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Predicting species’ abundances from occurrence data: Effects of sample size and bias

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  • Yañez-Arenas, Carlos
  • Guevara, Roger
  • Martínez-Meyer, Enrique
  • Mandujano, Salvador
  • Lobo, Jorge M.

Abstract

Modelling geographic patterns of abundance/density of species is an important step forward in ecological niche modelling, with implications for theoretical and applied ecology. The distance to the niche centroid approach (DNC) is a methodological development toward better understanding how the internal structure of species’ ecological niches is related to geographic patterns of abundance. We evaluated this approach under combinations of three sampling scenarios and three sampling intensities for a hypothetical species for which abundance patterns were ideal and strictly controlled. Our results indicate that predictive ability of the DNC approach increased with sample intensity, particularly under a strict random sampling scheme. Model performance under a sampling scenario biased by species' density fell slightly, but was importantly reduced when the source of the biases were attractor sites unrelated with species’ traits. We conclude that the DNC approach is only suitable to model species’ abundances/densities under particular conditions. First because it is necessary fulfill some assumptions (discussed in this paper), and second because its performance strongly depends on sampling characteristics that are unusual in most biodiversity data.

Suggested Citation

  • Yañez-Arenas, Carlos & Guevara, Roger & Martínez-Meyer, Enrique & Mandujano, Salvador & Lobo, Jorge M., 2014. "Predicting species’ abundances from occurrence data: Effects of sample size and bias," Ecological Modelling, Elsevier, vol. 294(C), pages 36-41.
    • Handle: RePEc:eee:ecomod:v:294:y:2014:i:c:p:36-41
    DOI: 10.1016/j.ecolmodel.2014.09.014
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    1. A. Townsend Peterson & Miguel A. Ortega-Huerta & Jeremy Bartley & Victor Sánchez-Cordero & Jorge Soberón & Robert H. Buddemeier & David R. B. Stockwell, 2002. "Future projections for Mexican faunas under global climate change scenarios," Nature, Nature, vol. 416(6881), pages 626-629, April.
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