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| 引用本文: | 孟洋洋,王丽雅,朱睿,周永强,施坤,张恩楼,张民.青藏高原湖泊浮游植物群落结构特征与驱动因素.湖泊科学,2025,37(2):415-428. DOI:10.18307/2025.0215 |
| Meng Yangyang,Wang Liya,Zhu Rui,Zhou Yongqiang,Shi Kun,Zhang Enlou,Zhang Min.Characteristics and driving factors of phytoplankton community structure in lakes on the Tibetan Plateau. J. Lake Sci.2025,37(2):415-428. DOI:10.18307/2025.0215 |
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| 青藏高原湖泊浮游植物群落结构特征与驱动因素 |
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孟洋洋1，2，3，王丽雅1，2，3，朱睿4，周永强1，2，施坤1，2，张恩楼1，2，3，张民1，2，3
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1.中国科学院南京地理与湖泊研究所，湖泊与流域水安全全国重点实验室，南京 211135 ；2.中国科学院南京地理与湖泊研究所，湖泊与环境国家重点实验室，南京 211135 ；3.中国科学院大学南京学院，南京 211135 ；4.南通大学地理科学学院，南通 226019
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| 摘要: |
| 青藏高原湖泊受气候变化影响强烈，浮游植物作为湖泊生态系统的主要初级生产者对环境变化极为敏感，是认识青藏高原湖泊生态系统响应气候变化的关键。本研究于2019年7月-2021年9月对青藏高原72个湖泊敞水区浮游植物进行了调查，分析了湖泊浮游植物群落结构特征和主要驱动因素。本次调查共鉴定出浮游植物8门91属，各湖泊浮游植物生物量变化范围为1.01~8742.24 μg/L，大部分湖泊浮游植物生物量处于100~1000 μg/L水平。青藏高原湖泊浮游植物主要为硅藻门和绿藻门，硅藻门藻类在35个湖泊中占比超过50%，绿藻门藻类在18个湖泊中占比超过50%。青藏高原湖泊浮游植物优势属为小球藻属（Chlorella）、卵囊藻属（Oocystis）、舟形藻属（Navicula）和小环藻属（Cyclotella）。在属水平，浮游植物丰富度指数变化范围为1~25属，平均值为8.60属；均匀度指数的变化范围为0.08~0.93，平均值为0.55；辛普森指数的变化范围为0~0.87，平均值为0.53；香农-威纳指数范围为0~2.39，平均值为1.16。Mantel test与RDA分析结果显示，盐度和电导率是青藏高原湖泊浮游植物群落结构差异的主要驱动因子，气温和pH是浮游植物生物量差异的主要驱动因子。本研究结果增强了对青藏高原湖泊浮游植物的认识，可为了解青藏高原湖泊生态系统对气候变化和人类活动的响应提供参考。 |
| 关键词: 青藏高原 湖泊 浮游植物 群落结构 生物量 驱动因子 |
| DOI：10.18307/2025.0215 |
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| 基金项目:第二次青藏高原综合科学考察项目（2019QZKK0202）；国家自然科学基金项目（32171546）联合资助 |
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| Characteristics and driving factors of phytoplankton community structure in lakes on the Tibetan Plateau |
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Meng Yangyang1，2，3，Wang Liya1，2，3，Zhu Rui4，Zhou Yongqiang1，2，Shi Kun1，2，Zhang Enlou1，2，3，Zhang Min1，2，3
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1.State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135 , P.R.China ；2.State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135 , P.R.China ；3.University of Chinese Academy of Sciences, Nanjing, Nanjing 211135 , P.R.China ；4.School of Geographical Science, Nantong University, Nantong 226019 , P.R.China
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| Abstract: |
| Lakes on the Tibetan Plateau are profoundly impacted by climate change. As the primary producers in lake ecosystems, phytoplankton are highly sensitive to environmental changes, making them key indicators for understanding how lake ecosystems on the Tibetan Plateau respond to climate change. From July 2019 to September 2021, we conducted an extensive study involving the collection, identification, and quantification of phytoplankton samples from 72 open water areas across lakes on the Tibetan Plateau. Our investigation revealed a total of 8 phyla and 91 genera of phytoplankton. The phytoplankton density in the lakes varied significantly, ranging from 1.01 μg/L to 8742.24 μg/L, with the majority of lakes exhibiting a density within the 100-1000 μg/L. The phytoplankton-predominant lake types in the region were Bacillariophyta and Chlorophyta, with Bacillariophyta organisms constituting over 50% in 35 lakes, and Chlorophyta organisms exceeding 50% in 18 lakes. Noteworthy the predominant phytoplankton species in lakes of the Tibetan Plateau included Chlorella, Oocystis, Navicula, and Cyclotella. The richness indices of lake phytoplankton were characterized by species, ranging from 1 genus to 25 genera, with an average value of 8.60 genera. The evenness index exhibited in a range of 0.08-0.93, with a mean of 0.55. Similarly, the Simpson index varied from 0 to 0.87, with the average of 0.53. The Shannon-Wiener index displayed a range of 0 to 2.39, with an average of 1.16. Using the Mantel test and RDA analysis, it was confirmed that salinity and conductivity were the primary drivers of differences in phytoplankton community structure, while pH and temperature were the main factors influencing the variations in phytoplankton biomass of the Tibetan Plateau. The findings of this study enhance the understanding of lake phytoplankton on the Tibetan Plateau, providing valuable insights on how lake ecosystems in the region respond to climate change and human activities. |
| Key words: Tibetan Plateau lakes phytoplankton community structure biomass driving factor |
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