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dc.contributor.authorCamp, Richard J.
dc.contributor.authorMiller, David L.
dc.contributor.authorThomas, Len
dc.contributor.authorBuckland, Stephen T.
dc.contributor.authorKendall, Steve J.
dc.date.accessioned2020-05-12T15:30:05Z
dc.date.available2020-05-12T15:30:05Z
dc.date.issued2020-07
dc.identifier.citationCamp , R J , Miller , D L , Thomas , L , Buckland , S T & Kendall , S J 2020 , ' Using density surface models to estimate spatio-temporal changes in population densities and trend ' , Ecography , vol. 43 , no. 7 , pp. 1079-1089 . https://doi.org/10.1111/ecog.04859en
dc.identifier.issn0906-7590
dc.identifier.otherPURE: 267831841
dc.identifier.otherPURE UUID: c9ad9a97-59ca-4f42-a7b2-192b2d886ce7
dc.identifier.otherScopus: 85083044933
dc.identifier.otherORCID: /0000-0002-7436-067X/work/74117826
dc.identifier.otherORCID: /0000-0002-9939-709X/work/74117829
dc.identifier.otherORCID: /0000-0001-7008-923X/work/74118148
dc.identifier.otherWOS: 000544487400012
dc.identifier.urihttp://hdl.handle.net/10023/19925
dc.descriptionFunding – Centre for Research into Ecological and Environmental Modelling, University of St Andrews and U.S. Geological Survey provided funding for this analysis through a studentship to RJC.en
dc.description.abstractPrecise measures of population abundance and trend are needed for species conservation; these are most difficult to obtain for rare and rapidly changing populations. We compare uncertainty in densities estimated from spatio–temporal models with that from standard design‐based methods. Spatio–temporal models allow us to target priority areas where, and at times when, a population may most benefit. Generalised additive models were fitted to a 31‐year time series of point‐transect surveys of an endangered Hawaiian forest bird, the Hawai'i ‘ākepa Loxops coccineus. This allowed us to estimate bird densities over space and time. We used two methods to quantify uncertainty in density estimates from the spatio–temporal model: the delta method (which assumes independence between detection and distribution parameters) and a variance propagation method. With the delta method we observed a 52% decrease in the width of the design‐based 95% confidence interval (CI), while we observed a 37% decrease in CI width when propagating the variance. We mapped bird densities as they changed across space and time, allowing managers to evaluate management actions. Integrating detection function modelling with spatio–temporal modelling exploits survey data more efficiently by producing finer‐grained abundance estimates than are possible with design‐based methods as well as producing more precise abundance estimates. Model‐based approaches require switching from making assumptions about the survey design to assumptions about bird distribution. Such a switch warrants carefully considered. In this case the model‐based approach benefits conservation planning through improved management efficiency and reduced costs by taking into account both spatial shifts and temporal changes in population abundance and distribution.
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofEcographyen
dc.rightsCopyright © 2020 This article is a U.S. Government work and is in the public domain in the USA. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos. 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.subjectDensity estimationen
dc.subjectDistance samplingen
dc.subjectPoint-transect samplingen
dc.subjectSpatio–temporal smootheren
dc.subjectVariance propagationen
dc.subjectQE Geologyen
dc.subjectQH301 Biologyen
dc.subjectEcology, Evolution, Behavior and Systematicsen
dc.subjectDASen
dc.subject.lccQEen
dc.subject.lccQH301en
dc.titleUsing density surface models to estimate spatio-temporal changes in population densities and trenden
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Statisticsen
dc.contributor.institutionUniversity of St Andrews.School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews.Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews.Applied Mathematicsen
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.doihttps://doi.org/10.1111/ecog.04859
dc.description.statusPeer revieweden


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