1. WHAT IS A MANAGEMENT PROCEDURE (MP) / MANAGEMENT STRATEGY EVALUATION (MSE) AND WHY IS IT IMPORTANT?
Doug S Butterworth
MARAM (Marine Resource Assessment and Management Group)
Department of Mathematics and Applied Mathematics
University of Cape Town, Rondebosch 7701, South Africa

2. OUTLINE What is an MP/MSE? (IWC/CCSBT interpretation)
Why is it important to implement an MP/ MSE?
Feedback control
When should an MP/MSE be implemented?
Objectives (and their relation to reference points)
An example

3. What is a Management Procedure?
I. WHAT IS AN MP/MSE?
What is a Management Procedure?
An agreed formula, with an agreed set of data inputs, used to calculate a recommendation for a fisheries management measure – typically a TAC
What the IOTC Glossary describes as a Harvest Strategy .
MSE is the process of developing and agreeing a Management Procedure
(At least for our purposes today)

4. II. WHY IS IT IMPORTANT TO IMPLEMENT AN MP/MSE?
Because of shortcomings with best-assessment-based management

5. BEST-ASSESSMENT-BASED MANAGEMENT
E.g. US Magnuson-Stevens Act with its MSY-related recovery targets
“Best Assessment” of resource
TAC
Catch control law

6. DIFFICULTIES FOR THE BEST-ASSESSMENT-BASED APPROACH
Inter-annual best assessment/TAC variation (including MSY-related Reference points)
No consideration of longer term trade-offs (which requires taking account of management responses to future resource monitoring data)
Lengthy haggling
What if the “best assessment” is wrong?
Default decision of “no change”
Too little too late
Ecolabeling and other external pressures on RFMOs 6

7. BUT WHY IS FISHERIES MANAGEMENT SO DIFFICULT?
SUSTAINABLE UTILISATION
Pensioner must live off interest
What’s my capital?
What’s the interest rate?
Multiply the two
Don’t spend more than that!
EASY!! 7

8. THE SOURCE OF THE DIFFICULTY
THE SOURCE OF THE DIFFICULTY . FISHERIES HAVE UNCO-OPERATIVE BANK TELLERS
They won’t tell you the interest rate, which in any case is highly variable
Recruitment fluctuations
They will advise your balance only once a year, with a typically +-50% error, and in the wrong currency
Surveys (or abundance indices) are typically available annually only, their results have high variance, and their bias is unknown 8

9. MANAGEMENT PROCEDURES (MSE) .
WHAT NEW DO THEY BRING TO ASSIST SOLVE THE PROBLEM?
FEEDBACK CONTROL!
Monitor stock changes and adjust management measures (e.g. TACs) accordingly 9

10. A FINANCIAL ANALOGY $1 000 000 invested at 5% p.a.
Each year withdraw $ 
Investment sustainably maintained at $
ton fish stock grows naturally at 5% p.a.
Each year catch tons 
Sustainable exploitation: resource kept at tons

11. After 5 years, someone MAY have stolen $300 000 from your investment
You keep withdrawing $ per year
No theft
Theft
After 5 years, recruitment failure or IUU fishing MAY have reduced abundance by 30%
Catches maintained at tons per year
If this event did occur, resource is rapidly reduced

12. WHY’S THERE ANY PROBLEM?
Ask the teller for account balance.
If this has fallen to $ , reduce annual withdrawal to $ 
Sustainability maintained.
BUT
The teller will advise balance only once a year
with 50% error
Resource abundance known only through annual surveys or abundance indicies (e.g. CPUE) which have large associated errors

13. CAN YOU TELL WHETHER $300 000 WAS STOLEN FROM YOUR ACCOUNT ?
(Equivalently, whether fish abundance was reduced by 30%?)
In each of the following scenarios shown, the theft occurred in only one of the two cases
Can you tell which one?

29. IMPRESSIONS It wasn’t easy to tell
It needed usually about 20 years of new data
to be certain
By that time, account was almost exhausted
(if theft had occurred)
By the time the adverse effect of recruitment failure or IUU fishing is detectable, the resource is already heavily depleted

30. THREE STRATEGIES (MPs)
I: Withdraw $ every year
II: Withdraw 5% of the teller-advised balance
each year
III: Withdrawal this year = 80% last year’s
withdrawal + 1% teller’s (erroneous) balance
Strategy must “work” whether or not theft occurred

31. Annual Withdrawal I II III
No theft
Theft I II
III

32. Balance in Account I II III
No theft
Theft I II
III

33. Annual Withdrawal I II III
No theft
Theft I II
III

34. Balance in Account I II III
No theft
Theft I II
III

35. “Feedback control” (MP basis)
PERFORMANCE
I: Going bankrupt if theft occurred
II: Stabilises balance in account, but annual
withdrawals too variable
III: Best of the three – stabilises balance
without too much change from year to year
Formula III automatically corrects for effect of recruitment failure/IUU fishing if it occurred.
“Feedback control” (MP basis)

36. Formula for TAC recommendation
SO WHAT EXACTLY IS AN MP ?
Formula for TAC recommendation
Pre-specified inputs to formula 36

37. But isn’t this the same as the traditional approach ?
Almost, but not quite 37

38. So what’s the difference ?
Pre-specifications prevent haggling
Simulation checks that formula works even if the “best” assessment is wrong 38

39. How is the MP formula chosen from amongst alternative candidates ?
Compare simulated catch / risk / catch variability trade-offs for alternatives
Check adequate for plausible variations on “best” assessments 39

40. SOUTHERN BLUEFIN TUNA EXAMPLE
TRADE OFF
More recovery
More catch
Catch Biomass
Year
Different MP options

41. What are the advantages of the MP approach ?
Less time haggling of little long term benefit
Proper evaluation of risk
Sound basis to impose limits on TAC variability
Consistent with Precautionary Principle
Provides framework for interactions with stakeholders, particularly re objectives and trade-offs amongst them
Use haggling time saved towards more beneficial longer term research 41

42. What are the disadvantages of the MP approach ?
Lengthy evaluation time
Rigid framework (though 5-9 yearly revisions in IWC/CCSBT)
BUT
Provides default
Addresses problems for RFMOs with “no change” 42

43. When should scientists change the TAC recommendation from a MP?
New information / understanding shows actual resource situation is outside range tested
A MP is like an auto-pilot
BUT
The real pilot remains to check that nothing unanticipated has occurred (i.e. usually annual routine assessments continue) 43

44. How should managers react to MP-based scientific recommendations ?
Treat as default (replacing “no change”) so that necessary action is not delayed
Require compelling reasons to change i.e. such a decision should not be taken lightly 44

45. THE MANAGEMENT PROCEDURE APPROACH (MSE) . .
Specify alternative plausible models of resource and fishery (Simulation or Operating Models – OMs)
Fit (condition) these models to data (effectively alternative assessments); pre-specify future data inputs to MP
Agree performance measures to quantify the extent to which objectives are attained (more on objectives later)
Select amongst candidate MPs for the one showing the “best” trade-offs in performance measures across objectives and different OMs in simulation testing
1 and 2: Scientists responsibility
3 and 4: Scientists and Managers/Stakeholders jointly

46. III. WHEN SHOULD MSE BE IMPLEMENTED?
Scenario A
A large quantity of non-conflicting data covering a few decades
An agreed assessment giving estimates of high precision
Few serious uncertainties about assessment assumptions
Little argument about the TAC recommendation to follow from the assessment
No urgency to implement MSE
ARE YOU IN UTOPIA OR DENIAL ??!!

47. III. WHEN SHOULD MSE BE IMPLEMENTED?
Scenario B
Few data
Perhaps an historical catch series (accuracy questionable)
No more than a rudimentary:
i) Estimate of abundance in absolute terms; or
ii) Very short series of a relative abundance index
USE A PRECAUTIONARY DATA-POOR METHOD TO PROVIDE ADVICE
Focus first on developing an improved index of relative abundance before considering MSE

48. III. WHEN SHOULD MSE BE IMPLEMENTED?
Scenario C (Minimally – do any IO tuna stocks match this profile? )
Historical catches, with some idea of their accuracy
A relative abundance index good and long enough plus sufficient biological information to develop at least a simple assessment
Nevertheless assessment results that vary considerably depending on assessment (and data) assumptions that are under debate/dispute
Reasonable certainty that the abundance index will continue to be available for each coming year
IMPLEMENT MSE

49. ABUNDANCE INDEX/INDICES
Indian Ocean tuna populations For which are reasonably long and reliable relative abundance series available? MSE simulation testing needs Not only such series continuing But also the extent of their variability about the true underlying abundance trend

50. SOUTH AFRICAN DEEPWATER HAKE – SOUTH COAST CPUE - DATA

51. SOUTH AFRICAN DEEPWATER HAKE – SOUTH COAST CPUE – MODEL FIT
CV = 0.20
autocorrelation = 0.60

52. IV. OBJECTIVES HIGH LEVEL (POLICY) LOWER LEVEL
e.g. Conserve the stock
Responsibility of policy makers/managers
LOWER LEVEL
Performance statistics/indicators to provide a basis to quantify objectives
e.g. Objective: Maximise probability that stock stays above a ……certain spawning biomass level (LRP) ……Performance statistic: Proportion of years B > 0.2 B0
Responsibility of scientists (in interaction with managers/stakeholders)

53. Lower Level Objectives and Example of Performance Statistics
High catches
Total catch over the next 10 years
Small chance of reducing resource to low abundance
Distribution of lowest biomass over the next ten years
Small changes in catch from year to year
Average annual proportional change in catch
These can often surrogate for socio-economic aspects

54. Relation to Reference Points
Tune the control parameters of MPs to achieve values of performance statistics that may correspond to the reference points of Best-assessment-based-management Examples LRP: Tune for a 5% probability that the lowest abundance over the next 10 years falls below the biomass limit reference point TRP: Tune so that the median abundance in 10 years time is equal to the biomass target reference point

55. Trade-offs Amongst Objectives
High catch
Small chance of reducing resource to low level
Small changes in catch from year to year
Conflicting
Find a management procedure which:
Provides desired trade-offs
Is (through feedback) reasonably robust in achieving this performance to changes in the operating model (underlying reality)
Trade-offs
Aim of MSE 55

56. V: EXAMPLE - SOUTH AFRICAN HAKE OPERATIONAL MPs (OMPs)
Actually two species:
M. capensis – shallow-water hake
M. paradoxus – deep-water hake

57. Hake Distribution

58. The 2006 Situation - Past Annual Catches
TAC for 2006: 150’000 tons

59. Major Uncertainties Natural death rate (“Natural mortality”)
Split of catches between two species
Shape of offspring-parent relationship (“Stock-recruitment curve”)
Recent recruitment levels
Results to be shown reflect 24 possible combinations of these factors

60. Medians for spawning biomass Bsp with full range of values
Past Resource Trends
Medians for spawning biomass Bsp with full range of values

61. What was the main problem for the industry?

62. What could be done to solve the problem?
WSSD:
RETURN TO Bmsy BY 2014
IF POSSIBLE
MAINTAIN
CURRENT TAC

63. What could be done to solve the problem?
CONSEQUENCES

64. Trade-Offs Neither solution is acceptable:
a) the first soon destroys the resource
b) the second leads to severe socio economic dislocation
A biological/socio-economic trade-off is required
Objectives and their trade-offs must be agreed, and a way found of achieving them in the face of scientific uncertainties that are only partially resolvable

65. Hake-OMP Data Inputs . CPUE Survey
M. paradoxus M. capensis M. paradoxus M. capensis

66. Objectives agreed for OMP-2006
1. Get catch rates up quickly in the short- medium term
2. Get M. paradoxus back to Bmsy over 20 years
3. After likely initial cuts to achieve 1), secure greater TAC stability over time.

67. Projections OMP (Option 1)

68. Projections OMP (Option 1)

69. Projections OMP (Option 1)

70. Projections OMP (Option 1)

71. Projections OMP (Option 1)

72. OMP (Option 1)

73. OMP (Option 2)

74. HAKE MP-2006 APPLICATION
Catch ('000 mt)
Perc. annual change in TAC 74

75. HAKE OMP 2010: Final selection
OMPf1b: Average annual TAC t
Max annual incr: 10%; max decr: 5%
TAC BIOMASS
Median
95% PI % PI % PI

76. What’s happened Applying OMP-2010.
Catch ('000 mt)
Perc. annual change in TAC

77. Hake OMP – 2014 Revision Process
Update assessments
Review monitoring data availability
Revisit objectives and trade-offs
Modify OMP formulae selection if considered necessary
Make final selection in September 2014 to apply for the next four years
Implement OMP-2014 to provide 2015 hake TAC recommendation in October 2014

78. Thank you for your attention
With thanks for assistance to:
Andre Punt
Rebecca Rademeyer