Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24598-0_14 | CAEPIA |
Keywords | Field | DocType |
Terrestrial vertebrate species richness,Continuous Bayesian networks,Probabilistic reasoning,Regression | Ecology,Species richness,Naive Bayes classifier,Regression,Bayesian network,Probabilistic logic,Statistics,Geography | Conference |
Volume | ISSN | Citations |
9422 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. D. Maldonado | 1 | 1 | 1.07 |
| R. F. Ropero | 2 | 6 | 2.26 |
| P. A. Aguilera | 3 | 67 | 5.28 |
| Rafael Rumí | 4 | 352 | 26.55 |
| Antonio Salmerón | 5 | 595 | 58.71 |