title: A future for models and data in environmental science.
abstract: Together, graphical models and the Bayesian paradigm provide powerful new tools that promise to change the way that environmental science is done. The capacity to merge theory with mechanistic understanding and empirical evidence, to assimilate diverse sources of information and to accommodate complexity will transform the collection and interpretation of data. As we discuss here, we specifically expect a shift from a focus on simple experiments with inflexible design and selection among models that embrace parts of processes to a synthesis of integrated process models. With this potential come new challenges, including some that are specific and technical and others that are general and will involve reexamination of the role of inference and prediction.
authors.affiliation: Nicholas School of the Environment and Department of Biology, Duke University, Durham, NC 27708, USA. jimclark@duke.edu
authors.forename: James S
authors.initials: JS
authors.lastname: Clark
authors.forename: Alan E
authors.initials: AE
authors.lastname: Gelfand
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D003625:Data Collection / Q000379:methods*; D004463:Ecology / Q000379:methods*; D005544:Forecasting; D006801:Humans; D008954:Models, Biological; D011153:Population