Using Range Condition Assessment to Optimize Wildlife Stocking in Tindress Wildlife Sanctuary, Nakuru District, Kenya
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CitationMuya, S. M., Kamweya, A. M., Muigai, A. W., Kariuki, A., & Ngene, S. M. (2013). Using range condition assessment to optimize wildlife stocking in Tindress Wildlife Sanctuary, Nakuru District, Kenya. Rangeland Ecology & Management, 66(4), 410-418.
PublisherSociety for Range Management
JournalRangeland Ecology & Management
AbstractOver 70% of Kenya’s wildlife resources occur outside protected areas, in areas where land use practices do not necessarily conform to wildlife conservation standards. Ensuring that land use practices in these areas accommodate wildlife conservation is vital in effectively conserving wildlife in this country. Tindress Farm in Rift Valley offers a good example of a place where economic activities and wildlife conservation can work harmoniously. The farm has set up a 320-ha wildlife sanctuary in the hilly parts of the property to provide a haven for wildlife displaced by human settlements in the surrounding environs. The Tindress Farm management needed to know the diversity and optimum number of wildlife species that the sanctuary could accommodate. This study set out to 1) outline a set of models for objectively calculating wildlife stocking levels and 2) demonstrate the practical use of these models in estimating optimum stocking levels for a specific wildlife sanctuary. After comparing models using forage inventory methods models and utilization-based methods (UM), we opted to use UM models because of their focus on ecological energetics. This study established that the range condition in Tindress Wildlife Sanctuary varied from poor to good (29-69%) and recommended a total stocking density of 158.9 grazer units and 201.4 browser units shared out by the various herbivore species. These estimates remain a best-case scenario. The effects of rainfall, range condition, and condition of the animals should be monitored continuously to allow for adjustments through active adaptive management.