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Demography, dynamics and data : building confidence for simulating changes in the world's forests

Eckes-Shephard, Annemarie H. LU orcid ; Argles, Arthur P.K. ; Brzeziecki, Bogdan ; Cox, Peter M. ; De Kauwe, Martin G. ; Esquivel-Muelbert, Adriane LU ; Fisher, Rosie A. ; Hurtt, George C. ; Knauer, Jürgen and Koven, Charles D. , et al. (2025) In New Phytologist
Abstract

Vegetation demographic models (VDMs) are advanced tools for simulating forest responses to climate and land-use changes, and are essential for projecting carbon cycling and large-scale forest management strategies. Despite their increasing incorporation into Earth System Models, VDMs differ in their demographic assumptions, with no prior quantitative comparison of their performance. We benchmarked nine VDMs against observational data from boreal, temperate and tropical sites, assessing their accuracy in predicting tree growth, carbon turnover, biomass stocks and size distributions. Models were simulated under consistent climate conditions with postdisturbance recovery monitored for at least 420 yr. Postdisturbance carbon recovery... (More)

Vegetation demographic models (VDMs) are advanced tools for simulating forest responses to climate and land-use changes, and are essential for projecting carbon cycling and large-scale forest management strategies. Despite their increasing incorporation into Earth System Models, VDMs differ in their demographic assumptions, with no prior quantitative comparison of their performance. We benchmarked nine VDMs against observational data from boreal, temperate and tropical sites, assessing their accuracy in predicting tree growth, carbon turnover, biomass stocks and size distributions. Models were simulated under consistent climate conditions with postdisturbance recovery monitored for at least 420 yr. Postdisturbance carbon recovery trajectories showed significant variability while remaining within observational ranges. Initial regrowth rates varied substantially (0.03–0.60, 0.18–0.70 and 0.35–1.10 kgCm−2 yr−1 for boreal, temperate and tropical sites, respectively), influenced by each model's initial forest state. Models captured mature forest carbon content but showed compensating effects between overestimated growth and underestimated mortality rates. This first multi-model benchmarking identifies growth and mortality rates as critical calibration targets and highlights the need to refine postdisturbance establishment conditions for model development. We outline specific benchmarking variables needed to improve predictions of forest responses to environmental change.

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organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
demographic vegetation model benchmarking, forest demography, growth–mortality dynamics, land-surface modelling, model intercomparison, postdisturbance recovery, self-thinning, vegetation carbon
in
New Phytologist
publisher
Wiley-Blackwell
external identifiers
  • pmid:41128161
  • scopus:105019594245
ISSN
0028-646X
DOI
10.1111/nph.70643
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025 Crown copyright. Lawrence Berkeley National Lab and The Author(s). New Phytologist © 2025 New Phytologist Foundation. This article is published with the permission of the Controller of HMSO and the King's Printer for Scotland.
id
2cc436ef-b105-44c3-8a69-a1da2f3cd741
date added to LUP
2025-11-01 13:08:18
date last changed
2025-11-04 03:41:26
@article{2cc436ef-b105-44c3-8a69-a1da2f3cd741,
  abstract     = {{<p>Vegetation demographic models (VDMs) are advanced tools for simulating forest responses to climate and land-use changes, and are essential for projecting carbon cycling and large-scale forest management strategies. Despite their increasing incorporation into Earth System Models, VDMs differ in their demographic assumptions, with no prior quantitative comparison of their performance. We benchmarked nine VDMs against observational data from boreal, temperate and tropical sites, assessing their accuracy in predicting tree growth, carbon turnover, biomass stocks and size distributions. Models were simulated under consistent climate conditions with postdisturbance recovery monitored for at least 420 yr. Postdisturbance carbon recovery trajectories showed significant variability while remaining within observational ranges. Initial regrowth rates varied substantially (0.03–0.60, 0.18–0.70 and 0.35–1.10 kgCm<sup>−2</sup> yr<sup>−1</sup> for boreal, temperate and tropical sites, respectively), influenced by each model's initial forest state. Models captured mature forest carbon content but showed compensating effects between overestimated growth and underestimated mortality rates. This first multi-model benchmarking identifies growth and mortality rates as critical calibration targets and highlights the need to refine postdisturbance establishment conditions for model development. We outline specific benchmarking variables needed to improve predictions of forest responses to environmental change.</p>}},
  author       = {{Eckes-Shephard, Annemarie H. and Argles, Arthur P.K. and Brzeziecki, Bogdan and Cox, Peter M. and De Kauwe, Martin G. and Esquivel-Muelbert, Adriane and Fisher, Rosie A. and Hurtt, George C. and Knauer, Jürgen and Koven, Charles D. and Lehtonen, Aleksi and Luyssaert, Sebastiaan and Marqués, Laura and Ma, Lei and Marie, Guillaume and Moore, Jonathan R. and Needham, Jessica F. and Olin, Stefan and Peltoniemi, Mikko and Piltz, Karl and Sato, Hisashi and Sitch, Stephen and Stocker, Benjamin D. and Weng, Ensheng and Zuleta, Daniel and Pugh, Thomas A.M.}},
  issn         = {{0028-646X}},
  keywords     = {{demographic vegetation model benchmarking; forest demography; growth–mortality dynamics; land-surface modelling; model intercomparison; postdisturbance recovery; self-thinning; vegetation carbon}},
  language     = {{eng}},
  month        = {{10}},
  publisher    = {{Wiley-Blackwell}},
  series       = {{New Phytologist}},
  title        = {{Demography, dynamics and data : building confidence for simulating changes in the world's forests}},
  url          = {{http://dx.doi.org/10.1111/nph.70643}},
  doi          = {{10.1111/nph.70643}},
  year         = {{2025}},
}