The giant panda is an example of a species that has faced extensive historical habitat fragmentation and anthropogenic disturbance, and is assumed to be isolated in numerous subpopulations with limited gene flow between them. To investigate the population size, health and connectivity of pandas in a key habitat area, we noninvasively collected a total of 539 fresh wild giant panda fecal samples for DNA extraction within Wolong Nature Reserve, Sichuan, China. Seven validated tetra-microsatellite markers were used to analyze each sample, and a total of 142 unique genotypes were identified. Non-spatial and spatial capture-recapture models estimated the population size of the reserve at 164 and 137 individuals (95% confidence intervals 153-175 and 115-163), respectively. Relatively high levels of genetic variation and low levels of inbreeding were estimated, indicating adequate genetic diversity. Surprisingly, no significant genetic boundaries were found within the population despite the national road G350 that bisects the reserve, which is also bordered with patches of development and agricultural land. We attribute this to high rates of migration, with 4 giant panda road-crossing events confirmed within a year based on repeated captures of individuals. This likely means that giant panda populations within mountain ranges are better connected than previously thought. Increased development and tourism traffic in the area and throughout the current panda distribution poses a threat of increasing population isolation, however. Maintaining and restoring adequate habitat corridors for dispersal is thus a vital step for preserving the levels of gene flow seen in our analysis and the continued conservation of the giant panda meta-population in both Wolong and throughout their current range.

Comprehending the population trend and understanding the distribution range dynamics of species is necessary for global species protection. Recognizing what causes dynamic distribution change is crucial for identifying species’ environmental preferences and formulating protection policies. Here, we studied the rear-edge population of the flagship species, giant pandas (Ailuropoda melanoleuca), to 1) assess their population trend using their distribution patterns, 2) evaluate their distribution dynamics change from the 2nd (1988) to the 3rd (2001) surveys (2–3 Interval) and 3rd to the 4th (2013) survey (3–4 Interval) using a machine learning algorithm (The Extremely Gradient Boosting), and 3) decode model results to identify driver factors in the first known use of SHapley Additive exPlanations. Our results showed that the population trends in Liangshan Mountains were worst in the 2nd survey (k = 1.050), improved by the 3rd survey (k = 0.97), but got worse by the 4th survey (k = 0.996), which indicates a worrying population future. We found that precipitation had the most significant influence on distribution dynamics among several potential environmental factors, showing a negative correlation between precipitation and giant panda expansion. We recommend that more study is required to understand the micro-environment and animal distribution dynamics. We provide a fresh perspective on the dynamics of Giant Panda distribution, highlighting novel focal points for ecological research on this species. Our study offers theoretical underpinnings that could inform the formulation of more effective conservation policies. Also, we emphasize the uniqueness and importance of the Liangshan Mountains giant pandas as the rear-edge population, which is at a high risk of population extinction.

Details of the unique genotypes including four reintroduced giant pandas in Liziping National Nature Reserve

Inbreeding can have negative consequences on population and individual fitness, which could be counteracted by inbreeding avoidance mechanisms. However, the inbreeding risk and inbreeding avoidance mechanisms in endangered species are less studied. The giant panda, a solitary and threatened species, lives in many small populations and suffers from habitat fragmentation, which may aggravate the risk of inbreeding. Here, we performed long-term observations of reproductive behaviour, sampling of mother-cub pairs and large-scale genetic analyses on wild giant pandas. Moderate levels of inbreeding were found in 21.1% of mating-pairs, 9.1% of parent-pairs and 7.7% of panda cubs, but no high-level inbreeding occurred. More significant levels of inbreeding may be avoided passively by female-biased natal dispersal rather than by breeding dispersal or active relatedness-based mate choice mechanisms. The level of inbreeding in giant pandas is greater than expected for a solitary mammal and thus warrants concern for potential inbreeding depression, particularly in small populations isolated by continuing habitat fragmentation, which will reduce female dispersal and increase the risk of inbreeding.

Tables showing haplotype distribution of giant pandas for mtDNA CR and Cyt b, information for historical and modern samples, bottleneck analysis, modern and historical effective population sizes, and time since population change in the Minshan and Qionglai populations using Storz and Beaumont’s method and habitat area available, and traditional and re-estimated population sizes of giant pandas during different periods.

QL178ArlequinThe microsatellite data of 178 giant pandas from the Qinling Mountains, in Arlequin format.

Founder and habitat contributions to the captive panda population. (XLSX 100 kb)

Climate change affects biodiversity through multidimensional impacts, influencing not only shifts in habitat range but also changes in habitat quality. In this context, habitat area and bioclimatic velocity have become critical metrics for assessing species-specific vulnerabilities to climate change. Here, we assessed the extinction risk and exposure risk of giant pandas (Ailuropoda melanoleuca) based on habitat area and bioclimatic velocity, respectively, and examined the differences between these two risks to inform climate-adaptive conservation strategies. Our findings indicate that under the SSP2-4.5 scenario, degraded giant panda habitats are projected to total 13,846.1 km2, with the Qinling (QL), Liangshan (LS), and Daxiangling (DXL) populations experiencing substantial habitat loss of 3,790.4 km2, 2,722.8 km2, and 1,135.4 km2, respectively. Bioclimatic velocities across different populations range from -0.468 to 0.309 km yr-1, with higher velocities observed in southeastern Minshan (MS) and Qionglaishan (QLS), and Liangshan (LS) regions, suggesting potential declines in habitat suitability and substantial challenges to population survival. Our results also reveal that while most populations exhibit consistent risk patterns when assessed by both habitat area change and bioclimatic velocity, notable discrepancies still remain. Populations with high extinction risk generally also face high exposure risks; however, some populations with low extinction risk may still encounter substantial exposure risks (e.g., DXL_A and MS_K). These findings highlight the limitations of relying on single-dimensional assessments of species’ vulnerability to climate change, as evidenced by the variability in risk assessment outcomes. Therefore, integrating changes in both habitat area and bioclimatic velocity provides a more comprehensive understanding of species’ vulnerability, reveal local adaptation mechanisms, and offers a robust scientific basis for formulating targeted climate-resilient conservation strategies.

Understanding population history and genetic structure are key drivers of ecological research. Here we studied two highly fragmented and isolated populations (Xiaoxiangling and Daxiangling) of giant pandas (Ailuropoda melanoleuca) at the extreme southwestern edge of their distribution. This area also contains the Dadu River, national road 108 and various human infrastructure and development, providing an ideal region in which we can identify the effects of different barriers on animal movements. We used partial mitochondrial control region (mtDNA) and nine microsatellite loci (nuclear DNA) data derived from 192 fecal and one blood sample collected from the wild. We found 136 genotypes corresponding to 53 unique multilocus genotypes and eight unique control region haplotypes (653 bp). Significant genetic boundaries correlated spatially with the Dadu River (K=2). We estimate that a major divergence took place between these populations 26 000 YBP, at around the similar time the rock surface of valley bottom formed in Dadu River. The national road has resulted in further recent population differentiation (Pairwise FS on mtDNA and nuclear DNA) so that in effect, four smaller sub-populations now exist. Promisingly, we identified two possible first generation migrants and their migration paths, and recommended the immediate construction of a number of corridors. Fortunately, the Chinese government has accepted our advice and is now planning corridor construction.

Background: Evaluating patterns of genetic variation is important to identify conservation units (i.e., evolutionarily significant units [ESUs], management units [MUs], and adaptive units [AUs]) in endangered species. While neutral markers could be used to infer population history, their application in the estimation of adaptive variation is limited. The capacity to adapt to various environments is vital for the long-term survival of endangered species. Hence, analysis of adaptive loci, such as the major histocompatibility complex (MHC) genes, is critical for conservation genetics studies. Here, we investigated 4 classical MHC class I genes (Aime-C, Aime-F, Aime-I, and Aime-L) and 8 microsatellites to infer patterns of genetic variation in the giant panda (Ailuropoda melanoleuca) and to further define conservation units. Results: Overall, we identified 24 haplotypes (9 for Aime-C, 1 for Aime-F, 7 for Aime-I, and 7 for Aime-L) from 218 individuals obtained from 6 populations of giant panda. We found that the Xiaoxiangling population had the highest genetic variation at microsatellites among the 6 giant panda populations and higher genetic variation at Aime-MHC class I genes than other larger populations (Qinling, Qionglai, and Minshan populations). Differentiation index (FST)-based phylogenetic and Bayesian clustering analyses for Aime-MHC-I and microsatellite loci both supported that most populations were highly differentiated. The Qinling population was the most genetically differentiated. Conclusions: The giant panda showed a relatively higher level of genetic diversity at MHC class I genes compared with endangered felids. Using all of the loci, we found that the 6 giant panda populations fell into 2 ESUs: Qinling and non-Qinling populations. We defined 3 MUs based on microsatellites: Qinling, Minshan-Qionglai, and Daxiangling-Xiaoxiangling-Liangshan. We also recommended 3 possible AUs based on MHC loci: Qinling, Minshan-Qionglai, and Daxiangling-Xiaoxiangling-Liangshan. Furthermore, we recommend that a captive breeding program be considered for the Qinling panda population.

Genetic composition of the new generation from three plans of habitat-controlled breeding. (XLSX 58 kb)

Genetic composition of the new generation from the recommended mating pairs based on the MSI scores. (XLSX 3317 kb)

SSR genotyping

Genetic composition and inbreeding coefficients of hypothetical offspring of the 1630 mating pairs free of pedigree and hidden inbreeding. (XLSX 463 kb)

Genetic variation plays a significant role in maintaining the evolutionary potential of a species. Comparing the patterns of adaptive and neutral diversity in extant populations is useful for understanding the local adaptations of a species. In this study, we determined the fine-scale genetic structure of 6 extant populations of the giant panda (Ailuropoda melanoleuca) using mtDNA and DNA fingerprints, and then overlaid adaptive variations in 6 functional Aime-MHC class II genes (DRA, DRB3, DQA1, DQA2, DQB1, and DQB2) on this framework. We found that: (1) analysis of the mtDNA and DNA fingerprint-based networks of the 6 populations identified the independent evolutionary histories of the 2 panda subspecies; (2) the basal (ancestral) branches of the fingerprint-based Sichuan-derived network all originated from the smallest Xiaoxiangling (XXL) population, suggesting the status of a glacial refuge in XXL; (3) the MHC variations among the tested populations showed that the XXL population exhibited extraordinary high levels of MHC diversity in allelic richness, which is consistent with the diversity characteristics of a glacial refuge; (4) the phylogenetic tree showed that the basal clades of giant panda DQB sequences were all occupied by XXL-specific sequences, providing evidence for the ancestor-resembling traits of XXL. Finally, we found that the giant panda had many more DQ alleles than DR alleles (33∶13), contrary to other mammals, and that the XXL refuge showed special characteristics in the DQB loci, with 7 DQB members of 9 XXL-unique alleles. Thus, this study identified XXL as a glacial refuge, specifically harboring the most number of primitive DQB alleles.

Habitat contributions and inbreeding coefficients of hypothetical offspring of all 17,640 possible mating pairs between 140 male and 126 female breeding candidates. (XLSX 5238 kb)

Context

I always wanted to access a data set that was related to the world’s population (Country wise). But I could not find a properly documented data set. Rather, I just created one manually.

Content

Now I knew I wanted to create a dataset but I did not know how to do so. So, I started to search for the content (Population of countries) on the internet. Obviously, Wikipedia was my first search. But I don't know why the results were not acceptable. And also there were only I think 190 or more countries. So then I surfed the internet for quite some time until then I stumbled upon a great website. I think you probably have heard about this. The name of the website is Worldometer. This is exactly the website I was looking for. This website had more details than Wikipedia. Also, this website had more rows I mean more countries with their population.

Once I got the data, now my next hard task was to download it. Of course, I could not get the raw form of data. I did not mail them regarding the data. Now I learned a new skill which is very important for a data scientist. I read somewhere that to obtain the data from websites you need to use this technique. Any guesses, keep reading you will come to know in the next paragraph.

https://fiverr-res.cloudinary.com/images/t_main1,q_auto,f_auto/gigs/119580480/original/68088c5f588ec32a6b3a3a67ec0d1b5a8a70648d/do-web-scraping-and-data-mining-with-python.png" alt="alt text">

You are right its, Web Scraping. Now I learned this so that I could convert the data into a CSV format. Now I will give you the scraper code that I wrote and also I somehow found a way to directly convert the pandas data frame to a CSV(Comma-separated fo format) and store it on my computer. Now just go through my code and you will know what I'm talking about.

Below is the code that I used to scrape the code from the website

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3200273%2Fe814c2739b99d221de328c72a0b2571e%2FCapture.PNG?generation=1581314967227445&alt=media" alt="">

Acknowledgements

Now I couldn't have got the data without Worldometer. So special thanks to the website. It is because of them I was able to get the data.

Inspiration

As far as I know, I don't have any questions to ask. You guys can let me know by finding your ways to use the data and let me know via kernel if you find something interesting

Intestinal diseases caused by opportunistic pathogens seriously threaten the health and survival of giant pandas. However, our understanding of gut pathogens in different populations of giant pandas, especially in the wild populations, is still limited. Here, we conducted a study based on 52 giant panda metagenomes to investigate the composition and distribution of gut pathogens and virulence factors (VFs) in five geographic populations (captive: GPCD and GPYA; wild: GPQIN, GPQIO, and GPXXL). The results of the beta-diversity analyzes revealed a close relationship and high similarity in pathogen and VF compositions within the two captive groups. Among all groups, Proteobacteria, Firmicutes, and Bacteroidetes emerged as the top three abundant phyla. By using the linear discriminant analysis effect size method, we identified pathogenic bacteria unique to different populations, such as Klebsiella in GPCD, Salmonella in GPYA, Hafnia in GPQIO, Pedobacter in GPXXL, and Lactococcus in GPQIN. In addition, we identified 12 VFs that play a role in the intestinal diseases of giant pandas, including flagella, CsrA, enterobactin, type IV pili, alginate, AcrAB, capsule, T6SS, urease, type 1 fimbriae, polar flagella, allantoin utilization, and ClpP. These VFs influence pathogen motility, adhesion, iron uptake, acid resistance, and protein regulation, thereby contributing to pathogen infection and pathogenicity. Notably, we also found a difference in virulence of Pseudomonas aeruginosa between GPQIN and non-GPQIN wild populations, in which the relative abundance of VFs (0.42%) of P. aeruginosa was the lowest in GPQIN and the highest in non-GPQIN wild populations (GPXXL: 23.55% and GPQIO: 10.47%). In addition to enhancing our understanding of gut pathogens and VFs in different geographic populations of giant pandas, the results of this study provide a specific theoretical basis and data support for the development of effective conservation measures for giant pandas.

Intestinal diseases caused by opportunistic pathogens seriously threaten the health and survival of giant pandas. However, our understanding of gut pathogens in different populations of giant pandas, especially in the wild populations, is still limited. Here, we conducted a study based on 52 giant panda metagenomes to investigate the composition and distribution of gut pathogens and virulence factors (VFs) in five geographic populations (captive: GPCD and GPYA; wild: GPQIN, GPQIO, and GPXXL). The results of the beta-diversity analyzes revealed a close relationship and high similarity in pathogen and VF compositions within the two captive groups. Among all groups, Proteobacteria, Firmicutes, and Bacteroidetes emerged as the top three abundant phyla. By using the linear discriminant analysis effect size method, we identified pathogenic bacteria unique to different populations, such as Klebsiella in GPCD, Salmonella in GPYA, Hafnia in GPQIO, Pedobacter in GPXXL, and Lactococcus in GPQIN. In addition, we identified 12 VFs that play a role in the intestinal diseases of giant pandas, including flagella, CsrA, enterobactin, type IV pili, alginate, AcrAB, capsule, T6SS, urease, type 1 fimbriae, polar flagella, allantoin utilization, and ClpP. These VFs influence pathogen motility, adhesion, iron uptake, acid resistance, and protein regulation, thereby contributing to pathogen infection and pathogenicity. Notably, we also found a difference in virulence of Pseudomonas aeruginosa between GPQIN and non-GPQIN wild populations, in which the relative abundance of VFs (0.42%) of P. aeruginosa was the lowest in GPQIN and the highest in non-GPQIN wild populations (GPXXL: 23.55% and GPQIO: 10.47%). In addition to enhancing our understanding of gut pathogens and VFs in different geographic populations of giant pandas, the results of this study provide a specific theoretical basis and data support for the development of effective conservation measures for giant pandas.

Clarification of the genetic structure and population history of a species can shed light on impacts of landscapes, historical climate change and contemporary human activities, and thus enables evidence-based conservation decisions for endangered organisms. The red panda (Ailurus fulgens) is an endangered species distributing at the edge of the Qinghai-Tibetan Plateau and is currently subject to habitat loss, fragmentation and population decline, thus representing a good model to test the influences of the above factors on a plateau edge species. We combined nine microsatellite loci and 551 bp of mitochondrial control region (mtDNA CR) to explore the genetic structure and demographic history of this species. 123 individuals were sampled from 23 locations across five populations. High levels of genetic variation were identified for both mtDNA and microsatellites. Phylogeographic analyses indicated little geographic structure, suggesting historically wide gene flow. However, microsatellit...