alexander e. r. woodcock, ph.d. allan falconer, ph.d. aerw: scolopax international consultants,...

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 FocusThe Costs to Developing Countries of

Adapting to Climate Change: New Methods and EstimatesThe 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 2


The Costs to Developing Countries of Adapting to Climate Change: New Methods and Estimates

Study addresses 8 sectorsInfrastructureCoastal zonesIndustrial and municipal water supply and

riverine (riparian) flood protectionAgricultureFisheriesHuman healthForestry and ecosystem servicesExtreme weather events


THE FISHERIES SECTOR: A Global ConcernHow to measure fish stocks?

ModellingModels 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 outcomesPCM and PPH Models AERW & AF © 2010 5

Literature Abounds


Case Studies: The North Atlantic Cod


The Northwest Atlantic Cod 1


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)

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.

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"

The North Atlantic Cod


Data source: FAO Fishery Statistics programme (FIGIS Online),

The Northeast Atlantic Cod


ECOPATH Mass Balance ModelProduction = catches + predation mortality +

biomass accumulation + net migration + other mortalityand

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.


The Western Tropical Pacific Ocean “Warm Pool”


Pacific Yellow fin Tuna


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-Prey-Harvesting (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



A Policy Cycle-based Model (PCM) can manage a Predator-Prey-Harvesting (PPH) model of a notional ecosystem

Management of Harvesting Process

Policy Cycle Model

Prey SpeciesHarvesting

Prey Species

Management of Predator Prey Species

Prey Growth

Predator-Prey-HarvestingDynamics

Predator Death

Predator Species

Prey Predation

Predator Growth

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.



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)

A Problem for Government

Stage I: Agenda Setting

Stage II: Policy Formulation

Stage III: Policy Implementation

Stage IV: Policy Evaluation

Stage V: Policy Change

Stage VI: Policy Termination

The Policy Cycle


Development and Use of Prototype Systems Dynamics-Based Models of the Policy Cycle and Predator-Prey-

Harvesting in STELLA™ provides insight into the impact of the responsiveness of bureaucratic processes on

policy outcomes


PolTermProc

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Policy Cycle Model

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


TmmStrtPreyMobilization

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pophvstmult

Population Dynamics: Logistic (density-dependent) Growth and Prey Harvesting

Implementation of the Predator-Prey-

Harvesting 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


130.19NewPolicy

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Control Panel Device Settings and Data Output Displays for the Policy Cycle Ecosystem Management Model

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.



Experiment 1—With PreyGrowthRatem1 = 0.05 and PrHvstRte = 0.0 the first peak in the notional prey population occurs at Time 283

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Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes


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Experiment 1—With PreyGrowthRatem1 = 0.1 and PrHvstRte = 0.0 the first peak occurs at Time 149



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Experiment 1—With PreyGrowthRatem1 = 0.7 and PrHvstRte = 0.0 the first peak occurs at Time 39



Growth Rate Time Peak 1 Mag. Peak 10.05 283 13420.1 149 1149

0.15 104 11490.2 81 1092

0.25 69 10240.3 61 967

0.35 55 9540.4 50 975

0.45 47 9760.5 45 936

0.55 43 9340.6 41 989

0.65 39 9740.7 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



Experiment 1—The impact of prey growth rate (PreyGrowthRatem1) on the Time to Peak 1 without harvesting (PrHvstRte = 0.0) and policy involvement

Time to Peak 1; Harvest Rate = 0

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Series1



Experiment 1—The impact of prey growth rate (PreyGrowthRatem1) on the Magnitude of Peak 1 without harvesting (PrHvstRte = 0.0) and policy

involvement

Growth Rate; Magnitude Peak 1; No Harvest

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Experiment 2—With PrHvstRte = 0.0, PreyGrowthRatem1 = 0.4 and no policy involvement the first peak occurs at Time 50

Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement

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Experiment 2—With PrHvstRte = 0.3 and PreyGrowthRatem1 = 0.4, the first peak occurs at Time 147; 14,520 units of prey were harvested

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Experiment 2—With PrHvstRte = 0.35, and PreyGrowthRatem1 = 0.4 the first peak occurs at Time 299; 15,327 units of prey were harvested



Harvest Rate Time Peak 1 Mag. Peak 1 Harvest Amt.0 50 975 0

0.05 54 960 26470.1 59 931 4949

0.15 68 896 77660.2 80 910 10063

0.25 102 882 125370.3 147 802 14520

0.35 299 567 153270.4 0 0 600

Growth Rate = 0.4; No Policy Involvement

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



Experiment 2—Increasing the Prey Harvesting Rate (PrHvstRte) with no policy involvement and Prey Growth Rate (PreyGrowthRatem1) = 0.4 delays Peak 1

Impact of Harvesting on Time to Peak 1; No Policy Involvement

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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

Effect of Harvesting on Magnitude of Peak 1

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Effect of Harvesting on Harvest Amount

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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



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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

Experiment 3: Policy Cycle-based Prey Resource Management


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Policy Cycle Dynamics

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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



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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



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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



Policy Mult Time Peak 1 Mag. Peak 1 Harvest Amt. Stop Hvst. Time0 147 802 14520 -

0.001 132 986 12153 400+0.002 124 1129 8802 400+0.003 118 1250 6057 3190.004 113 1363 4562 2420.005 109 1446 3767 2130.006 107 1556 3056 1850.007 104 1650 2731 1560.008 102 1737 2481 1470.08 79 2197 497 58

Growth Rte = 0.4; Harvest Rte = 0.3; With Policy Involvt.

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



Policy Cycle Impact on Time to Peak 1

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Experiment 3—PCM-mediated control shows that increased policy multiplier pophvstmult values of harvesting reduces the time of occurrence of Peak 1



Policy Impact on Peak 1 Magnitude

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Experiment 3—PCM-mediated control shows that increased policy multiplier pophvstmult values of harvesting reduces the Magnitude of Peak 1


Policy Impact on Amount Harvested

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Experiment 3—PCM-mediated control shows that increased policy multiplier pophvstmult values of harvesting reduces the amount of prey species harvested


Policy Impact on Harvesting Stop Time

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Experiment 3—Policy Impact on the time at which PCM-related actions order a halt to prey harvesting


Experiment 3: Study 1—Slowing the Policy CyclePolicy Params.Policy MultTime Peak 1Mag. Peak 1Harvest Amt.Stop Hvst. Time

0.8 0.005 109 1446 3767 2130.1 0.005 125 1464 4751 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

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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


11:55 PM Mon, Mar 8, 2010


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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


Experiment 3: Study 2—Starting Prey Monitoring at Time (TmmStrt) = 0

Start Time Policy Params.Policy MultTime Peak 1Mag. Peak 1Harvest Amt.Stop Hvst. Time45 0.8 0.005 109 1446 3767 2130 0.8 0.005 82 1334 2224 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

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With TmmStrt = 0 and PreyGrowthRatem1 = 0.4, PrHvstRte = 0.3, pophvstmult = 0.005; and the policy variables = 0.8


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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

Experiment 3: Study 2—Starting Prey Monitoring at Time (TmmStrt) = 0


Summary, Discussion, and Questions:

Toward the sustainable management of fish stocks impacted by climate change

and changing supply conditions

alexander e. r. woodcock, ph.d. allan falconer, ph.d. aerw: scolopax international consultants,...

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