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.

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.

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

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), ...

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)

Average genomic similarity measures and distances between the four largest habitatsa.

Comprehensive population and demographic data for Panda Village

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.

Average genomic inbreeding coefficient () by habitata.

Comprehensive population and demographic data for Bamhori Panda Village

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

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.

Significance test of differences in genomic inbreeding coefficientsa between habitats.

Comprehensive population and demographic data for Dibara Panda Village

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.

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.

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.

Comprehensive population and demographic data for Panda Padar Village

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.