ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models

By Robert Muscarella, Peter J. Galante, Mariano Soley-Guardia, Robert A. Boria, J. Kass, María Uriarte, R. P. Anderson. 2014

1,878 citations recorded by the source corpus.

Subfields: Ecological Modeling, Genetics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/58d5ebec52b2b37060f98be3c162f8518a7fed29

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Citations in the corpus

Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics (2018, methodology): “, 2006), with species-specific settings selected using the ENMeval (Muscarella et al., 2014) R package.”
ntbox: An r package with graphical user interface for modelling and evaluating multidimensional ecological niches (2020, neutral): “…distribu - tion modelling: NicheA (Qiao et al., 2016), kuenm (Cobos, Peterson, Barve, & Osorio-Olvera, 2019), Wallace (Kass et al., 2018), ENMeval (Muscarella et al., 2014), biomod2 (Thuiller, Lafourcade, Engler, & Araújo, 2009), dismo (Hijmans, Phillips, Leathwick, & Elith, 2019), sdm (Naimi &…”
Predicting range shifts of Asian elephants under global change (2019, methodology): “MaxEnt is known to facilitate species‐specific tuning of modelling decisions for improved performance and the calibration and evaluation of models via spatially independent training and test datasets to reduce model overfitting (Muscarella et al., 2014).”
ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions (2021, methodology): “*The two omission rates provided automatically for validation/testing data correspond to the following thresholds determined based on training data: minimum training presence and 10% omission of training presences (Muscarella et al., 2014)”
Diverse Mycorrhizal Associations Enhance Terrestrial C Storage in a Global Model (2019, methodology): “Niche models were created using theMaxent algorithm (Phillips et al., 2006) and validated against overfitting and accuracy via the ENMEval R package (Muscarella et al., 2014) following Kivlin et al. (2017).”
kuenm: an R package for detailed development of ecological niche models using Maxent (2019, background): “Other options for Maxent model calibration exist (e.g., Muscarella et al., 2014 ); however, we introduce an alternative that allows consideration of more parameter settings (particularly different sets of environmental variables) and a more robust metric of statistical significance (i.e., partial…”
Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, methodology): “…exhibit poorer performance in some cross-validation transferability assessments (e.g., slightly lower discriminatory ability on withheld testing data; Muscarella et al., 2014), although this may be confounded by geographic sampling bias present within training datasets (Galante et al., 2018).”
Environmental filtering improves ecological niche models across multiple scales (2019, methodology): “We used rasters and the ENMeval package in R (Muscarella et al., 2014) to calculate pairwise Schoener's D. Schoener's D statistic compares similarity in predictions on a scale from 0 (no overlap in predicted suitability) to 1 (identical predictions).”
Evaluating presence‐only species distribution models with discrimination accuracy is uninformative for many applications (2019, methodology): “Geographic partitioning: Same conditions as (1) but presence and background points for each species were split into four quadrants using ENMEval (Muscarella et al., 2014).”
Optimizing ensembles of small models for predicting the distribution of species with few occurrences (2018, methodology): “P; Muscarella et al., 2014) and the maxent r- package (to tune Max.”