title: Maximum information entropy: a foundation for ecological theory.
abstract: The maximum information entropy (MaxEnt) principle is a successful method of statistical inference that has recently been applied to ecology. Here, we show how MaxEnt can accurately predict patterns such as species-area relationships (SARs) and abundance distributions in macroecology and be a foundation for ecological theory. We discuss the conceptual foundation of the principle, why it often produces accurate predictions of probability distributions in science despite not incorporating explicit mechanisms, and how mismatches between predictions and data can shed light on driving mechanisms in ecology. We also review possible future extensions of the maximum entropy theory of ecology (METE), a potentially important foundation for future developments in ecological theory.
authors.affiliation: Energy and Resources Group, University of California at Berkeley, 310 Barrows Hall, Berkeley, CA 94720, USA; Department of Environmental Science, Policy, and Management, University of California at Berkeley, 130 Mulford Hall, Berkeley, CA 94720, USA. Electronic address: jharte@berkeley.edu.
authors.forename: John
authors.initials: J
authors.lastname: Harte
authors.affiliation: Energy and Resources Group, University of California at Berkeley, 310 Barrows Hall, Berkeley, CA 94720, USA; Department of Environmental Science, Policy, and Management, University of California at Berkeley, 130 Mulford Hall, Berkeley, CA 94720, USA.
authors.forename: Erica A
authors.initials: EA
authors.lastname: Newman
mesh_terms: D063147:Animal Distribution; D004463:Ecology; D008954:Models, Biological; D015233:Models, Statistical; D063148:Plant Dispersal; D011156:Population Density
keywords: MaxEnt; ecological theory; information entropy; macroecology; maximum entropy theory of ecology