To cite biomod2 in publications, please use (and check your version):

Guéguen M, Blancheteau H, Lemaire-Patin R, Thuiller W (2026). “'biomod2' - extending presence-absence species distribution models to multiple data types.” Ecography, 2026(9), e08588. doi:10.1002/ecog.08588. R package version 4.3-4-7 (2026-10-07), https://biomodhub.github.io/biomod2/.

Corresponding BibTeX entry:

  @Article{,
    author = {Maya Guéguen and Hélène Blancheteau and Rémi
      Lemaire-Patin and Wilfried Thuiller},
    title = {'biomod2' - extending presence-absence species
      distribution models to multiple data types},
    journal = {Ecography},
    volume = {2026},
    number = {9},
    pages = {e08588},
    year = {2026},
    doi = {10.1002/ecog.08588},
    url = {https://biomodhub.github.io/biomod2/},
    note = {R package version 4.3-4-7 (2026-10-07)},
    keywords = {abundance, habitat distribution model, reproducibility,
      species distribution model},
    abstract = {The R package 'biomod2' is one of the most widely used
      and versatile tools for species distribution modelling (SDM),
      enabling ecologists to calibrate, evaluate, and project
      species-environment relationships across space and time using
      multiple modelling algorithms and ensemble forecasting. Here, we
      present its latest and most comprehensive version, which
      substantially expands its scope by accommodating diverse
      ecological data types within a unified modelling workflow.
      Importantly, 'biomod2' is designed to handle each data type (e.g.
      presence-only, presence-absence, counts, multi-class abundance,
      or relative/absolute abundance) independently, ensuring
      methodological rigor and avoiding the integration of incompatible
      data formats or model types. This approach allows users to model
      biomass, land cover, habitat suitability, and other ecological
      metrics without conflating distinct data streams or analytical
      frameworks.  We have fully restructured the workflow to improve
      usability and reproducibility, introducing clearer function
      organization, standardized parameter names, and substantially
      revised documentation. The update also includes new
      methodological features such as enhanced cross-validation
      schemes, improved pseudo-absence selection strategies, expanded
      model parametrization options, additional algorithms, and new
      tools for exploring and visualizing outputs. Together, these
      developments provide a more flexible, transparent, and easily
      shareable framework for SDM, supporting comparison, uncertainty
      analyses, and ensemble predictions across a broad range of
      ecological questions.},
  }