AM Consultancy – Morris WMD, Nov 2007 1 1 Adaptive management – motivation and principles An overview for the Minnesota Grasslands Management Workshop

AM Consultancy – Morris WMD, Nov 2007

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Adaptive management – motivation and principles

An overview for theMinnesota Grasslands Management

Workshop(FWS-USGS Adaptive Management

Consultancy)

Clint Moore, USGS Patuxent Wildlife Research Center

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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

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Wildlife management is decision making

• Populations, habitats, people• Almost always under uncertainty• But whose wildlife training included principles

of formal decision making?

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UncertaintyDoesn’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

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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

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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)

+2 +3 +4 …

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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

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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)

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• Trial and error: try something and see how it works– Outcome is…

• Favorable – repeat the decision next time• Unfavorable – try something else

Adaptive Management Consultancy – Feb 2006 (conference call)

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This year: Try both

Outcome: burning better than mowing

Outcome: burning not better than mowing

Next year

Next year

Burning established as "best" option

Mowing established as "best" option

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• 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

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• Experimentation– Actions designed to resolve uncertainty as quickly as

possible


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Year 1 Year 2 Year 3

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• 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

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An adaptive approach

• Designed to…– Indicate good decisions in face of uncertainty– Make use of decision outcomes to reduce uncertainty

• Requires…1. Objective statement2. Set of decision alternatives3. 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)

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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…

Mow 0% 10

10% 7

20% 2

Burn 0% 1

10% 5

20% 9

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What are we uncertain about?The role of competing predictive models

Average response(# stems, LAI, area coverage, etc.)

Model 1: nodifference

Model 2: 10%improvement

Model 3: 20%improvement

MowingBurning

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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

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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

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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


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How do we gain knowledge and adapt?

The monitoring program• Following application of treatments, collect

data on the response

Observed response(# stems, LAI, area coverage, etc.)

Mowing Burning

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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

Model Initial Updated

1: no difference 0.50 0.40

2: burning 10% greater 0.33 0.40

3: burning 20% greater 0.17 0.20

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The monitoring program

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This year

10

6

Next year

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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

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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

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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

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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

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Making a sequential decision

• Situation 1:Control of a dynamic resourceSingle population: harvests of deer, releases of condors Multiple units: prescribed burning of forest compartments

time

Decision Decision Decision Decision

Population Population Population Population

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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

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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

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A few references

• Adaptive Management Guidebook for the Department of Interior (2007)

• Nichols and Williams (2006) Trends in Ecology and Evolution 21:668-673

• Gregory et al. (2006) Ecological Applications 16:2411-2425

• Schreiber et al. (2004) Ecological Management and Restoration 5:177-182

• Williams et al. (2002) Analysis and Management of Animal Populations (Academic Press)

• Walters (1986) Adaptive Management of Renewable Resources (McGraw-Hill)

Documents

AM Consultancy – Morris WMD, Nov 2007 1 1 Adaptive management – motivation and principles An overview for the Minnesota Grasslands Management Workshop