Cyberinfrastructure to support ecological forecasting challenges
Year: 2026
Type: article
Abstract: Ecological forecasting challenges, formalized efforts to empower the ecology community to develop near‐term iterative forecasts of yet‐to‐be‐collected data, are a powerful tool to advance predictive capacities in ecology. The National Ecological Observatory Network (NEON) Ecological Forecasting Challenge, which has processed over 82 million individual forecasts across 81 sites since 2021, exemplifies this approach. However, the cyberinfrastructure (CI) required to support such large‐scale, community‐driven forecasting efforts has not been fully described. Here, we present the design principles, technical implementation, and lessons learned from the CI developed for the NEON Challenge. This serverless, open‐source platform automates key tasks, including target data generation, probabilistic forecast submission‐processing, evaluation using proper scoring rules, and dissemination of results. The CI leverages cloud‐native technologies, including Simple Storage Service (S3)‐based storage, GitHub Actions, and Docker containers, to ensure scalability, reproducibility, and vendor‐agnostic deployment. The CI also includes tools to reduce barriers for contributors, such as R packages for forecast submission, access to weather forecasts as model drivers, and tutorials. A dashboard and searchable catalog enhance transparency and accessibility. Our experience demonstrates that forecasting challenges can serve as focal points for community engagement, education, and synthesis. The modular design of the CI enables adaptation to new observatories and evolving technologies, providing a roadmap for future ecological forecasting efforts. By documenting this infrastructure, we aim to empower researchers to launch and sustain forecasting challenges that advance ecological understanding through prediction.
Source: Ecosphere
DOI: 10.1002/ecs2.70682
Authors: R. Quinn Thomas, Carl Boettiger
Institutions: Virginia Tech, University of California, Berkeley
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,430
Awards: LTREB: Integrating real-time open data pipelines and forecasting to quantify ecosystem predictability at day to decadal scales, Collaborative Research: Frameworks: DeCODER (Democratized Cyberinfrastructure for Open Discovery to Enable Research), Collaborative Research: Elements: FaaSr: Enabling Cloud-native Event-driven Function-as-a-Service Computing Workflows in R, NEON RCN: The Ecological Forecasting Initiative RCN: Using NEON-enabled near-term forecasting to synthesize our understanding of predictability across ecological systems and scales, G6773449537, G8663942523, G8719133382, G8721245735
OpenAlex ID: W4414601891