Uncertainty and Decision Making

1. Uncertainty and Decision Making • Presence of uncertainty is one of the most significant characteristics of environmental management decisions • Statistical errors and burden of proof (NRC chap. 8, 9) • Assessing threat and conservation priorities (Todd and Burgman 1998) • Managing Kirtland’s warblers in the face of environmental uncertainty (Marshall et al. 1998) • Adaptive Management is one way to increase certainty over time

2. Uncertainty (Todd and Burgman 1998) • Statistical uncertainty • Subjective judgement • Systematic error • Incomplete knowledge • Temporal variation • Inherent stochasticity • MOST CONSERVATION DECISIONS IGNORE IT

3. Statistical Uncertainty • Type I error: probability of rejecting Ho when Ho is actually true • scientist’s set this low so as to rarely incorrectly reject hypotheses and therefore slow or confuse the progress of science (=0.05) • Type II error: probability of not rejecting the Ho when it is actually false • usually high because high precision (large sample size) is needed to reduce it and as type I error is reduced type II error increases

4. Power---the tradeoff between Type I and Type II error • Power = 1-  ( = probability of type II error) • probability of correctly rejecting the Ho • increases with increasing Type I error • increases with increasing precision (N)

5. Type I Error Reject true Ho Claim an effect when none exists Protect more species than necessary Lose scientific credibility Increase socioeconomic costs more than necessary Type II error Accept false Ho Claim no effect when one exists Protect species less than necessary may loose species Lose practical and scientific credibility Permit activities that should not have been approved Trading Off Type I and Type II Error in Biological Terms (Noss 1992)

6. Burden of Proof • Who has to demonstrate convincingly that a conservation action is needed? • Minimize type I error means burden of proof is on the party trying to conserve a species • cost of incorrectly concluding there is a problem (Type I) is greater than cost of incorrectly concluding there is not a problem (Type II) • Poor Information on status of species reduces power and increases Type II error • burden of proof is again on the conservationist

7. Do We Just Minimize Type II Error? • Simple shifting of burden of proof from conservationists to resource users • Unnecessary socioeconomic hardship? • Need to explicitly consider what shifting burden of proof means for conservation and then argue the prudent route • Precautionary Principle • better to err on side of caution when effect is not reversible • EXTINCTION

8. Formal Evaluation of Uncertainty • Kirtland’s warblers (Marshall et al. 1998) • Want to minimize costs of management (maximize timber harvest return) while keeping P(N<S)< • N is pop size at end of management • S is population size the manager wants (target) • 1- = “margin of safety” • recognizes that management is not certain • probability quantifies our uncertainty of hitting S

9. Margin of Safety • If we want to only undershoot our target 5% of the time then we have a 95% “margin of safety” • Increasing the margin of safety, means reducing the chances of undershooting our target • This costs money! • For Kirtlands’ warblers it means harvest less timber so you are sure you wont’t end up with too few warblers

10. Costs of “Safety” • Safety costs because of uncertainty: • we are not sure what warbler population will do without management (environmental and demographic stochasiticity) • we are not sure what the forest will do (growth models) • we are not sure how warblers will respond to forest management (habitat suitability models)

11. Quantifying the Costs • Increasing from a 90% to 99% margin of safety doubles the costs (reduces harvest) • Less certainty means you have to be especially conservative in resource management which costs more (less resource removed) • Irreversible effects (extinction) command greater safety margins

12. Including Uncertainty (Todd and Burgman 1998) • Rather than using a simple point estimate for a variable, you can encode variation in the variable and combine variation among several variables using FUZZY SET THEORY • Estimate of population size • point is mean of 550 • fuzzy set used to calculate likelihood of membership in population of 0 - 10,000 • cumulative probability distribution using SE

13. Fuzzy Sets Point = 550 Fuzzy Sets define a range of likely values rather than just a point estimate Point= 70

14. Degree of Membership • Can be from a cumulative probability distribution • Population Size • SE=300 • Can be assigned by expert opinion • Degree of “belief”---Range Size • could be up to 900km2 1 .526 Cumulative Distribution 0 500 Population Size

15. Combination of Fuzzy Sets • Can take unions and intersections just like with crisp sets • Intersection gives degree of membership in both outcomes Most Likely given uncertainty Point Estimate

16. Adaptive Management Recognizes That Managers Need to Work Before Mechanisms are Understood Solutions for Many of Wildlife Science’s Current Shortcomings Hypothetico- deductive Methods Relevant Spatial and Temporal Scale Validation via Monitoring Long-term Research Increased Manager- Researcher Partnerships

17. Benefits and Compromises of Adaptive Management Compromise Short-term Performance by Implementing Some Poor Alternatives Increase Area for Humans and Wildlife Value Management Maximize Wildlife Population Viability and Urban Development Hypothesis Development Learn How to Provide Habitat Effectively and Efficiently Compromise Statistical Rigor But Gain Scale Sensitivity and Relevance Test Alternative Hypotheses Research

18. Summary • Uncertainty is a certainty • We usually deal with it by minimizing type I error • Need to lay out the implications to conservation of type I versus type II error • May be able to incorporate uncertainty in the decision making process by modeling stochasticity (PVAs and the warbler example) or combining information with fuzzy set statistics • Adaptive Management may allow refinement of management techniques as uncertainty is reduced

19. References • Marshall, E., Haight, R. and F. R. Homans. 1998. Incorporating environmental uncertainty into species management decisions: Kirtland’s warbler habitat management as a case study. Cons. Biol. 12:975-985. • Todd, C.R. and M. A. Burgman. 1998. Assessment of threat and conservation priorities under realistic levels of uncertainty and reliability. Cons. Biol. 12:966-974. • Noss, R. F. 1992. Biodiversity: many scales and many concerns. Pp17-22 in Kerner, H. F. (ed.) Proceedings of the symposium on biodiversity of northwestern california.