Assessment of predictions of hydrologic function based on aquatic DNA fragments

Oregon State University, Corvallis OR

Investigators

Abstract

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.