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Navigating for Noah: Setting New Directions for Endangered Species Protection in the 21st Century
Abstract
The Endangered Species Act (ESA) recognizes the impact of human activities on animals and plants and expresses Congress’ intent
to halt extinction and restore species to their natural abundance. Although the goals of the ESA include conserving the ecosystems
upon which endangered species depend, none of the statute’s implementation provisions directly address ecosystem protection.
Rather, they are focused on the individual species themselves. Saving species one at a time is not a successful strategy for
saving wildlife, especially in the face of climate change. Habitat loss is the primary threat to species; habitat conservation
is the best way to address the problem of species extinction. Vitalizing the ecosystem goal of the ESA and creating a biological
diversity land conservation system in the United States are two ways to assure long term species survival.
... By analyzing the close relations among species, we can further research the potential relevance of different species. It will have a positive impact on endangered species protection [38], bioarchaeologists, animal classification [39], etc. ...
We present a cluster boundary detection scheme that exploits MeanShift and Parzen window in high-dimensional space. To reduce the noises interference in Parzen window density estimation process, the kNN window is introduced to replace the sliding window with fixed size firstly. Then, we take the density of sample as the weight of its drift vector to further improve the stability of MeanShift vector which can be utilized to separate boundary points from core points, noise points, isolated points according to the vector models in multi-density data sets. Under such circumstance, our proposed BorderShift algorithm doesn’t need multi-iteration to get the optimal detection result. Instead, the developed Shift value of each data point helps to obtain it in a liner way. Experimental results on both synthetic and real data sets demonstrate that the F-measure evaluation of BorderShift is higher than that of other algorithms.
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