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UNL seminar, Jan 2008 Adaptive Management Clear Direction in an Uncertain World Clint Moore USGS Patuxent Wildlife Research Center Warnell School of Forestry.

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Presentation on theme: "UNL seminar, Jan 2008 Adaptive Management Clear Direction in an Uncertain World Clint Moore USGS Patuxent Wildlife Research Center Warnell School of Forestry."— Presentation transcript:

1 UNL seminar, Jan 2008 Adaptive Management Clear Direction in an Uncertain World Clint Moore USGS Patuxent Wildlife Research Center Warnell School of Forestry and Natural Resources University of Georgia, Athens

2 UNL seminar, Jan 2008 Natural resource management is all about decision making  How much duck harvest can be allowed this year?  What management practices should be applied to restore grasslands?  How many birds to release annually to reestablish a population?  What water release regimes best maintain ecosystem integrity?

3 UNL seminar, Jan 2008 But why can decisions be so hard to make? Boat traffic in nursery areas must be decreased to protect whales. We want to save the whales too, but the real problem is toxicity. A dispute over the science? Or a hidden interest? Pig! Freak!

4 UNL seminar, Jan 2008 Structured Decision Making Example: Construction of a Conservation Reserve  Decision alternatives  Prediction of outcome under each decision  Valuation of outcomes Build Reserve ? Yes Species Extirpated No Species Extirpated Species Persists

5 UNL seminar, Jan 2008 Structured Decision Making  Conservation reserve problem is a single (one-off) decision  But many (most?) natural resource problems involve sequential decisions Recurrent decisions  Dynamic systems Replicative decisions  Collections of one-time decisions

6 UNL seminar, Jan 2008 Recurrent decisions for a dynamic system time Decision Resource Yield  e.g., harvests (of animals, forests) Management objective is periodic yield  e.g., restoration (of species, communities, habitats) Management objective is desired end state Desired Resource State

7 UNL seminar, Jan 2008 Replicated one-time decisions Site A Site B Site C Site D Site E Site F Site G time  e.g., dam removals, mine restorations

8 UNL seminar, Jan 2008 Everyday example of dynamic DM?  When did you last drive a car? Objective:  Arrive at destination quickly, safely Decision points:  Continuous – every point along route System state:  Current conditions of daylight, traffic, weather, surface Decisions:  Apply gas, apply brake, steer left, steer right, etc. Prediction model:  Expected response by car to driver inputs

9 UNL seminar, Jan 2008 What makes decision making difficult?  Uncertainty about consequence of decision Outcome is unpredictable System is not well understood  Inability to assign values to outcomes Stakeholder disagreement over objectives Build Reserve ? Yes Extirpated No Extirpated Persists

10 UNL seminar, Jan 2008 Uncertainty in decision making  Partial observability  Seeing the system in its apparent state, not its real state  e.g., decision based on estimated population abundance  Environmental stochasticity  Random, unpredictable variation around a mean response  e.g., stochastic effects of demography, climate  Partial controllability  Inability to carry out a targeted action  e.g., incomplete prescribed burn, destructive burn

11 UNL seminar, Jan 2008 Uncertainty is accommodated in SDM Partial Observability Environmental Stochasticity Partial Controllability Sequential decision making One-time decision making Structured Decision Making

12 UNL seminar, Jan 2008 A 4 th kind of uncertainty: Structural/Parametric Uncertainty  System behavior is unknown or disputed Inability to state an average system response to an action  Multiple, plausible descriptions of the system may exist Examples:  Is average rate of forest succession fast or slow?  Are demographic rates of wild-hatched birds equivalent to those of their captive-released parents?  Is soil disturbance required to release the native seed bank?  Is the harvest partially compensated for by reductions in non-harvest mortality or increases in productivity?  Does fire accelerate or slow salt marsh loss?

13 UNL seminar, Jan 2008 Structural uncertainty: Implies more than 1 plausible system model H1: 59 H2: 45 H1: 50 H2: Build Reserve ? Yes Species Extirpated No Species Extirpated Species Persists 0.5 H 1H 2

14 UNL seminar, Jan 2008 Adaptive Management: Exploiting sequential decision making to account for and reduce structural uncertainty Partial Observability Environmental Stochasticity Partial Controllability Sequential decision making One-time decision making Structural Uncertainty Structured Decision Making Adaptive Management

15 UNL seminar, Jan 2008 Adaptive Management: Iterated decision making in the face of structural uncertainty, with a focus on its reduction B. K. Williams

16 UNL seminar, Jan 2008 AM: Iterated decision making in the face of structural uncertainty, with a focus on its reduction  Emergence of AM 1890: Chamberlin  "The method of multiple working hypotheses" 1950s-60s: Bellman and others  Theory of optimal control of uncertain dynamic systems 1970s-80s: Walters, Holling, Hilborn  Theory & model development for regulation of fisheries 1980s-90s: Williams, Johnson, Nichols, & others  First widely successful wildlife management application Currently:  Applications development in endangered species reintroduction, habitat & landscape management, etc.

17 UNL seminar, Jan 2008 AM: Iterated decision making in the face of structural uncertainty, with a focus on its reduction  Elements of SDM Set of decision alternatives Prediction of outcome to each action Decision objective  Additional elements required for AM: A sequence of decisions through time Set of predictive models  Discrete set or continuous set Measure of relative confidence applied to each model Monitoring program to assess model predictions

18 UNL seminar, Jan 2008 Model confidence weights Point to a best decision under uncertainty H1: 59 H2: 45 H1: 50 H2: Build Reserve ? Yes Species Extirpated No Species Extirpated Species Persists 0.5 H 1H

19 An illustration of AM 1. A decision is made under model uncertainty e.g., population size, species richness, habitat structure, etc. UNL seminar, Jan 2008

20 Prediction phase An illustration of AM 2. Given decision, predict outcome under each model UNL seminar, Jan 2008

21 Action phase An illustration of AM 3. Carry out action, observe (monitor) outcome UNL seminar, Jan 2008

22 Updating phase An illustration of AM 4. Assess predictions, update model influence weights UNL seminar, Jan 2008 Bayesian updating

23 An illustration of AM Next time steps: repeat prediction, action, updating steps UNL seminar, Jan 2008

24 An illustration of AM Next time steps: repeat prediction, action, updating steps UNL seminar, Jan 2008

25 An illustration of AM Next time steps: repeat prediction, action, updating steps UNL seminar, Jan 2008

26 An illustration of AM Next time steps: repeat prediction, action, updating steps UNL seminar, Jan 2008

27  An illustration of AM Next time steps: repeat prediction, action, updating steps UNL seminar, Jan 2008

28 An illustration of AM Next time steps: repeat prediction, action, updating steps UNL seminar, Jan 2008 

29 Everyday example of adaptive management?  Car rental Key uncertainty:  Does the rental car perform/behave about the same as my own car, or nothing like it? Periods of greatest uncertainty Periods of great information gain

30 UNL seminar, Jan 2008 Example: Forest harvest scheduling for perpetuating an old-growth stage Greg Lasley Larkin Powell

31 UNL seminar, Jan 2008 Forest harvest scheduling  Objective: Maximize total area of old-growth (80+ yrs) pine over 1000 yrs  Decisions: Annual area to harvest (forest-wide) from 3 seral classes of pine: P2 (16-40 yrs) P3 (40-80 yrs) P4 (80+ yrs)  Constraints: Forest composition at next period depends on: - Current forest composition - Decisions made - Stochastic disturbances

32 UNL seminar, Jan 2008 Forest harvest scheduling Stage transition model P yr UH P yr P yr P4 80+ yr

33 UNL seminar, Jan 2008 Forest harvest scheduling Stage transition model P yr UH P yr P yr P4 80+ yr Regeneration decisions

34 UNL seminar, Jan 2008 Forest harvest scheduling Stage transition model P yr UH P yr P yr P4 80+ yr Stochastic forest disturbances

35 UNL seminar, Jan 2008 Forest harvest scheduling Stage transition model P yr UH P yr P yr P4 80+ yr Stochastic hardwood succession

36 UNL seminar, Jan 2008 Forest harvest scheduling How fast does pine succeed to hardwood? slow (  =0.015) moderate (  =0.03) fast (  =0.06) Model uncertainty Mean rate of hardwood succession

37 UNL seminar, Jan 2008 Forest harvest scheduling Optimal policy  Two examples of decision making under uncertainty / P4P3P2SlowModFastUHP4P3P2P1 Optimal Cutting Amounts Current Information State Current Forest Composition /

38 UNL seminar, Jan 2008 Example: Waterfowl harvest under uncertainty about population dynamics

39 UNL seminar, Jan 2008 Harvest management of waterfowl  Choice of best decision option for mallard hunting (season length, bag limit) is model sensitive  Degree of density dependence in survival and recruitment  But what model to “believe”?  Results of large management experiments were equivocal  Vigorous disagreement over degree of density dependence  Harvest decisions are indexed to current …  Population size  Habitat conditions  Relative belief in alternative models

40 Ponds (millions) Mallards (millions) Harvest Decision Policy = Liberal option = Moderate option = Restrictive option = Very restrictive option = Season closed UNL seminar, Jan 2008

41 Ponds Mallards Harvest Decision Policy Information State UNL seminar, Jan 2008

42 Decide on objective Decide on regulatory options Propose candidate models Assign belief weights to models Measure current population and habitat conditions Find optimal decision action, given: 1. current conditions 2. current weights on models Conduct harvest Predict spring population size by each competing model Measure the spring population size and habitat Compare each prediction to the measured population value Update model weights based on differences: Better-performing models get more belief weight ( Spring ) ( Fall ) ( Spring ) Adaptive Harvest Management Decision Cycle Work conducted spring 1995 UNL seminar, Jan 2008

43 X Ponds (millions) Mallards (millions) Harvest Decision Policy for 1995 Season L M R VR C Information State: Equal weights on models UNL seminar, Jan 2008

44 X SaRs  25.0 SaRw  25.0 ScRs 25.0 ScRw 25.0 Choose optimal action, given system state and current model weights Decision matrix Current system state Current model weights UNL seminar, Jan 2008

45 X SaRs  25.0 SaRw  25.0 ScRs 25.0 ScRw 25.0 Given decision, predict response by each model 4 predictions of outcome UNL seminar, Jan 2008

46 X SaRs  25.0 SaRw  25.0 ScRs 25.0 ScRw 25.0 Carry out decision, observe (monitor) system response New system state UNL seminar, Jan 2008

47 SaRs  SaRw  ScRs 25.0<0.1 ScRw 25.0<0.1 X Update model weights and compute new decision policy Updated model weights New decision matrix UNL seminar, Jan 2008

48 X SaRs  SaRw  ScRs 25.0<0.1 ScRw 25.0<0.1 X Repeat process for 1996 decision UNL seminar, Jan 2008

49 X SaRs  SaRw  ScRs 25.0< ScRw 25.0<0.1 XX 1997 decision UNL seminar, Jan 2008

50 X SaRs  SaRw  ScRs 25.0<0.10.1<0.1 ScRw 25.0<0.1 XXX 1998 decision UNL seminar, Jan 2008

51 X SaRs  SaRw  ScRs 25.0<0.10.1<0.1 ScRw 25.0< XXXX 1999 decision UNL seminar, Jan 2008

52 X SaRs  SaRw  ScRs 25.0<0.10.1<0.1 ScRw 25.0< <0.1 XXXXX 2000 decision UNL seminar, Jan 2008

53 X SaRs  SaRw  ScRs 25.0<0.10.1<0.1 ScRw 25.0< <0.1 XXXXXX 2001 decision UNL seminar, Jan 2008

54 Threatened/endangered species  Reintroduction of a nonmigratory whooping crane flock in Florida How many birds to release annually? Is survival/productivity of wild- hatched birds similar to captive- released parents?  Management of Mead's milkweed Uncertainty about effects of burning – when should it be applied?

55 UNL seminar, Jan 2008 Habitat and landscape-level management  Grassland management Which treatments (burning, grazing) and frequencies inhibit cool-season invasives?  Incentive-based quail habitat conservation Should a proposal receive greater consideration if enrolled acreage is nearby?

56 UNL seminar, Jan 2008 Adaptive management…  …is not unfocused trial and error The decision components (objectives, models, weights) provide clear decision direction  …is not experimentation Learning is valued, but only to the extent that management is measurably improved  …does not necessarily imply different decisions triggered by changing resource or environmental conditions Adaptation is all about change in the model credibility weights  …is not a consensus tool for resolving different stakeholder values Competing objectives must be resolved externally of AM

57 UNL seminar, Jan 2008 Bringing AM into DOI agencies  DOI Guidebook on adaptive management  Training Courses on modeling, structured decision making, adaptive management at FWS National Conservation Training Center  FWS Refuge System Refuge Cooperative Research Program Adaptive Management Consultancy  Informal efforts Adaptive Management Conference Series ARM for TES workshops

58 UNL seminar, Jan 2008 An appeal to natural resource educators  Graduate-level training in decision sciences Modeling Estimation Optimization, simulation Valuation (i.e., human dimensions)  Programs in structured decision making and adaptive management

59 UNL seminar, Jan 2008 Thank You!  Photo credits U.S. Fish and Wildlife Service Florida Fish and Wildlife Conservation Commission South Carolina DNR

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