Global patterns of tree wood density

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木材密度（wood density）是与树木生物力学和水力功能相关的基础属性，同时在通过体积反演与质量估算结合评估植被碳储量的过程中发挥关键作用。本研究基于包含28822株单木木材密度测量数据的全球数据库，借助四种决策树机器学习模型，生成了空间分辨率为0.01°（约1公里）的全球木材密度分布高分辨率地图。由四款表现最优的模型集成，并结合八种交叉验证策略，结果展现出极强的一致性，所呈现的木材密度分布模式具有显著的空间异质性。全球分布格局显示，北欧、西北欧以及加拿大林区的木材密度值较低，西伯利亚森林、美国西部和中国南部的数值略高。与之形成对比的是，热带区域尤其是湿润热带地区，木材密度普遍偏高。气候预测因子可解释49%~63%的空间变异，其次为植被特征（25%~31%）与土壤属性（11%~16%）。值得注意的是，在所有入选的预测协变量中，叶片类型（常绿 vs 落叶）与叶片习性类型（阔叶 vs 针叶）是占比最高的单一特征。相较于裸子植物针叶树，被子植物阔叶树的木材密度通常更高，对于常绿树种而言这一差异尤为明显。按叶片类型与叶片习性类型划分的木材密度分布，与实测得到的木材密度特征具有良好的一致性。本全球量化木材密度分布的地图，可助力提升森林碳储量的精准预测能力，为生态系统功能与碳循环研究提供更深入的见解，例如未来气候变化背景下森林对水力胁迫与热胁迫的脆弱性评估。 研究资助 GlobBiomass DUE项目，资助编号：4000113100/14/I-NB 德国联邦经济事务与气候行动部，资助编号：50EE1904 ESM2025 H2020欧洲研究理事会，资助编号：855187 国际马克斯·普朗克生物地球化学循环研究学院 ESA IFBN项目，资助编号：4000114425/15/NL/FF/gp ESA FRM4BIOMASS项目，资助编号：4000142684/23/I-EF-bgh 波兰国家研究与发展中心REMBIOFOR项目，资助编号：BIOSTRATEG1/267755/4/NCBR/2015