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; AF: Professor of Geography, George Mason University;

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 © 20102

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

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 © 20104

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 © 20105

Literature Abounds PCM and PPH Models AERW & AF © 20106

Case Studies: The North Atlantic Cod PCM and PPH Models AERW & AF © 20107

The Northwest Atlantic Cod 1 PCM and PPH Models AERW & AF © 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 ) Newfoundland's northern cod fishery traces back to the 16 th 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 PCM and PPH Models AERW & AF © Today [2007], it's estimated that offshore cod stocks are at one per cent of what they were in 1977"

The North Atlantic Cod PCM and PPH Models AERW & AF © Data source: FAO Fishery Statistics programme (FIGIS Online),

The Northeast Atlantic Cod PCM and PPH Models AERW & AF ©

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 ©

The Western Tropical Pacific Ocean “Warm Pool” PCM and PPH Models AERW & AF ©

Pacific Yellow fin Tuna PCM and PPH Models AERW & AF ©

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 PCM and PPH Models AERW & AF ©

PCM and PPH Models AERW & AF © 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 Species Harvesting Prey Species Management of Predator Prey Species Prey Growth Predator-Prey- Harvesting Dynamics 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. PCM and PPH Models AERW & AF ©

PCM and PPH Models AERW & AF © 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., 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

PCM and PPH Models AERW & AF © 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

PCM and PPH Models AERW & AF © 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 © 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

PCM and PPH Models AERW & AF © 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. PCM and PPH Models AERW & AF ©

PCM and PPH Models AERW & AF © Experiment 1 — With PreyGrowthRatem1 = 0.05 and PrHvstRte = 0.0 the first peak in the notional prey population occurs at Time 283 Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes

PCM and PPH Models AERW & AF © Experiment 1 — With PreyGrowthRatem1 = 0.1 and PrHvstRte = 0.0 the first peak occurs at Time 149 Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes

PCM and PPH Models AERW & AF © Experiment 1 — With PreyGrowthRatem1 = 0.7 and PrHvstRte = 0.0 the first peak occurs at Time 39 Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes

PCM and PPH Models AERW & AF © 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: Impact of Prey Population Growth Rate Without Policy Changes

PCM and PPH Models AERW & AF © Experiment 1 — The impact of prey growth rate (PreyGrowthRatem1) on the Time to Peak 1 without harvesting (PrHvstRte = 0.0) and policy involvement Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes

PCM and PPH Models AERW & AF © Experiment 1 — The impact of prey growth rate (PreyGrowthRatem1) on the Magnitude of Peak 1 without harvesting (PrHvstRte = 0.0) and policy involvement Experiment 1: Impact of Prey Population Growth Rate Without Policy Changes

PCM and PPH Models AERW & AF © 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

PCM and PPH Models AERW & AF © Experiment 2 — With PrHvstRte = 0.3 and PreyGrowthRatem1 = 0.4, the first peak occurs at Time 147; 14,520 units of prey were harvested Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement

PCM and PPH Models AERW & AF © Experiment 2 — With PrHvstRte = 0.35, and PreyGrowthRatem1 = 0.4 the first peak occurs at Time 299; 15,327 units of prey were harvested Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement

PCM and PPH Models AERW & AF © 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: Impact of Prey Harvesting Rate Without Policy Involvement

PCM and PPH Models AERW & AF © Experiment 2 — Increasing the Prey Harvesting Rate (PrHvstRte) with no policy involvement and Prey Growth Rate (PreyGrowthRatem1) = 0.4 delays Peak 1 Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement

PCM and PPH Models AERW & AF © 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 Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement

PCM and PPH Models AERW & AF © 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 Experiment 2: Impact of Prey Harvesting Rate Without Policy Involvement

PCM and PPH Models AERW & AF © 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

PCM and PPH Models AERW & AF © 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 Experiment 3: Policy Cycle-based Prey Resource Management

PCM and PPH Models AERW & AF © 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 Experiment 3: Policy Cycle-based Prey Resource Management

PCM and PPH Models AERW & AF © 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 Experiment 3: Policy Cycle-based Prey Resource Management

PCM and PPH Models AERW & AF © 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 Experiment 3: Policy Cycle-based Prey Resource Management

PCM and PPH Models AERW & AF © Experiment 3 — PCM-mediated control shows that increased policy multiplier pophvstmult values of harvesting reduces the time of occurrence of Peak 1 Experiment 3: Policy Cycle-based Prey Resource Management

PCM and PPH Models AERW & AF © Experiment 3: Policy Cycle-based Prey Resource Management 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 © Experiment 3: Policy Cycle-based Prey Resource Management 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 © Experiment 3: Policy Cycle-based Prey Resource Management Experiment 3 — Policy Impact on the time at which PCM-related actions order a halt to prey harvesting

PCM and PPH Models AERW & AF © Experiment 3: Study 1 — Slowing the Policy Cycle 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 = 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 © 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 © Experiment 3: Study 2 — Starting Prey Monitoring at Time (TmmStrt) = 0 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 © 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 = and the policy cycle variables = 0.8 Experiment 3: Study 2 — Starting Prey Monitoring at Time (TmmStrt) = 0

PCM and PPH Models AERW & AF © Summary, Discussion, and Questions: Toward the sustainable management of fish stocks impacted by climate change and changing supply conditions