Abstract
Age information is central to assessment and management of fish populations. Age information must be reliable to have value, which depends on its quality. Quality assurance (QA) and quality control (QC) are processes often applied to some aspects of producing age information (e.g., annulus validation). However, we advocate for a more holistic approach which leverages QA/QC measures across all phases of the age information-generating process. Systematic implementation of QA/QC measures in a repetitive process is common in the manufacturing industry where it is known as a quality management system (QMS) but this framework is not well described in the fisheries literature. We designed and implemented a QMS that incorporates QA/QC measures across all phases of fish age information development: Collection, Interpretation, and Distribution. These measures are guided by six principles: Train, Simplify, Validate, Compare, Record, and Improve. In our QMS, the Train, Simplify, and Validate principles are largely guidance for QA measures, while Compare, Record, and Improve guide QC measures. We provide examples of common errors (or sources of error) in each phase, and how the guiding principles in our QMS address these errors. This is a QMS crafted as a holistic approach to managing the quality of fish age information; however, it has broad application as a conceptual framework for other repetitive processes in fisheries.
•Fish age information development can be broken down into three phases: Collection, Interpretation, and Distribution.•Most traditional literature pertaining to quality and fish age information is focused on the interpretation of fish ages.•Implementing a QMS ensures all three phases of age information development are covered by QA/QC processes.•We describe the six principles that guide our QMS: Train, Simplify, Validate, Compare, Record, and Improve.