Show simple item record

dc.contributor.authorOliveira, Teresa
dc.contributor.authorSanchez, David Carricondo
dc.contributor.authorMattisson, Jenny
dc.contributor.authorVogt, Kristina
dc.contributor.authorCorradini, Andrea
dc.contributor.authorLinnell, John Durrus
dc.contributor.authorOdden, John
dc.contributor.authorHeurich, Marco Dietmar
dc.contributor.authorRodríguez-Recio, Mariano
dc.contributor.authorKrofel, Miha
dc.date.accessioned2023-01-17T08:50:56Z
dc.date.available2023-01-17T08:50:56Z
dc.date.created2022-12-02T12:38:20Z
dc.date.issued2022
dc.identifier.citationEcological Applications. 2022, .en_US
dc.identifier.issn1051-0761
dc.identifier.urihttps://hdl.handle.net/11250/3043867
dc.description© 2022 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en_US
dc.description.abstractKill rates are a central parameter to assess the impact of predation on prey species. An accurate estimation of kill rates requires a correct identification of kill sites, often achieved by field-checking GPS location clusters (GLCs). However, there are potential sources of error included in kill-site identification, such as failing to detect GLCs that are kill sites, and misclassifying the generated GLCs (e.g., kill for nonkill) that were not field checked. Here, we address these two sources of error using a large GPS dataset of collared Eurasian lynx (Lynx lynx), an apex predator of conservation concern in Europe, in three multiprey systems, with different combinations of wild, semidomestic, and domestic prey. We first used a subsampling approach to investigate how different GPS-fix schedules affected the detection of GLC-indicated kill sites. Then, we evaluated the potential of the random forest algorithm to classify GLCs as nonkills, small prey kills, and ungulate kills. We show that the number of fixes can be reduced from seven to three fixes per night without missing more than 5% of the ungulate kills, in a system composed of wild prey. Reducing the number of fixes per 24 h decreased the probability of detecting GLCs connected with kill sites, particularly those of semidomestic or domestic prey, and small prey. Random forest successfully predicted between 73%–90% of ungulate kills, but failed to classify most small prey in all systems, with sensitivity (true positive rate) lower than 65%. Additionally, removing domestic prey improved the algorithm's overall accuracy. We provide a set of recommendations for studies focusing on kill-site detection that can be considered for other large carnivore species in addition to the Eurasian lynx. We recommend caution when working in systems including domestic prey, as the odds of underestimating kill rates are higher.en_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectdomestic preyen_US
dc.subjectEurasian lynxen_US
dc.subjectGPS location clustersen_US
dc.subjectGLCsen_US
dc.subjectGPS-fix scheduleen_US
dc.subjectkill sitesen_US
dc.subjectmultiprey systemen_US
dc.subjectrandom foresten_US
dc.titlePredicting kill sites of an apex predator from GPS data in different multi-prey systemsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Økologi: 488en_US
dc.source.pagenumber12en_US
dc.source.journalEcological Applicationsen_US
dc.identifier.doi10.1002/eap.2778
dc.identifier.cristin2087726
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal