Spatial planning under uncertainty Brendan Wintle and Mark Burgman.

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

Old Growth

Solar

datadata

TopographyTemperature

Presence/Absence Data

modelsmodels

habitat quality ~ environmental attributes

Habitat

Model

pr(occupancy) ~ α + β1X1 + β2X2 + … βkXk

)(1

)(bxae

bxaep

Habitat mapsHabitat maps

• 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?

FOR1

FOR2

EDG1 GUL1 ASR1

EDG2 GUL2 ASR2

Subjective uncertainties

Bounds Bounds

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


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




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




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)


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)


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



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




mean uncertainty


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




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):




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?




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)


Case study – Hunter ValleyCase study – Hunter Valley


α = 0 α = 2 α = 3

Increasing robustness to uncertainty in habitat quality estimates


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.


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


ConclusionsConclusions

5. life without uncertainty is boring

the future6. make this easier7. extension - case studies – variable costs8. rules of thumb


ReferencesReferences


This one’s the easiest to follow!

This one’s the easiest to follow!




Mark Burgman, Brendan Wintle

[email protected]@unimelb.edu.au+61 3 8344 4572

Spatial planning under uncertainty Brendan Wintle and Mark Burgman.

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