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Elusive mustelids-18 months in the search of near-threatened stoat ( Mustela erminea ) and weasel ( M. nivalis ) reveals low captures.

Sofie Nørgaard KonradsenLinnea Worsøe HavmøllerCharlotte KragPeter Rask MøllerRasmus Worsøe Havmøller
Published in: Ecology and evolution (2024)
Stoat ( Mustela erminea ) and weasel ( M. nivalis) are hard to monitor as they are elusive of nature and leave few identifying marks in their surroundings. Stoat and weasel are both fully protected in Denmark and are thought to be widely distributed throughout the country. Despite this stoat and weasel were listed on the Danish Red List as Near Threatened in 2019, as their densities and population trends are unknown. Using a modified novel camera trapping device, the Double-Mostela, a wooden box comprising a tracking tunnel and two camera traps, we attempted to obtain density estimates based on identification of individual stoats and weasels. We deployed camera traps both inside Double-Mostela traps and externally in three different study areas in northern Zealand, Denmark, and tested commercial, American scent-based lures to attract stoat and weasel. We obtained very low seasonal trapping rates of weasel in two study areas, but in one study area, we obtained a seasonal trapping rate of stoat larger compared to another study using the Mostela. In one study area, both species were absent. We observed no effect of scent-based lures in attracting small mustelids compared to non-bait traps. Potential reasons behind low capture rates of weasel and stoat are suboptimal habitat placement and timing of deployment of the Double-Mostelas, land-use changes over the last 200 years, predation from larger predators, as well as unintended secondary poisoning with rodenticides. Due to the scarcity of weasel and stoat captures, we were unable to make density estimates based on identification of individuals; however, we identified potential features that could be used for identification and density estimates with more captures.
Keyphrases
  • transcription factor
  • machine learning
  • risk assessment
  • ultrasound guided
  • convolutional neural network