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Evan H. Campbell Grant US Geological Survey Amphibian Research and Monitoring Initiative Patuxent Wildlife Research Center SO Conte Anadromous Fish Research.

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Presentation on theme: "Evan H. Campbell Grant US Geological Survey Amphibian Research and Monitoring Initiative Patuxent Wildlife Research Center SO Conte Anadromous Fish Research."— Presentation transcript:

1 Evan H. Campbell Grant US Geological Survey Amphibian Research and Monitoring Initiative Patuxent Wildlife Research Center SO Conte Anadromous Fish Research Laboratory Turners Falls, MA ADAPTING TO NATURE IN THE NEW NORMAL IMPROVING NATURAL RESOURCE DECISION MAKING UNDER UNCERTAINTY

2 PROBLEM: AMPHIBIAN POPULATIONS ARE DECLINING WORLDWIDE

3 PROBLEMS ARE COMPLEX AND INTERACTING Matt Gray, UT Huntington et al. 2009. Can J. For. Res.

4 http://armi.usgs.gov/index.php Are amphibians in the USA declining?

5

6 IN THE NORTHEAST…

7 HOTTER DRIER UNPREDICTABLE IN THE NORTHEAST…

8 PROBLEM: HABITAT PROTECTION ALONE MIGHT NOT CUT IT Number of droughts

9 9 PRESCRIPTION FOR RESOLVING 10,000 DECISIONS Keeney 2004. Making better decision makers. Decision Analysis 1:193-204.

10 10 PRESCRIPTION FOR RESOLVING 10,000 DECISIONS Keeney 2004. Making better decision makers. Decision Analysis 1:193-204.

11  The structuring of a decision problem  in terms of choices, outcomes, and values  to identify the choice that is most likely to achieve the values of the decision maker.  Decisions involve  valuing the outcomes  predicting outcomes from alternative choices  The first part is the (subjective) role of society; the second part is the (objective) role of science 11 WHAT IS DECISION ANALYSIS?

12  Clear Objectives  Creative management Alternatives  Models linking actions to objectives, generate predictions  Optimization to determine best approach, given observations and objectives  Implement a decision  Monitor system state changes STRUCTURED DECISION MAKING Elements:

13  Clear Objectives  Creative management Alternatives  Models linking actions to objectives, generate predictions  Optimization to determine best approach, given observations and objectives  Implement a decision  Monitor system state changes STRUCTURED DECISION MAKING Values Science

14 14 SDM IS NOT A PANACEA OBJECTIVES Agreed upon Disputed OUTCOMES Well Understood UncertainDisputed Structured Decision- Making Conflict Resolution Joint Fact Finding Objectives – Alternatives – Models – Optimization – Implementation - Monitoring

15 EXAMPLE FROM C&O CANAL NHP  ‘Potomac Gorge’ area of C&O  Threats (Allen and Flack 2001)  Urbanization  Invasive/alien species  Isolation  Climate change (and variability)

16 WHAT IS VALUED BY RESOURCE MANAGEMENT: OBJECTIVES  Maintain average amphibian species richness at C&O Canal NHP wetlands.  Minimize cost of doing management. * Can include other competing objectives (e.g., visitor use and enjoyment, access to recreation, other species-specific goals) Objectives – Alternatives – Models – Optimization – Implementation - Monitoring

17 Monitoring data: Since 2005 Observe a decline in occupancy for all species

18 Relate occupancy to measured variables (*hydroperiod) Permanent Semi-permanent Temporary

19 Make predictions Permanent Semi-permanent Temporary

20 THINKING BACK TO OUR FORECASTS HOTTER DRIER UNPREDICTABLE

21  Best Alternative: increase hydroperiod of temporary wetlands  Optimization: Rank wetlands by the expected increase in richness, to choose most suitable sites for management each year USING THE MODEL TO GUIDE MANAGEMENT: OPTIMIZATION Objectives – Alternatives – Models – Optimization – Implementation - Monitoring

22  Typical response is to want to understand what’s causing declines,  But there is a tradeoff in waiting for more information (which may be imperfect) and a need for action  Both have components of uncertainty. NOW WHAT?

23 When to initiate a decision? 1 5 10 Utility threshold Average wetland richness Year that management is implemented Objectives – Alternatives – Models – Optimization – Implementation - Monitoring

24 1 5 10 Average wetland richness Manage half Manage a quarter Manage none Objectives – Alternatives – Models – Optimization – Implementation - Monitoring

25 1 5 10 Marginal benefit of managing 50% of sites in year 1, vs. no management, assuming poor outcome. Average wetland richness Objectives – Alternatives – Models – Optimization – Implementation - Monitoring

26 1 5 10 Average wetland richness Marginal benefit of implementing management of 50% of sites in year 1 vs. 5, assuming best outcome Objectives – Alternatives – Models – Optimization – Implementation - Monitoring

27 1 5 10 Marginal benefit of managing 50% vs. 25% of sites in year 1, assuming average outcome. Average wetland richness Objectives – Alternatives – Models – Optimization – Implementation - Monitoring

28 1 5 10 Marginal benefit of managing 50% of sites in year 1 vs. 25% of sites in year 5, assuming learning occurred Average wetland richness Objectives – Alternatives – Models – Optimization – Implementation - Monitoring

29 IN SUMMARY  Amphibians are in trouble (or may be…)  Even amphibians in protected areas are at risk under climate change

30  IF we value amphibians, where they are, we need to make hard decisions about active management UNCERTAINTY IS SCARY, BUT -

31 A proactive approach to conservation  Designed to preempt (or respond to) climate change effects – short term focus  Maintain community and prevent LOCAL extinctions  A structured approach to decision making ehgrant@usgs.gov


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