3.2 Standard procedures and guidelines for data collection

Overview     Introduction   Standard procedures   Other considerations      Resources      Results and next steps

Why Standard Procedures?

The broad purpose of monitoring programs is to draw inferences about the condition of key population indicators and metrics through carefully designed data collection projects and measurements.  In the context of this website these inferences can be viewed as the roof of a house that is supported by the various monitoring wheel components.  Standard procedures ensure that this foundation is strong and that inferences are reliable.  Standards are recommended for use here not to dictate how a researcher designs and implements a specific project, but as a resource to help increase the value of the research beyond individual project objectives.  The project design is intended to provide the specific quality desired, in terms of accuracy, precision, and reliability, to attain specific objectives, and can vary for any given monitoring project.  While the methods of collecting field data are well described in the scientific fisheries literature, their specific application can differ widely among project locations and species in order to balance a number of sampling design variables.  Therefore, it is essential to document design characteristics, methods and sampling protocols used for a monitoring project to communicate the quality of data collected and provide an understanding of any potential limitations for its use.   A useful example about these concepts in the context of spawning escapement estimation can be found here.

     As noted in the "Basic Information" section reached via the Home page, the suggestions and guidelines of this salmon-oriented web site are equally applicable to programs that monitor habitat variables. In the context of this current page on standard procedures for data collection, we therefore encourage users to see the Roper et al. (2010) paper for a comprehensive comparison of performance and compatability of seven sets of protocols for measuring stream habitat.

Quality Assurance and Quality Control (QA/QC)

Quality assurance (QA) typically refers to an overall system for ensuring quality of the monitoring project's outcomes while quality control (QC) generally refers to actual steps taken to ensure that routine procedures are tested and validated with respect to their assumptions and intended results.  These concepts certainly apply to the overall monitoring project as much as they apply to each individual component such as data collection.  The most effective QA/QC programs are those that are documented in writing and consistently followed by project staff.  

Incorporating robust QA/QC procedures for monitoring projects is essential for:

• Building confidence in the credibility of inferences
• Reducing the chances of collecting useless information or worse, i.e., producing information that is believed to be credible, but is actually biased and potentially misleading.

Principles

A QA/QC plan should address some basic elements including: precision, accuracy, representativeness, completeness, and comparability.  The plan should enable any project member or interested party to answer standard questions about the project.  Some example questions are provided below, as drawn from Preparing Quality Assurance Project Plans by Kansas Department of Health and Environment:

• Who is involved in the project and what do they do?
• Why is the sampling needed?
• What are the proposed sampling activities?
• Are there any special training requirements or safety issues?
• Where will the samples be collected and why?
• Are the samples representative of actual site conditions?
• Exactly how will the samples be collected, transported, stored and analyzed?
• How will the data be validated, stored and backed-up?
• What will be data be used for?
• Will the data be used for decision making, or will the data be used for education only?
• How will the data be reported and who is the target audience?

An applied example of a quality assurance plan can be found in the Washington Department of Ecology's plan for status and trends monitoring of watershed health and salmon recovery (click here).  The data attributes within this plan involve physical habitat features and water quality but the approach is directly relevant to fish population monitoring as well.

For a specific example of applying quality control concepts to a salmon data collection activity, please refer to this Fisheries and Oceans Canada case study related to coded-wire tag detection. 

Field Automation

Automated data collection tools can greatly enhance data quality in monitoring programs by eliminating potential for later transcription errors, incorporating built-in data error checking and validation routines and recording sampling locations and times. Tools such as hand held data recorders and GPS units can be important components of any robust QA/QC plan.

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