What is PECAn?

The Predictive Ecosystem Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PEcAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation. PEcAn is an open source utility that encapsulates:
  1. acquisition of meteorological inputs
  2. synthesis of physiological trait data as the posterior distribution of a Bayesian meta-analysis
  3. sampling trait meta-analysis posterior distributions to parameterize ensembles of ED2 and other e ecophysiological models
  4. probabilistic forecasts
  5. postprocessing to constrain forecasts and model parameters with field, meterological, eddy flux, and spectral data, and 6) provenance tracking

PECAn integrates available data into ecological forecasts by running ensembles of a terrestrial ecosystem model that is parameterized by the posterior distribution from a meta-analysis of available plant trait data. These trait data are assembled from field research and primary literature, and are stored in a database. Current development focused on biofuel crops uses BETYdb. In addition to generating forecasts that reflect available data, PEcAn quantifies the contribution of each parameter to model uncertainty. This information informs targeted data collection and synthesis efforts that most efficiently reduce forecast uncertainty.

Current development is focused on developing PEcAn into a real-time data assimilation and forecasting system. This system will provide a detailed analysis of the past and present ecosystem functioning that seamlessly transitions into forecasts.

Getting Started

Stable Releases

The best way to get started is to work through hands-on demonstrations developed for teaching the principles of data-model fusion: Hands-On Demo (Niwot Flux Course 2012) and Flux Course Presentations. PEcAn 1.1 also demonstrates the use of PEcAn in the context of reproducible research, in that it reproduces document#28.

  1. download virtual box and install (
  2. download PEcAn 1.2.6 vm (ova file) (below)
  3. double click the ova file, this will create the virtual machine (assuming 64 bit below, otherwise all
  4. in virtual box start the virtual machine
  5. once the vm is running, you can either login using ssh (localhost:6422, putty etc) or you can login to the console.
  6. login/password is pecan/pecan
  7. change password (enter passwd at the command line)
  8. you can access the webpages using http://localhost:6480/pecan

Basic Information

  1. FAQ
  2. Settings Files all about "settings.xml"
  3. Pecan Setup: How to set up and use development version of PEcAn*
  4. Directory Structure
  5. Module Documentation

Setting up a PEcAn working environment

  1. Using VM's
  2. VM Creation
  3. Setup a PEcAn Desktop Environment Example of PEcAn 1.2.6 desktop, a modification of the VM Creation instructions
  4. PEcAn Rails dev environment -- Ubuntu 10.04 A local development environment for PEcAn using rails 2.3.11 and MySQL

Development Guidelines (Required Reading For Developers)

  1. Redmine is the place to report bugs, request or discuss features, and obtain support)
  2. Using Bazaar Version Control
  3. R Programming Language
    1. Overview and Coding Style
    2. Testing: testthat R package
    3. Documenting Code: Roxygen2 R package
  4. Logging


Journal Articles

  • DS LeBauer, D Wang, KT Richter, CD Davidson, and MC Dietze. in press. Facilitating feedbacks between field measurements and ecosystem models. Ecological Monographs. document#28 doi:10.1890/12-0137.1
  • Wang, D., DS LeBauer, and MC Dietze, in press. Predicted yields of short-rotation hybrid poplar (Populus spp.) for the contiguous US. document#32 doi:10.1890/12-0854.1


  • C. Davidson, 2012. "The modeled effects of fire on carbon balance and vegetation abundance in Alaskan Tundra" M.S. Thesis, University of Illinois Department of Plant Biology.


  • Dietze, M.C., S.P. Serbin, D. LeBauer, R. Kooper, K. McHenry, and A. Desai. 2012. Reconciling inventory, tower, and remotely-sensed carbon estimates across northern Wisconsin through model-data fusion. Ecological Society of America Meeting, Portland, Oregon, August 10, 2012.
  • LeBauer, D. S., D. Wang, X. Feng, and M. C. Dietze (2010): PECAn, a workflow management tool for real-time data assimilation and forecasting: evaluation of a switchgrass (Panicum virgatum) cropping system. Presented to the Combining Experiments, Process Studies, and Models to Forecast the Future of Ecosystems, Communities, and Populations Organized Oral Session at the Ecological Society of America Meeting, Pittsburgh, PA, August 5, 2010. doi:10.1038/npre.2011.5533.1 document#8
  • LeBauer, D.S., D. Wang, C. Bernacchi, M. Zeri, M. C. Dietze. 2011. Plant trait meta-analysis and flux data assimilation contraints on parameterizations of ecosystem models. document#9