2013
DOI: 10.1111/j.1600-0587.2013.07872.x
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A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter

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 … Show more

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Cited by 2,838 publications
(2,973 citation statements)
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“…the 'relative occurrence rate', ROR) [34]. To facilitate comparison across species, we rescaled ROR values within species to range from 0 to 1 across the study extent.…”
Section: (C) Ecological Niche Modelsmentioning
confidence: 99%
“…the 'relative occurrence rate', ROR) [34]. To facilitate comparison across species, we rescaled ROR values within species to range from 0 to 1 across the study extent.…”
Section: (C) Ecological Niche Modelsmentioning
confidence: 99%
“…The models for the overall distribution of leopards and jaguars and the spatial distribution of melanism were generated using the maximum entropy algorithm implemented in the software Maxent [34] along with associated statistical tools [35][36][37][38]. The total set of location records divided by each phenotype for both species, 70% of which were used for training and 30% for testing the models, was employed.…”
Section: Modeling Procedures and Statistical Analysesmentioning
confidence: 99%
“…The algorithm used by MaxEnt is one of the most robust models of species distribution (MSD) in terms of successfully estimating the area from only a few records of presence (Hernández et al 2006), as occurs with most tropical epiphytes. MaxEnt has been widely tested for species idoneity in both present and future conditions (Merow et al 2013, Phillips 2008, Reina-Rodríguez et al 2016 and has been used with actual species from more than ten locations (Ramírez-Villegas et al 2014, Wiz et al 2008. In this study, to avoid over-fitting (Barbet-Massin et al 2012), 2120 pseudo-absences were included, with ten for each presence obtained from our database (herbaria data and fieldwork) in TDF-delimited areas.…”
Section: Methodsmentioning
confidence: 99%