MSA: Quantifying whole-stream denitrification and nitrogen fixation with integrated modeling of N2 and O2 fluxes

MSA：通过 N2 和 O2 通量的集成建模量化全流反硝化和固氮

• 批准号: 2307284
• 负责人: Amy Marcarelli
• 金额: $ 30万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2307284&HistoricalAwards=false
• 关键词: MSA; Quantifying; whole; stream; denitrification

Human activities on the landscape have increased the input of nitrogen (N) and phosphorus (P) to freshwater environments. This nutrient pollution has led to algae blooms and low-oxygen conditions in streams, lakes and the coastal ocean. Nitrogen can by removed by denitrification, a natural process carried out by microbes that transforms dissolved nitrogen (nitrate) into N2 gas. It is important to understand where, when, and how much denitrification occurs in streams and rivers to understand how to manage and protect these ecosystems. However, measuring denitrification rates requires using separate substrates in enclosed chambers, which creates unrealistic conditions for microbes. In this project, we will develop modeling approaches to quantify denitrification in stream sections by measuring concentrations of N2 gas over day-night cycles. We will test these models in experimental and natural streams across varying concentrations and ratios of N and P. We will create sampling methods and share our model using an open-source framework so that our approach can be adopted by researchers to study other streams and rivers. For broader impact activities, we will provide training opportunities for undergraduate students and create a summer short-course on coding and data training in ecology for high school students.Models that estimate freshwater denitrification rates using day-night cycles of N2 concentrations have recently been improved by integrating inverse modeling approaches that have been widely studied for estimating rates of primary production and respiration using oxygen gas (O2) concentrations. However, many different processes contribute to changes in N2 concentrations besides denitrification that have not yet been integrated into these models. For example, N2 gas can be removed from ecosystems by physical processes like diffusion and bubble formation, as well as by biological processes like nitrogen fixation (the biologically-mediated conversation of N2 gas to ammonium). We aim to improve existing models to simultaneously resolve denitrification and nitrogen fixation through a combination of experimental and field survey measurements. First, we will apply open-water N2 flux models in experimental streams where the relative activity of denitrification and N2 fixation will be manipulated by varying water column N:P, and where we will quantify bubble formation and gas exchange to parameterize our model. Second, we will apply open-water models to nine streams that are part of the National Ecological Observatory Network (NEON), selected to have a gradient of N:P and where prior NSF-funded research has documented a gradient of denitrification and N2 fixation activity. Together, these activities will both provide much improved estimates of N2 fluxes from streams in different ecoregions in the United States, and provide improved modeling techniques that can be applied by ourselves and others to better understand nitrogen cycling and removal in streams and rivers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人类在景观上的活动增加了对淡水环境的氮（N）和磷（P）的输入。这种养分污染导致溪流，湖泊和沿海海洋中的藻类开花和低氧条件。氮可以通过反硝化去除，这是由微生物进行的自然过程，将溶解的氮（硝酸盐）转化为N2气体。重要的是要了解流和河流中的何时，何时以及何时以及多少反硝化化，以了解如何管理和保护这些生态系统。但是，测量反硝化率需要在封闭的腔室中使用单独的底物，这为微生物带来了不切实际的条件。在这个项目中，我们将通过测量昼夜周期中的N2气体浓度来开发建模方法来量化流部分的非硝化方法。我们将在N和P的不同浓度和比率的实验和自然流中测试这些模型。我们将创建抽样方法并使用开源框架共享我们的模型，以便研究人员可以采用我们的方法来研究其他流和河流。对于更广泛的影响活动，我们将为本科生提供培训机会，并为高中生的生态学编码和数据培训创建夏季短途培训。最近，使用N2浓度的淡水循环估算淡水淡化率的模型，最近通过整合了使用原始生产速率和次级浓度的浓度来估算均衡速率（OXY浓度）（OEAXY浓度）（OE OXY 2），从而改善了N2浓度的循环。但是，除非硝化外，尚未集成到这些模型之外，许多不同的过程导致N2浓度的变化。例如，可以通过诸如扩散和气泡形成之类的物理过程以及诸如氮固定（N2气体与铵的生物介导的对话）等生物学过程等物理过程中去除N2气体。我们旨在改善现有模型，通过实验和现场测量测量的结合，同时解决反硝化和氮的固定。首先，我们将在实验流中应用开放水的N2通量模型，在这些流中，反硝化和N2固定的相对活性将通过不同的水列N：P来操纵，我们将量化气泡形成和气体交换来参数化我们的模型。其次，我们将将开放水模型应用于九个流的流，这些流是国家生态观测网络（NEON）的一部分，被选为N：P的梯度，以及先前的NSF资助的研究已记录了反硝化和N2固定活动的梯度。这些活动将共同提供对美国不同生态区域中N2通量的大大改进，并提供了改进的建模技术，我们自己和其他人可以更好地理解氮气环境和河流中的氮气环节和拆除。这是NSF的法定任务，反映出值得评估的企业的范围，这一奖项已被认为是由构成的构成师的构成师的范围。