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表流湿地细菌群落结构特征
引用本文:魏佳明,崔丽娟,李伟,雷茵茹,于菁菁,秦鹏,穆泳林,梁钊瑞. 表流湿地细菌群落结构特征[J]. 环境科学, 2016, 37(11): 4357-4365
作者姓名:魏佳明  崔丽娟  李伟  雷茵茹  于菁菁  秦鹏  穆泳林  梁钊瑞
作者单位:中国林业科学研究院湿地研究所, 北京 100091;湿地生态功能与恢复北京市重点实验室, 北京 100091;北京汉石桥湿地生态系统国家定位观测研究站, 北京 101399,中国林业科学研究院湿地研究所, 北京 100091;湿地生态功能与恢复北京市重点实验室, 北京 100091;北京汉石桥湿地生态系统国家定位观测研究站, 北京 101399,中国林业科学研究院湿地研究所, 北京 100091;湿地生态功能与恢复北京市重点实验室, 北京 100091;北京汉石桥湿地生态系统国家定位观测研究站, 北京 101399,中国林业科学研究院湿地研究所, 北京 100091;湿地生态功能与恢复北京市重点实验室, 北京 100091;北京汉石桥湿地生态系统国家定位观测研究站, 北京 101399,中国林业科学研究院湿地研究所, 北京 100091;湿地生态功能与恢复北京市重点实验室, 北京 100091;北京汉石桥湿地生态系统国家定位观测研究站, 北京 101399,中国林业科学研究院湿地研究所, 北京 100091;湿地生态功能与恢复北京市重点实验室, 北京 100091;北京汉石桥湿地生态系统国家定位观测研究站, 北京 101399,中国林业科学研究院湿地研究所, 北京 100091;湿地生态功能与恢复北京市重点实验室, 北京 100091;北京汉石桥湿地生态系统国家定位观测研究站, 北京 101399,中国林业科学研究院湿地研究所, 北京 100091;湿地生态功能与恢复北京市重点实验室, 北京 100091;北京汉石桥湿地生态系统国家定位观测研究站, 北京 101399
基金项目:中央级公益性科研院所基本科研业务费专项(CAFYBB2014QA029)
摘    要:为探讨表流湿地细菌群落结构的空间分布以及细菌环境影响因子,采用高通量测序方法,对表流湿地沿水流方向10个不同处理单元细菌群落结构进行分析,并利用冗余分析对其与环境因子的关系进行了探究.研究表明:细菌群落多样性指数(香农-威纳指数)平均值为6.57,细菌群落主要属于Proteobacterice(38.97%)、Bacteroidetes(15.63%)等18个门类,丰度大于1%的共有22个属;沿程细菌多样性总体呈现先升高后降低的波动性变化,最终处理单元与最初处理单元的多样性均较其余各处理单元低;细菌丰度与pH、ORP、NH_4~+-N、NO_2~--N、TN均有相关性.

关 键 词:表流湿地  细菌群落  水环境    高通量测序
收稿时间:2016-02-28
修稿时间:2016-06-08

Characteristics of Bacterial Communities in Surface-flow Constructed Wetlands
WEI Jia-ming,CUI Li-juan,LI Wei,LEI Yin-ru,YU Jing-jing,QIN Peng,MU Yong-lin and LIANG Zhao-rui. Characteristics of Bacterial Communities in Surface-flow Constructed Wetlands[J]. Chinese Journal of Environmental Science, 2016, 37(11): 4357-4365
Authors:WEI Jia-ming  CUI Li-juan  LI Wei  LEI Yin-ru  YU Jing-jing  QIN Peng  MU Yong-lin  LIANG Zhao-rui
Affiliation:Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China;The Beijing Key Laboratory of Wetland Ecological Function and Restoration, Beijing 100091, China;Beijing Hanshiqiao National Wetland Ecosystem Research Station, Beijing 101399, China,Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China;The Beijing Key Laboratory of Wetland Ecological Function and Restoration, Beijing 100091, China;Beijing Hanshiqiao National Wetland Ecosystem Research Station, Beijing 101399, China,Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China;The Beijing Key Laboratory of Wetland Ecological Function and Restoration, Beijing 100091, China;Beijing Hanshiqiao National Wetland Ecosystem Research Station, Beijing 101399, China,Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China;The Beijing Key Laboratory of Wetland Ecological Function and Restoration, Beijing 100091, China;Beijing Hanshiqiao National Wetland Ecosystem Research Station, Beijing 101399, China,Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China;The Beijing Key Laboratory of Wetland Ecological Function and Restoration, Beijing 100091, China;Beijing Hanshiqiao National Wetland Ecosystem Research Station, Beijing 101399, China,Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China;The Beijing Key Laboratory of Wetland Ecological Function and Restoration, Beijing 100091, China;Beijing Hanshiqiao National Wetland Ecosystem Research Station, Beijing 101399, China,Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China;The Beijing Key Laboratory of Wetland Ecological Function and Restoration, Beijing 100091, China;Beijing Hanshiqiao National Wetland Ecosystem Research Station, Beijing 101399, China and Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China;The Beijing Key Laboratory of Wetland Ecological Function and Restoration, Beijing 100091, China;Beijing Hanshiqiao National Wetland Ecosystem Research Station, Beijing 101399, China
Abstract:Employing high-throughput sequencing as the method, this study analyzed the relationship between the water environment and the microbial community structure in the surface-flow constructed wetland. The results showed that: the mean Shannon-Wiener index was 6.57 and there were mainly 18 categories in the microbial community, including Proteobacterice (38.97%), Bacteroidetes (15.63%) etc. Of these, the total content of 22 genera was over 1%. The microbial biodiversity showed an increasing trend at the beginning and then turned to a decreasing trend in the flowing direction. The results also revealed that pH, ORP, NH4+-N, NO2--N and TN acted as important restricting factors for the microbial community.
Keywords:surface-flow constructed wetlands  microbial community  water environment  nitrogen  high-throughput sequencing
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