Opportunities and challenges for near-term ecological forecasting with biodiversity and EO data

Near-term ecological forecasting offers a powerful early-warning system for ecological change, enabling proactive and adaptive management. Yet the ability to generate accurate, real-time forecasts remains limited. Although ecological monitoring and retrospective analyses have advanced rapidly, forecasting is still constrained by the scarcity of biodiversity datasets that provide frequent and standardized observations from the same locations.Global biodiversity aggregators—such as the Global Biodiversity Information Facility and the Ocean Biodiversity Information System—and their sources provide invaluable broad-scale information, but their records are typically opportunistic and infrequently revisited, making them better suited to retrospective analyses than to near-term forecasting. Closing this gap requires collaboration with local data providers (e.g., national parks, protected areas, long-term monitoring networks) and the development of coordinated, open-access biodiversity monitoring infrastructures across larger spatial scales. Initiatives such as the Global Ecosystem Research Infrastructure, the Group on Earth Observations Biodiversity Observation Network and BioTime represent important but still underutilized foundations.Earth observation (EO) data can help bridge several of these limitations by offering spatially synchronized measurements, high temporal resolution, and near-real-time environmental information. However, EO alone cannot generate ecological predictions. The most promising path forward lies in integrating frequently updated biodiversity observations with real-time EO indicators to build automated, transferable, and scalable forecasting pipelines. Cloud-based EO platforms (e.g., Google Earth Engine, WEkEO, CREODIAS) enable reproducible environmental data processing, while modern automation tools (e.g., GitHub Actions, Cron jobs) make continuous model updating feasible. Interactive, user-centred dashboards then provide accessible pathways for communicating forecasts.Despite progress, challenges persist: biodiversity monitoring remains spatially uneven, temporal resolution is often insufficient, and forecasting systems lack standardized validation frameworks, robust uncertainty communication, and sustained software engineering support. Overcoming these barriers requires interoperable infrastructures that integrate biodiversity monitoring, EO processing, automated forecasting, and forecast dissemination.Building such systems—grounded in INSPIRE and FAIR data principles—is essential for achieving the near-term ecological forecasting capacity needed to meet global policy commitments, including the Kunming–Montreal Global Biodiversity Framework and the Sustainable Development Goals. Strengthening the connections among local biodiversity monitoring, EO-derived indicators, and automated forecasting pipelines will substantially enhance our ability to anticipate ecological change and deliver timely, evidence-based guidance for conservation and resilience planning.