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