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Geology topics

Peter A. Lindsey

Publications and source records attributed to Peter A. Lindsey.

2 recordsLinked to original sources

Targeting wildlife crime interventions through geographic profiling

Seeing an animal hanging lifelessly from a snare is a heart-wrenching experience. Knowing that most animals caught in snares are left to rot without being used for meat or any other purpose might be worse. Over an eight-year period, 2001–2009, we recorded 10,231 incidents of illegal hunting in a wildlife conservation area in southeastern Zimbabwe, the Savé Valley Conservancy (SVC). Sixty-three percent of these incidents used snares, which is an illegal form of hunting in Zimbabwe. Almost fifty-nine percent of animals caught in snares were left to rot on the snare lines. What if we could prevent these unnecessary losses? The SVC is home to many iconic wildlife species such as elephants, lions, rhinos, giraffes, and buffalos. However, with the onset of political turmoil in the early 2000s, large sections of wildlife fencing surrounding SVC were removed, enough to make over 400,000 wire snares, many of which were recovered by anti-poaching teams. We found illegal hunting to be widespread throughout SVC. During the period of our study, we discovered the deaths of at least 6,454 wild animals, equating to a minimum of USD 1 million in financial losses annually – the ecological and financial scale of the problem is massive. However, in an area like SVC, which covers 3,450 km2, tackling the problem of illegal hunting is challenging.

Savé Valley Conservancy

A spatial approach to combatting wildlife crime

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.

Conservation Biology