Defining the Roles of Microbial Taxa in Soil Nitrogen Turnover

土壤微生物，特别是细菌和真菌，是有机物分解的重要参与者。它们的活动释放和封存植物可利用的营养物质，如氮。尽管成千上万种不同的细菌和真菌参与了氮循环，但人们对哪些物种最积极地参与或它们对不同形式的氮的偏好知之甚少。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是如何变化的。这项研究获得的信息有可能加深我们对微生物如何处理土壤中的氮的理解，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，那么当前的氮循环模型对物种组成的变化是健壮的；然而，如果存在专业化，那么通过纳入这些差异，模型将得到实质性的改进。最终，这些信息将有助于更好地了解氮素是如何循环的，并有可能改善氮素如何管理以维持陆地生态系统的生产力。研究人员将把一种新的、高灵敏度的稳定同位素探测方法(CHIP-SIP)与用于测量土壤总氮循环速率的15N同位素池稀释法结合起来。ChIP-SIP使用NanoSIMS同位素成像来定量测量15N底物与与系统发育微阵列杂交的微生物rRNA的掺入。其目的是确定土壤中吸收有机和无机氮的优势微生物类群，这些土壤具有高氮和低氮投入的历史。来自三个有30年高氮和低氮投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着有机氮与无机氮的同化，它是否依赖于N输入历史？2)NH4+同化/硝化比率如何与优势类群的类型和多样性有关，这种关系是否受到N输入历史的影响？为了解决这些问题，将通过添加示踪剂水平(低浓度，高浓缩)的15N标记底物来确定N的同化，并使用同位素稀释速率来计算总N过程速率，并使用Chip-SIP来识别积极同化N的微生物。所产生的数据将为深入了解土壤N循环中关键生物地球化学过程的微生物结构-功能关系提供洞察，这些过程对于自然和管理的陆地生态系统的生产力和可持续性是不可或缺的。在低氮和高氮输入条件下，ChIP-SIP和同位素库稀释相结合的新应用将识别吸收有机氮和无机氮的优势微生物类群。通过将这些分类群与加工速率相关联，并测量当不同功能基团的活动被抑制时，N加工速率和活跃分类群如何变化，本研究将提供对它们相互作用的本质的见解，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，在N循环模型中加入特定类群的N同化参数有望提高它们的预测能力。

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.