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dc.contributor.authorMerkel, Benjamin
dc.contributor.authorPhillips, Richard A.
dc.contributor.authorDescamps, Sébastien
dc.contributor.authorYoccoz, Nigel Gilles
dc.contributor.authorMoe, Børge
dc.contributor.authorStrøm, Hallvard
dc.coverage.spatialSouth Georgianb_NO
dc.identifier.citationMovement Ecology 2016, 4nb_NO
dc.description.abstractBackground: The use of light level loggers (geolocators) to understand movements and distributions in terrestrial and marine vertebrates, particularly during the non-breeding period, has increased dramatically in recent years. However, inferring positions from light data is not straightforward, often relies on assumptions that are difficult to test, or includes an element of subjectivity. Results: We present an intuitive framework to compute locations from twilight events collected by geolocators from different manufacturers. The procedure uses an iterative forward step selection, weighting each possible position using a set of parameters that can be specifically selected for each analysis. The approach was tested on data from two wide-ranging seabird species - black-browed albatross Thalassarche melanophris and wandering albatross Diomedea exulans – tracked at Bird Island, South Georgia, during the two most contrasting periods of the year in terms of light regimes (solstice and equinox). Using additional information on travel speed, sea surface temperature and land avoidance, our approach was considerably more accurate than the traditional threshold method (errors reduced to medians of 185 km and 145 km for solstice and equinox periods, respectively). Conclusions: The algorithm computes stable results with uncertainty estimates, including around the equinoxes, and does not require calibration of solar angles. Accuracy can be increased by assimilating information on travel speed and behaviour, as well as environmental data. This framework is available through the open source R package probGLS, and can be applied in a wide range of biologging studies. Keywords: Animal tracking, Global Location Sensors, GLS, Method assessment, Sea surface temperature, Probability sampling, probGLS, Threshold methodnb_NO
dc.rightsNavngivelse-Ikkekommersiell 3.0 Norge*
dc.subjectAnimal trackingnb_NO
dc.subjectGlobal location sensorsnb_NO
dc.subjectMethod assessmentnb_NO
dc.subjectSea surface temperaturenb_NO
dc.subjectProbability samplingnb_NO
dc.subjectThreshhold methodsnb_NO
dc.titleA probabilistic algorithm to process geolocation datanb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.subject.nsiVDP::Matematikk og naturvitenskap: 400::Basale biofag: 470nb_NO
dc.subject.nsiVDP::Mathematics and natural scienses: 400::Basic biosciences: 470nb_NO
dc.source.journalMovement Ecologynb_NO

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Navngivelse-Ikkekommersiell 3.0 Norge
Except where otherwise noted, this item's license is described as Navngivelse-Ikkekommersiell 3.0 Norge