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dc.contributor.authorChudzińska, Magda
dc.contributor.authorAyllón, Daniel
dc.contributor.authorMadsen, Jesper
dc.contributor.authorNabe-Nielsen, Jacob
dc.date.accessioned2018-01-19T11:30:08Z
dc.date.available2018-01-19T11:30:08Z
dc.date.issued2016-01-24
dc.identifier252091956
dc.identifierc79b426e-cce0-4f62-821e-e88993e87445
dc.identifier84946887538
dc.identifier.citationChudzińska , M , Ayllón , D , Madsen , J & Nabe-Nielsen , J 2016 , ' Discriminating between possible foraging decisions using pattern-oriented modelling : the case of pink-footed geese in Mid-Norway during their spring migration ' , Ecological Modelling , vol. 320 , pp. 299-315 . https://doi.org/10.1016/j.ecolmodel.2015.10.005en
dc.identifier.issn0304-3800
dc.identifier.otherORCID: /0000-0001-9568-1504/work/40797773
dc.identifier.urihttps://hdl.handle.net/10023/12519
dc.descriptionThis study was a part of MC's PhD project funded by Aarhus University. D. Ayllón was funded by a Marie Curie Intraeuropean Fellowship (PIEF-GA-2012-329264) for the project EcoEvolClim.en
dc.description.abstractForaging decisions and their energetic consequences are critical to capital Arctic-breeders migrating in steps, because there is only a narrow time window with optimal foraging conditions at each step. Optimal foraging theory predicts that such animals should spend more time in patches that enable them to maximise the net rate of energy and nutrient gain. The type of search strategy employed by animals is, however, expected to depend on the amount of information that is involved in the search process. In highly dynamic landscapes, animals are unlikely to have complete knowledge about the distribution of the resources, which makes them unable to forage on the patches that enable them to maximise their net energy intake. Random search may, however, be a good strategy in landscapes where patches with profitable resources are abundant. We present simulation experiments using an individual-based model (IBM) to test which foraging decision rule (FDR) best reproduces the population patterns observed in pink-footed geese during spring staging in an agricultural landscape in Mid-Norway. Our results suggested that while geese employed a random search strategy, they were also able to individually learn where the most profitable patches were located and return to the patches that resulted in highest energy intake. Such asocial learning is rarely reported for flock animals. The modelled geese did not benefit from group foraging, which contradicts the results reported by most studies on flocking birds. Geese also did not possess complete knowledge about the profitability of the available habitat. Most likely, there is no one single optimal foraging strategy for capital breeders but such strategy is site and species-specific. We discussed the potential use of the model as a valuable tool for making future risk assessments of human disturbance and changes in agricultural practices.
dc.format.extent17
dc.format.extent1945375
dc.language.isoeng
dc.relation.ispartofEcological Modellingen
dc.subjectAgent-based simulation modelen
dc.subjectAnser brachyrhynchusen
dc.subjectHeterogeneous landscapeen
dc.subjectLearningen
dc.subjectOptimal foragingen
dc.subjectQH301 Biologyen
dc.subjectG Geography (General)en
dc.subjectSF Animal cultureen
dc.subjectEcological Modellingen
dc.subjectNDASen
dc.subjectSDG 2 - Zero Hungeren
dc.subject.lccQH301en
dc.subject.lccG1en
dc.subject.lccSFen
dc.titleDiscriminating between possible foraging decisions using pattern-oriented modelling : the case of pink-footed geese in Mid-Norway during their spring migrationen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Sea Mammal Research Uniten
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doi10.1016/j.ecolmodel.2015.10.005
dc.description.statusPeer revieweden
dc.identifier.urlhttp://www.sciencedirect.com/science/article/pii/S0304380015004639?via%3Dihub#sec0180en


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