Advances in Fish Tagging and Marking Technology

Report on the Panel Discussion on Reconsideration of Stock Assessment Models Using Electronic Tag Data

Andrew C. Seitz


The panel consisted of Drs. Roger Hill of Wildlife Computers, Barbara Block of Stanford University, and Julian Melcalfe and David Righton of the Centre for Environment, Fisheries & Aquaculture Science. The purpose of the discussion was to bring together experts from tag manufacturing companies, government agencies, and academia to discuss how to integrate electronic tag data into assessment and management processes. The following summary of the panel discussion is not intended to be an objective and exhaustive literature review, but rather it is a synopsis of the discussion among the expert panel and the participants in the audience.

Before the discussion, the panel agreed to avoid archival tagging subjects euphemistically referred to as “old chestnuts,” which are subjects or ideas that have been discussed excessively. These ideas were identified as building smaller and cheaper tags, improved light-based geolocation accuracy, development of additional sensors and memory, and improved tag and data recovery. Several of these “old chestnuts,” such as additional sensors and increased memory storage, have become reality.

Rather, the panel suggested focusing the discussion on “new chestnuts,” or ideas that are just coming to the forefront of fish and fisheries research, which in this case is integrating our improved understanding of fish behavior, physiology and ecology from archival tagging into fisheries assessment and management processes. Frequently, these new ideas are old issues that must be re-examined because of recent findings from electronic tagging experiments and the development of these new ideas is occurring at an increasingly rapid pace. To keep the discussion focused on this “new chestnut,” the following question was posed: “how have we used archival tagging data to challenge current stock assessment models?” This information is essential as modelers develop “next-generation” models that incorporate habitat parameters and population structure. Examples of challenges to stock assessment models were provided from several fish taxa and they generally fall under the three broad categories of dispersal patterns, behavior and habitat use.