复制
生物
人口
生态学
微生物种群生物学
利基
甲烷菌
进化动力学
基因组
群落结构
厌氧消化
数学
甲烷
细菌
遗传学
基因
统计
社会学
人口学
作者
Inka Vanwonterghem,Paul D. Jensen,Paul G. Dennis,Philip Hugenholtz,Korneel Rabaey,Gene W. Tyson
出处
期刊:The ISME Journal
[Springer Nature]
日期:2014-04-17
卷期号:8 (10): 2015-2028
被引量:325
标识
DOI:10.1038/ismej.2014.50
摘要
Abstract A replicate long-term experiment was conducted using anaerobic digestion (AD) as a model process to determine the relative role of niche and neutral theory on microbial community assembly, and to link community dynamics to system performance. AD is performed by a complex network of microorganisms and process stability relies entirely on the synergistic interactions between populations belonging to different functional guilds. In this study, three independent replicate anaerobic digesters were seeded with the same diverse inoculum, supplied with a model substrate, α-cellulose, and operated for 362 days at a 10-day hydraulic residence time under mesophilic conditions. Selective pressure imposed by the operational conditions and model substrate caused large reproducible changes in community composition including an overall decrease in richness in the first month of operation, followed by synchronised population dynamics that correlated with changes in reactor performance. This included the synchronised emergence and decline of distinct Ruminococcus phylotypes at day 148, and emergence of a Clostridium and Methanosaeta phylotype at day 178, when performance became stable in all reactors. These data suggest that many dynamic functional niches are predictably filled by phylogenetically coherent populations over long time scales. Neutral theory would predict that a complex community with a high degree of recognised functional redundancy would lead to stochastic changes in populations and community divergence over time. We conclude that deterministic processes may play a larger role in microbial community dynamics than currently appreciated, and under controlled conditions it may be possible to reliably predict community structural and functional changes over time.
科研通智能强力驱动
Strongly Powered by AbleSci AI