Previously, the exact phylogenetic relationship between the giant panda (Ailuropoda melanoleuca) and the red panda (Ailurus fulgens) has been unclear, but with the advent of molecular sequencing technologies, these two animals are now known to be distantly related. Although taxonomically distinct, these two panda species have several things in common, primarily their almost exclusively bamboo diet, but also their similar physical adaptations to their diet and their sympatry in some locations. What was unknown was if their similarities also extended to their gastrointestinal tract (GIT) microbiota. Using next-generation 16S rRNA parallel sequencing technologies, we uncovered several factors that impact bacterial communities of the pandas and also found that the two panda species harbor distinct microbial communities. In general, the Firmicutes and the Proteobacteria dominate both pandas’ gut microbiomes, with lesser contributions by the Actinobacteria and the Bacteroidetes. However, for the red panda, sex, age, and season significantly alter GIT microbiota. For giant pandas, we found that bacterial communities differed significantly between their normal fecal samples and mucus excretions. Together, these data display the plasticity of their bacterial communities, with several factors shaping GIT microbiota. As both species are highly threatened, understanding the relationship between the panda and their gut microbiome has given insight into the overall health of these uniquely herbivorous carnivores.

This thesis is based and developed on the ambiguous and open conception, iconic species, which reveals the relationship between human society and non-human species that goes be- yond its biological status. From the case of the giant panda, I attempt to deconstruct the shap- ing process of an iconic species from multiple perspectives, which includes how a specific cultural context, or a specific historical period contributes to the shaping process and how the government and the public diverge or even clash around the shaping process. Then, I introduce my fieldwork where I through observing both giant pandas and red pandas in the exhibition centres called panda bases to analyse the encountering an iconic species in reality and their different influences on public awareness of the wildlife conservation. I also attempt to explore the various representative forms of an iconic species such as the Tibetan antelope ranging from the mascot to the film and follow the changes of its symbolic meanings in different forms. In conclusion, the thesis is aimed to reflect the hybrid features of the iconic species and pro- vide in-depth interpretation of the endless interactions between the human beings and other species.

Appropriate conservation decisions and efforts must be based on real−time and accurate information about wildlife populations. However, it is extremely challenging to monitor the population demography and physiological traits of many threatened and secretive animal species through direct observation and capture. Near infrared spectroscopy (NIRS) has the potential to be a remote tool to address questions concerning wildlife physiology and demography by analyzing “signs” of animals without seeing or capturing them. In this dissertation, two species, the giant panda (Ailuropoda melanoleuca) and red panda (Ailurus fulgens) are used as a case study, to demonstrate NIRS’ feasibility in studying their physiological properties. The aim of this study is to test NIRS’ potential as a real−time analytical tool for in the nutritional foodscape and demographic analysis using less processed or non−processed field fecal and forage samples with the help of the mode−cloning technique to transfer the master model (dry and ground samples) under laboratory conditions to satellite modes (wet or dry but unground) in field conditions. Mode−cloning is conducted using either slope and bias correction (SBC) or two spectral correction methods, piecewise direct standardization (PDS) and external parameter orthogonalization (EPO). The following four hypotheses are tested this dissertation: (1) by using mode−cloning with both SBC and PDS, unprocessed wet or unground dry bamboo leaves (pandas’ food) can be used to determine the crude protein contents; (2) machine learning−based classification models using less processed field feces after mode−cloning with spectral correction approaches (PDS and EPO) can differentiate between sexes of the giant panda; (3) mode−cloned machine learning classification models using field feces can detect pregnancy of female giant pandas; (4) with the application of mode−cloning, field fecal samples can provide sex differentiation of the red panda. This dissertation demonstrates that NIRS coupled with mode−cloning and machine learning has the potential to provide real−time and accurate prediction to determine bamboo foodscape quality and reproductive status of the giant panda and red panda using minimally processed biological samples, thus allowing quick decision-making for both in situ population monitoring of these two species and ex situ husbandry preparations for pregnant female giant pandas.