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dc.contributor.authorSenay, S. D.en
dc.contributor.authorIkeda, Takayoshien
dc.contributor.authorWorner, Susan P.en
dc.date.accessioned2013-10-22T20:20:34Z
dc.date.issued2013-08en
dc.identifier.citationSenay S.D., Worner S.P., Ikeda T. (2013). Novel three-step pseudo-absence selection technique for improved species distribution modelling. PLoS ONE, 8(8): e71218. doi:10.1371/journal.pone.0071218en
dc.identifier.issn1932-6203en
dc.identifier.urihttps://hdl.handle.net/10182/5696
dc.description.abstractPseudo-absence selection for spatial distribution models (SDMs) is the subject of ongoing investigation. Numerous techniques continue to be developed, and reports of their effectiveness vary. Because the quality of presence and absence data is key for acceptable accuracy of correlative SDM predictions, determining an appropriate method to characterise pseudo-absences for SDM’s is vital. The main methods that are currently used to generate pseudo-absence points are: 1) randomly generated pseudo-absence locations from background data; 2) pseudo-absence locations generated within a delimited geographical distance from recorded presence points; and 3) pseudo-absence locations selected in areas that are environmentally dissimilar from presence points. There is a need for a method that considers both geographical extent and environmental requirements to produce pseudo-absence points that are spatially and ecologically balanced. We use a novel three-step approach that satisfies both spatial and ecological reasons why the target species is likely to find a particular geolocation unsuitable. Step 1 comprises establishing a geographical extent around species presence points from which pseudo-absence points are selected based on analyses of environmental variable importance at different distances. This step gives an ecologically meaningful explanation to the spatial range of background data, as opposed to using an arbitrary radius. Step 2 determines locations that are environmentally dissimilar to the presence points within the distance specified in step one. Step 3 performs K-means clustering to reduce the number of potential pseudo-absences to the desired set by taking the centroids of clusters in the most environmentally dissimilar class identified in step 2. By considering spatial, ecological and environmental aspects, the three-step method identifies appropriate pseudo-absence points for correlative SDMs. We illustrate this method by predicting the New Zealand potential distribution of the Asian tiger mosquito (Aedes albopictus) and the Western corn rootworm (Diabrotica virgifera virgifera).en
dc.description.sponsorshipThis research was supported by a PhD and writing scholarship award to SS in Lincoln University by the Bio-Protection Research Centre. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.format.extente71218-e71218en
dc.language.isoenen
dc.publisherPLOSen
dc.relationThe original publication is available from - PLOS - https://doi.org/10.1371/journal.pone.0071218en
dc.relation.urihttps://doi.org/10.1371/journal.pone.0071218en
dc.rights© 2013 Senay et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectpseudo-absence selection techniqueen
dc.subjectinvasive speciesen
dc.subjectbiological controlen
dc.subjectspatial distribution modelsen
dc.subjectconservationen
dc.subjectGeneral Science & Technologyen
dc.subject.meshAnimalsen
dc.subject.meshCulicidaeen
dc.subject.meshModels, Statisticalen
dc.subject.meshReproducibility of Resultsen
dc.subject.meshEnvironmenten
dc.subject.meshEcosystemen
dc.subject.meshPopulation Dynamicsen
dc.subject.meshNew Zealanden
dc.subject.meshSpatial Analysisen
dc.titleNovel three-step pseudo-absence selection technique for improving species distribution modellingen
dc.typeJournal Article
lu.contributor.unitLincoln Universityen
lu.contributor.unitBio-Protection Research Centreen
dc.identifier.doi10.1371/journal.pone.0071218en
dc.relation.isPartOfPLoS Oneen
pubs.issue8en
pubs.organisational-group/LU
pubs.organisational-group/LU/BPRC
pubs.publication-statusPublisheden
pubs.volume8en
dc.rights.licenceAttributionen
dc.rights.licenceAttributionen


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