Leaf nitrogen from the perspective of optimal plant function.

Area of Science:

• Plant Functional Ecology
• Ecosystem Modelling
• Biogeochemistry

Background:

• Leaf traits like LMA, Vcmax, and leaf nitrogen (Narea, Nmass) are crucial for plant ecology and ecosystem models.
• Current understanding and modeling of these traits, particularly leaf nitrogen regulation, lack consensus.
• Optimality theory suggests traits should align with environmental conditions, but empirical validation is needed.

Purpose of the Study:

• To confirm if leaf nitrogen can be accurately estimated from LMA and Vcmax.
• To test if global variations in LMA and Vcmax are predictable by climate variables according to optimality theory.
• To model leaf nitrogen as a consequence of environmentally optimized traits.

Main Methods:

• Utilized a global compilation of field measurements to establish empirical relationships between leaf N, Vcmax25, and LMA.
• Estimated relationships between Vcmax25, LMA, and climate variables, comparing them to theoretical predictions.
• Assessed soil effects on trait predictions by analyzing biases.

Main Results:

• LMA was the primary predictor of Narea (positive) and Nmass (negative); LMA and Vcmax25 explained 60% of Narea and 31% of Nmass variation.
• Climate variables showed significant relationships with Vcmax25 and LMA, largely consistent with optimality theory predictions.
• Predicted traits explained substantial global variation in observed Vcmax25 (21%), LMA (43%), and Narea (31%), with soil type introducing biases.

Conclusions:

• Global patterns of leaf nitrogen are explained by climate-driven optimization of Vcmax and LMA.
• Leaf nitrogen should be modeled as a consequence of Vcmax and LMA optimization to the environment, not a primary driver.
• Future research should focus on whole-plant carbon allocation for modeling nitrogen limitation and improving coupled carbon-nitrogen cycle models.