The collection and use of tagging data to estimate abundance, fishing mortality, and movement for tuna stocks has a long history. This is due to the highly distributed nature of tuna stocks such that it is generally not possible to conduct dedicated (trawl) surveys as conducted for groundfish and other species. Traditionally, the tagging data is analyzed outside the stock assessment model. However, integrating tagging data inside tuna stock assessment models (MULTIFAN-CL) was one of the first applications (Hampton and Fournier, 2001) and has been used in the assessment of tropical tunas in the WCPO ever since. However, tagging tuna is complicated due to the practicalities of catching tuna in a way that post release survival is high. Therefore, the tagging opportunities are often restricted in space, time and in the sizes of the tuna that can be released. One of the major impediments of using the tagging data is that the tagged fish are not fully mixed with the total population, or that full mixing takes a longtime compared to their lifespan, such that much of the information is lost to account for the mixing period (e.g., estimating a separate F for the tagged population that differs from the total population).
Fine scale spatial-temporal modelling of tagging data (e.g., Mildenberger et al., 2024) can avoid bias from non-mixing while maximizing the content of the data from recaptures before the tags are fully mixed with the total population. Therefore, integrating the raw tagging data into the stock assessment model would require a fine spatial-temporal scale stock assessment mode.
Can a single framework be coded that enables running course and fine-scale models independently (estimate movement from a high resolution tagging model then pass these estimates to a spatially stratified assessment) as well as fully integrated (i.e., a multiscalar assessment)?
For operational use and given existing runtime constraints, is it feasible to integrate a high resolution tagging/movement model within a spatially stratified stock assessment (e.g., Thorson et al., 2021)?
Would a fully length-based assessment model be more appropriate for tuna with tagging data to accommodate the length-based tagging data and length-based processes?
How can various tag types be used synergistically?
Conventional: determine movement pathways and interannual variability from high sample size tag releases;
Satellite tags: inform mixing timeframes and unobserved transitions for conventional tags;
Otolith microchemistry: identify natal origin and potential reproductive mixing (or lack thereof);
Genetics: provide comparative estimates when nuisance parameters are removed (e.g., reporting, tag loss).
Can high resolution spatio-temporal tagging models overcome tagging data issues (e.g., tag mixing and tag representativeness)?
Are the current or future tuna tagging data sufficient to support the data needs of a fine-scale spatiotemporal assessment model (e.g., Cao et al., 2020; Olmos et al., 2023)?
Can the issues (tagging related mortality, tag loss, reporting rates, tag mixing) caused by the practicalities of conventional tagging programs be overcome and/or accounted for?
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