Global land model development: time to shift from a plant functional type to a plant functional trait approach

Abstract:

This project will advance global land models by shifting from the current plant functional type approach to one that better utilizes what is known about the importance and variability of plant traits, within a framework of simultaneously improving fundamental physiological relations that are at the core of model carbon cycling algorithms. Existing models represent the global distribution of vegetation types using the Plant Functional Typeconcept. Plant Functional Types are classes of plant species with similar evolutionary and life history withpresumably similar responses to environmental conditions like CO₂, water and nutrient availability. Fixedproperties for each Plant Functional Type are specified through a collection of physiological parameters, or traits.These traits, mostly physiological in nature (e.g., leaf nitrogen and longevity) are used in model algorithms to estimate ecosystem properties and/or drive calculated process rates. In most models, 5 to 15 functional types represent terrestrial vegetation; in essence, they assume there are a total of only 5 to 15 different kinds of plants on the entire globe. This assumption of constant plant traits captured within the functional type concept has serious limitations, as a single set of traits does not reflect trait variation observed within and between species and communities. While this simplification was necessary decades past, substantial improvement is now possible. Rather than assigning a small number of constant parameter values to all grid cells in a model, procedures will be developed that predict a frequency distribution of values for any given grid cell. Thus, the mean and variance, and how these change with time, will inform and improve model performance.

The trait-based approach will improve land modeling by (1) incorporating patterns and heterogeneity of traits into model parameterization, thus evolving away from a framework that considers large areas of vegetation to have near identical trait values; (2) utilizing what is know about trait-trait, -soil, and -climate relations to improve algorithms used to predict processes at multiple stages; and (3) allowing for improved treatment of physiological responses to environment (such as temperature and/or CO₂ response of photosynthesis or respiration).