Ecological forecasting truly epitomizes this year’s theme of “Harnessing the ecological data revolution”. With recent advances in data availability, models, statistics, and policy, the time is ripe for rapid progress in making ecology more predictive. Advances in sensor technologies, computational capacities, and statistical/data science techniques, when combined with reduced data latency, have made possible a new 21st century approach to near real-time ecology that is accelerating basic research. Consequently, the practice of ecological forecasting, i.e., the process of predicting the future states of ecological systems and ecological services with fully specified uncertainties, is an area of high interest and rapid growth in ecology that was barely possible a generation ago. Ecological forecasting is also supporting new 21st century approaches to conservation and management that are critical at a time of increasing variability and high uncertainty in many ecosystems. This session brings together ecologists from a diversity of study systems to highlight cutting-edge examples of near-term ecological forecasting that span ecosystem boundaries, time scales, and basic/applied applications. With the rapid growth of forecasting comes a need to share information about new approaches for model-data assimilation, quantification and analysis of uncertainties, best practices, and more. This session highlights speakers from across a range of career stages tackling ecological forecasting problems that directly provide data and models to inform environmental decision-makers. This session is organized by the Ecological Forecasting Initiative (EFI), an interdisciplinary, grassroots consortium that brings together scientists, managers, and policymakers developing near-term (daily to decadal) ecological forecasts. EFI’s overarching goal - to advance ecological forecasting within the ecology research community - is motivated by the goal to improve our ability to respond to complex environmental challenges by providing environmental decision-makers with the best available science in hand.