Evaluating the Effects of Climate Change on Spatial Aggregation of Giant Pandas and Sympatric Species in a Mountainous Landscape

Understanding how climate change alters the spatial aggregation of sympatric species is important for biodiversity conservation. Previous studies usually focused on spatial shifting of species but paid little attention to changes in interspecific competitions under climate change. In this study, we evaluated the potential effects of climate change on the spatial aggregation of giant pandas (Ailuropoda melanoleuca) and three sympatric competitive species (i.e., black bears (Ursus thibetanus), golden takins (Budorcas taxicolor), and wild boars (Sus scrofa)) in the Qinling Mountains, China. We employed an ensemble species distribution modeling (SDM) approach to map the current spatial distributions of giant pandas and sympatric animals and projected them to future climate scenarios in 2050s and 2070s. We then examined the range overlapping and niche similarities of these species under different climate change scenarios. The results showed that the distribution areas of giant pandas and sympatric species would decrease remarkably under future climate changes. The shifting directions of the overlapping between giant pandas and sympatric species vary under different climate change scenarios. In conclusion, future climate change greatly shapes the spatial overlapping pattern of giant pandas and sympatric species in the Qinling Mountains, while interspecific competition would be intensified under both mild and worst-case climate change scenarios.

Field metabolic rates of giant pandas reveal energetic adaptations

Knowledge of energy expenditure informs conservation managers for long term plans for endangered species health and habitat suitability. We measured field metabolic rate (FMR) of free-roaming giant pandas in large enclosures in a nature reserve using the doubly labeled water method. Giant pandas in zoo like enclosures had a similar FMR (14,182 kJ/day) to giant pandas in larger field enclosures (13,280 kJ/day). In winter, giant pandas raised their metabolic rates when living at − 2.4 °C (36,108 kJ/day) indicating that they were below their thermal neutral zone. The lower critical temperature for thermoregulation was about 8.0 °C and the upper critical temperature was about 28 °C. Giant panda FMRs were somewhat lower than active metabolic rates of sloth bears, lower than FMRs of grizzly bears and polar bears and 69 and 81% of predicted values based on a regression of FMR versus body mass of mammals. That is probably due to their lower levels of activity since other bears actively forage for food over a larger home range and pandas often sit in a patch of bamboo and eat bamboo for hours at a time. The low metabolic rates of giant pandas in summer, their inability to acquire fat stores to hibernate in winter, and their ability to raise their metabolic rate to thermoregulate in winter are energetic adaptations related to eating a diet composed almost exclusively of bamboo. Differences in FMR of giant pandas between our study and previous studies (one similar and one lower) appear to be due to differences in activity of the giant pandas in those studies.

Virome in Fecal Samples From Wild Giant Pandas (Ailuropoda Melanoleuca)

The giant panda (Ailuropoda melanoleuca) is one of the most endangered mammals in the world; anthropogenic habitat loss and poaching still threaten the survival of wild pandas. Viral infection has become one of the potential threats to the health of these animals, but the available information related to these infections is still limited. In order to detect possible vertebrate viruses, the virome in the fecal samples of seven wild giant pandas from Qinling Mountains was investigated by using the method of viral metagenomics. From the fecal virome of wild giant pandas, we determined six nearly complete genomes belonging to the order Picornavirales, two of which may be qualified as a novel virus family or genus. In addition, four complete genomes belonging to the Genomoviridae family were also fully characterized. This virological investigation has increased our understanding of the gut viral community in giant pandas. Whether these viruses detected in fecal samples can really infect giant panda needs further research.

Skin Mycobiota of the Captive Giant Panda (Ailuropoda melanoleuca) and the Distribution of Opportunistic Dermatomycosis-Associated Fungi in Different Seasons

Dermatomycosis is the second major cause of morbidity in giant pandas (Ailuropoda melanoleuca), and seriously endangers its health. Previous observations indicated that the occurrence of dermatomycosis in the giant panda varies in different seasons. The skin microbiota is a complex ecosystem, but knowledge on the community structure and the pathogenic potentials of fungi on the skin of the giant panda remains limited. In this study, samples from the giant panda skin in different seasons were collected, and the mycobiota were profiled by 18S rRNA gene sequencing. In total, 375 genera in 38 phyla were detected, with Ascomycota, Basidiomycota, Streptophyta, and Chlorophyta as the predominant phyla and Trichosporon, Guehomyces, Davidiella, Chlorella, Asterotremella, and Klebsormidium as the predominant genera. The skin mycobiota of the giant panda changed in the seasons, and the diversity and abundance of the skin fungi were significantly higher in spring, autumn, and summer than in the winter. Several dermatomycosis-associated fungi were detected as opportunists in the skin mycobiota of healthy giant pandas. Clinical dermatomycosis in the giant panda is observed more in summer and autumn. In this study, the results indicated that the high diversity and abundance of the skin fungi may have enhanced the occurrence of dermatomycosis in autumn and summer, and that dermatomycosis-associated fungi are the normal components of the skin mycobiota.

The giant panda is cryptic

The giant panda (Ailuropoda melanoleuca) is an iconic mammal, but the function of its black-and-white coloration is mysterious. Using photographs of giant pandas taken in the wild and state-of-the-art image analysis, we confirm the counterintuitive hypothesis that their coloration provides camouflage in their natural environment. The black fur blends into dark shades and tree trunks, whereas white fur matches foliage and snow when present, and intermediate pelage tones match rocks and ground. At longer viewing distances giant pandas show high edge disruption that breaks up their outline, and up close they rely more on background matching. The results are consistent across acuity-corrected canine, feline, and human vision models. We also show quantitatively that the species animal-to-background colour matching falls within the range of other species that are widely recognised as cryptic. Thus, their coloration is an adaptation to provide background matching in the visual environment in which they live and simultaneously to afford distance-dependent disruptive coloration, the latter of which constitutes the first computational evidence of this form of protective coloration in mammals.

The Effects of the Spatial Extent on Modelling Giant Panda Distributions Using Ecological Niche Models

Climate change and biodiversity loss have become increasingly prominent in recent years. To evaluate these two issues, prediction models have been developed on the basis of ecological-niche (or climate-envelope) models. However, the spatial scale and extent of the underlying environmental data are known to affect results. To verify whether the difference in the modelled spatial extent will affect model results, this study uses the MaxEnt model to predict the suitability range of giant pandas in the Min Mountain System (MMS) area through modelling performed (1) at a nationwide scale and (2) at a restricted MMS extent. The results show that, firstly, both models performed well in terms of accuracy. Secondly, extending the modelling extent does help improve the modelling results when the distribution data is incomplete. Thirdly, when environmental information is insufficient, the qualitative analysis should be combined with quantitative analysis to ensure the accuracy and practicality of the research. Finally, when predicting a suitability distribution of giant pandas, the modelling results under different spatial extents can provide management agencies at the various administrative levels with more targeted giant panda protective measures.