Opening the black box: an open-source release of Maxent

By Steven J. Phillips, R. P. Anderson, Miroslav Dud'ik, Robert E. Schapire, Mary E. Blair. 2017

2,479 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/4ce7ec015d2561ecfed7a3c0b0f082fcb15c5a69

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In Ecological Modeling

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In Nature and Landscape Conservation

Citations in the corpus

Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, methodology): ““Default”: Models were built using current Maxent defaults—regularization multiplier (RM) of 1 and using linear, quadratic, hinge and product features (LQHP) for models calibrated using > 80 occurrence points (Phillips et al., 2017).”
Comprehensiveness of conservation of useful wild plants: An operational indicator for biodiversity and sustainable development targets (2019, background): “, 2006; Phillips and Dudik, 2008) and because of recent advances in making the algorithm’s underlying methodology openly accessible (Phillips et al., 2017).”
Predicting the Potential Distribution of Paeonia veitchii (Paeoniaceae) in China by Incorporating Climate Change into a Maxent Model (2019, methodology): “veitchii and the environmental parameters that were utilized, 141 the maximum entrophy method was adopted for the analysis of SDM for the current climate 142 conditions [33].”
ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions (2021, methodology): “When users prefer to select models with AICc, which comes with a statistical caveat for Maxent (Warren & Seifert, 2011), ENMeval 2.0 can additionally quantify how well such models predict withheld data compared with null models (Kass et al., 2020).”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, methodology): “Newer versions of Maxent software (e.g., version 3.4.0 and later) implement a new function to produce an estimate of occurrence probability: the complementary log–log function (Phillips et al. 2017).”
Two alternative evaluation metrics to replace the true skill statistic in the assessment of species distribution models (2019, methodology): “In this context, it has to be stressed that the default number of background points (10,000) in MAXENT, a broadly adopted maximum entropy machine-learning SDM algorithm (Phillips et al. 2004, 2017), is often insufficient and – unless increased sufficiently to capture the range of existing environmen”