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A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter

Cory Merow, John A. Silander, and Matthew J. Smith


The MaxEnt software package is one of the most popular tools for Species Distribution Modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: i) MaxEnt typically outperforms other methods and ii) the software is particularly easy to use. MaxEnt users can choose from a wide variety of settings and consequently must make decisions about how they should select and prepare their input data. The underlying basis for making these decisions is unclear in many studies, despite them often influencing model results. For example, MaxEnt’s default settings are often chosen as a consequence of unfamiliarity with the maximum entropy modeling method, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions about when preparing data, choosing settings and interpreting output .We explain how predictor variables are created and selected, how the choice of background samples reflects prior assumptions, how to account for environmentally biased presence samples, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt’s calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt’s outputs vary in response to different settings to highlight the need for making ecologically motivated modeling decisions.


Publication typeArticle
Published inEcography

Newer versions

Mindy M Syfert, Lucas N Joppa, Matthew J Smith, David A Coomes, Steven P Bachman, and Neil A Brummitt. Using species distribution models to inform IUCN Red List assessments, Biological Conservation, Elsevier, June 2014.

Previous versions

Mindy M. Syfert, Matthew J. Smith, and David A. Coomes. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models , PLOS One, PLoS, February 2013.

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