The objective of ecological theory is to provide a mechanistic understanding of patterns that can be universally applied across space and time. Long-term ecological research programs are science frameworks that inform theory and emerging science observatory networks. The aim of this organized session is to distill lessons learned from long-term ecological research from diverse organisms, ecosystems and biomes, how it has informed core ecological theories, and how it can be used to integrate large data networks from national and international science observatories. More specifically, presenters will discuss how their synthesis efforts have addressed one or more of the following questions: How are theories informed by multiple long-term data sets? How does the use of long-term data in theoretical frameworks enable more accurate forecasts of future scenarios (e.g., novel ecosystem structure and/or function? Biodiversity in future climate scenarios)? Where have theories been refuted and what are the lessons learned? Where has the application of theory led to ambiguous results, and what kinds of data do we need to better test mechanism-based models to understand whether they apply broadly? What are potential high-payoff investments for ecological synthesis? What theories developed in one system may apply in other systems (e.g., metacommunity theory applied in streams, ponds, has potential for understanding community assembly in marine benthic systems)? What types of data collected by one group/discipline may be ideal for testing the theory in another discipline?