| 引用本文: | 巢欣,杨胜娴,刘惠秋,闫冰洁,卫佩佩,吴湘君,巴桑.雅鲁藏布江下游浮游植物群落构建机制及驱动因素.湖泊科学,2025,37(1):215-228. DOI:10.18307/2025.0135 |
| Chao Xin,Yang Shengxian,Liu Huiqiu,Yan Bingjie,Wei Peipei,Wu Xiangjun,Ba Sang.Mechanism and driving factors of phytoplankton community construction in the lower reaches of Yarlung Zangbo River. J. Lake Sci.2025,37(1):215-228. DOI:10.18307/2025.0135 |
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| 摘要: |
| 为探究雅鲁藏布江下游浮游植物群落多样性、构建机制及驱动因子，本文于2022年5月(春季)、2023年7月(夏季)和9月(秋季)对雅鲁藏布江下游34个样点进行了浮游植物样品采集和环境因子的调查，利用显微镜鉴定浮游植物物种，分析该水域浮游植物群落结构特征，并探究了不同季节浮游植物群落差异及群落构建的驱动因素。结果表明：1) 该水域共鉴定出浮游植物269种及变种，隶属于8门10纲22目40科87属，细胞丰度为春季＞夏季＞秋季，物种数为秋季＞春季＞夏季，浮游植物群落结构整体呈现硅藻—绿藻—蓝藻型，共筛选出优势种12 种，均为硅藻；2) Shannon多样性指数、Pielou均匀度指数、Simpson多样性指数及Margalef 丰富度指数在夏季最低，主坐标分析结果表明浮游植物群落组成在季节上存在差异，通过分析3个季节的β多样性及组分分解得知，这种差异主要来源于周转，且周转组分在春季占比最大；3) 中性群落模型及校正化随机率表明，随机性过程主导了雅鲁藏布江下游3个季节浮游植物群落的构建，方差分解分析结果显示地理因子的解释率(10.13%)大于环境因子(7.46%)，经度、纬度和海拔是影响浮游植物群落构建的主要地理因子，水温、pH是影响浮游植物群落构建的主要环境因子；4) 共现网络分析结果表明浮游植物间的相互作用以协作为主，且秋季的群落结构相较于春季和夏季更精简、稳定。 |
| 关键词: 浮游植物 群落多样性 中性模型 校正化随机率 随机性过程 驱动因子 雅鲁藏布江下游 |
| DOI：10.18307/2025.0135 |
| 分类号: |
| 基金项目:国家自然科学基金项目(32070418)；2022 年中央财政支持地方高校改革发展专项资金项目(藏财预指[2022]1 号)；西藏大学研究生“高水平人才培养计划”项目(2021-GSP-S049)联合资助 |
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| Mechanism and driving factors of phytoplankton community construction in the lower reaches of Yarlung Zangbo River |
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Chao Xin1，2，3，Yang Shengxian1，2，3，Liu Huiqiu1，2，3，Yan Bingjie1，2，3，Wei Peipei1，2，3，Wu Xiangjun1，2，3，Ba Sang1，2，3
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1.Laboratory of Tibetan Plateau Wetland and Watershed Ecosystem, School of Ecology and Environment, Tibet University, Lhasa 850000 , P.R.China ；2.Provincial Level of Mitika Wetland Ecosystem Observation and Research Station in Tibet Autonomous Region, Tibet University, Nagqu 852000 , P.R.China ；3.Center for Carbon Neutrality in the Earth's Third Pole, Tibet University, Lhasa 850000 , P.R.China
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| Abstract: |
| In order to explore phytoplankton community diversity and its driving factors in the lower reaches of the Yarlung Zangbo River, phytoplankton samples were collected at 34 sites in May 2022 (spring), July 2023 (summer) and September 2023 (autumn). Phytoplankton species were identified by microscopy. Environmental factors during the studied period were investigated. Based on the measured data, the phytoplankton community structure was characterized. The diverse phytoplankton community in different seasons and its driving factors were explored. The results showed that: 1) a total of 269 species and varieties of phytoplankton were identified, belonging to 8 phyla, 10 classes, 22 orders, 40 families and 87 genera. The order of cell abundance was spring＞summer＞autumn. the order of species number was autumn＞spring＞summer. The phytoplankton community structure as a whole showed diatom-green algae-cyanobacteria type. Twelve dominant species were selected, all of which were diatoms. 2) Shannon diversity index, Pielou evenness index, Simpson diversity index and Margalef richness index were lowest in summer. The principal coordinate analysis results showed that there were seasonal variations in phytoplankton community composition. By analyzing β diversity and component decomposition of these three seasons, we found that this difference was mainly due to turnover, in particularly in spring. 3) The neutral community model and the corrected random rate showed that the random process dominated the dynamics of phytoplankton communities in the lower reaches of the Yarlung Zangbo River in these three seasons. The variance decomposition analysis showed that the explanation rate of geographic factors (10.13%) was greater than that of environmental factors (7.46%). Latitude and altitude were the main geographic factors affecting the dynamics of phytoplankton communities. Water temperature was the main environmental factor affecting the dynamics of phytoplankton community. 4) The results of cooccurrence network analysis showed that phytoplankton interaction was mainly cooperative. The community structure in autumn was more concise and stable than that in spring and summer. |
| Key words: Phytoplankton community diversity neutral model modified stochsticity ratio stochastic process driving factors the lower reaches of Yarlung Zangbo River |
