Author(s)

Honour Chinoitezvi¹, Samuel Kusangaya¹, Chaabilo P. Muzamba², Mukululi Ndlovu³, Christopher Hungwe³

¹Department of Geography Geospatial Sciences and Earth Observation, University of Zimbabwe, Harare, Zimbabwe.
²Department of Animal and Wildlife Sciences, Midlands State University, Gweru, Zimbabwe.
³Zimbabwe Parks and Wildlife Authority, Sengwa Wildlife Research Institute, Gokwe, Zimbabwe.

Protected areas offer opportunities for natural resources management including biodiversity conservation. However, their success is incessantly stalled by non-compliant activities especially illegal hunting of wildlife. The use of empirical and spatially explicit information in understanding spatial patterns of wildlife poaching risk areas within protected areas is thus of paramount importance in implementing effective law enforcement towards anti-poaching. The use of species distribution models (SDM) in the field of wildlife research offers opportunities for increasing the understanding of poacher behavior in data scarce regions. However, the application of SDM in improving the understanding of wildlife poaching is still in its infancy. Predictive modelling of wildlife poaching risk was conducted for Sengwa Wildlife Research Area (SWRA) using Maximum Entropy modeling, a presence-only SDM. Results revealed that six predictor variables explained 80% of poaching incidents. These were SAVI, slope, distance from rivers, distance from roads, distance from settlements and general wildlife distribution. Riverine areas presented the most poaching risk zones with areas of steep slopes being of least poaching risks. Findings of this research can be used as a guiding tool in SWRA by park managers, to make informed conservation management decisions and effectively establish anti-poaching strategies by prioritizing areas of high risk. These results are very informative especially in situations where conservation resources are limited. Because of limited resources, wildlife managers are constrained to explicitly identify zones with the highest poaching risks for proactive resource allocation so as to combat illegal wildlife hunting. The modelling framework used in this study provides a crucial baseline for identifying potentially high-risk poaching zones and the main predictors, knowledge that can be utilized for proactive resource allocation towards anti-poaching activities. In addition, these results can be up scaled to any other conservation areas where poaching is problematic.

Modelling, Maximum Entropy, Poaching, Predictor Variables, Wire Snares

Chinoitezvi, H. , Kusangaya, S. , Muzamba, C. , Ndlovu, M. and Hungwe, C. (2024) Modelling Poaching Risk Zones in Sengwa Wildlife Research Area: A Progressive Step towards Poaching Management. Open Access Library Journal, 11, 1-16. doi: 10.4236/oalib.1111509.