Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Annis Water Resources Institute, Grand Valley State University, Muskegon, MI 49441, USA.
Chemosphere. 2017 Oct;184:299-308. doi: 10.1016/j.chemosphere.2017.06.003. Epub 2017 Jun 2.
Phytoplankton and bacterioplankton are integral components of aquatic food webs and play essential roles in the structure and function of freshwater ecosystems. However, little is known about how phyto- and bacterioplankton may respond synchronously to changing environmental conditions. Thus, we analyzed simultaneously the composition and structure of phyto- and bacterioplankton on a monthly basis over 12 months in cyanobacteria-dominated areas of Lake Taihu and compared their responses to changes in environmental factors. Metric multi-dimensional scaling (mMDS) revealed that the temporal variations of phyto- and bacterioplankton were significant. Time lag analysis (TLA) indicated that the temporal pattern of phytoplankton tended to exhibit convergent dynamics while bacterioplankton showed highly stable or stochastic variation. A significant directional change was found for bacterioplankton at the genus level and the slopes (rate of change) and regression R (low stochasticity or stability) were greater if Cyanobacteria were included, suggesting a higher level of instability in the bacterial community at lower taxonomy level. Consequently, phytoplankton responded more rapidly to the change in environmental conditions than bacterioplankton when analyzed at the phylum level, while bacterioplankton were more sensitive at the finer taxonomic resolution in Lake Taihu. Redundancy analysis (RDA) results showed that environmental variables collectively explained 51.0% variance of phytoplankton and 46.7% variance of bacterioplankton, suggesting that environmental conditions have a significant influence on the temporal variations of phyto- and bacterioplankton. Furthermore, variance partitioning indicated that the bacterial community structure was largely explained by water temperature and nitrogen, suggesting that these factors were the primary drivers shaping bacterioplankton.
浮游植物和细菌浮游生物是水生食物网的组成部分，对淡水生态系统的结构和功能起着至关重要的作用。然而，人们对浮游植物和细菌浮游生物如何同步响应环境变化知之甚少。因此，我们在太湖蓝藻占优势的地区进行了为期 12 个月的每月浮游植物和浮游细菌组成和结构的同步分析，并比较了它们对环境因子变化的响应。度量多维标度（mMDS）显示浮游植物和浮游细菌的时间变化具有显著意义。时滞分析（TLA）表明，浮游植物的时间格局倾向于表现出趋同动态，而浮游细菌则表现出高度稳定或随机变化。在属水平上，浮游细菌的时间格局发生了显著的定向变化，如果包括蓝藻，斜率（变化率）和回归 R（低随机性或稳定性）更大，这表明在较低分类水平上，细菌群落的不稳定性更高。因此，在门水平上进行分析时，浮游植物对环境条件的变化比浮游细菌反应更快，而在太湖水体中，细菌对更精细的分类分辨率更敏感。冗余分析（RDA）结果表明，环境变量共同解释了浮游植物 51.0%的方差和浮游细菌 46.7%的方差，这表明环境条件对浮游植物和浮游细菌的时间变化有显著影响。此外，方差分解表明，细菌群落结构主要由水温、氮素解释，表明这些因素是塑造细菌群落的主要驱动因素。
