AM Consultancy – Morris WMD, Nov Adaptive management – motivation and principles An overview for the Minnesota Grasslands Management Workshop (FWS-USGS Adaptive Management Consultancy) Clint Moore, USGS Patuxent Wildlife Research Center

AM Consultancy – Morris WMD, Nov Organization of presentation Wildlife management is decision making A management case study: prairie restoration –Customary approaches to management –An adaptive approach –How do the approaches compare? Criteria of all AM applications

AM Consultancy – Morris WMD, Nov Wildlife management is decision making Populations, habitats, people Almost always under uncertainty But whose wildlife training included principles of formal decision making?

AM Consultancy – Morris WMD, Nov Uncertainty Doesn’t make life (decision making) easier! Structured Decision Making Partial Observability Inability to accurately see or measure system Environmental Variation “Randomness” around expected mean response Partial Controllability Indirect control; realized action differs from intended Structural Uncertainty System behavior is unknown or disputed Adaptive Management

AM Consultancy – Morris WMD, Nov Adaptive Management – A management / science partnership Usual relationship: –Science provides information; management acts on it –No further interaction beyond this transfer of info … and this is a problem, why? –When the decision is not properly structured, it often leads to misdirection and management paralysis –Displacement behavior: always need “more information”, “more research”, “more monitoring”  Bolsters public view of science as a never-ending and mostly useless exercise AM integrates science and management –Science helps predict how system will respond to actions –Information focus is on what is needed to reduce uncertainty

AM Consultancy – Morris WMD, Nov Prairie restoration case study Objective: –Achieve as much annual growth as possible for a target forb (through reduction of competition) on a restoration area –Do this in a cost-effective way each spring 2 decision alternatives: Mowing or burning This year (0) Next year (+1) …

AM Consultancy – Morris WMD, Nov Prairie restoration case study Uncertainty about treatment –Both treatments are known to be effective, but is burning any more effective than mowing? (Structural Uncertainty) Is average effect 10% more effective than mowing? 20%? 0%? –No matter the average difference, the real difference any one year is unpredictable (Environmental Variation) What decision should be made? –If cost was not an issue, we would prefer to burn every time Provides additional ecological benefits not provided by mowing –But cost is an issue Burning is far more expensive than mowing

AM Consultancy – Morris WMD, Nov Customary approaches to management under uncertainty Strategies that sidestep uncertainty –Assertion: Uncertainty doesn’t exist –Uncertainty judged inconsequential: Uncertainty exists, but we decide it’s not meaningful in context of decision –Risk-aversive decision making: Uncertainty exists, but choose decision to minimize chance of worst possible outcome Risk of –Really bad decisions –Controversy about decision process (inquiries, litigation)

AM Consultancy – Morris WMD, Nov Customary approaches to management under uncertainty Trial and error: try something and see how it works –Outcome is… Favorable – repeat the decision next time Unfavorable – try something else

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Adaptive Management Consultancy – Feb 2006 (conference call) This year: Try both Outcome: burning better than mowing Outcome: burning not better than mowing Next year Burning established as "best" option Mowing established as "best" option 11

AM Consultancy – Morris WMD, Nov Customary approaches to management under uncertainty Trial and error: try something and see how it works –Outcome is… Favorable – repeat the decision next time Unfavorable – try something else –Learning is informal and accidental, even illusory: Hard to make sense of chance events that obscure outcome No contingency for ever challenging a “best” (traditional) decision No means of reconciling contradictory experiences

AM Consultancy – Morris WMD, Nov Customary approaches to management under uncertainty Experimentation –Actions designed to resolve uncertainty as quickly as possible

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Adaptive Management Consultancy – Feb 2006 (conference call) Year 1Year 2Year 3 15

AM Consultancy – Morris WMD, Nov Customary approaches to management under uncertainty Experimentation –Actions designed to resolve uncertainty as quickly as possible –Direct focus is on resolving uncertainty, not improving management Experimentation may be costly, impractical, or infeasible Management returns may be put on hold while experiment is conducted Quick reduction of uncertainty may impose too much risk to resource

AM Consultancy – Morris WMD, Nov An adaptive approach Designed to… –Indicate good decisions in face of uncertainty –Make use of decision outcomes to reduce uncertainty Requires… 1.Objective statement 2.Set of decision alternatives 3.Competing, predictive models of decision outcome Models link decisions, outcomes, and objective To describe uncertainty & provide basis for reducing it 4.Measures of confidence on each model Reflects current degree of influence on decision by each model 5.Program to monitor response To update confidence measures (reduce uncertainty)

AM Consultancy – Morris WMD, Nov What do we want out of management? The objective statement A subjective value placed on each outcome of each decision (e.g., scale of 0 – 10) If we choose this… and the true improvement of burning over mowing is this… then we will assign this value to the decision… Mow0%10 10%7 20%2 Burn0%1 10%5 20%9

AM Consultancy – Morris WMD, Nov What are we uncertain about? The role of competing predictive models

AM Consultancy – Morris WMD, Nov What are we uncertain about? The role of competing predictive models AM requires specifying alternative, plausible models –They serve as alternative hypotheses about management –They reflect breadth of uncertainty about management among decision makers and stakeholders (i.e., “bounding uncertainty”) Inclusive feature of AM: stakeholder beliefs are admitted and then evaluated on a level, transparent playing field But AM doesn't require that one model be selected or declared a "winner" –Instead, models are allocated and de-allocated influence over time

AM Consultancy – Morris WMD, Nov How do we measure uncertainty? Model confidence weights Numbers (proportions adding to 1.0) are assigned to each model Example: –We believe that chances are about 50/50 that burning is any better than mowing –If burning is better than mowing, we suppose chances are 2:1 that improvement is only moderate (i.e., 10% better) –Possible initial assignment of confidence weights: Model 1 (no difference)0.50 Model 2 (burning 10% better)0.33 Model 3 (burning 20% better)0.17

AM Consultancy – Morris WMD, Nov How do we measure uncertainty? Model confidence weights The best decision under uncertainty emerges when confidence weights are combined with objective values –Weights of (0.50, 0.33, 0.17) favor the mowing decision but do not exclude the burning decision –Other weight assignments could be chosen Each choice influences how likely each action is chosen or how often each action is represented

Adaptive Management Consultancy – Feb 2006 (conference call)

AM Consultancy – Morris WMD, Nov How do we gain knowledge and adapt? The monitoring program Following application of treatments, collect data on the response

AM Consultancy – Morris WMD, Nov How do we gain knowledge and adapt? The monitoring program Using a simple probability formula, model weights are updated based on support by the data for each alternative model –Observed difference in means: Burning 12% greater than mowing (95% CI: -6% - 25%) Model confidence weights ModelInitialUpdated 1: no difference : burning 10% greater : burning 20% greater

AM Consultancy – Morris WMD, Nov How do we gain knowledge and adapt? The monitoring program 124 This year 106 Next year

AM Consultancy – Morris WMD, Nov What happens next? Cycle of decision making, prediction, data collection, and updating is continued Management "adapts" as information is collected and knowledge is gained Possible improvements for this example –Incorporate measurement of a "state variable" (e.g., soil moisture) to make smarter judgments about use of fire vs mowing  greater control over environmental variation –Implement at multiple sites to increase experience with treatments over broader conditions  greater control over environmental variation –Incorporate objectives other than vegetation growth and cost

AM Consultancy – Morris WMD, Nov AM compared to customary management Trial-and-error –AM puts in place a decision and learning structure that Is transparent Resolves ambiguous or contradictory decision outcomes Accommodates unexpected outcomes, surprises Provides a formal record of management Experimentation –AM maintains focus on management objectives Decisions chosen to maximize objectives, not merely to return information Arbitrary "significance" thresholds are not required (nor are they desired) under AM: AM can proceed in cases where the experiment returns an ambiguous "not significant" outcome But, most effective when combined with good science design: –Randomization, replication, control

AM Consultancy – Morris WMD, Nov Some applications of adaptive management Adaptive Harvest Management of waterfowl Objective: Maximize cumulative harvest Principal uncertainties: Population response to harvest, relationship between regulations and harvest rates Monitoring data: Numbers of breeding waterfowl and habitat condition in spring Pine harvest management for RCW Objective: Maintain supply of old-growth forest through timber harvest Principal uncertainty: Rates of pine succession to hardwood Monitoring data: Forest composition in pine age classes and in hardwood R5 Impoundment Study Objective: Create seasonal wetland habitat for migrating shorebirds Principal uncertainty: Effects of drawdown timing and rate of drying on bird use Monitoring data: Pond hydrography, vegetation, bird abundance

AM Consultancy – Morris WMD, Nov Criteria of all AM applications A sequential decision must be made –Affecting a single resource or applied to multiple units –Series of one-time decisions, e.g., restoration projects

AM Consultancy – Morris WMD, Nov Making a sequential decision Situation 1: Control of a dynamic resource Single population: harvests of deer, releases of condors Multiple units: prescribed burning of forest compartments time Decision Population

AM Consultancy – Morris WMD, Nov Making a sequential decision Situation 2: Series of replicated, one-time decisions Examples: Dam removals, mine restorations time Site A Site B Site C Site D Site E Site F Site G

AM Consultancy – Morris WMD, Nov Criteria of all AM applications A sequential decision must be made A clear, measurable objective is (or can be) stated Manager is faced with real decision alternatives –None that are politically or practically implausible –Decisions aren't just "tweaks" of a default action A key uncertainty stands in the way –Litmus test: If I knew the true state of things, would it make a difference in the action I take? A way to predict outcomes for different actions –Each hypothesis represented by a unique model A way to test those predictions –A focused monitoring program can be put in place

AM Consultancy – Morris WMD, Nov A few references Adaptive Management Guidebook for the Department of Interior (2007) Nichols and Williams (2006) Trends in Ecology and Evolution 21: Gregory et al. (2006) Ecological Applications 16: Schreiber et al. (2004) Ecological Management and Restoration 5: Williams et al. (2002) Analysis and Management of Animal Populations (Academic Press) Walters (1986) Adaptive Management of Renewable Resources (McGraw-Hill)