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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.date.accessioned2017-01-10T10:22:29Z
dc.date.available2017-01-10T10:22:29Z
dc.date.issued2016-11-18
dc.description.abstract<p><i>Background: </i>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.</p> <p><i>Results:</i> 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 <i>Thalassarche melanophris</i> and wandering albatross <i>Diomedea exulans</i> – 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).</p> <p><i>Conclusions:</i> 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.</p>en_US
dc.descriptionSource at <a href=https://doi.org/10.1186/s40462-016-0091-8>https://doi.org/10.1186/s40462-016-0091-8</a>.
dc.identifier.citationMerkel, B., Phillips, R.A., Descamps, S., Yoccoz, N.G., Moe, B. & Strøm, H. (2016). A probabilistic algorithm to process geolocation data. <i>Movement Ecology, 4</i>, 26. https://doi.org/10.1186/s40462-016-0091-8en_US
dc.identifier.cristinIDFRIDAID 1402529
dc.identifier.doi10.1186/s40462-016-0091-8
dc.identifier.issn2051-3933
dc.identifier.urihttps://hdl.handle.net/10037/10133
dc.language.isoengen_US
dc.publisherBioMed Centralen_US
dc.relation.ispartofMerkel, B. (2019). Migration in seabirds: seasonal structure in space and environment across species, populations and individuals. (Doctoral thesis). <a href=https://hdl.handle.net/10037/15239>https://hdl.handle.net/10037/15239</a>
dc.relation.projectIDAndre: Norwegian Oil and Gas Associationen_US
dc.relation.projectIDUtenriksdepartementet: Norwegian SEATRACK Programmeen_US
dc.relation.projectIDMiljødirektoratet: Norwegian SEATRACK Programmeen_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Matematikk og naturvitenskap: 400::Basale biofag: 470en_US
dc.subjectVDP::Mathematics and natural scienses: 400::Basic biosciences: 470en_US
dc.subjectAnimal trackingen_US
dc.subjectGlobal Location Sensorsen_US
dc.subjectGLSen_US
dc.subjectMethod assessmenten_US
dc.subjectSea surface temperatureen_US
dc.subjectProbability samplingen_US
dc.subjectprobGLSen_US
dc.subjectThreshold methoden_US
dc.titleA probabilistic algorithm to process geolocation dataen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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