Illegal activity within protected areas is a primary driver of species decline and threatens conservation efforts. In the western corridor of the Serengeti Ecosystem of northern Tanzania, the Grumeti and Ikorongo Game Reserves and Ikona Wildlife Management Area provide an important buffer between permanent settlements and Serengeti National Park, while simultaneously maintaining critical wildlife habitat. Understanding the spatial distribution and environmental drivers of illegal activity is critical to optimize biodiversity protection efforts in this important ecosystem. We examined a rare dataset containing detailed records of reserve game scout patrol effort and occurrences of illegal activity between 2013 and 2016. We used presence only data to construct predictive models of five categories of illegal activity. We derived spatial predictions of the likelihood of different activities and identified the environmental variables predictive of risk for each activity. The highest risk areas were located along reserve edges, further from roads and scout camps, suggesting avoidance of enforcement presence. Activities associated with wildlife offtake were the most widely distributed in the study area and extended into the national park. Permanent scout camps were more effective deterrents of all illegal activities than observation posts, but their limited spatial influence demonstrated additional enforcement strategies are required. K E Y W O R D SAfrica, bushmeat, predictive modeling, protected areas, risk assessment, snare
In the western Serengeti of Tanzania, African elephant Loxodonta africana populations are increasing, which is rare across the species’ range. Here, conservation objectives come into conflict with competing interests such as agriculture. Elephants regularly damage crops, which threatens livelihoods and undermines local support for conservation. For damage reduction efforts to be successful, limited resources must be used efficiently and strategies for mitigation and prevention should be informed by an understanding of the spatial and temporal distribution of crop damage. We assessed historical records of crop damage by elephants to describe the dynamics and context of damage in the western Serengeti. We used binary data and generalized additive models to predict the probability of crop damage at the village level in relation to landscape features and metrics of human disturbance. During 2012–2014 there were 3,380 reports of crop damage by elephants submitted to authorities in 42 villages. Damage was concentrated in villages adjacent to a reserve boundary and peaked during periods of crop maturity and harvest. The village-level probability of crop damage was negatively associated with distance from a reserve, positively with length of the boundary shared with a reserve, and peaked at moderate levels of indicators of human presence. Spatially aggregated historical records can provide protected area managers and regional government agencies with important insights into the distribution of conflict across the landscape and between seasons, and can guide efforts to optimize resource allocation and future land use planning efforts.
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