As urbanisation is set to continue, understanding the impact on wildlife becomes increasingly important if we are to be able to conserve biodiversity. As an excellent group of bioindicators, invertebrates can allow us to understand some of the forces in urban areas which impact upon biodiversity and wildlife populations. This paper discusses some of the trends in the abundance, diversity and richness of invertebrates related to urbanisation and the specific urban environmental and traffic factors which may be at play., Elizabeth L. Jones, Simon R. Leather., and Obsahuje seznam literatury
Modern forestry may alter avian reproductive success indirectly through affecting predator-prey interactions. Here we evaluate the influence of road types on nest predation of ground-nesting birds in a highly fragmented forest area interspersed by a dense network of roads and forest paths, with one third of the area covered by a red-deer enclosure. Experimental nests (n = 276) resembling black grouse ( Tetrao tetrix) nests were proportionally installed along three types of roads discriminated by utility (road type, from frequently used to unused: tarred roads, gravel roads and forest paths) and inside/outside the red-deer enclosure. The nests were placed in couples, with one nest placed close to the road edge and the second placed inside the surrounding forest habitat to assess the “travel line” hypothesis. The “travel line” hypothesis was not supported because there was a similar predation rate among edge and interior nests. Even if predators can be discouraged along busy roads, type of road also did not affect nest predation. Nevertheless, nest predation inside the enclosure was significantly lower than in the surrounding, suggesting that frequent human disturbances in these habitats may have a repellent effect on predators of ground nests.
With the gradual improvement of the telecommunication infrastructure in China, the Internet and other new technologies have been frequently used. The Internet technology also brings many network security threats, for example, botnet, while bringing convenience. Botnet is a network formed between hosts controlled by malicious code. One of the most serious threat to network security faced by the Internet is a variety of malicious network attacks on the carrier of botnet. Back propagation (BP) neural network is proposed to detect botnet threat transmission. In this study, a botnet detection model was established using BP neural network system. BP neural network classifier could identify the botnet traffic and normal traffic. Moreover a test was carried out to detect botnet traffic using BP neural network; the performance of the BP neural network classifier was evaluated by the detection rate and false positive rate. The results showed that it had high detection rate and low false positive rate, which indicated that the BP neural network had a good performance in detecting the traffic of botnet threat transmission.