International Journal of Environmental Protection and Policy
Volume 1, Issue 2, July 2013, Pages: 24-31
Received: May 28, 2013;
Published: Jul. 20, 2013
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Shadrack Mumo Ngene, Kenya Wildlife Service, Biodiversity Research and Monitoring, Biodiversity Research and Monitoring, Nairobi, Kenya
Andrew Skidmore, University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands
Hein van Gils, University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands
Francis Kamau Muthoni, University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands
Wycliffe Mutero, Kenya Wildlife Service, Biodiversity Research and Monitoring, Biodiversity Research and Monitoring, Nairobi, Kenya
Patrick Omondi, Kenya Wildlife Service, Biodiversity Research and Monitoring, Biodiversity Research and Monitoring, Nairobi, Kenya
This study investigates the impact of contrasting wildlife management regimes on the probability of elephant presence in the Tsavo ecosystem base on the New York “zero tolerance policing”. Point data for the location of elephant, elephant carcasses, cattle, buffalo, giraffe, occupied bomas, charcoal kilns, settlements and farms, and water points were collected through aerial survey from 7-12 February 2011. Secondary data layers included main rivers, 30m resolution digital elevation model, and moderate resolution imaging spectro-radiometer (MODIS) 250m spatial resolution normalized difference vegetation index (NDVI) data for January 2011. Information on the three management regimes (none, passive, and active) adopted by protected areas and ranches in Tsavo ecosystem was identified through interviews. The Maxent algorithm was used for modeling the probability of elephant presence in the ecosystem. Multicollinearity of the fourteen explanatory variables was tested using Eigen values and Condition Index (CI). We used Maxent with elephant location points as the presence only data and the twelve explanatory variables as environmental variables. Bootstrapping of ten replications was included in the model. The accuracy of the model was determined using the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) function. The results indicated that elephants were significantly more likely to be found in the protected areas than the non-protected areas. The northern sector of Tsavo West and the Voi sector of Tsavo East were the most likely areas to record elephants. Sectors with protected areas and ranches that practiced active management were more likely to show elephant than those with passive or no management. The areas with high probability of elephant occurrence coincided with actual high elephant density. Elephant carcasses, buffalo, giraffe, and settlements were the main variables that predicted the probability of elephant presence. Elephants are more likely to be in protected areas and ranches that were managed actively than those passively or not managed. In order to capitalize on the notion of protection and active management, we propose a wildlife management model based on the New York ‘zero tolerance’ policing. Any misdemeanor is not tolerated, especially illegal charcoal burning and livestock grazing in the ranches.
Shadrack Mumo Ngene,
Hein van Gils,
Francis Kamau Muthoni,
Zero Tolerance: Evolving Wildlife Management in Kenya, International Journal of Environmental Protection and Policy.
Vol. 1, No. 2,
2013, pp. 24-31.
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