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dc.contributor.authorNightingale, Glenna Faith
dc.contributor.authorLaland, Kevin Neville
dc.contributor.authorHoppitt, William John Edward
dc.contributor.authorNightingale, Peter
dc.date.accessioned2015-07-14T14:10:02Z
dc.date.available2015-07-14T14:10:02Z
dc.date.issued2015-07-02
dc.identifier.citationNightingale , G F , Laland , K N , Hoppitt , W J E & Nightingale , P 2015 , ' Bayesian spatial NBDA for diffusion data with home-base coordinates ' PLoS One , vol. 10 , no. 7 , e0130326 . DOI: 10.1371/journal.pone.0130326en
dc.identifier.issn1932-6203
dc.identifier.otherPURE: 201364585
dc.identifier.otherPURE UUID: deeeab5d-3260-42cd-a95e-7c4522b0ab7a
dc.identifier.otherScopus: 84940421912
dc.identifier.urihttp://hdl.handle.net/10023/6981
dc.identifier.urihttp://dx.doi.org/10.17630/598D93F6-8B24-45D1-B606-05950144691Cen
dc.description.abstractNetwork-based diffusion analysis (NBDA) is a statistical method that allows the researcher to identify and quantify a social influence on the spread of behaviour through a population. Hitherto, NBDA analyses have not directly modelled spatial population structure. Here we present a spatial extension of NBDA, applicable to diffusion data where the spatial locations of individuals in the population, or of their home bases or nest sites, are available. The method is based on the estimation of inter-individual associations (for association matrix construction) from the mean inter-point distances as represented on a spatial point pattern of individuals, nests or home bases. We illustrate the method using a simulated dataset, and show how environmental covariates (such as that obtained from a satellite image, or from direct observations in the study area) can also be included in the analysis. The analysis is conducted in a Bayesian framework, which has the advantage that prior knowledge of the rate at which the individuals acquire a given task can be incorporated into the analysis. This method is especially valuable for studies for which detailed spatially structured data, but no other association data, is available. Technological advances are making the collection of such data in the wild more feasible: for example, bio-logging facilitates the collection of a wide range of variables from animal populations in the wild. We provide an R package, spatialnbda, which is hosted on the Comprehensive R Archive Network (CRAN). This package facilitates the construction of association matrices with the spatial x and y coordinates as the input arguments, and spatial NBDA analyses.en
dc.language.isoeng
dc.relation.ispartofPLoS Oneen
dc.rights© 2015 Nightingale et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are crediteden
dc.subjectBayesian inferenceen
dc.subjectSocial learningen
dc.subjectNBDAen
dc.subjectSpatialen
dc.subjectPoint processesen
dc.subjectQH301 Biologyen
dc.subjectHA Statisticsen
dc.subjectQA76 Computer softwareen
dc.subjectNDASen
dc.subject.lccQH301en
dc.subject.lccHAen
dc.subject.lccQA76en
dc.titleBayesian spatial NBDA for diffusion data with home-base coordinatesen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. Geography & Sustainable Developmenten
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.contributor.institutionUniversity of St Andrews. Centre for Social Learning & Cognitive Evolutionen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. Centre for Interdisciplinary Research in Computational Algebraen
dc.contributor.institutionUniversity of St Andrews. Centre for Biological Diversityen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0130326
dc.description.statusPeer revieweden


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