Civil and Environmental Engineering Department, University of Wisconsin-Madison, Madison, WI 53706, USA.
ISME J. 2013 Mar;7(3):680-4. doi: 10.1038/ismej.2012.118. Epub 2012 Oct 11.
With an unprecedented decade-long time series from a temperate eutrophic lake, we analyzed bacterial and environmental co-occurrence networks to gain insight into seasonal dynamics at the community level. We found that (1) bacterial co-occurrence networks were non-random, (2) season explained the network complexity and (3) co-occurrence network complexity was negatively correlated with the underlying community diversity across different seasons. Network complexity was not related to the variance of associated environmental factors. Temperature and productivity may drive changes in diversity across seasons in temperate aquatic systems, much as they control diversity across latitude. While the implications of bacterioplankton network structure on ecosystem function are still largely unknown, network analysis, in conjunction with traditional multivariate techniques, continues to increase our understanding of bacterioplankton temporal dynamics.
利用来自温带富营养化湖泊的空前的十年时间序列,我们分析了细菌和环境的共现网络,以深入了解群落水平的季节性动态。我们发现:(1) 细菌共现网络是非随机的;(2) 季节解释了网络的复杂性;(3) 共现网络的复杂性与不同季节下群落多样性呈负相关。网络的复杂性与相关环境因素的变化无关。在温带水生系统中,温度和生产力可能像控制纬度多样性一样,驱动着多样性在季节间的变化。尽管浮游细菌网络结构对生态系统功能的影响在很大程度上仍然未知,但网络分析与传统的多元技术相结合,继续加深了我们对浮游细菌时间动态的理解。