Maximum information entropy: a foundation for ecological theory

John Harte; Erica A Newman

doi:10.1016/j.tree.2014.04.009

Maximum information entropy: a foundation for ecological theory

Trends Ecol Evol. 2014 Jul;29(7):384-9. doi: 10.1016/j.tree.2014.04.009. Epub 2014 May 23.

Authors

John Harte¹, Erica A Newman²

Affiliations

¹ 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.
² 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.

PMID: 24863182
DOI: 10.1016/j.tree.2014.04.009

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.

Keywords: MaxEnt; ecological theory; information entropy; macroecology; maximum entropy theory of ecology.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Review

MeSH terms

Animal Distribution*
Ecology*
Models, Biological*
Models, Statistical
Plant Dispersal*
Population Density