Show simple item record

dc.contributor.authorIturbide Martínez de Albéniz, Maialen
dc.contributor.authorBedía Jiménez, Joaquín 
dc.contributor.authorHerrera García, Sixto 
dc.contributor.authorHierro, Oscar del
dc.contributor.authorPinto, Miriam
dc.contributor.authorGutiérrez Llorente, José Manuel
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2017-06-14T14:37:00Z
dc.date.available2017-06-14T14:37:00Z
dc.date.issued2015-06-10
dc.identifier.issn0304-3800
dc.identifier.issn1872-7026
dc.identifier.urihttp://hdl.handle.net/10902/11240
dc.description.abstractSpecies distribution models (SDMs) are an important tool in biogeography and phylogeography studies, that most often require explicit absence information to adequately model the environmental space on which species can potentially inhabit. In the so called background pseudoabsences approach, absence locations are simulated in order to obtain a complete sample of the environment. Whilst the commonest approach is random sampling of the entire study region, in its multiple variants, its performance may not be optimal, and the method of generation of pseudoabsences is known to have a significant influence on the results obtained. Here, we compare a suite of classic (random sampling) and novel methods for pseudo-absence data generation and propose a generalizable three-step method combining environmental profiling with a new technique for background extent restriction. To this aim, we consider 11 phylogenetic groups of Oak (Quercus sp.) described in Europe. We evaluate the influence of different pseudo-absence types on model performance (area under the ROC curve), calibration (reliability diagrams) and the resulting suitability maps, using a cross-validation approach. Regardless of the modelling algorithm used, randomsampling models were outperformed by the methods that incorporate environmental profiling of the background, stressing the importance of the pseudo-absence generation techniques for the development of accurate and reliable SDMs. We also provide an integrated modelling framework implementing the methods tested in a software package for the open source R environment.es_ES
dc.format.extent33 p.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rights© Elsevier.es_ES
dc.sourceEcological Modelling 312: 166-174 (2015)es_ES
dc.titleA framework for species distribution modelling with improved pseudo-absence generationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1016/j.ecolmodel.2015.05.018es_ES
dc.rights.accessRightsopenAccesses_ES
dc.type.versionsubmittedVersiones_ES


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record