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Pandas, Giant

Pandas, Giant (Ailuropoda melanoleuca) are large, black-and-white bears native to the mountainous regions of central China.
These solitary, bamboo-eating herbivores are recognized for their distinctive appearance and are classified as a vulnerable species due to habitat loss and fragmentation.
Pandas, Giant play a crucial role in their ecosystems, acting as seed dispersers and contributing to the preservation of bamboo forests.
Reserach on Pandas, Giant helps inform conservation efforts and advance our understanding of this iconic species.

Most cited protocols related to «Pandas, Giant»

The data matrix of craniodental features employed in the cladistic analysis, including 82 characters and 19 taxa (see Table S1), was coded on the basis of original specimens of osteological and fossil material, casts, and published figures and descriptions. For Canis lupus, Ursus arctos, Ursus americanus, Tremarctos ornatus, Ailuropoda melanoleuca, Ursavus brevirhinus, Indarctos vireti, I. arctoides and I. punjabiensis, we had direct access to skulls and mandibles. For Ursus thibetanus and Helarctos malayanus, we relied on casts of mandibles and skulls. For the remaining species, we used either photographs or images from scientific papers. The character description can be consulted in the Text S1. The matrix was generated using MacClade 4.08a OS X, and was analyzed using PAUP* (Version 4.0b10 for Macintosh [45] . A maximum-parsimony analysis was performed using the branch-and-bound method, with Canis lupus as the outgroup. Even though C. lupus is not a member of the Ursidae, its cranial, mandibular and dental morphologies are supposed to be similar to the ancestor of the Arctoidea, and therefore a quite accurate choice as an outgroup for this analysis. In order to test clade robusticity, a bootstrap analysis with 1,000 replicates was performed using the branch-and-bound search option.
Publication 2012
Bears CD3EAP protein, human Character Cranium Dental Health Services Grizzly Bears Lupus Vulgaris Mandible Pandas, Giant Spectacled Bear Ursus Wolves
In this study, fecal samples were collected from both wild and captive giant pandas. Fecal samples were collected from wild giant pandas at the Fengtongzhai National Nature Reserve (FNNR, n = 14) and the Wolong National Nature Reserve (WNNR, n = 67) by experienced trackers, immediately frozen in liquid nitrogen, and stored at −80 °C until later use. Fecal samples from captive giant pandas were collected from the China Conservation and Research Center for the Giant Panda (CCRC; n = 49) and also stored at −80 °C until later use. Demographics for all pandas sampled in this study are shown in Table S1. Seven tetra-microsatellites including GPL-60, gpz-47, gpz-20, GPL-44, GPL-29, GPL-53, and gpz-6 were used to distinguish the wild individuals, and this DNA analysis was performed by Qiao et al., therefore the samples of wild giant pandas were from their research (Table S2) [25 (link)].
DNA was extracted from the fecal samples using the Mo Bio PowerFecal DNA isolation kit (Mo Bio Laboratories, Carlsbad, CA, USA) according to the manufacturer’s instructions. Variable region 4 (V4) forward primer: GTGYCAGCMGCCGCGGTAA, reverse primer: GGACTACHVGGGTWTCTAAT. The PCR reaction (50 µl total volume) contained 2 µL DNA (20 ng), 19 µL PCR grade water, 25 µL 2× Es Taq Master Mix (CW BIO, Beijing, China) (including 2× Es Taq Polymerase, 0.075 µM Mg2+, and 10 µM dNTP mix), 2 µL (0.4 µM) forward primer, and 2 µL (0.4 µM) reverse primer. PCR was performed at 94 °C for 1 min, followed by 30 cycles of 94 °C for 20 s, 59 °C for 25 s, and 68 °C for 45 s, followed by a final extension at 68 °C for 10 min. Variable region 4 was sequenced at the Beijing Genomics Institute (Beijing, China).
Shotgun metagenomic sequencing was performed at Novogene (Beijing, China). DNA libraries were constructed according to Illumina’s instructions. Briefly, DNA was sheared to 300–400 bp fragments, and the DNA from each individual sample was barcoded uniquely. Since most of the demographic data for the wild pandas are unknown, we intended to choose a wider range of samples from the captive samples to see if the environment is still the major driver of the gut metagenome. Therefore, we randomly chose three cubs (≤2-year-olds), one sub-adult (>2-year-olds and <5-year-olds) and three adults (≥5-year-olds and ≤20-year-olds) (including three males and four females) for metagenome analysis (Table S1). Correspondingly, seven random samples were collected from wild giant pandas at the Fengtongzhai National Nature Reserve and were chosen for comparative analysis of metagenomics with captive giant pandas. Fourteen samples were sequenced on an Illumina HiSeq platform. Sequencing depth, quality control, and pre-processing details are listed in Table S3.
The datasets used in this study are accessible from the National Centre for Biotechnology Information Sequence Read Archive (SRA; http://www.ncbi.nlm.nih.gov/sra) [26 (link)], accession bio project numbers: PRJNA356809 and PRJNA358755.
Publication 2019
Adult BP 400 DNA Library Feces Females Freezing isolation Males Metagenome Nitrogen Oligonucleotide Primers Pandas, Giant Short Tandem Repeat Taq Polymerase Tetragonopterus
We merged overlapping paired end reads with SeqPrep40 and mapped either both merged and unmerged reads (modern samples), or merged reads only (ancient samples), to the reference genome assembly of the giant panda19 (link) with bwa aln v0.7.741 (link). To account for the evolutionary divergence between the reference genome and the samples, we increased the allowed mismatch rate by setting the −n flag to 0.01 rather than the default of 0.04. We excluded reads with a MapQuality score less than 30 and removed duplicate reads with samtools v0.1.1942 (link).
We selected the giant panda reference genome rather than the less evolutionarily distant and more contiguous polar bear reference genome36 for evolutionary analyses. As an ingroup to the group of samples being studied, the polar bear reference genome might introduce bias into our mappings that would disproportionately impact admixture inference. Cave bear reads from regions of the cave bear genome potentially introgressed from the polar/brown bear lineage would have a greater probability of mapping to the polar bear genome than reads from other parts of the genome because of their lower divergence. This biased assembly would produce the artefact of an inflated frequency of shared derived alleles between polar bears and cave bears in the cave bear assemblies. By contrast, mapping to the outgroup giant panda reference would have no bias for or against mapping introgressed regions making it a more suitable reference genome for this study.
For analysis, we generated haploidized sequences for each individual by randomly selecting a single high quality base call (BaseQuality ≥30, read MapQuality ≥30) at each site in the panda reference genome. This method better represents non-reference alleles for low coverage samples than genotype calling, which tends to be biased toward the reference allele, potentially confounding downstream analyses2 (link). To avoid inclusion of repetitive or duplicated genomic elements, we masked sites where an individual’s coverage was above the 95th percentile of genome wide coverage.
Publication 2018
Alleles Bears Biological Evolution Genome Genome Components Genotype Gigantism Pandas, Giant Polar Bears
We tested for admixture between cave bears and their nearest extant relatives, polar bears and brown bears, with the D statistic (ABBA, BABA test)2 (link),21 (link). All D statistics calculated during this study are reported in the Supplementary information (Supplementary Data 14). To avoid bias resulting from ancient DNA cytosine deamination (C- > T error) damage, we restricted our analysis to transversion sites. To test for significance, we applied the weighted block jackknife2 (link). Because of the low contiguity of the giant panda genome, we used 1 Mb non-overlapping blocks, rather than the 5 Mb non-overlapping blocks used in previous studies2 (link),10 (link),12 , which would exclude most of the panda scaffolds (N50 = 1,281,781)19 (link). Despite their smaller size, 1 Mb non-overlapping bins are adequate for testing cave bear introgression into brown bears because they are substantially longer than the longest estimated length of introgressed blocks of cave bear ancestry which is 175 kb (Supplementary Table 5). We consider results more than three standard errors different from zero (Z > 3) as strong evidence of admixture, and more than two standard errors different from zero (Z > 2) as providing moderate evidence of admixture.
We investigated four alternative outgroups for admixture tests. These were, in order of increasing phylogenetic distance from the focal clade13 (link): American black bear, Asiatic black bear, spectacled bear and giant panda. All outgroups resulted in a significant signal of admixture between all cave bears and all modern brown bears, following the divergence of polar bears and brown bears (Supplementary Fig. 6). In contrast, tests involving the Late Pleistocene brown bear similarly supported admixture between brown bears and cave bears when the less divergent outgroups were used, but the two most divergent outgroups resulted in a reversal of the admixture signal (Supplementary Fig. 6). We attribute this effect to accumulated errors in the ancient pseudohaploid sequences as a result of both sequencing error and spurious read mapping, both of which tend to occur at higher rates in ancient compared to modern DNA datasets. Specifically, at sites where the outgroup has a private allele, accumulated errors in an ancient sample occupying the P2 position would convert a proportion of these BBBA sites to BABA sites, causing that individual to appear unadmixed relative to P1. This effect is amplified with more divergent outgroups since they will posses more private alleles relative to the ingroup. For the American black bear outgroup, we also observed consistently elevated D values relative to those generated using other outgroups, suggesting differential allele sharing between American black bear and P1 and P2 ingroup lineages (polar and brown bear, in this case). We calculated D statistics to assess this imbalance using the giant panda as outgroup, and found a significant excess of derived alleles shared between American black bear and polar bears, relative to brown bears, in almost all comparisons (Supplementary Fig. 7). This may reflect admixture between American black bear and polar bear, admixture with a ghost lineage, ancestral population structure, or the transfer of archaic alleles into brown bears by a cave bear vector, but we did not investigate these alternative explanations further. All other outgroups did not show any clear or consistent imbalance in allele sharing with either ingroup lineage. Overall, we selected Asiatic black bear as the most appropriate outgroup, being sufficiently closely related to the ingroup to avoid artefacts associated with ancient datasets (Supplementary Fig. 6), and showing no consistent pattern of differential allele sharing with either polar bears or brown bears (Supplementary Fig. 7). Asiatic black bear was thus used as outgroup for all subsequent admixture tests.
To quantify the amount of admixture, we used the statistic21 (link), which is the excess of shared derived alleles between the admixed individual and candidate introgressor standardized by the maximum excess of shared derived alleles expected in an entirely (100%) introgressed individual. All values calculated during this study are reported in the Supplementary Data 5 and 6. The expected value is best calculated by using individuals that we hypothesize best approximate the diversity within the introgressing populations. We considered the European cave bears to best represent the diversity within a potentially introgressing cave bear lineage. For brown bear introgressors, we selected Eurasian brown bears as best representing diversity in a potential brown bear introgressor. As with the D statistic, we determined significance based on weighted block jackknife with 1 Mb blocks.
Publication 2018
Alleles Allelic Imbalance Bears Black Bears Cloning Vectors Cytosine Deamination DNA, Ancient Europeans Genome Pandas, Giant Polar Bears Population Group Red Cell Ghost Spectacled Bear
The complete mt sequences of three bear species in genus Ursus, the polar bear (U. maritimus), the brown bear (U. arctos), and the American black bear (U. americanus), have been determined in previous studies of genome evolution [38 (link)]. Thus, the availability of the other five mt genome sequences from Ursidae was of considerable interest for phylogenetic reconstruction. We extracted total DNA from fresh blood or frozen tissues of the Asiatic black bear (U. thibetanus), the sloth bear (U. ursinus), the sun bear (U. malayanus), the spectacled bear (Tremarctos ornatus) and the giant panda (Ailuropoda melanoleuca) using standard proteinase K, phenol/chloroform extraction [39 ].
Mt genome sequences were initially amplified with sets of universal primers (73 in total) described in Delisle and Strobeck's original study (2002) [38 (link)]. In the case of poor PCR performance with universal primers, 31 additional species-specific oligonucleotide primers were designed (underlined in Figure 6). Primer sequence information was available upon request. A "touch-down" PCR amplification was carried out using the following parameters: 95°C hot start (5 min), 10 cycles of 94°C denaturation (1 min), 60–50°C annealing (1 min; °C/cycle), 72°C extension (1 min), and finally 25 cycles of 94°C denaturation (1 min), 50°C annealing (1 min), 72°C extension (1 min). The amplified DNA fragments were purified and sequenced in both directions with an ABI PRISM™ 3700 DNA sequencer following the manufacturer's protocol. Mt sequences obtained were checked carefully to ensure that they did not include nuclear copies of mtDNA-like pseudogenes. The exact length of the control region in the mt genome cannot be determined due to the presence of long tandem repeated sequences. Newly determined genomes have been deposited in GenBank under Accession No. EF19661EF19665.
Publication 2007
Bears Biological Evolution Black Bears BLOOD Chloroform DNA, Mitochondrial Endopeptidase K Freezing Genome Oligonucleotide Primers Pandas, Giant Phenol Polar Bears prisma Pseudogenes Sloths Spectacled Bear Tandem Repeat Sequences Tissues Touch Ursus

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Publication 2023
Climate Females Males neuro-oncological ventral antigen 2, human Pandas, Giant Ticks

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Publication 2023
Childbirth Climate Forests Homo sapiens Human Body Iron Microtubule-Associated Proteins Pandas, Giant Physical Examination Pressure Retreatments Solar Energy Tracheophyta Woman X-linked mental retardation Gustavson type

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Publication 2023
Anesthesia Animals Dry Ice Ear Face Head Human Body Neck Pandas, Giant Physical Examination Protein Subunits Retreatments RNA, Ribosomal, 16S Ticks
From October 2020 to April 2022, the data of giant panda traces were recorded using the sample line method and infrared camera monitoring method, and a sample survey was conducted in March–April and October–November (from 2020 to 2022) in the areas where giant panda traces were recorded to determine the preferred environmental factors of the giant panda habitat. A total of 34 sample lines ≥3 km were set at intervals of ≥500 m. The sample lines covered as many vegetation types and as many potential giant panda distribution areas as possible. Combining data from 158 infrared cameras placed in the study area, the entire Daxiangling Reserve was divided into 145 square grids of 2 km2 each, with each camera spaced at least 500 m apart to ensure uniform camera coverage (Figure 1). Ten microhabitat variables were recorded in a 10 × 10 m sample square centered on the site of giant panda traces. The classification criteria for different environmental variables are shown in Table 1. A control sample was randomly set up along the sample line for every 500 m of walking or 100 m of elevation climb without traces of giant panda activity to reflect the environmental background information, and the setting and habitat variables of the control sample were recorded in the same way as the utilization sample [27 (link)]. A total of 348 samples were set up [23 (link)].
These habitat selection and ecological niche data were input into Excel for the relevant conversions. Following the conversion, the data were entered into SPSS13.0 for normality testing via the one-sample K-S test. Data that conformed to a normal distribution were tested through one-way analysis of variance (ANOVA), and data that did not conform to a normal distribution were tested using the Mann–Whitney U test.
Publication 2023
Niche, Ecological Pandas, Giant
The estimation of suitable habitats for giant pandas in the study area was performed using the MaxEnt model. A total of 62 giant panda occurrence sites were obtained in the field, with 44 and 18 occurrence sites in the rainy and snow seasons, respectively. To reduce autocorrelation, an 1125 m radius buffer was generated in ArcGIS 10.2 with giant panda occurrence sites in the rainy and snow seasons. When the occurrence site buffers overlapped with each other, one of them was randomly retained, and the rest were eliminated, resulting in 20 and 10 occurrence sites retained in the rainy and snow seasons, respectively [27 (link)]. The giant panda has rigorous requirements for habitat, usually choosing primary forests with low human interference [28 (link),29 (link)]. Climate and land-use types are also important factors that influence the spatial distribution of the giant panda [9 (link),30 (link)]. Therefore, climate, topography, vegetation, and human disturbance are important factors affecting the spatial distribution of the giant panda. In the construction of the model, the above variables were selected to evaluate the habitat. Access to each variable is shown in Table 2 [23 (link)]. Because the prediction accuracy of the model was affected by the correlation between environmental factors, the “caret” function in R 4.2.1 was used to remove the highly correlated variables, and the factors with Pearson correlation coefficients greater than 0.8 were removed. Finally, nine factors were retained (Table 2).
In total, 75% of the occurrence sites of the giant panda were selected for modeling, and 25% of the occurrence sites were retained for validation. The importance of each environmental factor was assessed using the Jackknife method, and the output was in the logistic format. The model prediction results were tested using the receiver operating characteristic (ROC) curve [31 (link)]. The evaluation criteria were as follows: the area enclosed by the ROC curve and the area under the curve (AUC value) was 0.5–0.6 for failure, 0.6–0.7 for poor, 0.7–0.8 for fair, 0.8–0.9 for good, and 0.9–1.0 for excellent [32 (link)]. The means of 10 calculation results were averaged to gain the habitat suitability index (HSI). The suitable habitat range in the study area was divided using Youden’s index as the threshold.
Publication 2023
Buffers Climate Forests Homo sapiens Muscle Rigidity Pandas, Giant Radius Rain Snow

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More about "Pandas, Giant"

Pandas, Giant (Ailuropoda melanoleuca) are large, iconic black-and-white bears native to the mountainous regions of central China.
These solitary, bamboo-eating herbivores are recognized for their distinctive appearance and are classified as a vulnerable species due to habitat loss and fragmentation.
Pandas, Giant play a crucial role in their ecosystems, acting as seed disperseres and contributing to the preservation of bamboo forests.
Research on Pandas, Giant helps inform conservation efforts and advance our understanding of this beloved species.
The Pandas, Giant community has access to a range of tools and techniques to support their research.
The QIAamp DNA Stool Mini Kit and DNeasy Blood and Tissue Kit can be used for extracting high-quality DNA from Pandas, Giant samples.
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