Influence of rewetting on microbial communities involved in nitrification and denitrification in a grassland soil after a prolonged drought period.

Scientific reports(2019)

引用 17|浏览17
暂无评分
摘要
The frequency of extreme drought and heavy rain events during the vegetation period will increase in Central Europe according to future climate change scenarios, which will affect the functioning of terrestrial ecosystems in multiple ways. In this study, we simulated an extreme drought event (40 days) at two different vegetation periods (spring and summer) to investigate season-related effects of drought and subsequent rewetting on nitrifiers and denitrifiers in a grassland soil. Abundance of the microbial groups of interest was assessed by quantification of functional genes (amoA, nirS/nirK and nosZ) via quantitative real-time PCR. Additionally, the diversity of ammonia-oxidizing archaea was determined based on fingerprinting of the archaeal amoA gene. Overall, the different time points of simulated drought and rewetting strongly influenced the obtained response pattern of microbial communities involved in N turnover as well as soil ammonium and nitrate dynamics. In spring, gene abundance of nirS was irreversible reduced after drought whereas nirK and nosZ remained unaffected. Furthermore, community composition of ammonia-oxidizing archaea was altered by subsequent rewetting although amoA gene abundance remained constant. In contrast, no drought/rewetting effects on functional gene abundance or diversity pattern of nitrifying archaea were observed in summer. Our results showed (I) high seasonal dependency of microbial community responses to extreme events, indicating a strong influence of plant-derived factors like vegetation stage and plant community composition and consequently close plant-microbe interactions and (II) remarkable resistance and/or resilience of functional microbial groups involved in nitrogen cycling to extreme weather events what might indicate that microbes in a silty soil are better adapted to stress situations as expected.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要