Transcript Adaptive management - Auburn University College of Agriculture

Adaptive management
Dr. e. r. irwin
FISH 7380
Managing “Adaptively”
Adaptation defined:
The adjustment of strategy based on improved
understanding or observed change
The term “adaptive” predates natural resources by at
least a generation
First used to describe management of engineering systems
Based on the fact that you don’t always fully understand the
system you’re managing
What ARM is Claimed to Be
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Resource tracking
Goal-directed management
Strategic planning
Sequential decision making
Assessment of management impacts
Applied Science
What I’ve been doing all along
What ARM is Not
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Not just the doing of science, even if managementoriented
Not just the tracking of resources, or activities, or
even impacts
Not strategic planning per se
Not the identification of goals and objectives
Not a post-hoc assessment of management
Most likely not what you’ve been doing all along
What ARM is
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“Managing natural resources in the face of
uncertainty, with a focus on its reduction”
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Dual management focus
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Achieving the goals of resource management
Increasing the level of understanding about resource
dynamics pursuant to these goals
Emphasis on uncertainty, and the value of reducing
uncertainty through learning
Framework for Resource Management
So what makes good decisions so difficult?
uncertain
resource status
environmental
action
variation
action
action
action
resource
status
resource
status
resource
status
resource
status
return
return
return
imprecise
control
return
uncertain
resource processes
ambiguous objectives
time
Conditions for an Adaptive
Approach
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Sequential decision-making
Agreed-upon management objectives
Acceptable range of available actions
Limited understanding of the biological processes
driving resource dynamics
Opportunity to improve management through a
better understanding about these processes
Opportunity to gain that understanding through
smart decision-making
So What’s New?
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Explicit accounting for uncertainty
 Typically through the use of models incorporating
different hypotheses about system dynamics
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Focus on improving management through improved
biological understanding
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Use of data accumulated over time
 Involves acquisition of useful data as a goal of
management
 Involves design (or redesign) of monitoring
programs specifically to reduce uncertainty
Adaptive Decision-making
…
decisiont
decisiont+1
monitoring
assessment
…
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Management objectives guide decision making at each point in time
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System responses to decisions are predicted with resource models
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Monitoring used to track actual system responses
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Actual vs. predicted responses are used to improve understanding
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Biological status and improvement in understanding are used in the
next round of decision-making in the next time period
What Makes it Adaptive?
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You account for where you are and what you know at
each point in time
You learn by doing, and learn as you go
You anticipate how well your decisions will
contribute to both management and understanding
Management is used to support assessment, just as
assessment is used to support management
Basically, the process
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Recognizes competing hypotheses about resource dynamics
Recognizes uncertainties about which is most appropriate
Accounts for uncertainties in decision-making, so as to
reduce them in the future
Alternatives to ARM
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Ad hoc management
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Wait-and-see
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Risk-aversive strategy that seeks to minimize
management impacts as information accumulates
Steady-state management
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Seat-of-the pants management
Based on anecdotal information, absence of stated
objectives
Inadequate biological basis for action
Attempts to sustain resource system in some targeted
steady state
Conventional objective-based management
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Optimal management decisions based on an assumed
resource model
Example: Adaptive Harvest
Management
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Used for setting annual waterfowl harvest
regulations over the last decade
 Regulations are used to influence harvest rates,
which in turn influence population dynamics
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Harvest regulations are set each year based on
 Breeding population status
 Pond conditions on the breeding grounds
 Uncertainty about regulations impacts
What is good for the duck
is good for the darter:
adaptive flow management.
E. R. Irwin & M. C. Freeman
USGS
Adaptive Flow Management
(AFM)
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Iterative approach to management that
acknowledges uncertainty and the need to learn.
Process where all stakeholders decide initial flow
treatment and assessment ensues.
Return to table to evaluate success of flow
management.
Re-prescribe flow treatment if needed; continue
assessment.
Objectives
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Assess the potential to use adaptive flow
management to define suitable criteria for
productive fisheries and community diversity,
while accommodating economic and societal
needs.
Summarize empirical relations among biological
and hydrological parameters from research in
regulated Southeastern rivers.
Stakeholders decide flow
regime based on management
goals.
AFM
Resource
Societal
Economic
Assessment =
management and research
to define ecological relations as
system is managed
Transfer knowledge
Approach
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Compiled data from multiple projects to
determine components of flow regime
essential for biological processes.
Quantify changes in flow regime.
Constructed hypotheses testable in an
Adaptive Flow Management framework.
What is required for AFM?
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Stakeholders that realize “adaptive” allows
for adjustment of management regime as
new information becomes available
Testable hypotheses with measurable
objectives to refine management
Ability to embrace paradigm shifts, radical
thinking
Baseline and reference data (?)
Examples of AFM scenarios
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Striped bass in the Roanoke River, VA.
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Long-term flow and juvenile recruitment data were
evaluated to establish alternative flows from dam.
Robust Redhorse sucker in Oconee River, GA.
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Spring flows provided to allow for spawning
windows.
Flow-advisory team established to monitor success of
management and potential modifications.
Adaptive management roadmap
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Identify stakeholders with respect to flows
below Harris Dam
Meet with potential stakeholders and
explain the adaptive management process
Form a workgroup of individuals
representing all stakeholders
Stakeholders
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Middle Tallapoosa
Property Owners
Lake Harris HOBOs
Alabama Rivers
Alliance
Bass Federation
Alabama Power
Company
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USFWS
NPS
USFS
AL DCNR
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USGS
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Next step---Workgroup
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Identify clear, focused management objectives
that represent all legitimate uses of the river. For
example:
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Maintain biotic integrity within a certain range in
specified segments in the river;
Increase angler catch rates of sport fishes to a certain
level in specified segments in the river;
Maintain the economic value of the project at a
specified percentage of current value;
Setting biological management goals
(versus flow
management goals)
Establish Management Goals
(versus setting fixed-flow criteria)
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Multiple-use riverine systems; all stakeholders
goals must be considered.
Not only a habitat-based approach for
establishing flow criteria for fishes.
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Fish-habitat relations not linear; species specific.
We don’t know “how much”, “how variable”or “how
long.”
Allows for flexibility in relation to natural flows.
Manipulation/Predicted Response
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Implementation of a
continuous flow.
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Increase density and
diversity of fishes and
invertebrates.
Provision of stable flows
and mitigate temperature.
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Provide predictable
boatable flow windows.
Increased recruitment,
growth, and
abundance of fishes.
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Increased recreational
use.
Workgroup
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Identify the array of flow management
options. For example:
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Provide a baseflow during non-generation
periods.
Provide a certain number of contiguous days
during which flow fluctuations are limited,
during specified seasons.
"Ramp" flows up and down at the beginning
and end of peaking releases.
Workgroup
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Identify limits of acceptable management
outcomes for APC and for the regulatory
agencies. What must management achieve
to be acceptable from all perspectives
represented in the workgroup?
Construct a set of meaningful hypotheses
about relations between management
objectives and flow parameters
Workgroup
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Incorporate alternative hypotheses into a
set of models (decision analysis) that that
predict outcomes with respect to
management objectives given different
flow management strategies and observed
levels of variation in inflow (using
historical gage data)
Faunal response: e.g. Fish Abundance, IBI
a
b
d
c
Presen
t
Threshold
Base flow (during non-generation intervals)
Workgroup
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Estimate the relative likelihood that each
model (i.e., using alternative hypotheses)
appropriately describes outcomes as a
result of a change in flow management
strategies
Decision Support Models
• Powerful tools for assessment, learning and
defining options for management.
• Demonstrate how these models will help us
decide what to do at R.L. Harris.
• Discuss the methods by which we will build
the models.
Bridging the GAP
Conservation
assessment
Resource
management
Development of Quantitative Planning
Tools for the Flint River Basin
Resource Management Decision-Making
The Traditional “Black Box” Approach
Resource
Development
Expected effects
Conservation
Populations
Restoration
Habitats
Quantitative Decision Modeling
Management
Actions
External
Physical
Influences
External
Biological
Influences
Stakeholder
benefits
Aquatic
Community
Explicitly incorporates uncertainty
Types of Uncertainty
System uncertainty
due to environmental and demographic variation
Statistical uncertainty
due to the use of sample data to estimate parameters
Process uncertainty
due to incomplete understanding of system dynamics
Factor A
or
Population
response
Factor A
Factor B
Factor B
or
Population
response
Population
response
Quantifying Uncertainty
Empirical Models
Expert Judgement
Combination
Reducing Uncertainty: Bayesian Learning
New Information
Prior Estimate
Posterior Estimate
Learning How a System Works
(Adaptation)
Infot
Current
state
Infot+1
Management
action
Model A
(hypothesis)
Actual
future
state
Predicted
State A
Bayes’
Rule
Model B
(hypothesis)
Predicted
future
State B
Max pulse length (d)
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
1930
Etowah River
>10,050 cfs
1940
1950
1960
5.7 fish/PAE
17 spp.
< 1 ind. = 10 spp.
24 spp. est.
78 known spp.
58% recent
1970
1980
1990
Lower
Tallapoosa River
25
>10,800 cfs
20
15
10
5
0
1920
1930
1940
1950
1960
2000
1970
1980
1990
15.7-20.7 fish/PAE
33, 41 spp.
< 1 ind. = 25, 35 spp.
43, 47 spp. est.
76 known spp.
2000 86% recent
Redbreast Sunfish Spawning
Success
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156 nests monitored daily (23 May-24 June
1999).
Mean daily nest failure was 14% for all life
stages.
Nest failure = 32% after 2-unit generation event.
71% of nests with swim-up fry failed (1-unit).
Only a total of 3 SUF observed after 2-units.
Daily flow pattern shows loss of stable-flow
periods in hydropeaking regime
Thermal regimes are altered by Harris Dam
operations
Spawning Windows
Number of YOY/100 PAEs
100
90
80
70
P. palmaris
60
Percina sp.
50
C. callistia
40
C. venusta
30
20
10
0
0
50
100
150
200
250
300
Longest period without hydropeaking July-August (hours)
Years between stable low-flow
periods of >10 days in hydropeaking
reaches
Data for July-September
Recurrence Interval (years)
7
6
5
Middle T allapoosa
Lower Etowah
Lower T allapoosa
Oostanaula
Lower Coosawattee
4
3
2
1
0
Pre-dam
Post-dam
Flow regime below Harris Dam on the Tallapoosa River
Hourly flows, April - August 1995
Availability of shallow habitats is high in a
hydropeaking reach of the Tallapoosa River…
PHABSIM data; Freeman, Bowen, Bovee and Irwin, 2001, Ecol. Appl. 11:179-190
80
80
60
60
40
40
20
0
1994
1995
20
Habitat availability, April-June,
0
based on hourly flows
1996 1997
1994 1995
Shallow-fast
Shallow-slow
1996
Deep-fast
1997
But hydropeaking greatly reduces temporal habitat
stability
Freeman, Bowen, Bovee and Irwin, 2001, Ecol. Appl. 11:179-190
80
80
60
60
40
40
20
0
1994
20
Maximum period of habitat stability, AprilJune, based on hourly
flows
0
1995 1996 1997
1994 1995 1996
Shallow-fast
Shallow-slow
Deep-fast
1997
Reservoir Inflow
Wet
22.3
Normal 31.8
Dry
46.0
Continuous non-generati...
hours0 100
0
Hours 100 200
0
HoursGT 200 100
Dam operation
Status quo
Inc baseNo
Inc base W
No base W
0
0
0
0
Degree days
high
100
moderate
0
low
0
Redbreast sunfish abund...
high
0
moderate
100
low
0
Slow_Cover amounts
High
0
Moderate
100
Low
0
Reservoir Inflow
Wet
34.0
Normal 42.0
Dry
24.0
Continuous non-generati...
hours0 100
9.70
Hours 100 200 13.9
HoursGT 200 76.4
Dam operation
Status quo
Inc baseNo
Inc base W
No base W
0
0
0
0
Degree days
high
41.3
moderate 29.3
low
29.3
Redbreast sunfish abund...
high
31.7
moderate
43.7
low
24.5
Slow_Cover amounts
High
33.3
Moderate 33.3
Low
33.3
Reservoir Inflow
Wet
22.3
Normal 31.8
Dry
46.0
Continuous non-generati...
hours0 100
0
Hours 100 200
0
HoursGT 200 100
Dam operation
Status quo
Inc baseNo
Inc base W
No base W
0
0
0
0
Degree days
high
100
moderate
0
low
0
Redbreast sunfish abund...
high
0
moderate
100
low
0
Slow_Cover amounts
High
0
Moderate
100
Low
0
Reservoir Inflow
Wet
33.4
Normal 41.6
Dry
25.0
Continuous non-generati...
hours0 100
8.40
Hours 100 200 14.2
HoursGT 200 77.4
Dam operation
Status quo
Inc baseNo
Inc base W
No base W
0
0
0
0
Degree days
high
44.7
moderate 28.2
low
27.1
Redbreast sunfish abund...
high
22.1
moderate
60.9
low
17.1
Slow_Cover amounts
High
27.7
Moderate 35.5
Low
36.8
What is next?
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Refine models using empirical evidence or expert
opinion.
Add to the model.
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All other fundamental objectives.
To do this we will need to input appropriate data.
We need to change something at the dam.
Remember, this is a learn as you go process.
Dam operation
Status quo
Inc base ...
Inc base ...
Inc base ...
Inc Base ...
No base W
Reservoir Inflow
Wet
34.0
Normal 42.0
Dry
24.0
Shallow
High
Moderate
Low
fast amounts
33.1
33.9
32.9
Small fish abundance
high
30.7
moderate 25.4
low
43.9
Continuous non-generati...
hours0 100
18.5
Hours 100 200 23.2
HoursGT 200 58.3
Small fish stability
high
30.7
moderate 25.4
low
43.9
0
0
0
0
0
0
Degree days
high
32.5
moderate 35.0
low
32.5
Redbreast sunfish abund...
high
30.6
moderate
25.4
low
44.0
power production
high
med
low
Slow_Cover amounts
High
33.3
Moderate 33.3
Low
33.3
Boatable days
BD GT 50
33.3
BD GT 100 33.3
BD GT 200 33.3
lake_levels
high
33.3
moderate 33.3
low
33.3
Redbreast sunfish stability
high
30.6
moderate
25.4
low
44.0
Workgroup
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Identify a starting point for changing the
flow regime below Harris Dam; the starting
point should have a high likelihood
(according to the models) of achieving
management objectives. Use models to
identify an appropriate time-frame for
assessing whether or not management
objectives are met
Workgroup and technical advisors
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Design a monitoring program designed to
assess attainment of management goals
under a given flow management strategy
Collect data under new management
regime for appropriate time-period
Workgroup and technical advisors
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After the agreed-upon period for
monitoring, use monitoring results to
assess attainment of management goals.
Based on the monitoring information,
revise likelihood estimates for alternative
models. Reassess the probabilities of
attaining management objectives under
alternative management strategies
Workgroup and technical advisors
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If management objectives are not being
met under the current flow regime, choose
a new strategy more likely to be successful
based on the revised models. Return to
step (k)
Workgroup
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Stakeholders agree to implement the
change in flow regime, to monitor results
for the appropriate period, how and when
attainment of objectives will be assessed,
and to then further modify the flow regime
depending on outcomes relative to
management objectives
Where are we now?
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Utility has provided some data that will be
incorporated into the model. We need more
disclosure.
Utility has been “secretly” testing options at the
dam.
The other stakeholders are restless.
The scientists are frustrated (but still hopeful?)
A facilitator (or group dynamics psychologist) is
needed for the next stakeholder meeting.
Values need to be added.
http://www.freshwaters.org/framework/
The Ecologically Sustainable Water Management (ESWM)
Framework
Framework:
1. Define ecosystem flow
requirements
develop initial numerical estimates of
key aspects of river flow necessary to
sustain native species and natural
ecosystem functions;
2. Determine the influence of human
activities
accounting for human uses of water,
both current and future, through
development of a computerized
hydrologic simulation model that
facilitates examination of humaninduced alterations to river flow
regimes;
3. Identify areas of incompatibility
assessing incompatibilities between
human and ecosystem needs with
particular attention to their spatial and
temporal character;
4. Search for collaborative solution
collaboratively searching for solutions
to resolve incompatibilities;
5. Conduct water management
experiments
design and implement water
management experiments to resolve
critical uncertainties that frustrate
efforts to integrate human and
ecosystem needs; and
6. Design and implement an
adaptive management plan
using the knowledge gained in steps 15, create an adaptive management
program to facilitate ecologically
sustainable water management for the
long term.