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Achieving higher standards in species distribution modeling by leveraging the diversity of available software

  • Jamie M. Kass
  • , Adam B. Smith
  • , Dan L. Warren
  • , Sergio Vignali
  • , Sylvain Schmitt
  • , Matthew E. Aiello-Lammens
  • , Eduardo Arlé
  • , Ana Márcia Barbosa
  • , Olivier Broennimann
  • , Marlon E. Cobos
  • , Maya Guéguen
  • , Antoine Guisan
  • , Cory Merow
  • , Babak Naimi
  • , Michael P. Nobis
  • , Ian Ondo
  • , Luis Osorio-Olvera
  • , Hannah L. Owens
  • , Gonzalo E. Pinilla-Buitrago
  • , Andrea Sánchez-Tapia
  • Wilfried Thuiller, Roozbeh Valavi, Santiago José Elías Velazco, Alexander Zizka, Damaris Zurell
  • Tohoku University
  • Okinawa Institute of Science and Technology Graduate University
  • Missouri Botanical Garden
  • University of Bern
  • UPR Forêts et Sociétés
  • University Montpellier
  • Pace University
  • Tel Aviv University
  • German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
  • University of Porto
  • University of Lausanne
  • University of Kansas
  • Laboratoire d'Ecologie Alpine
  • University of Connecticut
  • Utrecht University
  • Swiss Federal Institute for Forest, Snow and Landscape Research
  • Royal Botanic Gardens Kew
  • UN Environment World Conservation Monitoring Centre
  • Universidad Nacional Autónoma de México
  • CONAHCyT
  • University of Copenhagen
  • Florida Museum of Natural History
  • City University of New York
  • Instituto de Pesquisas Jardim Botânico do Rio de Janeiro
  • CSIRO
  • Instituto de Biología Subtropical
  • San Diego State University
  • Philipps-Universität Marburg
  • Potsdam University

Research output: Contribution to journalArticlepeer-review

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Abstract

The increasing online availability of biodiversity data and advances in ecological modeling have led to a proliferation of open-source modeling tools. In particular, R packages for species distribution modeling continue to multiply without guidance on how they can be employed together, resulting in high fidelity of researchers to one or several packages. Here, we assess the wide variety of software for species distribution models (SDMs) and highlight how packages can work together to diversify and expand analyses in each step of a modeling workflow. We also introduce the new R package ‘sdmverse' to catalog metadata for packages, cluster them based on their methodological functions, and visualize their relationships. To demonstrate how pluralism of software use helps improve SDM workflows, we provide three extensive and fully documented analyses that utilize tools for modeling and visualization from multiple packages, then score these tutorials according to recent methodological standards. We end by identifying gaps in the capabilities of current tools and highlighting outstanding challenges in the development of software for SDMs.

Original languageEnglish
Article numbere07346
Pages (from-to)1-14
Number of pages14
JournalEcography
Volume2025
Issue number2
Early online date19 Nov 2024
DOIs
Publication statusPublished - Feb 2025

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