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dc.contributor.authorOedekoven, Cornelia S.
dc.contributor.authorElston, David A.
dc.contributor.authorHarrison, Philip J.
dc.contributor.authorBrewer, Mark J.
dc.contributor.authorBuckland, Stephen T.
dc.contributor.authorJohnston, Alison
dc.contributor.authorFoster, Simon
dc.contributor.authorPearce-Higgins, James W.
dc.identifier.citationOedekoven , C S , Elston , D A , Harrison , P J , Brewer , M J , Buckland , S T , Johnston , A , Foster , S & Pearce-Higgins , J W 2017 , ' Attributing changes in the distribution of species abundance to weather variables using the example of British breeding birds ' , Methods in Ecology and Evolution , vol. 8 , no. 12 , pp. 1690-1702 .
dc.identifier.otherPURE: 249720944
dc.identifier.otherPURE UUID: 3a31a5fe-fde7-41f9-880f-aef7646fe5b0
dc.identifier.otherScopus: 85020900780
dc.identifier.otherWOS: 000417239200005
dc.identifier.otherORCID: /0000-0002-5610-7814/work/61978848
dc.identifier.otherORCID: /0000-0002-9939-709X/work/73701005
dc.identifier.otherORCID: /0000-0001-8221-013X/work/103866022
dc.descriptionThe BBS is undertaken by the British Trust for Ornithology (BTO) and jointly funded by the BTO, the Joint Nature Conservation Committee and the Royal Society for the Protection of Birds.en
dc.description.abstract1. Modelling spatio-temporal changes in species abundance and attributing those changes to potential drivers such as climate, is an important but difficult problem. The standard approach for incorporating climatic variables into such models is to include each weather variable as a single covariate whose effect is expressed through a low-order polynomial or smoother in an additive model. This, however, confounds the spatial and temporal effects of the covariates. 2. We developed a novel approach to distinguish between three types of change in any particular weather covariate. We decomposed the weather covariate into three new covariates by separating out temporal variation in weather (averaging over space), spatial variation in weather (averaging over years) and a space-time anomaly term (residual variation). These three covariates were each fitted separately in the models. We illustrate the approach using generalized additive models applied to count data for a selection of species from the UK’s Breeding Bird Survey, 1994-2013. The weather covariates considered were the mean temperatures during the preceding winter and temperatures and rainfall during the preceding breeding season. We compare models that include these covariates directly with models including decomposed components of the same covariates, considering both linear and smooth relationships. 3. The lowest QAIC values were always associated with a decomposed weather covariate model. Different relationships between counts and the three new covariates provided strong evidence that the effects of changes in covariate values depended on whether changes took place in space, in time, or in the space-time anomaly. These results promote caution in predicting species distribution and abundance in future climate, based on relationships that are largely determined by environmental variation over space. 4. Our methods estimate the effect of temporal changes in weather, while accounting for spatial effects of long-term climate, improving inference on overall and/or localized effects of climate change. With increasing availability of large-scale data sets, need is growing for appropriate analytical tools. The proposed decomposition of the weather variables represents an important advance by eliminating the confounding issue often inherent in analyses of large-scale data sets.
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.rights© 2017 The Authors. Methods in Ecology and Evolution © 2017 British Ecological Society. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at /
dc.subjectClimate changeen
dc.subjectDecomposition of spatial, temporal and anomaly effectsen
dc.subjectGeneralized additive modelen
dc.subjectSpatio-temporal modellingen
dc.subjectSpecies abundanceen
dc.subjectUKCP09 climate projections.en
dc.subjectGeneralized linear modelsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.titleAttributing changes in the distribution of species abundance to weather variables using the example of British breeding birdsen
dc.typeJournal articleen
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.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.contributor.institutionUniversity of St Andrews.Statisticsen
dc.description.statusPeer revieweden

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