环境科学  2023, Vol. 44 Issue (9): 5145-5153   PDF    
引用本文
罗颖, 李敬伟, 袁浩, 包智华. 稀土-重金属共污染土壤中真菌群落结构特征及主导影响因素[J]. 环境科学, 2023, 44(9): 5145-5153.
LUO Ying, LI Jing-wei, YUAN Hao, BAO Zhi-hua. Characteristics and Dominant Influencing Factors of the Fungal Community Structure in Soils Co-contaminated with Rare Earth Elements and Heavy Metals[J]. Environmental Science, 2023, 44(9): 5145-5153.
稀土-重金属共污染土壤中真菌群落结构特征及主导影响因素
罗颖1, 李敬伟2, 袁浩3, 包智华4     
1. 内蒙古工业大学能源与动力工程学院, 内蒙古自治区高等学校环境污染控制与修复重点实验室, 呼和浩特 010051;
2. 内蒙古尚清环保科技有限公司, 呼和浩特 010049;
3. 内蒙古自治区自然资源保护与利用研究中心, 呼和浩特 010000;
4. 内蒙古大学生态与环境学院, 呼和浩特 010070
收稿日期: 2022-10-02; 修订日期: 2022-11-30
基金项目: 国家自然科学基金项目（41963008，41563009）；内蒙古自治区科技重大专项（ZDZX2018054）；内蒙古科技计划项目（JH20180633）；内蒙古教育厅项目（RZ2300000522）；内蒙古自然科学基金项目（RZ2300001583）
作者简介: 罗颖(1982~), 女, 博士研究生, 讲师, 主要研究方向为稀土和重金属污染土壤微生物生态, E-mail: luoying82@126.com
摘要: 稀土元素已被列为新兴污染物，稀土元素经常与重金属在土壤中富集，造成生态危机.稀土污染的生态效应已逐渐受到关注，但忽视了稀土与重金属共存的协同效应.了解稀土和重金属共污染土壤中真菌群落结构及主导影响因素，有助于制定土壤修复策略，以减少或补救人类生产活动对环境的影响.目前，长期稀土和重金属污染对土壤真菌群落的影响尚不清楚.以包头稀土尾矿坝为研究区域，利用ITS1基因扩增子Illumina高通量测序分析稀土和重金属共污染土壤中真菌群落多样性和结构特征.结果表明，在共污染环境中，土壤环境变量的异质性决定了真菌群落在小范围内的分布，构成独特的生态位.污染土壤的真菌群落丰富度和多样性显著低于未污染土壤，且真菌群落组成差异明显.随机森林分析结果表明，TN是影响真菌群落丰富度和多样性的最主要因素，其次是稀土元素、重金属Zn和AK.CCA分析结果表明，重金属Zn是影响真菌群落结构的最关键因素.VPA分析结果显示，所检测环境变量能够解释土壤真菌群落变化93.3%的差异，土壤理化性质与污染因子（稀土和重金属）的综合效应解释了总差异的58.5%，二者单独解释的贡献度分别为13.5%和21%，污染因子的贡献度稍高于土壤理化性质；稀土和重金属的协同效应对总差异的贡献度为40.1%，各自的单独作用分别为21.8%和17.9%.因此，共污染环境中真菌群落结构和组成受土壤理化性质、稀土和重金属的共同调控，稀土和重金属的协同效应大于各自的单独作用，研究结果表明需要进一步加强土壤环境中稀土元素和重金属共污染的风险管控.
关键词: 稀土元素(REEs)      重金属(HMs)      共污染      土壤理化性质      土壤真菌群落      协同效应     
Characteristics and Dominant Influencing Factors of the Fungal Community Structure in Soils Co-contaminated with Rare Earth Elements and Heavy Metals
LUO Ying1 , LI Jing-wei2 , YUAN Hao3 , BAO Zhi-hua4     
1. Key Laboratory of Environmental Pollution Control and Remediation at Universities of Inner Mongolia Autonomous Region, College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China;
2. Inner Mongolia Shang Qing Environmental Protection Technology Co., Ltd., Hohhot 010049, China;
3. Center for Conservation and Utilization of Inner Mongolia Natural Resources, Hohhot 010000, China;
4. School of Ecology and Environment, Inner Mongolia University, Hohhot 010070, China
Abstract: Rare earth elements (REEs) have been listed as emerging pollutants and are often enriched together in soils with heavy metals (HMs), which results in ecological crises. The ecological effects caused by REEs have been attracting increasing amounts of attention, but most studies neglect the synergistic effect of REEs and HMs. The soil fungal community plays an important role in maintaining ecosystem functions, and understanding the fungal community structure and its dominant influencing factors in the co-contaminated soils will help to develop soil remediation strategies that could reduce or remedy the impacts of human production activities on the environment. Currently, the effects of long-term contamination of REEs and HMs on the soil fungal communities remain unclear. The Baotou rare earth tailings dam (Inner Mongolia, China) was used as the area of study, and soil samples co-contaminated with REEs and HMs were collected. Illumina high-throughput sequencing with ITS1 gene amplicons was used to analyze the fungal community diversity and structural characteristics. The results showed that the heterogeneity of soil environmental variables determined the distribution of fungal communities in a small area and constituted its own unique ecological niche in the co-contaminated environment. The fungal community richness and diversity in the co-contaminated soils were significantly lower than those in the uncontaminated soils, and the composition of the fungal community was significantly different. The results of a random forest (RF) analysis showed that TN was the most important factor that affected the fungal community richness and diversity, followed by REEs, Zn, and AK. The results of a canonical correspondence analysis (CCA) showed that Zn was the most important factor that affected the fungal community structure. A variation partitioning analysis (VPA) was performed to quantify the relative contributions of different environmental variables on the changes in fungal community structure, and the analytical results showed that all the detected environmental variables could explain 93.3% of the variation in soil fungal community. The combined effect of soil physicochemical properties and pollution factors (REEs and HMs) accounted for 58.5% of the total variation, and their contribution alone accounted for 13.5% and 21%, respectively. The effects of these pollution factors on the fungal communities were slightly higher than those of the soil physicochemical properties. The synergistic contributions of REEs and HMs were 40.1%, and their individual effects were 21.8% and 17.9%, respectively. Therefore, the soil physicochemical properties, REEs, and HMs regulated the fungal community structure and composition in concert. The synergistic contributions of REEs and HMs were greater than their individual effects, and these results suggest that it is necessary to further strengthen the risk control of the co-contamination of REEs and HMs in the soil environment.
Key words: rare earth elements (REEs)      heavy metals (HMs)      co-contamination      soil physicochemical properties      soil fungal community      synergistic effect     

稀土元素(rare earth elements, REEs) 是由17种物理化学性质相似的金属组成, 由于稀土具有优异的特性被广泛应用, 全球对稀土的需求正在迅速增加[1].有研究表明, 大规模、密集的稀土开采活动和地表尾矿的堆积导致了一系列的环境污染和生态危机[2~4], 甚至对人类健康具有潜在危害[5].由于稀土元素具有环境持久性、生物富集性和毒性, 人们开始逐渐关注稀土污染引起的生态效应[6], 稀土元素已被列为新兴污染物[7].然而, REEs经常与重金属(heavy metals, HMs)同时出现在环境中, 导致严重的REEs-HMs共污染, 如稀土的开采和精炼过程, 废弃的稀土矿山或尾矿库的堆积, 甚至在电子废弃物中[8~10].人们往往仅关注稀土引起的环境问题却忽视了其与重金属共存的协同效应.

细菌和真菌都是土壤微生物群的核心组成部分, 在陆地生态系统中发挥着关键的生态功能[11].然而, 与关于重金属污染土壤中细菌群落的大量研究相比, 有关真菌群落的研究相对较少, 并且有研究显示, 可能由于细菌和真菌菌体结构和代谢物质存在差异的原因, 在重金属富集的土壤环境中影响细菌和真菌群落结构和组成的关键因素不同[12, 13].研究土壤真菌群落结构和多样性对了解地下生态系统的功能也至关重要.

大多数情况下, REEs或HMs的积累对土壤微生物具有毒性作用.有研究表明, 重金属污染会对土壤微生物群落结构及其生态功能造成不利影响.高稀土含量对土壤微生物丰度和功能具有持续的抑制作用, 随着REEs含量的增加, 抑制作用可能会增强, 而且很难在短期消除[14].REEs和HMs的共同出现极有可能对土壤微生物产生协同毒性效应.然而, 目前REEs-HMs共污染对土壤真菌群落的影响知之甚少.此外, 影响污染土壤微生物群落的关键因素也不同, 例如, 矿山废弃地普遍缺乏必要的养分, 进一步限制了土壤微生物群落[15, 16].有研究者指出, 土壤理化性质(包括土壤有机质、湿度、pH值和土壤类型)可影响金属毒性[17~19], 土壤化学性质在调节微生物对不同金属的适应方面很重要[20].微生物群落结构的变化不仅与土壤金属含量有关, 还与土壤理化性质参数有关.共污染环境中环境因子与真菌群落的关系也不清楚.

本文以稀土尾矿坝附近共污染土壤为研究对象, 采用高通量测序技术分析长期REEs-HMs共污染土壤中真菌群落多样性和结构特征, 评价污染因子和土壤理化性质对真菌群落结构和组成的影响.本研究填补了REEs-HMs共污染土壤中影响真菌群落结构主导因素的研究空白, 对共污染环境生态风险评估具有指导意义.

1 材料与方法 1.1 研究区概况及样本采集

rare earth tailings dam研究区域为内蒙古西部包头稀土尾矿坝, 该地区干燥少雨, 全年盛行西北风, 气温较低, 周边土壤主要为栗钙土.自1965年开始运行以来, 由于坝漏、浮尘和雨水侵蚀等原因, 尾矿坝周围环境受到了REEs和HMs的污染, 坝体东南侧土壤污染最为严重[21].因此, 该稀土尾矿坝是探索微生物对REEs-HMs共污染响应的良好自然栖息地.本文在尾矿坝以东、东南和南侧距离200 m的5个土壤采样样地(BR1、BR2、BR3、BR4和BR5)采集了样品.对照样地(CK)选自距离大坝20 km的相对无污染区域[21], 气候和土壤类型与坝区相同.总共选用了6个采样样地, 样地位置如图 1所示.每个样地随机设置3个平行采样点, 采用多点采样法采集表层土(0~20 cm), 经混合和均质后, 为每个采样点创建复合样本.共采集18个复合样品, 装入无菌袋中, 并立即用冰盒运送到实验室.所有土壤复合样品分为两部分：一部分用于理化分析及金属含量分析, 另一部分进行微生物分析.

图 1 包头稀土尾矿坝采样点示意 Fig. 1 Locations of soil sampling sites in Baotou rare earth tailings dam

1.2 土壤理化及金属含量分析

土壤pH值按土水比1∶2.5(质量浓度)混合, 使用玻璃电极pH计进行测定.土壤可溶性盐含量(soluble salt content, Salinity)采用残渣干燥法进行测定.土壤含水率(water content, WC)采用重量差法进行测定.测定具体方法参见《土壤农化分析》[22].土壤总有机碳(total organic carbon, TOC)和总氮(total nitrogen, TN)使用元素分析仪进行测定.总磷(total phosphorus, TP)和总钾(total potassium, TK)是土壤经微波消解后, 采用电感耦合等离子体发射光谱仪(inductively coupled plasma emission spectrometer, ICP-OES)进行测定[23].土壤中速效磷(total phosphorus, AP)和速效钾(available potassium, AK)是土壤经浸提后, 采用ICP-OES进行测定[24].

土壤样品中REEs使用HCl、HNO3和HClO4(3∶1∶1, 体积比)开放式消解, HMs使用HCl、HNO3、HF和HClO4(3∶1∶1∶1, 体积比)微波消解, 采用ICP-OES进行测定[25].

1.3 高通量测序及分析

使用土壤DNA试剂盒(Omega Bio-tek, USA)提取土壤的总DNA, 经纯化、质检合格后, 使用引物ITS1F(5′-CTTGGTCATTTAGAGGAAGTAA-3′)和ITS1R(5′-GCTGCGTTCTTCATCGATGC-3′)对真菌18S rRNA基因位点的ITS1区域进行PCR扩增[26], 扩增后使用磁珠法对PCR产物进行扩增目标片段纯化回收后, 送至上海派森诺生物技术有限公司进行高通量测序.制备测序文库后, 采用Illumina NovaSeq平台对群落DNA片段进行双端(Paired-end)测序(2×250), 以确定土壤真菌群落的组成和多样性.

采用QIIME2 (2019.4)[27]对获得的高质量序列进行微生物组生物信息学分析.经质量过滤、去噪、拼接和去除嵌合体后[28], 进行去重, 获得有效序列.使用DADA2质控后产生的每个去重的序列称为ASVs (amplicon sequence variants), 或称为特征序列.在QIIME2环境下, 使用feature-classifier插件中的classification-sklearn算法[29], 以默认参数通过Naive Bayes分类器参照UNITE数据库(版本8.0/ITS-fungi数据库)对每个ASVs特征序列进行真菌物种注释.

使用QIIME2的多样性插件进行多样性指数、丰富度和测序深度估算.以随机抽取的方式对每个样本进行抽平, 计算ASVs相对丰度, 用于后续的生信分析.

1.4 数据统计与分析

本文采用SPSS(25.0版)进行数据统计分析.采用单因素方差分析(ANOVA)以P＜0.05的阈值进行显著性差异分析.使用R中的“randomForest”软件包(版本4.0.2)进行了分类随机森林(random forest, RF)分析[30, 31].使用R Studio中的“Vegan”软件包, 基于weighted-normalized unifrac距离进行非度量多维尺度(nonmetric multidimensional scaling, NMDS)分析和相似性分析(analysis of similarity, ANOSIM)[32], 以检测样本之间的真菌群落差异.利用Canoco(5.0版)软件, 采用典范对应分析(canonical correspondence analysis, CCA)和方差分解分析(variation partition analysis, VPA)确定细菌群落与环境变量之间的关系[33, 34].

2 结果与分析 2.1 土壤理化性质及稀土元素、重金属元素含量

土壤样本理化指标如表 1所示, REEs和HMs含量如表 2所示.总的来说, 6个土壤样地的土壤环境变量之间存在显著差异, 其中, 污染土壤中稀土La和Ce的含量, 重金属Cr和Zn的含量较高.与我国内蒙古土壤背景值[25]相比, 部分污染土壤中REEs和HMs(除Cd外)的含量高出3倍(表 2).BR5样地Cr的含量和BR2、BR3样地Zn的含量高于我国土壤环境质量标准(GB 15618-2018)中风险筛查值(pH>7.5, 表 2).表明稀土尾矿坝存在严重的REEs-HMs共污染.BR1样地的TOC最低(1.55 g·kg-1).BR3样地的Salinity最高(83.6 g·kg-1), 总氮最为匮乏[C/N=20.5/1; ω(TN)=0.36 g·kg-1].污染土壤pH值为8.00~8.78, 尾矿坝土壤为盐碱化土壤.污染土壤中AK和AP均显著高于对照区.结果表明, 即使在小范围采样区域内, 尾矿坝土壤也具有明显的REEs-HMs共污染特征, 环境变量波动较大, 具有高度的异质性.

表 1 检测土壤样本的理化性质1) Table 1 Soil physicochemical properties in the studied soils

表 2 检测土壤样本中稀土元素和重金属含量1) Table 2 Contents of REEs and HMs in the studied soils

2.2 真菌群落多样性及群落结构特征 2.2.1 土壤真菌群落多样性变化

本研究共检测到914 655条有效、高质量的序列, 平均长度为265 bp.每个样品的有效序列覆盖率高于99%(表 3), 表明测序深度足够.本文确定了土壤真菌群落的Chao1、Shannon和PD_whole_tree这3个α-多样性指数, 以评估物种丰富度和多样性(包括遗传多样性), 如表 3所示.结果显示, CK样地的真菌群落最多样化、最稳定, 而污染样地BR4真菌丰富度和遗传多样性最低, BR2样地的真菌物种多样性最低.与对照区相比, 污染土壤的真菌群落丰富度和多样性显著降低(P＜0.05, 表 3).此外, 污染样地间的真菌群落丰富度和多样性差异显著(P＜0.05, 表 3).

表 3 6个土壤样地中真菌群落丰富度和多样性指数1) Table 3 Fungal richness and diversity indices of six soil sites

2.2.2 土壤真菌群落结构特征

采用NMDS和ANOSIM相似性分析对样本中的真菌群落结构进行差异性分析, 如图 2所示.结果表明, 6个样地的真菌群落各自聚为一簇, BR1和BR5样地较为接近.ANOSIM检验表明, 6个样地间的组间差异显著大于组内差异(R=0.662, P=0.002).以上结果表明, 在REEs-HMs共污染环境中, 土壤环境变量的异质性决定了真菌群落在小范围内的分布, 每个样地都构成了自己独特的生态位.

图 2 基于weighted-normalized unifrac距离真菌群落在属水平的NMDS和ANOSIM检测 Fig. 2 NMDS and ANOSIM tests of the fungal communities based on the weighted-normalized unifrac distance at the genus level

2.3 土壤环境变量对真菌多样性和群落结构的影响 2.3.1 多样性随机森林分析

通过RF分析确定影响真菌多样性波动的环境参数主要贡献者, 如图 3所示.结果表明, TN是影响真菌群落丰富度(Chao1指数)和多样性(Shannon指数)的最主要因素, 其次是5个稀土元素、重金属Zn和AK.所选环境参数的重要性顺序解释了77.58%的真菌丰富度差异[Chao1指数, 图 3(a)]和61.61%的真菌多样性差异[Shannon指数, 图 3(b)], Chao1指数的模型拟合系数R2为0.71(P＜0.001), Shannon指数R2为0.48 (P＜0.001).因此, 所检测土壤环境变量的异质性可以解释真菌丰富度和多样性的差异.

IncMSE为精度平均减少值, 值越大, 说明该变量越重要; 1.TOC, 2.TN, 3.pH, 4.Salility, 5.AP, 6.AK, 7.WC, 8.Cr, 9.Pb, 10.Zn, 11.Cd, 12.La, 13.Ce, 14.Pr, 15.Nd, 16.Sm; 显著性水平, *表示P＜0.05和**表示P＜0.01 图 3 基于随机森林模型确定环境变量对真菌群落α-多样性的相对影响重要性 Fig. 3 Relative influence importance of environmental variables on the fungal communities α-diversity based on the random forest model

2.3.2 真菌群落结构CCA和VPA分析

采用CCA和VPA确定真菌群落与环境变量之间的关系, 如图 4所示.分析表明, CCA1和CCA2分别解释了群落结构总变异的28.95%和19.32%[图 4(a)]. 考虑单因素时, 重金属Zn是影响真菌群落结构变化的最主要因素, 并发现理化指标TOC、TN、AK、pH和Salinity与CCA2显著相关, 而Zn、Cr、Pb、La、Ce、Nd和Cd与CCA1显著相关[图 4(a)].通过VPA分析, 量化不同环境因子对真菌群落结构组成变化的相对贡献, 如图 4(b)所示.所检测环境变量能够解释土壤真菌群落93.3%的变化, 土壤理化性质(TOC、TN、pH、WC、Salinity、AK、AP)和污染因子(La、Ce、Pr、Nd、Sm、Cr、Zn、Pb、Cd)的综合效应解释了58.5%的总变异, 这两部分环境变量分别单独解释了13.5%和21%的变化[图 4(b)], 污染因子(REEs和HMs)的单独作用稍大于土壤理化性质.稀土、重金属和两者共同效应对真菌群落差异的贡献度分别为21.8%、17.9%和40.1% [图 4(b)].结果表明, 即使在小范围内, 真菌群落的变化也主要是由土壤化学性质和污染因子(即REEs和HMs)共同驱动的, 且污染因子的作用稍大于土壤理化性质; 在污染因子中, REEs和HMs的协同作用大于二者单独作用的影响.

(a)真菌群落结构与环境变量相关性的CCA分析, (b)不同环境因子综合作用解释真菌群落差异的VPA分析; Salinity为土壤可溶性盐含量, WC为水含率, TN为全氮, TOC为全有机碳, TK为全钾, TP为全磷, AK为速效钾, AP为速效磷 图 4 真菌群落结构与环境变量相互关系的CCA和VPA分析 Fig. 4 CCA and VPA showed the relationship between the fungal community structure and the environmental variables

2.4 真菌群落组成及差异性分析

根据分类学分析, 在采集的土壤样本中总共发现了11个真菌门, 真菌群落优势菌门的相对丰度如图 5所示.优势菌门(>1%)为子囊菌门(Ascomycota)、未分类菌门、罗兹菌门(Rozellomycota)、壶菌门(Chytridiomycota)、担子菌门(Basidiomycota)、被孢霉菌门(Mortierellomycota)和球囊菌门(Glomeromycota), 其中子囊菌门、罗兹菌门、壶菌门和担子菌门相对丰度在所检测的样本间存在显著差异(P＜0.05, 图 5), 罗兹菌门和壶菌门在BR1中相对丰度最高, 子囊菌门在BR2中的相对丰度最高, 球囊菌门在BR3中的相对丰度最高, 担子菌门在CK中相对丰度最高.与CK相比, 污染土壤样本中被孢霉菌门和担子菌门的相对丰度均明显降低.结果表明, 所有优势菌门相对丰度均受到尾矿长期堆积的影响.

n=3; others表示在任一样地中平均相对丰度＜1%; 采用单因素方差分析(One-way ANOVA)进行物种相对丰度组间差异显著性检验, *表示0.01≤P＜0.05; **表示0.001≤P＜0.01; ***表示P＜0.001 图 5 各样地中真菌群落门水平的相对丰度 Fig. 5 Relative abundances of fungal communities in all soil samples at phylum level

3 讨论

稀土尾矿坝经60多年的积累, 导致周边生态系统发生了明显变化, 地表植被稀少, 土壤盐碱化, 表现出较强的土壤异质性.REEs-HMs共污染土壤的真菌群落丰富度和多样性均显著低于对照土壤(表 3), 这与以往有关稀土/重金属污染土壤研究的结果一致[34, 35].本研究发现TN是影响真菌群落丰富度(Chao1指数)和多样性(Shannon指数)的最主要因素, 其次是5个稀土元素、重金属Zn和AK.前人的研究也有类似的报道, 在金属污染土壤中有机质是影响微生物丰富度和多样性的最重要因素[36].在稀土开采活动中, 关键调节因子包括植物物种丰富度、限制营养素(TOC和TN)和REEs对土壤微生物有显著影响[34].通常情况下, 土壤理化性质直接影响微生物多样性和群落组成[37], 在矿山废弃地普遍缺乏必要的养分, 进一步限制了土壤微生物生长繁殖[15, 16].本研究中污染样地的TN显著低于对照土壤(表 1), 因此, 有限氮源成为影响共污染土壤中真菌群落的丰富度和多样性的最主要因素.除限制性养分外, REEs和HMs也显著影响土壤真菌丰富度和多样性(图 2)及其群落组成(图 5).REEs持续抑制了采矿土壤中的微生物组成和丰度[38].Hao等[39]研究报道, 500 g·L-1 La能显著降低微生物α-多样性指数, 显著改变微生物群落组成.此外, Ce、Pr和Nd可以作为抗菌试剂[40, 41]和诱变剂[42].在土壤中, 高浓度重金属会影响蛋白质合成等代谢功能[43], 且几乎对所有微生物都有毒性, 导致微生物生物量和多样性发生变化[44, 45].

污染土壤微生物群落组成的变化依赖于物种更替, 其中耐药物种取代较敏感的物种, 导致微生物多样性和群落结构的变化[46].REEs和HMs会抑制不具有耐性真菌的生长, 致使污染土壤中真菌群落多样性降低, 但耐性菌株由于其适应性而成为优势菌, 从而使REEs-HMs共污染土壤中真菌群落结构发生改变.NMDS分析结果表明, 6个样地的真菌群落各自聚为一簇, 每个样地都构成了自己独特的生态位(图 2).

REEs-HMs共污染土壤中微生物与土壤环境的关系是复杂的.土壤微生物群落结构受多种环境因素共同调控, 而不是由单一因素决定的[47, 48].VPA结果表明, 长期污染土壤中的真菌群落结构受土壤理化性质和污染因子(REEs和HMs)的共同调控[图 4(b)].在受重金属污染的土壤中也报道了类似的结果[49].在污染因子中, REEs和HMs的协同作用大于REEs和HMs单独作用的影响[图 4(b)].有关多种REEs和HMs元素对真菌影响的相关研究较少, 没能获得更多的比较.以往的研究显示生物体可以同时富集和积累REEs和HMs[50, 51], 袁浩等[52]分离出同时具有重金属和稀土耐性的曲霉菌属, 证实较高的REEs和HMs浓度对微生物具有显著的抑制作用.另外, 有研究显示, 混合稀土元素的含量在150 mg·kg-1时, 会降低微生物生物量和生产力[53].杨元根等[54]研究表明, 混合稀土元素和单个稀土元素对土壤微生物的毒性是不同的, 混合稀土元素的毒性更强; 当混合稀土元素的含量≥350 mg·kg-1时, 会严重影响土壤微生物群落结构, 这可能是由于REEs具有相似的性质, 导致对土壤微生物具有叠加作用.因此, 多种REEs和HMs对土壤微生物具有协同效应, 并且大于各自的单独作用.

4 结论

(1) 稀土尾矿长期堆存对周边土壤造成了严重的稀土和重金属共污染, 导致了尾矿坝周围土壤的异质性, 在共污染土壤中真菌群落丰富度和多样性显著降低, TN是最主要的影响因素, 其次是5个稀土元素、重金属Zn和AK.考虑单因素时, 重金属Zn是影响真菌群落结构的最关键因素.

(2) 在综合贡献方面, 真菌群落结构和组成受土壤理化性质、REEs和HMs的共同调控, 污染因子对真菌群落结构差异的贡献度稍高于土壤理化性质, REEs和HMs的协同效应大于各自的单独作用.

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