摘要
为探究白洋淀浮游生物群落结构动态特征及其关键影响因子,基于2017年秋,2018年春、夏3个季节的水环境因子与浮游生物野外采样调查结果,采用冗余分析(RDA)、广义可加模型(GAMs)等方法探究了白洋淀不同季节的浮游生物群落动态变化,建立了浮游植物生物量与环境因子的非线性关系并进行了率定验证。结果表明:与1992年白洋淀干淀后重新蓄水时相比,浮游植物与浮游动物物种数分别下降了59%与37%,优势群落从绿藻门转化为蓝藻门。根据RDA排序结果可知,温度、透明度是影响浮游生物丰度的关键环境因子。运用GAMs构建了环境因子与浮游植物生物量的定量模型,最优模型结构为Biomass-phy^s(T)+s(TN)+s(Cond)+s(Biomass-zoo)+s(Biomass-pro)+te(Biomass-proTN)+te(Biomass-proCond)+te(Biomass-proBiomass-zoo)。将2019年6月采样数据进行模型验证,计算模拟效果良好,实现了对浮游植物生物量的定量模拟,能够为白洋淀生态恢复与保护提供决策支持。
To explore the dynamic characteristics of plankton community structure and its key influencing factors in Baiyangdian Lake, the study is based on the water environment and plankton field sampling survey in the autumn of 2017, spring and summer of 2018 using the Redundancy analysis(Redundancy analysis, RDA) and Generalized additive models(Generalized additive models, GAMs) method to explore the Baiyangdian Lake phytoplankton community dynamic changes of different seasons as well as the quantitative simulation of the nonlinear relationship between phytoplankton biomass and environmental factors and the model is calibrabed and validated. The results show that compared with 1992, the number of phytoplankton and zooplankton has decreased by 59% and 37%, respectively, and the dominant community has changed from Chlorophyta to Cyanophyta. According to RDA sequencing results, temperature and transparency are key environmental factors affecting plankton abundance. A quantitative model of environmental factors and phytoplankton biomass is constructed by using GAMs. The optimal model structure is Biomass-phy ~ s(T) + s(TN) + s(Cond) + s(Biomass-zoo) + s(Biomass-pro)+ te(Biomass-pro_TN) + te(Biomass-pro_Cond) + te(Biomass-pro_Biomass-zoo). The model is verified with the sampling data in June 2019, and the results show that the simulation performance of the model is good, which has realized the quantitative simulation of phytoplankton biomass, and provided a foundation for the ecological restoration and protection of Baiyangdian Lake.
作者
林楚翘
易雨君
李春晖
唐彩红
宋劼
LIN Chuqiao;YI Yujun;LI Chunhui;TANG Caihong;SONG Jie(State Key Laboratory of Water Environment Simulation,School of Environment,Beijing Normal University,Beijing 100875,China;Ministry of Education Key Laboratory of Water and Substrate Science,School of Environment,Beijing Normal University,Beijing 100875,China)
出处
《水利水电技术》
北大核心
2020年第12期169-179,共11页
Water Resources and Hydropower Engineering
基金
国家重点研发计划(2017YFC0404500)。
