RCN

The Ecological Forecasting Initiative RCN:
Using NEON-enabled near-term forecasting to synthesize our understanding of predictability across ecological systems and scales (EFI-RCN)

Vision: Solve the challenge of predicting nature and, ultimately, forecast the effects of alternative environmental policies and actions.

Goal: Create a community of practice that builds capacity for ecological forecasting by leveraging NEON data products. 

Objectives:

  1. Define community standards and best practices for developing, sharing, and archiving forecasts and models
  2. Increase the number and diversity of NEON-enabled forecasts by developing and hosting the NEON Ecological Forecasting Challenge
  3. Create educational materials to empower scientists at all career stages to forecast using NEON data products
  4. Support the creation of software to produce NEON-enabled forecasts at intensive and collaborative coding-focused workshops
  5. Align forecast outputs and decision support with the needs of forecast users at mission-driven agencies to guide decision making, and
  6. Synthesize forecasts to examine how limits to forecastability vary across ecological systems and scales.

Proposed Network Activities:

Leadership:

Project PI: R. Quinn Thomas (Virginia Tech, rqthomas@vt.edu)

Steering Committee: Michael Dietze (Boston University), Melissa Kenney (University of Minnesota), Christine Laney (NEON), Jason McLachlan (Notre Dame), Carl Boettiger (UC Berkeley), Cayelan Carey (Virginia Tech), Andy Fox (UCAR), Leah Johnson (Virginia Tech), and Jake Weltzin (USGS)

Interested in being involved?  Join the Ecological Forecasting Initiative and sign-up for the EFI newletter to learn about EFI-RCN activities.