1. Management Procedures (Prof Ray Hilborn)

2. Current Management Cycle Fishery: Actual Catches Data Collection Assessment Management Decision

3. This system is unsatisfactory for the industry: Stock status is uncertain, and can easily change with new interpretation of data or change of models. There are no rules about how quotas will be set and thus no transparency and understanding of what will happen. There are no rules about what model assumptions will be used and thus no transparency of understanding of what will happen.

4. This system is also unsatisfactory for managers: With no rules about how quotas will be set, there is no way to determine the long term consequences of the quota set for any individual year. The assessment process is not transparent to managers. At best “risk” can be evaluated by calculating impacts of constant catch levels. But we know that we wouldn’t hold the catch constant if data changed dramatically. Thus managers have no way of evaluating the probability of achieving objectives.

5. Management Procedures: A Way Forward

6. Elements of a Management Procedure A list of data that will be collected. Rules about how the data will be used to determine the quota for the next year. The rules may be data based: If CPUE is going up raise catches, if going down lower catches. The rules may be model based – but the details of the models will be specified and not subject to annual changes.

7. Testing Possible Management Procedures Define a set of “operating models” that are alternative hypotheses about the stock status. Stock productive and will increase under current catch. Stock unproductive and will decrease under current catch. Pretend the “operating models” are true, and test possible management procedures to find those that are robust to uncertainty in stock status. Our goal is to find a management procedure that will do as well as possible with whatever stock status and SBT biology happen to actually be true.

8. CONDITIONING COMPONENT Operating Model Historic Data Current Stock Size & Catches  Population and Fishery Dynamics  Stochastic in dynamics  Alternative Hypotheses/Model Formulations to Capture Real Uncertainty; e.g. -stock/recruitment -  selectivity/  catchability -mortality rates –density dependent  Consistent with Historic Data Evaluation: Simulation Model

9. “True” Population Actual Catches Operating Model Sampling Model Catch, length,age, effort, tagging, acoustic, etc Assessment Model Summary Statistic of Stock Status and Trends (e.g. F’s, N’s,  CPUE) Management Model Management Action (e.g. TAC) F PROJECTION MODEL

10. Performance Indicators Evaluation of Management Summary Module FEVALUATION COMPONENT

11. CONDITIONING COMPONENT Operating Model Historic Data Current Stock Size & Catches “True” Population Actual Catches Operating Model Sampling Model Catch, length, age, effort, tagging, acoustic, etc Assessment Model Statistic Stock on Status & Trends ( F’s, N’s,  SSB) Managemen t Model Management Action (e.g. TAC) FPROJECTION COMPONENT Performance Indicators Evaluation of Management Summary Module FEVALUATION COMPONENT

12. Evaluation – Operating Model A key issue: Ensuring that the operating model appropriately incorporates the full uncertainty about the real stock, its dynamics and the sampling processes.

13. Limitations Robust only to what has been evaluated. Generation of hypotheses for what we don’t know ? Low probability events do happen. (“Surprisingly often”. Hilborn) Tinkering – pressure to modify to meet short term expediency. Tinkering – genuine desire to improve ( endless development cycle/ highly complex assessment)

14. Examples: International Whaling Commission – RMP/AWMP. South Africa – small pelagic, lobster, etc. Namibia – small pelagic. New Zealand – rock lobster. Australia – gemfish, trawl fisheries, tuna. CCSBT (under development) –international large pelagic. Increasingly Being Used

15. What we need from industry and managers: Objectives: what should a MP achieve ? What performance indicators to provide ? Which Uncertainties should be considered ?

16. A Simple CPUE Based Rule TAC next year = (TAC Last year * %carryover) + (1-%carryover)*TAC Last year *(1+slope of cpue*K)