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.

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.

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.

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)

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.

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.

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.

Die Statistik zeigt die Anzahl der in Wildnis lebenden Großen Pandas in den Jahren 1974, 1985, 2004 und 2015. In Jahr 2015 gab es weltweit ungefähr 1.864 in der Wildnis lebende Große Pandas.

Overview This dataset re-shares cartographic and demographic data from the U.S. Census Bureau to provide an obvious supplement to Open Environments Block Group publications.These results do not reflect any proprietary or predictive model. Rather, they extract from Census Bureau results with some proportions and aggregation rules applied. For additional support or more detail, please see the Census Bureau citations below. Cartographics refer to shapefiles shared in the Census TIGER/Line publications. Block Group areas are updated annually, with major revisions accompanying the Decennial Census at the turn of each decade. These shapes are useful for visualizing estimates as a map and relating geographies based upon geo-operations like overlapping. This data is kept in a geodatabase file format and requires the geopandas package and its supporting fiona and DAL software. Demographics are taken from popular variables in the American Community Survey (ACS) including age, race, income, education and family structure. This data simply requires csv reader software or pythons pandas package. While the demographic data has many columns, the cartographic data has a very, very large column called "geometry" storing the many-point boundaries of each shape. So, this process saves the data separately, with demographics columns in a csv file and geometry in a gpd file needed an installation of geopandas, fiona and DAL software. More details on the ACS variables selected and derivation rules applied can be found in the commentary docstrings in the source code found here: https://github.com/OpenEnvironments/blockgroupdemographics. ## Files While the demographic data has many columns, the cartographic data has a very, very large column called "geometry" storing the many-point boundaries of each shape. So, this process saves the data separately, with demographics columns in a csv file named YYYYblcokgroupdemographics.csv. The cartographic column, 'geometry', is shared as file named YYYYblockgroupdemographics-geometry.pkl. This file needs an installation of geopandas, fiona and DAL software.

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)

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.

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.

Dataset

This dataset was created by Shark Deng

Contents

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.

This is the supplementary material of the paper "Wealth Consumption, Sociopolitical Organization, and Change: A Perspective from Burial Analysis on the Middle Bronze Age in the Carpathian Basin" (accessible over doi: https://doi.org/10.1515/opar-2022-0281). Please consult the publication for in depth description of the data, its context and for the method applied on the data, as well as references to primary sources. The data tables comprise the burial data of the Hungarian Middle Bronze Age cemeteries of Dunaújváros-Duna-dűlő, Dömsöd, Adony, Lovasberény, Csanytelek-Palé, Kelebia, Hernádkak, Gelej, Pusztaszikszó and Streda nad Bodrogom. The script "supplementary_material_2_wealth_index_calculation.py" provides the calculation of a wealth index, based on grave goods, for the provided data. The script "supplementary_material_3_population_estimation.py" models the living population of Dunaújváros-Duna-dűlő. Both can be run by double-click. Requirements to be installed to run the scripts: Python 3 (https://www.python.org/) with the packages numpy (https://numpy.org/), pandas (https://pandas.pydata.org/), matplotlib (https://matplotlib.org/), seaborn (https://seaborn.pydata.org/) and scipy (https://scipy.org/); all included in Ancaonda (Python-Distribution, https://www.anaconda.com/).

Dataset

This dataset was created by Maxenso

Contents

It contains the following files:

Dataset

This dataset was created by Maxenso

Contents

Significance test of differences in genomic inbreeding coefficientsa between habitats.