中文关键词有机污染浮游细菌群落多样性冗余分析指示物种 英文关键词organic pollutionbacterioplankton communitydiversityredundancy analysisbio-indicator species

作者	单位	E-mail
宣丽霞	宁波大学海洋学院, 宁波 315211	xuan_lixia@163.com
戴文芳	宁波大学海洋学院, 宁波 315211 浙江海洋高效健康养殖协同创新中心, 宁波 315211
郁维娜	宁波大学海洋学院, 宁波 315211 浙江海洋高效健康养殖协同创新中心, 宁波 315211
周素明	宁波大学海洋学院, 宁波 315211
欧昌荣	宁波大学海洋学院, 宁波 315211
熊金波	宁波大学海洋学院, 宁波 315211 浙江海洋高效健康养殖协同创新中心, 宁波 315211	xiongjinbo@nbu.edu.cn

中文摘要 杭州湾海域剧烈的人为干扰及大量的陆源输入，导致有机物污染严重，是我国近海污染最严重的海域之一.微生物在污染物降解过程中发挥着关键作用并能作为评价污染程度的生物标记，但对于浮游细菌群落如何响应有机污染物尚不清楚.因此，本研究从杭州湾采集了13个站点的表层水样（水表下0.5 m），并采集了邻近外海8个站点的水样作为对照，利用Illumina MiSeq测序技术测定细菌16S rRNA基因，研究不同有机污染程度对浮游细菌群落的影响.结果表明杭州湾有机污染值A（13.2±1.6）显著（P<0.001）高于邻近外海（5.4±3.0），且两区域浮游细菌分布和多样性存在显著差异.杭州湾浮游细菌群落优势菌群为γ-变形菌纲（γ-Proteobacteria，24.4%±5.5%）、α-变形菌纲（α-Proteobacteria，16.5%±7.7%）和浮霉菌门（Planctomycetes，13.9%±8.6%），邻近海域主要优势菌群为蓝细菌（Cyanobacteria，20.1%±7.5%）、拟杆菌（Bacteroidetes，18.4%±1.5%）、放线菌门（Actinobacteria，17.5%±4.2%）、γ-变形菌纲（16.6%±1.2%）和α-变形菌纲（14.3%±1.7%）.多元回归树（multivariate regression tree，MRT）分析表明：悬浮颗粒（suspended particulates，SP）、亚硝酸盐（NO₂^-）、油污（oil）、有机污染是影响浮游细菌群落多样性的最主要因素，分别解释了22.0%、6.5%、6.0%和5.5%的多样性变异.冗余分析（redundancy analysis，RDA）表明：驱动浮游细菌群落变异的环境因子有机污染、化学需氧量（COD）、叶绿素a（Chla）、总氮（TN）、硝酸盐（NO₃^-）和盐度对物种分布的总解释量为71.0%，而有机污染作为单因子控制了6.5%的群落变异，高于其它单一因子.此外，采用指示值方法共挑选出了35个敏感物种，这些物种相对丰度与有机污染显著相关，可用来指示近海有机污染水平.综上，浮游细菌群落对环境污染敏感，为评价海洋有机污染水平提供了敏感的生物学指标. 英文摘要 Hangzhou Bay suffers from intensive anthropogenic disturbances and a huge amount of terrestrial inputs, and thus has become one of the most seriously contaminated coastal zones in China. There is evidence that microbes play a dominant role in pollutant biodegradation and serve as biomarkers for pollution levels. However, it remains unclear how the bacterioplankton communities respond to organic contaminants. To fill this knowledge gap, we collected surface water samples (0.5 m below the surface layer) from 13 sites across Hangzhou Bay and 8 control sites across its adjacent offshore areas. Using Illumina sequencing based on analysis of the bacterial 16S rRNA gene, we explored the effects of increasing organic pollution levels on the bacterioplankton community compositions (BCCs). The results revealed that the organic pollution level (A) in Hangzhou Bay (13.2±1.6) was significantly (P<0.001) higher than in the control zone (5.4±3.0). The distribution and diversity of bacterioplankton communities were significantly distinct between the two zones. The dominant bacterioplankton lineages in Hangzhou Bay were γ-Proteobacteria (24.4%±5.5%), α-Proteobacteria (16.5%±7.7%), and Planctomycetes (13.9%±8.6%), whereas those in the adjacent zones were Cyanobacteria (20.1%±7.5%), Bacteroidetes (18.4%±1.5%), Actinobacteria (17.5%±4.2%), γ-Proteobacteria (16.6%±1.2%), and α-Proteobacteria (14.3%±1.7%). Multivariate regression tree (MRT) analysis showed that the bacterioplankton community diversity was primarily affected by suspended particulates (SP), nitrite, oil, and organic pollutants, which respectively explained 22.0%, 6.5%, 6.0%, and 5.5% of the variance in diversity. Redundancy analysis (RDA) illustrated that the bacterioplankton community distribution was controlled by organic pollutants, COD, Chla, TN, nitrate, and salinity, which cumulatively governed 71.0% of the variation in BCCs. Organic pollutants alone controlled 6.5% variance, which was higher than any other single factor. Additionally, 35 sensitive species were identified via the indicator value method and their relative abundances were significantly associated (P<0.05 in each case) with the organic pollution level, thereby indicating their potential for evaluating coastal pollution. Collectively, our work demonstrates that BCCs are sensitive to coastal pollution and provides biomarkers for elevated pollution levels.

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