Spatial planning under uncertainty

1. Spatial planning under uncertainty Brendan Wintle and Mark Burgman

2. The engineer’s taxonomy of uncertainty Natural variation (aleatory uncertainty) Lack of knowledge (epistemic uncertainty) Probability arithmetic, ‘classical’ decision theory, Monte Carlo

3. Linguistic uncertainty (Regan et al 2002) • 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.

4. Underspecificity There’s a 70% chance of rain Gigerenzer, Hertwig, van den Broek, Fasolo, & Katsikopoulos, Risk Analysis (in press) • 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)

5. Habitat maps in conservation planning Landscape data Reserve planning exercise Habitat maps Decisions

6. Presence/Absence Data Topography Temperature Solar Old Growth data

7. models habitat quality ~ environmental attributes Habitat Model pr(occupancy) ~ α + β1X1 + β2X2 + …βkXk

8. Habitat maps

9. Samba Deer • Introduced from Asia • Contradictory laws • Hunters: utility • Conservation: • ecological damage

10. Questions • How many are there? • Where are they likely to disperse? • Can we manipulate the landscape to slow dispersal?

11. Subjective uncertainties

12. Bounds

14. 95% CIs What are they? The Sooty Owl in the Eden Region What is the probability the species is present? How reliable is the probability? Is the map reliable ‘enough’? Upper 95% Mean prediction Lower 95%

15. 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

16. 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

17. 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)

18. YBG SQGL SOWL GRGL POWL The Data ETC.. predicted distribution of yellow-bellied glider habitat in the hunter region (Wintle,Elith,Potts (2005) Austral Ecology)

19. The uncertainty Uncertainty: Imperfect spatial representation of habitat quality for focal species habitat quality ~ environmental attributes

20. The uncertainty Uncertainty: Imperfect spatial representation of habitat quality for focal species pr(occupancy) ~ α + β1X1 + β2X2 + …βkXk

21. poorly mapped variables: classification error, measurement error • modelling method: • glm/gam • gdm/gbm • boosted regression • mars/cart • garp/neural nets positional accuracy distal variables data age parameter uncertainty sampling bias detectability-classification error model structure uncertainty non-independence The uncertainty pr(occupancy) ~ α + β1X1 + β2X2 + …βkXk NON EQUILIBRIUM STATES

22. poorly mapped variables: classification error, measurement error • modelling method: • glm/gam • gdm/gbm • boosted regression • mars/cart • garp/neural nets positional accuracy distal variables data age parameter uncertainty sampling bias detectability-classification error model structure uncertainty non-independence The uncertainty pr(occupancy) ~ α + β1X1 + β2X2 + …βkXk NON EQUILIBRIUM STATES

23. poorly mapped variables: classification error, measurement error • modelling method: • glm/gam • gdm/gbm • boosted regression • mars/cart • garp/neural nets positional accuracy distal variables data age parameter uncertainty sampling bias detectability-classification error model structure uncertainty non-independence The uncertainty pr(occupancy) ~ α + β1X1 + β2X2 + …βkXk NON EQUILIBRIUM STATES

24. poorly mapped variables: classification error, measurement error • modelling method: • glm/gam • gdm/gbm • boosted regression • mars/cart • garp/neural nets positional accuracy distal variables data age parameter uncertainty sampling bias detectability-classification error model structure uncertainty non-independence The uncertainty pr(occupancy) ~ α + β1X1 + β2X2 + …βkXk NON EQUILIBRIUM STATES

25. The uncertainty Uncertainty: Imperfect spatial representation of habitat quality for focal species mean uncertainty

26. Case study – Hunter Valley • 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 • maximize robustness to uncertainty while achieving a satisfactory outcome –infogap decision theory

27. Case study – Hunter Valley • 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 • uncertainty characterized by bounds on p • solution - info-gap decision theory (Ben-Haim 2001):

28. design2 Case study – Hunter Valley two questions: is this amount of uncertainty plausible? what is this minimumally satisfactory performance? design 1 habitat included in reserve (ha) horizon of uncertainty (α)

29. 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)

30. Case study – Hunter Valley pr(occupancy) ~ α + β1X1 + β2X2 + …βkXk α = 0 α = 2 α = 3 Increasing robustness to uncertainty in habitat quality estimates

31. Adaptive 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.

32. Conclusions • it is possible (though not trivial) to explicitly identify management strategies that are most robust to uncertainty • optimal policies are often not robust to uncertainty • including all uncertainties is hard, but including as many as possible is worth it • your decision will definitely be wrong, so have a plan for learning and adapting

33. the future • make this easier • extension - case studies – variable costs • rules of thumb Conclusions • life without uncertainty is boring

34. References

35. References

36. This one’s the easiest to follow!

37. Prioritizing under uncertainty:data, models, decision theory Mark Burgman, Brendan Wintle brendanw@unimelb.edu.au markab@unimelb.edu.au +61 3 8344 4572