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 0.01° (~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 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.

Descrição

Funding Information: We acknowledge Thomas Ibanez for his invaluable assistance in collecting wood density measurements. H.Y. is supported by the Project Office BIOMASS (grant number 50EE1904) funded by the German Federal Ministry for Economic Affairs and Climate Action. R.S. was supported by ESM2025. D.S. was supported by the ESA IFBN (ESA Contract No. 4000114425/15/NL/FF/gp) and ESA FRM4BIOMASS (ESA Contract No. 4000142684/23/I-EF-bgh) projects. Á.M.-M. was supported by the European Research Council under the ERC-SyG-2019 USMILE project (grant agreement 855187). S.W. and W.Z. acknowledge support from the International Max Planck Research School for Biogeochemical Cycles (IMPRS-gBGC). N.C. acknowledges contribution by the GlobBiomass DUE Project (ESA Contract No. 4000113100/14/I-NB). The data from Poland used for analysis were collected under REMBIOFOR project entitled “Remote sensing-based assessment of woody biomass and carbon storage in forests”, which was financially supported by the National Centre for Research and Development (Poland), under the BIOSTRATEG programme (Agreement No. BIOSTRATEG1/267755/4/NCBR/2015). Open Access funding enabled and organized by Projekt DEAL. Funding Information: We acknowledge Thomas Ibanez for his invaluable assistance in collecting wood density measurements. H.Y. is supported by the Project Office BIOMASS (grant number 50EE1904) funded by the German Federal Ministry for Economic Affairs and Climate Action. R.S. was supported by ESM2025. D.S. was supported by the ESA IFBN (ESA Contract No. 4000114425/15/NL/FF/gp) and ESA FRM4BIOMASS (ESA Contract No. 4000142684/23/I‐EF‐bgh) projects. Á.M.‐M. was supported by the European Research Council under the ERC‐SyG‐2019 USMILE project (grant agreement 855187). S.W. and W.Z. acknowledge support from the International Max Planck Research School for Biogeochemical Cycles (IMPRS‐gBGC). N.C. acknowledges contribution by the GlobBiomass DUE Project (ESA Contract No. 4000113100/14/I‐NB). The data from Poland used for analysis were collected under REMBIOFOR project entitled “Remote sensing‐based assessment of woody biomass and carbon storage in forests”, which was financially supported by the National Centre for Research and Development (Poland), under the BIOSTRATEG programme (Agreement No. BIOSTRATEG1/267755/4/NCBR/2015). Open Access funding enabled and organized by Projekt DEAL. Publisher Copyright: © 2024 The Authors. Global Change Biology published by John Wiley & Sons Ltd.

Palavras-chave

carbon stocks climate stresses machine learning plant traits tree physiology vegetation resilience Global and Planetary Change Environmental Chemistry Ecology General Environmental Science SDG 13 - Climate Action

URI

http://hdl.handle.net/10362/172642

DOI

10.1111/gcb.17224

Coleções

Home collection (FCT)

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