1. Policy Evaluation II (Feedback strategies)
Lecture 8

2. Why Evaluate Feedback Strategies?
The strategies we examined in lecture 6 (constant catch, constant effort, etc.) were based on calculations that assumed that the strategies would be applied for the next 20 years irrespective of any data that might be collected.
This isn’t very realistic.

3. The Management “Cycle”
The Fishery
(actual removals)
Data
Collection
Management
Decisions
Data
Analysis
We wish to capture this entire system within our decision
analysis.

4. Evaluating a Feedback Approach
Operating
Model
The Fishery
(actual removals)
Data
Generation
Management
Strategy
Management
Model
Assessment
Model
Management Strategy = “Decision rule”, “Management
Procedure”, “Operational Management Procedure”, etc.

5. The operating model The management strategy
The fishery
Assessment
data
Stock assessment
The stocks
Model outputs:
abundance, MSY
The catch
control law
The catch
limit
The environment

6. The Operating Model
Includes the biology of the system, the fishery, and how any data are generated.
Represents the “real world” for the analyses
Several operating models need to be considered: each is an alternative state of nature.
Several types of uncertainties (model, process, estimation, etc.) can be represented as states of nature.

7. A Management Strategy Specifies: which data are to be collected;
how those data are to be analyzed (incl. CPUE standardization; population modelling…); and
how the results of the analysis of the data determine management measures.

8. Example 1 – Cape Hake (The Management Strategy)
Data collection:
Catches and catch-rates.
Data analysis:
Fit a biomass dynamics model to estimate the time-trajectory of biomass.
Management decision:
Quota is r/2 multiplied by current biomass.
The management strategy is trying to mimic a fixed proportion strategy, except that the estimates of biomass are obtained using a method of stock assessment (instead of being assumed to be known exactly).

9. Example 1 – Cape Hake (Equations for the Management Strategy)

10. Example 1 – Cape Hake (Generating Future CPUE Data)
Future catch-rate data are assumed to be log-normally
distributed, i.e.:

11. Example 1 – Cape Hake (Computational Aspects)
A separate program has been written to fit the biomass dynamics model and determine the quota.
For each future year, the projection software:
creates an input file for the assessment program;
calls the program; and
reads the results back into the projection program.

12. Example 1 – Cape Hake (Results)
q=0.15
113 75
0.37
0.01
q=0.25 74
0.40
C=113 97
0.17
Comparing the results for different values of  permits an
evaluation of the value of collecting additional data.
Comparing the results for constant catch against those for
the feedback strategy allows the value of being reactive to
data to be evaluated.

13. Example 1 – Cape Hake (Issues Arising-I)
This example considered several sources of uncertainty:
Model uncertainty: The structure of the biomass dynamics model differs from that of the (age-structured) operating model.
Process uncertainty: Recruitment in the operating model is log-normal.
Observation uncertainty: The data used by the management strategy are subject to (log-normal) observation error.

14. Example 1 – Cape Hake (Issues Arising-II)
There are many ways to extend this analysis:
Management strategy:
Use additional data (e.g. survey estimates of biomass).
Use a different rule for determining the quota based on the results of assessments (e.g. reduce quotas by the extent of uncertainty, i.e. ABCs).
Operating model:
Consider additional sources of uncertainty.
Outputs:
We could see how well the estimates of biomass from the biomass dynamics model correspond to the biomass in the operating model.

15. Example 2 (Anchovy and Sardine)
Catches (by mass) off southern Africa are dominated by those of the pelagic species (anchovy, sardine and round herring).
The pelagic fishery is affected substantially by technological interactions:
Adult anchovy are targeted in January- March, but the fishery is primarily a recruit fishery.
Adult sardine are targeted throughout the year.
Mixed shoaling of juvenile anchovy and sardine means that anchovy fishing causes a by-catch of juvenile sardine.
Fishing for round herring leads to a by-catch of adult sardine.

16. Example 2 (Overview) Sardine Fishery Anchovy Fishery Human Consumption
Fish Meal

17. Example 2 (Biomass trends)

18. Example 2 (Structure of the analysis)
The operating model:
Includes two species (anchovy and sardine).
Includes technical interactions between the two species (catching anchovy leads to catches of juvenile sardine).
Includes process uncertainty in recruitment (and bycatch rates).
Generates survey indices of abundance for the two species.
The management strategies:
Include separate rules for the two species, but makes allowance for technical interactions.
Modify catch limits during the year based on the results of surveys and the amount of bycatch.

19. Example 2 (Management Objectives)
1. Maintain biomass of stocks at biologically “safe” levels
2. Minimise year to year variability in TACs
3. Allow for growth in pilchard biomass (recovery)
4. Within the constraints of 1. to 3., maximise average annual TAC
5. In order to achieve 4., and complying with 2. and 3., provide adequate bycatch TAC of juvenile sardine

20. Example 2 (Performance measures)
Risk-related performance measures:
Anchovy – no more than an 30% probability of dropping below 15% of the virgin biomass over the next 20 years.
Sardine - no more than an 10% probability of dropping below 20% of the virgin biomass over the next 20 years.
Reward-related performance measure:
Average annual catch of sardine and anchovy.

21. Example 2 (Results) Sardine only fishery So what is this?
Anchovy only fishery

22. Example 2 (Impact of additional data collection)

23. Example 3 (Northern Prawn Fishery)
Value ~ A$150 million
Over 17.5 million pounds landed.
Premium product (Banana, Tiger, Endeavour prawns)
Over 90% exported
500 species of bycatch
Australia

24. Example 3 (The operating model)
Uncertainties:
“Effort creep”
Implementation error
Dealing with banana prawns
Effort allocation
Recruitment variation
2 tiger species
5 stocks of each species

25. Example 3 (Modeling the Management Controls)
Allow for
Implementation
Error
Advice on effort and
season length for
tiger prawns
Split effort to week
Generate a banana
prawn season
Split weekly effort to
week & area
Allocate weekly
effort by area
to species

26. Example 3 (Implementation Error)
This is an “input control” fishery so the actual effort in the
fishery (fishing days) may differ from the number of days of
fishing expected by the managers. There is also a 1 in 3
chance that scientific management advice will not be followed
at all.

27. Example 3 (Management Strategies-I)
Three assessment types:
Biomass dynamics model.
Empirical.
Age-structured model.
Various control rules:
Conservative
Aggressive
Spawning stock in current year
Intended target effort
1.2Smsy
Emsy
Smsy
1.0Smsy

28. Example 3 (Management Strategies-II)
The season (the weeks that the fishery is open for) depends
on how depleted the stocks are assessed to be.

29. Example 3 (Strategies and Operating Models)

30. Example 4 (Multispecies considerations)

31. Management Model Structures and Management Systems Age-; sex-
Cull scenarios
Age-; sex-
structured
Hake Management
Procedure
Fixed F
Multi-species models are being developed to examine the implications of changes in the biomass of top predators on system behavior (including fishery catches). This particular chart shows the structure of a model used to examine the implications of culling Cape fur seals on the catches of Cape hake off South Africa.
Species-; age-
structured
Delay-difference

32. Developing a Management Strategy (A Final Overview)
Agree on Objectives
Implement Strategy No
Yes
Agree?
Select Performance
Measures No
Develop
Testing Framework
Discuss Results
Test a Range of
Management Strategies

33. References (some) Butterworth. 2007. ICES J. mar. Sci. 64: 613-617.
Butterworth & Punt ICES J. mar. Sci. 56:
De Oliveira & Butterworth ICES J. mar. Sci. 61:
Dichmont et al Fish. Res. 82:
Dichmont et al Fish. Res. 94:
Dichmont et al J. Appl. Ecol. 50:
Punt & Butterworth S. Afr. J. Mar. Sci. 15:
Punt & Donovan ICES J. mar. Sci. 64:
Punt et al Fish and Fish. 17: