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Webinar: Improving and Verifying Ecological Restoration Outcomes: Recently Published EPA Guidance
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Pre-approved for 1 CEC under SER's CERP program

When: December 10, 2019
1:00 PM ET
Where: United States
Presenter: Louis Blume, Lynn Walters, Craig Palmer
Contact: Alexis Gibson

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Have you ever questioned the reliability of your monitoring data? (Be honest!) In ecological restoration projects, reliable data are needed to accurately assess ecosystem conditions, track progress toward stated restoration goals, determine the effectiveness of restoration practices, and provide evidence of restoration success.  EPA’s Great Lakes National Program Office (GLNPO) and the Interagency Ecological Restoration Quality Committee are pleased to announce the recent publication of the Application of Quality Assurance and Quality Control Principles to Ecological Restoration Project Monitoring (EPA-905-K19-001, April 2019) to address the unique challenges associated with controlling and documenting the quality of ecological monitoring data. It describes quality assurance and quality control strategies that can improve the quality of data collected and increase certainty in decision-making, with a broader goal of improving the success of ecological restoration activities. This webinar by two of its primary authors will describe the contents of this guidance, particularly how it addresses quality best practices for the collection of observer-determined data.

About the Presenters
Louis Blume is a Quality Manager for U.S. EPA Great Lakes National Program Office (GLNPO), a position held since 1995. Lynn Walters is a senior project manager and environmental scientist with GDIT.  She has a degree in Environmental Science and Biology, and has spent the last 30 years supporting environmental data gathering activities for the Office of Water, GLNPO, and other EPA offices. Dr. Craig Palmer (BSc. Geology and Agronomy; Ph.D Soil Chemistry) has over 35 years of experience developing ecological research and monitoring programs. He has managed numerous projects involving field data collection, quality assurance and data management in support of these programs.