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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 Ecology (http://www.esajournals.org/loi/ecol).eng
dc.description.abstractThere is increasing interest in predicting ecological processes. Methods to accomplish such predictions must account for uncertainties in observation, sampling, models, and parameters. Statistical methods for spatio-temporal processes are powerful, yet difficult to implement in complicated, high-dimensional settings. However, recent advances in hierarchical formulations for such processes can be utilized for ecological prediction. These formulations are able to account for the various sources of uncertainty, and can incorporate scientific judgment in a probabilistically consistent manner. In particular, analytical diffusion models can serve as motivation for the hierarchical model for invasive species. We demonstrate by example that such a framework can be utilized to predict spatially and temporally, the house finch relative population abundance over the eastern United States.eng
dc.description.sponsorshipThis research has been 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.citationEcology, 84, 1382-1394.eng
dc.publisherEcological Society of Americaeng
dc.relation.ispartofStatistics publications (MU)eng
dc.subjectstate spaceeng
dc.subject.lcshBayesian statistical decision theoryeng
dc.subject.lcshHouse fincheng
dc.subject.lcshIntroduced organismseng
dc.titleHierarchical Bayesian Models for Predicting The Spread of Ecological Processeseng

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