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ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions

Jamie M. KassBiodiversity and Biocomplexity Unit Okinawa Institute of Science and Technology Graduate University Okinawa JapanRobert MuscarellaPlant Ecology and Evolution Evolutionary Biology Centre Uppsala University Uppsala SwedenPeter J. GalanteCenter for Biodiversity and Conservation American Museum of Natural History New York NY USACorentin BohlGonzalo E. Pinilla‐BuitragoDepartment of Biology City College of New York City University of New York New York NY USARobert A. BoriaQuantitative and Systems Biology Graduate group University of California‐Merced Merced CA USAMariano Soley‐GuardiaEscuela de Biología Universidad de Costa Rica, and Ciudad Universitaria San Pedro Costa RicaRobert P. AndersonDepartment of Biology City College of New York City University of New York New York NY USA
2021en
ABI

Аннотация

Abstract 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. ​

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