Motivation Bayes Evaluating actions Example Conclusions

Better ignorant than misled: Includinguncertainty in forecasts supporting management

and policy

N. Thompson Hobbs

Natural Resource Ecology Laboratory, Department of Ecosystem Science andSustainability, and Graduate Degree Program in Ecology,

Colorado State University

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

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Motivation Bayes Evaluating actions Example Conclusions

Honest synthesis

Published

research

Monitoring

Experiments Physical

laws

Theory

Management and Policy

Process

studies

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Motivation Bayes Evaluating actions Example Conclusions

The problem of management:

What actions will allow us to meetgoals 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 insupport of management

[E,θprocess,θdata |data] ∝

[data|θdata ,Et ] [Et |θprocess ,Et−1] [θprocess ,θdata ,E0]

data

EtEt�1

✓data✓process

Process

Parameters

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Motivation Bayes Evaluating actions Example Conclusions

Posterior predictive distributions[E ′|data] =∫

θθθ

∫E [E ′|E ,θdata ,θprocess ] [E ,θdata ,θprocess |data]dEdθdataθprocess

Time

Stat

e of

Pop

ulat

ion

(E o

r E’

)St

ate

of E

colo

gica

l Sys

tem

(E o

r E')

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Motivation Bayes Evaluating actions Example Conclusions

Posterior predictive distribution of future states, E ′

Future state of system, E'

Prob

abilit

y de

nsity

Time

Stat

e of

Pop

ulat

ion

(E o

r E’

)St

ate

of E

colo

gica

l Sys

tem

(E o

r E')

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Motivation Bayes Evaluating actions Example Conclusions

How to evaluate actions?

Objective: reduce state below a target

Future state of system, E'

Pro

babi

lity

dens

ity

Objective

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Motivation Bayes Evaluating actions Example Conclusions

Objective: maintain state within acceptable range

Future state of system, E'

Pro

babi

lity

dens

ity

Range

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Motivation Bayes Evaluating actions Example Conclusions

Objective: increase state above a target

Future state of system, E'

Pro

babi

lity

dens

ity

Objective

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Motivation Bayes Evaluating actions Example Conclusions

Action: do nothing

Future state of system, E'

Pro

babi

lity

dens

ity

Objective

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Motivation Bayes Evaluating actions Example Conclusions

Action: implement managment

Future state of system, E'

Pro

babi

lity

dens

ity

Objective

Management

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Motivation Bayes Evaluating actions Example Conclusions

Net effect of management

Future state of system, E'

Pro

babi

lity

dens

ity

Objective

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Motivation Bayes Evaluating actions Example Conclusions

Net effect of management

Future state of system, E'

Pro

babi

lity

dens

ity

Objective

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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 infectionby half in five years.

Action: Annually vaccinate 200sero-positive females.

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Motivation Bayes Evaluating actions Example Conclusions

Bayesian matrix model with multiple sources of data

Serology Ysero[t]

Mark recaptureyϕ[t]

Removalsyr[t]

Censusyn.obs[t]yn.sd[t]

Aerial classificationsyratio.calf[t]

yσ[t]

A[t]n[t�1] � r[t]

n[t]asd, bsd

psight

Ground classificationsyp.µ[t]yp.σ[t]

�p

Removal age, sexyr.age[t]

Removal serologyyr.sero[t]

rcomp[t]

rsero[t]

�, fc, fn, fp, , s, v

rinfect[t]

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Motivation Bayes Evaluating actions Example Conclusions

Effect of vaccination: Treat 200 sero-positive / year

0 1 2 3 4 5

0.00

0.05

0.10

0.15

0.20

0.25

Years into the future

Pro

babi

lity

of tr

ansm

issi

on

Do nothingVaccinate

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Motivation Bayes Evaluating actions Example Conclusions

Effect of vaccination

0 1 2 3 4 5

0.00

0.10

0.20

0.30

Years into the future

Pro

babi

lity

of tr

ansm

issi

on

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Motivation Bayes Evaluating actions Example Conclusions

Effect of vaccination

Objective: reduce transmission probability by half

0.0 0.1 0.2 0.3 0.4

02

46

810

Five years in the future

Probablity of tranmission

Density

Do nothingVaccinate

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Motivation Bayes Evaluating actions Example Conclusions

Comparison of alternatives

Management action Probability of meeting goal

Do nothing .05

Vaccinate 200 sero-positives .33

Cull 200 sero-positives .29

Cull 200 females .15

Boundary hunting .03

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Motivation Bayes Evaluating actions Example Conclusions

Multiple objectives, multiple actions

Objectives

I Reduce P(infection) byhalf

I Sero prevalence < 40%

I Population size between3000 - 3500

I Appropriate demographiccomposition

Actions

I Vaccination

I Remove sero-positives

I Remove sero-negatives

I Boundary hunting (or removal)

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Motivation Bayes Evaluating actions Example Conclusions

Closing

I Value

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

I Limitations

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

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Motivation Bayes Evaluating actions Example Conclusions

Develop Bayesian model(s) of system

Forecast system behavior under different policy

options

Implement policy (or better, policies)

Observe system behavior

Update model(s)

Walters, C. J. 1986. Adaptive Management of Renewable Resources. Macmillan, New York.

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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

Award DEB 000347455

A workshop for faculty and agency researchers. $1000 stipend. See www.nrel.colostate.edu/projects/bayesworkshop/

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