Environmental and Ecological Statistics

, Volume 12, Issue 1, pp 27–44

Prediction of potential areas of species distributions based on presence-only data

  • Jorge A. Argáez
  • J. Andrés Christen
  • Miguel Nakamura
  • Jorge Soberón
Article

DOI: 10.1007/s10651-005-6816-2

Cite this article as:
Argáez, J., Andrés Christen, J., Nakamura, M. et al. Environ Ecol Stat (2005) 12: 27. doi:10.1007/s10651-005-6816-2

Abstract

We introduce a methodology to infer zones of high potential for the habitat of a species, useful for management of biodiversity, conservation, biogeography, ecology, or sustainable use. Inference is based on a set of sites where the presence of the species has been reported. Each site is associated with covariate values, measured on discrete scales. We compute the predictive probability that the species is present at each node of a regular grid. Possible spatial bias for sites of presence is accounted for. Since the resulting posterior distribution does not have a closed form, a Markov chain Monte Carlo (MCMC) algorithm is implemented. However, we also describe an approximation to the posterior distribution, which avoids MCMC. Relevant features of the approach are that specific notions of data acquisition such as sampling intensity and detectability are accounted for, and that available a priori information regarding areas of distribution of the species is incorporated in a clear-cut way. These concepts, arising in the presence-only context, are not addressed in alternative methods. We also consider an uncertainty map, which measures the variability for the predictive probability at each node on the grid. A simulation study is carried out to test and compare our approach with other standard methods. Two case studies are also presented.

Keywords

biodiversity ecology mixture model predictive probability map prior elicitation 

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Jorge A. Argáez
    • 1
    • 2
  • J. Andrés Christen
    • 1
  • Miguel Nakamura
    • 1
  • Jorge Soberón
    • 3
    • 4
  1. 1.Centro de Investigación en Matemáticas, A. C.GuanajuatoMéxico
  2. 2.Centro de Investigación Científica de Yucatán A. C.México
  3. 3.Instituto de Ecología Universidad Nacional Autónoma de MéxicoParque del PedregalMéxico
  4. 4.Universidad Nacional Autónoma de México, Comisión Nacional para el Conocimiento y Uso de la BiodiversidadParque del PedregalMéxico

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