Eco-evolutionary optimality as a means to improve vegetation and land-surface models

Area of Science:

• Ecology
• Plant Physiology
• Climate Science
• Computational Biology

Background:

• Global vegetation and land-surface models are crucial for projecting environmental change impacts.
• Current models exhibit systematic errors and large discrepancies in carbon and water cycle projections.
• Limitations stem from current process formulations in representing plant and ecosystem behavior.

Purpose of the Study:

• To review how eco-evolutionary optimality (EEO) principles can offer novel, parameter-sparse representations of plant and vegetation processes.
• To demonstrate the potential of EEO in improving core plant functions within global models.
• To highlight areas of active research and integration possibilities for EEO in ecological modeling.

Main Methods:

• Review of existing literature on eco-evolutionary optimality (EEO) principles applied to plant functions.
• Analysis of case studies demonstrating EEO's application to leaf-level processes (photosynthesis, respiration, stomatal behavior).
• Discussion of EEO's potential for representing the leaf economics spectrum and community-level dynamics.

Main Results:

• EEO provides parsimonious, testable, and evidence-supported representations of core plant processes.
• EEO approaches to photosynthesis, respiration, and stomatal behavior are suitable for integration into global models.
• EEO offers a unifying framework for understanding plant adaptations across different timescales.

Conclusions:

• Eco-evolutionary optimality (EEO) principles can significantly enhance the accuracy and parsimony of global vegetation models.
• Further research into EEO applications for community ecology and trait variation is warranted.
• Integrating EEO-derived modules into existing modeling frameworks will improve climate change impact assessments.