1. SEDAR Uncertainty Workshop
Tab G, No. 3
February 22-26, 2010
Charlotte, North Carolina

2. Some Major Sources of Uncertainty For the Stock Assessment Process
Sampling/Observation Error
Input Parameter Uncertainty
Model Uncertainty/Structural Complexity
Projection Uncertainty
Stock Vulnerability
Management Implementation Uncertainty
Slide from: Jon Brodziak
NOAA National Marine Fisheries Service
Pacific Islands Fisheries Science Center

3. Input Error Age reader error Ex: red porgy (SEDAR 1)
An extreme example of red porgy. This was dealt with through a sensitivity run assuming one reader group was correct and then running the other through a correction matrix. This issue has since been resolved and was the result of whole versus sectioning reading.
South-Atlantic-Assessment-Uncertainty-Review-EHW-final.ppt – slide 5

4. Input Parameter Uncertainty Red Snapper
study
age 0-1
age 1
age 0
problems
Szedlmayer, age 0-1
0.54 ~2
Emigration could increase Z, no
trawling, so mostly M
Nichols (2004)
1.98
0.58
Incomplete selectivity of small fish
could decrease Z; Emigration could
increase Z
Brooks & Porch (2004) ?
inefficiency, no contrast in effort,
emigration
Gazey et al 2008
2.2
1.3 2
1.2
emigration to structure bias Z high
RE model est
low q for age 0, emigration to
structure bias Z high, model mispec.
RE model Dens Dep
ratios, linear reg.
3.48
3.1 NS
2.96
neg. survival, bias from error
stucture, nonsensical regression
SEDAR 7
1.5
0.98
0.6
based upon VPA, ratios of surveys
1999 assessment
0.5
0.3
Substantial uncertainty Z M
The strict continuity case (run 0) followed SEDAR 7 in using the total offshore (three-area) shrimp trawl effort series to index the mortality rate of juvenile red snapper caught by offshore shrimp trawlers. The panel agreed that the effort expended outside 10 fathoms better represented the observation that over 80% of the shrimp trawl bycatch of red snapper occurs outside of 10 fathoms, while much of the offshore shrimp effort occurs inside 10 fathoms. Accordingly, continuity model 0b and all of the other model formulations described below use the shrimp effort series from greater than 10 fathoms (two-area series).
SEDAR Uncertainty GOM_Calay.pptx – slide 37

5. Stock Assessment Model choice
Data needs
Data
Model type
PAA1
Removal
Indices
Effort2 M
Biology3
Statistical CAA x
Delay-difference
Age Structured SP
Stochastic SRA
Catch-survey (stage)
Tuned VPA
Cohort analysis
Surplus production
PSA x4
High
Group 2
Low
Table 1. Some common stock assessment model types and their data requirements, from most complex to least.
1observed proportion-at-age data are not needed in some age-structured models where age composition is inferred using input selectivities.
2fishery-dependent indices indirectly inform the analyses on effort
3some of the biological characteristics used to estimate spawning biomass for estimating spawner-recruit relations are not used in some model formulations
4Productivity-susceptibility analysis, as used in the Southeast U.S., include relative vulnerabilities to different fisheries

6. Presenting Model Uncertainty
Monte Carlo/Bootstrap procedure
Parameters and Output
Ex: red grouper
South-Atlantic-Assessment-Uncertainty-Review-EHW-final.ppt – slide 22

7. Trends of SSB ratios E-BFT
Projection Uncertainty Note how confidence intervals quickly widen
With further projections
Trends of SSB ratios E-BFT
50th percentile
80th percentile
Trends of SSB ratios E-BFT
An alternate illustration of output uncertainty
Uncertainty ICCAT Spp.pptx – slide 5
Eastern bluefin tuna

8. Management Implementation Uncertainty
Fishermen’s behavior
Enforcement
Weather
Economy
Biological unknowns (e.g., average weight of fish)

9. Gulf of Mexico Regional PSA Results: Snapper
How many with missing data?
Of 26 species, nine species are missing some information (the report noted 7 – typo).
6 sp are missing information for 1 attribute
1 sp is missing information for 2 attributes
1 sp is missing information for 3 attributes
1 sp is missing information for 4 attributes

10. Conclusions
Move beyond single ‘run’ when providing results and recommendations.
SEDAR should provide an OFL estimate and distribution around that estimate that addresses uncertainty and enables the SSC to determine ABC in accordance with its ABC control rules
SEDAR should better communicate uncertainties and the purpose of typical techniques used to evaluate uncertainties
SEDAR should strive to improve consistency between assessments
SEDAR should strive to explicitly identify the primary and most influential uncertainties at each step of the assessment process……and ensure these are carried forward to subsequent steps?
Potential management actions should be linked with projections made through population models

11. The End