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Predicting and Valuing Species Populations in an Integrated Economic/Ecosystem Model

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Objective:
Decision makers are continuously reminded that their policies directed toward economic activity impact ecosystems and that their policies directed toward ecosystems impact economic activity. The reciprocal impacts follow because important economic and ecosystem variables are jointly determined. Unfortunately, decision makers have very incomplete knowledge about the reciprocity; therefore, they cannot reasonably determine how economic welfare will be affected by poorly understood ecosystem impacts. Basic indicators of ecosystem impacts are changes in species populations. The threefold objective is to: 1) predict how species’ populations in an ecosystem are changed by economic activity; 2) value these population changes; and 3) predict how the population changes impact economic activity. The objective will be accomplished by using a new, tightly integrated economic/ecological model.

Approach:
This new approach has three components: 1) construct a computable general equilibrium ecosystem model (GEEM) using individual plant and animal optimizing behavior to predict population dynamics of many interacting species; 2) combine GEEM with a computable general equilibrium (CGE) economic model to obtain one seamless, integrated bioeconomic model; and 3) gather ecological and economic data for application to a real ecosystem/economy. A marine ecosystem in the Eastern Bering Sea and the Alaskan economy provide the application. This ecosystem contains some of the world’s largest fisheries in addition to endangered and keystone species, and the economy significantly depends on biological resources.

Expected Results:
Simultaneous predictions are obtained for economic variables including prices, quantities, investment, and incomes and profits for consumers and firms, and for ecological variables including species populations and biomass transfers between predators and prey. Obtaining changes in total biomass and biodiversity can be done using population changes. Valuing population changes depends on which of three categories a species falls into: 1) for a harvested species (e.g., fish), market values can be applied; 2) for a high-profile species (e.g., sea otter), existing results from contingent valuation studies that ask how much consumers are willing to pay for a change in population can be applied; and 3) for a low-profile species (e.g., plankton at the base of the food web), its value in the ecosystem can be converted to dollars by using the derived demands from GEEM that species in categories 1) and 2) have for plankton.

Supplemental Keywords:
economy/ecology integration, ecosystem, non-market valuation, public policy, integrated assessment, cost benefit, fisheries, general equilibrium. , Economic, Social, & Behavioral Science Research Program, Ecosystem Protection/Environmental Exposure & Risk, HUMAN HEALTH, Health, PHYSICAL ASPECTS, RFA, Scientific Discipline, Ecology and Ecosystems, Economics, Economics & Decision Making, Exposure, Monitoring/Modeling, Physical Processes, Risk Assessments, Social Science, decision-making, PCB, biomarkers, chemical exposure, computational model, contingent valuation, decision analysis, decision making, dietary exposure, dose-response, ecological risk, ecological risk assessment, environmental stress, fish-borne toxicants, human exposue, human exposure, human health risk, integrated ecological assessment model, integrated economic ecosytem model, market valuation models, multi-criteria decision analysis, multi-objective decision making, non-market valuation, pesticides, policy analysis, population based dose response model, population model, public policy, risk assessment model, standards of value, surveys

Metadata

EPA/NSF ID:
R830819
Principal Investigators:
Tschirhart, John
Finnoff, David
Technical Liaison:
Research Organization:
Wyoming, University of
Central Florida, University of
Funding Agency/Program:
EPA/ORD/Valuation
Grant Year:
2002
Project Period:
July 17, 2003 through February 28, 2005
Cost to Funding Agency:
$203,176
Project Status Reports:
2004
Objective:
Decisionmakers continuously are reminded that their policies directed toward economic activity impact ecosystems and that their policies directed toward ecosystems impact economic activity. The reciprocal impacts follow because important economic and ecosystem variables are determined jointly. Unfortunately, decisionmakers have incomplete knowledge about the reciprocity; therefore, they cannot reasonably determine how economic welfare will be affected by poorly understood ecosystem impacts. Basic indicators of ecosystem impacts are changes in species populations. The objectives of this research project are to: (1) predict how species populations in an ecosystem are changed by economic activity; (2) value these population changes; and (3) predict how the population changes impact economic activity. The objectives will be accomplished by using a new, tightly linked economic/ecological model.

Progress Summary:
The researchers' approach has three parts: (1) construct a computable general equilibrium ecosystem model (GEEM) that is founded in individual plant and animal optimizing behavior and can predict population dynamics of many interacting species; (2) combine the GEEM with a computable general equilibrium (GCE) economic model to obtain one seamless, integrated bioeconomic model; and (3) gather ecological and economic data for application to a real ecosystem/economy to predict mutually determined variables, including prices, quantities, incomes and profits in the economy, and species populations and biomass transfers in the ecosystem.

With respect to part one, the researchers have expanded the ecosystem model to include more species. Initially, our marine ecosystem model of the Eastern Bering Sea (EBS) and Aluetian Island (AI) off Alaska was comprised of eight species: various species of phytoplankton (1) are aggregated into a single species called phytoplankton, the plants in the EBS that compete for light. A kelp (2) forest comprises the plants in the AI, where kelp is an aggregation of various species of brown and red algae. Various species of zooplankton (3) are aggregated into a single species that feeds on phytoplankton. Pollock (4) is a groundfish in the EBS that feeds on zooplankton and supports an important fishery. Steller sea lions (5), an endangered pinneped species, feed on the pollock, whereas killer whales (6) feed on the sea lions. Killer whales also feed on sea otters (7) that in turn feed on various species of sea urchin (8) that in turn feed on the kelp. Pollock is the harvested species in the initial model, and the Steller sea lion is the species whose population is subject to an endangered species recovery program under the National Marine Fisheries Service.

The species that have been added include: (9) herring, (10) Pacific cod, (11) Northern fur seal, (12) sperm whale, and (13) blue whale. These additions allow for some interesting and complex food web interactions. We include both a toothed whale and a baleen whale because their feeding habits differ. The fur seals compete with sea lions for pollock and herring, and the cod also consumes pollock. The killer whales are the top predators, consuming both whale species, sea lions, fur seals, sea otters, pollock, and herring.

Data from approximately 1980 until now have been gathered on the five new species. The model has been calibrated to obtain steady-state populations sans humans, and it is running with convincing results. The expanded ecosystem is not yet linked to the economic model, although we are able to set constant harvest to see how the populations are impacted. For example, we have harvested the pollock and herring populations by 10 percent per year. As a result, the killer whales initially cut their consumption of both fish because the lower fish populations drive up the energy price that killer whales pay to capture them. The killer whales also reduced their consumption of fur seals, sea lions, and sperm whales, all of which depend on the fish. At the same time, the killer whale consumption of blue whales and sea otters increased because the latter two species do not prey on the harvested fish. In ecological terms, we are tracking the functional and numerical responses of the killer whales. We can explain these responses in more microlevel detail, however, than is typically done. The switching behavior exhibited by the killer whales depends on the relative energy prices that they must expend to capture their prey, where the energy prices equate the biomass demands and supplies between all predator and prey. Other multispecies models hold the ratios of prey consumptions constant, as they rely on input/output modeling instead of nonlinear, behavioral general equilibrium modeling.

With respect to parts two and three of the approach, a preliminary CGE model of the Alaskan economy is complete with limited data, and the researchers have obtained Impact Analysis for Planning (IMPLAN) data to expand the economy model. The investigators' main emphasis during the last year with respect to the economy has been to develop the theory of how to introduce the commercial fishing industry into the CGE framework. Incorporating a commercial fishery raises issues that require two modifications to the standard fishery models. First, where most of the fishery literature employs effort as the single human factor of production, capital and labor must be included in CGE so that the fishery interacts with the other industries that use capital and labor. Second, the nonfishery industries hire capital and labor in units per time period, in our case 1 year. In the fishery, however, capital and labor are employed considerably less than 1 year and may earn economic rents, or payments above what is needed to keep them in fishing. The researchers argue that rational workers in the fishery may demand higher than market payments in season in anticipation of being unemployed off season. If they do, seasonal wages will not be driven down to market levels in season, leaving positive seasonal rents. In each period, the general equilibrium calculations determine the divergence of the fishery wages from the prevailing market wages that also are determined in the calculations.

The model determines economic and ecological variables together by capturing the feedback between the two systems. If harvesting is decreased, the other nonfish species respond in ways that reflect their position in the food web. They change their preying habits as they switch from less to more available prey. There are increases in most marine mammal populations, and this positively impacts the tourism industry that partially depends on these populations. Labor and capital then will flow to tourism and other industries from the fishery. There are numerous price and income changes that reverberate across the Alaskan economy for domestic, imported and exported goods. We believe that our approach of linking general equilibrium to the economic and ecological models will be a very useful tool for policymakers who may now rely solely on economic models, ignoring or making educated guesses about how their policies change species populations and how the changes feed back to the economy.

Future Activities:
The researchers will conduct the following activities in Year 2 of the project:

1. The GEEM portion of the linked systems is complete, and we now can test it by running various historical scenarios of harvesting and climate shifts in the Alaskan area. For example, one hypothesis is that historical whaling is responsible for the large increase in pollock populations in the EBS, and this can be investigated with the model. Another hypothesis, the junk food hypothesis, states that the endangered Steller sea lion populations are partly a result of the fall in herring and the rise in pollock populations. Both fish are prey for sea lions, but the herring is an energy-rich species and more nutritious; thus, overharvesting herring may have negatively impacted the sea lion owing to the sea lion switching to the inferior pollock prey.

2. The CGE economic model of the Alaskan economy will be expanded with IMPLAN to include more sectors and superior data.

3. The expanded economy model will be linked to the GEEM. The full model will be used to investigate various fishing and endangered species policies. We will quantify economic welfare changes, owing to alternative policies, and we will assign portions of economic welfare changes that are attributable only to changes in ecosystem populations. This will allow us to obtain values for the ecological inputs into the Alaskan economy.

Publications and Presentations: Total Count: 1

TypeCitation
Journal ArticleTschirhart J. 2004. A new adaptive system approach to predator-prey modeling. Ecological Modelling 2004;176(3-4):255-276.
   
Supplemental Keywords:
ecological effects, organism, aquatic, integrated assessment, public policy, social science, Northwest, Pacific coast, EPA Region 10, food processing, , Economic, Social, & Behavioral Science Research Program, Ecosystem Protection/Environmental Exposure & Risk, HUMAN HEALTH, Health, PHYSICAL ASPECTS, RFA, Scientific Discipline, Ecology and Ecosystems, Economics, Economics & Decision Making, Exposure, Monitoring/Modeling, Physical Processes, Risk Assessments, Social Science, decision-making, PCB, biomarkers, chemical exposure, computational model, contingent valuation, decision analysis, decision making, dietary exposure, dose-response, ecological risk, ecological risk assessment, environmental stress, fish-borne toxicants, human exposue, human exposure, human health risk, integrated ecological assessment model, integrated economic ecosytem model, market valuation models, multi-criteria decision analysis, multi-objective decision making, non-market valuation, pesticides, policy analysis, population based dose response model, population model, public policy, risk assessment model, standards of value, surveys

 
Relevant Websites:
http://uwacadweb.uwyo.edu/tschirhart/exit EPA
Project Reports:

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