Professor Peter Reich

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. A primary goal for Earth-system modeling is to make accurate predictions of the future trajectory of the climate system, based on a mechanistic understanding of processes regulating fluxes of mass and energy among system components. Land plays an important role in modifying the earth's mass and energy balance, as a critical link in the global cycling of carbon, among others. Land surface models have developed to include mechanistic representations of vegetation physiology, carbon and nutrient dynamics in plants and soils, how they might respond to changing climate and chemistry, and how those changes might feedback to influence changes in atmospheric greenhouse gases themselves. This project addresses these processes.

Existing models represent the global distribution of vegetation types using the Plant Functional Type concept. Plant Functional Types are classes of plant species with similar evolutionary and life history with presumably similar responses to environmental conditions like CO₂, water and nutrient availability. Fixed properties 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. New remote sensing data products are enabling increasing accurate estimates of trait diversity at smaller and smaller scales. With data that show how diverse vegetation traits are at both fine and broad scales this project will use these new data sources to evaluate how diversity influences model estimates of carbon, water, and biogeochemical cycles. In addition to updating trait values into distributions the researchers will update key parameters related to photosynthesis that are available from public genetic databases. This will allow them to evaluate whether rubisco, the key enzyme in photosynthesis, varies across or within existing plant functional types.

The trait-based approach will improve land modeling by: 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; utilizing what is known about trait-trait, -soil, and -climate relations to improve algorithms used to predict processes at multiple stages; allowing for improved treatment of physiological responses to environment (such as temperature and/or CO₂ response of photosynthesis or respiration); and updating parameter values using novel datasets, such as genetic databases.