Global patterns of tree wood density

作者：Hui Yang, Siyuan Wang, Rackhun Son, Hoontaek Lee, Vitus Benson, Weijie Zhang, Yahai Zhang, Yuzhen Zhang, Jens Kattge, Gerhard Boenisch, Dmitry Schepaschenko, Zbigniew Karaszewski, Krzysztof Stereńczak, Álvaro Moreno‐Martínez, Cristina Nabais, Philippe Birnbaum, Ghislain Vieilledent, Ulrich Weber, Nuno Carvalhais · 发表于：Global Change Biology · 年份：2024 · DOI：10.1111/gcb.17224 · 被引用次数：42 · 研究领域：Forest ecology and management、Tree-ring climate responses、Plant Water Relations and Carbon Dynamics

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...