October 19, 2020

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Beyond Ecosystem Modeling: A Roadmap to Community Cyberinfrastructure for Ecological Data-Model Integration

Facilitating a new era of data-model integration will require investments in accessible, scalable, and transparent tools to synthesize data and knowledge.

The Science

The researchers explore limitations to rapid model-data integration and provide a vision for a new community cyberinfrastructure to reduce the disconnects between empirical research and modeling, including the lags between data collection and model ingest. The team details five key opportunities for community tool development designed to improve the fidelity of the models on which scientists, managers, and policymakers rely; reduce barriers to entry; and increase the speed at which new information is synthesized into a predictive framework.

The Impact

In an era of rapid global change, the capacity to predict the responses of Earth’s natural systems lags behind the ability to monitor and measure changes in the biosphere. A primary bottleneck to improvements in process understanding is the lack of community tools for informing models with observations, which reduces our ability to fully exploit the growing volume and variety datasets. Addressing this challenge will require new infrastructure investments to provide accessible, scalable, and transparent tools that integrate the expertise of modelers and empiricists to accelerate the pace of discovery.


Process-based ecosystem models are a primary tool used by scientists, managers, and policymakers to understand and project the impacts of global change on Earth’s natural and managed ecosystems. In recent years, the volume and diversity of observational data have significantly increased, and yet the ability to incorporate this new information into predictive frameworks has lagged behind, slowing the pace of progress in model capacity to forecast natural systems. Furthermore, the insufficient communication between the non-modeling and modeling communities represents an additional bottleneck to improving the representation of underlying processes in models. In addition, the complexity and diversity of process models lead to a technical barrier to entry for new researchers. Given the breadth and depth of these challenges that transcend individual research groups, empirical and modeling communities, and funding agencies, the team argue for the development of a new community-wide cyberinfrastructure: a computational environment facilitating seamless data flows into and out of models to more rapidly simulate natural phenomena, test new hypotheses, perform standardized model evaluation, and more easily interpret results and compare predictions across a range of models. The researchers specifically provide a roadmap for this cyberinfrastructure, including five key opportunities for the development of community tools addressing this need. The team feel this new modeling paradigm is a critical step toward meeting the needs for science and society in the 21st century.

Principal Investigator

Shawn Serbin
Brookhaven National Laboratory
[email protected]

Program Manager

Daniel Stover
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
[email protected]


This review was supported by NASA CMS (grant #80NSSC17K0711), and through the DOE Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA), which is sponsored by the Earth & Environmental Systems Modeling (EESM) Program in the Climate and Environmental Sciences Division (CESD), and the Next-Generation Ecosystem Experiments (NGEE-Arctic and NGEE- Tropics) supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science, as well as through the United States Department of Energy contract no. DE- SC0012704 to Brookhaven National Laboratory.

Related Links


Fer, I., A. K. Gardella, A. N. Shiklomanov, and E. E. Campbell, et al.. "Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration." Global Change Biology 27 (1), 13–26  (2021). https://doi.org/10.1111/gcb.15409.