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Shevtsova, E.; Jiang, G.; Vitkalova, A.; Gu, J.; Qi, J.; Chaika, M.I.; Guskov, V.Y.; Ning, Y.; Wang, M.; Kostyria, A.; Darman, Y.
Saving the Amur Tiger and Amur Leopard
2018  Full Book

The Amur tiger and Amur leopard, among the flagship species, are respectively designated as Endangered and Critically Endangered by IUCN Red List of Threatened Species. Population estimates reach over 500 for Amur tiger and over 60 for Amur leopard in the wild, with most of them live in Northeastern China and Far Eastern Russia, and a few individuals were assumed inhabit in the Democratic People's Republic of Korea (DPRK). As an apex predator residing at the top of a food chain, the Amur tiger and leopard play an important role for heathy ecosystem by balancing the number of wild species, and that they need large habitat area to search enough prey to feed themselves. Therefore, conservation of these two big cat species also includes sustainable management of ecosystem, such as preserving million square meters of the region's forests as well as other wild species. Reliable and precise scientific evidence underpinned by unified research methodologies has been identified as a key challenge for the range countries scaling up efforts on the conversation of endangered species and their habitats. To respond to this challenge, the joint study applied camera trapping and molecular genetic analysis of targeted species in transboundary areas of China and the Russian Federation to provide scientific assessment and consequently developed policy recommendations for better conservation plan and improved ecological corridor management among key member States in North-East Asia. With the support of NEASPEC Secretariat, the project also reached the following breakthrough that: (1) DNA samples of the Amur tiger and leopard across the Russian Federation border to China were collected for the first time for joint molecular genetic analysis, and (2) Chinese and Russian experts shared data collected through camera trapping and carried out molecular genetic analysis applying the same methodologies that significantly enhanced the data reliability.

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