How ESSA has successfully used Decision Analysis to overcome challenges in multi-objective resource management problems

1. How ESSA has successfully used Decision Analysis to overcome challenges in multi-objective resource management problems Developed by ESSA Technologies Ltd. General overview January 10 2002 David Marmorek, Calvin Peters, Ian Parnell, Clint Alexander

2. Common challenges in resource management • Getting stakeholder groups to agree on a course of action, given multiple values and objectives • Getting scientists to agree on which uncertainties most critically affect management decisions, and what decisions are most robust to these uncertainties • Evaluating the costs and benefits of adaptive management - is it worth it?

3. How decision analysis can help with these challenges • It provides a toolbox for handling multiple objectives / values, and analyzing tradeoffs among these objectives • It systematically analyzes the impacts of uncertainties on decisions • It can be used to evaluate the ability of Adaptive Management experiments to improve decisions • It provides a helpful way to integrate many techniques employed by managers and scientists (i.e. models, interactive workshops, sensitivity analysis) into products that better clarify management decisions

4. Three examples • Getting scientists to agree: PATH • Getting stakeholders to agree: Cheakamus • Evaluating adaptive management: Keenleyside

5. Multiple historical changes in Columbia and Snake River ecosystems and fisheries management practices Endangered species listings for Snake River salmon populations Multiple hypotheses and uncertainties held by different groups of scientists Duelling models representing these hypotheses and uncertainties Best management policies for species recovery? PATH: Decision Context

6. PATH: Washington State, US

7. Decision Analysis: 8 elements 1. List of alternative management actions 2. Management objectives composed of performance measures (to rank management actions) 3. Uncertainstates of nature (differenthypotheses) 4. Probabilities of those states (to account for uncertainty); 5. Model to calculate outcomes of each combination of management action and hypothesised state of nature; 6. Decision tree; 7. Rank actions based on expected value of the performance measures; and, 8. Sensitivity analyses.

8. Decision Analysis: Basic Elements

9. PATH Decision Tree

10. Benefits of decision analysis in PATH • Allowed evaluation of multiple hypotheses for 14 uncertainties - scientists did not have to agree! • Only 3 of these turned out to make a difference to the decision - created a common focus for AM, research • Preferred actions were those which were most robust to the critical uncertainties (drawdown A3) • Sensitivity analyses defined how much belief you would have to have in a given hypothesis to change decision

11. Recent Publications on PATH • Marmorek, David R. and Calvin Peters. 2001. Finding a PATH towards scientific collaboration: insights from the Columbia River Basin. Conservation Ecology 5(2): 8. [online] URL: <http://www.consecol.org/vol5/iss2/art8> • Deriso, R.B., Marmorek, D.R., and Parnell, I.J. 2001. Retrospective Patterns of Differential Mortality and Common Year Effects Experienced by Spring Chinook of the Columbia River. Can. J. Fish. Aquat. Sci. 58(12) 2419-2430 http://www.nrc.ca/cgi-bin/cisti/journals/rp/rp2_tocs_e?cjfas_cjfas12-01_58 • Peters, C.N. and Marmorek, D.R. 2001. Application of decision analysis to evaluate recovery actions for threatened Snake River spring and summer chinook salmon (Oncorhynchus tshawytscha). Can. J. Fish. Aquat. Sci. 58(12):2431-2446. <same web site as above> • Peters, C.N., Marmorek, D.R., and Deriso, R.B. 2001. Application of decision analysis to evaluate recovery actions for threatened Snake River fall chinook salmon (Oncorhynchus tshawytscha). Can. J. Fish. Aquat. Sci. 58(12):2447-2458. <same web site as above>

12. Cheakamus WUP: Decision Context • British Columbia Hydro, Water Use Planning: Stakeholder driven multi-objective consultation / decision process. • No formal incorporation of uncertainty as for PATH • Emphasis: values, objectives, performance measures, trade off analysis (DA steps 1, 2, 5 and 7). • Used PrOACT approach (Smart Choices, Hammond et al 1999)

13. Cheakamus WUP: Process WUP Steps

14. Cheakamus WUP:Decision Problem Select operating alternatives for Daisy Lake Dam that: 1) recognize multiple water uses in the Cheakamus and Squamish Rivers, and 2) achieve a balance between competing interests and needs.

15. Cheakamus WUP:Objectives and PMs Power First Nations Recreation Flooding Fish Aquatic Ecosystem

16. Cheakamus: WUP Alternatives • Consultative Committee specifies operating alternatives for Hydro operations model (AMPL). • Basic constraints: minimum flow at Brackendale gauge, minimum dam release. • AMPL model produces 32 water years of flow data for these control points • Flow data and other models used to calculate performance measures. • Performance measures summarize consequences of alternatives for objectives.

17. Cheakamus WUP: Consequences

18. Tradeoffs (or not) Win-Lose Win-Win

19. Cheakamus WUP: Filtering • Use PMs to Eliminate clearly inferior alternatives. • Drop insensitive PMs (e.g., rafting). • Drop Objectives that don’t help the decision (e.g., flooding). • Tradeoff analysis: Even Swaps • Elicit values behind decisions (e.g., rating exercises) • Develop new alternatives to address concerns (e.g., chum spawning vs. rainbow trout rearing).

20. Risk Biological flows too high reduce productive capacity, may drive population towards extinction Flow during spawning Economic smaller flows may reduce de-watering mortality but reduce potential $ and operational flexibility  stage Flow during incubation Proportion eggs in de-watered area Keenleyside Problem : Increased egg mortality from dam operation

21. Problem II: Uncertainty True whitefish recruitment dynamics? Given typical egg mortality, LARGE differences in abundance associated with these curves No reliable baseline information

22. Stage 1 - Decision Analysis w current uncertainty

23. Stage 1 Results: Current Uncertainty Objective: Maintain “least cost” whitefish population nearest to or greater than 45,000 adults

24. 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

25. 10 7.5 $Cnd mil Max. potential power revenues (per yr) 5 2.5 What would you change if you knew the “truth”?If population insensitive, then maximize power revenues (85 kcfs)If population sensitive, then minimize biological risk (~60 kcfs)

26. Example Stage 2 Results: Good monitoring is critical for differentiating hypotheses; flow manipulation had less effect than expected.

27. AM can “pay for itself”

28. Is AM and monitoring worth it? “Yes” If New information leads to choice of a different management action that better satisfies a particular objective, or rigorously confirms that current management action is appropriate.

29. No definitive “yes/no” Under AM practitioners control Can evaluate implications using decision analysis? Factor 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 Yes Maybe No No (can’t know without doing the experiment) No Yes Yes Yes Yes Yes Yes

30. 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 • Determine which uncertainties have strongest effect on choice of “best” management decision • Decisions more robust to uncertainties (reduces risk - integrates broader range of possible outcomes included) • Include new information as revised probabilities on hypotheses

31. Decision Analysis - Summary