We are physiological ecologists interested in interactions between plants (principally trees) and their abiotic environment. Specifically, we focus on the impacts of climate change on terrestrial ecosystems, and feedbacks of vegetation to the climate system. We use a combination of field measurements, remote sensing, laboratory analyses, as well as modeling and statistical analysis of large data sets, in our research. We are interested in both above- and below-ground processes, at spatial scales and levels of integration from individual stomata to entire ecosystems, and from leaves and roots to the biosphere.

--- Prof. Andrew Richardson

The Richardson Lab has moved!

  • We are now based at Northern Arizona University, located in beautiful Flagstaff, Arizona, on the southern edge of the Colorado plateau. The lab is affiliated with both the School of Informatics, Computing, and Cyber Systems (SICCS) and the Center for Ecosystem Science and Society (Ecoss). Students and postdocs interested in joining the lab should contact Professor Richardson at andrew.richardson@nau.edu.

Measurements and modeling of the terrestrial carbon cycle

  • Biotic and abiotic controls on ecosystem C sequestration
  • Eddy covariance measurements of surface-atmosphere greenhouse gas fluxes
  • Impacts of global change factors, management practices, and disturbance on C stocks and fluxes
  • Ecosystem C-cycle processes at time scales from minutes to decades

Plant Phenology

  • Impacts of climate change and climate variability on vegetation phenology
  • Relationships between phenology and ecosystem processes
  • Phenological control of vegetation feedbacks to the climate system
  • "Near surface" remote sensing using networked digital cameras

Carbon allocation and carbohydrate reserves in forest trees

  • Within-tree C allocation processes and C metabolism
  • Dynamics and age (via 14C) of nonstructural carbohydrate (NSC) reserves
  • Availability of NSC reserves to support metabolism in times of stress
  • Ecological role of NSCs, and their importance in forest C cycling

Model-data fusion

  • Inverse modeling and model-data fusion using Monte Carlo approaches
  • Optimization using multiple data constraints
  • Rigorous quantification of uncertainties in measurements, model parameters, and model predictions
  • Model selection and process representation