Environmental control of carbon allocation matters for modelling forest growth

We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest growth with a process-based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct envir...

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Bibliographic Details
Published inThe New phytologist Vol. 214; no. 1; pp. 180 - 193
Main Authors Guillemot, Joannès, Francois, Christophe, Hmimina, Gabriel, Dufrêne, Eric, Martin‐StPaul, Nicolas K., Soudani, Kamel, Marie, Guillaume, Ourcival, Jean‐Marc, Delpierre, Nicolas
Format Journal Article
LanguageEnglish
Published England New Phytologist Trust 01.04.2017
Wiley Subscription Services, Inc
Wiley
Subjects
biomass growth
Calibration
carbon
carbon (C) allocation
Carbon - metabolism
Castanea
cold stress
Environment
Environmental control
Environmental Sciences
forest growth
Forest productivity
Forests
Global Changes
Leaves
Low temperature
Models, Biological
organ phenology
Photosynthesis
Plant Development
Plant Leaves - anatomy & histology
Plant Leaves - physiology
prediction
process‐based modelling
Seasons
sink–demand
Time Factors
Water stress
Wood
Wood - growth & development
water stress
organ phenology
process-based modelling
biomass growth
carbon (C) allocation
sink-demand
allocation
accroissement de la biomasse
phenology
bilan environnemental
dynamique forestière
stress hydrique
modelling
sink–demand
environmental assessment
phénologie
allocation de carbone
carbon
source puits
process-based
modélisation
carbone
croissance du peuplement forestier
carbon (C)
Online AccessGet full text
ISSN0028-646X
1469-8137
1469-8137
DOI10.1111/nph.14320

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Abstract We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest growth with a process-based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model–data fusion procedure, using satellite-derived leaf production estimates and biometric measurements at c. 104 sites. The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year-to-year variability of aboveground forest growth along regional gradients. A significant effect of the previous year’s water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species. Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink–demand fluctuations, for the simulations of current and future forest productivity with process-based models.
AbstractList Summary We aimed to evaluate the importance of modulations of within‐tree carbon (C) allocation by water and low‐temperature stress for the prediction of annual forest growth with a process‐based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model–data fusion procedure, using satellite‐derived leaf production estimates and biometric measurements at c. 104 sites. The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year‐to‐year variability of aboveground forest growth along regional gradients. A significant effect of the previous year's water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species. Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink–demand fluctuations, for the simulations of current and future forest productivity with process‐based models.
We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest growth with a process-based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model–data fusion procedure, using satellite-derived leaf production estimates and biometric measurements at c. 104 sites. The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year-to-year variability of aboveground forest growth along regional gradients. A significant effect of the previous year's water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species.Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink–demand fluctuations, for the simulations of current and future forest productivity with process-based models.
We aimed to evaluate the importance of modulations of within‐tree carbon (C) allocation by water and low‐temperature stress for the prediction of annual forest growth with a process‐based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model–data fusion procedure, using satellite‐derived leaf production estimates and biometric measurements at c . 10 4 sites. The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year‐to‐year variability of aboveground forest growth along regional gradients. A significant effect of the previous year's water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species. Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink–demand fluctuations, for the simulations of current and future forest productivity with process‐based models.
* We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest growth with a process-based model. * A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model-data fusion procedure, using satellite-derived leaf production estimates and biometric measurements at c. 10 super(4) sites. * The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year-to-year variability of aboveground forest growth along regional gradients. A significant effect of the previous year's water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species. * Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink-demand fluctuations, for the simulations of current and future forest productivity with process-based models.
We aimed to evaluate the importance of modulations of within‐tree carbon (C) allocation by water and low‐temperature stress for the prediction of annual forest growth with a process‐based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model–data fusion procedure, using satellite‐derived leaf production estimates and biometric measurements at c. 10⁴ sites. The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year‐to‐year variability of aboveground forest growth along regional gradients. A significant effect of the previous year's water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species. Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink–demand fluctuations, for the simulations of current and future forest productivity with process‐based models.
Summary We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest growth with a process-based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model-data fusion procedure, using satellite-derived leaf production estimates and biometric measurements at c. 104 sites. The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year-to-year variability of aboveground forest growth along regional gradients. A significant effect of the previous year's water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species. Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink-demand fluctuations, for the simulations of current and future forest productivity with process-based models.
We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest growth with a process-based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model–data fusion procedure, using satellite-derived leaf production estimates and biometric measurements at c. 104 sites. The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year-to-year variability of aboveground forest growth along regional gradients. A significant effect of the previous year’s water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species. Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink–demand fluctuations, for the simulations of current and future forest productivity with process-based models.
We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest growth with a process-based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model-data fusion procedure, using satellite-derived leaf production estimates and biometric measurements at c. 10 sites. The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year-to-year variability of aboveground forest growth along regional gradients. A significant effect of the previous year's water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species. Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink-demand fluctuations, for the simulations of current and future forest productivity with process-based models.
We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest growth with a process-based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model-data fusion procedure, using satellite-derived leaf production estimates and biometric measurements at c. 104 sites. The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year-to-year variability of aboveground forest growth along regional gradients. A significant effect of the previous year's water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species. Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink-demand fluctuations, for the simulations of current and future forest productivity with process-based models.We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest growth with a process-based model. A new C allocation scheme was implemented in the CASTANEA model that accounts for lagged and direct environmental controls of C allocation. Different approaches (static vs dynamic) to modelling C allocation were then compared in a model-data fusion procedure, using satellite-derived leaf production estimates and biometric measurements at c. 104 sites. The modelling of the environmental control of C allocation significantly improved the ability of CASTANEA to predict the spatial and year-to-year variability of aboveground forest growth along regional gradients. A significant effect of the previous year's water stress on the C allocation to leaves and wood was reported. Our results also are consistent with a prominent role of the environmental modulation of sink demand in the wood growth of the studied species. Data available at large scales can inform forest models about the processes driving annual and seasonal C allocation. Our results call for a greater consideration of C allocation drivers, especially sink-demand fluctuations, for the simulations of current and future forest productivity with process-based models.
Author Kamel Soudani
Joannès Guillemot
Eric Dufrêne
Jean-Marc Ourcival
Gabriel Hmimina
Nicolas K. Martin-St Paul
Guillaume Marie
Christophe Francois
Nicolas Delpierre
Author_xml – sequence: 1
  givenname: Joannès
  surname: Guillemot
  fullname: Guillemot, Joannès
  email: joannes.guillemot@cirad.fr
  organization: UMR ECO&SOLS
– sequence: 2
  givenname: Christophe
  surname: Francois
  fullname: Francois, Christophe
  organization: Université Paris‐Saclay
– sequence: 3
  givenname: Gabriel
  surname: Hmimina
  fullname: Hmimina, Gabriel
  organization: Université Paris‐Saclay
– sequence: 4
  givenname: Eric
  surname: Dufrêne
  fullname: Dufrêne, Eric
  organization: Université Paris‐Saclay
– sequence: 5
  givenname: Nicolas K.
  surname: Martin‐StPaul
  fullname: Martin‐StPaul, Nicolas K.
  organization: INRA
– sequence: 6
  givenname: Kamel
  surname: Soudani
  fullname: Soudani, Kamel
  organization: Université Paris‐Saclay
– sequence: 7
  givenname: Guillaume
  surname: Marie
  fullname: Marie, Guillaume
  organization: Université Paris‐Saclay
– sequence: 8
  givenname: Jean‐Marc
  surname: Ourcival
  fullname: Ourcival, Jean‐Marc
  organization: UMR5175
– sequence: 9
  givenname: Nicolas
  surname: Delpierre
  fullname: Delpierre, Nicolas
  organization: Université Paris‐Saclay
BackLink https://www.ncbi.nlm.nih.gov/pubmed/27883190$$D View this record in MEDLINE/PubMed
https://hal.science/hal-01594822$$DView record in HAL
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ContentType Journal Article
Copyright 2016 New Phytologist Trust
2016 The Authors. New Phytologist © 2016 New Phytologist Trust
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Copyright © 2017 New Phytologist Trust
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– notice: 2016 The Authors. New Phytologist © 2016 New Phytologist Trust
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Issue 1
Keywords water stress
organ phenology
process-based modelling
biomass growth
carbon (C) allocation
sink-demand
allocation
accroissement de la biomasse
phenology
bilan environnemental
dynamique forestière
stress hydrique
modelling
sink–demand
environmental assessment
phénologie
allocation de carbone
carbon
source puits
process-based
modélisation
carbone
croissance du peuplement forestier
carbon (C)
Language English
License http://onlinelibrary.wiley.com/termsAndConditions#vor
2016 The Authors. New Phytologist © 2016 New Phytologist Trust.
Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0
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Snippet We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual forest...
Summary We aimed to evaluate the importance of modulations of within‐tree carbon (C) allocation by water and low‐temperature stress for the prediction of...
We aimed to evaluate the importance of modulations of within‐tree carbon (C) allocation by water and low‐temperature stress for the prediction of annual forest...
Summary We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of...
* We aimed to evaluate the importance of modulations of within-tree carbon (C) allocation by water and low-temperature stress for the prediction of annual...
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StartPage 180
SubjectTerms biomass growth
Calibration
carbon
carbon (C) allocation
Carbon - metabolism
Castanea
cold stress
Environment
Environmental control
Environmental Sciences
forest growth
Forest productivity
Forests
Global Changes
Leaves
Low temperature
Models, Biological
organ phenology
Photosynthesis
Plant Development
Plant Leaves - anatomy & histology
Plant Leaves - physiology
prediction
process‐based modelling
Seasons
sink–demand
Time Factors
Water stress
Wood
Wood - growth & development
Title Environmental control of carbon allocation matters for modelling forest growth
URI https://www.jstor.org/stable/90001562
https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fnph.14320
https://www.ncbi.nlm.nih.gov/pubmed/27883190
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https://hal.science/hal-01594822
Volume 214
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