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Request 2: new base model (Run2). New rec index; Revised mink food catches; Retention block at 2011 (IFQ); Remove 2003 OR/WA discard rate; Use empirical discard estimates;

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request 2 new base model run2
Request 2: new base model (Run2)
  • New rec index;
  • Revised mink food catches;
  • Retention block at 2011 (IFQ);
  • Remove 2003 OR/WA discard rate;
  • Use empirical discard estimates;
  • #5 – Not possible to input discard estimates (mt) into the model since discard rates were used in other years. Instead, use calculated discard rate (based on observed discard and retained amount), and reasonable small CVs. Outputs matched well with the estimates.
request 2 new base model run21
Request 2: new base model (Run2)

Comprisons with preSTAR base: Lower ln(R0), less depleted: 74.3%

request 3 2 0 and 0 5 pre 1930 ca catches run3
Request 3: 2.0 and 0.5 pre-1930 CA catches (Run3)

Doubling catches resulted in high R0 and Ms, no effects from halving catches

request 4 wallace s mesh study data run4

Request 4: Clarify Wallace (1996) mesh size study data were filtered adequately to inform fishery discard rates and catch composition.

Request 4: Wallace’s mesh study data (Run4)
  • Still unclear what exact mesh sizes were used to derive length comps. John provided a draft on the study (it was a background doc in one of early panels)
  • No discard rate from mesh study was used
  • Proposed new base model - to drop length comps from the study (2 rows of comp data for 1990 OR/WA): Run2 + removal of these two data sets.
  • Run4 – new base model? Depletion = 0.930, Mfemale=0.45; Mmale=0.55 (bit high, but still within ranges of priors); ln(R0) increased from 11.4 to 12.28 (NWFSC Q drops to 19.8 from 23, still very high), large uncertainties in biomass estimates.
  • May need more time to check model stability, etc.
request 4 wallace s mesh study data run41

Run4 – new base model? Depletion = 0.930, Mfemale=0.45; Mmale=0.55 (bit high, but still within ranges of priors); ln(R0) increased from 11.4 to 12.28 (NWFSC Q drops to 19.8 from 23, still very high), large uncertainties in biomass estimates.

  • Discard estimates are OK.
Request 4: Wallace’s mesh study data (Run4)
request 4 wallace s mesh study data run42

Run4 – new base model? Depletion = 0.930, Mfemale=0.45; Mmale=0.55 (bit high, but still within ranges of priors); ln(R0) increased from 11.4 to 12.28 (NWFSC Q drops to 19.8 from 23, still very high), large uncertainties in biomass estimates.

  • Discard estimates are OK.
Request 4: Wallace’s mesh study data (Run4)
request 5 triennial survey data removals

Request 5: Justify why only triennial survey index data were removed in the sensitivity run. Explore removing the length comp. data as well. Additionally, provide a sensitivity run removing the early triennial survey index and comp. data.

Request 5: Triennial survey data removals
  • Comp data are much less influenced by sampling designs, gear operation, and locations than index data, such as coverage, survey timing, etc.
  • Run5a: remove all early year triennial survey data -
  • Run5b: remove all triennial survey data -
request 5 triennial survey data removals1

Removing the early year triennial survey data have larger effects than removing all triennial data (bit odd?). This could mainly due to there are some conflicting signals between two data.

Request 5: Triennial survey data removals
request 6 test influence of age comp data
Request 6: Test influence of age comp data
  • Request 6: Test the influence of the fishery age comps. and survey conditional age-at-length data by 1) removing age comps., 2) fixing growth parameters from the base model and removing conditional age-at-length data, and 3) fixing growth parameters from the base model and removing all these data to explore reasons for the variable scale of the SSB. (set lambda=1)
  • Rationale: Examine the influence of the age comp. data on the estimated SSB.
  • Run61: remove age comps from CA and OR/WA fisheries (lambda = 0 for age comp of both fisheries): Mfemale=0.35, Mmale=0.44, SSB comparable to the new base, depletion=0.688;
  • Run62: remove NWFSC CAAL data (lambda = 0 for age comp for the survey, fleet=5), but keep age comps from CA and OR/WA fishery, and fixed growth paras (n paras -10): Mfemale=0.58, Mmale=0.69, very large uncertainty in SSB, depletion=1.194; ln(R0)=13.8;
  • Run63: remove age comps from CA and ORWA, and NWFSC survey CAAL and fixed growth paras (same as Run62, n para -10): Mfemale=0.64, Mmale=0.75, very large uncertainty in SSB; ln(R0)=14.3;
request 6 test influence of age comp data1
Request 6: Test influence of age comp data
  • Fishery age comp data have small influences on outputs
  • Without CAAL data, unreasonably high Ms, ln(R0) higher but Qs still high
request 7 profile on ln r0 with each likelihood component

Request 7: Profile on ln(R0) with each likelihood component (by fleet, survey, and data component).

Rationale: To understand which components are most influential on the estimated scale of SSB.

Unable to complete: Much more time needed to cut & paste from outputs. Contacted Ian Taylor and have not got response.

Request 7: Profile on ln(R0) with each likelihood component
request 8 simple production model to test r0 scale

All estimated parameters in the base model were turned off, except R0, and were set to be same as in the base model;

  • All recDevs = 0.0
  • r4SS somehow could not read outputs from this run – no r4SS comparison plots
  • Depletion: base=74.3%; Run8=85.2%
Request 8: Simple production model to test R0 scale