Journal
TRENDS IN ECOLOGY & EVOLUTION
Volume 29, Issue 7, Pages 384-389Publisher
ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tree.2014.04.009
Keywords
ecological theory; information entropy; macroecology; MaxEnt; maximum entropy theory of ecology
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Funding
- Gordon and Betty Moore Foundation
- US National Science Foundation (NSF) [NSF-EF-1137685]
- Emerging Frontiers
- Direct For Biological Sciences [1137685] Funding Source: National Science Foundation
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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.
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