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Title A Bayesian approach to habitat suitability prediction
URL
Publication Date
Date Available
Date Accessioned
Degree MS
Discipline/Department Marine Resource Management
Degree Level masters
University/Publisher Oregon State University
Abstract 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.
Subjects/Keywords Habitat suitability; Scaphopoda  – Habitat suitability index models  – Oregon  – Pacific Coast
Contributors Goldfinger, Chris (advisor); Gitelman, Alix (committee member)
Language en
Country of Publication us
Record ID handle:1957/28788
Repository oregonstate
Date Retrieved
Date Indexed 2017-03-17
Grantor Oregon State University
Issued Date 2012-03-27 00:00:00
Note [] Graduation date: 2012; [peerreview] no;

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…37 Spatial application of habitat suitability probability ......................................... 38 NB versus TAN models ........................................................................... 38 NB model temperature sensitivity test…

…the environment to support a species (Franklin, 2009). SDMs are also referred to as habitat suitability models when describing the 4 suitability of a habitat to support a species (Franklin, 2009). When used to predict in a…

…geographical space, they have been called “predictive habitat distribution models” (Guisan and Zimmerman, 2000). In this study, we use the term habitat suitability as described by Franklin (2009). We also equate the terms presence/absence…

…29 Incorporating case files ............................................................................ 29 Habitat suitability raster analyses ............................................................ 30 Confidence raster analyses…

…for a particular map cell in the study area ................................... 35 Figure 10. Mean error rates and standard deviations are compared .............. 37 Figure 11. The habitat suitability map spatially depicts posterior probabilities for…

…the NB model ...................................................................................... 39 Figure 12. The habitat suitability map spatially depicts posterior probabilities for the TAN model…

…memory of Mary Jo Lockett A Bayesian Approach to Habitat Suitability Prediction Introduction Wave Energy Development and the Benthic Environment The wave climate, coastal infrastructure, and electrical power demand along the west coast of North America…

…and true/false with habitat suitability probability (HSP). Why model species distribution? Advancements in remote sensing and geographic information systems and science (GIS) have accelerated interest in, and capacity to create…

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