Search Results (2)

It is vitally important to understand the ecological roles of medium and small carnivores in the context of the massive decline in the number of large carnivores around the world. Based on a spatial association network of terrestrial birds and mammals, this study analyzed the ecological roles of medium and small carnivores in the community in Liancheng National Nature Reserve. From October 2019 to June 2020, we obtained 3559 independent detections of 20 terrestrial birds and mammals from 112 camera traps. There are seven species that are medium and small carnivores present in the study area, including red fox (Vulpes vulpes), leopard cat (Prionailurus bengalensis), Chinese mountain cat (Felis bieti), stone marten (Martes foina), Asian badger (Meles leucurus), Siberian weasel (Mustela sibirica) and mountain weasel (Mustela altaica). By calculating the Phi coefficient of all species pairs, a spatial association network composed of twelve species was constructed. We analyzed the characterization of spatial associations by the Shannon–Wiener index and Lambda statistic. The results showed that: (1) the status of the network reflects the changes of community composition and structure after the decline in large carnivores and other species; (2) with the exception of the Chinese mountain cat and stone marten, the other five medium and small carnivores were located in the network, which played an important role in the complexity of the network and the maintenance of the community; (3) the medium and small carnivores could not take the place of the large carnivores in order to control the population of herbivores, such as Siberian roe deer (Capreolus pygargus) and Himalayan marmot (Marmota himalayana). The results of this study provide guidance for determining the direction and focus of conservation efforts. Full article

The COVID-19 pandemic has significantly changed urban life and increased attention has been paid to the pandemic in discussions of urban vulnerability. There is a lack of methods to incorporate dynamic indicators such as urban vitality into evaluations of urban pandemic vulnerability. In this research, we use machine learning to establish an urban Pandemic Vulnerability Index (PVI) that measures the city’s vulnerability to the pandemic and takes dynamic indicators as an important aspect of this. The proposed PVI is constructed using 140 statistic variables and 10 dynamic variables, using data from 47 prefectures of Japan. Factor Analysis is used to extract factors from variables that may affect city vulnerability, and the LambdaMART algorithm is used to aggregate factors and predict vulnerability. The results show that the proposed PVI can predict the relative seriousness of the COVID-19 pandemic in two weeks with a precision of more than 0.71, which is meaningful for taking controlling measures in advance and shaping the society’s response. Further analysis revealed the key factors affecting urban pandemic vulnerability, including city size, transit station vitality, and medical facilities, emphasizing precautions for public transport systems and new planning concepts such as the compact city. This research explores the application of machine learning techniques in the indicator establishment and incorporates dynamic factors into vulnerability assessments, which contribute to improvements in urban vulnerability assessments and the planning of sustainable cities while facing the challenges of the COVID-19 pandemic. Full article