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Connecting dynamic vegetation models to data - an inverse perspective

Connecting dynamic vegetation models to data - an inverse perspective
Connecting dynamic vegetation models to data - an inverse perspective
Dynamic vegetation models provide process-based explanations of the dynamics and the distribution of plant ecosystems. They offer significant advantages over static, correlative modelling approaches, particularly for ecosystems that are outside their equilibrium due to global change or climate change. A persistent problem, however, is their parameterization. Parameters and processes of dynamic vegetation models (DVMs) are traditionally determined independently of the model, while model outputs are compared to empirical data for validation and informal model comparison only. But field data for such independent estimates of parameters and processes are often difficult to obtain, and the desire to include better descriptions of processes such as biotic interactions, dispersal, phenotypic plasticity and evolution in future vegetation models aggravates limitations related to the current parameterization paradigm. In this paper, we discuss the use of Bayesian methods to bridge this gap. We explain how Bayesian methods allow direct estimates of parameters and processes, encoded in prior distributions, to be combined with inverse estimates, encoded in likelihood functions. The combination of direct and inverse estimation of parameters and processes allows a much wider range of vegetation data to be used simultaneously, including vegetation inventories, species traits, species distributions, remote sensing, eddy flux measurements and palaeorecords. The possible reduction of uncertainty regarding structure, parameters and predictions of DVMs may not only foster scientific progress, but will also increase the relevance of these models for policy advice.
bayesian statistics, calibration, data assimilation, forest models, inverse modelling, model selection, parameterization, plant functional types, predictive uncertainty, process-based models
0305-0270
2240-2252
Hartig, Florian
018354d6-781a-48c1-be3e-cd8a8b9fec94
Dyke, James
e2cc1b09-ae44-4525-88ed-87ee08baad2c
Hickler, Thomas
ee4ef9a1-5121-4f6e-a5d7-b5ee7339a1cb
Higgins, Steven I.
275bbf79-e7d2-4fb7-959d-3103baee81a8
O'hara, Robert B.
14c26381-4be2-4920-ac2c-a19d2d365100
Scheiter, Simon
87f6ef58-eaa1-4e36-adf8-4a48bbb02260
Huth, Andreas
610e0d7d-675b-46eb-96db-94c1d131cd81
Hartig, Florian
018354d6-781a-48c1-be3e-cd8a8b9fec94
Dyke, James
e2cc1b09-ae44-4525-88ed-87ee08baad2c
Hickler, Thomas
ee4ef9a1-5121-4f6e-a5d7-b5ee7339a1cb
Higgins, Steven I.
275bbf79-e7d2-4fb7-959d-3103baee81a8
O'hara, Robert B.
14c26381-4be2-4920-ac2c-a19d2d365100
Scheiter, Simon
87f6ef58-eaa1-4e36-adf8-4a48bbb02260
Huth, Andreas
610e0d7d-675b-46eb-96db-94c1d131cd81

Hartig, Florian, Dyke, James and Hickler, Thomas et al. (2012) Connecting dynamic vegetation models to data - an inverse perspective Journal of Biogeography, 39, (12), pp. 2240-2252. (doi:10.1111/j.1365-2699.2012.02745.x).

Record type: Article

Abstract

Dynamic vegetation models provide process-based explanations of the dynamics and the distribution of plant ecosystems. They offer significant advantages over static, correlative modelling approaches, particularly for ecosystems that are outside their equilibrium due to global change or climate change. A persistent problem, however, is their parameterization. Parameters and processes of dynamic vegetation models (DVMs) are traditionally determined independently of the model, while model outputs are compared to empirical data for validation and informal model comparison only. But field data for such independent estimates of parameters and processes are often difficult to obtain, and the desire to include better descriptions of processes such as biotic interactions, dispersal, phenotypic plasticity and evolution in future vegetation models aggravates limitations related to the current parameterization paradigm. In this paper, we discuss the use of Bayesian methods to bridge this gap. We explain how Bayesian methods allow direct estimates of parameters and processes, encoded in prior distributions, to be combined with inverse estimates, encoded in likelihood functions. The combination of direct and inverse estimation of parameters and processes allows a much wider range of vegetation data to be used simultaneously, including vegetation inventories, species traits, species distributions, remote sensing, eddy flux measurements and palaeorecords. The possible reduction of uncertainty regarding structure, parameters and predictions of DVMs may not only foster scientific progress, but will also increase the relevance of these models for policy advice.

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More information

e-pub ahead of print date: 21 August 2012
Published date: December 2012
Keywords: bayesian statistics, calibration, data assimilation, forest models, inverse modelling, model selection, parameterization, plant functional types, predictive uncertainty, process-based models
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 337710
URI: http://eprints.soton.ac.uk/id/eprint/337710
ISSN: 0305-0270
PURE UUID: 8ba2a838-c83c-4b84-a9cf-641ab110dfd0
ORCID for James Dyke: ORCID iD orcid.org/0000-0002-6779-1682

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Date deposited: 02 May 2012 08:01
Last modified: 18 Jul 2017 06:01

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Contributors

Author: Florian Hartig
Author: James Dyke ORCID iD
Author: Thomas Hickler
Author: Steven I. Higgins
Author: Robert B. O'hara
Author: Simon Scheiter
Author: Andreas Huth

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