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Determining the minimal background area for species distribution models: MinBAR package

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  • Rotllan-Puig, Xavier
  • Traveset, Anna

Abstract

One of the crucial choices when modelling species distributions using pseudo-absences and background approaches is the delineation of the background area to fit the model. We hypothesise that there is a minimum background area around the geographical centre of the species distribution that characterises well enough the range of environmental conditions needed by the species to survive. Thus, fitting the model within this geographical area should be the optimal solution in terms of both quality of the model and execution time. MinBAR is an R package that calculates the optimal background area by means of sequentially fitting several concentric species distribution models (SDMs) until a satisfactory model in terms of the included metrics is reached. The version 1.1.2 is implemented for MaxEnt (using either maxnet or the original java program) and uses Boyce Index as a metric to assess models performance. Three case studies are presented to test the hypothesis and assess package's functionality. We show how partial models trained with part of the species distribution often perform equal or better than those fitted on the entire extent. MinBAR is a versatile tool that helps modellers to objectively define the optimal solution.

Suggested Citation

  • Rotllan-Puig, Xavier & Traveset, Anna, 2021. "Determining the minimal background area for species distribution models: MinBAR package," Ecological Modelling, Elsevier, vol. 439(C).
    • Handle: RePEc:eee:ecomod:v:439:y:2021:i:c:s0304380020304191
    DOI: 10.1016/j.ecolmodel.2020.109353
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    1. Jarnevich, Catherine S. & Talbert, Marian & Morisette, Jeffery & Aldridge, Cameron & Brown, Cynthia S. & Kumar, Sunil & Manier, Daniel & Talbert, Colin & Holcombe, Tracy, 2017. "Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection," Ecological Modelling, Elsevier, vol. 363(C), pages 48-56.
    2. Duque-Lazo, J. & van Gils, H. & Groen, T.A. & Navarro-Cerrillo, R.M., 2016. "Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia," Ecological Modelling, Elsevier, vol. 320(C), pages 62-70.
    3. Melo-Merino, Sara M. & Reyes-Bonilla, Héctor & Lira-Noriega, Andrés, 2020. "Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence," Ecological Modelling, Elsevier, vol. 415(C).
    4. Barve, Narayani & Barve, Vijay & Jiménez-Valverde, Alberto & Lira-Noriega, Andrés & Maher, Sean P. & Peterson, A. Townsend & Soberón, Jorge & Villalobos, Fabricio, 2011. "The crucial role of the accessible area in ecological niche modeling and species distribution modeling," Ecological Modelling, Elsevier, vol. 222(11), pages 1810-1819.
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    1. Sillero, Neftalí & Campos, João Carlos & Arenas-Castro, Salvador & Barbosa, A.Márcia, 2023. "A curated list of R packages for ecological niche modelling," Ecological Modelling, Elsevier, vol. 476(C).

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