The University of Illinois at Urbana-Champaign and the University of Wisconsin - Madison are awarded collaborative grants to develop an integrated ecological bioinformatics toolbox dubbed the Predictive Ecosystem Analyzer (PEcAn) which 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 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, but is 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 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. 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 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 then 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.
The tools developed in this project will not only find broad use in the ecological community but will also have direct relevance to important policy and management debates about climate change mitigation and carbon credit markets. Specifically, it will allow a repeatable, scientifically defensible, and temporally up-to-date analysis of the state of the carbon cycle base on a broad synthesis of the best available data. Within the scientific community, these tools will be broadly applicable to numeous ecosystem models and facilitate the use and evaluation of predictive models by non-modelers. The tools developed here are also well-positioned to synthesize the large volumes of information coming out of a number of NSF-supported research networks, such as the LTER network and NEON. To encourage use and development, we will make open-source code, documentation, and tutorials available on the project website, pecanproject.org. To further disseminate these tools and methods, this project also has a strong education component consisting of three elements: 1) the development of a graduate seminar on eco-informatics that will be offered in both face-to-face and online formats, 2) the participation of the PIs in two existing summer courses, one of which is offered at a tribal college located within our study region, and 3) direct training of students and postdocs directly involved with the project.
