Dataset Information

Assessing vulnerability of giant pandas to climate change in the Qinling Mountains of China.

ABSTRACT: Climate change might pose an additional threat to the already vulnerable giant panda (Ailuropoda melanoleuca). Effective conservation efforts require projections of vulnerability of the giant panda in facing climate change and proactive strategies to reduce emerging climate-related threats. We used the maximum entropy model to assess the vulnerability of giant panda to climate change in the Qinling Mountains of China. The results of modeling included the following findings: (1) the area of suitable habitat for giant pandas was projected to decrease by 281 km² from climate change by the 2050s; (2) the mean elevation of suitable habitat of giant panda was predicted to shift 30 m higher due to climate change over this period; (3) the network of nature reserves protect 61.73% of current suitable habitat for the species, and 59.23% of future suitable habitat; (4) current suitable habitat mainly located in Chenggu, Taibai, and Yangxian counties (with a total area of 987 km²) was predicted to be vulnerable. Assessing the vulnerability of giant panda provided adaptive strategies for conservation programs and national park construction. We proposed adaptation strategies to ameliorate the predicted impacts of climate change on giant panda, including establishing and adjusting reserves, establishing habitat corridors, improving adaptive capacity to climate change, and strengthening monitoring of giant panda.

SUBMITTER: Li J

PROVIDER: S-EPMC5468157 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Assessing vulnerability of giant pandas to climate change in the Qinling Mountains of China.

Li Jia J Liu Fang F Xue Yadong Y Zhang Yu Y Li Diqiang D

Ecology and evolution 20170425 11

Climate change might pose an additional threat to the already vulnerable giant panda (<i>Ailuropoda melanoleuca</i>). Effective conservation efforts require projections of vulnerability of the giant panda in facing climate change and proactive strategies to reduce emerging climate-related threats. We used the maximum entropy model to assess the vulnerability of giant panda to climate change in the Qinling Mountains of China. The results of modeling included the following findings: (1) the area o ...[more]

PMID: 28616195

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Project description:Qinling-Daba Mountains (QDM), which are located in central China, are considered as a significant climatic boundary delimiting north and south. However, the influence of complex topographic and climatic features makes it challenging to identify the exact location of the boundary, and different scholars delimit the boundary with significant differences. In addition, there is a gradual transition between climate zones, and no real dividing line exists. To explore the climate regionalization of the QDM, we focused on the identification of the transition zone rather than the exact location of the boundary between subtropical and temperate zones. Thus, we proposed a new workflow for climate regionalization based on the Geodetector-SVM model (a combination of Geodetector and support vector machines). First, we selected the spatial distribution data of six vegetation types (including typical subtropical and temperate vegetation) to represent the spatial distribution of climatic zones. Environmental factors (such as topography, temperature, precipitation, and soil) were used as explanatory variables for the spatial distribution of vegetation. Second, using the Geodetector-SVM model, the distribution characteristics and suitable environment of typical vegetation in different climatic zones are comprehensively explored. By analyzing the multiple boundaries between subtropical and temperate vegetation, the location of the transition zone of the QDM was identified. The results revealed the following: (1) The new workflow for climate regionalization based on the Geodetector-SVM model is powerful for the identification of the transition zone. The q-statistics are generally greater than 0.35, indicating that the transition zone between subtropical and temperate zones can highly reflect the character of the QDM; (2) From west to east, the transition zone mainly passes through the cities of Heishui County, Kang County, Liuba County, and Yichuan County and is approximately 30 km wide.

Dataset Information

Assessing vulnerability of giant pandas to climate change in the Qinling Mountains of China.

Publications

Assessing vulnerability of giant pandas to climate change in the Qinling Mountains of China.

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