Contributions of Non-consumptive Wildlife Tourism to Conservation

Wildlife tourism has the potential to contribute to conservation through a variety of mechanisms. This chapter presents a preliminary assessment of the extent to which this potential is currently being realised, comparing tourism based on viewing of animals in captive settings (with a focus on federated zoos) with that in free-ranging situations (wildlife watching). The key mechanisms involved are: direct wildlife management and research; use of income derived from wildlife tourism to fund conservation; education of visitors to behave in a more conservation-friendly manner; political lobbying in support of conservation; and provision of a socio-economic incentive for conservation. All of these occur in at least some zoos and wildlife-watching situations, and collectively the contribution of non-consumptive wildlife tourism to conservation is significant, though impossible to quantify. The key strengths of the zoo sector in this regard are its inputs into captive breeding and its potential to educate large numbers of people. in contrast, wildlife watching provides significant socio-economic incentives for conservation of natural habitats. There seem to be significant opportunities for expanding the role of non-consumptive wildlife tourism in conservation.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.