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A methodology is presented which makes use of both theory-based and data-based information to structure and calibrate low-order dynamic models for lake ecosystem analysis. A brief historical review explores the inductive and deductive aspects of past modeling efforts and describes the motivation for this research. A methodology, summarized in a flowchart, is discussed in terms of data analysis, conceptualization, development of a mathematical model, calibration, and three feedback stages for validity checks. This approach has been applied to the study of nutrient cycling in the south basin of Lake George, NY. The results of several iterations of the methodology are given and discussed in terms of statistical validity and agreement with biological theory. The final model is a set of difference equations containing two state variables and three external variables, and shows a good fit to the existing data. This is one of the first lake ecosystem studies to make extensive use of data in order to develop model relationships and estimate parameters.