模拟的增温增雨对内蒙古温带草原土壤氨氧化微生物的影响

Responses of Soil Ammonia Oxidizers to Simulated Warming and Increased Precipitation in a Temperate Steppe of Inner Mongolia

土壤氨氧化微生物是草地生态系统氮循环过程特别是氨氧化过程的主要驱动者,对全球变化具有响应、适应和反馈机制.通过采集在内蒙古温带草原设置的长期增温增雨野外控制实验的土壤样品,应用定量PCR、限制性末端片段长度多态性(terminal restriction fragment length polymorphism,T-RFLP)和克隆文库等方法研究氨氧化古菌和细菌的丰度、多样性和群落结构对增温增雨的响应.结果表明,增雨显著升高了土壤pH,而增温显著降低了土壤呼吸.氨氧化微生物丰度在各处理之间没有显著差异.T-RFLP结果表明,增雨显著影响土壤氨氧化细菌的群落结构,增温和增雨对土壤氨氧化微生物群落结构的交互作用并不显著.结构方程模型的结果显示植物多样性与氨氧化古菌和细菌的群落结构有显著的相关关系,表明气候变化-微生物-植物三者之间存在着一定的关系.研究结果预示土壤微生物对长期气候变化有一定的适应能力,这对预测未来生态系统的变化具有重要的参考价值.

Soil ammonia oxidizers,as key players for the ammonia oxidation process in soil N cycling,could respond,adapt,and give feedback to global change. In this research,soil samples were collected from a long-term field experiment with increased precipitation and warming in a temperate steppe of Inner Mongolia. We analyzed the responses of the abundance,diversity,and community structure of ammonia-oxidizing archaea（ AOA） and ammonia-oxidizing bacteria（ AOB） to warming and increased precipitation using quantitative real-time PCR,terminal restriction fragment length polymorphism（ T-RFLP）,and clone library. The results showed that increased precipitation significantly stimulated soil pH and warming significantly reduced soil respiration（ SR）. No significant difference was detected regarding the abundances of amo A genes across all treatments,whereas increased precipitation significantly affected the community structure of soil AOB. However,the interactive effect between warming and increased precipitation had no significant influence on the community structure of soil ammonia oxidizers. The result of the structural equation model indicated that the plant diversity and community structures of soil ammonia oxidizers were significantly correlated, suggesting that there were certain relationships among climate change,microbes,and plants. In conclusion,this study confirmed that soil microorganisms had the ability to adapt to climate change,which could provide important information for predicting future changes in ecosystems.