Cross-forecast synthesis and cyberinfrastructure in Near-term ecological forecasting


John R. Foster1; Dietze, Michael C.1; Averill, Colin1; Bhatnagar, Jennifer M.1; Ladeau, Shannon L.2; Weathers, Kathleen C.2; Werbin, Zoey R.1; Wheeler, Kathryn I.1; Zarada, Katherine A.1

  1. Department of Earth and Environment, Boston University
  2. The Cary Institute of Ecosystem Studies


The Near-term Ecological Forecasting Initiative has set out to forecast a variety of ecological systems to analyze the uncertainties within individual forecasts (initial condition, model parameters, ecological drivers, and process error), and perform cross-forecast synthesis across disciplines. Focus areas where temporal forecasts are currently operational (automated and iterative) include 16-day carbon and water fluxes and phenology at a broadleaf forest site in Willow Creek, Wisconsin. More sites are coming soon and forecasts for tick-borne diseases (ticks and their small mammal hosts) will come on-line this summer. In addition, spatial forecasts of soil fungal and bacterial communities have been calibrated using community data and validated using out-of-sample data from NEON. All models are in a Bayesian state-space framework, which allows for the explicit separation of observation error from the process of interest.

To automate the forecasting process we have prototyped a generalized forecast cyberinfrastructure where any forecaster can drag-and-drop their model code, automatically schedule repeated forecasts, and archive results. Upon completion, the forecast is returned to an R shiny website for the user to explore results. This general workflow allows for scalable, repeatable research where competing forecasts can directly be compared.