Quantifying leaf-trait covariation and its controls across climates and biomes

Area of Science:

• Plant functional ecology
• Trait variation analysis
• Global plant trait prediction

Background:

• Quantifying plant trait variation is crucial for understanding ecological processes.
• Global datasets are needed to assess trait variation and its drivers across diverse environments.

Purpose of the Study:

• To analyze leaf trait variation and predictability in China.
• To identify the controls on trait variation, including climate, life-form, and family membership.
• To assess the predictive power of climate data for global trait values.

Main Methods:

• Field measurements on 483 species across 48 sites in China.
• Principal Components Analysis (PCA) for trait variation characterization.
• Redundancy Analysis (RDA) to assess climate effects and variance partitioning for multiple factors.

Main Results:

• Four key dimensions of trait variation identified: leaf area, CO2 ratio, leaf economics spectrum, and photosynthetic capacity.
• Leaf area and CO2 ratio correlated with moisture index.
• Site, climate, life-form, and family explained 70% of trait variance, with families showing significant effects on LDMC and SLA.
• Climate influences trait variation through selection on life forms and families.

Conclusions:

• Climate and taxonomic/life-form membership are key drivers of plant trait variation.
• RDA models using climate data demonstrate strong predictive power for global trait values.
• Continued systematic trait data collection across diverse biomes is essential for advancing plant functional ecology.