1. 458 Population Projections (policy analysis) Fish 458; Lecture 21

2. 458 Policy Evaluation-I It is often the objective for developing and fitting a model is to address “what if” questions. What is the impact of: removal limits (quotas: individual / Olympic); time / area closures; gear restrictions (number of pots, traps, gillnets); bag limits; minimum / maximum sizes; and vessel numbers / size of vessels.

3. 458 Policy Evaluation - II We are often not looking for optimal policies. Rather, we want to identify polices that are robust to: Estimation error. Uncertainty regarding the true model. Implementation uncertainty. Environmental variability and environmental change. “Optimal policies” can often be found if we know the true model but these may perform poorly if applied to the wrong model.

4. 458 Policy Evaluation-III (objectives and tactics) Policies are based on choosing tactics (quotas, minimum sizes, closed areas) to achieve management objectives / goals. Corollary - if we don’t know the management objectives we cannot (sensibly) compare different policies. Problem: often the decision makers have not agreed on any objectives (or are unwilling to state their actual objectives publicly).

5. 458 Policy Evaluation-IV (objectives and tactics) We distinguish between high-level objectives (e.g. conserve the stock) and operational (quantitative) objectives (the probability of dropping below 0.1K should not be greater than 0.1 over a 20-year period). Many decision makers confuse the tactics (what to do next year) with the objectives (why are we doing what we are doing next year).

6. 458 Objectives for Fisheries Management (typical high-level objectives) High level objectives arise from: National legislation (MMPA, Magnusson- Stevens Act, ESA). International Agreements (CCAMLR, IWC, UN Fish Stocks Agreement). Court decisions.

7. 458 Objectives for Fisheries Management (Objectives for commercial whaling) 1. Acceptable risk level that a stock not be depleted (at a certain level of probability) below some chosen level (e.g. some fraction of its carrying capacity), so that the risk of extinction of the stock is not seriously increased by exploitation; 2. Making possible the highest continuing yield from the stock; and 3. Stability of catch limits. The first objective was assigned highest priority but was not fully quantified.

8. 458 Objectives for Fisheries Management (Australian Fisheries Management Authority) 1. I mplementing efficient and cost-effective fisheries management on behalf of the Commonwealth; 2. Ensuring that the exploitation of fisheries resources and the carrying on of any related activities are conducted in a manner consistent with the principles of ecologically sustainable development and the exercise of the precautionary principle;   aximising economic efficiency in the exploitation of fisheries resources;   nsuring accountability to the fishing industry and to the Australian community; and 5. Achieving government targets in relation to the cost recovery.

9. 458 Operational and High-level objectives Operational objectives describe the high-level objectives quantitatively. Preserve biodiversity (have at least 80% of all species protected in a system of reserves). Protect endangered species (have an 80% probability that all currently endangered species are no longer endangered within 50 years). Protect ecosystem functioning (who knows what exactly what this means??)

10. 458 Techniques for Policy Evaluation We can sometimes evaluate the implications of a policy analytically (e.g. the impact of changes in fishing intensity on yield-per-recruit). More commonly, we have to evaluate policy alternatives using Monte Carlo simulation methods. Specify the high-level management objectives. Specify the operational management objectives. Develop models of the system to be managed (including their uncertainty). Use simulation to determine the implications of each policy. Summarize the results.

11. 458 Projecting Forward - I 1. Define the state of the system in the first year of the projection. 2. Calculate the catch limit based on the current state of the system. 3. Project ahead one year (there may be implementation error at this stage) and update the dynamics. 4. Repeat steps 2-3 for each future year. 5. Repeat steps 1-4 many times.

12. 458 The Simplest Decision Rules Constant catch (b=0). Constant harvest rate (a=0). Constant escapement (a<0).

13. 458 The Simplest Decision Rules (a=10,b=0) (a=0,b=10) (a=-2.5,b=12.5)

14. 458 Evaluating the Simplest Rule Model of the state of the system (Schaefer model): This a deterministic model so we only have to do a single simulation as there is no uncertainty.

15. 458 Average Catch / Population Size vs. slope and Intercept Intercept 0 500 0 Intercept

16. 458 Extending to a Stochastic Model Model of the state of the system (Schaefer model): This is now a stochastic model so we do 100 simulations (  p =0.1).

17. 458 Catch and Population Size Trajectories

18. 458 Average Catch / Population Size / CV vs. slope and Intercept Between simulation CV of average catch

19. 458 Average catch vs. Population Size

20. 458 CV of catch vs. Average Catch

21. 458 Allowing for Errors in Stock Assessment We now allow for correlated errors when conducting assessments (if this year’s assessment is wrong, next year’s is also likely to be wrong) : This approach to modeling assessment errors ignores biases in assessment results – also assessment errors are unlikely to be log-normally distributed.

22. 458 Allowing for Errors in Stock Assessment Measuring the within-year variance in catches: No Stock Assessment ErrorsWith Stock Assessment Errors

23. 458 Going Beyond the Simple Case Rather than assume assessment errors are log-normally distributed, simulate the process of conducting annual assessments (this is highly computationally intensive). Examine strategies designed to achieve specific management objectives (e.g. select catch limits so that the probability of recovery equals a desired level).

24. 458 Readings Burgman et al. (1993); Chapter 3. Hilborn and Walters (1992); Chapters 15-18. Quinn and Deriso (1999); Chapter 11.