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Effects of environmental and anthropogenic drivers on Amur tiger distribution in northeastern China |
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| Authors: | Guangshun Jiang Haiyi Sun Jianmin Lang Lijuan Yang Cheng Li Arnaud Lyet Barney Long Dale G Miquelle Changzhi Zhang Sergey Aramilev Jianzhang Ma Minghai Zhang |
| Institution: | 1. Feline Research Center of Chinese State Forestry Administration; College of Wildlife Resources, Northeast Forestry University, Harbin, 150040, China 2. Heilongjiang Provincial Academy of Forestry, Harbin, 150081, China 3. Jilin Hunchun Amur Tiger National Nature Reserve, Hunchun, 133300, China 4. Heilongjiang Provincial Dongfanghong Forestry Bureau, Dongfanghong Town, 158402, China 5. Jilin Provincial Huangnihe Nature Reserve, Huangnihe Town, 33704, China 6. World Wildlife Fund, US, 1250 24th Street NW, Washington, DC, 20037, USA 7. Wildlife Conservation Society, 2300 Southern Boulevard, Bronx, NY, 10460, USA 8. World Wildlife Fund, China, Harbin, 150040, China 9. World Wildlife Fund, Russia, Amur Branch, Verkhneportovaya 18A, Vladivostok, 690003, Russia |
| Abstract: | We examined environmental and anthropogenic factors drive range loss in large mammals, using presence data of Amur tigers opportunistically collected between 2000 and 2012, and anthropogenic and environmental variables to model the distribution of the Amur tiger in northeastern China. Our results suggested that population distribution models of different subregions showed different habitat factors determining tiger population distribution patterns. Where farmland cover was over 50 km2 per pixel (196 km2), distance was within 15 km to the railway in Changbaishan and road density (length per pixel) increased in Wandashan, the relative probability of Amur tiger occurrence exhibited monotonic avoidance responses; however, where distance was within 150 km of the Sino-Russia border, the occurrence probability of Amur tiger was relatively high. We analyzed the avoidance or preference responses of Amur tiger distribution to elevation, snow depth and Viewshed. Furthermore, different subregional models detected a variety of spatial autocorrelation distances due to different population clustering patterns. We found that spatial models significantly improved model fits for non-spatial models and made more robust habitat suitability predications than that of non-spatial models. Consequently, these findings provide useful guidance for habitat conservation and management. |
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