青草沙水库大型底栖动物群落结构及水质生物学评价
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上海海洋大学省部共建水产种质资源发掘与利用教育部重点实验室,上海城投原水有限公司青草沙水库管理分公司,上海城投原水有限公司青草沙水库管理分公司,上海海洋大学省部共建水产种质资源发掘与利用教育部重点实验室

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上海市教委水产动物遗传育种中心上海市协同创新中心项目(ZF1206);上海城投原水有限公司项目(D-8006-14-0055)


Macrozoobenthic community structure and bioassessment for water quality of Qingcaosha Reservoir
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Ministry of Education Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources,Shanghai Ocean University,Shanghai Smi Raw Water CoLtd Qingcaosha Reservoir Management Branch,Shanghai Smi Raw Water CoLtd Qingcaosha Reservoir Management Branch,Ministry of Education Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources,Shanghai Ocean University

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    摘要:

    2014年1-12月在青草沙水库设置11个站点对大型底栖动物进行逐月调查,结果表明:调查期间共采集到22种底栖动物,隶属于4门14科19属,其中多毛类7种、寡毛类4种、节肢动物3种、软体动物6种、线形动物1种;主要优势种类为圆锯齿吻沙蚕(Dentinephtys glabra)、日本角吻沙蚕(Goniada japonica)和日本旋卷蜾蠃蜚(Corophium volutator)。底栖动物年均丰度为(155.99±92.61)ind/m2,年均生物量为(0.52±0.35)g/m2,丰度和生物量在采样站点、季节间不存在显著差异(P>0.05)。底栖动物生物多样性指数、生物学污染指数(BPI)和Hilsenhoff生物指数(BI)时空差异不显著。运用丰度-生物量比较曲线(ABC曲线)、群落相似性聚类和MDS排序分析群落结构得出:青草沙水库大型底栖动物群落未受到干扰或干扰较轻,群落结构相对稳定。

    Abstract:

    In order to demonstrate macrozoobenthic community structure dynamics and conduct a biological evaluation of water quality in Qingcaosha Reservoir, seasonal samples were collected from January to December 2014 at 11 sampling sites. A total of 22 species belonging to 4 phyla, 14 families, and 19 genera were identified, including 7 polychaeta, 4 oligochaeta,3 arthropod, 5 molluscs and 1 nematomorpha. Dentinephtys glabra, Goniada japonica and Corophium volutator were the dominant species in Qingcaosha Reservoir. The mean annual density and biomass of macrozoobenthos were (160.30±90.15) ind/m2 and (0.52±0.35) g/m2, respectively. There were no significant differences in the macrobenthic density and biomass among the sampling sites and seasons (P>0.05). Based on the macrozoobenthic biodiversity index, biological pollution index (BPI) and Hilsenhoff biotic index (BI), there were no significant differences between stations and seasons. ABC curves, hierarchical clustering and MDS were used to analyze the data of community structure. The results showed that Qingcaosha Reservoir, suffering less disturbance, keeps a stable community structure.

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汤勇,王绍祥,陈立婧.青草沙水库大型底栖动物群落结构及水质生物学评价[J].上海海洋大学学报,2016,25(6):873-883.
TANG Yong, WANG Shaoxiang, CHEN Lijing. Macrozoobenthic community structure and bioassessment for water quality of Qingcaosha Reservoir[J]. Journal of Shanghai Ocean University,2016,25(6):873-883.

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  • 收稿日期:2016-02-18
  • 最后修改日期:2016-07-12
  • 录用日期:2016-09-13
  • 在线发布日期: 2016-11-25
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