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SEDAR Uncertainty Workshop. Tab G, No. 3. February 22-26, 2010 Charlotte, North Carolina. Some Major Sources of Uncertainty For the Stock Assessment Process. Sampling/Observation Error Input Parameter Uncertainty Model Uncertainty/Structural Complexity Projection Uncertainty - PowerPoint PPT Presentation
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SEDAR Uncertainty Workshop
February 22-26, 2010Charlotte, North Carolina
Tab G, No. 3
• Sampling/Observation Error
• Input Parameter Uncertainty
Model Uncertainty/Structural Complexity
Projection Uncertainty
Stock Vulnerability
Management Implementation Uncertainty
Some Major Sources of Uncertainty For the Stock Assessment Process
Input ErrorAge reader error
0
2
4
6
8
10
12
14
0 2 4 6 8 10 12
NC
Age
(jit
tere
d)
SC Age (jittered)
Ex: red porgy (SEDAR 1)
Input Parameter UncertaintyRed Snapper
study age 0-1 age 1 age 0 age 1 problems
Szedlmayer, age 0-1 2.1-3.2 0.54 ~2 0.54Emigration 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) 2.3-3.7 ? ?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 3.3-3.7 1.6-2.25 3.3-3.7 0.76-1.4
low q for age 0, emigration to structure bias Z high, model mispec.
RE model Dens Dep 2.6-3.5 0.6-1.3
low q for age 0, emigration to structure bias Z high, model mispec.
ratios, linear reg. 3.48 3.1 NS 2.96neg. survival, bias from error stucture, nonsensical regression
SEDAR 7 1.5 1.2 0.98 0.6 based upon VPA, ratios of surveys
1999 assessment 0.5 0.3 Substantial uncertainty
Z M
Stock Assessment Model choiceData
Model type PAA1 Removal Indices Effort2 M Biology3
Statistical CAA x x x x x x
Delay-difference x x x x x
Age Structured SP x x x x x
Stochastic SRA x x x x x
Catch-survey (stage) x x x x
Tuned VPA x x x x
Cohort analysis x x x
Surplus production x x
PSA x4 xTable 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 effort3some of the biological characteristics used to estimate spawning biomass for estimating spawner-recruit relations are not used in some model formulations4Productivity-susceptibility analysis, as used in the Southeast U.S., include relative vulnerabilities to different fisheries
High
Low
Data needs
Presenting Model UncertaintyParameters and Output
• Monte Carlo/Bootstrap procedure
Ex: red grouper
Trends of SSB ratios E-BFT
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
SSB/SSB_vrg
0
0.5
1
1.5
2
2.5
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
SSB/MxH_SSB
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
SSB/SSB_F01
0
2
4
6
8
10
12
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
SSB/SSB_Fmax
50th percentile80th percentile
Projection UncertaintyNote how confidence intervals quickly widen
With further projections
Trends of SSB ratios E-BFT
Management Implementation Uncertainty
•Fishermen’s behavior•Enforcement•Weather•Economy•Biological unknowns (e.g., average weight of fish)
Gulf of Mexico Regional PSA Results: Snapper
Gulf of Mexico Snapper
1.00
1.50
2.00
2.50
3.00
1.00 1.50 2.00 2.50 3.00
Productivity (high --- low)
Su
sc
ep
tib
ilit
y (
low
---
hig
h)
Silk Snapper Mutton Snapper Lane Snapper Red SnapperMangrove Snapper Yellow tail Snapper Vermilion Snapper
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
The End