Motivation

Bayes

Evaluating actions

Example

Conclusions

Better ignorant than misled: Including
uncertainty in forecasts supporting management
and policy
N. Thompson Hobbs
Natural Resource Ecology Laboratory, Department of Ecosystem Science and
Sustainability, and Graduate Degree Program in Ecology,
Colorado State University

Uncertainty Analysis: A Critical Step in Ecological Synthesis
Annual Meeting of the Ecological Society of America, 8/5/2013

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Honest synthesis
Management and Policy

Theory
Published
research

Process
studies

Experiments

Monitoring

Physical
laws

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Motivation

Bayes

Evaluating actions

Example

Conclusions

The problem of management:
What actions will allow us to meet
goals for the future?

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Synthesis with deterministic models

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Orrin H. Pilkey and Linda Pilkey-Jarvis 2007

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Motivation

Bayes

Evaluating actions

Example

Conclusions

A broadly applicable approach to ecological modeling in
support of management
[E, θprocess, θdata |data] ∝
[data|θdata , Et ] [Et |θprocess , Et−1 ] [θprocess , θdata , E0 ]

data
Et

Et

Process

✓process ✓data

Parameters

1

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Posterior predictive distributions

State of Population (E or E’)
State of Ecological System (E or E')

[E |data] =
θ E [E |E , θdata , θprocess ] [E , θdata , θprocess |data] dE dθdata θprocess

Time

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Probability density

State of Population (E or E’)
State of Ecological System (E or E')

Posterior predictive distribution of future states, E

Time

Future state of system, E'

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Motivation

Bayes

Evaluating actions

Example

Conclusions

How to evaluate actions?
Objective: reduce state below a target

Probability density

Objective

Future state of system, E'

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Objective: maintain state within acceptable range

Probability density

Range

Future state of system, E'

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Objective: increase state above a target

Probability density

Objective

Future state of system, E'

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Action: do nothing

Probability density

Objective

Future state of system, E'

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Action: implement managment

Probability density

Objective
Management

Future state of system, E'

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Net effect of management

Probability density

Objective

Future state of system, E'

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Net effect of management

Probability density

Objective

Future state of system, E'

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Example: Managing brucellosis in Yellowstone Bison

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Goal: Reduce probability of infection
by half in five years.
Action: Annually vaccinate 200
sero-positive females.

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Bayesian matrix model with multiple sources of data
Mark recapture
yϕ[t]
Serology
Ysero[t]

Ground
Aerial
classifications
classifications
yp.µ[t]
yratio.calf[t]
yp.σ[t]
yσ[t]

n[t]
p

A[t] n[t

Census
yn.obs[t]
yn.sd[t]

psight

asd , bsd

1]

r[t]

Removals
yr[t]
Removal
Removal
age, sex
serology
yr.age[t]
yr.sero[t]

rcomp[t]
rsero[t]
rinfect[t]

, fc , fn , fp , , s, v

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Motivation

Bayes

Evaluating actions

Example

Conclusions

0.20
0.15
0.10
0.05

Do nothing
Vaccinate

0.00

Probability of transmission

0.25

Effect of vaccination: Treat 200 sero-positive / year

0

1

2

3

4

5

Years into the future

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Motivation

Bayes

Evaluating actions

Example

Conclusions

0.30
0.20
0.10
0.00

Probability of transmission

Effect of vaccination

0

1

2

3

4

5

Years into the future

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Effect of vaccination
Objective: reduce transmission probability by half

10

Five years in the future

6
4
2
0

Density

8

Do nothing
Vaccinate

0.0

0.1

0.2

0.3

Probablity of tranmission

0.4

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Comparison of alternatives

Management action
Do nothing
Vaccinate 200 sero-positives
Cull 200 sero-positives
Cull 200 females
Boundary hunting

Probability of meeting goal
.05
.33
.29
.15
.03

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Multiple objectives, multiple actions

Objectives

Actions

Reduce P(infection) by
half

Vaccination

Sero prevalence < 40%

Remove sero-negatives

Population size between
3000 - 3500

Boundary hunting (or removal)

Remove sero-positives

Appropriate demographic
composition

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Closing

Value
Provides honest forecasts relevant to actions and goals.
Informs the conversation

Limitations
Demonic intrusions aren’t included.
Forecasting horizons are short.

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Motivation

Bayes

Evaluating actions

Example

Conclusions

Walters, C. J. 1986. Adaptive Management of
Renewable Resources. Macmillan, New York.
Develop Bayesian
model(s) of system

Update model(s)

Observe system
behavior

Forecast system
behavior under
different policy
options
Implement policy (or
better, policies)

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Motivation

Bayes

Evaluating actions

Example

Conclusions

May 21-30, 2013 Google “NREL Bayes”

Building capacity in Bayesian modeling
for practicing ecologists
A workshop for faculty and
agency researchers. $1000
stipend. See
www.nrel.colostate.edu/projects/
bayesworkshop/
Award DEB 000347455

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Hobbs: Better ignorant than misled: Including uncertainty in forecasts supporting management and policy.