spatial planning under uncertainty brendan wintle and mark burgman
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Spatial planning under uncertaintySpatial planning
under uncertainty
Brendan Wintle and Mark Burgman
t t+1Time
Po
pu
lation
size
Risk
Natural variation(aleatory uncertainty)
Lack of knowledge(epistemic uncertainty)
Probability arithmetic, ‘classical’ decision theory, Monte Carlo
The engineer’s taxonomy of uncertaintyThe engineer’s taxonomy of uncertainty
Linguistic uncertaintyLinguistic uncertainty
• Ambiguity – words have two or more meanings, and it is not clear which is meant (‘cover’).
• Vagueness – borderline cases (e.g., ‘river’)• Underspecificity – unwanted generality.• Context dependence – a failure to specify context.
(Regan et al 2002)
UnderspecificityUnderspecificity
Gigerenzer, Hertwig, van den Broek, Fasolo, & Katsikopoulos, Risk Analysis (in press)
There’s a 70% chance of rain
Possible interpretations• rain during 70% of the day• rain over 70% of the area• 70% chance of rain at a particular point (the weather station)
Habitat mapsReserve planning exercise
Landscape data
Habitat maps in conservation planning
Habitat maps in conservation planning
)(1
)(bxae
bxaep
Decisions
modelsmodels
habitat quality ~ environmental attributes
Habitat
Model
pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk
)(1
)(bxae
bxaep
• Introduced from Asia• Contradictory laws• Hunters: utility• Conservation:
ecological damage
Samba DeerSamba Deer
QuestionsQuestions
1. How many are there?
2. Where are they likely to disperse?
3. Can we manipulate the landscape to slow dispersal?
95% CIs What are they?
95% CIs What are they?
C.I.%95p
The Sooty Owl in the Eden Region
Mean prediction
Lower 95%
Upper 95%
C.I.%95p
What is the probability the species is present?
How reliable is the probability?
Is the map reliable ‘enough’?
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
Prioritizing under uncertainty:data, models, decision theory
Prioritizing under uncertainty:data, models, decision theory
How important is the uncertainty in my particular application?How can i find out?What can i do about it?
Decision Theory
Because the uncertainty is only important to the extent that it impacts on the quality or robustness of decisions
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
Prioritizing under uncertainty:data, models, decision theory
Prioritizing under uncertainty:data, models, decision theory
Case study: Spatial prioritization that is robust to uncertainty about habitat values.
Goal: Prioritize areas of high quality habitat for protection against development in the Hunter Valley, NSW, Australia
Uncertainty: Imperfect spatial representation of habitat quality for focal species
Case study: Spatial prioritization that is robust to uncertainty about habitat values.
Goal: Prioritize areas of high quality habitat for protection against development in the Hunter Valley, NSW, Australia
Uncertainty: Imperfect spatial representation of habitat quality for focal species
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
Prioritizing under uncertainty:data, models, decision theory
Prioritizing under uncertainty:data, models, decision theory
Decision: Choose the reserve design that satisfies a minimum representativeness requirement, and that is most robust to uncertainty in the estimates of habitat quality for focal species.
Decision theory: Info-gap decision theory (Ben-Haim 2002)
Decision: Choose the reserve design that satisfies a minimum representativeness requirement, and that is most robust to uncertainty in the estimates of habitat quality for focal species.
Decision theory: Info-gap decision theory (Ben-Haim 2002)
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
YBG
The DataThe Data
SQGLSOWL
GRGL
POWL
ETC..
predicted distribution of yellow-bellied glider habitat in the hunter region (Wintle,Elith,Potts (2005) Austral Ecology)
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
The uncertaintyThe uncertainty
habitat quality ~ environmental attributes
Uncertainty: Imperfect spatial representation of habitat quality for focal speciesUncertainty: Imperfect spatial representation of habitat quality for focal species
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
The uncertaintyThe uncertainty
pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk
Uncertainty: Imperfect spatial representation of habitat quality for focal speciesUncertainty: Imperfect spatial representation of habitat quality for focal species
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
The uncertaintyThe uncertainty
pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk
detectability-classification error
data age positional accuracy
modelling method:-glm/gam-gdm/gbm-boosted regression-mars/cart-garp/neural nets
non-independence
NON EQUILIBRIUM STATES
poorly mapped variables:classification error, measurement error
distal variables
model structure uncertainty
parameter uncertaintysampling bias
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
The uncertaintyThe uncertainty
pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk
detectability-classification error
data age positional accuracy
modelling method:-glm/gam-gdm/gbm-boosted regression-mars/cart-garp/neural nets
non-independence
NON EQUILIBRIUM STATES
poorly mapped variables:classification error, measurement error
distal variables
model structure uncertainty
parameter uncertaintysampling bias
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
The uncertaintyThe uncertainty
pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk
detectability-classification error
data age positional accuracy
modelling method:-glm/gam-gdm/gbm-boosted regression-mars/cart-garp/neural nets
non-independence
NON EQUILIBRIUM STATES
poorly mapped variables:classification error, measurement error
distal variables
model structure uncertainty
parameter uncertaintysampling bias
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
The uncertaintyThe uncertainty
pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk
detectability-classification error
data age positional accuracy
modelling method:-glm/gam-gdm/gbm-boosted regression-mars/cart-garp/neural nets
non-independence
NON EQUILIBRIUM STATES
poorly mapped variables:classification error, measurement error
distal variables
model structure uncertainty
parameter uncertaintysampling bias
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
The uncertaintyThe uncertainty
Uncertainty: Imperfect spatial representation of habitat quality for focal speciesUncertainty: Imperfect spatial representation of habitat quality for focal species
mean uncertainty
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
Case study – Hunter Valley
Case study – Hunter Valley
1. objective – identify the conservation strategy that maximizes our immunity to uncertainty (in habitat predictions) while achieving a satisfactory proportion of preserved habitat for each species (minimum area). robust satisfycing
2. maximize robustness to uncertainty while achieving a satisfactory outcome – infogap decision theory
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
Case study – Hunter Valley
Case study – Hunter Valley
1. objective – identify the conservation strategy that maximizes our immunity to uncertainty (in habitat predictions) while achieving a satisfactory proportion of preserved habitat for each species. robust satisfycing
2. uncertainty characterized by bounds on p
3. solution - info-gap decision theory (Ben-Haim 2001):
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
Case study – Hunter Valley
Case study – Hunter Valley
design 1
design2
horizon of uncertainty (α)
hab
itat i
ncl
ud
ed in
re
serv
e (h
a)
two questions:is this amount of uncertainty plausible?what is this minimumally satisfactory performance?
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
Case study – Hunter Valley
Case study – Hunter Valley
Solution (find a geek): implemented in Zonation (Moilanen et al. 2005)
- Implementation hardwired in Zonation for all to use
- Load in uncertainty files (prediction lower bounds)
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
Case study – Hunter ValleyCase study – Hunter Valley
pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk
α = 0 α = 2 α = 3
Increasing robustness to uncertainty in habitat quality estimates
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
Adaptive managementAdaptive management
Your decision will be wrong, so have a plan to learn and adapt
(adaptable spatial priorities?)
Linkov et al. 2006. Integ. Env. Ass. Manage.
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
ConclusionsConclusions
1. it is possible (though not trivial) to explicitly identify management strategies that are most robust to uncertainty2. optimal policies are often not robust to uncertainty3. including all uncertainties is hard, but including as many as possible is worth it4. your decision will definitely be wrong, so have a plan for learning and adapting
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
ConclusionsConclusions
5. life without uncertainty is boring
the future6. make this easier7. extension - case studies – variable costs8. rules of thumb
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
ReferencesReferences
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
ReferencesReferences
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
This one’s the easiest to follow!
This one’s the easiest to follow!
Wintle and Burgman - prioritizing under uncertainty www.botany.unimelb.edu.au/envisci
Prioritizing under uncertainty:data, models, decision theory
Prioritizing under uncertainty:data, models, decision theory
Mark Burgman, Brendan Wintle
[email protected]@unimelb.edu.au+61 3 8344 4572