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Modeling carbon allocation in trees: a search for principles

Franklin, Oskar; Johansson, Jacob; Dewar, Roderick; Dieckmann, Ulf; McMurtrie, Ross; Brannstrom, Ake; Dybzinski, Ray

Description

We review approaches to predicting carbon and nitrogen allocation in forest models in terms of their underlying assumptions and their resulting strengths and limitations. Empirical and allometric methods are easily developed and computationally efficient, but lack the power of evolution-based approaches to explain and predict multifaceted effects of environmental variability and climate change. In evolution-based methods, allocation is usually determined by maximization of a fitness proxy,...[Show more]

dc.contributor.authorFranklin, Oskar
dc.contributor.authorJohansson, Jacob
dc.contributor.authorDewar, Roderick
dc.contributor.authorDieckmann, Ulf
dc.contributor.authorMcMurtrie, Ross
dc.contributor.authorBrannstrom, Ake
dc.contributor.authorDybzinski, Ray
dc.date.accessioned2015-12-10T22:20:29Z
dc.identifier.issn0829-318X
dc.identifier.urihttp://hdl.handle.net/1885/51944
dc.description.abstractWe review approaches to predicting carbon and nitrogen allocation in forest models in terms of their underlying assumptions and their resulting strengths and limitations. Empirical and allometric methods are easily developed and computationally efficient, but lack the power of evolution-based approaches to explain and predict multifaceted effects of environmental variability and climate change. In evolution-based methods, allocation is usually determined by maximization of a fitness proxy, either in a fixed environment, which we call optimal response (OR) models, or including the feedback of an individual's strategy on its environment (game-theoretical optimization, GTO). Optimal response models can predict allocation in single trees and stands when there is significant competition only for one resource. Game-theoretical optimization can be used to account for additional dimensions of competition, e.g., when strong root competition boosts root allocation at the expense of wood production. However, we demonstrate that an OR model predicts similar allocation to a GTO model under the root-competitive conditions reported in free-air carbon dioxide enrichment (FACE) experiments. The most evolutionarily realistic approach is adaptive dynamics (AD) where the allocation strategy arises from eco-evolutionary dynamics of populations instead of a fitness proxy. We also discuss emerging entropy-based approaches that offer an alternative thermodynamic perspective on allocation, in which fitness proxies are replaced by entropy or entropy production. To help develop allocation models further, the value of wide-ranging datasets, such as FLUXNET, could be greatly enhanced by ancillary measurements of driving variables, such as water and soil nitrogen availability.
dc.publisherHeron Publishing
dc.sourceTree Physiology
dc.subjectKeywords: carbon; acclimation; adaptation; allometry; biomass allocation; carbon dioxide enrichment; climate change; data set; ecosystem response; environmental effect; evolutionarily stable strategy; fitness; game theory; growth rate; nitrogen cycle; nutrient dyna acclimation; evolutionarily stable strategy; functional balance; game theory; partitioning; plasticity; soil depth; theory; tree growth
dc.titleModeling carbon allocation in trees: a search for principles
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume32
dc.date.issued2012
local.identifier.absfor060705 - Plant Physiology
local.identifier.ariespublicationu4956746xPUB235
local.type.statusPublished Version
local.contributor.affiliationFranklin, Oskar, International Institute for Applied System Analysis
local.contributor.affiliationJohansson, Jacob, International Institute for Applied Systems Analysis
local.contributor.affiliationDewar, Roderick, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationDieckmann, Ulf, International Institute for Applied Systems Analysis
local.contributor.affiliationMcMurtrie, Ross, University of New South Wales
local.contributor.affiliationBrannstrom, Ake, International Institute for Applied Systems Analysis
local.contributor.affiliationDybzinski, Ray, Princeton University
local.description.embargo2037-12-31
local.bibliographicCitation.startpage648
local.bibliographicCitation.lastpage666
local.identifier.doi10.1093/treephys/tpr138
local.identifier.absseo970106 - Expanding Knowledge in the Biological Sciences
dc.date.updated2016-02-24T11:27:27Z
local.identifier.scopusID2-s2.0-84862992239
local.identifier.thomsonID000305585000003
CollectionsANU Research Publications

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