Global patterns of tree wood density

被引:9
|
作者
Yang, Hui [1 ]
Wang, Siyuan [1 ,2 ]
Son, Rackhun [1 ,3 ]
Lee, Hoontaek [1 ,2 ]
Benson, Vitus [1 ,4 ]
Zhang, Weijie [1 ]
Zhang, Yahai [5 ]
Zhang, Yuzhen [1 ]
Kattge, Jens [1 ,6 ]
Boenisch, Gerhard [1 ]
Schepaschenko, Dmitry [7 ]
Karaszewski, Zbigniew [8 ]
Sterenczak, Krzysztof [9 ]
Moreno-Martinez, Alvaro [10 ]
Nabais, Cristina [11 ]
Birnbaum, Philippe [12 ,13 ]
Vieilledent, Ghislain [12 ]
Weber, Ulrich [1 ]
Carvalhais, Nuno [1 ,4 ,14 ]
机构
[1] Max Planck Inst Biogeochem, Jena, Germany
[2] Tech Univ Dresden, Inst Photogrammetry & Remote Sensing, Dresden, Germany
[3] Pukyong Natl Univ, Dept Environm Atmospher Sci, Busan, South Korea
[4] ELLIS Unit Jena, Jena, Germany
[5] Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing, Peoples R China
[6] German Ctr Integrat Biodivers Res iDiv, Leipzig, Germany
[7] Int Inst Appl Syst Anal IIASA, Laxenburg, Austria
[8] Lukasiewicz Res Network, Ctr Sustainable Econ, Res Grp Chem Technol & Environm Protect, Poznan Inst Technol, Poznan, Poland
[9] Forest Res Inst, Dept Geomat, Raszyn, Poland
[10] Univ Valencia, Image Proc Lab IPL, Valencia, Spain
[11] Univ Coimbra, Ctr Funct Ecol, Dept Life Sci, Associate Lab TERRA, Coimbra, Portugal
[12] Univ Montpellier, INRAE, CNRS, CIRAD,IRD,AMAP, Montpellier, France
[13] Inst Agron Neo Caledonien IAC, Noumea, New Caledonia
[14] Univ Nova Lisboa, Fac Ciencias & Tecnol, Dept Ciencias & Engn Ambiente, DCEA,FCT, Caparica, Portugal
基金
欧盟地平线“2020”;
关键词
carbon stocks; climate stresses; machine learning; plant traits; tree physiology; vegetation resilience; PLANT TRAIT DATABASE; CLIMATE; GROWTH; BIOMASS; FORESTS; AGE;
D O I
10.1111/gcb.17224
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
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.
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页数:13
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