Unlike most laboratory studies, rigorous quality assurance/quality control (QA/QC) procedures may be lacking in ecosystem restoration (“ecorestoration”) projects, despite legislative mandates in the United States. This is due, in part, to ecorestoration specialists making the false assumption that some types of data (e.g. discrete variables such as species identification and abundance classes) are not subject to evaluations of data quality. Moreover, emergent behavior manifested by complex, adapting, and nonlinear organizations responsible for monitoring the success of ecorestoration projects tend to unconsciously minimize disorder, QA/QC being an activity perceived as creating disorder. We 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 “recalibration,” with results obtained by experts at the same plot treated as laboratory calibration standards. 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 revisits assess precision and accuracy of the entire data collection process following initial calibration. Rigorous QA/QC data in an ecorestoration project are essential for evaluating the success of a project, and they provide the only objective “legacy” of the dataset for potential legal challenges and future uses.
The Laurentian Great Lakes are undergoing intensive ecological restoration in Canada and the United States. In the United States, an interagency committee was formed to facilitate implementation of quality practices for federally funded restoration projects in the Great Lakes basin. The Committee's responsibilities include developing a guidance document that will provide a common approach to the application of quality assurance and quality control (QA/QC) practices for restoration projects. The document will serve as a "how-to" guide for ensuring data quality during each aspect of ecological restoration projects. In addition, the document will provide suggestions on linking QA/QC data with the routine project data and hints on creating detailed supporting documentation. Finally, the document will advocate integrating all components of the project, including QA/QC applications, into an overarching decision-support framework. The guidance document is expected to be released by the U.S. EPA Great Lakes National Program Office in 2017. Implications for Practice• Document will serve as a "how-to" guide for ensuring data quality during each aspect of a restoration project. • Document will recommend acceptance criteria that specify the level of quality needed for each datum, and data quality indicators (e.g. accuracy, bias, precision, completeness, comparability, representativeness, sensitivity) that can be used to determine if this level of quality has been achieved. • Document will provide suggestions on linking QA/QC data with the routine project data and hints on creating detailed supporting documentation. • Document will advocate integrating all components of the project, including QA/QC applications, into an overarching decision-support framework such as adaptive management.Reliable data are necessary for making wise decisions in ecological restoration projects (U.S. EPA 2002). Reliable data are characterized by documented methods, estimated uncertainties, and the ability to withstand rigorous quality assurance/quality control (QA/QC) inspection (e.g. Chapman 2005; ANSI-ASQ 2014). Furthermore, rigorous QA/QC data provide the only objective "legacy" of the dataset for potential legal challenges and for future uses (Stapanian et al. 2016 and references therein). In particular, sample design and methodology, data quality, and qualifications of sampling crews are often the first aspects of an ecological restoration program that are examined by legal teams in litigation. Therefore, the potential cost to an ecological restoration program that does not include rigorous QA/QC can be considerable. A rigorous QA/QC dataset linked with the original monitoring data allows secondary data users to decide whether the original data are suitable for their intended purpose. Practitioners of ecological restoration projects have struggled with inclusion of quantitative objectives and evidence-based assessments (e.g. Suding 2011) to ensure the reliability of the data and to reduce uncertainty. Rigorous QA/QC procedures may be lacking ...
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