1. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies A decision analysis of adaptive management experiments: Is it worth varying flows to reduce key uncertainties? An application to Columbia River whitefish management AFS - Spokane; Apr 27 to May 1 2002 Developed by ESSA Technologies Ltd. Clint Alexander, Paul Higgins*, David Marmorek, and Calvin Peters * Funded by BC Hydro Power Supply & Watershed Management

2. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Outline Study Area / Problem Objective Methods –Decision analysis –Model Results: Is it worth it? General conclusions

3. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Problem I Problem I: Increased egg mortality from dam operation Flow during spawning Flow during incubation stage Proportion eggs in de- watered area Some flexibility to regulate flows during spawning (January 1 - 21)

4. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Problem II Problem II: Uncertainty True whitefish recruitment dynamics? No reliable baseline information

5. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Study Objective Use Decision Analysis to evaluate benefits and costs of alternative spawning flows & monitoring programs

6. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Methods Decision Analysis Age-structured simulation model –1) population dynamics –2) simulated mark-recapture egg & age4 abundance estimates –site specific stage and wetted-area at depth data generated from hydraulic flow simulations (HEC2-RAS model)

7. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Management Objective Maintain least cost whitefish population nearest to or greater than 45,000 adults

8. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Stage 1 - Base Decision Analysis

9. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Stage 1 Results: Current Uncertainty

10. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies

11. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Stage 2 - Simulated learning from flow experiments and monitoring Uses same model and uncertain components but... Actions are now alternative experimental flow regimes + monitoring programs Assume a true relationship for population dynamics with process error

12. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies 4 experimental flow regimes

13. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Learning from AM experiments: a function of what practitioner can and cannot control

14. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Would you change if you knew the truth? 10 5 2.5 7.5 $Cnd mil Max. potential power revenues (per yr)

15. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Stage 2 Results: Good monitoring is critical for learning; flow manipulation has less effect than expected.

16. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Stage 2 Results: Its easier to identify a sensitive population than an insensitive one.

17. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Low measurement error critical to differentiating hypotheses

18. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies AM can pay for itself

19. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Caveats about the Keenleyside Results Small difference in performance of alternative experiments was surprising. Why? –Large uncontrollable natural variation in flows, at both spawning and hatching, creates year-to-year variability in egg mortality (Kootenay R influence) –Passive flows not actually that passive (large spawning flows informative) –Model added too much measurement error (true detection probability higher than shown)

20. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Is AM worth it? Yes If New information leads to choice of a different management action that better satisfies a particular objective

21. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Factor Under AM practitioners control Benefits of AM decision analysis Management objective (fish vs. power $) Ability to do well designed experiments Initial level of uncertainty in alternative hypotheses Magnitude of natural variability in the system What truth really is Inherent sensitivity of best action to uncertainty Yes May be known No No (cant know without doing the experiment) No Yes Can evaluate implications using decision analysis?

22. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies Natural variation in recruitment

23. AFS - Spokane Apr 27 to May 1, 2002ESSA Technologies General Conclusions Value of AM potentially large Whether to proceed depends on the kind of system you are in (i.e. previous factors) Decision Analysis is very helpful for evaluating these benefits –Decisions more robust to uncertainties (reduces risk - explicitly accounts for uncertainties) –forces clarification of problem & uncertainties –Determine which uncertainties have strongest effect on choice of best management decision (identify research priorities)