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.
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Pandas, Giant
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.
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»
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.
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 (
The datasets used in this study are accessible from the National Centre for Biotechnology Information Sequence Read Archive (SRA;
Adult
BP 400
DNA Library
Feces
Females
Freezing
isolation
Males
Metagenome
Nitrogen
Oligonucleotide Primers
Pandas, Giant
Short Tandem Repeat
Taq Polymerase
Tetragonopterus
Alleles
Bears
Biological Evolution
Genome
Genome Components
Genotype
Gigantism
Pandas, Giant
Polar Bears
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 Figure6 ). 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. EF19661 –EF19665 .
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
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
Most recents protocols related to «Pandas, Giant»
Protocol full text hidden due to copyright restrictions
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Climate
Females
Males
neuro-oncological ventral antigen 2, human
Pandas, Giant
Ticks
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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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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.
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.
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.
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.
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.
The TRIzol reagent and RNeasy Mini Kit are effective for RNA extraction, while the Agilent 2100 Bioanalyzer provides advanced analysis of nucleic acid quality and quantity.
The 2× Taq master Mix is a versatile PCR reagent, and the Shim-pack UFLC SHIMADZU CBM30A system enables advanced chromatographic separation and analysis.
For researchers working with Pandas, Giant, the Xten platform offers a innovative solution for streamlining the research process.
This AI-driven platform from PubCompare.ai helps users locate relevant protocols from literature, pre-prints, and patents, and provides advanced comparison tools to identify the best protocols and products.
By optimizing the research process and ensuring reproducibility, the Xten platform empowers the Pandas, Giant community to make groundbreaking discoveries and drive conservation efforts forward.
Whether you're studying the biology, ecology, or conservation of Pandas, Giant, the tools and technologies available today can help you achieve your research goals.
By leveraging the insights and capabilities of these resources, the Pandas, Giant community can continue to expand our understanding of this iconic species and ensure its long-term survival.
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.
The TRIzol reagent and RNeasy Mini Kit are effective for RNA extraction, while the Agilent 2100 Bioanalyzer provides advanced analysis of nucleic acid quality and quantity.
The 2× Taq master Mix is a versatile PCR reagent, and the Shim-pack UFLC SHIMADZU CBM30A system enables advanced chromatographic separation and analysis.
For researchers working with Pandas, Giant, the Xten platform offers a innovative solution for streamlining the research process.
This AI-driven platform from PubCompare.ai helps users locate relevant protocols from literature, pre-prints, and patents, and provides advanced comparison tools to identify the best protocols and products.
By optimizing the research process and ensuring reproducibility, the Xten platform empowers the Pandas, Giant community to make groundbreaking discoveries and drive conservation efforts forward.
Whether you're studying the biology, ecology, or conservation of Pandas, Giant, the tools and technologies available today can help you achieve your research goals.
By leveraging the insights and capabilities of these resources, the Pandas, Giant community can continue to expand our understanding of this iconic species and ensure its long-term survival.