Background: Advances in computing technology and the increasing interdisciplinarity of research have all encouraged the development of landscape-scale mechanistic models of coupled ecological-geophysical systems. There has been little consensus, however on the strategies and goals for these models. One challenge is that ecology and the geosciences have embraced different modeling epistemologies, with ecologists historically favoring inductive inference from generalized, phenomenological models and geoscientists favoring deductive inference from detailed first-principles models. Today, most models used for ecosystems management tend to be highly detailed, with ecological and geophysical components represented in different modules that are linked but not closely integrated. These observations highlight a need for cross-disciplinary dialogue about and convergence of ecosystem modeling objectives and approaches. The philosophies of pattern-oriented modeling in ecology and exploratory modeling in geophysics, including atmospheric sciences, have yielded advances in theoretical and applied knowledge, but they are not comprehensive across all aspects of ecosystem modeling. Here we explore the application of the Appropriate-Complexity Method (ACME), which builds upon these two philosophies to guide the development of ecosystem models. ACME helps modelers converge upon an optimal modeling structure based on systematic evaluation and unpacking of the attributes that comprise computational and representational detail. Our approach will build upon the following study now in press at Earth Science Reviews: Larsen LG, et al., Appropriate Complexity Landscape Modeling.
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