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

TLDR

MaxEnt is a widely used species‑distribution modeling tool, favored for its high predictive accuracy and user‑friendly interface, yet many users rely on default settings without clear guidance on data selection or parameter choices. This paper aims to explain MaxEnt’s internal workings and outline modeling options so users can make informed decisions about data preparation, parameter selection, and output interpretation. The authors describe how background sampling reflects prior assumptions, how nonlinear environmental features are generated and selected, how to correct for sampling bias, and how to interpret various output types, illustrating these concepts with simulated data and Proteaceae occurrence records. Results demonstrate that MaxEnt outputs change markedly with different settings, underscoring the importance of making biologically motivated modeling choices.

Abstract

The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, 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 when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, 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 biologically motivated modeling decisions.