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dc.contributor.authorKlappstein, Natasha J.
dc.contributor.authorPotts, Jonathan R.
dc.contributor.authorMichelot, Théo
dc.contributor.authorBörger, Luca
dc.contributor.authorPilfold, Nicholas W.
dc.contributor.authorLewis, Mark A.
dc.contributor.authorDerocher, Andrew E.
dc.identifier.citationKlappstein , N J , Potts , J R , Michelot , T , Börger , L , Pilfold , N W , Lewis , M A & Derocher , A E 2022 , ' Energy-based step selection analysis : modelling the energetic drivers of animal movement and habitat use ' , Journal of Animal Ecology , vol. Early View , 13687 .
dc.identifier.otherPURE: 278277027
dc.identifier.otherPURE UUID: f43fce46-44c3-44e2-8f39-e5ccb3820310
dc.identifier.otherRIS: urn:B34245BEBCDBDD401E1FBB51178859E8
dc.identifier.otherScopus: 85126869820
dc.identifier.otherWOS: 000771771800001
dc.descriptionWe acknowledge funding from Mitacs Canada, the Canadian Association of Zoos and Aquariums, Canadian Wildlife Federation, Environment and Climate Change Canada, Hauser Bears, Natural Sciences and Engineering Research Council of Canada, Polar Bears International, Polar Continental Shelf Project, Quark Expeditions, United States Department of the Interior (Bureau of Ocean Energy Management), and World Wildlife Fund Canada.en
dc.description.abstract1. The energetic gains from foraging and costs of movement are expected to be key drivers of animal decision-making, as their balance is a large determinant of body condition and survival. This fundamental perspective is often missing from habitat selection studies, which mainly describe correlations between space use and environmental features, rather than the mechanisms behind these correlations. 2. To address this gap, we present a novel parameterisation of step selection functions (SSFs), that we term the energy selection function (ESF). In this model, the likelihood of an animal selecting a movement step depends directly on the corresponding energetic gains and costs, and we can therefore assess how moving animals choose habitat based on energetic considerations. 3. The ESF retains the mathematical convenience and practicality of other SSFs and can be quickly fitted using standard software. In this article, we outline a workflow, from data gathering to statistical analysis, and use a case study of polar bears Ursus maritimus to demonstrate application of the model. 4. We explain how defining gains and costs at the scale of the movement step allows us to include information about resource distribution, landscape resistance and movement patterns. We further demonstrate this process with a case study of polar bears and show how the parameters can be interpreted in terms of selection for energetic gains and against energetic costs. 5. The ESF is a flexible framework that combines the energetic consequences of both movement and resource selection, thus incorporating a key mechanism into habitat selection analysis. Further, because it is based on familiar habitat selection models, the ESF is widely applicable to any study system where energetic gains and costs can be derived, and has immense potential for methodological extensions.
dc.relation.ispartofJournal of Animal Ecologyen
dc.rightsCopyright © 2022 The Authors. Journal of Animal Ecology; British Ecological Society. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectAnimal movementen
dc.subjectEnergy landscapesen
dc.subjectHabitat selectionen
dc.subjectMovement ecologyen
dc.subjectOptimal foraging theoryen
dc.subjectPolar bearen
dc.subjectStep selection functionsen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.titleEnergy-based step selection analysis : modelling the energetic drivers of animal movement and habitat useen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.description.statusPeer revieweden

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