Abstract:

The principle of maximum entropy (MaxEnt) was originally studied in information theory and statistical mechanics, and was widely employed in a variety of contexts. MaxEnt provides a statistical inference of unknown distributions on the basis of partial knowledge without taking into any unknown information. Recently there has been growing interest in the use of MaxEnt in ecology. In this review, to provide an intuitive understanding of this principle, we firstly employ an example of dice throwing to demonstrate the underlying basis of MaxEnt, and list the steps one should take when applying this principle. Then we focus on its applications in some fields of ecology and biodiversity, including the predicting of species relative abundances using community aggregated traits (CATs), the MaxEnt niche model of biogeography based on environmental factors, the studying of macroecology patterns such as species abundance distribution (SAD) and species-area relationship (SAR), inferences of species interactions using species abundance matrix or merely occurrence (presence/absence) data, and the predicting of food web degree distributions. We also highlight the main debates about these applications and some recent tests of these models' strengths and limitations. We conclude with the discussion of some matters of attention ecologists should keep in mind when using MaxEnt.

Key words: MaxEnt, Bayesian statistics, plant traits, species geographic distribution, macroecology, species interactions, degree distributions, neutral theory