Global patterns of tree wood density.
carbon stocks
climate stresses
machine learning
plant traits
tree physiology
vegetation resilience
Journal
Global change biology
ISSN: 1365-2486
Titre abrégé: Glob Chang Biol
Pays: England
ID NLM: 9888746
Informations de publication
Date de publication:
Mar 2024
Mar 2024
Historique:
revised:
10
02
2024
received:
02
12
2023
accepted:
12
02
2024
medline:
9
3
2024
pubmed:
9
3
2024
entrez:
9
3
2024
Statut:
ppublish
Résumé
Wood density is a fundamental property related to tree biomechanics and hydraulic function while playing a crucial role in assessing vegetation carbon stocks by linking volumetric retrieval and a mass estimate. This study provides a high-resolution map of the global distribution of tree wood density at the 0.01° (~1 km) spatial resolution, derived from four decision trees machine learning models using a global database of 28,822 tree-level wood density measurements. An ensemble of four top-performing models combined with eight cross-validation strategies shows great consistency, providing wood density patterns with pronounced spatial heterogeneity. The global pattern shows lower wood density values in northern and northwestern Europe, Canadian forest regions and slightly higher values in Siberia forests, western United States, and southern China. In contrast, tropical regions, especially wet tropical areas, exhibit high wood density. Climatic predictors explain 49%-63% of spatial variations, followed by vegetation characteristics (25%-31%) and edaphic properties (11%-16%). Notably, leaf type (evergreen vs. deciduous) and leaf habit type (broadleaved vs. needleleaved) are the most dominant individual features among all selected predictive covariates. Wood density tends to be higher for angiosperm broadleaf trees compared to gymnosperm needleleaf trees, particularly for evergreen species. The distributions of wood density categorized by leaf types and leaf habit types have good agreement with the features observed in wood density measurements. This global map quantifying wood density distribution can help improve accurate predictions of forest carbon stocks, providing deeper insights into ecosystem functioning and carbon cycling such as forest vulnerability to hydraulic and thermal stresses in the context of future climate change.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e17224Subventions
Organisme : GlobBiomass DUE Project
ID : 4000113100/14/I-NB
Organisme : German Federal Ministry for Economic Affairs and Climate Action
ID : 50EE1904
Organisme : ESM2025
Organisme : H2020 European Research Council
ID : 855187
Organisme : International Max Planck Research School for Biogeochemical Cycles
Organisme : ESA IFBN project
ID : 4000114425/15/NL/FF/gp
Organisme : ESA FRM4BIOMASS
ID : 4000142684/23/I-EF-bgh
Organisme : Poland National Centre for Research and Development REMBIOFOR project
ID : BIOSTRATEG1/267755/4/NCBR/2015
Informations de copyright
© 2024 The Authors. Global Change Biology published by John Wiley & Sons Ltd.
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