Published October 8, 2021 | Version v1.7.2
Software Open

The Predictive Ecosystem Analyzer (PEcAn) is an integrated ecological bioinformatics toolbox.

Creators

  • 1. University of Arizona
  • 2. Boston University
  • 3. National Center for Supercomputing Applications
  • 4. Boston Univeristy
  • 5. Pacific Northwest National Laboratory / University of Maryland
  • 6. Brookhaven National Laboratory
  • 7. University of Notre Dame
  • 8. Brigham Young University
  • 9. Pennsylvania State University
  • 10. University of Wisconsin-Madison
  • 11. Worchester Academy
  • 12. Morton Arboretum
  • 13. Purdue University
  • 14. University of New South Wales
  • 15. Finnish Meteorological Institute
  • 16. University of Illinois at Urbana-Champaign

Description

The Predictive Ecosystem Analyzer (PEcAn) (see pecanproject.org) is an integrated ecological bioinformatics toolbox (Dietze et al 2013, LeBauer et al, 2013) that consists of: 1) a scientific workflow system to manage the immense amounts of publicly-available environmental data and 2) a Bayesian data assimilation system to synthesize this information within state-of-the-art ecosystems models. This project is motivated by the fact that many of the most pressing questions about global change are not necessarily limited by the need to collect new data as much as by our ability to synthesize existing data. This project seeks to improve this ability by developing a accessibe framework for integrating multiple data sources in a sensible manner.

Notes

The PEcAn project is supported by the National Science Foundation (ABI #1062547, ABI #1458021, DIBBS #1261582, ARC #1023477, EF #1318164, EF #1241894, EF #1241891, EF #1501873, EF #1638577, EF #1702996), NASA Terrestrial Ecosystems #NNX14AH65G, NASA CMS #80NSSC17KO711, NASA NESSF #NNX16AO13H, the Department of Energy (ARPA-E awards #DE-AR0000594, #DE-AR0000598, and #SERDP RC-2636), the Energy Biosciences Institute, and an Amazon AWS in Education Grant.

Files

PecanProject/pecan-v1.7.2.zip

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Additional details

Related works

Is supplement to
https://github.com/PecanProject/pecan/tree/v1.7.2 (URL)

References

  • LeBauer, David S, Dan Wang, Katherine Richter, Carl Davidson, and Michael C Dietze (2013). Facilitating feedbacks between field measurements and ecosystem models. Ecological Monographs. doi:10.1890/12-0137.1
  • Wang, Dan, David S LeBauer, and Michael C Dietze (2013). Predicting yields of short-rotation hybrid poplar (Populus spp.) for the contiguous US through model-data synthesis. Ecological Applications doi:10.1890/12-0854.1
  • Dietze, Michael C, David S LeBauer, and Rob Kooper (2013). On improving the communication between models and data. Plant, Cell, & Environment doi:10.1111/pce.12043
  • Dietze, Michael C, Shawn P Serbin, Carl Davidson, Ankur R Desai, Xiaohui Feng, Ryan Kelly, Rob Kooper, David LeBauer, Josh Mantooth, Kenton McHenry, Dan Wang (2014) A quantitative assessment of a terrestrial biosphere model's data needs across North American biomes. Journal of Geophysical Research: Biogeosciences 119, no. 3 (2014): 286-300.
  • Viskari, Toni, Brady Hardiman, Ankur R. Desai, and Michael C. Dietze. (2015) Model-data assimilation of multiple phenological observations to constrain and predict leaf area index. doi:10.1890/14-0497.1
  • Shiklomanov. A, MC Dietze, T Viskari, PA Townsend, SP Serbin. (2016) Quantifying the influences of spectral resolution on uncertainty in leaf trait estimates through a Bayesian approach to RTM inversion. Remote Sensing of the Environment 183: 226-238
  • LeBauer, David S, Rob Kooper, Patrick Mulrooney, Scott Rohde, Dan Wang, Stephen P Long, & Michael C Dietze (2018). BETYdb: a yield, trait, and ecosystem service database applied to second generation bioenergy feedstock production. GCB Bioenergy, 10(1), 61-71. doi: 10.1111/gcbb.12420