白洋淀浮游植物群落的时空分布特征及其驱动因素研究

邓丽姣; 张益; 李浩; 曾军; 赖俊翔

doi:10.13205/j.hjgc.202605011

白洋淀浮游植物群落的时空分布特征及其驱动因素研究

doi: 10.13205/j.hjgc.202605011

邓丽姣^1,3,
张益²,
李浩^1, ,,
曾军¹,
赖俊翔¹

1. 广西科学院广西海洋科学院广西近海海洋环境科学重点实验室, 南宁 530007;
2. 北京师范大学环境学院, 北京 100875;
3. 北京中地泓科环境科技有限公司, 北京 100020

基金项目:

京津冀环境综合治理国家科技重大专项“白洋淀生态系统健康提升与保持关键技术研究与综合示范项目”（2025ZD1204700）；北京市科技新星交叉合作课题（20240484647）；广西科学院基本科研业务费资助项目（2024YWF2116）

详细信息

作者简介:
邓丽姣(1990—),女,工程师,主要研究方向为环境影响评价。17301150721@163.com

通讯作者:
李浩(1987—),男,助理研究员,主要研究方向为水生态修复。leslieleehao@163.com

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出版历程
- 收稿日期: 2026-01-22
- 网络出版日期: 2026-06-06

Spatiotemporal distribution characteristics of phytoplankton communities in Baiyangdian Lake and their driving factors

DENG Lijiao^1,3,
ZHANG Yi²,
LI Hao^{1
, ,},
ZENG Jun¹,
LAI Junxiang¹

1. Guangxi Key Laboratory of Marine Environmental Science, Guangxi Academy of Marine Sciences, Guangxi Academy of Sciences, Nanning 530007, China;
2. School of Environment, Beijing Normal University, Beijing 100875, China;
3. Beijing Zhong Di Hong Ke Environmental Science and Technology Company Limited, Beijing 100020, China

摘要

摘要: 浮游植物对环境变化高度敏感、响应迅速，其群落动态是湖泊生态系统健康状况的重要早期预警指标。以往研究侧重定性分析多种水体理化因素对浮游植物群落的总效应类型，然而，很少有学者定量区分不同水体理化因素对浮游植物群落的直接和间接效应。研究采用相关性分析、冗余分析及结构方程模型分析等方法，研究了白洋淀浮游植物群落的时空分布特征及其驱动因素。研究共记录浮游植物7门57属113种。浮游植物密度呈现显著的季节和空间变异性，在夏季出现最大值。春季以硅藻和绿藻为主，夏秋季转为蓝藻占优，冬季则复以绿藻为主。结构方程模型结果表明，水温对浮游植物生物量的总效应为正向（β=0.96），可分解为正向直接效应（β=0.94，P <0.05）和通过影响丰富度进而影响生物量的正向间接效应（β=0.02）。TN对浮游植物生物量的总效应为负向（β=-0.98），可分解为负向直接效应（β=-0.97，P<0.05）和通过影响丰富度进而影响生物量的负向间接作用（β=-0.01）。其中TN对丰富度有负向直接效应（β=-0.15，P<0.05）。研究可以为白洋淀的生态系统健康评估、富营养化防治对策制定及生物多样性保护提供科学依据。
- 浮游植物 /
- 群落 /
- 水体理化因素 /
- 结构方程模型 /
- 直接与间接效应
Abstract: Phytoplankton are highly sensitive to environmental changes and respond rapidly. Their community dynamics serve as important early warning indicators of lake ecosystem conditions. Previous studies have focused on qualitatively analyzing the combined effects of multiple physicochemical water quality factors on phytoplankton communities； however， few have quantitatively distinguished between the direct and indirect effects of different physicochemical water quality factors on these communities. This study used correlation analysis， redundancy analysis， and structural equation modeling to explore the spatial distribution characteristics of phytoplankton communities in Baiyangdian Lake and their driving factors. A total of 113 species belonging to 57 genera and 7 phyla were recorded. Phytoplankton density showed significant seasonal and spatial variability， reaching its maximum in summer. Diatoms and green algae dominated in spring， transitioning to cyanobacterial dominance in summer and autumn， and then reverting to green algae dominance in winter. Structural equation modeling revealed that water temperature had a total positive effect on phytoplankton biomass （β = 0.96）， consisting of a direct positive effect （β = 0.94， P < 0.05） and an indirect positive effect via increased abundance （β = 0.02）. Total nitrogen had a negative total effect on phytoplankton biomass （β = -0.96）， consisting of a direct negative effect （β = -0.95， P < 0.05） and an indirect negative effect via reduced abundance （β = -0.01）. Total nitrogen exerted a negative effect on abundance （β = -0.15， P < 0.05）. This study provides a scientific basis for ecosystem health assessment， eutrophication control strategies， and biodiversity protection in Baiyangdian Lake.
- phytoplankton /
- community /
- physicochemical water quality factors /
- structural equation modelling /
- direct and indirect effects

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