Estimation of Species Richness Using Bayesian Networks

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.