Collaborative Research: URoL:ASC: Determining the relationship between genes and ecosystem processes to improve biogeochemical models for nutrient management

清洁水对生命至关重要，对维持健康的生态系统至关重要。无论是在切萨皮克湾还是污水处理厂等工程系统中，管理清洁水都需要模拟这些生态系统如何应对由于修复或气候变化而导致的环境变化。微生物过程对这种生态系统响应至关重要，但无论是在模型开发还是验证期间，模型通常都不包括对生态系统（其微生物组）中微生物的任何直接测量。这个项目利用了生命的一个基本规则，即生物体内的细胞过程是由基因编码的，从而提供了对生态系统中微生物的直接测量。该团队将专注于反硝化过程，该过程可以去除多余的氮污染，对污水处理厂和切萨皮克湾都至关重要。他们将使用受控的实验室实验来研究影响反硝化作用的特定基因丰度（通过生态系统中所有微生物进行测量）与反硝化率之间关系的因素。该项目将确定基因丰度数据如何提高不同类型模型的预测价值，这些模型以不同的方式对反硝化进行编码，并用于管理切萨皮克湾和废水处理厂。通过在整个项目中听取水资源管理和社区合作伙伴的关注和意见，该团队将重点关注受废水和切萨皮克湾水质影响最大的人群的利益，最终改进指导管理决策的模型预测。本研究使用定量PCR测量的基因丰度作为微生物反硝化的非保守示踪剂，作为“功能群细胞密度”的代表，以估计废水、接收水体和下游生态系统的营养模型中细胞密度依赖的反应速率。像切萨皮克湾这样的生态系统模型通常不编码依赖于细胞密度的反应，但如果反应速率依赖于密度，并且使用基因丰度跟踪和校准功能群丰度，则可能更准确。受控生物反应器实验将测试基因丰度和反硝化速率之间的关系，以及这种关系是否会随着干扰频率的变化而变化。该项目将系统地比较编码反硝化作为细胞密度依赖或独立反应如何影响切萨皮克湾的模型结果。我们还将测试基因丰度是否可以用作非保守示踪剂，以校准海湾和废水处理模型中的其他细胞密度依赖反应。我们将与管理伙伴合作，了解将基因丰度测量纳入其典型监测和建模工作流程的预算和技术限制，并设计解决方案，以推进其作为反硝化措施的使用，以改进模型预测。这项工作将作为一个例子，说明基因丰度如何被用作编码一系列管理生态系统中微生物过程的模型的额外输入。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Clean water is essential to life and critical for maintaining healthy ecosystems. Whether in the Chesapeake Bay or engineered systems like wastewater treatment plants, managing clean water requires modelling how these ecosystems respond to environmental changes due to remediation or climate change. Microbial processes are crucial to such ecosystem responses, but models typically do not incorporate any direct measurements of the microbes in the ecosystem (its microbiome), either during model development or validation. This project takes advantage of a fundamental rule of life, that cellular processes are encoded by the genes in living things, to provide that direct measurement of microbes in an ecosystem. The team will focus on the process of denitrification, which removes excess nitrogen pollution and is critical in both wastewater treatment plants and Chesapeake Bay. They will use controlled, laboratory experiments to investigate factors influencing the relationship between the abundance of particular genes involved in denitrification, as measured across all the microbes in an ecosystem, and denitrification rates. The project will determine how gene abundance data can improve the predictive value of different types of models that encode denitrification in different ways and that are used in managing Chesapeake Bay and wastewater treatment plants. By listening to the concerns and input of water management and community partners throughout the project, the team will focus on efforts to benefit those most impacted by wastewater and Chesapeake Bay water quality, ultimately improving model predictions that guide management decisions.This work uses gene abundance, measured by quantitative PCR, as a non-conservative tracer of microbial denitrification, serving as a proxy for “functional group cell density”, to estimate cell-density dependent reaction rates in nutrient models of wastewater, receiving waterbodies, and downstream ecosystems. Models of ecosystems, like the Chesapeake Bay, typically do not encode cell-density dependent reactions but may be more accurate if reaction rates are density dependent and functional group abundance is tracked and calibrated using gene abundance. Controlled bioreactor experiments will test the relationship between gene abundance and denitrification rates, and whether that relationship changes in response to disturbance frequency. The project will systematically compare how encoding denitrification as either cell-density dependent or independent reactions influences model results in the Chesapeake Bay. We will also test whether gene abundance, can be used as a non-conservative tracer to calibrate other cell-density dependent reactions in both the Bay and wastewater treatment models. We will work with management partners to understand the budgetary and technical limitations of incorporating gene abundance measurements into their typical surveillance and modeling workflows, and design solutions to advance its use as a measure of denitrification to improve model predictions. This work will serve as an example of how gene abundance can be used as an additional input into models encoding microbial processes across a range of managed ecosystems.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.