C₃ and C₄ photosynthesis

Description

Carbon isotope values of plants reflect photosynthetic pathways, allowing for a clear distinction between C₃ and C₄ plants. Mechanistic models to predict the distributions of C₃ and C₄ plants provided one foundation for photosynthetic ecology at continental scales. Existing spatial models provide a foundation to predict equilibrium C₃-C₄ geographical distributions, based on photorespiration and quantum efficiency concepts. Equilibrium models already are in place to produce raster-based precitions at global scale, incorporating additional precipitation constraints.

Several initial projects are anticipated:

• First, when the biochemistry and spatial concepts are intergrated, the models can be used to explore equilibrium C₃-C₄ distributions under historically low and anticipated high atmospheric [CO₂], allowing extension to different time periods in the Earth's past and future. Together, these theories constitute a foundation for scaling biochemical and physiological processes relevant to photosynthetic pathway distributions to regional and continental scales.
• Second, isotopically, animals are what they eat , and knowing the carbon isotope values of animals we can reconstruct their dietary histories. Interpretation of such data as in source of information on dietary ecology requires understanding of the isotopic composition of the spatially and temporally varying dietary environment for animals. Conversely, the models might have applications to predicting animal distributions in the future, given a changing photosynthetic landscape.

The project will support a postdoc to integrate available C₃-C₄ plant, vegetation, and soil isotope data, ancillary climate and land use data, and animal carbon isotope data. These integrated data sets will be used to test and improve the continental-scale, biochemistry-based predictions of photosynthetic pathway distributions.

These syntheses and modeling efforts will provide a framework for interpreting the very large number of carbon isotope data that will emerge on both insets and vegetation at NEON sites across the US.

Affiliated faculty

Bowling, Cerling, Ehleringer, Pendall, Still, Wofsy