Martin Stapanian, Ph.D.
U.S. Geological Survey
Lake Erie Biological Station
6100 Columbus Avenue
Sandusky, OH 44870
Rigorous quality assurance/quality control (QA/QC) data in a restoration project are essential for evaluating the success of the project, and they provide the only objective legacy of the data set for potential legal challenges and future uses. However, QA/QC procedures are often lacking in ecological restoration projects, according to a recent report led by the U.S. Geological Survey.
Legislative mandates in the United States require highly reliable QA/QC procedures for ecological restoration projects. Unlike most laboratory studies, QA/QC are often neglected in these projects largely due to a common misconception that field data, either collected with a calibrated instrument or by best professional judgment, are not subject to evaluations of data quality.
“It is important to perform QA/QC activities in ecorestoration projects to avoid negative effects,” said lead author Dr. Martin Stapanian of the USGS in Sandusky, Ohio.
There is also a tendency to unconsciously minimize disorder in complex projects and non-linear organizations, and QA/QC is often perceived as creating disorder.
“However, history has shown that suppression of QA/QC for the sake of minimizing disorder can lead to much larger and more costly problems later,” said Stapanian.
In their paper, Stapanian and coauthors discuss similarities and differences in assessing precision and accuracy for field and laboratory data. Although the concepts for assessing precision and accuracy of ecorestoration field data are conceptually the same as laboratory data, the manner in which these data quality attributes are assessed is different.
“From a sample analysis perspective, a field crew is comparable to a laboratory instrument that requires regular ‘re-calibration,’ with results obtained by experts at the same plot treated as laboratory calibration standards,” Stapanian said.
Unlike laboratory standards and reference materials, the true value for many field variables is commonly unknown. In the laboratory, specific QA/QC samples assess error for each aspect of the measurement process whereas field re-visits assess precision and accuracy of the entire data collection process following initial calibration.
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