Journal of Risk Analysis and Crisis Response

Volume 7, Issue 4, December 2017, Pages 225 - 229

Risk Assessment of Canine Distemper in the Distribution Area of Giant Panda in Sichuan, Shaanxi and Gansu Provinces, China

Authors
Weigeng Shao, Feng Jiang, Liya Huang, Xiaolong Wang
Corresponding Author
Xiaolong Wang
Received 3 September 2017, Accepted 10 November 2017, Available Online 28 December 2017.
DOI
https://doi.org/10.2991/jrarc.2017.7.4.4How to use a DOI?
Keywords
Giant Panda, canine distemper, MaxEnt model, risk assessment
Abstract
Giant panda is the world-class precious endangered species, facing the canine distemper and other important infectious diseases on its wild and captive population of a serious threat. In this study, we used MaxEnt model and combined with ArcGIS analysis to predict the potential risk of canine distemper to giant panda habitat in Sichuan, Gansu and Shaanxi Provinces, China. The results showed that 35.05% and 19.47% of the distribution areas of the giant pandas were in the high risk and medium risk of canine distemper, respectively. The canine distemper pose a great risk to the healthy survival of giant pandas in China. In future, epidemic prevention, vaccine development and application of wild animals should be enhanced so as to effectively protect the giant panda.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
Journal of Risk Analysis and Crisis Response
Volume-Issue
7 - 4
Pages
225 - 229
Publication Date
2017/12/28
ISSN (Online)
2210-8505
ISSN (Print)
2210-8491
DOI
https://doi.org/10.2991/jrarc.2017.7.4.4How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Weigeng Shao
AU  - Feng Jiang
AU  - Liya Huang
AU  - Xiaolong Wang
PY  - 2017
DA  - 2017/12/28
TI  - Risk Assessment of Canine Distemper in the Distribution Area of Giant Panda in Sichuan, Shaanxi and Gansu Provinces, China
JO  - Journal of Risk Analysis and Crisis Response
SP  - 225
EP  - 229
VL  - 7
IS  - 4
SN  - 2210-8505
UR  - https://doi.org/10.2991/jrarc.2017.7.4.4
DO  - https://doi.org/10.2991/jrarc.2017.7.4.4
ID  - Shao2017
ER  -