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Continuous Bayesian networks for the estimation of species richness

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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 (TAN), regarding a set of environmental continuous predictors. First, we analysed the relationships between the response variable (called the terrestrial vertebrate species richness) and a set of environmental predictors. Second, the learnt model was used to estimate the species richness in Andalusia (Spain) and the results were depicted on a map. In addition to this, the TAN model was compared to three other methods commonly used for regression in terms of their root mean squared error. The experimental results showed that the TAN model not only was competitive from the point of view of accuracy but also 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. Data sources: Andalusian Environmental Network, Spanish Inventory of Terrestrial Species, Spanish National Geographic Institute and Multiterritorial Information System of Andalusia.

  2. Obtained from the Andalusian Land Use and Land Cover Map.

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Acknowledgments

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.

Additional information

A preliminary version of this work [26] was presented at the 16th Conference of the Spanish Association for Artificial Intelligence (CAEPIA’15).

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Maldonado, A.D., Ropero, R.F., Aguilera, P.A. et al. Continuous Bayesian networks for the estimation of species richness. Prog Artif Intell 4, 49–57 (2015). https://doi.org/10.1007/s13748-015-0067-8

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  • DOI: https://doi.org/10.1007/s13748-015-0067-8

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