Global patterns of tree wood density

Yang, H., Wang, S., Son, R., Lee, H., Benson, V., Zhang, W., Zhang, Y., Kattge, J., Boenisch, G., Shchepashchenko, D. ORCID: https://orcid.org/0000-0002-7814-4990, Karaszewski, Z., Krzysztof, S., Moreno-Martínez, Á., Nabais, C., Birnbaum, P., Vieilledent, G., Weber, U., & Carvalhais, N. (2024). Global patterns of tree wood density. 10.5281/zenodo.10692058.

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Abstract

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 0.01º (~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 USA, 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.

Research Funding

GlobBiomass DUE Project. Grant Number: 4000113100/14/I-NB

German Federal Ministry for Economic Affairs and Climate Action. Grant Number: 50EE1904

ESM2025

H2020 European Research Council. Grant Number: 855187

International Max Planck Research School for Biogeochemical Cycles

ESA IFBN project. Grant Number: 4000114425/15/NL/FF/gp

ESA FRM4BIOMASS. Grant Number: 4000142684/23/I-EF-bgh

Poland National Centre for Research and Development REMBIOFOR project. Grant Number: BIOSTRATEG1/267755/4/NCBR/2015

Item Type:	Data
Additional Information:	Creative Commons Attribution 4.0 International
Research Programs:	Advancing Systems Analysis (ASA) Advancing Systems Analysis (ASA) > Novel Data Ecosystems for Sustainability (NODES) Biodiversity and Natural Resources (BNR) Biodiversity and Natural Resources (BNR) > Agriculture, Forestry, and Ecosystem Services (AFE)
Depositing User:	Luke Kirwan
Date Deposited:	09 Jan 2025 13:43
Last Modified:	09 Jan 2025 13:43
URI:	https://pure.iiasa.ac.at/20316

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