Climate and traits drive bark decomposition patterns at global scale.

Area of Science:

• Ecology
• Biogeochemistry
• Climate Science

Background:

• Tree bark is a significant global carbon reservoir, crucial for carbon and nutrient cycling.
• Current understanding of global bark decomposition drivers and future climate impacts is limited.

Purpose of the Study:

• To globally assess bark decomposition rates (k values) and their spatial variability.
• To project future bark decomposition responses under various climate scenarios using machine learning.

Main Methods:

• Compilation of a global bark decomposition dataset.
• Application of three machine learning models to predict decomposition rates.
• Analysis of abiotic (climate) and biotic (bark phosphorus) drivers.

Main Results:

• Global bark decomposition is 2.9 times faster than previously estimated for tree trunks.
• Decomposition rates increase latitudinally, from boreal to tropical regions.
• Angiosperm bark decomposes faster than gymnosperm bark; temperature, precipitation, and bark phosphorus are key drivers.

Conclusions:

• Bark decomposition is a critical, underestimated component of global carbon cycling.
• Future climate change will differentially affect bark decomposition, with potential slowdowns in warm/wet areas and acceleration in cold/dry regions.
• Incorporating bark decomposition into carbon cycle models is essential for accurate climate projections.