Poaching can have devastating impacts on animal and plant numbers, and in many countries has reached crisis levels, with illegal hunters employing increasingly sophisticated techniques. Here, we show how geographic profiling – a mathematical technique originally developed in criminology and recently applied to animal foraging and epidemiology – can be adapted for use in investigations of wildlife crime, using data from an eight-year study in Savé Valley Conservancy, Zimbabwe that in total includes more than 10,000 incidents of illegal hunting and the deaths of 6,454 wild animals. Using a subset of these data for which the illegal hunters’ identities are known, we show that the model can successfully identify the illegal hunters’ home villages using the spatial locations of hunting incidences (for example, snares) as input, and show how this can be improved by manipulating the probability surface inside the Conservancy to reflect the fact that – although the illegal hunters mostly live outside the Conservancy, the majority of hunting occurs inside (in criminology, ‘commuter crime’). The results of this analysis – combined with rigorous simulations – show for the first time how geographic profiling can be combined with GIS data and applied to situations with more complex spatial patterns – for example, where landscape heterogeneity means that some parts of the study area are unsuitable (e.g. aquatic areas for terrestrial animals, or vice versa), or where landscape permeability differs (for example, forest bats tending not to fly over open areas). More broadly, these results show how geographic profiling can be used to target anti-poaching interventions more effectively and more efficiently, with important implications for the development of management strategies and conservation plans in a range of conservation scenarios.
|Publication Subtype||Journal Article|
|Title||A spatial approach to combatting wildlife crime|
|Series title||Conservation Biology|
|Contributing office(s)||Wetland and Aquatic Research Center|
|Google Analytic Metrics||Metrics page|