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dc.contributor.authorMasello, Juan F.
dc.contributor.authorRast, Wanja
dc.contributor.authorSchumm, Yvonne R.
dc.contributor.authorMetzger, Benjamin
dc.contributor.authorQuillfeldt, Petra
dc.date.accessioned2024-02-07T10:37:05Z
dc.date.available2024-02-07T10:37:05Z
dc.date.issued2023
dc.identifier.urihttps://jlupub.ub.uni-giessen.de//handle/jlupub/18979
dc.identifier.urihttp://dx.doi.org/10.22029/jlupub-18340
dc.description.abstractAccelerometers capture rapid changes in animal motion, and the analysis of large quantities of such data using machine learning algorithms enables the inference of broad animal behaviour categories such as foraging, flying, and resting over long periods of time. We deployed GPS-GSM/GPRS trackers with tri-axial acceleration sensors on common woodpigeons (Columba palumbus) from Hesse, Germany (forest and urban birds) and from Lisbon, Portugal (urban park). We used three machine learning algorithms, Random Forest, Support Vector Machine, and Extreme Gradient Boosting, to classify the main behaviours of the birds, namely foraging, flying, and resting and calculated time budgets over the breeding and winter season. Woodpigeon time budgets varied between seasons, with more foraging time during the breeding season than in winter. Also, woodpigeons from different sites showed differences in the time invested in foraging. The proportion of time woodpigeons spent foraging was lowest in the forest habitat from Hesse, higher in the urban habitat of Hesse, and highest in the urban park in Lisbon. The time budgets we recorded contrast to previous findings in woodpigeons and reaffirm the importance of considering different populations to fully understand the behaviour and adaptation of a particular species to a particular environment. Furthermore, the differences in the time budgets of Woodpigeons from this study and previous ones might be related to environmental change and merit further attention and the future investigation of energy budgets.de_DE
dc.language.isoende_DE
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.ddcddc:570de_DE
dc.subject.ddcddc:590de_DE
dc.titleYear-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learningde_DE
dc.typearticlede_DE
local.affiliationFB 08 - Biologie und Chemiede_DE
local.source.journaltitleBehavioral ecology and sociobiologyde_DE
local.source.volume77de_DE
local.source.articlenumber40de_DE
local.source.urihttps://doi.org/10.1007/s00265-023-03306-wde_DE


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