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.

Understanding the interaction between life history, demography and population genetics in threatened species is critical for the conservations of viable populations. In the context of habitat loss and fragmentation, identifying the factors that underpin the structuring of genetic variation within populations can allow conservationists to evaluate habitat quality and connectivity and help to design dispersal corridors effectively. In this study, we carried out a detailed, fine-scale landscape genetic investigation of a giant panda population for the first time, using a large microsatellite data set and examined the role of isolation-by-barriers (IBB), isolation-by-distance (IBD) and isolation-by-resistance (IBR) in shaping the genetic variation pattern of giant pandas in the Qinling Mountains. We found that the Qinling population comprises one continuous genetic cluster, and among the landscape hypotheses tested, gene flow was found to be correlated with resistance gradients for two topographic factors, rather than geographical distance or barriers. Gene-flow was inferred to be facilitated by easterly slope aspect and to be constrained by land surface with high topographic complexity. These factors are related to benign micro-climatic conditions for both the pandas and the food resources they rely on and more accessible topographic conditions for movement, respectively. We identified optimal corridors based on these results, aiming to promote gene flow between human-induced habitat fragments. These findings provide insight into the permeability and affinities of the giant panda habitat and offer important reference for the conservation of the giant panda and its habitat.

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.

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.

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

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

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)

The national surveys on giant panda (Ailuropoda melanoleuca) population and habitat quality have shown a high-density population of this species in the Qinling Mountains, China. We investigated five adjacent nature reserves (NR), i.e., the key distribution area of giant pandas in the Qinling Mountains, to model and identify the potential dispersal routes for giant pandas. We hypothesized that giant pandas will spread to neighboring areas when the population of the species keeps increasing. Habitat suitability was firstly evaluated based on environmental and disturbance factors. We then identified source and sink patches for giant pandas’ dispersal. Further, Minimum Cumulative Resistance (MCR) model was applied to calculate cost of movement. Finally, the Current Theory was adopted to model linkages between source and sink patches to explore potential dispersal routes of giant pandas. Our results showed that (1) the three large source patches and eight potential sink patches were identified; (2) the 14 potential corridors were predicted for giant pandas dispersing from source patches to the neighboring areas; (3) through the predicted corridors, the giant pandas in the source patches could disperse to the west, the south and the east sink patches. Our research revealed possible directional patterns for giant pandas’ dispersal in their key distribution area of the Qinling Mountains, and can provide the strong recommendations in policy and conservation strategies for improving giant panda habitat management in those identified sink patches and also potential dispersal corridors.

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, microsatellite-based Bayesian clustering clearly identified three groups (Qionglai-Liangshan, Xiaoxiangling and Gaoligong-Tibet). A significant isolation-by-distance pattern was detected only after removing Xiaoxiangling. For mtDNA data there was no statistical support for a historical population expansion or contraction for the whole sample or any population except Xiaoxiangling where a signal of contraction was detected. However, Bayesian simulations of population history using microsatellite data did pinpoint population declines for Qionglai, Xiaoxiangling and Gaoligong, demonstrating significant influences of human activity on demography. The unique history of the Xiaoxiangling population plays a critical role in shaping the genetic structure of this species, and large-scale habitat loss and fragmentation is hampering gene flow among populations. The implications of our findings for the biogeography of the Qinghai-Tibetan Plateau, subspecies classification and conservation of red pandas are discussed.

Please cite our paper if you publish material based on those datasets

G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata", in IEEE Trans. on Mobile Computing, 2018 (in Press). 10.1109/TMC.2018.2871156

Abstract

Communication-enabled devices that are physically carried by individuals are today pervasive,
which opens unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. We show that our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. Specifically, it achieves higher accuracy than that granted by previous equivalent solutions. In addition, our approach enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. Our results build on significant real-world mobile network metadata and relevant ground-truth information in multiple urban scenarios.

Dataset Columns

This dataset cover one month of data taken during the month of April 2015 for three Italian cities: Rome, Milan, Turin. The raw data has been provided during the Telecom Italia Big Data Challenge (http://www.telecomitalia.com/tit/en/innovazione/archivio/big-data-challenge-2015.html)

1. grid_id: the coordinate of the grid can be retrieved with the shapefile of a given city
2. date: format Y-M-D H:M:S
4. landuse_label: the land use label has been computed by through method described in [2]
5. presence: presence data of a given grid id as provided by the Telecom Italia Big Data Challenge
6. population: Census population of a given grid block as defined by the Istituto nazionale di statistica (ISTAT https://www.istat.it/en/censuses) in 2011
7. estimation: Dynamics density population estimation (in person) as the result of the method described in [1]
8. area: surface of the "grid id" considered in km^2
9. geometry: the shape of the area considered with the EPSG:3003 coordinate system (only with quilt)

Note

Due to legal constraints, we cannot share directly the original data from Telecom Italia Big Data Challenge we used to build this dataset.

Easy access to this dataset with quilt

Install the dataset repository:

$ quilt install vgauthier/DynamicPopEstimate

Use the dataset with a Panda Dataframe

>>> from quilt.data.vgauthier import DynamicPopEstimate
>>> import pandas as pd
>>> df = pd.DataFrame(DynamicPopEstimate.rome())

Use the dataset with a GeoPanda Dataframe

>>> from quilt.data.vgauthier import DynamicPopEstimate
>>> import geopandas as gpd
>>> df = gpd.DataFrame(DynamicPopEstimate.rome())

References

[1] G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Population estimation from mobile network traffic metadata", in proc of the 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1 - 9, 2016.

[2] A. Furno, M. Fiore, R. Stanica, C. Ziemlicki, and Z. Smoreda, "A tale of ten cities: Characterizing signatures of mobile traffic in urban areas," IEEE Transactions on Mobile Computing, Volume: 16, Issue: 10, 2017.

The taxonomy in woody bamboo faces a lot of difficulties due to its long blooming intervals and complicated morphological variation. Whether the current taxonomy would reflect the genuine species divergence within woody bamboo is an intriguing question. Fargesia spathacea complex comprises fifteen closely related species with sympatric distribution in China. Their classification has long been controversy because of only a handful of vegetative traits available, thus providing a good opportunity to explore the evolutionary relationship and genetic differentiation in woody bamboo. Here we presented a study with 750 individuals from 39 representative populations in Fargesia spathacea complex using 14 SSR markers. We found varying degrees of genetic diversity across populations of the Fargesia spathacea complex (He=0.07-0.81) and largely negative F values at the population level, implying an excess of heterozygotes in the populations. Phylogenetic analyses revealed that all populations were divided into two major groups (cluster A and B), with the majority of fifteen species representing distinct genetic lineages. Based on the population genetic analysis along with morphological evidence, we confirmed the identity of three species (F. decurvata, F. spathacea and F. murielae) and suggested invalidation of four other species (scabrida, F. robusta, F. denudata and F. nitida). The delimitation of the rest eight species was yet to be explored. The ecological factor and spatial autocorrelation analysis supported that altitude difference might account for the distinct genetic divergence between two major groups.

How high is the brand awareness of Panda Express in the United States?When it comes to restaurant chain customers, brand awareness of Panda Express is at 87% in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Panda Express in the United States?In total, 30% of U.S. restaurant chain customers say they like Panda Express. What is the usage share of Panda Express in the United States?All in all, 23% of restaurant chain customers in the United States use Panda Express. How loyal are the customers of Panda Express?Around 19% of restaurant chain customers in the United States say they are likely to use Panda Express again. What's the buzz around Panda Express in the United States?In October 2024, about 11% of U.S. restaurant chain customers had heard about Panda Express in the media, on social media, or in advertising over the past three months. If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

This layer displays the percentage of the population with access to electricity. Source: The World Bank

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.

This layer shows the overall 2016 Environmental Democracy Index for 70 countries around the world. The map also shows the total population of each country for reference.The Environmental Democracy Index is an average of three overall pillars: transparency, participation, and justice. These pillars are made up of 23 guidelines adopted by the United Nations Environment Programme (UNEP), which are arithmetic averages of 75 legal indicators. As described on the Background and Methodology page, the Environmental Democracy Index rides on the following:"Environmental democracy is rooted in the idea that meaningful public participation is critical to ensure that land and natural resource decisions adequately and equitably address citizens’ interests. At its core, environmental democracy involves three mutually reinforcing rights:the right to freely access information on environmental quality and problemsthe right to participate meaningfully in decision-makingthe right to seek enforcement of environmental laws or compensation for harm.Protecting these rights, especially for the most marginalized and vulnerable, is the first step to promoting equity and fairness in sustainable development. Without essential rights, information exchange between governments and the public is stifled and decisions that harm communities and the environment cannot be challenged or remedied. Establishing a strong legal foundation is the starting point for recognizing, protecting and enforcing environmental democracy. "The population estimate comes from the Esri 2016 World Population Estimate.

The transformation of natural to human-dominated areas continues to be the leading driver of habitat loss and fragmentation, and ultimately species extinction. In response, numerous conservation efforts have emerged, many of which have been successful at recovering the habitat and populations of wildlife species. Nevertheless, while habitat recovery may reduce fragmentation, such outcome may not always occur due to the complexity in the relationship between habitat extent and fragmentation. Although this complexity has important consequences for conservation, it has predominantly been evaluated empirically in landscapes exhibiting habitat loss, not in recovering landscapes, and mostly at a single scale. Using the recovery of the habitat of a global conservation icon (giant pandas, Ailuropoda melanoleuca) as a demonstration, we empirically examined the complexity of the relationship between habitat extent and fragmentation from the perspective of habitat recovery. Our analysis was also performed at different spatial scales, ranging from the average home range of panda individuals to the entire geographic range of the species. Results show that fragmentation not only decreased but also increased with habitat recovery. Furthermore, the increase in fragmentation with an increase in habitat extent occurred at all scales analyzed, although it occurred more often under low (e.g., < 50%) habitat extents and when the distance between newly formed and old habitat areas remained large with respect to the home range of individuals of the target species. In addition, giant pandas exhibited a lower probability of occupying areas that experienced habitat gains but that also increased in fragmentation. Therefore, the complex relationship between habitat extent and fragmentation needs to be properly considered for enhancing the positive outcomes, across multiple scales, of ongoing and future conservation and restoration programs.

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

Significance test of differences in genomic inbreeding coefficientsa between habitats.

Average genomic similarity measures of the four largest habitatsa.

The red panda (Ailurus fulgens) is a herbivorous carnivore that is protected worldwide. The gastrointestinal tract (GIT) microbial community has widely acknowledged its vital role in host health, especially in diet digestion; However, no study to date has revealed the GIT microbiota in the red panda. Here, we characterized the microbial biogeographical characteristics in the GIT of a red panda using high-throughput sequencing technology. Significant differences were observed among GIT segments by beta diversity of microbiota, which were divided into four distinct groups: the stomach, small intestine, large intestine, and feces. The stomach and duodenum showed less bacterial diversity, but contained higher bacterial abundance and the most unclassified tags. The number of species in the stomach and small intestine samples was higher than that of the large intestine and fecal samples. A total of 133 core operational taxonomic units were obtained from the GIT samples with 97% sequence identity. Proteobacteria (52.16%), Firmicutes (10.09%), and Bacteroidetes (7.90%) were the predominant phyla in the GIT of the red panda. Interestingly, Escherichia–Shigella were largely abundant in the stomach, small intestine, and feces whereas the abundance of Bacteroides in the large intestine was high. Overall, our study provides a deeper understanding of the gut biogeography of the red panda microbial population. Future research will be important to investigate the microbial culture, metagenomics and metabolism of red panda GIT, especially in Escherichia–Shigella.