Show simple item record

Files in this item


Item metadata

dc.contributor.authorMiller, David Lawrence
dc.contributor.authorBurt, M Louise
dc.contributor.authorRexstad, Eric
dc.contributor.authorThomas, Len
dc.identifier.citationMiller , D L , Burt , M L , Rexstad , E & Thomas , L 2013 , ' Spatial models for distance sampling data : recent developments and future directions ' , Methods in Ecology and Evolution , vol. 4 , no. 11 , pp. 1001-1010 .
dc.identifier.otherPURE: 47731379
dc.identifier.otherPURE UUID: 8cd9b7c0-5b0e-4057-9a4b-f5f6c59ccd85
dc.identifier.otherScopus: 84887200344
dc.identifier.otherORCID: /0000-0002-7436-067X/work/29591687
dc.identifier.otherORCID: /0000-0002-4323-8161/work/29574865
dc.description.abstractOur understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates. Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods. We offer a comparison of recent advances in the field and consider the likely directions of future research. In particular we consider spatial modelling techniques that may be advantageous to applied ecologists such as quantification of uncertainty in a two-stage model and smoothing in areas with complex boundaries. The methods discussed are available in an \textsf{R} package developed by the authors and are largely implemented in the popular Windows package Distance (or are soon to be incorporated). Density surface modelling enables applied ecologists to reliably estimate abundances and create maps of animal/plant distribution. Such models can also be used to investigate the relationships between distribution and environmental covariates.
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.rights© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en
dc.subjectAbundance estimationen
dc.subjectDistance softwareen
dc.subjectGeneralized additive modelsen
dc.subjectLine transect samplingen
dc.subjectPoint transect samplingen
dc.subjectPopulation densityen
dc.subjectSpatial modellingen
dc.subjectWildlife surveysen
dc.subjectQA Mathematicsen
dc.titleSpatial models for distance sampling data : recent developments and future directionsen
dc.typeJournal articleen
dc.contributor.sponsorOffice of Naval Researchen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.description.statusPeer revieweden

This item appears in the following Collection(s)

Show simple item record