Invited Appropriate complexity landscape modeling

Advances incomputing technology, new and ongoingrestoration initiatives, concernsaboutclimate change'sef- fects, and the increasing interdisciplinarity of research have encouraged the development of landscape-scale mechanistic models of coupled ecological-geophysical systems. However, communication barriers and uneven in ﬁ ltration of new strategies for data-driven induction persist in the context of simulation model development across disciplines. One challenge is that ecology and the geosciences have embraced different modeling episte- mologies, with ecologists historically favoring inductive inference from generalized, phenomenological models and geoscientists favoring deductive inference from detailed ﬁ rst-principles models. Today, many models used for environmental management, particularly for aquatic ecosystems, tend to be highly detailed, with ecological and geophysical components represented in different modules that are linked but often not closely integrated. These observations highlight a need for cross-disciplinary dialogue about landscape-scale modeling objectives andapproaches.Thephilosophiesofpattern-orientedmodelinginecologyandexploratorymodelingingeophys- icshaveyieldedadvancesintheoreticalandappliedknowledgeinbothofthosedisciplines,buttheyarenotcom-prehensive across all aspects of landscape-scale modeling. Here we de ﬁ ne and synthesize the “ Appropriate- Complexity Method ” (ACME), which builds upon these two philosophies to guide the development of process-oriented models across a spectrum of scienti ﬁ c and management objectives. ACME helps modelers ef ﬁ ciently converge upon an optimal modeling structure through: i) systematic evaluation of the attributes that comprise computationalandrepresentationaldetail,forwhichwehavedevelopedanoperationaldecisiontree;ii)iterative adjustment of models based on pattern-oriented model evaluation strategies; and iii) the use of appropriate datasets (where applicable) to build conceptual models and formulate predictions. Decisions about aspects of computational and representational detail are based on the landscape's emergent properties. They are also based on a hierarchy of classes of questions governing model objectives that represent a multi-attribute tradeoff among validation potential, interpretability, tractability, and generality as functions of computational and representational detail.Tradeoffcurves, togetherwith modelobjectives, provide furtherguidance for determining the “ appropriate ” level of complexity for representation of processes in models. Once deemed adequate for address-ing the original research question of interest, models may be used for projection and scenario testing. They may next undergo expansion that moves them down the hierarchy, where they can then be used to address research questionsofhigherspeci ﬁ city,detail,andvalidationpotential,thoughatacostoflowertractabilityandinterpret-ability on the tradeoffcurves.Thispractical,systematicprocedure provides clear guidance for the design andim- provement of landscape models that may be used to address a wide variety of questions relevant to restoration, over a spectrum of scales. © 2016 The Author(s). Published by Elsevier B.V. This is an open access under the CC