterial community variations and changes both on alpha and beta diversity levels in response to eutrophication. Here, we investigated cyanobacterial communities sampled at 140 sites from 59 lakes and reservoirs along a strong eutrophication gradient in eastern China through using CPC-IGS and 16S rRNA gene amplicon sequencing. We found that taxonomic diversity increased, but phylogenetic diversity decreased significantly along the eutrophication gradient. Both niche width and niche overlap of cyanobacteria significantly decreased from low- to high-nutrient waterbodies. Cyanobacterial community distance-decay relationship became weaker from mesotrophic to hypereutrophic waterbodies, while ecological uniqueness (i.e., local contributions to beta diversity) tended to increase in high-nutrient waterbodies. Latitude and longitude were more important in shaping cyanobacterial community structure than other environmental variables. These findings suggest that eutrophication affects alpha and beta diversity of cyanobacterial communities, leading to increasingly similar community structures in lakes and reservoirs with a higher level of eutrophication. Our work highlights how cyanobacterial communities respond to anthropogenic eutrophication and calls for an urgent need to develop conservation and management strategies to control lake eutrophication and protect freshwater biodiversity.

Abstract Image

Abstract Image

富营养化增加了湖泊和水库中蓝藻群落特征的相似性
内陆水域富营养化是影响全球水生生物多样性的主要人为现象,可能会改变生物群落结构和生态系统功能。然而,人们对蓝藻群落的变异模式以及富营养化在α和β多样性水平上的变化知之甚少。在此,我们采用 CPC-IGS 和 16S rRNA 基因扩增子测序技术,对中国东部 59 个湖泊和水库的 140 个蓝藻群落进行了调查。我们发现,沿着富营养化梯度,分类多样性有所增加,但系统发育多样性却显著下降。从低营养水体到高营养水体,蓝藻的生态位宽度和生态位重叠度都明显下降。从中度富营养化水体到高度富营养化水体,蓝藻群落距离-衰减关系变弱,而生态独特性(即对β多样性的局部贡献)在高营养水体中趋于增加。与其他环境变量相比,纬度和经度对蓝藻群落结构的形成更为重要。这些发现表明,富营养化会影响蓝藻群落的α和β多样性,从而导致富营养化程度较高的湖泊和水库中的群落结构越来越相似。我们的研究强调了蓝藻群落是如何对人为富营养化做出反应的,并呼吁迫切需要制定保护和管理策略来控制湖泊富营养化和保护淡水生物多样性。
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
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.
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