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Innovative Techniques for Estimating Illegal Activities in a Human-Wildlife-Management Conflict

Overview of attention for article published in PLOS ONE, January 2013
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Title
Innovative Techniques for Estimating Illegal Activities in a Human-Wildlife-Management Conflict
Published in
PLOS ONE, January 2013
DOI 10.1371/journal.pone.0053681
Pubmed ID
Authors

Paul Cross, Freya A. V. St. John, Saira Khan, Andrea Petroczi

Abstract

Effective management of biological resources is contingent upon stakeholder compliance with rules. With respect to disease management, partial compliance can undermine attempts to control diseases within human and wildlife populations. Estimating non-compliance is notoriously problematic as rule-breakers may be disinclined to admit to transgressions. However, reliable estimates of rule-breaking are critical to policy design. The European badger (Meles meles) is considered an important vector in the transmission and maintenance of bovine tuberculosis (bTB) in cattle herds. Land managers in high bTB prevalence areas of the UK can cull badgers under license. However, badgers are also known to be killed illegally. The extent of illegal badger killing is currently unknown. Herein we report on the application of three innovative techniques (Randomized Response Technique (RRT); projective questioning (PQ); brief implicit association test (BIAT)) for investigating illegal badger killing by livestock farmers across Wales. RRT estimated that 10.4% of farmers killed badgers in the 12 months preceding the study. Projective questioning responses and implicit associations relate to farmers' badger killing behavior reported via RRT. Studies evaluating the efficacy of mammal vector culling and vaccination programs should incorporate estimates of non-compliance. Mitigating the conflict concerning badgers as a vector of bTB requires cross-disciplinary scientific research, departure from deep-rooted positions, and the political will to implement evidence-based management.

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Geographical breakdown

Country Count As %
United Kingdom 4 2%
France 1 <1%
Australia 1 <1%
Mozambique 1 <1%
South Africa 1 <1%
Brazil 1 <1%
Mexico 1 <1%
United States 1 <1%
Unknown 172 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 37 20%
Student > Ph. D. Student 35 19%
Researcher 34 19%
Student > Doctoral Student 12 7%
Other 11 6%
Other 32 17%
Unknown 22 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 56 31%
Environmental Science 51 28%
Social Sciences 15 8%
Medicine and Dentistry 8 4%
Veterinary Science and Veterinary Medicine 6 3%
Other 17 9%
Unknown 30 16%