A modeling framework for inferring tree growth and allocation from physiological, morphological and allometric traits

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

Predictions of forest succession, diversity and function require an understanding of how species differ in their growth, allocation patterns and susceptibility to mortality. These processes in turn are affected by allometric constraints and the physiological state of the tree, both of which are coupled to the tree's labile carbon status. Ultimately, insight into the hidden labile pools and the processes affecting the allocation of labile carbon to storage, maintenance and growth will improve our ability to predict tree growth, mortality and forest dynamics. We developed the 'Allometrically Constrained Growth and Carbon Allocation' (ACGCA) model that explicitly couples tree growth, mortality, allometries and labile carbon. This coupling results in (1) a semi-mechanistic basis for predicting tree death, (2) an allocation scheme that simultaneously satisfies allometric relationships and physiology-based carbon dynamics and (3) a range of physiological states that are consistent with tree behavior (e.g., healthy, static, shrinking, recovering, recovered and dead). We present the ACGCA model and illustrate aspects of its behavior by conducting simulations under different forest gap dynamics scenarios and with parameter values obtained for two ecologically dissimilar species: loblolly pine (Pinus taeda L.) and red maple (Acer rubrum L.). The model reproduces growth and mortality patterns of these species that are consistent with their shade-tolerance and succession status. The ACGCA framework provides an alternative, and potentially improved, approach for predicting tree growth, mortality and forest dynamics.

Original languageEnglish (US)
Pages (from-to)587-605
Number of pages19
JournalTree Physiology
Volume29
Issue number4
DOIs
StatePublished - Apr 1 2009

Fingerprint

tree growth
Carbon
carbon
Growth
Pinus taeda
Acer rubrum
Mortality
Acer
physiological state
shade tolerance
forest succession
tree mortality
allometry
growth models
physiology
prediction
Maintenance
Forests

All Science Journal Classification (ASJC) codes

  • Physiology
  • Plant Science

Keywords

  • Acer rubrum
  • Carbon allocation
  • Carbon reserves
  • Carbon storage
  • Growth model
  • Labile carbon
  • Loblolly pine
  • Pinus taeda
  • Red maple
  • Retranslocation
  • Shade-tolerance
  • Succession
  • Tree mortality

Cite this

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abstract = "Predictions of forest succession, diversity and function require an understanding of how species differ in their growth, allocation patterns and susceptibility to mortality. These processes in turn are affected by allometric constraints and the physiological state of the tree, both of which are coupled to the tree's labile carbon status. Ultimately, insight into the hidden labile pools and the processes affecting the allocation of labile carbon to storage, maintenance and growth will improve our ability to predict tree growth, mortality and forest dynamics. We developed the 'Allometrically Constrained Growth and Carbon Allocation' (ACGCA) model that explicitly couples tree growth, mortality, allometries and labile carbon. This coupling results in (1) a semi-mechanistic basis for predicting tree death, (2) an allocation scheme that simultaneously satisfies allometric relationships and physiology-based carbon dynamics and (3) a range of physiological states that are consistent with tree behavior (e.g., healthy, static, shrinking, recovering, recovered and dead). We present the ACGCA model and illustrate aspects of its behavior by conducting simulations under different forest gap dynamics scenarios and with parameter values obtained for two ecologically dissimilar species: loblolly pine (Pinus taeda L.) and red maple (Acer rubrum L.). The model reproduces growth and mortality patterns of these species that are consistent with their shade-tolerance and succession status. The ACGCA framework provides an alternative, and potentially improved, approach for predicting tree growth, mortality and forest dynamics.",
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A modeling framework for inferring tree growth and allocation from physiological, morphological and allometric traits. / Ogle, Kiona; Pacala, Stephen Wilson.

In: Tree Physiology, Vol. 29, No. 4, 01.04.2009, p. 587-605.

Research output: Contribution to journalArticle

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