Abstract

为了解青海湖浮游植物群落结构特征、时空分布格局及其关键环境驱动因子,于2020年5月(春)、8月(夏)、10月(秋)对青海湖进行系统调查,分析浮游植物群落在3个季度和4个区域(湖滨带、进水区、浅水区和深水区)间的差异。3次调查共检出浮游植物6门39属65种,物种组成以硅藻(36种,占总物种数的55.38%)、绿藻(17种,26.15%)和蓝藻(7种,10.76%)为主。4个区域间的浮游植物丰度存在显著差别,其中深水区丰度显著高于其他区域,主要原因可能在于深水区的环境较为稳定。3个季节间,浮游植物丰度和生物量具有较大差异:夏季和秋季的丰度、生物量均为春季的近10倍;浮游植物的优势类群和种类也发生了较大的变化:春季最具优势类群为硅藻门,优势种也主要隶属于硅藻门,而夏、秋两季则以蓝藻门种属占据主要优势。春季浮游植物的Pielou均匀度和Simpson多样性指数显著高于夏、秋两季,秋季Margalef指数和Shannon-Wiener多样性指数高于夏季。PERMANOVA分析和NMDS分析显示,青海湖浮游植物群落结构在不同区域和不同季节间具有显著差异。此外,dbRDA分析表明:盐度、水温和总磷是影响青海湖浮游植物分布的关键环境因子。与历史调查结果相比,青海湖浮游植物丰度出现数量级程度的上升,主要优势类群由硅藻门逐渐演变为蓝藻门。本研究揭示了青海湖浮游植物群落的时空分布特征及驱动因素,可为后续青海湖保护和管理提供理论支撑。;To understand the spatio-temporal of phytoplankton communities and their key environmental drivers in the Lake Qinghai, we conducted a systematic survey during May (Spring), August (Summer), and October (Autumn) in 2020, and examined how phytoplankton communities varied among three seasons and four regions (lakeshore zone, inlet zone, shallow zone, and deep zone). A total of 65 species belonging to 39 genera and 6 phyla were identified, with Bacillariophyta (36 species, accounting for 55.38% of the total species) being the species-richest class, followed by Chlorophyta (17 species, 26.15%), and Cyanophyta (7 species, 10.76%). The abundance of phytoplankton differed significantly among four regions, with significantly higher values in deep zone than other zones, probably due to the more stable environment in deep zone. There were also significant differences in abundance and biomass of phytoplankton among the three seasons. The abundance and biomass of phytoplankton in summer and autumn were nearly 10 times higher than those in spring, and the dominant groups and species of phytoplankton also changed greatly. The most dominant class in Spring was Bacillariophyta, and the dominant species were also belonged to Bacillariophyta, while the dominant group and species in Summer and Autumn became Cyanophyta and its subset species. The Pielou's evenness and Simpson diversity indices of phytoplankton in Spring were significantly higher than those in Summer and Autumn, and the Margalef and Shannon-Wiener indices showed higher values in Autumn than in Summer. PERMANOVA and NMDS both indicated that the structure of phytoplankton communities varied significantly among different regions and seasons in the Lake Qinghai. In addition, the dbRDA analysis showed that salinity, water temperature and total phosphorus were the key environmental factors affecting the phytoplankton patterns in the Lake Qinghai. Compared with the historical surveys, this study indicated that the main dominant groups of phytoplankton community in Lake Qinghai gradually changed from Bacillariophyta to Cyanophyta. This study reveals the spatio-temporal pattern and key driving factors of phytoplankton communities in the Lake Qinghai, which can provide theoretical support for the subsequent protection and management of the Lake Qinghai.

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