| 东部湖区典型湖泊浮游植物群落变化规律及驱动因子 |
| 摘要点击 1020 全文点击 137 投稿时间：2024-03-14 修订日期：2024-04-24 |
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| 中文关键词 东部湖区 典型湖泊 浮游植物群落 驱动因子 季节差异 空间差异 富营养化 |
| 英文关键词 Eastern Lake Region typical lakes phytoplankton community driving factors seasonal differences spatial differences eutrophic |
| 作者 | 单位 | E-mail | | 刘杰 | 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 210008
中国科学院大学, 北京 100049
南京大学地理与海洋科学学院, 南京 210023 | liujie_ucas@163.com | | 邓建明 | 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 210008
中国科学院大学, 北京 100049 | jmdeng@niglas.ac.cn | | 蔡永久 | 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 210008
中国科学院大学, 北京 100049 | | | 龚志军 | 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 210008
中国科学院大学, 北京 100049 | | | 汤祥明 | 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 210008
中国科学院大学, 北京 100049 | |
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| 中文摘要 |
| 东部湖区是我国湖泊富营养化程度最严重、受人类活动影响最强的湖区. 近年来该湖区众多湖泊中浮游植物大量增殖,水华频发,针对区域尺度上湖泊浮游植物群落长期变化的驱动机制及空间差异仍不清楚. 其中,太湖、洪泽湖和骆马湖地处长江经济带兼具调蓄、饮用水及灌溉等重要生态功能,受人类活动影响较大,是东部湖区典型的湖泊. 利用2016~2021年水文气象数据、水体理化指标数据和浮游植物生物量数据,基于冗余分析,将层次分割和变差分解相结合,研究这3个典型湖泊浮游植物群落变化规律,并识别主要驱动因子. 结果表明,东部湖区典型湖泊间的气候背景长期变化趋势基本一致,但其自身营养盐、浮游植物群落及环境驱动因素存在差异. 太湖、洪泽湖和骆马湖浮游植物优势门类和优势属差异显著；以水深为主要表征的湖泊特征是导致典型湖泊间各季节浮游植物群落空间差异的最主要驱动因子（春、夏、秋和冬的解释率分别为：46.32%、30.79%、26.92%和35.80%,下同）,但次主要驱动因子存在季节差异,其中,春季次主要驱动因子为电导率（13.48%）和总氮（12.74%）,夏季次主要驱动因子为总磷（19.02%）和电导率（14.71%）,秋季次主要驱动因子为总磷（19.43%）和溶解性总氮（15.86%）,冬季次主要驱动因子为总磷（23.53%）和日最低温度（14.91%）. 量化不同驱动因子的贡献对今后开展湖泊富营养化治理和制定相应政策等均有重要意义. |
| 英文摘要 |
| The Eastern Lake Region is the most eutrophic in China and is most affected by human activities. In recent years, phytoplankton have proliferated in most lakes in the lake region, with the frequent occurrence of water blooms, and the driving mechanisms and spatial differences for long-term changes in the phytoplankton community of lakes at the regional scale remain unclear. Among them, Lake Taihu, Lake Hongze, and Lake Luoma are located in the Yangtze River Economic Zone and have important ecological functions such as storage, drinking water, and irrigation. They are greatly affected by human activities and are typical lakes in the Eastern Lake Region. We used hydro-meteorological data, physical and chemical index data, and phytoplankton biomass data from 2016 to 2021 to study the phytoplankton community changes in typical lakes in the Eastern Lake Region based on redundancy analysis and combined hierarchical partitioning and variance decomposition to identify the main drivers of phytoplankton community changes. The results showed that the long-term trends of climate background were generally consistent among typical lakes in the Eastern Lake Region, but their nutrients, phytoplankton community, and environmental driving factors were different. The dominant phytoplankton phyla and genera in Lake Taihu, Lake Hongze, and Lake Luoma were significantly different. The lake characteristic, mainly characterized by water depth, was the main driving factor that led to spatial differences in phytoplankton communities among typical lakes in different seasons. The explanatory rates of water depth in spring, summer, autumn, and winter were 46.32%, 30.79%, 26.92%, and 35.80%, respectively. However, the secondary driving factors had seasonal differences. Among them, in spring, the secondary driving factors were conductivity （13.48%） and total nitrogen （12.74%）. In summer, the secondary driving factors were total phosphorus （19.02%） and conductivity （14.71%）. In autumn, the secondary driving factors were total phosphorus （19.43%） and dissolved total nitrogen （15.86%）. In winter, the secondary driving factors were total phosphorus （23.53%） and the daily minimum temperature （14.91%）. Quantifying the contribution of different drivers was important for future lake eutrophication management and policy formulation. |
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