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

Kass Jamie M., Smith Adam B., Warren Dan L., Vignali Segio, Schmitt Sylvain, Aiello-Lammens Matthew E., Arlé Eduardo, Márcia Barbosa Ana, Broennimann Olivier, Cobos Marlon E., Guéguen Maya, Guisan Antoine, Merow Cory, Naimi Babak, Nobis Michael P., Ondo Ian, Osorio-Olvera Luis, Owens Hannah L., Pinill-Buitrago Gonzalo E., Sánchez-Tapia Andrea, Thuiller Wilfried, Valavi Roozbeh, Velazco Santiago José Elías, Zizka Alexander, Zurell Damaris. 2025. Achieving higher standards in species distribution modeling by leveraging the diversity of available software. Ecography, 2025 (2):e07346, 14 p.

Url - autres données associées : https://github.com/sylvainschmitt/sdmverse / Url - jeu de données - Entrepôt autre : https://doi.org/10.5281/zenodo.13927581 / Url - jeu de données - Entrepôt autre : https://doi.org/10.6084/m9.figshare.27312903 / Url - autres données associées : https://github.com/jamiemkass/sdm-pkg-plurality-tutorials / Url - jeu de données - Entrepôt autre : https://doi.org/10.5281/zenodo.14044044

Résumé : 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.

Mots-clés Agrovoc : distribution géographique, méthodologie, modèle de simulation, dynamique des populations, modélisation environnementale, écologie, modèle mathématique

Mots-clés libres : Code, Habitat, Niche, Programming, R package, Reproducibility

Projets sur financement : Non spécifié

Auteurs et affiliations

• Kass Jamie M., Tohoku University (JPN) - auteur correspondant
• Smith Adam B., Missouri Botanical Garden (USA)
• Warren Dan L., Okinawa Institute of Science and Technology Graduate University (JPN)
• Vignali Segio, Université de Berne (CHE)
• Schmitt Sylvain, CIRAD-ES-UPR Forêts et sociétés (FRA) ORCID: 0000-0001-7759-7106
• Aiello-Lammens Matthew E., Pace University (USA)
• Arlé Eduardo, Tel Aviv University (ISR)
• Márcia Barbosa Ana, University of Porto (PRT)
• Broennimann Olivier, UNIL (CHE)
• Cobos Marlon E., University of Kansas (USA)
• Guéguen Maya, Université de Savoie Mont Blanc (FRA)
• Guisan Antoine, UNIL (CHE)
• Merow Cory, University of Connecticut (USA)
• Naimi Babak, University of Utrecht (NLD)
• Nobis Michael P., Swiss Federal Research Institute (CHE)
• Ondo Ian, Royal Botanic Gardens (GBR)
• Osorio-Olvera Luis, UNAM (MEX)
• Owens Hannah L., UCPH (DNK)
• Pinill-Buitrago Gonzalo E., University of New-York (USA)
• Sánchez-Tapia Andrea, JBRJ (BRA)
• Thuiller Wilfried, Université Grenoble Alpes (FRA)
• Valavi Roozbeh, CSIRO (AUS)
• Velazco Santiago José Elías, Universidad Nacional de Misiones (ARG)
• Zizka Alexander, Philipps University Marburg (DEU)
• Zurell Damaris, University of Potsdam (DEU)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/612976/)

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