Exploring Anti-poaching Strategies for Wildlife Crime with a Simple and General Agent-Based Model
Understanding and preventing wildlife crime is challenging because of the complex interdependencies between animals, poachers, and rangers. To tackle this complexity, this study introduces a simple, general agent-based model of wildlife crime. The model is abstract and can be used to derive general conclusions about the emergence and prevention of wildlife crime. It can also be tailored to create scenarios which allows researchers and practitioners to better understand the dynamics in specific cases. This was illustrated by applying the model to the context of rhino poaching in South Africa. A virtual park populated by rhinos, poachers and rangers was created to study how an increase in patrol effort for two different anti-poaching strategies affect the number of poached rhinos. The results show that fence patrols are more effective in preventing wildlife crime than standard patrols. Strikingly, even increasing the number of ranger teams does not increase the effectiveness of standard patrols compared to fence patrols.
KeywordsAgent-based modeling Wildlife crime Anti-poaching strategies
The author would like to thank Jacob van der Ploeg and Michael Mäs (University of Groningen, the Netherlands) for their help in developing and creating the model. Furthermore, AM Lemieux (Netherlands Institute for the Study of Crime and Law Enforcement) and Craig Spencer (Balule Nature Reserve, South Africa) for their helpful comments and suggestions on the model.
- 1.Banks, D., Davies, C., Gosling, J., Newman, J., Rice, M., Wadley, J., Walravens, F.: Environmental crime: a threat to our future, p. 25 (2008)Google Scholar
- 4.Critchlow, R., Plumptre, A.J., Driciru, M., Rwetsiba, A., Stokes, E.J., Tumwesigye, C., Wanyama, F., Beale, C.M.: Spatiotemporal trends of illegal activities from ranger-collected data in a Ugandan National Park: trends in illegal activities. Conserv. Biol. 29, 1458–1470 (2015). doi: 10.1111/cobi.12538CrossRefGoogle Scholar
- 8.Birks, D.J., Donkin, S., Wellsmith, M.: Synthesis over analysis: towards an ontology for volume crime simulation. In: Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, pp. 160–192. IGI Global (2008)Google Scholar
- 9.Lemieux, A.M.: Introduction. In: Situational Prevention of Poaching (2014)Google Scholar
- 10.Milliken, T., Shaw, J.: The South Africa-Vietnam Rhino Horn Trade Nexus: A Deadly Combination of Institutional Lapses, Corrupt Wildlife Industry Professionals and Asian Crime Syndicates. TRAFFIC, Johannesburg (2012)Google Scholar
- 11.Wilensky, U.: NetLogo (1999). http://ccl.northwestern.edu/netlogo/
- 12.McKight, P.E., Najab, J.: Kruskal-Wallis Test. Corsini Encyclopedia of Psychology (2010). doi: 10.1002/9780470479216.corpsy0491
- 13.Moreto, W.: To conserve and protect: examining law enforcement ranger culture and operations in Queen Elizabeth National Park, Uganda. Thesis, Rutgers University (2013)Google Scholar
- 14.Bell, R.H.V., Clarke, J.E.: Funding and financial control. In: Conservation and Wildlife Management in Africa, pp. 543–546. US Peace Corps, Washington, DC (1986)Google Scholar
- 16.Eck, J.E., Weisburd, D.L.: Crime places in crime theory. Crime Place Crime Prev. Stud. 4, 1–33 (2015)Google Scholar
- 17.Eloff, C., Lemieux, A.M.: Rhino poaching in Kruger National Park, South Africa. In: Situational Prevention of Poaching (2014)Google Scholar