background on adaptive management and decision support … · integrated decision support tools are...
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Background on Adaptive
Management and Decision
Support Systems
James T. Peterson
USGS Oregon Cooperative Fish and Wildlife Research Unit
TRRP Scientific Advisory Board
Homepage: http://people.oregonstate.edu/~peterjam/
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Decision support tools
Ideally an easy-to-use quantitative
tool for evaluating tradeoffs
A tool that can: Incorporate monitoring data
Involve scientists working within TRRP
Allow decision makers to evaluate tradeoffs
Identify key uncertainties that make decisions difficult
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Integrated decision support tools are most
effective when:
1) Decisions are revisited regularly
2) Decisions can be informed by ongoing monitoring
3) Managers and scientists take ownership of the process
Decision support tools
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Decision analysis-- structured decision making--
adaptive resource management--- decision support
tools– decision support systems
Some background
Decision theoretic approaches
Developed turn of 20th century
Initially business applications
1970’s natural resource decision making
Adaptive resource management
Special case of DA
Feedback from monitoring
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Decision Support Process
Requires thought (time to think)
Break down problem into manageable parts
Management objectives
- what do you really want to accomplish?
Alternative decisions/ actions
Link decisions with objectives
- a model
Evaluate sensitivity of decisions to assumptions
identify monitoring endpoints
estimate required effort- evaluate tradeoffs
Link with monitoring (ARM)
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Don’t we already do this?
Confusion over objectives (fundamental vs. means)
Confusion between goals/ objectives and beliefs/ technical uncertainty
Failure to adequately consider sources of uncertainty (blind faith in models)
Failure to incorporate research and monitoring into decision making (to reduce uncertainty) inefficient use of resources
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Why model decisions?
Quantitative methods should guide and support decision making
Not a replacement for human intuition and subjectivity
At least- be an intelligent consumer of models for decision making
Better: collaborate on model development
Models for conservation are not about modeling-- - they are about conservation
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The Big Picture
TWS 2012 PORTLAND
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Decision support process
Identify the decision
situation and objectives
Identify the management alternatives
Break down and build model of the problem:
Identify the best alternative
Evaluate model sensitivity
Is further
analysis needed?
Implement the best alternative
NO
YES
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Why emphasize objectives?
Decision
Everything depends on your objectives
Everything depends on your objectives
Everything depends on your objectives
Model Objectives (must be quantifiable)
Step 2: Identify and Structure Objectives
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Basic types of objectives
Fundamental objectives: what the decision-maker really
wants to accomplish.
Means objectives: the things that need to be accomplished
to realize the fundamental objective
>>>>>> Clarity is essential <<<<<<<
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The importance of identifying and structuring
objectives common sticking point
Confusing fundamental and mean objectives
Stated (fundamental) objective of stream fishery manager:
Natural Hydrologic Regime
Time
Wa
ter
leve
l
I
regimes
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I
regimes
?
Possible outcome: The flow regime is natural but….
all the fish are dead
Would the fishery manager be happy with the outcome???
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Maximize native
fish biodiversity
Maintain natural
hydrologic regime
Maximize habitat
availability
Means objectives (sometimes) help
realize the fundamental objective
Means objectives often are hypotheses about
system dynamics
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More common problems
Please leave your
model at the door
Dismissing potential
objectives due to
perceived conflicts
Dismissing potential
objectives due to
perceived lack of
information or complexity
Values (objectives) masquerading as facts or process
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Decision Support Process
Step 1: Identify the problem / decision situation
Step 2: Identify and Structure Objectives
Step 3: Identify decision alternatives
These 3 steps = most difficult aspects of process
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Where do we get the information? Empirical data
Published reports (meta-analysis)
“Expert” judgment
Construct the model (decision support tool) Simple (simple is good!)
Complex (can do but-- the rule of 6)
But…Must include uncertainty
Step 4: Model building
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Linguistic
Epistemic Statistical uncertainty
Observational error
Structural uncertainty
Aleatory
Environmental variability
Demographic variability
Common forms of uncertainty in natural
resources management decision making
Reducible
Irreducible
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Structural (System) uncertainty
due to incomplete understanding of system dynamics
Popula
tio
n s
ize
Habitat
Incorporated into decision modeling using multiple models
and model probabilities (weights)
Often overlooked source of uncertainty
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Step 5: Identify key uncertainties—
Sensitivity Analysis
An essential step
Basis for model simplification
Focus monitoring on decision-making what do we need to know
how much is enough
Estimate value of information collecting monitoring data
more studies
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Example: Water availability for ecological
needs in the ACF Basin
Spatially explicit Stream segment
Flow, habitat, fish
metapopulation models (43
species)
Statistical uncertainty Flow and habitat model
errors
Structural (system)
uncertainty Alternative fish population
demographics models
Peterson and Freeman 2016
Apalachicola, Chattahoochee, Flint Basin
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-16%
-12%
-8%
-4%
0%
0 1.0 2.0 3.0 4.0
Daily water withdrawal (MGD)
Change in
tota
l fish s
pecie
s r
ichness (
%)
Acute low flows
(10-day low Q)
Chronic flows
(median seasonal)
Alternative (local)
extinction models
Averaged
estimate
What assumptions/inputs affect
the decision?
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Value of information
Expected value of decision if no uncertainty Model parameters
Model inputs and system state
Currency that is valued by the decision-makers Fish population size
Water available for use
Others
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Value of imperfect information
Value of sample information
Multi-species occupancy simulations
2 sample occasions, error (CV) ~ 35% True richness, given estimated 25: 25 +/- 4
Value of sample information: 0.26 MGD
Compare to EVPI = 0.61
4 sample occasions, error (CV) ~ 10%
True richness, given estimated 25: 25+/- 2
Value of sample information: 0.49 MGD
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Reducing Uncertainty
Retrospective studies
Can provide a good initial basis for prediction
Usually confounded with other factors
Experiments
Difficult to perform in many systems
Uncertainty reduction is not directed at resource objective (inefficient)
Adaptive management
Can be done in virtually any resource system
No tradeoff necessary in resource objective
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Learning how a system works
time
Decision Decision Decision Decision
Population Population Population Population
Sequential decision-making through time
C. Moore
Learn while managing (Adaptive Resource Management) Decisions are made
Requires sequential dynamic decision-making: time and/or space
Probing (aka experimentation) not necessary
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Sequential decision-making through space
Learning how a system works
time
Site A
Site B
Site C
Site D
Site E
Site F
Site G
C. Moore
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An illustration: Dueling professors
Professor Knowsitall, says that spawning habitat is limited.
She estimates that there will be 2500 juvenile suckers
produced if spawning habitat is increased, 1500 if not
Professor Nottoobrite thinks that juvenile rearing habitat is
limited. He estimates that there will be 3000 juvenile
suckers produced if rearing habitat is increased, 1500 if
not
We have two potential management actions for swamp suckers:
increasing spawning habitat or increase juvenile rearing habitat
(we don’t have enough money to do both!)
What do we do?….
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Create juvenile
habitat
Create spawning
habitat
Management
action
System
dynamics
Juvenile habitat
Spawning habitat
Juvenile habitat
Spawning habitat
0.5
0.5
0.5
0.5
2250
2000
0 1 2 3 4
Nu
mb
er
juve
nile
s
3000
1500
1500
2500
Outcome: 2100 juveniles
Estimated number
juveniles time t+1
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Create juvenile
habitat
Create spawning
habitat
Management
action
System
dynamics
Juvenile habitat
Spawning habitat
Juvenile habitat
Spawning habitat
0.39
0.61
0.39
0.61
2090
2110
0 1 2 3 4
Nu
mb
er
juve
nil
es
3000
1500
1500
2500
Estimated number
juveniles time t+1
Outcome: 1800 juveniles
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Create juvenile
habitat
Create spawning
habitat
Management
action
System
dynamics
Juvenile habitat
Spawning habitat
Juvenile habitat
Spawning habitat
0.59
0.41
0.59
0.41
2380
1910
0 1 2 3 4 Nu
mb
er
ou
tmig
ran
ts
3000
1500
1500
2500
Estimated number
juveniles time t+1
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Example: Reducing uncertainty
ACF Basin: monitoring
Spring and summer 2011- 2013
21 sites, 40- 100 m
Electrofishing and seining
Occupancy 2-3 visits season
Atlanta
Flint River Basin
Potato Creek
Ichawaynochaway
Creek
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8 Prior
After 2011
After 2012
Mosquitofish Spotted sucker
Update model probabilities
Peterson and Freeman 2016
Acu
te f
low
s m
od
el
pro
bab
ilit
y (
weig
ht)
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-12%
-8%
-4%
0%
0 1.0 2.0 3.0 4.0
Daily water withdrawal (MGD)
Change in
tota
l fish
specie
s r
ichness (
%)
Posterior weights
Averaged
estimate
Updated estimates of water use
effects
Prior weights
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Role of monitoring Traditional approach
Current state of the system
Trajectory (trends)
Integration of information indirect
Potential loss of information (knowledge)
Adaptive resource management (ARM)
Current state of the system
Information on system dynamics
Integration of information direct
Institutional memory contained in model(s)
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Stakeholder objectives
Management alternatives
Integrated models
Implement ‘best’ management
alternative
Evaluate evidence
(analysis)
Revise beliefs
(updating) Observe outcome
(monitoring)
Revise or develop new Objectives
Management alternatives
Models
Assess current assumptions Objectives
Management alternatives
Models
Single loop
Double loop
What if the models are wrong? Single and double loop learning