The University of Illinois at Urbana-Champaign and the University of Wisconsin – Madison have been awarded an NSF ABI grant to develop an integrated ecological bioinformatics toolbox dubbed the Predictive Ecosystem Analyzer (PEcAn). This toolbox consists of:

  1. A scientific workflow system to manage the immense amounts of publicly-available environmental data and stored in a variety of heterogeneous databases/archives and formats,
  2. A Bayesian data assimilation system to synthesize this information within state-of-the-art ecosystems models.

The 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 utilize existing data. This project seeks to improve this ability by developing a framework for integrating multiple data sources in a sensible manner. PEcAn is initially being developed around the Ecosystem Demography model (ED), one of the few terrestrial biosphere models capable of integrating a large suite of observational data at different spatial and temporal scales. At the same time PEcAn is being designed to interface with a wide class of ecosystem models. The output of the data assimilation system will be a regional-scale high-resolution estimate of both the terrestrial carbon cycle and plant biodiversity based on the best available data and with a robust accounting of the uncertainties involved. The workflow system will allow ecosystem modeling to be more reproducible, automated, and transparent in terms of operations applied to data, and thus ultimately more reusable and comprehensible to both peers and the public. It will reduce the redundancy of effort among modeling groups, facilitate collaboration, and make models more accessible the rest of the research community due to the open nature of the workflow system.

As a test bed for the development and application of these ecological bioinformatics tools, the project will focus on the temperate/boreal transition zone in northern Wisconsin, a region that is expected to show large climate change responses and is arguably the most ecologically data-rich region in the country. The tools developed here will enable us to partition carbon flux and pool variability in space and time and to attribute the regional-scale responses to specific biotic and abiotic drivers. The data-assimilation framework will partition different sources of uncertainty which will enable a better understanding of which are limiting our inference and provide a more complete propagation of uncertainty into model forecasts. ED will also be used to forecast regional-scale dynamics under decadal to centennial scale climate change scenarios. This approach will allow us to assess for the first time how much our uncertainty about the current state of the ecosystem impacts our ability to anticipate the future.

National Science Foundation – Model-data Synthesis and Forecasting Across the Upper Midwest: Partitioning Uncertainty and Environmental Heterogeneity in Ecosystem Carbon (DBI-1062547), 2011-2014

Michael Dietze (PI)
Kenton McHenry (Co-PI)