ABSTRACT

Computer simulations play an essential role in ecological research, the management of national forests and other public and private land resources, and projections of climate change impacts on ecosystem services at the local, state, national, and international level. However, at the moment, there are a number of barriers slowing the pace of model improvement and reducing their wider use. First, the software for using each model is unique and does not communicate well with other models. Second, because each model is unique, the tools to manage data going into models, analyze models, and visualize results are not shared. In this project PEcAn (Predictive Ecosystem Analyzer) is being developed to provide a common set of software tools for researchers and land managers to effectively work with multiple ecosystem models and data. Web technologies will be used to allow distant modeling teams to share information, work together, and better use public and private cloud and supercomputing resources. Other tools will be developed to identify model errors and combine new and existing applications into workflows to make ecological research more efficient, better forecast ecosystem services, and support evidence-based decision making. The PEcAn team will also develop training tools for new users and work with the scientific community to add more models to PEcAn. PEcAn will make ecological research more transparent, repeatable, and accountable.

PEcAn is an open-source ecoinformatics system designed for ecologists with a range of modeling backgrounds to be able to better and more easily parameterize, run, analyze, and assimilate data into ecosystem models at local and regional scales. This project will expand the PEcAn user community, incorporate more models, and develop tools that are more intuitive and accessible. Further, the project intends to transform tools for managing the flows of information into and out of ecosystem models into a resilient, scalable, and distributed peer-to-peer network for managing the flow of this information among modeling teams and with the broader community. To support a larger number of models, data processing workflows will be improved and tools will be developed for multi-model visualization and benchmarking. Applications that distribute analyses across the PEcAn network, cloud, and high-performance computing environments will be used to better understand model structural error using data mining approaches. Models will benchmarked over a range of environmental conditions, allowing model improvement to be tracked and users to select the best models for different applications in an informed manner. Finally, PEcAn tools will be combined into customizable workflows for real-time synthesis, forecasting, and decision support. By allowing modelers to focus on science rather than informatics, and allowing ecologists to easily compare their data to models, PEcAn will greatly accelerate the pace of model improvement and hypothesis testing. These activities are essential for improving ecosystem models and reducing uncertainty of the impacts of climate change on ecosystems and carbon cycle-climate feedbacks. Project information and results are available at http://pecanproject.org while project computer code is available at https://github.com/pecanproject.

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