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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

• Archival tags potentially provide much more information on movement

Treat as conventional tags

• Only use release and recapture information• Higher reward so better reporting rate• Popoff tags don’t rely on fishery• Ignores information (intermediate positions)

Treat as mark-recapture data

• Treat every observation as a re-release• Provides information on movement• “Recapture” probability = 1 for intermediate

observation since we know it is finally recaptured

• If it is not observed in the next period, it is due to a recording error and not because it wasn’t observed

Methods: References• Miller 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: 1196–1206.

• 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 issues

• Continuous vs discrete time• Observation vs process error• Composite likelihood vs separate likelihoods

for movement and recapture

Additional 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 Andersen

• Continuous movement rates with continuous F and M– Like Baranov catch equation

• Can model different times at release for each tag (I think it )

• Applied to conventional and archival tags• Should 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 location

• At a point in time• Most frequent region• Best judgment (Eveson et al.)• Probabilities– Model x percent in y then predictions of x percent

in z

Observation error vs process error

• For movement, use process error model– Treat each observation as the new starting

position• For mortality use the known positions and

observation error• Deal with missing data (bad location, battery

running out)– Treat like conventional tag

Composite likelihood vs separate likelihoods for movement and recapture

• Separate Movement and recapture likelihoods• Know fish locations for applying recapture

likelihood – Fit to the recaptures using a negative binomial

based likelihood– Mortality rate information

• Use process error for movement– multinomial based likelihood for the location at

each time period

Eveson 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.: summary

• State-space model• Conventional, archive, popup• Models probability of transition among states

and probability of observation given in a state• States ={on dead fish, shed, on live fish in

region 1, ….}• Multinomial probabilities

Separate stocks: Taylor et al. 2009

• Need to define stock of origin– Tagged in spawning area– Observation in spawning area– Genetics

• Some may have unknown origin

Popoff

• Popoff – at right time– Malfunction– Constant depth (mortality)– Captured

• Model the probability of popping off, why?– Because you need to model the probability that

the fish survived until pop-off time?

Issues

• Sample size is important because observations from one individual are correlated and imply pseudo replication

• Memory (non-markov) models: e.g. fidelity to spawning grounds

• Dealing with tags that were never recaptured? • Treat missing data like conventional tags

(model all possible states)

The End

Eveson et al. method

• Use 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).

Download - Integrating archival tag data into stock assessment models

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