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.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6776 |
| Date | 12 May 2023 |
| Creators | Sheng, Qingyu |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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