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A Bayesian network for prioritizing restoration of aquatic connectivity

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dc.contributor.advisor Tullos, Desiree D.
dc.creator Andersen, Eric J. (Eric John)
dc.date.accessioned 2010-07-19T16:14:56Z
dc.date.available 2010-07-19T16:14:56Z
dc.date.copyright 2010-05-26
dc.date.issued 2010-07-19T16:14:56Z
dc.identifier.uri http://hdl.handle.net/1957/16830
dc.description Graduation date: 2011 en
dc.description.abstract Re-establishing connectivity is a primary restoration activity for enhancing the recovery of migratory fishes, but actions are often limited by lack of funds and understanding of the benefits of individual projects. The objective of this study was to develop a Bayesian Network (BN) to assess priorities for restoration of aquatic connectivity as accomplished by replacement of culverts at road stream crossings that may act as passage barriers to winter run Steelhead (Oncorhynchus mykiss) in the North and South Santiam Rivers (state of Oregon). The model predicted the probability of biological benefit obtained by removal or replacement of a culvert. The degree of passage impairment, habitat suitability and probability of habitat use influenced the predicted biological benefit. This model structure was populated with conditional probability table values derived from expert opinion and a Bayesian learning algorithm to produce outcomes based on different model inputs. Both models were then used to assess 141 data scenarios land and fishery managers would likely encounter. Results of the BN indicate that culverts that 1) are barriers to adult and juvenile steelhead, 2) are located in Oregon Department of Environmental Quality (DEQ) designated cold core water habitat, 3) have a high capacity for rearing juvenile fish, and 4) have a high probability of habitat use will provide the highest overall benefit. As anticipated, culverts that are not barriers to upstream migrating fish provided the lowest benefit, regardless of habitat suitability or habitat use. In addition to specific results for the Santiam basin, comparison between the two models and across information scenarios illustrated the sensitivity of such models to various conditions likely to be encountered by decision makers; in general, the two models agreed when all input nodes were engaged by having a state value entered, yet disagreed as fewer input nodes were engaged. The passage impairment of a culvert and the probability of habitat use exerted a strong influence on model output. Finally, this model may serve as a template for providing a coarse evaluation of culverts in other basins or may be a foundation upon which additional nodes may be added. en
dc.language.iso en_US en
dc.relation Explorer Site::Oregon Explorer en
dc.subject Bayesian en
dc.subject Culverts en
dc.subject Prioritization en
dc.subject Restoration en
dc.subject Steelhead en
dc.subject.lcsh Steelhead (Fish) -- Oregon -- Santiam River Watershed en
dc.subject.lcsh Steelhead (Fish) -- Habitat -- Oregon -- Santiam River Watershed en
dc.subject.lcsh Fish habitat improvement -- Oregon -- Santiam River Watershed en
dc.subject.lcsh Culverts -- Oregon -- Santiam River Watershed en
dc.title A Bayesian network for prioritizing restoration of aquatic connectivity en
dc.type Thesis/Dissertation en
dc.degree.name Master of Science (M.S.) in Water Resources Science en
dc.degree.level Master's en
dc.degree.discipline Interdisciplinary Studies en
dc.degree.grantor Oregon State University en
dc.contributor.committeemember Dunham, Jason
dc.contributor.committeemember Luh, Hans


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