Prognostic Plant Traits

Mapping and predicting global plant trait distributions.

The Science

Variation in plant traits at local scales can be very large, of similar magnitude to the global range of values used to characterize mean traits for multiple plant functional types. This study generated global maps of plant trait distributions for use in Earth system models, representing trait means and trait variation in each global grid cell. It used environmental variables to estimate trait distributions so that trait variation under future conditions can be estimated as well.

The Impact

At present, Earth system models (ESMs) represent variation in plant life through the presence of a small set of plant functional types (PFTs), each of which represents hundreds or thousands of species across thousands of vegetated grid cells on land. By expanding plant traits from a single mean value per PFT to a full distribution per PFT that varies among grid cells, the trait variation present in nature is restored and may be propagated to estimates of ecosystem processes. Critical ecosystem processes tend to depend on the full trait distribution, which needs to be represented accurately. The maps reintroduce substantial local variation and permit a more accurate representation of the land surface in ESMs.


The global land surface is perhaps the most heterogeneous component of the Earth system. Reducing vegetation to a collection of plant functional types (PFTs) with fixed trait values has been the preferred method to constrain this heterogeneity and group similar biochemical and biophysical properties; however, this has been at the expense of functional diversity. The study used the largest available global plant traits database and global environmental datasets as observational inputs to a Bayesian prediction framework. It estimated trait distributions (mean and standard deviation) for three leaf traits known to play a critical role in predictions of photosynthesis and respiration: specific leaf area, leaf nitrogen concentration, and leaf phosphorus concentration. The team made global trait distribution estimates both with and without constraints from global maps of plant functional type distribution. This analysis quantifies the substantial magnitude of the previously ignored trait variation. The approach and methods presented here retain the simplicity of the PFT representation, but capture a wider range of functional diversity.

Principal Investigator(s)

Ethan Butler
University of Minnesota


This research was supported by multiple agencies in several countries including funding from the Energy Exascale Earth System Model (E3SM) project and the Earth System Modeling program, both funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (Grant DE-SC0012677).


Butler, E.E., A. Datta, et al. “Mapping local and global variability in plant trait distributions.” Proc. Nat. Acad. Sci. (2017). [DOI:10.1073/pnas.1708984114]