HR: 09:30h
AN: NB41E-05 INVITED     [Abstracts]
TI: Using knowledge elicitation to inform a Bayesian belief network model of a stream ecosystem
AU: * Black, P
EM: pblack@neptuneinc.org
AF: Neptune and Company, Inc., 1505 15th Street, Suite B, Los Alamos, NM 87544 United States
AU: Stockton, T
EM: stockton@neptuneinc.org
AF: Neptune and Company, Inc., 1505 15th Street, Suite B, Los Alamos, NM 87544 United States
AU: Yuan, L
EM: yuan.lester@epa.gov
AF: National Center for Environmental Assessment, U.S. Environmental Protection Agency, 1200 Pennsylvania Avenue, NW, Mail Code 8623N, Washington DC, DC 20460 United States
AU: Allan, D
EM: dallan@umich.edu
AF: School of Natural Resources and Environment, University of Michigan, Dana Building, 430 East University, Ann Arbor, MI 48109 United States
AU: Dodds, W
EM: wkdodds@ksu.edu
AF: Division of Biology, Kansas State University, 232 Ackert Hall, Manhattan, KS 66506 United States
AU: Johnson, L
EM: ljohnson@nrri.umn.edu
AF: Center for Water and the Environment, Natural Resources Research Institute, University of Minnesota, 5013 Miller Trunk Highway, Duluth, MN 55811 United States
AU: Palmer, M
EM: mp3@umail.umd.edu
AF: Department of Entomology, University of Maryland, Plant Sciences BLDG 4112, College Park, MD 20742 United States
AU: Wallace, B
EM: bwallace@uga.edu
AF: Institute of Ecology/Entomology, University of Georgia, 717A Biological Sciences Building, Athens, GA 30602 United States
AU: Stewart, A
EM: astewart@tnainc.com
AF: TN&Associates, Inc., 704 South Illinois Avenue Suite C-104, Oak Ridge, TN 37830 United States
AB: The identification of the causal pathways leading to stream impairment is a central challenge to our understanding of ecological relationships. Bayesian belief networks (BBN's) are a promising tool for modeling presumed causal relationships, providing a modeling structure within which different factors describing the ecosystem can be causally linked and uncertainties expressed for each linkage. Relationships can be specified empirically or by knowledge elicitation from a group of experts. We conducted a pilot study to examine the effectiveness of knowledge elicitation for a simple scenario (impairment of a Midwestern, low-gradient stream by excess fine sediments). Five stream ecologists guided by BBN facilitators then defined relevant chemical, physical, and biological aspects of the ecosystem and how the components interacted, and predicted quantitatively how different attributes of the macroinvertebrate assemblage would change in response to increased levels of fine sediment. The exercise provided insights into how best to adapt knowledge elicitation methods to ecological questions, and informed the assembled stream ecologists on the elicitation process and on the potential benefits of this modeling approach. The explicit quantification of uncertainty in the model not only enhances the utility of the model predictions but can also help guide future research
DE: 1871 Surface water quality
DE: 6309 Decision making under uncertainty
SC: North American Benthological Society [NB]
MN: 2005 Joint Assembly