Ecosystem Analysis Using Probabilistic Relational Modeling
In this paper, we present the results of initial explorations into the application of relational model discovery methods to building comprehensive ecosystem models from data. Working with collaborators at the USGS Biological Resources Discipline and at the Environmental Protection Agency, we are engaged in two projects that apply relational probabilistic model discovery to building “community-level” models of ecosystems. A community-level ecosystem model is an integrated model of the ecosystem as a whole. The goal of our modeling effort is to aid domain scientists in gaining insight into data. Our preliminary work leads us to believe the method has tremendous promise. At the same time, we have encountered some limitations in existing methods. We briefly describe two projects and make some observations, particularly with respect to the development of synthetic, or derived, variables. We describe specific extensions we made to solve problems we encountered, and suggest elements of an extended grammar for such variables.
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