Restoration synthesis: A meta-analysis of low-head, in-stream river restoration project outcomes
Laurel Larsen, Professor
Geography
Applications for Spring 2024 are closed for this project.
We are seeking 1-2 new apprentices to assist with a synthesis and meta-analysis project examining the outcomes of low-head river restoration projects. The final deliverable for this project is the creation of a public database of restoration projects and outcomes to help centralize the available data on these restoration strategies for restoration practitioners and researchers in the field.
In recent decades, there has been increasing interest in using low-head, in-stream structures to restore and manage degraded streams; these include natural structures such as beaver dams and log jams, and artificial biomimicry structures such as beaver dam analogs (BDAs) and engineered log jams (ELJs), among others. Broadly, the goal of these structures is to slow flow, laterally extend floodplains, increase groundwater infiltration, and improve habitat for aquatic species, among other project-specific outcomes. Despite increasing interest in these restoration approaches, much of the existing literature is site-specific, and relatively few works take a synthesis approach to analyzing the impact of low-head in-stream structures on streamflow and ecosystem services.
The goal of this project is to complete a comprehensive literature search using three major search databases and compile data on project location, restoration goals, post-restoration monitoring, and any reported data on project outcomes, including: surface water storage, hydrograph moderation, groundwater infiltration/surface water-groundwater interaction, water quality, sediment passage, channel morphology, and aquatic habitat assessment. Where sufficient data density is available (e.g. when enough projects of a similar restoration scheme report outcome data using similar metrics), we will analyze the magnitude and direction of post-restoration change to determine what consensus, if any, can be drawn on the impact of a given restoration scheme on different ecosystem services.
Apprentices involved with this project will gain valuable insight into the current state of river restoration practice while developing their organizational and data management skills. At least one student assistant will also assist with developing an interactive map and database tool using popular APIs (such as Google Earth Engine or ESRI Story Maps). If desired, the apprentice can also be involved in discussions about scientific communication, best practices in data analysis and transparency, and other topics that align with their research interests and professional goals.
Role: Tasks include: 1) acquiring full text PDFs and screening full text PDFs in Eppi-Reviewer, 2) compiling raw data from included literature into the restoration synthesis database, 3) mining other sources for restoration project data (such as other review papers or government agency databases), and 4) assisting with the online database and web tool development. Other tasks negotiable depending on what skills and experience the students may wish to acquire during the apprenticeship (such as statistical analysis, basic programming in R, contributing to scientific articles and conference presentations, etc).
Qualifications: -Familiar with literature databases (e.g. EBSCO, Ex Libris, Google Scholar, etc) and citation management software (e.g. EPPI-Reviewer, Mendeley, Zotero, etc)
-Comfortable with reading and interpreting scientific articles
-Strong organizational and data management skills
-Previous experience with GIS or web-based mapping tools (such as Google Earth Engine or ArcGIS StoryMaps) a plus
-Previous experience with meta-analysis and/or database management a plus
Day-to-day supervisor for this project: Sam Stein, Ph.D. candidate
Hours: to be negotiated
Related website: http://esdlberkeley.com
Related website: http://restorationsynthesis.org