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Likelihood profiling of differently weighted models Doug Kinzey, George M.Watters, and Christian S. Reiss NOAA SWFSC Antarctic Ecosystem Research Division.

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Presentation on theme: "Likelihood profiling of differently weighted models Doug Kinzey, George M.Watters, and Christian S. Reiss NOAA SWFSC Antarctic Ecosystem Research Division."— Presentation transcript:

1 Likelihood profiling of differently weighted models Doug Kinzey, George M.Watters, and Christian S. Reiss NOAA SWFSC Antarctic Ecosystem Research Division CAPAM Data Weighting Workshop, 19-23 November 2015

2 The base model for Antarctic krill uses 7 sources of data

3 Survey length-compositions for recruitment and growth estimation

4 Fishery length-compositions, not so much

5 Fishery catches (CV = 0.5)

6 MCMC samples for recruitment and spawning biomass (1967- 2034)

7 Likelihood profile on R 0

8 Seven alternative configurations Increase weighting on fishery length-compositions X10 (number of hauls X 10) Increase weighting on fishery length-compositions X20 (number of hauls X 20) Decrease weighting on fishery catches X2 (CV= 1.0) Decrease weighting on fishery catches X4 (CV= 2.0) No fishery length-compositions No survey compositions, increase fishery length-composition weighting X10 (number of hauls X 10) No survey compositions, increase fishery length-composition weighting X20 (number of hauls X 20)

9 Increase weighting on fishery compositions X10

10 Increase weighting on fishery compositions X20

11 Decrease weighting on fishery catches X2 (CV = 1.0)

12 Decrease weighting on fishery catches X4 (CV = 2.0)

13 No fishery compositions (1967-2014)

14 No survey compositions, increase fishery composition weighting X10

15 No survey compositions, increase fishery composition weighting X20

16 Summary Likelihood profiling can augment other model diagnostics by showing the effects of different data sources relative to one another on the parameters estimated in an integrated model. The effect of different data sources on the parameter estimates depend on what other data sources are fitted by the model and how they are weighted. Data weighting using the same time-series observations in the krill model can effect the estimates of a scaling parameter such as R 0 by over an order of magnitude.

17 Three stages of the krill model http://swfscftp.noaa.gov/thinclient/ username: anonymous password: anonymous change current folder name from: /users/anonymous/ to /users/dkinzey/public Model code, executable, and data: wg_SAM_14_20 FishRes_15 wg_EMM_15_51


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