A Bayesian approach to habitat suitability prediction Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/z029p788f

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  • For the west coast of North America, from northern California to southern Washington, a habitat suitability prediction framework was developed to support wave energy device siting. Concern that wave energy devices may impact the seafloor and benthos has renewed research interest in the distribution of marine benthic invertebrates and factors influencing their distribution. A Bayesian belief network approach was employed for learning species-habitat associations for Rhabdus rectius, a tusk-shaped marine infaunal Mollusk. Environmental variables describing surficial geology and water depth were found to be most influential to the distribution of R. rectius. Water property variables, such as temperature and salinity, were less influential as distribution predictors. Species-habitat associations were used to predict habitat suitability probabilities for R. rectius, which were then mapped over an area of interest along the south-central Oregon coast. Habitat suitability prediction models tested well against data withheld for crossvalidation supporting our conclusion that Bayesian learning extracts useful information available in very small, incomplete data sets and identifies which variables drive habitat suitability for R. rectius. Additionally, Bayesian belief networks are easily updated with new information, quantitative or qualitative, which provides a flexible mechanism for multiple scenario analyses. The prediction framework presented here is a practical tool informing marine spatial planning assessment through visualization of habitat suitability.
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  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2012-04-16T22:08:11Z (GMT) No. of bitstreams: 1 Lockett_Thesis_Final.pdf: 1900172 bytes, checksum: 56977c275851b508d1cf9a05383aadfe (MD5)
  • description.provenance : Made available in DSpace on 2012-04-17T20:50:07Z (GMT). No. of bitstreams: 1 Lockett_Thesis_Final.pdf: 1900172 bytes, checksum: 56977c275851b508d1cf9a05383aadfe (MD5) Previous issue date: 2012-03-27
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2012-04-17T20:50:06Z (GMT) No. of bitstreams: 1 Lockett_Thesis_Final.pdf: 1900172 bytes, checksum: 56977c275851b508d1cf9a05383aadfe (MD5)
  • description.provenance : Submitted by Dan Lockett (lockettd) on 2012-04-12T17:58:29Z No. of bitstreams: 1 Lockett_Thesis_Final.pdf: 1900172 bytes, checksum: 56977c275851b508d1cf9a05383aadfe (MD5)

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