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Conference of the Spanish Association for Artificial Intelligence

CAEPIA 2015: Advances in Artificial Intelligence pp 153–163Cite as

Estimation of Species Richness Using Bayesian Networks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9422))

Abstract

We propose a new methodology based on continuous Bayesian networks for assessing species richness. Specifically, we applied a restricted structure Bayesian network, known as tree augmented naive Bayes, regarding a set of environmental continuous predictors. Firstly, we analyzed the relationships between the response variable (called the terrestrial vertebrate species richness) and a set of environmental predictors. Secondly, the learnt model was used to estimate the species richness in Andalusia (Spain) and the results were depicted on a map. The model managed to deal with the species richness - environment relationship, which is complex from the ecological point of view. The results highlight that landscape heterogeneity, topographical and social variables had a direct relationship with species richness while climatic variables showed more complicated relationships with the response.

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Notes

  1. 1.

    Data sources: Andalusian Environmental Network, Spanish Inventory of Terrestrial Species, Spanish National Geographic Institute and Multiterritorial Information System of Andalusia.

  2. 2.

    Variables representing landscape structure [3], calculated from the Andalusian Land Use and Land Cover Map.

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Acknowledgements

This work has been supported by the Spanish Ministry of Economy and Competitiveness through project TIN2013-46638-C3-1-P, by Junta de Andalucía through projects P12-TIC-2541 and P11-TIC-7821 and by ERDF (FEDER) funds. A.D. Maldonado and R. F. Ropero are being supported by the Spanish Ministry of Education, Culture and Sport through an FPU research grant, FPU2013/00547 and AP2012-2117 respectively.

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Authors and Affiliations

  1. Department of Mathematics, University of Almería, Almería, Spain

    A. D. Maldonado, R. Rumí & A. Salmerón

  2. Informatics and Environment Laboratory, Department of Biology and Geology, University of Almería, Almería, Spain

    R. F. Ropero & P. A. Aguilera

Authors
  1. A. D. Maldonado
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  2. R. F. Ropero
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  3. P. A. Aguilera
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  4. R. Rumí
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  5. A. Salmerón
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Corresponding author

Correspondence to A. D. Maldonado .

Editor information

Editors and Affiliations

  1. University of Castilla-La Mancha, Albacete, Spain

    José M. Puerta

  2. University of Castilla-La Mancha, Albacete, Spain

    José A. Gámez

  3. University of Cadiz, Cadiz, Spain

    Bernabe Dorronsoro

  4. Public University of Navarre, Pamplona, Spain

    Edurne Barrenechea

  5. Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso

  6. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Bruno Baruque

  7. Public University of Navarre, Pamplona, Spain

    Mikel Galar

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Maldonado, A.D., Ropero, R.F., Aguilera, P.A., Rumí, R., Salmerón, A. (2015). Estimation of Species Richness Using Bayesian Networks. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_14

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  • DOI: https://doi.org/10.1007/978-3-319-24598-0_14

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