Integrating archival tag data into stock assessment models

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Integrating archival tag data into stock assessment models. Motivation. We have been integrating conventional tagging data into stock assessment models for over a decade We are starting to get a reasonable number of archival tags returned - PowerPoint PPT Presentation

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Integrating archival tag data into stock assessment models

Integrating archival tag data into stock assessment modelsMotivationWe have been integrating conventional tagging data into stock assessment models for over a decadeWe are starting to get a reasonable number of archival tags returnedArchival tags potentially provide much more information on movementTreat as conventional tagsOnly use release and recapture informationHigher reward so better reporting ratePopoff tags dont rely on fisheryIgnores information (intermediate positions)

Treat as conventional tagsOnly use release and recapture informationHigher reward so better reporting ratePopoff tags dont rely on fisheryIgnores information (intermediate positions)

Treat as mark-recapture dataTreat every observation as a re-releaseProvides information on movementRecapture probability = 1 for intermediate observation since we know it is finally recapturedIf it is not observed in the next period, it is due to a recording error and not because it wasnt observed

Treat as mark-recapture dataTreat every observation as a re-releaseProvides information on movementRecapture probability = 1 for intermediate observation since we know it is finally recapturedIf it is not observed in the next period, it is due to a recording error and not because it wasnt observed

Methods: ReferencesMiller and Andersen. 2008. A Finite-State Continuous-Time Approach for Inferring Regional Migration and Mortality Rates from Archival Tagging and Conventional Tag-Recovery Experiments. Biometrics 64: 11961206.Eveson, et al. (submitted). Using electronic tag data to improve parameter estimates in a tag-based spatial fisheries assessment model. Canadian Journal of Fisheries and Aquatic Sciences.Taylor et al. 2009. A multi stock tag integrated age structured assessment model for the assessment of Atlantic Bluefin tuna. SCRS/2008/097 Collect. Vol. Sci. Pap. ICCAT, 64(2): 513-531.

Methods: some issuesContinuous vs discrete timeObservation vs process errorComposite likelihood vs separate likelihoods for movement and recaptureAdditional Issues (Eveson et al.)There are a number of complicating factors when applying the integrated spatial model to real data:(1) position estimates from archival tags have large uncertainty;(2) many (most) fish tracks fit do not fit unambiguously into the spatial and temporal structure being assumed;(3) tracks estimated from archival tags often stop before the fish is caught and the tag recovered (due to a number of reasons such as the light sensor failing, the battery dying, etc).Continuous time: Miller and AndersenContinuous movement rates with continuous F and MLike Baranov catch equationCan model different times at release for each tag (I think it )Applied to conventional and archival tagsShould be able to approximate by small descrete time steps.Migration, M and F are not constant over time, so only an approximation anyway

Continuous time: Miller and AndersenContinuous movement rates with continuous F and MLike Baranov catch equationCan model different times at release for each tag (I think it )Applied to conventional and archival tagsShould be able to approximate by small descrete time steps.Migration, M and F are not constant over time, so only an approximation anyway

Discete time: choosing a locationAt a point in timeMost frequent regionBest judgment (Eveson et al.)ProbabilitiesModel x percent in y then predictions of x percent in zObservation error vs process errorFor movement, use process error modelTreat each observation as the new starting positionFor mortality use the known positions and observation errorDeal with missing data (bad location, battery running out)Treat like conventional tag

Composite likelihood vs separate likelihoods for movement and recaptureSeparate Movement and recapture likelihoodsKnow fish locations for applying recapture likelihood Fit to the recaptures using a negative binomial based likelihoodMortality rate informationUse process error for movementmultinomial based likelihood for the location at each time periodEveson et al.The probability of a fish released in region r1 in time period t being recaptured in region r2 in time period t+3 after having made transitions from r1 to r3 to r1 to r2 is just Pr(survive r1 in time period t)*Pr(move from r1 to r3)*Pr(survive r3 in time period t+1)*Pr(move from r3 to r1)*Pr(survive r1 in time period t+2)*Pr(move from r1 to r2)*Pr(caught in r2 in time period t+3). (what is likelihood)For a conventional tag, all possible intermediate transitions need to be accounted for.Taylor et al.: summaryState-space modelConventional, archive, popupModels probability of transition among states and probability of observation given in a stateStates ={on dead fish, shed, on live fish in region 1, .}Multinomial probabilities

Separate stocks: Taylor et al. 2009Need to define stock of originTagged in spawning areaObservation in spawning areaGeneticsSome may have unknown originPopoffPopoff at right timeMalfunctionConstant depth (mortality)CapturedModel the probability of popping off, why?Because you need to model the probability that the fish survived until pop-off time?

IssuesSample size is important because observations from one individual are correlated and imply pseudo replicationMemory (non-markov) models: e.g. fidelity to spawning groundsDealing with tags that were never recaptured? Treat missing data like conventional tags (model all possible states)The EndEveson et al. methodUse multinomial likelihood for conventional tags

Issues (Eveson et al.)(1) longitude estimates are generally much more accurate than latitude and should be sufficient to determine the broad regions needed for the model. In(2) the spatial and temporal structure of the model is clearly an oversimplification of the truth, and it can be difficult to accommodate some of the archival tag tracks within this structure. Again, we used our best judgement for each archival tag track to determine the most appropriate region designation in each season. (3), the model can be modified to accommodate incomplete archival tag tracks by treating each one the same as any archival tag up until the track stops, then treating it as a conventional tag that was released in the last observed region/time period (and recaptured in the region/time period where the fish was caught).

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