Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Michael C. Dietze, Andrew Fox, Lindsay M. Beck-Johnson, Julio L. Betancourt, Mevin B. Hooten, Catherine S. Jarnevich, Timothy H. Keitt, Melissa A. Kenney, Christine M. Laney, Laurel G. Larsen, Henry W. Loescher, Claire K. Lunch, Bryan C. Pijanowski, James T. Randerson, Emily K. Read, Andrew T. Tredennick, Rodrigo Vargas, Kathleen C. Weathers, Ethan P. White

Research output: Contribution to journalArticlepeer-review

379 Scopus citations


Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Original languageEnglish (US)
Pages (from-to)1424-1432
Number of pages9
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number7
StatePublished - Feb 13 2018
Externally publishedYes

Bibliographical note

Funding Information:
Some successful models that have emerged to fill gaps in graduate student skillsets are cross-institution fellowship programs (e.g., GLEON Fellows program) (63) and within-institution interdisciplinary training programs [e.g., National Science Foundation (NSF) Research Training Groups]. The next generation of ecological forecasters would also benefit from graduate and postdoctoral fellowships directed at forecasting or supplemental funding aimed at making existing analyses more updatable and automated. Short courses, held over a 1-to 2-wk period to obtain specific skills, are another model that holds promise, not only in academia but also in applied disciplines and professional societies (e.g., water quality, forestry), which often have continuing education certification requirements. By training practitioners in the latest forecast approaches, such certifications may also be particularly helpful for bridging ecological forecasts from research to operations.

Funding Information:
We thank James S. Clark, Tom Hobbs, Yiqi Luo, and Woody Turner, who also participated in the NEON forecasting workshop and contributed useful ideas and discussion. M.C.D. and K.C.W. were supported by funding from NSF Grants 1638577 and 1638575 and K.C.W. additionally by NSF Grant 1137327. E.P.W. and L.G.L. were supported by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grants GBMF4563 and GBMF4555. A.T.T. was supported by an NSF Postdoctoral Research Fellowship in Biology (DBI-1400370). R.V. and H.W.L. acknowledge support from NSF Grant 1347883. M.B.H. acknowledges support from NSF Grants 1241856 and 1614392. This paper benefited from feedback from John Bradford, Don DeAngelis, and two anonymous reviewers, as well as internal reviews by both NEON and USGS. This paper is a product of the “Operationalizing Ecological Forecasts” workshop funded by the National Ecological Observatory Network (NEON) and hosted by the US Geological Survey (USGS) Powell Center in Fort Collins, Colorado. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Publisher Copyright:
© 2018 National Academy of Sciences. All Rights Reserved.


  • ecology
  • Forecast
  • prediction


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