Achieving higher standards in species distribution modeling by leveraging the diversity of available software
2025
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
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.
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