Adaptively Managing
Protected Areas for
Climate Change
Tony Prato, Professor of Ecological
Economics, and Co-Director
Center for Agricultural, Resource, and
Environmental Systems
University of Missouri-Columbia
“…protected areas will themselves
need to be changed and adapted if
they are to meet the challenges
posed by global warming” World
Wildlife Fund 2003
Background
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There is considerable uncertainty
regarding the biophysical impacts of
climate change, social and ecological
responses to those impacts, and the
effectiveness of alternative strategies in
reducing vulnerability of protected areas
to climate change.
Protected area managers could respond to
this situation by doing nothing or by
developing and implementing adaptation
strategies aimed at reducing the social
and ecological vulnerability of protected
areas to climate change.
Objective
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Describe a modeling system that allows
protected area managers to reduce the
social and ecological vulnerability of
protected areas to climate change.
Adaptively Managing Protected Areas for
Climate Change (AMPAC) modeling
system.
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AMPAC incorporates a conceptual
framework for evaluating and selecting
adaptation strategies to increase the
biophysical and social resilience of
protected areas to climate change.
AMPAC is designed for use in individual
protected area ecosystems. However, the
framework is suitable for managing other
natural resource-based lands for the
impacts of climate change.
Explanation of Models
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The five models comprising AMPAC are
described in the context of the ecosystem
in which Glacier National Park is located.
The emphasis is on the models that
contribute most directly to the unique
aspects of the AMPAC modeling system.
Climate Change Model
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Climate change model: Used to develop
plausible climate scenarios for a protected
ecosystem.
The Climate Change Model for Glacier
National Park would specify plausible
climate scenarios for the park ecosystem
in terms of changes in annual and
seasonal patterns of precipitation,
temperature, and other factors.
Evaluating Adaptation Strategies
Effects of adaptation strategies
designed to reduce adverse impacts
of climate scenarios on indicator
variables can be evaluated using:
– The Ecosystem Response Model, if is
suitable for this purpose
– The Adaptive Management Model, or
– A combination of the two models
Ecosystem Response Model
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Climate response model: Simulates likely
biophysical and social responses to climate
scenarios in the absence of adaptation
strategies, and modifies those responses
for the potential effects of alternative
adaptation strategies.
The Ecosystem Response Model simulates
likely biophysical and social responses to
climate scenarios using models, such as
the Regional Hydro-Ecological Simulation
System (RHESSys), and social surveys.
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Simulated responses of indicator variables
to climate scenarios in the absence of
adaptation strategies are used construct
original probability distributions for
indicator variables.
Effects of adaptation strategies on
indicator variables are modeled using a
Delphi method and the results used to
modify the original probability
distributions.
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To illustrate the Ecosystem Response
Model, suppose a climate scenario for
Glacier National Park indicates higher
temperatures and associated decreases in
plant and animal species favored by
grizzly bear.
Other things equal, these changes are
likely to reduce grizzly bear population in
the park ecosystem, which could
adversely effect biodiversity and visitor
enjoyment of the park.
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Suppose the original probability
distribution for grizzly bear population for
a particular climate scenario is
represented by the triangular probability
distribution in the top diagram:
Pr
Original probability distribution
Pr
Modified probability distribution
Bear population
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Consider an
adaptation strategy
aimed at grizzly
bear that involves
decommissioning
selected roads in
Flathead National
Forest, east of the
park.
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Research shows a negative relationship
between road density and bear mortality.
Hence, road decommissioning is expected
to increase grizzly bear population (other
things equal), as illustrated in the bottom
diagram.
Pr
Original probability distribution
Pr
Modified probability distribution
Bear population
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This adaptation strategy could counteract
some but probably not all adverse impacts
of climate change on grizzly bear
population.
A similar procedure is used to construct
modified probability distributions for all
indicator variables.
Adaptive Management Model
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Adaptive management model:
Experimentally tests hypotheses about
how indicator variables are likely to
respond to adaptation strategies (active
adaptive management).
Model results are used to construct the
modified probability distributions for
indicator variables.
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The Adaptive Management Model is
illustrated for testing hypotheses about
the effects of lower road density on grizzly
bear population.
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Two hypotheses are tested:
– H1: bear mortality decreases as road density
decreases vs.
– H2: bear mortality increases or remains the
same as road density decreases.
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Hypotheses are sequentially tested using
Bayesian statistical methods.
H1 and H2 are illustrated in terms of the
triangular probability distributions:
The ratio is
estimated from
data collected in
the adaptive
management
experiments.
H1 implies that bear
mortality decreases as road
density decreases (R>0).
Pr
H1
0
Pr
H2 implies that bear
mortality increases as road
density decreases (R<0).
H2
R = ∆ bear mortality/∆ road density
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If results from the first round of
experiments indicates H1 should not be
rejected, then bear mortality after road
density is decreased equals bear mortality
before road density is decreased times
one minus the ratio selected from the
triangular distribution for H1.
Since ratios selected from the H1
distribution are likely to exceed zero, bear
mortality is expected to decrease as road
density decreases.
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In this case, reducing road density is
judged to be an effective adaptation
strategy for maintaining bear population,
for the initial round of experiments.
If H1 is rejected in favor of H2, then
reducing road density would not be
considered an effective adaptation
strategy for maintaining bear population.
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Similar adaptive management
experiments can be done for other
indicator variables.
Hypotheses should be periodically retested
to determine how the effectiveness of a
particular strategy changes over time.
Details on this method are given in: Prato,
T. 2005. Bayesian adaptive management
of ecosystems. Ecological Modelling 183:
147-156.
Ecosystem Vulnerability Model
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Ecosystem vulnerability model: Assesses
extent to which alternative adaptation
strategies reduce vulnerability of the park
ecosystem to climate change by
synthesizing the outputs of the previous
three models.
Synthesis
Indicator variables and their modified
probability distributions
Indicator elements
Ecosystem conditions
Park’s vulnerability to climate change
Example for Glacier Park
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Four indicator variables: floral diversity,
population of grizzly bear, water quality,
and opportunities for viewing glaciers.
Modified probability distributions for all
indicator variables under each climate
scenario are constructed as explained
earlier.
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Indicator elements are statements about
indicator variables, e.g., grizzly bear
population is moderately higher than the
target recovery population.
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Four ecosystem conditions: R1, R2, R3, and R4.
– R1: heavy loss in floral diversity, grizzly bear absent
from area, very high water quality degradation, and no
opportunities for viewing glaciers (all glaciers melted)
– R2: moderate loss in floral diversity, grizzly bear
population moderately below the target recovery
population, moderately high water quality degradation,
and moderately limited opportunities for viewing
glaciers.
– R3: good floral diversity, grizzly bear population
moderately higher than the target recovery population,
very little water quality degradation, and moderately
good opportunities for viewing glaciers; and
– R4: excellent floral diversity, grizzly bear population
significantly higher than the target recovery population,
no water quality degradation, and excellent
opportunities for viewing glaciers.
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Four ecosystem states of vulnerability to
climate change: M1 = very low; M2 =
moderately low; M3 = moderately high;
and M4 = very high.
M1 and M2 are desirable ecosystem states
(they imply resilience to climate change),
and M3 and M4 are undesirable ecosystem
states.
Subjective prior probabilities of ecosystem
states are p(M1), p(M2), p(M3), and p(M4),
which sum to one.
Evaluation of Indicator Elements
– Grizzly bear is absent from the park if the probability the
population is between zero and 25 percent of the target
recovery population is greater than or equal to 0.75;
– Grizzly bear population is moderately below the target
recovery population if the probability the population is
between 75 percent and 100 percent of the target
recovery population is greater than or equal to 0.75;
– Grizzly bear population is moderately higher than the
target recovery population if the probability the
population is between 100 percent and 125 percent of
the target recovery population is greater than or equal
to 0.75; and
– Grizzly bear population is significantly higher than the
target recovery population if the probability the
population is between 100 percent and 125 percent is
greater than or equal to 0.95.
Pr
Modified probability
distribution
a
c
Bear population
b
P*
∫
implies grizzly bear population is
f(x)dx ≥ 0.75 moderately below the target
recovery population (P*)
.75P*
f(x) = [2(x-a)/(b-a)(c-a)] for a≤x≤c, and
= [2(b-x)/(b-a)(b-c)] for c≤x≤b,
Inferring Ecosystem Conditions and States
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Inferring ecosystem conditions: Determine
the ecosystem condition from the
combination of indicator elements
simulated for each adaptation strategy
and climate scenario, and the evaluation
of those elements using modified
probability distributions of indicator
elements.
Inferring ecosystem states: Use Bayes
theorem to calculate posterior probabilities
for all ecosystem states conditional on
that ecosystem condition, and determine
the most likely ecosystem state, i.e., the
one with the highest posterior probability.
Errors in Inferring Ecosystem States
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Two mutually exclusive errors are
possible:
– 1. Decide ecosystem is vulnerable to climate
change when it is not. Causes implementation
of an adaptation strategy when it is not
necessary-Type I error.
– 2. Decide ecosystem is not vulnerable to
climate change when, in fact, it is. Causes no
implementation of an adaptation strategy
when one is needed. Runs the risk that the
ecosystem remains in or changes to a less
desirable state-Type II error.
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Bayes theorem can be used to minimize
inferential errors.
Optimal Strategy Model
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Optimal strategy model: Determines the
best adaptation strategy for each climate
scenario by applying the minimax regret
criterion to the posterior probabilities for
ecosystem states and the maximum
expected costs for alternative strategies.
Minimax regret criterion selects the
adaptation strategy for each climate
scenario that minimizes the maximum
expected cost unless the social cost of
that strategy is unacceptably high.
Example
(for a particular climate scenario)
E(s1) = s11p(M1|R3) + s12p(M2|R3) + s13p(M3|R3) + s14p(M4|R3)
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For example, T1 is the best adaptation
strategy for a particular climate scenario if
E(a1) + E(s1) < E(a2) + E(s2) and E(a1) +
E(s1) < E(a3) + E(s3).
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If scientists can assign subjective
probabilities to climate scenarios, then the
minimax regret criterion can be used to
identify the best adaptation strategy
across all climate scenarios.
Conclusions
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AMPAC has high informational and
scientific requirements that are not likely
to be satisfied in most protected areas at
this time.
This limitation might be diminishing due to
advances in environmental monitoring and
assessment, climate research, and
adoption of science-based management,
e.g., National Park Service’s Inventorying
and Monitoring Program and Natural
Resource Challenge, USGS’ global change
research program, and the Cooperative
Ecosystem Studies Units.
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Agencies that manage protected areas
may not consider it worthwhile and/or
may lack the financial and technical
resources to implement AMPAC.
This concern can be addressed by
initiating a pilot program that implements
AMPAC for a sample of protected areas
containing a range of natural resource and
environmental conditions, human uses
and values, and availabilities of scientific
knowledge and technical expertise.
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Information obtained from such a pilot
program can be used to assess the
expected benefits and expected costs of
using AMPAC in protected areas, and
identify the conditions under which its use
is economically feasible.
Questions?
Questions?