Cooperative Agreement for CESU-affiliated Partner with Piedmont-South Atlantic Coast Cooperative Ecosystem Studies Unit

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The USGS is offering a funding opportunity to a CESU partner for collaborative research to evaluate empirical support for hypothesized relations between river flow regimes and fish population dynamics using multiple, long-term datasets, and to prepare analytical code together with the assembled datasets for public release. The overall objective is to advance flow-ecology science and applications in water resource management by elucidating mechanisms linking river flows to changes in fish populations and communities. The funding recipient will provide at least one doctoral student, or equivalent technical expertise, to collaborate with the USGS principal investigator in: assembling and formatting data for analyses; coding and executing regression analyses to address the study objectives (below); and packaging datasets and code for public release (e.g., as R Markdown files, Jupyter Notebooks, or a similar format agreed to by USGS principal investigator and the project team). It is anticipated that the student(s) funded through this opportunity will collaborate with the USGS principal investigator and the project team (a diverse group of academic and agency scientists) to summarize and publish results of hypothesis testing in a journal-article format. Research Objectives: Complete compilation and standardization of 20+ time-series of annual fish count data, to allow joint analyses of population dynamics in relation to alternative flow metrics, and annotate each data-set with values for species-specific traits and river system characteristics. These compiled data sets will be used in analyses (Objective 2) and form a product for future work (Objective 3). Analyze population dynamics in relation to streamflow components to assess support for alternative, specified hypothesized streamflow drivers, accounting for effects of river context and species traits. Compile datasets used in analyses along with model code in a user-friendly and publicly-available format.

High