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  • Predicting kill sites of an apex predator from GPS data in different multi-prey systems [Elektronski vir]
    Oliveira, Teresa ...
    Kill 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 non-kill) that were not field-checked. Here, we address these two sources of error using a large GPS dataset of collared Eurasian lynx, an apex predator of conservation concern in Europe, in three multi-prey systems, with different combinations of wild, semi-domestic, and domestic prey. We first used a subsampling approach to investigate how different GPS-fix schedules affect the detection of GLCs indicating kill sites. Then, we evaluated the potential of the random forest algorithm to classify GLCs as non-kills, small prey kills, and ungulate kills. We show that the number of fixes can be reduced to from 7 to 3 fixes/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 semi-domestic 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 sites detection, which can be considered for other large carnivore species besides the Eurasian lynx. We recommend caution when working in systems including domestic prey, as the odds of underestimating kill rates are higher.
    Source: Ecological applications [Elektronski vir]. - ISSN 1939-5582 (Vol. 33, iss. 2 [e2778], 2023, 12 str.)
    Type of material - e-article
    Publish date - 2023
    Language - english
    COBISS.SI-ID - 130503171

    Link(s):

    https://doi.org/10.1002/eap.2778
    https://esajournals.onlinelibrary.wiley.com/doi/10.1002/eap.2778
    Repository of the University of Ljubljana – RUL
    DOI

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