Plant traits shape global spatiotemporal variations in photosynthetic efficiency

Area of Science:

• Ecology
• Plant Physiology
• Remote Sensing

Background:

• Photosynthetic efficiency (PE) is crucial for ecosystem productivity and the global carbon cycle.
• Significant unexplained variance (nearly 60%) exists in global terrestrial PE.
• Understanding drivers of PE is vital, especially under vegetation stress.

Purpose of the Study:

• To uncover the primary drivers of daily PE variations.
• To improve the accuracy of predicting ecosystem productivity.
• To enhance Earth system models for climate change impacts.

Main Methods:

• Integration of remote sensing data with eco-evolutionary optimality theory.
• Derivation of key plant traits (chlorophyll content, leaf longevity, leaf mass per area).
• Application of explainable machine learning and global eddy covariance observations.

Main Results:

• Incorporating plant traits increased explained daily PE variance to 80% for C3 and 84% for C4 vegetation.
• Key plant traits were consistently important across biomes and scales.
• Water availability and light conditions also critically regulate PE.

Conclusions:

• An integrative approach combining plant traits and climatic factors is essential for understanding PE.
• Remote sensing and eco-evolutionary optimality theory offer powerful tools for PE research.
• Accurate PE prediction is crucial for improving Earth system models under climate change.