Andr é E. Punt 1 School of Aquatic and Fishery Sciences, UW

1. How has Strategic Advice Been Usedin a Global LMR Context: A GlobalPerspective on How to Deal With Ecosystem Model Uncertainty André E. Punt 1School of Aquatic and Fishery Sciences, UW 2CSIRO Marine and Atmospheric Research

2. Outline • Some definitions (to provide context). • A process for strategic evaluation. • Assigning plausibility weights • Case studies I & II (environmental drivers of recruitment) • Case studies III & IV (trophic interactions & MRMs) • Case study V (whole of ecosystem models)

3. Examples • International Whaling Commission: • Aboriginal and commercial whaling. • Australia: • Management of the SESSF. • South Africa: • Penguins and anchovy. • USA: • Evaluation of the GOA pollock harvest control rule.

4. Definitions andcontext

5. Tactical Advice -> What is next year’s catch limit for pollock? Strategic Advice -> How well does the approach we use for determining next year’s catch limit for pollock perform relative to a set of agreed management objectives Tactical Advice -> A number. Strategic Advice -> A set of trade-offs.

6. Strategic Evaluation For this talk a “strategic evaluation” asks the question: How well does a set of tactics (monitoring, assessment, decision making) achieve a set of (agreed) management goals. Strategic evaluation is not: • How to determine what the goals should be? • Perfect knowledge analyses • Constant F projections

7. An “Ecosystem model”: Anything which is NOT a single-species, single-area, population dynamics model driven by random perturbations in recruitment and fishery selection. Non-fisheries drivers Environmental drivers Standard model Spatial structure Trophic interactions

8. Elements of a Strategic Evaluation (aka MSE): • A set of management goals (appropriately quantified). • A set of candidate strategies to evaluate. • A set of operating models which span the space • of possible realities. • Uncertainty arises because of: • model uncertainty (is our model right?). • process uncertainty (are the parameters constant?). • parameter uncertainty (given a model, can we • estimate its parameters?). • implementation uncertainty (given a management • decision, can we implement it as anticipated?).

9. The Indirect Approach Simulation trials were run for changes in climate in an indirectway Carrying capacity (or natural mortality) Time [IWC testing of its revised management procedure]

10. A Process for Strategic Evaluation

11. Qualitative management objectives (aka the M-S Act) Hypotheses for system behaviour Quantitative performance measures Models of system behaviour System simulation Models weights Candidate strategies Strategy ranks Data and priors

12. A model weighting scheme How strong is the basis for the hypothesis; • in the actual data for the system under consideration; • in the actual data for a similar system; • for any system; or • in theory. After Butterworth et al. (1996); Rep. Int. Whal. Comm 46: 637-40.

13. An IWC interpretation-I • Step 1 of the previous scheme requires a belief in the objective function (aka AIC, DIC, etc.); this is rarely possible. • The IWC approach: • Assign each hypothesis (model) a rank of ‘high’, ‘medium’, ‘low’ or ‘no agreement’ using a “Delphi” approach. • Each rank is associated with an agreed (conservation) performance standard.

14. An IWC interpretation-II What makes a hypothesis “low” plausibility? • Obvious conflict with actual data. • Obvious conflict with auxiliary information.

15. Quantitative Tools for Model Weighting In order of relative ease: • Fit diagnostics (observed versus predicted data; residual plots, q-q plots, etc). • Sensitivity tests • Variance estimates • Bayesian; Bootstrap; delta method

16. Case Studies I & II Environmental Drivers of Recruitment

17. Incorporating climate forcing(An empirical approach) Climate indices Age-structured operating model Link to recruitment TAC Data Management Strategy “Climate” Decision rule??

18. Gulf of AlaskaPollock A’mar et al. (2009); IJMS 66: 1614-32

19. Data from surveys and the fishery Acceptable Biological Catch (ABC) Stock assessment model Fishing mortality relative to F35% Stock size, productivity Target and limit reference points Stock size relative to SB47%

20. The performance of this approach to setting TAC can be quantified in terms of: • high stable catches; • low probability of reducing stock size to • undesirable (low) levels; and • accurate and precise estimates of biomass (and • status relative to target biomass levels). [essentially • hindcast skill]

21. What drives pollock recruitment? Estimated recruitment (from assessment) Predicted recruitment (with environment) Kendall et al. Fish Ocean (1996)

22. Performance when: • the assessment is (almost) correct • recruitment varies about a mean • the stock is left above the target and • the average catch is ~ 150,000t.

23. Spawning biomass: • Generally downward • Depends on model for • forecasting future climate • (two of eight IPCC models) Spawning biomass Year

24. Spawning biomass: • Generally downward • Depends on model for • forecasting future climate • (two of eight IPCC models) Catch • Catches: • React faster than • abundance, especially for • a declining resource. Year

25. Uncertainty • Model uncertainty • Choice of IPCC model • Relationship between environmental indices and recruitment • Process uncertainty • Variation in recruitment about the assumed relationship • Estimation uncertainty • Parameter uncertainty (Bayesian analysis)

26. Eastern North Pacific Gray Whales Brandon and Punt (2009): IWC Document SC/61/AWMP2

27. Eastern North Pacific Gray Whale Ice conditions in the Bering Sea have been postulated to impact calf production.

28. Objectives and Strategies • Objectives • Satisfy aboriginal need (Russia and the US) • Achieve stock conservation objectives • Management strategy (default) • Surveys (of absolute abundance) every 5-10 years. • Strike limits based on the IWC’s “Gray whale SLA”.

29. With climate Previous Assessment

30. Performance Evaluation • Model uncertainty: • Sea-ice impacts calf production • Future catastrophic events are: • random • related to population density. • Process uncertainty: • Random variation in calf production. • Estimation uncertainty: • Parameters are based on Bayesian • estimation.

31. Other Studies • Rock Lobsters off Southern Australia • Pacific Sardine off the west coast of the US

32. Cases StudiesIII and IV Trophic Interactions (MRMs)

33. MRM Types • Biological interactions • Competition, predation, etc. • Technical interactions • Interactions through bycatch.

34. Anchovy and Penguins How does penguin breeding success and adult survival depend on the abundance of pelagic fish?

35. Penguins as output statistics Anchovy and sardine control rule

36. Uncertainty (sardine and pilchard) • Model uncertainty • Stock-recruitment relationships • Process uncertainty • Variation in recruitment • Variation in bycatch rates • Estimation uncertainty • Quantified using bootstrapping

37. Gulf of AlaskaPollock A’mar et al. Fish. Res. (Submitted)

38. { Arrowtooth flounder Predator functional relationship Predator harvest policy (const F) Pacific Halibut Pacific cod GOA pollock Pollock harvest policy

39. M really isn’t constant it seems… Type I Type II Type III

40. Uncertainty • Model uncertainty • With / without predation mortality • Predator feeding relationship • Fishing mortality on the predators • Process uncertainty • Variation in recruitment • Estimation uncertainty • Parameter uncertainty (Bayesian analysis)

41. Other Studies • Predator-prey interactions: • SSLA for krill management (CCAMLR) • Cod and minke whales in the Barents Sea • Technical interactions: • Hake off South Africa. • Coral trout and red throat emperor off the Great Barrier Reef, Australia. • Prawns off Northern Australia.

42. Cases StudiesV Whole of System Models

43. South East AustraliaWhole of System Review Beth Fulton, pers. commn

44. EEZ Claimable shelf SE Australian Atlantis-I • Aim: To rethink management • arrangements in the SESSF • Complications: • Multi-everything • Relatively data poor • Many objectives • Atlantis: • Physical component. • Biological component. • Assessment component. • Management component. • Social component. • Non-fishing impacts.

45. SE Australian Atlantis-II • Advantages: • Considerable “realism” • Appeals to decision makers • Key difficulties: • Driven to an unknown extent • byassumptions • Very difficult to calibrate • No variance estimates (ever) • relative performance of high • level policies (at a PEIS level; • perhaps even beyond • “strategic”).

46. Calibration Tests for Atlantis-I Observed and predicted diet composition for gummy shark

47. Calibration Tests for Atlantis-II Forecast based on Atlantis

48. Uncertainty • Model uncertainty • Productivity / susceptibility – alternative parameterizations. • Structural sensitivity (loop analysis; social network theory). • External forcing scenarios. • Process uncertainty • Emergent property of the model. • Estimation uncertainty • In a formal sense - N/A (ever?)`

49. Uncertainty of StrategicEvaluation(Adoption, Uncertainty, and the State of the Art)

50. Strategic Evaluations(directly used!) Minke whales cod Sardine Mackerel Hake Anchovy Sardine Rock lobster Toothfish Rock lobster