SEDAR Uncertainty Workshop

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

3. Input Error Age reader error Ex: red porgy (SEDAR 1)

4. Z M study age 0-1 age 1 age 0 age 1 problems Emigration could increase Z, no Szedlmayer, age 0-1 2.1-3.2 0.54 ~2 0.54 trawling, so mostly M Incomplete selectivity of small fish could decrease Z; Emigration could Nichols (2004) 1.98 0.58 increase Z inefficiency, no contrast in effort, Brooks & Porch (2004) 2.3-3.7 ? ? emigration Gazey et al 2008 2.2 1.3 2 1.2 emigration to structure bias Z high low q for age 0, emigration to structure bias Z high, model mispec. 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 neg. survival, bias from error ratios, linear reg. 3.48 3.1 NS 2.96 stucture, nonsensical regression based upon VPA, ratios of surveys SEDAR 7 1.5 1.2 0.98 0.6 1999 assessment 0.5 0.3 Substantial uncertainty Input Parameter UncertaintyRed Snapper

5. Stock Assessment Model choice Data needs High 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

7. Projection UncertaintyNote how confidence intervals quickly widen With further projections Trends of SSB ratios E-BFT 50th percentile 80th percentile Trends of SSB ratios E-BFT

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

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