ESSA Technologies AFS - Spokane Apr 27 to May 1, 2002 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
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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?
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ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002
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 byESSA Technologies Ltd.
Clint Alexander, Paul Higgins*, David Marmorek, and Calvin Peters
* Funded by BC Hydro Power Supply & Watershed Management
ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002
Outline
• Study Area / Problem• Objective• Methods
– Decision analysis
– Model
• Results: Is it worth it?• General conclusions
ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002
Problem IProblem I: Increased egg mortality from dam operation
Flow during spawningFlow during spawning
Flow during Flow during incubationincubation
stage
Proportion eggs in de-watered area
Some flexibility to regulate flows during spawning (January 1 - 21)
ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002
Problem IIProblem II: Uncertainty True whitefish recruitment dynamics?
No reliable baseline information
Alternative Hypotheses
-
5,000
10,000
15,000
20,000
25,000
0 5 10 15 20 25
Eggs Just Prior to Hatching (millions)
Age
4 W
hite
fish
Very Sensitive
Sensitive
Neutral
Insensitive
Very Insensitive
ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002
Study Objective
Use Decision Analysis to evaluate benefits and costs of alternative
spawning flows & monitoring programs
ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002
Active $0.6 (5.8) $0.6 (7.6) $0.2 (17.4) $0.6 (7.6)
$Cnd millionsNumbers in brackets = experimental pay-back interval in yearsBlue = things under AM practitioners controlRed = beyond AM practitioners control
I ncrease in annual power revenues from operating with experimental information (insensitive population only, 10-year experiments)
Low Nat Variability High Natural Variability
Natural Variability and Measurement Error
ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002
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)
ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002
Is AM worth it?Is AM worth it?
“Yes” IfNew information leads to choice of a
different management action that better satisfies a particular objective
ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002
FactorUnder AM practitioners control
Benefits of AM decision analysisBenefits 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
Yes
May be known
No
No (can’t know without doing the experiment)
No
Yes
Yes
Yes
Yes
Yes
Yes
Can evaluate implications using decision analysis?
ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002
Natural variation in recruitmentNatural variation in recruitment
Low natural variation: sigma = 0.25
0
20000
40000
60000
80000
1 11 21
High natural variation: sigma = 0.45
0
20000
40000
60000
80000
1 11 21
ESSA TechnologiesAFS - Spokane Apr 27 to May 1, 2002
General ConclusionsGeneral 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)