Bayesian Data—Model Integration in Plant Physiological and Ecosystem Ecology

  • Kiona Ogle
  • Jarrett J. Barber
Part of the Progress in Botany book series (BOTANY, volume 69)

This paper reviews and illustrates the use of modern methods for integrating diverse data sources with process-based models for learning about plant physiological and ecosystem processes. The particular focus is on how such data sources and models can be coupled within a hierarchical Bayesian modeling framework. This framework, however, has been underutilized in physiological and ecosystem ecology, despite its great potential for data—model integration in these areas. This paper provides a summary of the use of Bayesian methods in ecological research and gives detailed examples highlighting existing and potential uses of Bayesian and hierarchical Bayesian methods in plant physiological and ecosystem ecology. This paper also provides an overview of the statistical theory underlying the development of hierarchical Bayesian methods for analyzing complex ecological problems. The methods are applied to specific examples that include a detailed illustration of a hierarchical Bayesian analysis of leaf-level gas exchange data that are integrated with models of photosynthesis and stomatal conductance, and Bayesian approaches to estimating parameters in complex ecosystem simulation models. The paper concludes with some practical issues and thoughts on the direction of hierarchical Bayesian modeling in plant physiological and ecosystem ecology.


Posterior Distribution Stomatal Conductance Carbon Stock Ecosystem Ecology Bayesian Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kiona Ogle
    • 1
  • Jarrett J. Barber
    • 1
  1. 1.Department of Botany and Department of Statistics, Department of BotanyUniversity of WyomingLaramieUSA

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