Landscape Ecology

, Volume 18, Issue 5, pp 487–502

Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model

  • Mevin B. Hooten
  • David R. Larsen
  • Christopher K. Wikle
Article

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.

Bayesian statistics Hierarchical Bayesian models Landscape vegetation prediction Spatial modeling Missouri, USA, Ozark Highlands 

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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Mevin B. Hooten
    • 1
  • David R. Larsen
    • 2
  • Christopher K. Wikle
    • 1
  1. 1.Department of StatisticsUniversity of MissouriColumbiaUSA
  2. 2.Department of ForestryUniversity of MissouriColumbiaUSA

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