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dc.contributor.authorHooten, Mevin B., 1976-eng
dc.contributor.authorLarsen, David R. (David Rolf)eng
dc.contributor.authorWikle, Christopher K., 1963-eng
dc.contributor.otherUniversity of Missouri-Columbia. College of Arts and Sciences. Department of Statisticseng
dc.descriptionThis is the pre-print version of the article found in Landscape Ecology. The original publication is available at www.springerlink.com.eng
dc.description.abstractAccomodation of important sources of uncertainty in ecological models is essential to realistically predicting ecological processes. The purpose of this project is to develop a robust methodology for modeling natural processes on a landscape while accounting for the variability in a process by utilizing environmental and spatial random effects. A hierarchical Bayesian framework has allowed the simultaneous integration of these effects. This framework naturally assumes variables to be random and the posterior distribution of the model provides probabilistic information about the process. Two species in the genus Desmodium were used as examples to illustrate the utility of the model in Southeast Missouri. In addition, two validation techniques were applied to evaluate the qualitative and quantitative characteristics of the predictions.eng
dc.description.sponsorshipNASA and the University of Montana provided funding through the EOS Training Center Project. Wikle's research was supported by a grant from the U.S. Environmental Protection Agency's Science to Achieve Results (STAR) program, Assistance Agreement No. R827257-01-0.eng
dc.identifier.citationLandscape Ecology, 18, 487-502.eng
dc.publisherLandscape Ecologyeng
dc.relation.ispartofStatistics publications (MU)eng
dc.subjectspatial modelingeng
dc.subjectlandscape vegetation predictioneng
dc.subjectBayesian statisticseng
dc.subject.lcshBayesian statistical decision theoryeng
dc.subject.lcshSpatial ecologyeng
dc.subject.lcshLandscape ecologyeng
dc.titlePredicting the Spatial Distribution of Ground Flora on Large Domains Using a Hierarchical Bayesian Modeleng

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