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Technical Note: Approximate Bayesian Parameterization of a Complex Tropical Forest Model : Volume 10, Issue 8 (07/08/2013)

By Hartig, F.

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Book Id: WPLBN0003986123
Format Type: PDF Article :
File Size: Pages 32
Reproduction Date: 2015

Title: Technical Note: Approximate Bayesian Parameterization of a Complex Tropical Forest Model : Volume 10, Issue 8 (07/08/2013)  
Author: Hartig, F.
Volume: Vol. 10, Issue 8
Language: English
Subject: Science, Biogeosciences, Discussions
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Huth, A., Wiegand, T., Dislich, C., & Hartig, F. (2013). Technical Note: Approximate Bayesian Parameterization of a Complex Tropical Forest Model : Volume 10, Issue 8 (07/08/2013). Retrieved from

Description: UFZ – Helmholtz Centre for Environmental Research, Department of Ecological Modelling, Permoserstr. 15, 04318 Leipzig, Germany. Inverse parameter estimation of process-based models is a long-standing problem in ecology and evolution. A key problem of inverse parameter estimation is to define a metric that quantifies how well model predictions fit to the data. Such a metric can be expressed by general cost or objective functions, but statistical inversion approaches are based on a particular metric, the probability of observing the data given the model, known as the likelihood.

Deriving likelihoods for dynamic models requires making assumptions about the probability for observations to deviate from mean model predictions. For technical reasons, these assumptions are usually derived without explicit consideration of the processes in the simulation. Only in recent years have new methods become available that allow generating likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics.

We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional MCMC, performs well in retrieving known parameter values from virtual field data generated by the forest model. We analyze the results of the parameter estimation, examine the sensitivity towards the choice and aggregation of model outputs and observed data (summary statistics), and show results from using this method to fit the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss differences of this approach to Approximate Bayesian Computing (ABC), another commonly used method to generate simulation-based likelihood approximations.

Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can successfully be applied to process-based models of high complexity. The methodology is particularly suited to heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models in ecology and evolution.

Technical Note: Approximate Bayesian parameterization of a complex tropical forest model

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