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ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions
Author(s) -
Kass Jamie M.,
Muscarella Robert,
Galante Peter J.,
Bohl Corentin L.,
PinillaBuitrago Gonzalo E.,
Boria Robert A.,
SoleyGuardia Mariano,
Anderson Robert P.
Publication year - 2021
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13628
Subject(s) - computer science , overfitting , data mining , field (mathematics) , software , metadata , spatial analysis , model selection , r package , data science , machine learning , remote sensing , mathematics , artificial neural network , pure mathematics , programming language , geology , operating system , computational science
Quantitative evaluations to optimize complexity have become standard for avoiding overfitting of ecological niche models (ENMs) that estimate species’ potential geographic distributions. ENMeval was the first R package to make such evaluations (often termed model tuning) widely accessible for the Maxent algorithm. It also provided multiple methods for partitioning occurrence data and reported various performance metrics. Requests by users, recent developments in the field, and needs for software compatibility led to a major redesign and expansion. We additionally conducted a literature review to investigate trends in ENMeval use (2015–2019). ENMeval 2.0 has a new object‐oriented structure for adding other algorithms, enables customizing algorithmic settings and performance metrics, generates extensive metadata, implements a null‐model approach to quantify significance and effect sizes, and includes features to increase the breadth of analyses and visualizations. In our literature review, we found insufficient reporting of model performance and parameterization, heavy reliance on model selection with AICc and low utilization of spatial cross‐validation; we explain how ENMeval 2.0 can help address these issues. This redesigned and expanded version can promote progress in the field and improve the information available for decision‐making.