A Wood Biology Agenda to Support Global Vegetation Modelling

Pieter A. Zuidema*, Benjamin Poulter, David C. Frank

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)

Abstract

Realistic forecasting of forest responses to climate change critically depends on key advancements in global vegetation modelling. Compared with traditional ‘big-leaf’ models that simulate forest stands, ‘next-generation’ vegetation models aim to track carbon-, light-, water-, and nutrient-limited growth of individual trees. Wood biology can play an important role in delivering the required knowledge at tissue-to-individual levels, at minute-to-century scales and for model parameterization and benchmarking. We propose a wood biology research agenda that contributes to filling six knowledge gaps: sink versus source limitation, drivers of intra-annual growth, drought impacts, functional wood traits, dynamic biomass allocation, and nutrient cycling. Executing this agenda will expedite model development and increase the ability of models to forecast global change impact on forest dynamics.

Original languageEnglish
Pages (from-to)1006-1015
JournalTrends in Plant Science
Volume23
Issue number11
Early online date9 Sep 2018
DOIs
Publication statusPublished - Nov 2018

Fingerprint

Biological Sciences
vegetation
dry matter partitioning
global change
forest stands
biogeochemical cycles
drought
climate change
carbon
nutrients
leaves
water

Keywords

  • Climate change
  • Earth system models
  • forests
  • individual-based models
  • vegetation modelling

Cite this

Zuidema, Pieter A. ; Poulter, Benjamin ; Frank, David C. / A Wood Biology Agenda to Support Global Vegetation Modelling. In: Trends in Plant Science. 2018 ; Vol. 23, No. 11. pp. 1006-1015.
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A Wood Biology Agenda to Support Global Vegetation Modelling. / Zuidema, Pieter A.; Poulter, Benjamin; Frank, David C.

In: Trends in Plant Science, Vol. 23, No. 11, 11.2018, p. 1006-1015.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - A Wood Biology Agenda to Support Global Vegetation Modelling

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AU - Poulter, Benjamin

AU - Frank, David C.

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N2 - Realistic forecasting of forest responses to climate change critically depends on key advancements in global vegetation modelling. Compared with traditional ‘big-leaf’ models that simulate forest stands, ‘next-generation’ vegetation models aim to track carbon-, light-, water-, and nutrient-limited growth of individual trees. Wood biology can play an important role in delivering the required knowledge at tissue-to-individual levels, at minute-to-century scales and for model parameterization and benchmarking. We propose a wood biology research agenda that contributes to filling six knowledge gaps: sink versus source limitation, drivers of intra-annual growth, drought impacts, functional wood traits, dynamic biomass allocation, and nutrient cycling. Executing this agenda will expedite model development and increase the ability of models to forecast global change impact on forest dynamics.

AB - Realistic forecasting of forest responses to climate change critically depends on key advancements in global vegetation modelling. Compared with traditional ‘big-leaf’ models that simulate forest stands, ‘next-generation’ vegetation models aim to track carbon-, light-, water-, and nutrient-limited growth of individual trees. Wood biology can play an important role in delivering the required knowledge at tissue-to-individual levels, at minute-to-century scales and for model parameterization and benchmarking. We propose a wood biology research agenda that contributes to filling six knowledge gaps: sink versus source limitation, drivers of intra-annual growth, drought impacts, functional wood traits, dynamic biomass allocation, and nutrient cycling. Executing this agenda will expedite model development and increase the ability of models to forecast global change impact on forest dynamics.

KW - Climate change

KW - Earth system models

KW - forests

KW - individual-based models

KW - vegetation modelling

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