Quality Assurance Best Practices to Ensure the Reliability of Ecological Restoration Monitoring Data

Session: 34. - Aquatic Habitat Evaluation and Assessment

Lynn Walters, CSRA, [email protected]
Molly Middlebrook Amos, CSRA, [email protected]
Joan Cuddeback, CSRA, [email protected]
Brick Fevold, [email protected]
Craig Palmer, CSRA, [email protected]
Louis Blume, U.S. EPA Great Lakes National Program Office, [email protected]

Abstract

Monitoring data generated during habitat assessment and restoration projects provide the empirical evidence necessary to make informed management decisions when evaluating the effectiveness of ongoing restoration strategies, and to determine if and when established project objectives have been achieved. However, collection of reliable habitat monitoring data presents a number of challenges, particularly for restoration projects that quantify variables considered transitory or that simply cannot be easily measured using a scientific instrument. Methods used to quantify such variables often involve the use of field personnel observations and best professional judgment. In contrast, variables considered static or easily measured using an instrument that provides reproducible results are generally collected with greater confidence in their reliability. Regardless of the type of variable quantified or data collected, it is crucial to assess their quality in reference to established Data Quality Indicators (DQIs) to identify limitations that might compromise their reliability in effective decision-making, and ultimately impact the ability of project planners to achieve desired short-, mid-, and long-term restoration outcomes. This presentation discusses the importance of incorporating quality assurance best practices for the review and assessment of data quality as part of comprehensive project planning, effective decision making, and continual quality improvement.

1. Keyword
ecosystems

2. Keyword
habitats

3. Keyword
planning

4. Additional Keyword
restoration

5. Additional Keyword
data quality

6. Additional Keyword
GLRI