Assessment of predictions of hydrologic function based on aquatic DNA fragments

为了公众的安全和保障，能够在没有流量计的地方估计流经河流和溪流的水量是很重要的。在这些地方，收集一些其他类型的信息对于理解流模式非常有用。这个项目研究如何利用生物材料的碎片来预测河流和溪流的流量。本项目研究的生物材料是在水样中发现的微生物的脱氧核糖核酸（DNA）。这种材料可以快速、方便、廉价地收集和分析。通过使用先进的生物技术，在溪流中发现的DNA可以转化为不同类型微生物的相对丰度。这个项目是基于这样一种认识，即不同的环境条件，包括河流的流动模式，会导致不同的微生物种群变得或多或少丰富。该项目支持一个由教师和学生组成的跨学科小组开发新的工具，将溪流和河流微生物与水文流动模式联系起来。该项目与一个地方组织合作，致力于将代表性不足的社区与科学专业人员联系起来。该项目的重点是在一套跨越太平洋西北部生态水文条件的长期测量站收集和测序水流DNA。利用16s rRNA扩增子测序，在每个位置量化不同微生物群落成员的相对丰度，并利用机器学习技术将群落组成模式与河流流量联系起来。然后将这些方法扩展到区域和国家层面的河流微生物组组成数据集，以确定不同尺度的河流DNA中包含的宏观水文信息。在这个项目期间，一个由高中生、本科生和研究生组成的团队致力于先进的生物学、水文学和机器学习技术，以研究河流DNA和流域功能之间的联系。通过这个项目开发的水文学和微生物工具和技术将以各种形式向更广泛的社区传播，包括传统的学术渠道和作为开放源码的互动电子文本，供水文学普通教育使用。该项目包括从高中到博士阶段的学生在科学、技术、工程和数学（STEM）方面的培训。该项目与一家当地组织合作，致力于将代表性不足的社区与STEM专业人士联系起来。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。

For the safety and security of the public, it is important to be able to estimate how much water flows through rivers and streams at locations where no gauges of flow exist. In these places, the collection of some other type of information can be remarkably useful in understanding flow patterns. This project investigates how fragments of biological material can be used in predicting river and stream flows. The biological material investigated in this project is the deoxyribonucleic acid (DNA) of microbes found within water samples. This material can be collected and analyzed quickly, easily, and inexpensively. By using advanced biological techniques, the DNA found in streams can be translated into the relative abundance of different types of microbes. This project is based on the understanding that different environmental conditions, including flow patterns in rivers, cause different populations of microbes to become more or less abundant. This project supports an interdisciplinary group of faculty and students to develop new tools that relate stream and river microbes to hydrologic flow patterns. The project partners with a local organization focused on connecting under-represented communities with science professionals.This project focuses on the collection and sequencing of streamwater DNA at a suite of long-term gauging stations spanning a range ecohydrologic conditions across the Pacific Northwest. Using 16s rRNA amplicon sequencing, the relative abundance of different microbial community members is quantified at each location, and patterns in community composition is related to river flows with machine learning techniques. These methods are then extended to regional and national level datasets of streamwater microbiome composition to determine the macroscale hydrologic information contained within streamwater DNA at different scales. For the duration of this project, a team consisting of high-school, undergraduate, and graduate students is engaged in advanced biological, hydrologic, and machine learning techniques to investigate connections between streamwater DNA and watershed function. Both hydrologic and microbial tools and techniques developed through this project will be disseminated to the wider community in a variety of forms, including traditional scholarly outlets and as open-source interactive electronic text for general education about hydrology. This project includes training in science, technology, engineering, and mathematics (STEM) for students from high school to the PhD level. The project partners with a local organization focused on connecting under-represented communities with STEM professionals.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.