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.

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.

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.

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

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)

Comprehensive population and demographic data for Panda 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 ***** in der Wildnis lebende Große Pandas.

Regional GAM Species Counts

Observing Animal Species Abundance and Distribution

By [source]

About this dataset

This dataset captures detailed information about the abundance and distribution of multiple animal species in different parts of the Regional GAM network. By analyzing this data, researchers gain valuable insight into species trends over time, species population growth or decline, seasonal migration patterns, and other important ecological patterns. Moreover, this dataset helps us to understand risks associated with animal populations and ecosystems; aiding decision-making related to land use for conservation and sustainability initiatives. This data provides an easily accessible resource for monitoring changes in animals' ranges and distributions across the region – enabling powerful analysis which can inform sound management decisions to promote conservation efforts. In sum, this dataset holds great promise for scientists seeking an improved understanding of wildlife dynamics; making it a powerful tool for both monitoring biodiversity in our changing world as well as informing proactive management strategies that will ultimately help keep our planet healthy into the future

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How to use the dataset

This dataset contains information about animal species and their occurrence per site, which can be used to gain insights into species abundance and distribution in the Regional GAM network. This data can help researchers analyze species trends, population growth or decline, animal migrations, and other important ecological factors.

Users of this dataset can analyze the presence or absence of a particular species in different sites across the region, as well as their abundance by counting individual sightings. Additionally, by combining datasets such as those contained in this one with other environmental factors (e.g., water levels), users can gain further insight into animals’ behavior and ecology within any given location over time.

The following steps outline how to use this dataset to analyze animal populations: - Download all necessary files from Kaggle for your analysis - Use an online tool such as Pandas or RStudio to extract desired data from each file into one unified table - Select relevant columns for your analysis (e.g., Species Name, Location/Site Name), specify date ranges if necessary and arrange them in an easily readable manner using sorting tools within the software program you’re using
- Filter entries related to a certain period of time (e.g., last 7 days), location or unique combination of both if needed 5) Choose appropriate chart or graph types depending on what kind of data you want to present visually 6) Finally plot/display your findings on a map / basis plot / 3D-model / etc…for best clarity

This dataset provides valuable insight into environmental conditions which may affect wildlife behavior. By following these simple steps researchers should be able visualize trends associated with certain areas over periods of time allowing them better understand how animal populations are affected by land-use decisions and climate change among others!

Research Ideas

• Species Conservation: This data set can be used to assess the health of a species' population in a particular region and how this varies over time. Researchers can use data trends to identify declining populations and areas of conservation needs, allowing them to create appropriate management plans focused on species protection.
• Wildlife Monitoring: Observing the species count at different sites can provide researchers with an insight into animal behavior, migration patterns and habitat usage which in turn informs wildlife management plans.
• Climate Change: By assessing population changes over time, researchers can use this dataset to explore how climate change is impacting specific animal populations and inform conservation initiatives accordingly/

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Dataset multispecies Regional GAM.csv | Column name | Description ...

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

Dataset Description

This dataset contains the population statistics of countries and dependencies sourced from Wikipedia. It provides the latest figures of population estimates along with each country’s share of the world’s total population.

Columns Included:

• Country – Name of the country or dependency
• Population – Estimated total population (as per Wikipedia data)
• % of World – Share of the global population

Source:

The data was collected using Python libraries:

• requests for fetching the HTML page
• BeautifulSoup for parsing and extracting the table
• pandas for cleaning and structuring the data

Use Cases:

• Exploratory Data Analysis (EDA)
• Visualization of global population distribution
• Practice dataset for data cleaning, preprocessing, and feature engineering
• Building beginner-friendly machine learning projects with demographic data

Notes:

• Data is directly scraped from Wikipedia and may change over time as updates are made.
• Useful as a starting point for population-related research, data visualization, or ML practice projects.

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)

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...

Comprehensive population and demographic data for Panda Bigha Village

SSR genotyping

Comprehensive population and demographic data for Panda Chak Village