Quantifying wood decomposition by insects and fungi using computed tomography scanning and machine learning

Area of Science:

• Ecology
• Biogeochemistry
• Forest Science

Background:

• Wood decomposition is crucial for global carbon and nutrient cycling.
• Accurate quantification of insect and fungal roles in wood decomposition is essential.
• Traditional methods like dry mass loss have limitations, including destructive sampling.

Purpose of the Study:

• To develop and validate a non-destructive method for quantifying wood decomposition by insects and fungi.
• To compare the new method with traditional approaches for assessing wood decomposition.
• To provide spatially explicit data on decomposition processes.

Main Methods:

• Computed tomography (CT) scanning and semi-automatic image analysis were employed.
• The study involved a field experiment with manipulated beetle communities.
• Quantified volumes of insect tunnels and fungal decay were compared to dry mass loss and ergosterol content.

Main Results:

• CT scanning accurately quantified beetle tunnel volume, correlating with dry mass loss and beetle functional groups.
• Fungal decay was identified with high accuracy and strongly correlated with ergosterol content.
• The CT-based method is non-destructive, covers entire logs, and provides spatial data.

Conclusions:

• Computed tomography scanning offers a powerful, non-destructive approach to quantify wood decomposition by insects and fungi.
• This method enhances understanding of carbon and nutrient cycling in forest ecosystems.
• Researchers are encouraged to publish training data to facilitate the development of general decomposition models.