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Habitat estimation through synthesis of species presence/absence information and environmental covariate data

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dc.contributor.authorDornan, Grant J., author
dc.contributor.authorGivens, Geof H., advisor
dc.contributor.authorHoeting, Jennifer A., committee member
dc.contributor.authorChapman, Phillip L., committee member
dc.contributor.authorMyrick, Christopher A., committee member
dc.date.accessioned2007-01-03T08:21:15Z
dc.date.available2007-01-03T08:21:15Z
dc.date.issued2011
dc.description.abstractThis paper investigates the statistical model developed by Foster, et al. (2011) to estimate marine habitat maps based on environmental covariate data and species presence/absence information while treating habitat definition probabilistically. The model assumes that two sites belonging to the same habitat have approximately the same species presence probabilities, and thus both environmental data and species presence observations can help to distinguish habitats at locations across a study region. I develop a computational method to estimate the model parameters by maximum likelihood using a blocked non-linear Gauss-Seidel algorithm. The main part of my work is developing and conducting simulation studies to evaluate estimation performance and to study related questions including the impacts of sample size, model bias and model misspecification. Seven testing scenarios are developed including between 3 and 9 habitats, 15 and 40 species, and 150 and 400 sampling sites. Estimation performance is primarily evaluated through fitted habitat maps and is shown to be excellent for the seven example scenarios examined. Rates of successful habitat classification ranged from 0.92 to 0.98. I show that there is a roughly balanced tradeoff between increasing the number of sites and increasing the number of species for improving estimation performance. Standard model selection techniques are shown to work for selection of covariates, but selection of the number of habitats benefits from supplementing quantitative techniques with qualitative expert judgement. Although estimation of habitat boundaries is extremely good, the rate of probabilistic transition between habitats is shown to be difficult to estimate accurately. Future research should address this issue. An appendix to this thesis includes a comprehensive and annotated collection of R code developed during this project.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierDornan_colostate_0053N_10879.pdf
dc.identifierETDF2011400291STAT
dc.identifier.urihttp://hdl.handle.net/10217/70684
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectabsence
dc.subjectestimation
dc.subjectGauss-Seidel algorithm
dc.subjecthabitat
dc.subjectpresence
dc.subjectspecies
dc.titleHabitat estimation through synthesis of species presence/absence information and environmental covariate data
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineStatistics
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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