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

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 and context

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. Standard model Environmental drivers Trophic interactions Spatial structure Non-fisheries drivers

8. 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 Time Carrying capacity (or natural mortality ) [IWC testing of its revised management procedure]

10. A Process for Strategic Evaluation

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

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) Link to recruitment Climate indices Age-structured operating model Management Strategy TAC Data “Climate” Decision rule??

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

19. Data from surveys and the fishery Stock assessment model Target and limit reference points Stock size, productivity Fishing mortality relative to F 35% Stock size relative to SB 47% Acceptable Biological Catch (ABC)

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

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) Year Spawning biomass

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

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. Previous Assessment With climate

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 Studies III 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 Alaska Pollock A’mar et al. Fish. Res. (Submitted)

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

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 Studies V Whole of System Models

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

44. SE Australian Atlantis-I EEZ Claimable shelf Aim: To rethink management arrangements in the SESSF Complications: 1.Multi-everything 2.Relatively data poor 3.Many objectives Atlantis: 1.Physical component. 2.Biological component. 3.Assessment component. 4.Management component. 5.Social component. 6.Non-fishing impacts.

45. SE Australian Atlantis-II

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 Strategic Evaluation (Adoption, Uncertainty, and the State of the Art)

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

51. Overall Summary (the State of the Art?) Sensitivity tests / model scenarios IPCC data sets (pollock) Productivity scenarios (Atlantis) Predation functions (pollock) Process uncertainty Climate-recruitment (pollock) Ice coverage – birth rate (gray whales) Variance estimation Gray whales, pollock, etc.

52. How are strategies based ecosystem models used? USA Pollock A requirement for (continuing) MSC certification. Presented to the NPFMC SSC (but validates current management strategy). Pacific Sardine Included in the PFMC CPS FMP IWC Aboriginal subsistence whaling and commercial whaling management schemes all tested accounting for “ecosystem changes” The ENP gray whale analysis w ill form (part of) the basis for the review of the current Strike Limit Algorithm for gray whales in 2010.

53. How are strategies based ecosystem models used? South Africa (OMPs have legal status) Penguin model currently “on hold” while it is being refined. Management strategies for sardine and pilchard have taken technical interactions (and between sector- allocation) into account for over a decade. Australia Used to “guide” decision making rules. Atlantis provided direction that helped set policy directions in SESSF (gears, spatial, quota, etc.)

54. Pre-pre- Implementation Assessment (1) First Intersessional Workshop Agree completed at an Annual Meeting First Annual Meeting Pre- Implementation Assessment (2+) Second Intersessional Workshop Second Annual Meeting Option or options presented to the Commission Catch limit? Commission 2 years

55. What is actually necessary to provide strategic advice? The Objective: How robust are the current / alternative strategies (note that strategies which are “deterministically optimal” will not necessarily be given uncertainty). Stakeholder Buy-in: Most successful applications are associated with strong stakeholder involvement: Workshops to identify candidate strategies, hypotheses, desired trade-offs. Stakeholder involvement is key when “implementation uncertainty” is important.

56. What is actually necessary to provide strategic advice? Think carefully about candidate strategies: The default strategy should always be the current one. A TAC which is 20% of current biomass will always be preferred to the outcome of complicated (e.g. ecosystem) model. Look for a “good enough” solution which is easily explained rather than “complex perfection”. Avoidance of “unrealistic” scenarios Avoid scenarios which “while interesting” are not strongly supported by the data (IWC “rejects” all scenarios which are “low” plausibility).

57. What is actually necessary to provide strategic advice? Capture the major uncertainties, but avoid 1,000 scenarios: Consider when to “integrate” (process error) and “scenario”. A balance here is key. Always include assessment error (at realistic levels). Keep the scenarios “balanced” (e.g. high vs low productivity) Combinations for factors are nice, but usually just add confusion.

58. University of Washington  Teresa A’mar  John Brandon CSIRO Beth Fulton Eva Plaganyi-Lloyd UCT Eva Plaganyi-Lloyd KEY Acknowledgements