Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Bayesian synthesis for quantifying uncertainty in predictions from process models.

Edwin J. Green1, David W. MacFarlane, Harry T. Valentine

  • 1Department of Ecology, Evolution and Natural Resources, Rutgers University, 14 College Farm Road, New Brunswick, NJ 08901-8551, USA.

Tree Physiology
|March 26, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Initializing a model stand for process-based projection.

Tree physiology·2003
Same journal

A river runs through it: intraspecific variability in hotter-drought-related physiological traits across the Mississippi divide in Pinus taeda L.

Tree physiology·2026
Same journal

From microscopic movements to forest-scale insights: the Nordic Dendrometer Meeting and the emergence of the Global Dendrometer Network.

Tree physiology·2026
Same journal

Drought-induced carbon reallocation in European beech: linking non-structural carbohydrates, xylem anatomy, and water use efficiency.

Tree physiology·2026
Same journal

PyWRKY48 directly activates PyMTP10 to confer cadmium tolerance and accumulation in poplar.

Tree physiology·2026
Same journal

Effects of fertilization on drought responses in saplings of three European trees species.

Tree physiology·2026
Same journal

Good neighbours: current-year needles in Nordmann fir rely on their one-year-old neighbouring needles for adequate nutrient supply.

Tree physiology·2026
See all related articles

Bayesian synthesis is a valuable method for analyzing uncertainty in process-based forest models. It provides posterior distributions and interval estimates for model parameters and outputs, incorporating all available information.

Area of Science:

  • Forestry science
  • Ecological modeling
  • Statistical analysis

Background:

  • Process-based forest models are crucial for understanding ecosystem dynamics.
  • Quantifying uncertainty in these models is essential for reliable predictions.
  • Existing methods may not fully integrate all available data.

Purpose of the Study:

  • To review and evaluate the Bayesian synthesis method for forest modeling.
  • To demonstrate its utility in determining posterior distributions and interval estimates.
  • To highlight its comprehensive approach to uncertainty analysis.

Main Methods:

  • Review of the Bayesian synthesis methodology.
  • Application to process-based forest models.
  • Estimation of posterior distributions for parameters and response variables.

Related Experiment Videos

Main Results:

  • Bayesian synthesis effectively determines posterior distributions and interval estimates.
  • The method provides correlation estimates among model parameters and outputs.
  • It uniquely incorporates all investigator and model information.

Conclusions:

  • Bayesian synthesis is a powerful tool for uncertainty quantification in forest models.
  • It offers a robust framework for integrating diverse data sources.
  • The method enhances the reliability and interpretability of forest model outputs.