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.

木材密度是关联树木生物力学与水力功能的基础属性，同时通过衔接体积反演与质量估算，在植被碳储量评估中发挥关键作用。本研究基于包含28822株树木木材密度实测数据的全球数据库，采用4种决策树（decision trees）机器学习（machine learning）模型，构建了空间分辨率为0.01°（约1公里）的全球树木木材密度分布高分辨率地图。本研究集成4种表现最优的模型，并结合8种交叉验证（cross-validation）策略，所得结果一致性极佳，呈现出的木材密度分布格局具有显著的空间异质性。全球分布格局显示，北欧与西北欧、加拿大林区的木材密度较低，而西伯利亚森林、美国西部与中国南部的木材密度略高；与之形成鲜明对比的是，热带地区尤其是湿润热带区域的木材密度较高。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，叶型（常绿 vs 落叶）与叶习性类型（阔叶 vs 针叶）是贡献度最高的单个特征。被子植物阔叶树的木材密度通常高于裸子植物针叶树，常绿类树种尤为明显。按叶型与叶习性类型分类的木材密度分布，与实测木材密度所呈现的特征具有良好的一致性。本套全球木材密度分布量化地图可助力提升森林碳储量预测的准确性，为深入理解生态系统功能与碳循环（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）提供更深刻的认知。

搜集汇总

背景与挑战

背景概述

该数据集提供了全球高分辨率（0.01º）的树木木材密度分布图，基于28,822个树种测量数据和机器学习模型生成。研究发现热带地区尤其是湿润热带区域木材密度较高，而北欧、加拿大森林区域密度较低，气候因素是影响木材密度空间变异的主要因素（贡献49~63%）。