Fine-Scale Evaluation of Giant Panda Habitats and Countermeasures against the Future Impacts of Climate Change and Human Disturbance (2015–2050): A Case Study in Ya’an, China

Jing Zhen, Xinyuan Wang, Qingkai Meng, Jingwei Song, Ying Liao, Bo Xiang, Huadong Guo, Chuansheng Liu, Ruixia Yang and Lei Luo
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Jing Zhen: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Xinyuan Wang: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Qingkai Meng: State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
Jingwei Song: Centre for Autonomous Systems, University of Technology Sydney, Ultimo, NSW 2007, Australia
Ying Liao: Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China
Bo Xiang: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Huadong Guo: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Chuansheng Liu: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Ruixia Yang: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Lei Luo: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

Sustainability, 2018, vol. 10, issue 4, 1-19

Abstract: The accelerating impact of climate change on giant panda ( Ailuropoda melanoleuca ) habitats have become an international research topic. Recently, many studies have also focused on medium-sized mountain ranges or entire giant panda habitats to predict how habitats will change as the climate warms, but few say in detail what to do or where to focus efforts. To fill this gap, this paper presents a new method to take comprehensive, fine-scale evaluations incorporating climate change, human disturbance, and current conservation networks and translate them into practical countermeasures in order to help decision-makers set priority regions for conservation. This study looked at the core area of the Sichuan Giant Panda Sanctuaries United Nations Educational, Scientific and Cultural Organisation (UNESCO) World Natural Heritage site, namely Ya’an Prefecture, as a case study. The research employs the Maximum Entropy (MaxEnt) modeling algorithm to analyze how climate change will affect the habitats by 2050 under two scenarios: only considering the influence of climate change, and thinking about the coupled influence of climate change and human disturbance together. The results showed the following: (1) only considering climate change, the overall habitat that can be used by giant pandas in this region will increase, which differs from most of the previous results showing a decrease; (2) the new suitable habitat will shift westward, northward and eastward in this region; (3) conversely, the suitable habitat will be significantly reduced (about 58.56%) and fragmentized when taking into account human disturbance factors; (4) at present, the three small nature reserves are far from each other and cannot cover the present habitat well nor protect the potentially suitable habitats. Based on the comprehensive analysis of habitat shifts and our two field investigations, we suggest two regions that can be expanded into the conservation network to contain more potentially suitable habitats in the future. Furthermore, we used a geographical information system to incorporate high-resolution remote-sensing images from the GF-1 satellite, land-cover maps, and a digital elevation model (DEM) to verify the possibility of our two suggested regions.

Keywords: climate change; habitat evaluation; MaxEnt; geographic information system; giant panda; species distribution modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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