Defining the Roles of Microbial Taxa in Soil Nitrogen Turnover

土壤微生物，特别是细菌和真菌，是有机质分解的重要参与者。它们的活动既释放又隔离植物可用的营养物质，如氮。虽然有成千上万种不同的细菌和真菌参与氮循环，但人们对哪些物种最活跃地参与或它们对不同形式氮的偏好知之甚少。这项研究将使用一种新的、最先进的方法来识别使用不同形式氮的微生物物种，以及这种变化在一系列自然和管理土壤中的变化。这项研究所获得的信息有可能加深我们对微生物如何处理土壤中氮的理解，这对我们预测植物和生态系统对环境变化的反应的能力具有影响。例如，如果不同类型的真菌和细菌以相同的方式处理氮，那么目前的氮循环模型对物种组成的变化是稳健的;然而，如果存在专门化，那么模型将通过纳入这些差异而得到实质性改进。最终，这些信息将有助于更好地了解氮是如何循环的，并有可能改善氮的管理，以维持陆地生态系统的生产力。研究人员将结合一种新的，高灵敏度的稳定同位素探测方法（芯片SIP），与成熟的方法15 N同位素池稀释用于测量土壤中的总氮循环速率。Chip-SIP使用NanoSIMS同位素成像来定量测量15 N底物掺入与系统发育微阵列杂交的微生物rRNA中。其目的是确定的优势微生物类群同化的有机和无机形式的N在土壤中的历史高和低的N输入。来自三个具有30年以上高氮和低氮输入历史的地点的土壤将被用来回答两个问题：1）哪些微生物类群主导有机氮与无机氮源的同化，它是否取决于氮输入历史？2)NH 4+同化/硝化的比例与优势类群的类型和多样性有何关系，这种关系是否受氮输入历史的影响？为了解决这些问题，N同化将通过添加示踪剂水平（低浓度，高富集）的15 N标记的底物，并使用同位素稀释率来计算总N处理率，和芯片SIP来确定微生物积极同化N。所产生的数据将提供深入了解微生物结构功能关系的关键土壤氮循环，这是不可或缺的自然和管理的陆地生态系统的生产力和可持续性的地球化学过程。这种新的应用芯片SIP结合同位素池稀释在土壤中将确定优势微生物类群同化有机与无机N在低和高N输入的条件下。通过将这些类群与处理速率相关联，并测量当不同功能组的活动被抑制时，N处理速率和活性类群如何变化，本研究将深入了解它们相互作用的性质，并确定N输入是否影响N循环过程中的竞争和促进行为。最后，将特定类群的N同化参数纳入N循环模型有望提高其预测能力。

Soil microorganisms, especially bacteria and fungi, are essential players in organic matter decomposition. Their activities both release and sequester plant-available nutrients, such as nitrogen. Although thousands of different species of bacteria and fungi participate in nitrogen cycling, little is known about which species most actively participate or their preferences for different forms of nitrogen. This research will use a new, state-of-the-art method to identify microbial species that use different forms of nitrogen and how this changes across a range of natural and managed soils. The information gained by this research has the potential to deepen our understanding of how microorganisms process nitrogen in soils, which has implications for our ability to forecast plant and ecosystem responses to environmental change. For example, if different types of fungi and bacteria process nitrogen in the same way, then current models of nitrogen cycling are robust to changes in species composition; however, if there is specialization, then models would be substantially improved by incorporating these differences. Ultimately, this information will lead to better understanding of how nitrogen is cycled and has the potential for improving how nitrogen is managed to sustain the productivity of terrestrial ecosystems.The researchers will couple a new, high-sensitivity stable isotope probing method (Chip-SIP), with the well-established method of 15N isotope pool dilution used to measure gross N cycling rates in soil. Chip-SIP uses NanoSIMS isotopic imaging to quantitatively measure incorporation of 15N substrates into microbial rRNA hybridized to a phylogenetic microarray. The objective is to identify the dominant microbial taxa assimilating organic and inorganic forms of N in soils with histories of high and low N inputs. Soils from three sites with a 30-year+ history of high and low N inputs will be used to answer two questions: 1) Which microbial taxa dominate the assimilation of organic N vs. inorganic N sources, and does it depend on N input history? 2) How does the ratio of NH4+ assimilation/nitrification relate to the types and diversity of dominant taxa, and is this relationship influenced by N input history? To address these questions, N assimilation will be determined by adding tracer levels (low concentrations, high enrichments) of 15N-labeled substrates, and using the rate of isotope dilution to calculate gross N process rates, and Chip-SIP to identify microorganisms actively assimilating N. The data generated will provide insights into microbial structure-function relationships for key biogeochemical processes in the soil N cycle, which are integral to productivity and sustainability of natural and managed terrestrial ecosystems. This novel application of Chip-SIP combined with isotope pool dilution in soils will identify the dominant microbial taxa assimilating organic vs. inorganic N under conditions of low and high N inputs. By correlating these taxa with process rates, and measuring how N processing rates and active taxa change when the activities of different functional groups are suppressed, this research will provide insights into the nature of their interactions and establish if N inputs affect competitive and facilitative behaviors in N cycle processes. Finally, incorporation of taxa-specific N assimilation parameters into N cycle models holds promise for improvement of their predictive capability.