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.

人类景观活动增加了淡水环境中氮（N）和磷（P）的输入。这种营养物污染导致了溪流、湖泊和沿海海洋中的藻类大量繁殖和低氧条件。氮可以通过反硝化作用去除，反硝化作用是微生物进行的一种自然过程，将溶解的氮（硝酸盐）转化为氮气。了解溪流和河流中发生反硝化的地点、时间和程度对于了解如何管理和保护这些生态系统非常重要。然而，测量反硝化率需要在封闭的室内使用单独的基质，这为微生物创造了不切实际的条件。在该项目中，我们将开发建模方法，通过测量昼夜循环中氮气的浓度来量化河段中的反硝化作用。我们将在不同浓度和比例的 N 和 P 的实验和自然溪流中测试这些模型。我们将创建采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溪流和河流。对于更广泛的影响活动，我们将为本科生提供培训机会，并为高中生开设有关生态学编码和数据培训的夏季短期课程。使用 N2 浓度昼夜循环来估计淡水反硝化率的模型最近已得到改进通过整合已被广泛研究的逆建模方法，用于使用氧气 (O2) 浓度估算初级生产和呼吸速率。然而，除了反硝化作用之外，许多不同的过程也会导致 N2 浓度的变化，但这些过程尚未整合到这些模型中。例如，氮气可以通过扩散和气泡形成等物理过程以及固氮（氮气与铵的生物介导对话）等生物过程从生态系统中去除。我们的目标是通过实验和现场调查测量相结合来改进现有模型，以同时解决反硝化和固氮问题。首先，我们将在实验流中应用开放水域 N2 通量模型，其中反硝化和 N2 固定的相对活动将通过改变水柱 N:P 来控制，并且我们将量化气泡形成和气体交换以参数化我们的模型。其次，我们将开放水域模型应用于国家生态观测站网络 (NEON) 的九条溪流，选择具有 N:P 梯度，并且先前 NSF 资助的研究已记录反硝化和 N2 固定的梯度活动。总之，这些活动将大大改进对美国不同生态区溪流的氮通量的估计，并提供改进的建模技术，我们自己和其他人可以应用这些技术更好地了解溪流和河流中的氮循环和去除。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。