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Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods

Author

Listed:
  • Giovanelli, João G.R.
  • de Siqueira, Marinez Ferreira
  • Haddad, Célio F.B.
  • Alexandrino, João

Abstract

We here examine species distribution models for a Neotropical anuran restricted to ombrophilous areas in the Brazilian Atlantic Forest hotspot. We extend the known occurrence for the treefrog Hypsiboas bischoffi (Anura: Hylidae) through GPS field surveys and use five modeling methods (BIOCLIM, DOMAIN, OM-GARP, SVM, and MAXENT) and selected bioclimatic and topographic variables to model the species distribution. Models were first trained using two calibration areas: the Brazilian Atlantic Forest (BAF) and the whole of South America (SA). All modeling methods showed good levels of predictive power and accuracy with mean AUC ranging from 0.77 (BIOCLIM/BAF) to 0.99 (MAXENT/SA). MAXENT and SVM were the most accurate presence-only methods among those tested here. All but the SVM models calibrated with SA predicted larger distribution areas when compared to models calibrated in BAF. OM-GARP dramatically overpredicted the species distribution for the model calibrated in SA, with a predicted area around 106km2 larger than predicted by other SDMs. With increased calibration area (and environmental space), OM-GARP predictions followed changes in the environmental space associated with the increased calibration area, while MAXENT models were more consistent across calibration areas. MAXENT was the only method that retrieved consistent predictions across calibration areas, while allowing for some overprediction, a result that may be relevant for modeling the distribution of other spatially restricted organisms.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:2:p:215-224
    DOI: 10.1016/j.ecolmodel.2009.10.009
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    References listed on IDEAS

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    1. Norman Myers & Russell A. Mittermeier & Cristina G. Mittermeier & Gustavo A. B. da Fonseca & Jennifer Kent, 2000. "Biodiversity hotspots for conservation priorities," Nature, Nature, vol. 403(6772), pages 853-858, February.
    2. Christopher J. Raxworthy & Enrique Martinez-Meyer & Ned Horning & Ronald A. Nussbaum & Gregory E. Schneider & Miguel A. Ortega-Huerta & A. Townsend Peterson, 2003. "Predicting distributions of known and unknown reptile species in Madagascar," Nature, Nature, vol. 426(6968), pages 837-841, December.
    3. VanDerWal, Jeremy & Shoo, Luke P. & Graham, Catherine & Williams, Stephen E., 2009. "Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?," Ecological Modelling, Elsevier, vol. 220(4), pages 589-594.
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    Cited by:

    1. Azuaje-Rodríguez, Roxiris A. & Silva, Sofia Marques & Carlos, Caio J., 2022. "Not going with the flow: Ecological niche of a migratory seabird, the South American Tern Sterna hirundinacea," Ecological Modelling, Elsevier, vol. 463(C).
    2. 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).
    3. 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).

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