Spatial capture–recapture with multiple noninvasive marks: An application to camera-trapping data of the European wildcat (Felis silvestris) using R package multimark
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Spatial capture–recapture with multiple noninvasive marks: An application to camera-trapping data of the European wildcat (Felis silvestris) using R package multimark

  • Published Date:

    2020

  • Source:
    Ecology and Evolution, 10(24), 13968-13979
Filetype[PDF-1.27 MB]


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  • Description:
    In Switzerland, the European wildcat (Felis silvestris), a native felid, is protected by national law. In recent decades, the wildcat has slowly returned to much of its original range and may have even expanded into new areas that were not known to be occupied before. For the implementation of efficient conservation actions, reliable information about the status and trend of population size and density is crucial. But so far, only one reliable estimate of density in Switzerland was produced in the northern Swiss Jura Mountains. Wildcats are relatively rare and elusive, but camera trapping has proven to be an effective method for monitoring felids. We developed and tested a monitoring protocol using camera trapping in the northern Jura Mountains (cantons of Bern and Jura) in an area of 100 km2. During 60 days, we obtained 105 pictures of phenotypical wildcats of which 98 were suitable for individual identification. We identified 13 individuals from both sides and, additionally, 5 single right-sided flanks and 3 single left-sided flanks that could not be matched to unique individuals. We analyzed the camera-trap data using the R package multimark, which has been extended to include a novel spatial capture–recapture model for encounter histories that include multiple “noninvasive” marks, such as bilaterally asymmetrical left- and right-sided flanks, that can be difficult (or impossible) to reliably match to individuals. Here, we present this model in detail for the first time. Based on a “semi-complete” data likelihood, the model is less computationally demanding than Bayesian alternatives that rely on a data-augmented complete data likelihood. The spatially explicit capture–recapture model estimated a wildcat density (95% credible interval) of 26 (17–36) per 100 km2 suitable habitat. Our integrated model produced higher abundance and density estimates with improved precision compared to single-sided analyses, suggesting spatially explicit capture–recapture methods with multiple “noninvasive” marks can improve our ability to monitor wildcat population status.
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    CC BY
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