Global patterns of tree wood density.

Area of Science:

• Ecology
• Forest Science
• Biogeography

Background:

• Wood density is vital for tree biomechanics, hydraulics, and carbon stock assessment.
• Accurate global wood density distribution data is essential for ecological modeling.

Purpose of the Study:

• To create a high-resolution global map of tree wood density.
• To identify key drivers of spatial variation in wood density.
• To improve forest carbon stock estimations and climate change impact assessments.

Main Methods:

• Utilized a global database of 28,822 tree-level wood density measurements.
• Employed an ensemble of four decision trees machine learning models.
• Incorporated climatic, vegetation, and edaphic predictors.

Main Results:

• Generated a ~1km resolution global wood density map showing high spatial heterogeneity.
• Identified climate (49%-63%), vegetation (25%-31%), and soil properties (11%-16%) as key drivers.
• Leaf type and habit were dominant predictors, with angiosperm broadleaf trees showing higher density than gymnosperm needleleaf trees.

Conclusions:

• The global wood density map enhances forest carbon stock predictions.
• Findings provide insights into ecosystem functioning and carbon cycling.
• The map aids in assessing forest vulnerability to climate change stresses.