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Assessing the effects of pseudo-absences on predictive distribution model performance

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  • Chefaoui, Rosa M.
  • Lobo, Jorge M.

Abstract

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudo-absence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study shows that if we do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:210:y:2008:i:4:p:478-486
    DOI: 10.1016/j.ecolmodel.2007.08.010
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    1. Chefaoui, Rosa M. & Serrão, Ester A., 2017. "Accounting for uncertainty in predictions of a marine species: Integrating population genetics to verify past distributions," Ecological Modelling, Elsevier, vol. 359(C), pages 229-239.
    2. Hengl, Tomislav & Sierdsema, Henk & Radović, Andreja & Dilo, Arta, 2009. "Spatial prediction of species’ distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging," Ecological Modelling, Elsevier, vol. 220(24), pages 3499-3511.
    3. Václavík, Tomáš & Meentemeyer, Ross K., 2009. "Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?," Ecological Modelling, Elsevier, vol. 220(23), pages 3248-3258.
    4. Senait D Senay & Susan P Worner & Takayoshi Ikeda, 2013. "Novel Three-Step Pseudo-Absence Selection Technique for Improved Species Distribution Modelling," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-16, August.
    5. 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.
    6. Laze, Kuenda & Gordon, Ascelin, 2016. "Incorporating natural and human factors in habitat modelling and spatial prioritisation for the Lynx lynx martinoi," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 16(1), pages 17-31.
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    9. Watling, James I. & Romañach, Stephanie S. & Bucklin, David N. & Speroterra, Carolina & Brandt, Laura A. & Pearlstine, Leonard G. & Mazzotti, Frank J., 2012. "Do bioclimate variables improve performance of climate envelope models?," Ecological Modelling, Elsevier, vol. 246(C), pages 79-85.
    10. Doko, Tomoko & Fukui, Hiromichi & Kooiman, Andre & Toxopeus, A.G. & Ichinose, Tomohiro & Chen, Wenbo & Skidmore, A.K., 2011. "Identifying habitat patches and potential ecological corridors for remnant Asiatic black bear (Ursus thibetanus japonicus) populations in Japan," Ecological Modelling, Elsevier, vol. 222(3), pages 748-761.
    11. Haider, Saira M. & Benscoter, Allison M. & Pearlstine, Leonard & D'Acunto, Laura E. & Romañach, Stephanie S., 2021. "Landscape-scale drivers of endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) presence using an ensemble modeling approach," Ecological Modelling, Elsevier, vol. 461(C).
    12. Jiang, Dong & Wang, Qian & Ding, Fangyu & Fu, Jingying & Hao, Mengmeng, 2019. "Potential marginal land resources of cassava worldwide: A data-driven analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 167-173.
    13. Brice B Hanberry & Hong S He & Brian J Palik, 2012. "Pseudoabsence Generation Strategies for Species Distribution Models," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-12, August.
    14. Mouton, Ans M. & De Baets, Bernard & Goethals, Peter L.M., 2010. "Ecological relevance of performance criteria for species distribution models," Ecological Modelling, Elsevier, vol. 221(16), pages 1995-2002.
    15. Sillero, Neftalí & Arenas-Castro, Salvador & Enriquez‐Urzelai, Urtzi & Vale, Cândida Gomes & Sousa-Guedes, Diana & Martínez-Freiría, Fernando & Real, Raimundo & Barbosa, A.Márcia, 2021. "Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling," Ecological Modelling, Elsevier, vol. 456(C).
    16. Peiwei Fan & Mengmeng Hao & Fangyu Ding & Dong Jiang & Donglin Dong, 2020. "Quantifying Global Potential Marginal Land Resources for Switchgrass," Energies, MDPI, vol. 13(23), pages 1-13, November.
    17. 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.
    18. Stokland, Jogeir N. & Halvorsen, Rune & Støa, Bente, 2011. "Species distribution modelling—Effect of design and sample size of pseudo-absence observations," Ecological Modelling, Elsevier, vol. 222(11), pages 1800-1809.

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