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Welcome to the updated NEON website! This site features more intuitive navigation and a seamlessly integrated Biorepository portal, making it easier to explore NEON data, samples and resources. For a brief summary of changes visit this page. Your feedback is welcome through our webform through February 20.

Research and Collaborations

  • Environmental Data Science Innovation and Inclusion Lab
  • Collaboration with DOE BER User Facilities and Programs
  • EFI-NEON Ecological Forecasting Challenge
  • NEON Great Lakes User Group
  • NCAR-NEON-Community Collaborations

Breadcrumb

  1. Get Involved
  2. Research and Collaborations
  3. EFI-NEON Ecological Forecasting Challenge

EFI RCN NEON Ecological Forecasting Challenge

EFI

The Ecological Forecasting Initiative

The NSF funded EFI Research Coordination Network (EFI-RCN) is hosting a NEON Ecological Forecast Challenge with the goal to create a community of practice that builds capacity for ecological forecasting by leveraging NEON data products. As a community, we are excited to learn more about the predictability of ecological processes by forecasting NEON data prior to its release. What modeling frameworks, mechanistic processes, and statistical approaches best capture community, population, and ecosystem dynamics?

These questions are answerable by a community generating a diverse array of forecasts. The Challenge is open to any individual or team that wants to submit forecasts and includes categories for different career stages. This program started in 2020, and will continue through at least 2024, with rotating challenges over the years that will span multiple research themes. The current research challenge themes include forecasts of terrestrial water and carbon fluxes, beetle abundance, tick population size, water temperature and dissolved oxygen, and canopy greenness. Individuals can submit forecasts for any or all themes!

All About EFI

From videos to wikis to FAQs, the EFI website is chock full of the information you'll need to learn more about ecological forecasting and join the challenge.

Visit EFI

NEON Data

NEON data are used in all of the challenges. Explore what's available and think about what you might want to forecast!

Data Catalog

NEON Field Sites

NEON intensively monitors 81 sites across a range of ecosystem types, providing ample opportunity to forecast across a wide array of gradients.

Explore Field Sites

Video Resources

Learning How To Forecast

NEON and Ecological Forecasting

New To Forecasting?

Ecological forecasting is an emerging interdisciplinary research area that combines concepts, tools, and methods from ecology, statistics, computer science, the social and decision sciences, and the physical environmental sciences with the aim of simultaneously improving environmental decision making and improving our understanding of the predictability of natural systems. We've put together a series of videos explaining the fundamentals of ecological forecasting.

Educational Videos

Learn About The Current Themes

Ongoing challenges, all of which use NEON data, include Beetle Abundance, Terrestrial Carbon & Water Fluxes, Water Temperature and Dissolved Oxygen, Tick Population Size, and Canopy Spring and Fall Phenology.

Challenge Theme Clips

All About the Challenge

This extensive document on the EFI site gives details on each Challenge Theme, as well as sections with an example workflow, participation and submission instructions, FAQs, and video resources on how to participate.

Find Challenge Details

NASA Satellite Data

Get MODIS, VIIRS, DayMet, and more, specifically clipped to NEON field sites, from the Oak Ridge National Laboratory DAAC.

Get satellite data

For more information about the Forecasting Challenge, be sure to visit the EFI website. For questions about NEON data, send us a message through our Contact Us form.

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The National Ecological Observatory Network is a major facility fully funded by the U.S. National Science Foundation.

Any opinions, findings and conclusions or recommendations expressed in this material do not necessarily reflect the views of the U.S. National Science Foundation.