贵州草海秋季浮游植物群落结构与水质因子的关系
Autumn Phytoplankton Community Structure and Its Relationship with Water Quality Parameters in Caohai Wetland of Guizhou Province
投稿时间:2018-07-03  修订日期:2020-04-21
中文关键词:草海湿地  浮游植物  群落结构  环境因子
英文关键词:Caohai wetland  phytoplankton  community structure  environmental factors
基金项目:贵州科学院院基金项目(黔科院字[2016]01);贵州科学院青年基金项目(黔科院J字[2018]05& [2018]24);贵州省林业科研课题(黔林科合[2015]14号)
作者单位E-mail
代亮亮 贵州省生物研究所贵州 贵阳 550000 1441712390@qq.com 
李莉杰 中国科学院水生生物研究所 淡水生态与生物技术国家重点实验室湖北 武汉 430072  
何梅 贵州省生物研究所贵州 贵阳 550000  
龙云川 贵州省生物研究所贵州 贵阳 550000  
吕敬才 贵州省生物研究所贵州 贵阳 550000  
万合锋 贵州省生物研究所贵州 贵阳 550000  
袁 果 贵州省生物研究所贵州 贵阳 550000  
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中文摘要:
      为了探究贵州草海浮游植物群落结构特征及其与水质环境因子的关系,为其水资源保护和可持续发展提供参考依据。于2017年11月(秋季)对贵州草海16个采样点的浮游植物种类组成、细胞密度、优势种及环境因子进行调查分析,同时利用分层聚类分析、冗余分析(RDA)和主成分分析(PCA)的方法探讨浮游植物群落结构、环境因子、采样点三者之间的关系。结果显示,本次调查共鉴定出浮游植物7门、66属、139种;其中蓝藻门12属、22种,绿藻门28属、69种,硅藻门16属、29种,隐藻门2属、3种,裸藻门4属、11种,甲藻门3属、4种,金藻门1属、1种;其中绿藻为优势门类,占浮游植物总数的49.64%;硅藻次之,占20.86%;蓝藻居第三位,占15.83%,金藻最少,仅占0.72%。调查期间,样点6、7、11、12的叶绿素a含量较高,均高于5.0 μg/L,细胞密度也较大,均超过了107 个/L。各采样点浮游植物的群落结构聚类分析将样点7、11、12聚为一类,其优势门类均为蓝藻,且位于靠近威宁县城的沿岸带。各采样点与环境变量之间的主成分分析(PCA)结果显示,6、7、11、12号采样点主要受营养盐、总溶解固体和盐度的影响,浮游植物与环境因子的冗余分析(RDA)得出蓝藻受营养盐的影响较大。研究表明,威宁县城沿岸带的藻类细胞密度明显高于其它位点,其优势门类为蓝藻,营养盐是导致威宁县城沿岸带藻类细胞密度过高的主要原因。
英文摘要:
      Wetlands are the kidneys of Earth and wetland ecosystems are vital for maintaining the environmental balance. Caohai Lake, located on the Yunnan-Guizhou Plateau in the Yangtze River watershed, is one of the three plateau freshwater lakes in China and plays an important role in the downstream water balance. The aquatic environment of Caohai wetland has changed dramatically in recent years and the existing data is out of date. In this study, we investigated the community structure of phytoplankton and analyzed its relationship with environmental factors in Caohai wetland, aiming to provide a reference for water resource protection and sustainable development of Caohai wetland. The investigation was carried out at 16 sampling sites during the autumn of 2017 and focused on phytoplankton species composition, abundance, dominant species and environmental factors. Hierarchical cluster analysis, redundancy analysis (RDA) and principal component analysis (PCA) were used to reveal the relationship between community structure of phytoplankton, environmental factors and sampling sites. A total of 139 phytoplankton species were identified, belonging to 66 genera and 7 phyla. Chlorophyta (69 species, 28 genera) were dominant and accounted for 49.64% of the total phytoplankton species, followed by Bacillariophyta (29 species, 16 genera), Cyanophyta (22 species, 12 genera), and Chrysophyta (1 species, 1 genera), respectively accounting for 20.86%, 15.83% and 0.72% of the total species. The chlorophyll-a concentration and phytoplankton abundance at Sites 6, 7, 11, and 12 were much higher than at other sites, with respective values of 6.37 μg/L and 1.29×107 cells/L, 14.13 μg/L and 2.74×107 cells/L, 13.93 μg/L and 2.94×107 cells/L, 11.45 μg/L and 1.31×107 cells/L. At other sites the chlorophyll-a concentrations were less than 5.0 μg/L and phytoplankton abundances were less than 5×106cells/L, except for Site 1. Cluster analysis of the phytoplankton community for each sampling site shows that Sites 7, 11 and 12 formed one group and all were located in the coastal zone of Weining county with cyanobacteria as the dominant taxa. Principal component analysis of sampling sites and environmental factors shows that site 6, 7, 11 and 12 were mainly influenced by nutrients, total dissolved solids and salinity. Redundancy analysis of phytoplankton species with environmental variables shows that nutrients have a dramatic affect on cyanobacteria. High nutrient levels in the coastal zone of Weining county has resulted in significantly higher algae cell densities, dominated by cyanobacteria.
代亮亮,李莉杰,何梅,龙云川,吕敬才,万合锋,袁 果.2020.贵州草海秋季浮游植物群落结构与水质因子的关系[J].水生态学杂志,41(2):62-67.
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