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

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.

Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

“…Maximum entropy (MaxEnt) [13] and support vector machines (SVMs) [14,15] are flexible and very powerful techniques. MaxEnt is a machine learning algorithm with a high capability in artificial fitting rules or functional connections (e.g., nonlinear relation) according to appearance information, usage of species' presence, and background data for the prediction of species distribution and habitat suitability [8,[16][17][18]. The MaxEnt algorithm is applied to detect the maximum entropy distribution likelihood and is used to forecast the possible distribution of a target species according to its maximum entropy under different conditions.…”

“…Their suitability for the assessment of species distribution and habitat suitability models has led them to become popular methods for evaluating habitat requirements in recent years. Both methods are applicable for predicting distribution patterns of plants and assessing their habitat suitability [6,7,15,28,29], biodiversity in the natural landscape [30,31], and the distribution pattern of living creatures [18,32,33]. Genus Juniperus is a coniferous plant with a variety of species occurring in the cool and temperate zones of the Northern Hemisphere's mountainous regions.…”

Potential Current and Future Distribution of the Long-Whiskered Owlet (Xenoglaux loweryi) in Amazonas and San Martin, NW Peru

“…Human disturbance and climate change variables were combined to assess integrated habitat impacts for X. loweryi following the analysis of Zhent et al [67]. In that sense, we selected four types of human disturbances, (urban areas from the National Geographic Institute (https://www.gob.pe/ign (accessed on 18 April 2020)), roads obtained from the Ministry of Transport and Communications [68], and hydroelectric power plants and power transmission lines from GEO GPS PERU (www.geogpsperu.com accessed on 28 October 2020) to represent the most frequently occurring disturbances in the study area.…”

Digital Heritage

“…The giant pandas are now confined to six mountain systems, from north to south: Qinling Mountain, Minshan Mountain, Qionglai Mountain, Big Facies Mountain, Small Facies Mountain and Liangshan Mountain. Based on spatial information technology, the Wang Xinyuan Research Group (Song et al 2014;Zhen et al 2018) carried out a habitat suitability assessment of giant panda habitat.…”