Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model |
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| Authors: | Hooten Mevin B Larsen David R Wikle Christopher K |
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| Institution: | (1) Department of Statistics, University of Missouri, 222 Mathematical Sciences Building, Columbia, 65211, USA;(2) Department of Forestry, University of Missouri, Columbia, 65211, USA |
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| Abstract: | Accomodation 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, USA. In addition, two validation techniques were applied to evaluate the qualitative and quantitative characteristics of the predictions.This revised version was published online in May 2005 with corrections to the Cover Date. |
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| Keywords: | Bayesian statistics Hierarchical Bayesian models Landscape vegetation prediction Spatial modeling Missouri USA Ozark Highlands |
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