My talk will be about a Trans-Disciplinary Data-Model Integration (TDMI) approach that focuses on spatio-temporal modeling and cross-scale interactions, and employs interactive machine learning strategies. Applied to ecological problems, my approach integrates knowledge and data on: (1) biological processes, (2) spatial heterogeneity in the land surface template, and (3) variability in environmental drivers using data and knowledge drawn from multiple lines of evidence (i.e., observations, experimental manipulations, analytical and numerical models, products from imagery, conceptual model reasoning, theory). I will apply this approach to a suite of increasingly complex ecologically-relevant problems, and will show how the framework can be linked with a Data Science Integration System (DSIS) to allow more complex questions to be addressed in the future.