2. 河海大学浅水湖泊综合治理与资源开发教育部重点实验室,南京 210098
2. Key Laboratory of Integrated Regulation and Resource Department on Shallow Lakes, Ministry of Education, Hohai University, Nanjing 210098, China
细菌在河流中分布广泛,种类繁多,参与生物地球化学循环和能量流动,在维持河流生态系统的健康稳定方面起着关键作用[1, 2]. 细菌群落具有高度可变性,对环境变化极其敏感[3, 4]. 因而河流浮游细菌可用于反映河流的整体变化,是河流生态系统响应外界压力干扰和衡量系统稳定性的重要指标之一[5]. 浮游细菌之间的共存关系是影响河流生态系统的重要因素,通过共存网络分析河流生态系统中浮游细菌之间的共存关系是当今的研究重点. 与传统分析方法相比,共存网络分析能更好地了解微生物共存关系在维持群落稳定和生态系统功能方面的作用,以及微生物响应环境干扰的情况[6, 7]. Cui等[8]在研究金沙江不同河段微生物群落的影响因素时发现,受不同河段环境条件的影响,微生物的共存关系存在显著差异. 然而,现有研究多集中探究营养盐或单一污染物等特定压力源对浮游细菌群落的影响[9~11],缺乏浮游细菌对多因素共存响应方面的深入分析. 因此,综合考虑多种压力源对浮游细菌群落的影响,有利于深入理解自然河流中浮游细菌对多环境压力共存的响应机制.
饶河是汇入鄱阳湖的五大河流之一,由昌江和乐安河两条支流汇合而成[12]. 近年来,随着经济的快速发展,饶河附近人类活动对河流的干扰逐渐增强. 昌江和乐安河附近居民生活污水、养殖废水和农业废水排入河流,造成河流中营养盐和抗生素质量浓度上升[13]. 德兴铜矿和银山铅锌矿的采矿活动产生的工业废水排入乐安河,导致乐安河重金属污染较为严重[14]. 因此,饶河成为研究多压力源下浮游细菌多样性和共存关系的理想区域. 本研究采集了饶河入湖段及其支流昌江和乐安河的33个水样,分析饶河不同河段中水体营养盐、重金属和抗生素质量浓度差异及其对浮游细菌群落的多样性和共存关系的影响规律. 本研究结果有助于丰富多压力源下河流浮游细菌群落特征的认知,以期为河流生态系统健康状况评价提供数据支撑.
1 材料与方法 1.1 样品采集与处理2021年3月沿饶河及其支流采集33个水样(图 1),包括昌江11个采样点(C1~C11),乐安河17个采样点(L1~L17)和入湖段4个采样点(R1~R4). 使用GPSMAP 62s(Germin,KS,USA)记录每个采样点的经度和纬度. 使用Hydrobios Ruttner采水器(Altenholz,Germany)在各个采样点,按照左、中和右这3点等比例混合采集表层水样10 L(0~50 cm),每个采样点3个重复. 一部分水样采集后立即用孔径为0.22 μm的聚碳酸酯滤膜(GTTP04700,上海希言科学仪器有限公司)过滤收集浮游微生物,滤膜避光保存于-80℃冰箱,用于后续微生物分析;另一部分水样运回实验室,用于营养盐、重金属和抗生素质量浓度的测定.
![]() |
图 1 饶河采样点分布示意 Fig. 1 Layout of sampling sites in Raohe River |
参照Liu等[15]的方法测定水样中的总氮(TN)、氨氮(NH4+-N)、硝氮(NO3--N)、亚硝氮(NO2--N)、总磷(TP)和磷酸盐(PO43--P)的质量浓度. 使用等离子体质谱法(ICP-MS 7500,USA)测定水样中的锌(Zn)、镉(Cd)、铅(Pb)、镍(Ni)和铜(Cu)的质量浓度,利用原子荧光光谱法(AFS 8330,Beijing)测定水样中砷(As)的质量浓度. 选取磺胺类(SAs)的磺胺甲唑(SMX)、磺胺甲嘧啶(SMZ)、磺胺间甲氧嘧啶(SMM)和磺胺喹啉(SQX),四环素类(TCs)的四环素(TC)、金霉素(CTC)、土霉素(OTC)和强力霉素(DOX),喹诺酮类(QNs)的环丙沙星(CIP)、氧氟沙星(OFL)、恩诺沙星(ENR)和氟罗沙星(FLE),以及大环内酯类(MLs)的阿奇霉素(AZM)、克拉霉素(CTM)、红霉素(ERY)和罗红霉素(RTM)共16种抗生素进行检测. 参照Chi等[16]的方法测定水样中的抗生素质量浓度,其中仅有6种抗生素(SMX、SMZ、SMM、SQX、OTC和DOX)被检出.
1.3 DNA提取和高通量测序使用FastDNA Spin Kit for Soil(MP Biomedicals,USA)的DNA提取试剂盒,按照说明书要求提取滤膜上的DNA. 提取的DNA通过Illumina NovaSeq PE250平台(Illumina,San Diego,CA,USA),进行细菌的16S rRNA基因高通量扩增子测序. 参照Li等[17]的方法,选取带有barcode的特异性引物314F(5′-CCTAY GGGRBGCASCAG-3′)和806R(5′-GGACTACNNGG GTATCTAAT-3′),对16S rRNA基因的V3-V4高变区进行聚合酶链式反应(PCR)扩增. 根据Li等[18]的方法进行基因文库的构建,各样品的PCR产物以等量浓度混合,并在Illumina NovaSeq PE250平台上测序. 采用Quantitative Insights Into Microbial Ecology(QIIME,version 1.8.0)对测序数据进行质量过滤,丢弃与引物或条形码不匹配,以及长度小于200 bp的序列[19]. 将过滤后的高质量序列以97%的序列相似性水平聚类成操作分类单元(operational taxonomic units,OTU). 根据SILVA138.1数据库(http://www.arb-silva.de)对OTUs代表序列进行分类学注释.
1.4 数据分析本文数据均对样品的3个平行重复采用平均值±标准偏差计算. 使用单因素方差分析(one-way ANOVA)中的Games Howell检验方法[20]检验同样点间水体中营养盐、重金属和抗生素质量浓度,以及与浮游细菌相关指数的差异显著性. 以上分析使用SPSS软件(version 25.0,IBM Corporation,NY,USA)进行计算分析. 计算浮游细菌群落的alpha多样性指数(Chao 1指数和Shannon指数). 基于Bray-Curtis距离分析浮游细菌群落的beta多样性,使用非度量多维尺度分析(non-metric multidimensional scaling analysis,NMDS)对细菌群落结构的差异性进行可视化,并利用置换多元方差分析(permutational multivariate analysis of variance,PERMANOVA)评估3段河流中浮游细菌群落结构之间是否具有显著性差异. 基于方差膨胀因子(variance inflation factor,VIF)分析筛选影响因子,剔除VIF值大于10的环境因子[21, 22],然后使用冗余分析(redundancy analysis,RDA)探究水体中营养盐、重金属和抗生素与浮游细菌群落之间的相关性. 利用方差分解分析(variation partitioning analysis,VPA)确定营养盐、重金属和抗生素对饶河浮游细菌群落结构和共存关系的解释率. 通过Mantel检验,分析单个环境因子与浮游细菌子网络拓扑指标的相关性. 以上分析使用R 3.5.1软件中的“vegan”包进行计算分析. 利用R中的“pheatmap”包绘制物种丰度的聚类热图,以及浮游细菌与营养盐、重金属和抗生素的Spearman相关性热图. 共存网络用于研究饶河浮游细菌之间的共存关系,参照Gao等[7]的方法,使用Cytoscape软件(version 3.7.1)构建共存网络,并使用Gephi软件(version 0.9.2)实现网络的可视化. 使用R中的“igraph”包计算共存网络中子网络的节点数、连接数、平均度和平均路径长度等拓扑指标. Zi(模块内连通性)和Pi(模块间连通性)的阈值用于确定网络中单个节点的拓扑角色,其中的节点可分为4部分,分别为:外围节点(Zi ≤ 2.5,Pi ≤ 0.62)、连接节点(Zi ≤ 2.5,Pi > 0.62)、模块枢纽(Zi > 2.5,Pi ≤ 0.62)和网络枢纽(Zi > 2.5,Pi > 0.62)[23]. 网络枢纽、模块枢纽和连接节点被认为在微生物群落的抵抗力上起着关键作用,因此以上节点被定义为关键物种[24]. 关键物种的相对丰度是通过将所有关键物种的相对丰度进行Z score标准化转化后,再计算平均值得到的[25, 26].
2 结果与分析 2.1 饶河水体营养盐、重金属和抗生素质量浓度如图 2(a)所示,昌江、乐安河和入湖段之间水体TP和PO43--P的质量浓度没有显著差异. 3组河段的ρ(TN)均超过2.0 mg·L-1,河流污染情况较为严重. 在入湖段,NH4+-N和NO3--N的质量浓度明显高于昌江和乐安河,入湖段和昌江中NO2--N质量浓度显著高于乐安河. 饶河不同河段的重金属质量浓度,如图 2(b)所示,Ni和Cu在各河段中的质量浓度无显著性差异. 乐安河和入湖段的As质量浓度显著高于昌江. 乐安河中Zn和Cd质量浓度显著高于昌江,其Zn污染相对较为严重,ρ(Zn)的最大值为68.6 μg·L-1. 入湖段的Pb质量浓度显著高于昌江和乐安河. 检测的16种抗生素中共有6种被检出[图 2(c)],包含4种SAs和2种TCs. DOX质量浓度在饶河各河段中没有显著差异,入湖段中SMX、SQX和OTC的质量浓度显著高于昌江和乐安河,乐安河中SMZ、SMM和OTC的质量浓度显著高于昌江. 由此可知,入湖段营养盐、重金属和抗生素污染较为严重,乐安河重金属污染较为严重.
![]() |
(a)营养盐,(b)重金属,(c)抗生素;不同小写字母表示两者之间显著差异(P < 0.05),ns表示没有显著差异 图 2 昌江、乐安河和入湖段水体中环境因子的质量浓度 Fig. 2 Mass concentrations of environmental factors in the water of Changjiang, Le'an River, and the lake entry segment |
饶河水体中获得的高通量序列数范围为26 781~60 838,平均值为46 552. 每个水样中鉴定出的OTUs数量介于796~1 707之间. 使用alpha多样性指数(Chao 1指数和Shannon指数)评估浮游细菌群落的丰富度和多样性. 如图 3(a)所示,乐安河的Chao 1指数和Shannon指数显著低于昌江和入湖段. 采用NMDS分析[图 3(b)]和PERMANOVA分析(表 1)探究了饶河中浮游细菌群落结构的空间分布特征,结果表明饶河不同河段水体中浮游细菌群落结构存在显著差异.
![]() |
(a)alpha多样性指数(Chao 1指数和Shannon指数),(b)NMDS分析,(c)门水平浮游细菌的相对丰度;不同小写字母表示两者之间显著差异(P < 0.05) 图 3 饶河浮游细菌群落的alpha多样性指数(Chao 1指数和Shannon指数),NMDS分析和门水平浮游细菌的相对丰度 Fig. 3 The alpha diversity indices (Chao 1 index and Shannon index), NMDS analysis, and relative abundance of planktonic bacterial community at phylum level in Raohe River |
![]() |
表 1 饶河浮游细菌群落的PERMANOVA分析 Table 1 PERMANOVA analysis of planktonic bacterial community in Raohe River |
如图 3(c)所示,饶河中浮游细菌优势门包括变形菌门(Proteobacteria)、拟杆菌门(Bacteroidetes)和放线菌门(Actinobacteria),它们的相对丰度总和在不同河段中均高于85%. 乐安河(69.47%)和入湖段(71.93%)中变形菌门的相对丰度高于昌江(57.05%);而昌江段放线菌门相对丰度(16.21%)高于乐安河(10.11%)和入湖段(11.87%);昌江(15.22%)和乐安河(14.84%)中拟杆菌门的相对丰度高于入湖段(9.81%). 可见,饶河不同河段中浮游细菌的优势门存在差异.
基于VIF分析筛选出了共现性较高(VIF > 10)的影响因子,其中PO43--P(VIF =187.4)、Cu(VIF = 477.8)和Ni(VIF = 150.7)被剔除. RDA分析表明[图 4(a)],营养盐、重金属和抗生素对饶河浮游细菌群落结构变化前两轴的贡献率分别为69.06%和11.82%,其中NO3--N、Zn和NH4+-N是主要的影响因子. 通过随机森林[图 4(b)]进一步分析表明,Zn(16.8%)和NO3--N(15.1%)是导致饶河浮游细菌群落分布差异的主要环境因子. VPA分析显示[图 4(c)],营养盐、重金属和抗生素分别单独解释了饶河浮游细菌群落差异性的18.0%、15.5%和13.1%,三者共同解释了64.5%. 饶河浮游细菌Chao 1指数与NH4+-N显著正相关,与Zn和SMM显著负相关,Shannon指数与NH4+-N显著正相关,与Zn和SMZ显著负相关[图 4(d)]. 而NO3--N与变形菌门显著正相关,与拟杆菌门显著负相关;Zn和SMZ与变形菌门显著正相关;As与放线菌门显著负相关.
![]() |
(a)饶河浮游细菌群落与营养盐、重金属和抗生素的冗余分析(RDA),(b)方差分解分析(VPA),(c)随机森林模型,(d)营养盐、重金属和抗生素与浮游细菌群落alpha多样性和相对丰度前10细菌门的相关性分析;b1.Zn,b2.NO3--N,b3.NH4+-N,b4.TN,b5.SMX,b6.As,b7.Pb,b8.SMM,b9.DCC,b10.NO2--N,b11.TP,b12.SMZ,b13.Cd,b14.SQX,b15.OTC;d1.TN,d2.NH4+-N,d3.NO3--N,d4.NO2--N,d5.TP,d6.Zn,d7.As,d8.Cd,d9.Pb,d10.SMX,d11.SMZ,d12.SMM,d13.SQX,d14.DOX,d15.OTC;*表示P < 0.05,**表示P < 0.01 图 4 饶河浮游细菌群落与营养盐、重金属和抗生素的相关性分析 Fig. 4 Correlation analysis of planktonic bacterial community with nutrients, heavy metals, and antibiotics in Raohe River |
对饶河浮游细菌的OTUs进行共存网络分析(图 5),结果显示共存网络包含290个节点和738条边,正相关和负相关的连接数占比分别为63.35%和36.55%. 入湖段的节点数、连接数和平均度显著高于昌江和乐安河,而平均路径长度显著较低,表明入湖段的浮游细菌群落比昌江和乐安河的浮游细菌群落具有更复杂的共存关系. 共存网络中的节点主要属于变形菌门(61.51%),拟杆菌门(18.44%)和放线菌门(13.52%)等细菌门.
![]() |
(a)饶河浮游细菌共现网络;子网络拓扑指标:(b)节点数、(c)连接数、(d)平均度和(e)平均路径长度;不同小写字母表示两者之间显著差异(P < 0.05);百分数分别表示连接线正负相关性的占比和节点所属细菌门水平的占比 图 5 饶河浮游细菌群落的共现网络和子网络拓扑指标 Fig. 5 Co-occurrence network and subnetwork topological features of planktonic bacterial community in Raohe River |
基于模块内连接度(Zi)和模块间连接度(Pi)可知[图 6(a)],饶河浮游细菌共存网络中,96.55%的节点属于外围节点,2.07%的节点位于模块枢纽和1.38%的节点位于接连节点. 位于模块枢纽和接连节点中的10个节点可被认为是浮游细菌群落中的关键物种,它们在细菌纲水平上可归为6类,其中40%的节点属于alpha变形菌纲(alpha-Proteobacteria),20%的节点属于拟杆菌纲(Bacteroidia)[图 6(b)]. 对关键物种的相对丰度进行标准化可知[图 6(c)],关键物种在入湖段的相对丰度显著高于昌江和乐安河.
![]() |
(a)模块内连接度-模块间连接度(Zi-Pi)图,(b)关键物种在所属纲水平上占比,(c)关键物种的相对丰度;1.Acidimicrobiia,2.Cyanobacteriia,3.Verrucomicrobiae,4.Gamma-Proteobacteria,5.Bacteroidia,6.alpha-Proteobacteria;不同小写字母表示两者之间显著差异(P < 0.05) 图 6 饶河浮游细菌群落中关键物种的分析 Fig. 6 Analysis of key species of planktonic bacterial community in Raohe River |
如图 7(a)所示,多数营养盐、重金属和抗生素与关键物种、节点数、连接数和平均度显著正相关,而与平均路径长度显著负相关. 关键物种与节点数和连接数显著正相关,与平均路径长度显著负相关. 营养盐、重金属和抗生素可解释浮游细菌子网络拓扑指标51.0%的变化[图 7(b)],各自单独的解释率分别为12.4%、11.3%和6.3%. Mantel分析结果如表 2所示,浮游细菌子网络拓扑指标与NO3--N、As、SMM和TN表现出显著相关性. 这说明与其他环境因子相比,NO3--N、As、SMM和TN质量浓度对浮游细菌共存关系的复杂性的影响较大.
![]() |
(a)饶河水体营养盐、重金属和抗生素与拓扑指标之间的相关性热图分析,(b)营养盐、重金属和抗生素对拓扑指标的方差分解分析(VPA);1.NH4+-N,2.NO3--N,3.NO2--N,4.TN,5.TP,6.Zn,7.As,8.Cd,9.Pb,10.SMX,11.SMZ,12.SMM,13.SQX,14.DOX,15.TOC,16.关键物种,17.节点数,18.连接数,19.平均数,20.平均路径长度;*表示P < 0.05,**表示P < 0.01 图 7 饶河浮游细菌子网络拓扑指标与营养盐、重金属和抗生素相关性分析 Fig. 7 Correlation analysis of planktonic bacterial subnetwork topological indicators with nutrients, heavy metals, and antibiotics in Raohe River |
![]() |
表 2 Mantel检验分析显示饶河水体营养盐、重金属和抗生素对浮游细菌子网络拓扑指标的影响 Table 2 Mantel tests showing the effects of nutrients, heavy metals, and antibiotics on planktonic bacterial subnetwork topological indicators in Raohe River |
3 讨论 3.1 饶河浮游细菌群落的多样性及其影响因素
有研究发现,高质量浓度的Zn会干扰细菌蛋白质合成等代谢过程,抑制水体中部分浮游细菌的生长,导致浮游细菌生物量减少[27, 28]. 此外,河流中残留的抗生素会影响浮游细菌群落,导致浮游细菌群落多样性和丰富性下降[11]. 乐安河中Zn、SMM和SMZ质量浓度较高,可能是导致乐安河浮游细菌alpha多样性显著低于昌江和入湖段的主要原因[图 3(a)和图 4(d)]. 入湖段区域独特的水动力条件使得营养盐容易在该区域累积,使得入湖段水体中NH4+-N质量浓度升高[29, 30],这为浮游细菌的生长繁殖提供了更多的能量[31],导致浮游细菌的alpha多样性增加. 本研究发现NH4+-N质量浓度与alpha多样性存在显著正相关[图 4(d)],且在入湖段较高. 因此,这可能是入湖段alpha多样性高于其他河段的原因.
乐安河毗邻德兴铜矿和银山铅锌矿,矿区的尾矿可能在雨水冲刷下释放出Zn离子,随后渗透至地下水或者直接进入河流,导致乐安河中Zn质量浓度升高,从而影响乐安河中浮游细菌的群落结构[10, 32]. 昌江和乐安河附近人类活动产生的农业、养殖业废水和居民生活污水排入河流,导致饶河水体受到营养盐和抗生素污染,进而使得饶河浮游细菌群落结构差异显著[33]. 这可能解释了为何本研究中浮游细菌群落结构差异主要与Zn、NO3--N、NH4+-N、TN和SMX质量浓度显著相关[图4(a)和4(b)].
有研究发现,变形菌门可以广泛分布于富营养化以及重金属和抗生素等污染环境中[34, 35]. 这可能是NO3--N、Zn和SMZ质量浓度较高的乐安河和入湖段,变形菌门相对丰度高于昌江的原因[图 4(d)]. Basu等[36]发现,放线菌门对As质量浓度高度敏感,其丰度随As质量浓度的增加而降低. 这可能解释了为何本研究中As质量浓度较低的昌江中放线菌门相对丰度反而较高[图 4(d)]. Wu等[37]对于巢湖水体的研究表明,在拟杆菌门丰度较高的情况下,由于反硝化过程致使NO3--N质量浓度降低. 由此可推测拟杆菌门参与反硝化的过程,会消耗河流中的NO3--N,导致其质量浓度的降低. 因此,在NO3--N质量浓度较低的昌江和乐安河中,拟杆菌门的相对丰度较高[图 4(d)].
3.2 饶河浮游细菌共存网络关系及其驱动因素微生物的共存关系可以被用来探究微生物群落对不同环境变化的响应[38, 39]. 微生物共存网络中正、负连接分别表征微生物间的互惠和拮抗关系[40]. 饶河的浮游细菌共存网络中正相关连接数比例高于负相关,表明饶河浮游细菌之间协作关系占主导作用[图 5(a)]. 微生物共存网络中的子网络拓扑指标可以反映微生物间的复杂程度和紧密程度[41]. 其中节点数、连接数和平均度数值越大,说明微生物之间有更复杂和更连通的共存关系[42]. 平均路径长度越小,说明外界的干扰能在网络中快速传达,表明网络中物种之间的共存关系更为密切[43]. 本研究中入湖段浮游细菌群落具有更紧密和更复杂的共存关系[图 5(b)~5(e)],表明其能迅速响应外界干扰[44]. 较高浓度的营养盐为浮游细菌种间的物质交换提供了充足的能量,有利于增强浮游细菌种间的共存关系[45]. 在重金属和抗生素污染压力下,微生物会通过增强种间共存关系,来增强对污染物的抗性,从而适应环境压力[46]. 本研究通过相关性分析进一步证明了,营养盐、重金属和抗生素质量浓度较高的入湖段浮游细菌之间的共存网络关系更复杂[图 7(a)].
关键物种在微生物抵御外界环境干扰方面起着重要作用[47, 48]. 本研究中,关键物种主要属于alpha变形菌纲和拟杆菌纲[图 6(b)],这两个纲被报道可在重金属和抗生素污染压力下存活,并在N循环过程中发挥关键作用[49~51],这说明环境条件差异会影响关键物种组成[图 7(a)]. 关键物种与节点数、连接数和平均路径长度显著相关[图 7(a)],表明这些具有重要功能的关键物种在环境压力下,对维持浮游细菌群落紧密的共存关系至关重要. 这说明饶河不同河段的环境条件差异对关键物种的影响可能会进一步影响浮游细菌的共存关系.
营养盐和重金属质量浓度对饶河不同河段浮游细菌群落结构和共存关系的影响大于抗生素[图4(c)和7(b)],这可从以下4点解释:①营养盐是浮游细菌生长和代谢的必要元素,显著影响浮游细菌群落组成和多样性[45];营养盐质量浓度的增加为浮游细菌种间的物质转移提供了充足的能量,使得浮游细菌种间的共存关系更复杂[52];②重金属会对浮游细菌群落产生选择压力,改变浮游细菌群落结构和共存关系[53];③本研究发现NO3--N质量浓度的增加,使得能利用该营养物质的类群比例上升,进而影响浮游细菌的群落结构[31, 53];重金属Zn和As能与浮游细菌细胞中的DNA和酶等分子结合,导致细胞损伤和死亡[54],其中Zn和As质量浓度越大,对浮游细菌生长产生的毒害作用越大[28, 36]. 因此,这可能是NO3--N、Zn和As与其他环境因子相比,对饶河浮游细菌群落结构和共存关系影响更大的原因;④抗生素对某些特定种类的细菌有较强的选择性压力,但对非目标细菌的压力较低,因而对浮游细菌群落的整体影响较弱[55].
4 结论饶河不同河段间浮游细菌群落结构具有明显差异,其中乐安河浮游细菌alpha多样性显著低于昌江和入湖段. 饶河浮游细菌的主要优势菌门为变形菌门、拟杆菌门和放线菌门. 入湖段浮游细菌网络复杂性和关键物种相对丰度,与昌江和乐安河相比显著增加,进一步表明浮游细菌在饶河入湖段具有更复杂和更紧密的共存关系. 此外,营养盐和重金属质量浓度是饶河不同河段浮游细菌群落多样性和共存关系的关键驱动因素.
[1] | Madsen E L. Microorganisms and their roles in fundamental biogeochemical cycles[J]. Current Opinion in Biotechnology, 2011, 22(3): 456-464. DOI:10.1016/j.copbio.2011.01.008 |
[2] | Liu S, Wang P F, Wang C, et al. Ecological insights into the disturbances in bacterioplankton communities due to emerging organic pollutants from different anthropogenic activities along an urban river[J]. Science of the Total Environment, 2021, 796. DOI:10.1016/j.scitotenv.2021.148973 |
[3] | Yang Y G, Chen H H, Abdullah Al M, et al. Urbanization reduces resource use efficiency of phytoplankton community by altering the environment and decreasing biodiversity[J]. Journal of Environmental Sciences, 2022, 112: 140-151. DOI:10.1016/j.jes.2021.05.001 |
[4] | Hou D W, Huang Z J, Zeng S Z, et al. Environmental factors shape water microbial community structure and function in shrimp cultural enclosure ecosystems[J]. Frontiers in Microbiology, 2017, 8. DOI:10.3389/fmicb.2017.02359 |
[5] |
璩伟卿, 张博美, 黄雪, 等. 基于16S rRNA测序技术的青藏高原河流细菌群落多样性[J]. 环境科学, 2023, 44(1): 262-271. Qu W Q, Zahng B M, Huang X, et al. Bacterial community and diversity of river ecosystems on the Qinghai-Tibet Plateau based on 16S rRNA gene sequencing[J]. Environmental Science, 2023, 44(1): 262-271. |
[6] | Niu L H, Guo Y T, Li Y, et al. Degradation of river ecological quality in Tibet plateau with overgrazing: a quantitative assessment using biotic integrity index improved by random forest[J]. Ecological Indicators, 2021, 120. DOI:10.1016/j.ecolind.2020.106948 |
[7] | Gao H, Chen J, Wang C, et al. Diversity and interaction of bacterial and microeukaryotic communities in sediments planted with different submerged macrophytes: responses to decabromodiphenyl ether[J]. Chemosphere, 2023, 322. DOI:10.1016/j.chemosphere.2023.138186 |
[8] | Cui G, Chen J, Wang C, et al. Planktonic archaea reveal stronger dispersal limitation and more network connectivity than planktonic bacteria in the Jinsha River of Southwestern China[J]. Freshwater Biology, 2023, 68(11): 1995-2010. DOI:10.1111/fwb.14170 |
[9] | Ma K, Ren Z, Ma J M, et al. Compositional changes and co-occurrence patterns of planktonic bacteria and microeukaryotes in a subtropical estuarine ecosystem, the Pearl River Delta[J]. Water, 2022, 14(8). DOI:10.3390/w14081227 |
[10] | Jiang Y H, Xie H Q, Zhang H, et al. Dissolved heavy metals distribution and risk assessment in the Le'an River subjected to violent mining activities[J]. Polish Journal of Environmental Studies, 2018, 27(4): 1559-1572. DOI:10.15244/pjoes/77033 |
[11] | Ye M Q, Chen G J, Du Z J. Effects of antibiotics on the bacterial community, metabolic functions and antibiotic resistance genes in mariculture sediments during enrichment culturing[J]. Journal of Marine Science and Engineering, 2020, 8(8). DOI:10.3390/jmse8080604 |
[12] |
圣平, 于一尊, 田晓娟, 等. 鄱阳湖7个河口水体中细菌多样性和组成特征[J]. 农业现代化研究, 2016, 37(3): 606-612. Sheng P, Yu Y Z, Tian X J, et al. Bacterial diversities and compositions in seven different estuarine water columns of Poyang Lake[J]. Research of Agricultural Modernization, 2016, 37(3): 606-612. |
[13] |
程华, 张冬. 饶河流域水污染防治规划研究[J]. 水利规划与设计, 2020(10): 32-35. Cheng H, Zhang D. Study on water pollution control planning of Raohe River Basin[J]. Water Resources Planning and Design, 2020(10): 32-35. DOI:10.3969/j.issn.1672-2469.2020.10.008 |
[14] | Xiao H Y, Zhou W B, Zeng F P, et al. Water chemistry and heavy metal distribution in an AMD highly contaminated river[J]. Environmental Earth Sciences, 2010, 59(5): 1023-1031. DOI:10.1007/s12665-009-0094-5 |
[15] | Liu L M, Yang J, Yu X Q, et al. Patterns in the composition of microbial communities from a subtropical river: effects of environmental, spatial and temporal factors[J]. PLoS One, 2013, 8(11). DOI:10.1371/journal.pone.0081232 |
[16] | Chi S L, Xu W H, Han Y R. ARGs distribution and high-risk ARGs identification based on continuous application of manure in purple soil[J]. Science of the Total Environment, 2022, 853. DOI:10.1016/j.scitotenv.2022.158667 |
[17] | Li Y, Sun Y, Zhang H J, et al. The responses of bacterial community and N2O emission to nitrogen input in lake sediment: estrogen as a co-pollutant[J]. Environmental Research, 2019, 179. DOI:10.1016/j.envres.2019.108769 |
[18] | Li Y, Liu G, Gong R Q, et al. Gut microbiome dysbiosis in patients with endometrial cancer vs. healthy controls based on 16S rRNA gene sequencing[J]. Current Microbiology, 2023, 80(8). DOI:10.1007/s00284-023-03361-6 |
[19] | Shen C C, Xiong J B, Zhang H Y, et al. Soil pH drives the spatial distribution of bacterial communities along elevation on Changbai Mountain[J]. Soil Biology and Biochemistry, 2013, 57: 204-211. DOI:10.1016/j.soilbio.2012.07.013 |
[20] | Ruxton G D, Beauchamp G. Time for some a priori thinking about post hoc testing[J]. Behavioral Ecology, 2008, 19(3): 690-693. DOI:10.1093/beheco/arn020 |
[21] |
周石磊, 孙悦, 岳哿丞, 等. 雄安新区-白洋淀冬季冰封期水体好氧反硝化菌群落空间分布特征及驱动因素[J]. 环境科学, 2020, 41(5): 2177-2187. Zhou S L, Sun Y, Yue G C, et al. Spatial distribution characteristics and driving factors of aerobic denitrification bacterial community structure from Baiyangdian Lake in Xiong'an New Area during the winter freezing period[J]. Environmental Science, 2020, 41(5): 2177-2187. |
[22] | Zhang L Y, Delgado-Baquerizo M, Shi Y, et al. Co-existing water and sediment bacteria are driven by contrasting environmental factors across glacier-fed aquatic systems[J]. Water Research, 2021, 198. DOI:10.1016/j.watres.2021.117139 |
[23] | Ling N, Zhu C, Xue C, et al. Insight into how organic amendments can shape the soil microbiome in long-term field experiments as revealed by network analysis[J]. Soil Biology and Biochemistry, 2016, 99: 137-149. DOI:10.1016/j.soilbio.2016.05.005 |
[24] | Tylianakis J M, Morris R J. Ecological networks across environmental gradients[J]. Annual Review of Ecology, Evolution, and Systematics, 2017, 48: 25-48. DOI:10.1146/annurev-ecolsys-110316-022821 |
[25] | Chen J, Wang P F, Wang C, et al. How dam construction affects the activity of alkaline phosphatases in reservoir sediments: a study of two highly regulated rivers[J]. Environmental Research, 2022, 207. DOI:10.1016/j.envres.2021.112236 |
[26] | Chen J, Zhang B, Wang C, et al. Insight into the enhancement effect of humic acid on microbial degradation of triclosan in anaerobic sediments[J]. Journal of Hazardous Materials, 2024, 461. DOI:10.1016/j.jhazmat.2023.132549 |
[27] | Zhao J, Zhao X, Chao L, et al. Diversity change of microbial communities responding to zinc and arsenic pollution in a river of Northeastern China[J]. Journal of Zhejiang University-Science B, 2014, 15(7): 670-680. DOI:10.1631/jzus.B1400003 |
[28] | Zhang H, Wan Z W, Ding M J, et al. Inherent bacterial community response to multiple heavy metals in sediment from river-lake systems in the Poyang Lake, China[J]. Ecotoxicology and Environmental Safety, 2018, 165: 314-324. DOI:10.1016/j.ecoenv.2018.09.010 |
[29] | Yang B, Lin H, Bartlett S L, et al. Partitioning and transformation of organic and inorganic phosphorus among dissolved, colloidal and particulate phases in a hypereutrophic freshwater estuary[J]. Water Research, 2021, 196. DOI:10.1016/j.watres.2021.117025 |
[30] | Ji N N, Liu Y, Wang S R, et al. Buffering effect of suspended particulate matter on phosphorus cycling during transport from rivers to lakes[J]. Water Research, 2022, 216. DOI:10.1016/j.watres.2022.118350 |
[31] | Niu L H, Xie X D, Li Y, et al. Effects of nitrogen on the longitudinal and vertical patterns of the composition and potential function of bacterial and archaeal communities in the tidal mudflats[J]. Science of the Total Environment, 2022, 806. DOI:10.1016/j.scitotenv.2021.151210 |
[32] |
王森, 陈建文, 张红, 等. 亳清河水体细菌群落的结构和分布特征[J]. 环境科学, 2023, 44(4): 2113-2121. Wang S, Chen J W, Zhang H, et al. Structure and distribution characteristics of bacterial community in Boqing River water[J]. Environmental Science, 2023, 44(4): 2113-2121. |
[33] |
吴怡, 王成, 王华, 等. 基于CCME-WQI方法的鄱阳湖流域乐安河水质分析[J]. 环境科学, 2024, 45(9): 5235-5243. Wu Y, Wang C, Wang H, et al. Analysis of water quality of Le'an river in Poyang Lake Basin based on CCME-WQI method[J]. Environmental Science, 2024, 45(9): 5235-5243. |
[34] | Feris K, Ramsey P, Frazar C, et al. Differences in hyporheic-zone microbial community structure along a heavy-metal contamination gradient[J]. Applied and Environmental Microbiology, 2003, 69(9): 5563-5573. DOI:10.1128/AEM.69.9.5563-5573.2003 |
[35] | Wang L Q, Zhu M J, Li Y, et al. Deterministic assembly process dominates bacterial antibiotic resistome in wastewater effluents receiving river[J]. Environmental Science and Pollution Research, 2022, 29(60): 90207-90218. DOI:10.1007/s11356-022-22096-8 |
[36] | Basu S, Paul T, Yadav P, et al. Molecular study of indigenous bacterial community composition on exposure to soil arsenic concentration gradient[J]. Polish Journal of Microbiology, 2017, 66(2): 209-221. DOI:10.5604/01.3001.0010.7838 |
[37] | Wu L, Shu F, Ou Z, et al. Compositions of prokaryote communites and their relationship to physiochemical factors in december in Chaohu Lake and three urban rivers in China[J]. Applied Ecology and Environmental Research, 2019, 17(4): 7265-7281. |
[38] | Kéfi S, Miele V, Wieters E A, et al. How structured is the entangled bank? The surprisingly simple organization of multiplex ecological networks leads to increased persistence and resilience[J]. Plos Biology, 2016, 14(8). DOI:10.1371/journal.pbio.1002527 |
[39] | Ishimoto C K, Aono A H, Nagai J S, et al. Microbial co-occurrence network and its key microorganisms in soil with permanent application of composted tannery sludge[J]. Science of the Total Environment, 2021, 789. DOI:10.1016/j.scitotenv.2021.147945 |
[40] | Faust K, Raes J. Microbial interactions: from networks to models[J]. Nature Reviews Microbiology, 2012, 10(8): 538-550. DOI:10.1038/nrmicro2832 |
[41] | Ma J, Lu Y Q, Chen F, et al. Molecular ecological network complexity drives stand resilience of soil bacteria to mining disturbances among typical damaged ecosystems in China[J]. Microorganisms, 2020, 8(3). DOI:10.3390/microorganisms8030433 |
[42] | Zhang B G, Zhang J, Liu Y, et al. Co-occurrence patterns of soybean rhizosphere microbiome at a continental scale[J]. Soil Biology and Biochemistry, 2018, 118: 178-186. DOI:10.1016/j.soilbio.2017.12.011 |
[43] | Yuan M M, Guo X, Wu L W, et al. Climate warming enhances microbial network complexity and stability[J]. Nature Climate Change, 2021, 11(4): 343-348. DOI:10.1038/s41558-021-00989-9 |
[44] | Deng Y, Zhang P, Qin Y J, et al. Network succession reveals the importance of competition in response to emulsified vegetable oil amendment for uranium bioremediation[J]. Environmental Microbiology, 2016, 18(1): 205-218. DOI:10.1111/1462-2920.12981 |
[45] | He D, Shen W J, Eberwein J, et al. Diversity and co-occurrence network of soil fungi are more responsive than those of bacteria to shifts in precipitation seasonality in a subtropical forest[J]. Soil Biology and Biochemistry, 2017, 115: 499-510. DOI:10.1016/j.soilbio.2017.09.023 |
[46] | Chen J W, Li J J, Zhang H, et al. Bacterial heavy-metal and antibiotic resistance genes in a copper Tailing dam area in Northern China[J]. Frontiers in Microbiology, 2019, 10. DOI:10.3389/fmicb.2019.01916 |
[47] | Gao H, Wang C, Chen J, et al. Enhancement effects of decabromodiphenyl ether on microbial sulfate reduction in eutrophic lake sediments: a study on sulfate-reducing bacteria using dsrA and dsrB amplicon sequencing[J]. Science of the Total Environment, 2022, 843. DOI:10.1016/j.scitotenv.2022.157073 |
[48] | Wang M, Chen S B, Chen L, et al. Responses of soil microbial communities and their network interactions to saline-alkaline stress in Cd-contaminated soils[J]. Environmental Pollution, 2019, 252: 1609-1621. DOI:10.1016/j.envpol.2019.06.082 |
[49] | Karimi B, Terrat S, Dequiedt S, et al. Biogeography of soil bacteria and archaea across France[J]. Science Advances, 2018, 4(7). DOI:10.1126/sciadv.aat1808 |
[50] | Eggers S, Safdar N, Kates A, et al. Urinary lead level and colonization by antibiotic resistant bacteria: evidence from a population-based study[J]. Environmental Epidemiology, 2021, 5(6). DOI:10.1097/EE9.0000000000000175 |
[51] | Dang H Y, Lovell C R. Microbial surface colonization and biofilm development in marine environments[J]. Microbiology and Molecular Biology Reviews, 2016, 80(1): 91-138. DOI:10.1128/MMBR.00037-15 |
[52] | Dai Y, Yang Y Y, Wu Z, et al. Spatiotemporal variation of planktonic and sediment bacterial assemblages in two plateau freshwater lakes at different trophic status[J]. Applied Microbiology and Biotechnology, 2016, 100(9): 4161-4175. DOI:10.1007/s00253-015-7253-2 |
[53] |
刘洋, 刘琦, 田雨露, 等. 滦河干流中上游浮游细菌群落多样性及其影响因素[J]. 生态学报, 2022, 42(12): 5103-5114. Liu Y, Liu Q, Tian Y L, et al. Characteristics of bacterioplankton community with relations to environmental parameters in upstream and midstream of the Luanhe River, China[J]. Acta Ecologica Sinica, 2022, 42(12): 5103-5114. |
[54] | Gupta S, Graham D W, Sreekrishnan T R, et al. Exploring the impacts of physicochemical characteristics and heavy metals fractions on bacterial communities in four rivers[J]. Journal of Environmental Management, 2023, 325. DOI:10.1016/j.jenvman.2022.116453 |
[55] | Sánchez-Baena A M, Caicedo-Bejarano L D, Chávez-Vivas M. Structure of bacterial community with resistance to antibiotics in aquatic environments. A systematic review[J]. International Journal of Environmental Research and Public Health, 2021, 18(5). DOI:10.3390/ijerph18052348 |