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Modelling Poaching Risk Zones in Sengwa Wildlife Research Area: A Progressive Step towards Poaching Management

DOI: 10.4236/oalib.1111509, PP. 1-17

Subject Areas: Environmental Sciences, Geography

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

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Abstract

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.

Cite this paper

Chinoitezvi, H. , Kusangaya, S. , Muzamba, C. P. , 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, e1509. doi: http://dx.doi.org/10.4236/oalib.1111509.

References

[1]  Muzhingi, D.T. (2012) Environmental Niche Modelling of Elephant Poaching Sites in the Matusadona National Park, Zimbabwe. University of Johannesburg, Johannesburg, 5.
[2]  Kassa, S., Claudia, B.-C., Costa, J. and Lugolobi, R. (2022) Determinants and Drivers of Wildlife Trafficking: A Qualitative Analysis in Uganda. Journal of International Wildlife Law & Policy, 24, 314-342.  https://doi.org/10.1080/13880292.2021.2019381
[3]  Wittemyer, G., Elsen, P., Bean, W.T.A.C.O. and Brashares, J.S. (2008) Accelerated Human Population Growth at Protected Area Edges. Science, 321, 123-126.  https://doi.org/10.1126/science.1158900
[4]  Iossa, G., Soulsbury, C.D. and Harris, S. (2007) Mammal Trapping: A Review of Animal Welfare Standards of Killing and Animal Welfare. Animal Welfare, 16, 335-352. https://doi.org/10.1017/S0962728600027159
[5]  Fa, J.E. and Brown, D. (2009) Impacts of Hunting on Mammals in African Tropical Moist Forests: A Review and Synthesis. Mammal Review, 39, 231-264.  https://doi.org/10.1111/j.1365-2907.2009.00149.x
[6]  Zvidzai, M., et al. (2023) Application of Maximum Entropy (MaxEnt) to Understand the Spatial Dimension of Human-Wildlife Conflict (HWC) Risk in Areas Adjacent to Gonarezhou National Park of Zimbabwe. Ecology and Society, 28, 18.  https://doi.org/10.5751/ES-14420-280318
[7]  Mohammadi, A.K., et al. (2021) Integrating Spatial Analysis and Questionnaire Survey to Better Understand Human-Onager Conflict in Southern Iran. Scientific Reports, 11, Article No. 12423. https://doi.org/10.1038/s41598-021-91921-w
[8]  Sharma, P.N., et al. (2020) Mapping Human-Wildlife Conflict Hotspots in a Transboundary Landscape, Eastern Himalaya. Global Ecology and Conservation, 24, e01284. https://doi.org/10.1016/j.gecco.2020.e01284
[9]  Mahakata, I. (2022) Seasonal Trend and Distribution of Wire-Snaring Activities and Possible Hotspots in the Sengwa Wildlife Area (SWRA), Zimbabwe. Open Access Library Journal, 9, e9287. https://doi.org/10.4236/oalib.1109287
[10]  Pisa, L.S. and Katsande, S. (2021) Human Wildlife Conflict in Relation to Human Security in the Gonarezhou National Park, Zimbabwe. International Journal of Earth Sciences Knowledge and Applications, 3, 96-106.
[11]  Tafangenyasha, C., Ngorima, P., Musungwa, S. and Kavhu, B. (2018) Modifications of the Flora Zambeziaca in the Zambezi Basin by Environmental Antecedent Factors: Termites, Fire and Elephant. International Journal of Environmental Sciences & Natural Resources, 12, Article 555840.
[12]  Mhiripiri, S. and Mlambo, D. (2021) The Effects of Land Use and Microsite Availability on Early Seedling Recruitment of Acacia Tortilis (Synonym: Vachelia tortilis) in a Southern African Savanna. Tropical Ecology, 62, 82-94.  https://doi.org/10.1007/s42965-020-00128-z
[13]  Manyenye, N.S.H. (2008) Identification and Mapping risk areas for Zebra Poaching: A case of Tarangire National Park, Tanzania. International Institute for Geo-Information Science and Earth Observation Enschede, Netherlands.
[14]  Muposhi, V.K., et al. (2016) Habitat Heterogeneity Variably Influences Habitat Selection by Wild Herbivores in a Semi-Arid Savanna Ecosystem. PLOS ONE, 11, e0163084. https://doi.org/10.1371/journal.pone.0163084
[15]  Asmaa, N.M.E., Olatubara, C.O. and Ewemoje, T.A. (2020) Inland Wetland Time-Series Digital Change Detection Based on SAVI and NDWI Indecies: Wadi EI-Rayan Lakes, Egypt. Remote Sensing Applications: Society and Environment, 19, Article 100347. https://doi.org/10.1016/j.rsase.2020.100347
[16]  Ndaimani, H., Tagwirei, P., Sebele, L. and Madzikanda, H. (2016) An Ecological Paradox: The African Wild Dog (Lycaon pictus) Is Not Attracted to Water Points When Water Is Scarce in Hwange National Park, Zimbabwe. PLOS ONE, 11, e0146263. https://doi.org/10.1371/journal.pone.0146263
[17]  Padalia, H., Srivastava, V. and Kushwaha, S. (2014) Modelling Potential Invasion Range of Alien Invasive Species, Hyptis suaveolens (L) Piot. in India: Comparison of MaxEnt and GARP. Ecological informatics, 22, 36-43.  https://doi.org/10.1016/j.ecoinf.2014.04.002
[18]  Philip, S.J. (2005) A Brief Tutorial on MaxEnt. AT&T Research, 190, 231-259.
[19]  Thiault, L., et al. (2019) Predicting Poaching Risk in Marine Protected Area for Improved Patrol Efficiency. Elsevier, Paris.  https://doi.org/10.1016/j.jenvman.2019.109808
[20]  Sibanda, M., et al. (2016) Understanding the Spatial Distribution of Elephant (Loxodonta africana) Poaching Incidences in the Mid-Zambezi Valley, Zimbabwe Using Geographic Information Systems and Remote Sensing. Geocarto International, 31, 1006-1018. https://doi.org/10.1080/10106049.2015.1094529
[21]  Haines, A.M., et al. (2012) Spatially Explicit Analysis of Poaching Activity as a Conservation Management Tool. Wildlife Society Bulletin, 36, 685-692.  https://doi.org/10.1002/wsb.194
[22]  Mannathoko, L.B., et al. (1990) A Case Study on Land Use, Land Resources and Aspects of Human Geography in Parts of Lom Kao District of Petchabun Province, Thailand. Research Report, ITC, Enschede.

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