Costs and benefits of the presence of leopards to the sport-hunting industry and local communities in Niassa national reserve, mozambique

Sport hunting is often proposed as a tool to support the conservation of large carnivores. However, it is challenging to provide tangible economic benefits from this activity as an incentive for local people to conserve carnivores. We assessed economic gains from sport hunting and poaching of leopards (Panthera pardus), costs of leopard depredation of livestock, and attitudes of people toward leopards in Niassa National Reserve, Mozambique. We sent questionnaires to hunting concessionaires (n = 8) to investigate the economic value of and the relative importance of leopards relative to other key trophy-hunted species. We asked villagers (n = 158) the number of and prices for leopards poached in the reserve and the number of goats depredated by leopard. Leopards were the mainstay of the hunting industry; a single animal was worth approximately U.S.$24,000. Most safari revenues are retained at national and international levels, but poached leopard are illegally traded locally for small amounts ($83). Leopards depredated 11 goats over 2 years in 2 of 4 surveyed villages resulting in losses of $440 to 6 households. People in these households had negative attitudes toward leopards. Although leopard sport hunting generates larger gross revenues than poaching, illegal hunting provides higher economic benefits for households involved in the activity. Sport-hunting revenues did not compensate for the economic losses of livestock at the household level. On the basis of our results, we propose that poaching be reduced by increasing the costs of apprehension and that the economic benefits from leopard sport hunting be used to improve community livelihoods and provide incentives not to poach.

Race Detection for Android Applications

Programming environments for smartphones expose a concurrency model that combines multi-threading and asynchronous event-based dispatch. While this enables the development of efficient and feature-rich applications, unforeseen thread interleavings coupled with non-deterministic reorderings of asynchronous tasks can lead to subtle concurrency errors in the applications. In this paper, we formalize the concurrency semantics of the Android programming model. We further define the happens-before relation for Android applications, and develop a dynamic race detection technique based on this relation. Our relation generalizes the so far independently studied happens-before relations for multi-threaded programs and single-threaded event-driven programs. Additionally, our race detection technique uses a model of the Android runtime environment to reduce false positives. We have implemented a tool called DROIDRACER. It generates execution traces by systematically testing Android applications and detects data races by computing the happens-before relation on the traces. We analyzed 1 5 Android applications including popular applications such as Facebook, Twitter and K-9 Mail. Our results indicate that data races are prevalent in Android applications, and that DROIDRACER is an effective tool to identify data races.