2012 Pre-Season Forecasts for the Stillaguamish River Chinook

1. 2012 Pre-Season Forecasts for the Stillaguamish River Chinook EMPAR(Environmental Model Predicting Adult Returns) January 14, 2012 Developed By: Jason Hall1 and Dr. Correigh Greene2 1Hall and Associates Consulting, Inc. Jason@haaci.com 2NOAA Northwest Fisheries Science Center Correigh.Greene@noaa.gov

2. Background: EMPARConcept • Return rates are driven by survival across multiple life stages • Unique environmental conditions are experienced during each life stage • Life stage specific environmental conditions can influence survival • Forecasts that consider life-stage specific environmental conditions may provide better forecasts • EMPAR developed to provide an accurate and robust forecast model that incorporates life-stage specific environmental conditions • Approach adapted from Greene et al. (2005)* • EMPAR development started with 2009 return year forecast *Greene, C.M., D.W. Jensen, G.R. Pess, and E.A. Steel. 2005. Effects of environmental conditions during stream, estuary, and ocean residency on Chinook salmon return rates in the Skagit River, Washington. Transactions of the American Fisheries Society 134:1562-1581.

3. Background: Life stage concept Age 2 Spawners

4. Background: Broodyear Model Concept RY 2000 Spawners (S) RY 2003 Spawners (S) RY 2002 Spawners (S) RY 2001 Spawners (S) Age 2 Age 3 Age 4 Age 5 SPSt = spawners per spawner in year t Nt = adult escapement in year t Px,t = proportion of age x in return year t 2002 2003 2004 2005 2006 2007 2008

5. Background: Age-Specific ModelConcept RY 2003 Spawners (S) RY 2002 Spawners (S) RY 2001 Spawners (S) RY 2000 Spawners (S) Age 2 Age 3 Age 4 Age 5 *Return rate calculated for each age class – Age 3 example shown here 2002 2003 2004 2005 2006 2007 2008

6. Background: EMPAR Updates • Removed some environmental factors from consideration: • Infrequent data update schedule • Forecast years rely on estimated data • Added 2009 and 2010 return years to model training set: • Increases sample size by almost 10% • Return year 2011 was used as sole test set • Working with age-specific models only: • Removes errors associated with applying average age structure • Makes more sense from a biological standpoint

7. Background: EMPAR Updates • Incorporated Principle Components Analysis (PCA): • Common factor analysis technique • Synthesize multiple factors within a life stage into primary components • Longer temporal patterns can be considered • More arbitrary than using actual factors, but is more robust • Allows trends in many factors within a life stage to be considered

8. PCA Approach: • PCA for Freshwater Life Stage (1989-2010) • EGG, PKCM, HATCH, and QMAX • 62% of variance explained with first two components • PCA for Delta/Nearshore Life Stage (1989-2010) • DO, TEMP, and SAL • 50% of variance explained with first two components • PCA for Ocean Life Stage (1949-2010) • SST, UWI, PDO, SOI, and SL • 73% of variance explained with first two components

9. PCA Approach: • Linear regression models: • Combination of PCA components and selected raw factors • 2 freshwater, 1 delta/near, and 2 ocean life stage factors • PCA components (representing multiple factors) count as 1 factor • Over-parameterized model? • Significant increase in predictive power for key age groups • Describes complicated life cycle well • Several evaluation techniques indicate that these models are not over-parameterized

10. Test Set Validation: SNOR Models *MSE decays as test set increases *Similar patterns observed for factor coefficients

11. Results: PCA EMPAR Model Summaries * Factors in parentheses have negative coefficients ** Pearson’s correlation calculated based on sum of predicted returns of each age class by return year and observed escapement

12. Results: SHOR Model Output

13. Results: SNOR Model Output

14. Results: FNOR Model Output

15. EMPAR Performance: Previous Models • With return years 2009 – 2011 as test sets: • Derived forecast from all three selected EMPAR models • PCA model shows best track record when compared on equal terms

16. Recommendations: • Use EMPAR models that incorporate Principle Components Analysis (PCA): • Forecast trends track well with observed trends • Factor sensitivity does not appear to be an issue as compared to the full permutation AIC based approaches • Forecast performance comparisons indicate that the PCA model has better predictive accuracy • PCA model does not appear to be over-parameterized and training set appears valid • More arbitrary than using actual factors, but is a more statistically robust procedure • Allows consideration of trends in multiple factors within a life stage

17. Results: 2012 Forecast Escapement with Fishing Escapement without Fishing (assumes average exploitation rates)

18. Results: 2012 Forecast FRAM Conversion FRAM Input MM Run FRAM Recruits

19. EMPAR Supporting Information: The following slides are supplemental information to support the presentation and detailed questions…

20. PCA Example: Delta/Nearshore Life Stage PCA

21. PCA Example: Ocean Life Stage PCA

22. EMPAR Validation: Forecast Compensation

23. Test Set Validation: SNOR Age 3 Model *Coefficient change decays as test set increases

24. Background: Model Structures • Broodyear Model: • Simplest model structure • Calculate return rates for each broodyear • One model for all spawners produced from each broodyear • Separate model for SNOR, SHOR, and FNOR • Allocate predicted returns by average age structure • Age-Specific Model: • More complicated model structure • Calculate return rates for each age class by broodyear • Separate model for each age class • Separate model for SNOR, SHOR, and FNOR

25. Background: Life stage factors • Nearshore(Jun – Oct) • Surface DO • Surface Temp • Sea Level • Upwelling Index • Ocean Year 1 – 4 (Oct – Sep) • Sea Surface Temperature • Upwelling Index • Pacific Decadal Oscillation • Southern Oscillation Index • Sea Level • Aleutian Low Pressure Index* • SVI boreal copepod* • SVI southern copepod* • Freshwater(Aug – Feb) • Egg deposition • Pink and Chum escapement • Hatchery Releases • Max incubation flow • Min spawning flow • Delta(Feb – Jun) • Surface DO • Surface Temp • Surface Salinity • Sea Level *Removed from candidate list

26. Background: Life stage factors

27. Background: EMPAR Approaches • Several model selection and model development approaches have been considered during the development of EMPAR: • Full permutation models with Akaike's Information Criterion score (AICc) model selection • Stepwise regression techniques • Principle Components Analysis (PCA) based approach

28. EMPAR Approaches: AIC Models • Full permutation models with Akaike's Information Criterion score (AICc) model selection • Multiple models provide information about dependent variables • The best models are those that have strong predictive power but use fewer independent variables • AIC scores models based on their ability to reduce uncertainty but penalizes by the number of variables in the model • Not sensitive to the order variables enter as in stepwise regressions • Model structure caveats • Large test model sets increases risk of selecting randomly correlated models • Sensitivity to collinearities were initially a problem, but were subsequently resolved in later models • Forecast outputs show sensitivity to variations in strong factors, but were more accurate than stepwise regression models

29. EMPAR Approaches: Stepwise Regression • Stepwise regression model selection • Common and well established approach • An aggressive fitting technique that can be overly greedy • Model structure caveats • Sensitive to factor order • Favors models with fewer factors, and therefore does not consider all life stages • Stepwise regression approaches appear to produce less accurate forecasts despite the caveats associated with the full permutation AIC approach

30. EMPAR Approaches: PCA • Principle Components Analysis • Common factor analysis technique • Reduces the number of variables and detects structure within a set of factors • Can be used to synthesize multiple factors within a life stage into primary components • Longer temporal patterns can be considered since components can be derived independently • Model Structure Caveats • Models using PCA components can be more conservative • Interpretation of the influence of factors within components is not as direct as in AIC or stepwise regression techniques

31. EMPAR: Previous Model Forecasts