Temporal constraints on leaf-level trait plasticity for next-generation land surface models.

Area of Science:

• Ecology
• Climate Science
• Plant Physiology

Background:

• Dynamic global vegetation models (DGVMs) are crucial for climate research but face challenges with uncertainty and complexity.
• Eco-evolutionary optimality (EEO) theory offers a framework to improve DGVMs by optimizing leaf carbon gain relative to resource costs.
• Current EEO models lack systematic incorporation of the timescales at which plant traits adjust to environmental changes.

Purpose of the Study:

• To identify temporal constraints on key leaf photosynthetic and functional traits.
• To develop a conceptual framework for integrating temporal leaf trait dynamics into EEO-based models.
• To enhance the accuracy and reduce uncertainty in DGVMs.

Main Methods:

• Literature review of temporal responses in leaf traits (stomatal, hydraulic, biochemical, morphological, lifespan).
• Categorization of response times (physiological, phenotypical, evolutionary).
• Development of a conceptual framework separating dynamics of leaf CO2 concentration ratio (ci:ca) and stomatal conductance (gsmax) responses.

Main Results:

• Identified 15 crucial leaf traits with their response mechanisms and timescales for EEO modeling.
• Physiological and phenotypical responses are most relevant for EEO trait dynamics; evolutionary constraints limit response ranges.
• Demonstrated a framework separating near-instantaneous to week-scale ci:ca dynamics and minute-scale to annual-scale gsmax dynamics.

Conclusions:

• Leaf trait dynamics can be constrained in EEO-based models using physiological, phenotypical, and evolutionary response mechanisms.
• This approach offers a pathway to improve the representation of terrestrial ecosystems in Earth system models.
• Enhanced DGVMs will improve quantification of ecosystem roles in the climate system.