R Quinn Thomas, Carl Boettiger, Cayelan C Carey, Michael C Dietze, Leah R Johnson, Melissa A Kenney, Jason S McLachlan, Jody A Peters, Eric R Sokol, Jake F Weltzin, Alyssa Willson, Whitney M Woelmer

01 April 2023

Frontiers in Ecology and the Environment, Volume 21, Issue 3 p. 112-113

The 21st century continues to be characterized by major changes to the environment and the ecosystem services upon which society depends. Anticipating and responding to these changes requires that scientists explicitly forecast future conditions in real time (Dietze et al, 2018)Ecological forecasting, like weather and epidemiological forecasting, involves integrating data and models to generate quantitative predictions of thefuture state of ecological systems before observations are collected. The iterative cycle of creating forecasts, evaluating them with new observations, updating the models, and then making new forecasts has the potential to accelerate learning across many ecological subdisciplines. This cycle builds on openly available data, often published soon after collection, as is increasingly common in ecological observatory networks, such as the National Ecological Observatory Network (NEON). To accelerate improvements in ecological forecasting, we designed and launched the NEON Ecological Forecasting Challenge (hereafter, “Challenge”) (Figure 1), an open platform for the ecological and data science communities to forecast NEON data before they are collected.