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dc.contributor.authorYuan, Y.
dc.contributor.authorBachl, F. E.
dc.contributor.authorLindgren, F.
dc.contributor.authorBorchers, David Louis
dc.contributor.authorIllian, J. B.
dc.contributor.authorBuckland, S. T.
dc.contributor.authorRue, H.
dc.contributor.authorGerrodette, T.
dc.date.accessioned2018-01-04T13:30:07Z
dc.date.available2018-01-04T13:30:07Z
dc.date.issued2017-12
dc.identifier243307360
dc.identifier1a0e48e7-3ebd-4166-ab57-1980c2e0f9a4
dc.identifier85042675293
dc.identifier000418893000022
dc.identifier.citationYuan , Y , Bachl , F E , Lindgren , F , Borchers , D L , Illian , J B , Buckland , S T , Rue , H & Gerrodette , T 2017 , ' Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales ' , Annals of Applied Statistics , vol. 11 , no. 4 , pp. 2270-2297 . https://doi.org/10.1214/17-AOAS1078en
dc.identifier.issn1932-6157
dc.identifier.otherArXiv: http://arxiv.org/abs/1604.06013v1
dc.identifier.otherArXiv: http://arxiv.org/abs/1604.06013v4
dc.identifier.otherORCID: /0000-0002-3944-0754/work/72842485
dc.identifier.otherORCID: /0000-0002-9939-709X/work/73701100
dc.identifier.urihttps://hdl.handle.net/10023/12427
dc.description.abstractDistance sampling is a widely used method for estimating wildlife population abundance. The fact that conventional distance sampling methods are partly design-based constrains the spatial resolution at which animal density can be estimated using these methods. Estimates are usually obtained at survey stratum level. For an endangered species such as the blue whale, it is desirable to estimate density and abundance at a finer spatial scale than stratum. Temporal variation in the spatial structure is also important. We formulate the process generating distance sampling data as a thinned spatial point process and propose model-based inference using a spatial log-Gaussian Cox process. The method adopts a flexible stochastic partial differential equation (SPDE) approach to model spatial structure in density that is not accounted for by explanatory variables, and integrated nested Laplace approximation (INLA) for Bayesian inference. It allows simultaneous fitting of detection and density models and permits prediction of density at an arbitrarily fine scale. We estimate blue whale density in the Eastern Tropical Pacific Ocean from thirteen shipboard surveys conducted over 22 years. We find that higher blue whale density is associated with colder sea surface temperatures in space, and although there is some positive association between density and mean annual temperature, our estimates are consistent with no trend in density across years. Our analysis also indicates that there is substantial spatially structured variation in density that is not explained by available covariates.
dc.format.extent28
dc.format.extent1863423
dc.language.isoeng
dc.relation.ispartofAnnals of Applied Statisticsen
dc.subjectDistance samplingen
dc.subjectSpatio-temporal modelingen
dc.subjectStochastic partial differential equationsen
dc.subjectINLAen
dc.subjectSpatial point processen
dc.subjectGE Environmental Sciencesen
dc.subjectQA Mathematicsen
dc.subject3rd-NDASen
dc.subjectBDCen
dc.subjectR2Cen
dc.subject.lccGEen
dc.subject.lccQAen
dc.titlePoint process models for spatio-temporal distance sampling data from a large-scale survey of blue whalesen
dc.typeJournal articleen
dc.contributor.sponsorEPSRCen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. St Andrews Sustainability Instituteen
dc.identifier.doi10.1214/17-AOAS1078
dc.description.statusPeer revieweden
dc.identifier.grantnumberEP/K041061/1en


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