# Presentation: 504753

## Management Strategy Evaluation and the Gulf of Alaska walleye pollock fishery: including indicators of decadal change 

**Teresa A’mar**
**Andr****é Punt**
**University of Washington**
**Martin Dorn**
**NOAA – NMFS, Alaska Fisheries Science Center**
**28 August 2007**

## Outline

- Objectives
- Management Strategy Evaluation
- MSE and the Gulf of Alaska walleye pollock fishery
- Regime shifts
- Environmental variability
- Next steps

## Objectives

- Examine the performance of the current management strategy under several hypotheses about decadal change in productivity
- Evaluate alternative management strategies that are designed to be more responsive to decadal changes in productivity
- Examine methods of incorporating indicators of environmental change into advice for fisheries management

## Management Strategy Evaluation

- Simulation testing of a feedback-control management strategy
- accounting for process, measurement and model uncertainty
- using performance measures based on identified management goals and objectives
- This method is used in other countries, IWC

## GOA pollock fishery management strategy

- Annual stock assessment estimates F40%, SSB0, SSB40%, and current SSB

- Overfishing level (OFL) definition
- NPFMC Tier 3 decision rule
- Dorn’s decision rule

## The MSE approach

- Estimate
- current
- state of
- population

- Project
- population
- with
- catch

- Apply
- decision rule
- using
- estimates

- Generate
- data from
- population

- Estimate
- current
- state of
- population

- Calculate
- performance
- measures

- Loop over projection period

- Management Strategy

## Components of the GOA pollock MSE

- An operating model
- Conditioning part
- Projection part
- A management strategy
- A stock assessment model
- A decision rule
- Performance measures

## Performance measures

- Computed by comparing the operating model (‘true’) and stock assessment model (‘estimated’) values
- Estimation measures
- How well is spawning biomass estimated?
- How well is fishing mortality estimated?
- Management measures
- How often spawning biomass is below SSB20%
- How often fishing mortality is greater than F40% or FOFL
- Decision rule results for catch and catch variability

## Simulations

- The operating model estimates the state of the population in 2005 for simulations that start in 2006
- Bayesian analysis characterizes parameter uncertainty (a subset of 100 MCMC parameter vectors is used for the simulations)
- Each simulated population is projected forward with uncorrelated process error and catches set by the management strategy
- There is uncorrelated observation error in the data generated for the stock assessment

## Some results

## Simulation scenarios

- Base scenario
- 30 years, 2006 – 2035
- No environmental or ecosystem forcing
- Regime shift
- 50 years, 2006 – 2055
- Changes in average level of recruitment
- Environmental variability
- 45 years, 2006 – 2050
- Climate indices affect annual recruitment
- Dorn’s decision rule is used in all scenarios

## Base scenario – management performance: SSB/SSB40%

- Is the spawning biomass near the target level?

- Inter-simulation
- quantiles

- The stock is close to the target level on average

- Target level

## Base scenario – management performance: ABC

- Errory = 100(Esty-Truey)/Truey

- Annual management
- decision based on
- assessment results
- and decision rule

## Regime shifts

- All forcing is on age-1 recruitment
- Fixed regime shifts
- the average level of recruitment changes in specific years during the projection period
- Random regime shifts
- probability of an annual change in the average level of recruitment and recruitment variability
- modelled using a homogeneous Markov process

## Fixed regime shifts

- Model 1
- Shifts occur in 2006 (higher), 2023 (lower), and 2040 (higher)
- Higher level of recruitment: 1971 – 1980
- Lower level of recruitment: all other years
- Model 2
- Shifts occur in 2006 (very low), 2016 (lower), and 2041 (higher)
- Higher level of recruitment: 1971 – 1980
- Lower level of recruitment: 1981 – 2005

## Random regime shifts

- 2-state homogeneous Markov process
- Model 1
- Equal probability of starting in and staying in the higher and lower productivity regimes
- High probability of staying in the current regime
- Model 2
- Higher probability of starting in and staying in the lower productivity regime
- High probability of staying in the current regime

## Regime shifts – management performance: SSB/SSB40%

- Regime shifts – management performance: SSB/SSB40%

## Regime shifts – management performance: ABC

## Environmental variability

| Mechanism | Index | Season | Source/Citation |
| --- | --- | --- | --- |
| Primary production | Precipitation | Winter | Bailey et al. 2005 |
| Primary production | Wind mixing energy | Winter | Bailey et al. 2005 |
| Concentration of prey and larvae | Eddy formation due to freshwater input – Precipitation | Spring | FOCI |
| Concentration of prey and larvae | Upwelling and transport – Wind mixing energy | Spring | FOCI |
| Stage duration | Temperature | Spring | FOCI |
| Water column turbulence, eddies, transport, advection, upwelling | Precipitation | Spring | Ciannelli et al.2004,
Bailey et al. 2005 |
| Water column turbulence, eddies, transport, advection, upwelling | Wind mixing energy | Spring,
Summer | Bailey and Macklin 1994,
Ciannelli et al. 2004,
Bailey et al. 2005 |
| Temperatures affect amount of prey and amount of pelagic habitat for juveniles and age-0 animals | Sea surface temperature  (may interact with other environmental factors) | Summer, Autumn | Bailey 2000,
Bailey et al. 2005 |

## Environmental variability

- Climate indices
- precipitation (on Kodiak Island)
- wind mixing energy (Shelikof Strait)
- sea surface temperature (Shelikof Strait)
- North Pacific PDO anomaly
- Jan 1962 – Dec 2006 (historical data)
- Jan 2001 – Dec 2050 (8 sets of output from IPCC models)

## Climate indices

## Environmental variability

- Multiple scenarios for climate forcing on age-1 recruitment
- Accounts for some of the process error using the climate indices
-       decreased from 1.0 to 0.6

## Environmental variability

- Model 1
- Winter precipitation (+), Summer SST (-)
- Model 2
- Winter precipitation (+), Winter WME (+), Winter SST (-), Summer SST (-)
- Model 3
- Winter precipitation (+), Winter WME (+),    (Winter WME)2 (-), Winter SST (-),  Summer SST (-)

## Environmental variability – management performance: SSB/SSB40%

- Environmental variability – management performance: SSB/SSB40%

## Environmental variability – management performance: ABC

## Next steps

## Alternative management strategies

- Use 25-year sliding window for average recruitment used in calculating SSB40%
- Develop software to detect regime shifts using estimated age-2 recruitment
- Rodionov and Overland (2005)
- Find change points using sequential t-test analysis
- Use results of regime shift analysis to influence how the biological reference points are calculated        changes in ABC

## Acknowledgements

- André Punt, Martin Dorn, Ray Hilborn, and Dan Huppert
- Funding through NOAA FATE
- Anne Hollowed, Nick Bond, Bern Megrey
- NOAA Personnel at AFSC
- The Punt Lab at UW

## Slide 29