RII Track-2 FEC: Aquatic Intermittency Effects on Microbiomes in Streams (AIMS)​

Summary: 

Understanding of links among microbial communities (microbiomes), stream health, and water quality relies on studies of perennially flowing streams. However, more than half of global stream-miles do not flow continuously. These intermittent streams occur across the entire country--from western deserts to eastern forests. Despite their ubiquity, research on intermittently flowing streams is impeded by a lack of: 

1. physical infrastructure designed to measure intermittency, and 
   
2. scientific training that straddles aquatic and terrestrial ecology.
   

The Aquatic Intermittency effects on Microbiomes in Streams (AIMS) project will address the first obstacle by creating a network of instrumented sites designed to generate “Big Data” to quantify flow intermittency, stream microbiomes, and water quality. AIMS will confront the second obstacle by using its network to provide training in collaborative science and interdisciplinary methods to study intermittent streams, and by providing workforce training in environmental "Big Data" tools through a new On Ramps to Data Science program, which will focus on data generated by microbiome sequencing, environmental sensors, and Geographic Information Systems (GIS). This infrastructure and training will support a team of 18 investigators, including nine early career scientists spanning five EPSCoR jurisdictions (AL, ID, KS, MS, OK).

To build capacity in team science, 11 graduate students and two postdoctoral associates will be recruited using a cohort model that will provide cross-jurisdictional training in scientific communication, inclusive mentoring, data management and collaboration. Students will be trained through AIMS Undergraduate Program (AIMS UP), which will recruit participants from regional partners, such as Haskell Indian Nations University, Alabama A&M, and the Shoshone-Bannock Summer Youth Program. Our overarching objective is to create research infrastructure and training capable of integrating big data sources needed to address water quality at the critical nexus between intermittent and perennial streams.