PLoS ONE (Jan 2019)

Predicting badger visits to farm yards and making predictions available to farmers.

  • Andrew Robertson,
  • Joanna Judge,
  • G J Wilson,
  • Ian J Vernon,
  • Richard J Delahay,
  • Robbie A McDonald

DOI
https://doi.org/10.1371/journal.pone.0216953
Journal volume & issue
Vol. 14, no. 5
p. e0216953

Abstract

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The use of agricultural resources or environments by wildlife may result in opportunities for transmission of infections amongst wild animals, livestock and humans. Targeted use of biosecurity measures may therefore reduce disease risks, although this requires practical knowledge of where such measures would be most effective, and effective means of communicating risks so that stakeholders can make informed decisions about such investment. In parts of Europe, the European badger Meles meles may act as a wildlife reservoir for Mycobacterium bovis, the causative agent of bovine tuberculosis, and badger visits to farmyards may provide potential opportunities for transmission of M. bovis to cattle. Biosecurity measures are effective in reducing badger activity in farmyards, although it is unclear which farms should be targeted with such measures. We used cameras to monitor badger activity in 155 farmyards in south west England and Wales, and related variations in the presence and frequency of badger visits to farm characteristics. Badgers were recorded on camera in 40% of farmyards monitored. However, the frequency of visits was highly variable, with badgers recorded on >50% of nights in only 10% of farms. The presence of badgers in farmyards was positively associated with the density of badger setts, the number of feed stores and the number of cattle sheds, and negatively associated with the distance to the nearest active badger sett, the presence of a house/dwelling and the number of cattle housed on the farm. The frequency of visits was negatively associated with the distance to the nearest active badger sett and the number of cattle housed. Models predicted the presence/absence of badgers in farmyards with 73% accuracy (62% sensitivity, 81% specificity, using a cut off value of 0.265). Models could not distinguish between farms with low/high frequency of visits, although farms predicted as having badgers present typically had a higher frequency of visits than those that were not. We developed and present an interactive web based application: the Badger Farm Assessment Tool (BFAT), to allow users to enter the characteristics of a farm and generate a relative risk score describing the likelihood of badger visits.