Global patterns of tree wood density

Yang Hui, Wang Siyuan, Son Rackhun, lee Hoontaek, Benson Vitus, Zhang Weijie, Zhang Yahai, Zhang Yuzhen, Kattge Jens, Boenisch Gerhard, Schepaschenko Dmitry, Karaszewski Zbigniew, Stereńczak Krzysztof, Moreno-Martínez Álvaro, Nabais Cristina, Birnbaum Philippe, Vieilledent Ghislain, Weber Ulrich, Carvalhais Nuno. 2024. Global patterns of tree wood density. Global Change Biology, 30 (3):e17224, 13 p.

Url - jeu de données - Entrepôt autre : https://doi.org/10.5281/zenodo.10692059 / Url - autres données associées : https://gitlab.gwdg.de/siyuan.wang/global-wood-density.git

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.

Mots-clés Agrovoc : densité du bois, propriété du bois, physiologie végétale, apprentissage machine, séquestration du carbone, changement climatique, carbonisation du bois, bois tropical, cycle du carbone, distribution spatiale, facteur climatique, mesure (activité)

Mots-clés libres : Carbon stocks, Climate stresses, Machine Learning, Plant traits, Tree physiology, Vegetation resilience

Classification Agris : K50 - Technologie des produits forestiers
U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : CTS 7 (2019-) - Hors champs stratégiques

Agences de financement européennes : European Research Council

Agences de financement hors UE : German Federal Ministry for Ecoconomic Affairs and Climate Action, International Max Planck Research School for Biogeochemical Cycles, Poland National Centre for Research and Development

Projets sur financement : (DEU) GlobBiomass, (DEU) ESA IFBN, (POL) REMBIOFOR

Auteurs et affiliations

• Yang Hui, Max Planck Institute for Biogeochemistry (DEU) - auteur correspondant
• Wang Siyuan, Max Planck Institute for Biogeochemistry (DEU)
• Son Rackhun, Max Planck Institute for Biogeochemistry (DEU)
• lee Hoontaek, Max Planck Institute for Biogeochemistry (DEU)
• Benson Vitus, Max Planck Institute for Biogeochemistry (DEU)
• Zhang Weijie, Max Planck Institute for Biogeochemistry (DEU)
• Zhang Yahai, Beijing Normal University (CHN)
• Zhang Yuzhen, Max Planck Institute for Biogeochemistry (DEU)
• Kattge Jens, Max Planck Institut für Biogeochemie (DEU)
• Boenisch Gerhard, Max Planck Institute for Biogeochemistry (DEU)
• Schepaschenko Dmitry, International Institute for Applied System Analysis (AUS)
• Karaszewski Zbigniew, Łukasiewicz Research Network Poznań Institute of Technology Center of Sustainable Economy (POL)
• Stereńczak Krzysztof, Forest Research Institute (POL)
• Moreno-Martínez Álvaro, Universidad de Valencia (ESP)
• Nabais Cristina, University of Coimbra (PRT)
• Birnbaum Philippe, CIRAD-BIOS-UMR AMAP (NCL)
• Vieilledent Ghislain, CIRAD-BIOS-UMR AMAP (FRA) ORCID: 0000-0002-1685-4997
• Weber Ulrich, Max Planck Institut für Biogeochemie (DEU)
• Carvalhais Nuno, Max Planck Institut für Biogeochemie (DEU) - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/609112/)

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