Informing Spatially Explicit Stock Assessment Models with Fisheries-Independent Movement Probabilities

Accounting for movement and mixing of fish stocks has the potential to improve stock assessments.  However movement rates are difficult to estimate, because they are often confounded with estimates of recruitment, mortality and selectivity. Conventional tagging studies typically rely on fishery recaptures, so tag recovery observations are most appropriately modeled within a tag-integrated stock assessment model, because they are influenced by patterns of fishing mortality and selectivity. As an alternative, electronic tagging provides fishery-independent observations that can support inferences of movement that are external to stock assessment models. Advection-diffusion parameters derived from estimated tracks of tagged fish can be used in simulations to determine seasonal residency in differing geographic regions and movement among regions. The resulting age specific seasonal transfer rates can then be used as input information for spatially-explicit stock assessment models.  This framework is applied to Atlantic bluefin tuna using age and time based subsets of a large pop-up satellite tag database. Incorporating complex movement patterns into stock assessment models represents a possible mechanism for consideration of electronic tag data in stock assessments and a basis for evaluation of potential changes in distribution using fishery independent information.