As a scientist in Core Science Systems, Daniel Wieferich helps develop, manage, and analyze national scale biogeographic data. Daniel has worked with Science Analytics and Synthesis Program since 2014.
Daniel has a B.S. and M.S. in Fisheries and Wildlife from Michigan State University, with a specialization in Spatial Information Processing. Daniel also holds a Computer Programmer/Analyst Certificate from Lansing Community College. His graduate work focused on population dynamics and distribution of an invasive forest insect (Beech Scale) in Michigan. In 2009, Daniel started working for the Aquatic Landscape Ecology Lab in the Department of Fisheries and Wildlife at Michigan State University. Here he played a leading role of geospatial data management and analyses for regional and national assessments of fish habitat through collaborative efforts with the National Fish Habitat Partnership, U.S. Geological Survey, U.S. Fish and Wildlife Service, and Midwest states.
As a member of the Science Analytics and Synthesis Program Daniel continues an active role in the National Fish Habitat Partnership (NFHP) serving as a co-lead of the National Science and Data Committee and a USGS point of contact for NFHP data management and display efforts. Daniel is also an active member of the USGS Community of Data Integration.
Science and Products
Chesapeake Bay Aquatic Habitat Assessments
Aquatic Gap Analysis Vision
Knowledge Extraction Algorithms (KEA): Turning Literature Into Data
National Stream Summarization: Standardizing Stream-Landscape Summaries
National Dam Removal Database: A living database for information on dying dams
Science and Products
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Chesapeake Bay Aquatic Habitat Assessments
Eastern Ecological Science Center research ecologists are working with state and local partners to develop multiple biological assessments of non-tidal stream and river conditions in the Chesapeake Bay watershed.Aquatic Gap Analysis Vision
The Aquatic Gap Analysis Project (AGAP) works to synthesize existing data and generate new data products to answer complex questions about aquatic species, their habitats and their conservation needs at multiple scales. AGAP is working to build a national data framework that makes data management and sharing efficient.Knowledge Extraction Algorithms (KEA): Turning Literature Into Data
Identifying, extracting, and mobilizing information from current and historical literature is a time-consuming part of organizing and collating synthetic data productions. This project explored the use of algorithm-based methods to identify and extract occurrence information from the GeoDeepDive (GDD) literature database to support upkeep of the Nonindigenous Aquatic Species (NAS) data. The GeoDeeNational Stream Summarization: Standardizing Stream-Landscape Summaries
As research and management of natural resources shift from local to regional and national scales, the need for information about aquatic systems to be summarized to multiple scales is becoming more apparent. Recently, four federally funded national stream assessment efforts (USGS Aquatic GAP, USGS National Water-Quality Assessment Program, U.S. Environmental Protection Agency [EPA] StreamCat, andNational Dam Removal Database: A living database for information on dying dams
The United States has over 2 million dams on rivers and streams (Graf, 1999), and more than 84,000 of the larger dams are documented in the congressionally mandated National Inventory of Dams (U.S. Army Corps of Engineers, 2015). The average age of these National Inventory of Dams is 52 years; by the year 2030, over 80 percent will be at least 50 years old (American Society of Civil Engineers, 201 - Data
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