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The influence of the number and distribution of background points in presence-background species distribution models

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  • Whitford, Anna M.
  • Shipley, Benjamin R.
  • McGuire, Jenny L.

Abstract

Species distribution models (SDMs), which relate recorded observations (presences) and absences or background points to environmental characteristics, are powerful tools used to generate hypotheses about the biogeography, ecology, and conservation of species. Although many researchers have examined the effects of presence and background point distributions on model outputs, they have not systematically evaluated the effects of various methods of background point sampling on the performance of a single model algorithm across many species. Therefore, a consensus on the preferred methods of background point sampling is lacking. Here, we conducted presence-background SDMs for 20 vertebrate species in North America under a variety of background point conditions, varying the number of background points used, the size of the buffer used to constrain the background points around the occurrences, and the percentage of background points sampled within the buffer (“spatial weighting”). We evaluated the accuracy and transferability of the models using Boyce index, overlap with expert-generated range maps, and area overpredicted and underpredicted by the SDM (and AUC for comparability with other studies).

Suggested Citation

  • Whitford, Anna M. & Shipley, Benjamin R. & McGuire, Jenny L., 2024. "The influence of the number and distribution of background points in presence-background species distribution models," Ecological Modelling, Elsevier, vol. 488(C).
    • Handle: RePEc:eee:ecomod:v:488:y:2024:i:c:s0304380023003344
    DOI: 10.1016/j.ecolmodel.2023.110604
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    References listed on IDEAS

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    1. Giovanelli, João G.R. & de Siqueira, Marinez Ferreira & Haddad, Célio F.B. & Alexandrino, João, 2010. "Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods," Ecological Modelling, Elsevier, vol. 221(2), pages 215-224.
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    4. Grimmett, Liam & Whitsed, Rachel & Horta, Ana, 2020. "Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics," Ecological Modelling, Elsevier, vol. 431(C).
    5. Chefaoui, Rosa M. & Lobo, Jorge M., 2008. "Assessing the effects of pseudo-absences on predictive distribution model performance," Ecological Modelling, Elsevier, vol. 210(4), pages 478-486.
    6. Iturbide, Maialen & Bedia, Joaquín & Herrera, Sixto & del Hierro, Oscar & Pinto, Miriam & Gutiérrez, Jose Manuel, 2015. "A framework for species distribution modelling with improved pseudo-absence generation," Ecological Modelling, Elsevier, vol. 312(C), pages 166-174.
    7. 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. Steen, Bart & Broennimann, Olivier & Maiorano, Luigi & Guisan, Antoine, 2024. "How sensitive are species distribution models to different background point selection strategies? A test with species at various equilibrium levels," Ecological Modelling, Elsevier, vol. 493(C).

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