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Ecosystem Management Project
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Developing Computational Tools that are Useful to Decision Makers
Virginia DaleEnvironmental Sciences Division Oak Ridge National Laboratory
February 2005
Ecosystem Management Project
Ecosystem Management Project
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Who is interested in ecological modeling for resource management? A spectrum that runs between the pure scholar and the decision maker
Academic audience: Environmental scientists Social scientists Economists Landscape architects Planners Risk communicators
Practitioner community: Resource planners Private and public land
owners Sociologists who deal with
goal and values Environmental engineers Applied ecologists Resource managers (e.g.,
foresters and fisheries researchers and managers)
Risk analysts Environmental lawyers. “Savvy” decision makers
Training audience
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A brief history of modeling for resource management
Beginnings of ecological modeling: Lotka Voterra, etc. Attempts to understand human population dynamics Explanations of dynamics of laboratory animals Fisheries management Development of computers Systems ecology Risk assessment Models of land-use change
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Overcoming barriers to the use of models in decision making
Generalized Barriers Communication Humans are risk averse Institutional Logistical Model capabilities
Barriers specific to groups involved in decision-making
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Overcoming barriers (continued)
Solutions to barriers Collaborative decision making Improved technology transfer
Outstanding issues modeling vs scientific questions modeling at different stages of decision process
• Scoping
• Analysis
• Decision making
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New Directions Trends
Use of computers is growing More complex view of world Environmental pressures are increasing Awareness of environmental pressures increasing World is becoming networked Globalization of economy Stakeholders more educated and more actively involved
Philosophies of modeling A parsimonious approach Assumptions are key Generic modeling approaches
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New Directions (continued)
Future directions of ongoing saga of using ecological models for resource management Know problem Keep close communication between modeler and
decision makers Continue exploring diversity of modeling approaches Use models to direct data collection Explore alternative futures in a gaming mode
Species-specifichabitat quality
Focal species
and communities
How RSim Models Stressor Effects on Focal Species & Communities
Stressors
Noise
ToxicitySmog
Air quality
Water quality
Land cover
RSim (Regional Simulation of Environmental Change)
Focal species and communities
Endangered Migratory species birds
Species-specifichabitat quality
Interactions of RSim with Environmental Laws and Regulations
Stressors
ToxicitySmog
RSim can be a scoping tool for the National Environmental Policy Act (NEPA)
Air quality
Water quality
Land cover
Compliance with Endangered Species Act
Compliance with Clean Air Standards
Compliance with Clean Water Standards
Compliance with Local and Federal Noise Standards
Wetland loss
Noise
Compliance with Migratory Bird Act
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Whoever, in the pursuit of science, seeks after immediate practical utility, may generally rest assured that he will seek in vain. All that science can achieve is a perfect knowledge and a perfect understanding of the action of natural and moral forces.
Hermann Ludwig Ferdinand van HelmholtzAcademic discourse, Heidelberg, 1862
Relating Science to Decision Making
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The scientific research uses several methods to develop this knowledge:
Observe nature Organize data Use logic Form a hypothesis Express findings
mathematically
Indicator ResearchProject 1
Indicator Research Project 2
Indicator Research Project 3
ThresholdResearch Project 1
ThresholdResearch project 2
Procedure for Including Indications in Decision Making Process= Research + Characterization + Management Needs
Suite of Indicators
Data repository
IntegratedPlanning Database
Monitoring And
Analysis Plan
Scie
nce
inte
grat
ion
scre
en
Management
needs scre
en
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Scientists can discover new options by: Exploring future options with
computer simulation models Examining past situations Determining causes of observed
phenomenon
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Most scientists are “introverted, intuitive, thinking, judging” (INTJ)
Are visionary and excel at creating systems Can understand complex and difficult subjects Enjoy creative and intellectual challenges Are good at theoretical and technical analysis and
logical problem solving Work well alone and are determined even in the face
of opposition.
(Tieger, P.D. and Barron-Tieger, B. 1992. Boston: Little, Brown and Company)
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However, INTJs can also Be less interested in projects after creative
problem solving is completed Drive others as hard as they drive
themselves Be too independent to adapt to corporate
culture Have difficulty working with or for others
they consider less competent Be inflexible and single-minded about their
ideas.
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Politicians are most often extraverted, intuitive, feeling, judging (ENFJ)
Tend to promote harmony and build cooperation
Respect a variety of opinions Are decisive and organized Are natural leaders.
(Tieger, P.D. and Barron-Tieger, B. 1992. Boston: Little, Brown and Company)
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However, ENFJs also
May have trouble dealing with conflict Tend to sweep problems under the rug May not be attentive to factual accuracy May take criticism too personally
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Resource managers are both blessed and cursed by the attention of biologists.
Are rewarded for novelty Often suggest and
sometimes test new ideas for management.
Are expected to employ the latest scientific develops
Find that application of untried ideas can result in failure
Are often unsure as to what is the most appropriate technology.
New ideas should be tested with empirical data before new methods are adopted.
(Schwartz, M.W, 1999. Annual Review of Ecology and Systematics 30: 83-108)
Scientists: Managers:
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Scientists and decision makers come from different fields of endeavor
Scientist who wish to contribute to policy are a subset of all scientists
Science only has value for decision-making when it can influence a decision.
Neither scientists nor decision makers are a singular entity.
Scientists need to better understand the spectrum of concerns in the policy process.
Decision makers typically accept a different level of uncertainty than scientists.
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Communication between scientists and decision makers is critical
Scientific information is only useful to decision making when policy questions are addressed.
Decision makers often not aware that science can pertain to a policy issue.
Regular discussions between scientists and decision makers can enhance communications and build mutual respect.
Communication is a two-way street. Scientific results are rarely expressed in terms that
have meaning or value to decision-makers.
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General questions about science and decision making
Does “better” science translate to “better” decision making?
How much does and should science influence decision making (and vice versa)?
How can we quantify the value of scientific information to decision makers? How can we use this information to make science more useful?
How can the linkages between science and environmental policy be facilitated?
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Questions dealing with communications How do decision makers receive scientific
information? What are the major barriers influencing effective
communication of science to decision makers? What aspects of the science should be communicated
to decision makers? What are the best ways to communicate science to
decision makers? Who are the decision makers? Do different decision makers want information in different
styles?
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How might resource managers use indicators (or models)?
Their responses: Planning budgets Provide a “heads up” regarding
compliance Heading toward non-compliance?
Signal whether on right path toward achieving longer term goals
Signal whether on right path to achieve shorter term objectives
Suggest need for targeted research The “holy cow” scenario
Photo: Fort Bragg
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Resource managers noted that some criteria are conditional
“Cheaper is better, but more expensive might be ok”If associated with Critical land use needs Red list (Endangered Species) Isolated populations (“lucrative targets”)
Broad applicability is better, but narrow applicability might be ok
Indicator criteria—relates to modeling criteria