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. / Graduation date: 2012
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/28788 |
| Date | 27 March 2012 |
| Creators | Lockett, Daniel Edwin IV |
| Contributors | Goldfinger, Chris, Henkel, Sarah |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Relation | Oregon Explorer |
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