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| DOI | 10.1111/gcb.17224 |
| Global patterns of tree wood density | |
| 发表日期 | 2024 |
| ISSN | 1354-1013 |
| EISSN | 1365-2486 |
| 起始页码 | 30 |
| 结束页码 | 3 |
| 卷号 | 30期号:3 |
| 英文摘要 | 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 degrees (similar to 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. |
| 英文关键词 | carbon stocks; climate stresses; machine learning; plant traits; tree physiology; vegetation resilience |
| 语种 | 英语 |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| WOS类目 | Biodiversity Conservation ; Ecology ; Environmental Sciences |
| WOS记录号 | WOS:001181376800001 |
| 来源期刊 | GLOBAL CHANGE BIOLOGY
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/301259 |
| 作者单位 | Max Planck Society; Technische Universitat Dresden; Pukyong National University; Beijing Normal University; International Institute for Applied Systems Analysis (IIASA); Forest Research Institute; University of Valencia; Universidade de Coimbra; Universite de Montpellier; Institut de Recherche pour le Developpement (IRD); CIRAD; Centre National de la Recherche Scientifique (CNRS); INRAE; Universidade Nova de Lisboa |
| 推荐引用方式 GB/T 7714 | . Global patterns of tree wood density[J],2024,30(3). |
| APA | (2024).Global patterns of tree wood density.GLOBAL CHANGE BIOLOGY,30(3). |
| MLA | "Global patterns of tree wood density".GLOBAL CHANGE BIOLOGY 30.3(2024). |
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