The need for spatial stock assessment models that match the spatiotemporal management and biological structure of marine species is growing. Spatially explicit, tag-integrated models can emulate complex population structure, because they are able to estimate connectivity among population units by incorporating tag-recovery data directly into the combined objective function of the assessment. However, the limited scope of many small-scale tagging studies along with difficulty addressing major assumptions of tagging data has prevented more widespread utilization of tag-recovery data sets within tag-integrated models. A spatially explicit simulation-estimation framework that simulates metapopulation dynamics with two populations and time-varying connectivity was implemented for three life history (i.e., longevity) scenarios to explore the relative utility of tagging data for use in spatial assessment models across a range of tag release designs (e.g., annual, historical, periodic, and opportunistic tagging). Model scenarios also investigated the impacts of not accounting for incomplete tag mixing or assuming all fish were fully selected (i.e., that the age composition of tagged fish was unknown). Results demonstrated that periodic tagging (e.g., releasing tags every five years) may provide the best balance between tag program cost and parameter bias. For cost-effective tagging programs, tag releases should be spread over a longer time period instead of focusing on release events in consecutive years, while releasing tags in tandem with existing surveys could further improve the practicality of implementing tag-recovery experiments. However, care should be taken to fully address critical modeling assumptions (e.g., by estimating tag mixing parameters) before incorporating tagging data into an assessment model.
Goethel, D. R., Bosley, K. M., Hanselman, D. H., Berger, A. M., Deroba, J. J., Langseth, B. J., & Schueller, A. M. (2019). Exploring the utility of different tag-recovery experimental designs for use in spatially explicit, tag-integrated stock assessment models. Fisheries Research, 219. https://doi.org/10.1016/j.fishres.2019.105320