Alexander E. R. Woodcock, Ph.D.
Allan Falconer, Ph.D.
AERW: Scolopax International Consultants, Burke, Virginia. and Affiliate Professor, School of Public Policy, George
Mason University; e-mail: [email protected]
AF: Professor of Geography, George Mason University; e-mail: [email protected]
A New International Focus
 The Costs to Developing Countries of Adapting
to Climate Change: New Methods and
Estimates
The Global Report of the Economics of Adaptation
to Climate Change Study - Consultation Draft
 Author(s): The World Bank
 Year: 2009
PCM and PPH Models AERW & AF © 2010
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The Costs to Developing Countries of
Adapting to Climate Change: New
Methods and Estimates
PCM and PPH Models AERW & AF © 2010
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Study addresses 8 sectors
 Infrastructure
 Coastal zones
 Industrial and municipal water supply and riverine





(riparian) flood protection
Agriculture
Fisheries
Human health
Forestry and ecosystem services
Extreme weather events
PCM and PPH Models AERW & AF © 2010
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THE FISHERIES SECTOR:
A Global Concern
 How to measure fish stocks?
 Modelling
 Models are used to estimate populations
 Simple Malthusian models (Resources grow linearly,
Demand grows exponentially)
 Fish stocks grow exponentially but with predator/prey
dynamics
 Ecological models accommodate multiple influences
 Models of cumulative effects predict outcomes
PCM and PPH Models AERW & AF © 2010
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Literature Abounds
PCM and PPH Models AERW & AF © 2010
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Case Studies: The North Atlantic Cod
PCM and PPH Models AERW & AF © 2010
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The Northwest Atlantic Cod 1
Newfoundland's northern cod fishery traces back to the 16th century.
(Some) 300,000 tonnes of cod was landed annually until the 1960s…
(when)…advances in technology enabled factory trawlers to take larger
catches.. (and).. by 1968, landings for the fish peaked at 800,000 tonnes
before a gradual decline set in.
This aggressive technology resulted in a crash in the fishery in the United
States and Canada during the early 1990s.
With the reopening of the limited cod fisheries last year [2006], nearly
2,700 tonnes of cod were hauled in. (paraphrased from Wikipedia 6-19-10)
PCM and PPH Models AERW & AF © 2010
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The Northwest Atlantic Cod 2
Today [2007], it's estimated that offshore cod stocks
are at one per cent of what they were in 1977"
PCM and PPH Models AERW & AF © 2010
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The North Atlantic Cod
Data source: FAO Fishery Statistics programme (FIGIS Online),
PCM and PPH Models AERW & AF © 2010
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The Northeast Atlantic Cod
PCM and PPH Models AERW & AF © 2010
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ECOPATH Mass Balance Model
 Production = catches + predation mortality + biomass
accumulation + net migration + other mortality
and
 Consumption = production + respiration + unassimilated food


Ecopath models require the input of three of the following four parameters for each of the groups, the model estimates the
missing parameter by assuming mass balance:

total biomass, B (tWM/km2)



production to biomass ratio P/B equivalent to total mortality (Allen 1971) (year-1)
consumption to biomass ratio, Q/B (year-1)
ecotrophic efficiency, EE (fraction of 1).
Diet composition as well as fisheries catch (in tWM/km2/y) for each group are also needed.
PCM and PPH Models AERW & AF ©
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The Western Tropical Pacific
Ocean “Warm Pool”
PCM and PPH Models AERW & AF ©
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Pacific Yellow fin Tuna
PCM and PPH Models AERW & AF ©
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Our Agenda
Motivation: The Management of Fish Stocks Requires Informed and
Intelligent Assessment and Command and Control Processes
Building and using Prototype Policy Cycle (PCM) and Predator-PreyHarvesting (PPH) Models as shown by:






Experiment 1: Impact of Prey Population Growth Rate Without Policy
Involvement.
Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement.
Experiment 3: Policy Cycle-based Prey Resource Management
Toward the sustainable management of fish stocks impacted by climate
change and changing supply conditions
PCM and PPH Models AERW & AF © 2010
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Management of
Harvesting Process
Prey Species
Harvesting
Management of
Predator Prey Species
Policy Cycle Model
Prey Species
Prey Growth
Prey Predation
Predator Death
Predator-PreyHarvesting
Dynamics
Predator Growth
Predator Species
A Policy Cycle-based Model (PCM) can manage a Predator-PreyHarvesting (PPH) model of a notional ecosystem
PCM and PPH Models AERW & AF © 2010
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The policy cycle involves defining an agenda and then
formulating, implementing, evaluating, changing or terminating
a policy (after: Lester and Stewart)
 Stage I: Agenda Setting ‘The list of subjects or problems to which





government officials ... are paying ... serious attention.’
Stage II: Policy Formulation ‘The passage of legislation designed to remedy
some past problems or prevent some future public policy problems’ such as
abandoned toxic waste dumps.
Stage III: Policy Implementation ‘What happens after a bill becomes law.’
Stage IV: Policy Evaluation ‘What happens after a policy is implemented’
Does increasing the funding for education increase achievement; how
successful is a toxic clean up policy?
Stage V: Policy Change Modification of policies in response to changing
needs and circumstances.
Stage VI: Policy Termination The ending of outdated or inadequate policies.
PCM and PPH Models AERW & AF © 2010
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Stage VI: Policy Termination
A Problem for Government
Stage V: Policy Change
Stage I: Agenda Setting
Stage IV: Policy Evaluation
Stage II: Policy Formulation
Stage III: Policy Implementation
The Policy Cycle
The Policy Cycle involves identifying a problem for government, setting an
agenda, and formulating, implementing, evaluating, changing and/or
termination of a policy aimed at addressing the problem (Modified after: Lester,
James P. and Joseph Stewart, Jr., 2000. Public Policy An Evolutionary
Approach, Second Edition, Belmont California: Wadsworth)
PCM and PPH Models AERW & AF © 2010
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Development and Use of Prototype Systems DynamicsBased Models of the Policy Cycle and Predator-PreyHarvesting in STELLA™ provides insight into the
impact of the responsiveness of bureaucratic processes on
policy outcomes
PCM and PPH Models AERW & AF © 2010
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Implementation of the Policy Cycle Model in Systems Dynamics
software involves use of system-provided icons and the specification
of the nature of the components used to construct the model
PCM and PPH Models AERW & AF © 2010
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Implementation of the
Predator-PreyHarvesting Model
provides facilities for
assessing the impact of
prey growth, predation,
and harvesting rates and
other parameters on the
dynamics of a notional
aquatic ecosystem
PCM and PPH Models AERW & AF © 2010
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Control Panel Device Settings and Data Output Displays for the
Policy Cycle Ecosystem Management Model
PCM and PPH Models AERW & AF © 2010
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Computer Experiments can Examine Policy
Making, Management, and Harvesting Dynamics
1.
Experiment 1: Impact of Prey Population Growth Rate Without
Policy Involvement. Increased rates of growth increased the rate of
oscillation of the prey population in the absence of prey harvesting.
2.
Experiment 2: Impact of Prey Harvesting Rate Without Policy
Involvement. Increased rates of harvesting reduced the rate of
predator-prey oscillation; sufficiently large harvesting rates prevented
any oscillations from taking place.
3.
Experiment 3: Policy Cycle-based Prey Resource Management. The
impact of harvesting levels on predator-prey dynamics can be off-set
by Policy Cycle-triggered reductions in harvesting rates.
PCM and PPH Models AERW & AF © 2010
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Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes
Experiment 1—With PreyGrowthRatem1 = 0.05 and PrHvstRte = 0.0 the
first peak in the notional prey population occurs at Time 283
PCM and PPH Models AERW & AF © 2010
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Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes
Experiment 1—With PreyGrowthRatem1 = 0.1 and PrHvstRte = 0.0 the first
peak occurs at Time 149
PCM and PPH Models AERW & AF © 2010
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Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes
Experiment 1—With PreyGrowthRatem1 = 0.7 and PrHvstRte = 0.0
the first peak occurs at Time 39
PCM and PPH Models AERW & AF © 2010
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Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes
Growth Rate Time Peak 1 Mag. Peak 1
0.05
283
1342
0.1
149
1149
0.15
104
1149
0.2
81
1092
0.25
69
1024
0.3
61
967
0.35
55
954
0.4
50
975
0.45
47
976
0.5
45
936
0.55
43
934
0.6
41
989
0.65
39
974
0.7
39
955
Harvest = 0; No Policy Involvement
Experiment 1—The impact of Prey Growth Rate (PreyGrowthRatem1)
on the Time to Peak 1, and the Magnitude of Peak 1 without harvesting
of Prey resources (PrHvstRte = 0.0) and no Policy Cycle involvement
PCM and PPH Models AERW & AF © 2010
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Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes
Time to Peak 1; Harvest Rate = 0
300
250
Time to Peak 1
200
150
Series1
100
50
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Growth Rate
Experiment 1—The impact of prey growth rate (PreyGrowthRatem1) on the
Time to Peak 1 without harvesting (PrHvstRte = 0.0) and policy involvement
PCM and PPH Models AERW & AF © 2010
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Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes
Growth Rate; Magnitude Peak 1; No Harvest
1600
1400
Magnitude Peak 1
1200
1000
800
600
400
200
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Growth Rate
Experiment 1—The impact of prey growth rate (PreyGrowthRatem1) on the
Magnitude of Peak 1 without harvesting (PrHvstRte = 0.0) and policy involvement
PCM and PPH Models AERW & AF © 2010
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Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement
Experiment 2—With PrHvstRte = 0.0, PreyGrowthRatem1 = 0.4 and no
policy involvement the first peak occurs at Time 50
PCM and PPH Models AERW & AF © 2010
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Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement
Experiment 2—With PrHvstRte = 0.3 and PreyGrowthRatem1 = 0.4, the
first peak occurs at Time 147; 14,520 units of prey were harvested
PCM and PPH Models AERW & AF © 2010
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Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement
Experiment 2—With PrHvstRte = 0.35, and PreyGrowthRatem1 = 0.4 the first
peak occurs at Time 299; 15,327 units of prey were harvested
PCM and PPH Models AERW & AF © 2010
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Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement
Harvest Rate Time Peak 1 Mag. Peak 1
0
50
975
0.05
54
960
0.1
59
931
0.15
68
896
0.2
80
910
0.25
102
882
0.3
147
802
0.35
299
567
0.4
0
0
Growth Rate = 0.4; No Policy Involvement
Harvest Amt.
0
2647
4949
7766
10063
12537
14520
15327
600
Experiment 2—Impact of Prey Harvest Rate (PrHvstRte) with Prey Growth
rate (PreyGrowthRatem1) = 0.4 and no policy involvement on the Time to
Peak 1, the Magnitude of Peak 1, and the size of the notional prey harvest
PCM and PPH Models AERW & AF © 2010
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Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement
Impact of Harvesting on Time to Peak 1; No Policy Involvement
350
300
Time to Peak 1
250
200
150
100
50
0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Harvest Rate
Experiment 2—Increasing the Prey Harvesting Rate (PrHvstRte) with no policy
involvement and Prey Growth Rate (PreyGrowthRatem1) = 0.4 delays Peak 1
PCM and PPH Models AERW & AF © 2010
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Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement
Effect of Harvesting on Magnitude of Peak 1
1200
Magnitude of Peak 1
1000
800
600
400
200
0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Harvesting Rate
Experiment 2—Increasing the Prey Harvesting Rate (PrHvstRte) with no
policy involvement and Prey Growth Rate (PreyGrowthRatem1) = 0.4 reduces
the Magnitude of Peak 1
PCM and PPH Models AERW & AF © 2010
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Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement
Effect of Harvesting on Harvest Amount
18000
16000
Harvesting Amount
14000
12000
10000
8000
6000
4000
2000
0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Harvesting Rate
Experiment 2—Increasing the Prey Harvesting Rate (PrHvstRte) with no policy
involvement and Prey Growth Rate (PreyGrowthRatem1 = 0.4) increases the
amount of notional Prey Harvest until system collapse occurs
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Policy Cycle-based Prey Resource Management
Experiment 3—With PreyGrowthRatem1 = 0.4, PrHvstRte = 0.3, pophvstmult =
0.001; and the policy variables = 0.8 the first peak occurs at Time = 132
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Policy Cycle-based Prey Resource Management
Experiment 3—With PreyGrowthRatem1 = 0.4, PrHvstRte = 0.3, pophvstmult =
0.001; and Policy Cycle variables BureauProcRte = 0.8 ImpleRte = 0.8,
PolTermRte = 0.8, PolEvalRte = 0.8, and PolChangeRte = 0.8, the Policy Cycle
generates rapid activity in the Formulate, Implement, Evaluation, and
PolicyChange model entities
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Policy Cycle-based Prey Resource Management
Experiment 3—With PreyGrowthRatem1 = 0.4, PrHvstRte = 0.3, pophvstmult =
0.001; and the policy variables = 0.8, the Policy Cycle generates a NewPolicy
output that reduces the rate of prey harvesting rate shown by the decline in the
value of the ModHvstRte trace
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Policy Cycle-based Prey Resource Management
Experiment 3—With PreyGrowthRatem1 = 0.4, PrHvstRte = 0.3, pophvstmult
= 0.005; and the policy variables = 0.8, the Policy Cycle generates a
NewPolicy output that causes a reduction in the rate of prey harvesting to zero
at Time 213 as shown by the ModHvstRte trace
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Policy Cycle-based Prey Resource Management
Policy Mult Time Peak 1 Mag. Peak 1 Harvest Amt.
0
147
802
14520
0.001
132
986
12153
0.002
124
1129
8802
0.003
118
1250
6057
0.004
113
1363
4562
0.005
109
1446
3767
0.006
107
1556
3056
0.007
104
1650
2731
0.008
102
1737
2481
0.08
79
2197
497
Growth Rte = 0.4; Harvest Rte = 0.3; With Policy Involvt.
Stop Hvst. Time
400+
400+
319
242
213
185
156
147
58
Experiment 3—Impact of PCM model-related policy involvement
(represented by the Policy Multiplier (pophvstmult)) parameter on the Time
to Peak 1, the Magnitude of Peak 1, the amount of prey species harvested in
the PPH model, and time of policy-directed cessation of harvesting
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Policy Cycle-based Prey Resource Management
Policy Cycle Impact on Time to Peak 1
160
140
Time to Peak 1
120
100
80
60
40
20
0
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
Policy Multiplier
Experiment 3—PCM-mediated control shows that increased policy multiplier
pophvstmult values of harvesting reduces the time of occurrence of Peak 1
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Policy Cycle-based Prey Resource Management
Policy Impact on Peak 1 Magnitude
2000
1800
1600
Magnitude of Peak 1
1400
1200
1000
800
600
400
200
0
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
Policy Multiplier
Experiment 3—PCM-mediated control shows that increased policy multiplier
pophvstmult values of harvesting reduces the Magnitude of Peak 1
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Policy Cycle-based Prey Resource Management
Policy Impact on Amount Harvested
16000
14000
Amount Harvested
12000
10000
8000
6000
4000
2000
0
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
Policy Multiplier
Experiment 3—PCM-mediated control shows that increased policy multiplier
pophvstmult values of harvesting reduces the amount of prey species harvested
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Policy Cycle-based Prey Resource Management
Policy Impact on Harvesting Stop Time
350
300
Harvest Stop Time
250
200
150
100
50
0
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
Policy Multiplier
Experiment 3—Policy Impact on the time at which PCM-related actions order a
halt to prey harvesting
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Study 1—Slowing the Policy Cycle
Policy Params. Policy Mult
0.8
0.005
0.1
0.005
Time Peak 1
109
125
Mag. Peak 1
1446
1464
Harvest Amt.
3767
4751
Stop Hvst. Time
213
238
Reducing the policy parameters from 0.8 to 0.1 increases the time of
occurrence of Peak 1, the magnitude of the peak, the amount of harvested
prey, and the time at which harvesting is stopped by PCM action
Setting all policy variables at 0.1 (compared with 0.8) with policy
implementation multiplier = 0.005 prolongs the harvesting to Time = 238
compared with 213 when the policy variables are set at 0.8 units
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Study 1—Slowing the Policy Cycle
Experiment 3—Setting the policy
variables at 0.1 units delays the flow of
information through the Formulate,
Implement, Evaluation, and PolicyChange
entities compared with the more rapid
movement when they were set at 0.8 units
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Study 2—Starting Prey Monitoring at Time (TmmStrt) = 0
Start Time
45
0
Policy Params. Policy Mult
0.8
0.005
0.8
0.005
Time Peak 1
109
82
Mag. Peak 1
1446
1334
Harvest Amt.
3767
2224
Stop Hvst. Time
213
161
Monitoring of prey availability at the outset (TmmStrt = 0) compared with
(TmmStrt = 45) speeds up the appearance of Peak 1 and reduces the Magnitude
of Peak 1 and the amount of harvested prey
With TmmStrt = 0 and PreyGrowthRatem1 = 0.4, PrHvstRte = 0.3,
pophvstmult = 0.005; and the policy variables = 0.8
PCM and PPH Models AERW & AF © 2010
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Experiment 3: Study 2—Starting Prey Monitoring at Time (TmmStrt) = 0
Experiment 3—Starting prey level monitoring (TmmStrt) at Time = 0 compared
with Time = 45 speeds up the occurrence of Peak 1 from Time = 109 to Time 82
with pophvstmult = 0.005 and the policy cycle variables = 0.8
PCM and PPH Models AERW & AF © 2010
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Summary, Discussion, and Questions:
Toward the sustainable management of
fish stocks impacted by climate change
and changing supply conditions
PCM and PPH Models AERW & AF © 2010
50