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dc.contributor.authorJones, Esther Lane
dc.contributor.authorRendell, Luke Edward
dc.contributor.authorPirotta, Enrico
dc.contributor.authorLong, Jed A.
dc.date.accessioned2016-06-15T08:30:05Z
dc.date.available2016-06-15T08:30:05Z
dc.date.issued2016-11
dc.identifier.citationJones , E L , Rendell , L E , Pirotta , E & Long , J A 2016 , ' Novel application of a quantitative spatial comparison tool to species distribution data ' Ecological Indicators , vol. 70 , pp. 67-76 . DOI: 10.1016/j.ecolind.2016.05.051en
dc.identifier.issn1470-160X
dc.identifier.otherPURE: 243135012
dc.identifier.otherPURE UUID: 97905024-cef5-4a62-a538-d9d5de98e2e7
dc.identifier.otherScopus: 84974687027
dc.identifier.urihttp://hdl.handle.net/10023/8985
dc.descriptionE.L.J. was funded under Scottish Government grant MMSS001/11. Sperm whale data were collected with support from One World Wildlife, the Natural Environment Research Council (NER/I/S/2002/00632), Whale and Dolphin Conservation (WDC), and J.M. Brotons of the Balearic Government Office of Fisheries Management. L.R. was supported by the MASTS pooling initiative, funded by the Scottish Funding Council (HR09011) and contributing institutions and their support are gratefully acknowledged.en
dc.description.abstractComparing geographically referenced maps has become an important aspect of spatial ecology (e.g. assessing change in distribution over time). Whilst humans are adept at recognising and extracting structure from maps (i.e. identifying spatial patterns), quantifying these structures can be difficult. Here, we show how the Structural Similarity (SSIM) index, a spatial comparison method adapted from techniques developed in computer science to determine the quality of image compression, can be used to extract additional information from spatial ecological data. We enhance the SSIM index to incorporate uncertainty from the underlyin g spatial models, and provide a software algorithm to correct for internal edge effects so that loss of spatial information from the map comparison is limited. The SSIM index uses a spatially-local window to calculate statistics based on local mean, variance, and covariance between the maps being compared. A number of statistics can be calculated using the SSIM index, ranging from a single summary statistic to quantify similarities between two maps, to maps of similarities in mean, variance, and covariance that can provide additional insight into underlying biological processes. We demonstrate the applicability of the SSIM approach using a case study of sperm whales in the Mediterranean Sea and identify areas where local-scale differences in space-use between groups and singleton whales occur. We show how novel insights into spatial structure can be extracted, which could not be obtained by visual inspection or cell-by-cell subtraction. As an approach, SSIM is applicable to a broad range of spatial ecological data, providing a novel, implementable tool for map comparison.en
dc.format.extent10en
dc.language.isoeng
dc.relation.ispartofEcological Indicatorsen
dc.rights© 2016 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).en
dc.subjectEdge effectsen
dc.subjectMap comparisonsen
dc.subjectMoving windowen
dc.subjectSperm whaleen
dc.subjectSSIM indexen
dc.subjectUncertaintyen
dc.subjectGC Oceanographyen
dc.subjectQH301 Biologyen
dc.subject.lccGCen
dc.subject.lccQH301en
dc.titleNovel application of a quantitative spatial comparison tool to species distribution dataen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews. Sea Mammal Research Uniten
dc.contributor.institutionUniversity of St Andrews. Centre for Social Learning & Cognitive Evolutionen
dc.contributor.institutionUniversity of St Andrews. Bioacoustics groupen
dc.contributor.institutionUniversity of St Andrews. Geography & Sustainable Developmenten
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. Bell-Edwards Geographic Data Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Biological Diversityen
dc.identifier.doihttps://doi.org/10.1016/j.ecolind.2016.05.051
dc.description.statusPeer revieweden


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