As a source of animal and plant population data, the Global Population Dynamics Database (GPDD) is unrivalled. Nearly five thousand separate time series are available here. In addition to all the population counts, there are taxonomic details of over 1400 species. The type of data contained in the GPDD varies enormously, from annual counts of mammals or birds at individual sampling sites, to weekly counts of zooplankton and other marine fauna. The project commenced in October 1994, following discussions on ways in which the collaborating partners could make a practical and enduring contribution to research into population dynamics. A small team was assembled and, with assistance and advice from numerous interested parties we decided to construct the database using the popular Microsoft Access platform. After an initial design phase, the major task has been that of locating, extracting, entering and validating the data in all the various tables. Now, nearly 5000 individual datasets have been entered onto the GPDD. The Global Population Dynamics Database comprises six Tables of data and information. The tables are linked to each other as shown in the diagram shown in figure 3 of the GPDD User Guide (GPDD-User-Guide.pdf). Referential integrity is maintained through record ID numbers which are held, along with other information in the Main Table. It's structure obeys all the rules of a standard relational database.

The most common regional threat to wildlife populations is habitat loss or degradation. This is where habitats are impacted by human activities, such as logging, agriculture, and mining. This was followed by species overexploitation. Since 1970, the wildlife populations have fallen an average of 69 percent.

Context

Animals are multicellular, eukaryotic organisms in the biological kingdom Animalia. With few exceptions, animals consume organic material, breathe oxygen, have myocytes and are able to move, can reproduce sexually, and grow from a hollow sphere of cells, the blastula, during embryonic development. As of 2022, 2.16 million living animal species have been described—of which around 1.05 million are insects, over 85,000 are mollusks, and around 65,000 are vertebrates. It has been estimated there are around 7.77 million animal species. Animals range in length from 8.5 micrometers (0.00033 in) to 33.6 meters (110 ft). They have complex interactions with each other and their environments, forming intricate food webs. The scientific study of animals is known as zoology.

Content

This dataset encompasses a diverse array of attributes pertaining to various animal species worldwide. The dataset prominently includes fields such as Animal, Height (cm), Weight (kg), Color, Lifespan (years), Diet, Habitat, Predators, Average Speed (km/h), Countries Found, Conservation Status, Family, Gestation Period (days), Top Speed (km/h), Social Structure, and Offspring per Birth. These columns collectively offer a comprehensive understanding of animal characteristics, habitats, behaviors, and conservation statuses. Researchers and enthusiasts can utilize this dataset to analyze animal traits, study their habitats, explore dietary patterns, assess conservation needs, and conduct a wide range of ecological research and wildlife studies.

Dataset Glossary (Column-wise)

• Animal - Name of the animal.
• Height (cm) - Height range in centimeters for the animal.
• Weight (kg) - Weight range in kilograms for the animal.
• Color - Common colors associated with the animal's appearance.
• Lifespan (years) - Average lifespan of the animal in years.
• Diet - Type of diet the animal primarily follows (e.g., Carnivore, Herbivore).
• Habitat - Typical habitat or environment where the animal is found.
• Predators - Natural enemies or organisms that prey on the animal.
• Average Speed (km/h) - The average speed range the animal can achieve in kilometers per hour.
• Countries Found - Countries or regions where the animal is commonly found.
• Conservation Status - The conservation status of the animal as per relevant conservation organizations.
• Family - Taxonomic family the animal belongs to.
• Gestation Period (days) - Range of days representing the gestation or pregnancy period of the animal.
• Top Speed (km/h) - The maximum speed the animal can achieve in kilometers per hour.
• Social Structure - Information about the social behavior or structure of the animal (e.g., Solitary, Group-based).
• Offspring per Birth - The typical number of offspring born per birth or reproduction event for the animal.

Structure of the Dataset

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Acknowledgement

This dataset was generated using information from: https://www.wikipedia.org/. If you wish to delve deeper, you can explore the website.

Cover Photo by: Image by brgfx on Freepik

Thumbnail by: Dog icons created by Flat Icons - Flaticon

Animal populations worldwide have fallen an average of 73 percent from 1970 to 2020. The most dramatic decline has been experienced in Latin America and the Caribbean, where the Living Planet Index (LPI) dropped by 95 percent in the past five decades. Biodiversity around the world is under threat from human activities such as changes in land use. Humankind's impact on climate change is also having an effect on these delicate ecosystems.

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 ...

The Living Planet Index (LPI) is a measure of the state of global biological diversity based on population trends of vertebrate species from around the world. The index represents 31,821 populations of 5,230 species. All indices are weighted by species richness, giving species-rich taxonomic groups in terrestrial, marine and freshwater systems more weight than groups with fewer species. Using a method developed by ZSL and WWF, these species population trends are aggregated to produce indices of the state of biodiversity.

The index value is measured relative to species' populations in 1970 (i.e. 1970 = 1).

To calculate an LPI, a generalised additive modelling framework is used to determine the underlying trend in each population time-series. Average rates of change are then calculated and aggregated to the species level. For the global LPI, the method of aggregation has recently been revised to include a weighting system which gives trends from more species-rich systems, realms and groups more weight in the final index.

Data Source: World Wildlife Fund (WWF) and Zoological Society of London (2022). Living Planet Report 2022 – Building a nature-positive society. Almond, R.E.A., Grooten, M., Juffe Bignoli, D. & Petersen, T. (Eds). WWF, Gland, Switzerland.

Photo by Jacob Campbell on Unsplash

We used this dataset to assess the strength of isolation due to geographic and macroclimatic distance across island and mainland systems, comparing published measurements of phenotypic traits and neutral genetic diversity for populations of plants and animals worldwide. The dataset includes 112 studies of 108 species (72 animals and 36 plants) in 868 island populations and 760 mainland populations, with population-level taxonomic and biogeographic information, totalling 7438 records. Methods Description of methods used for collection/generation of data: We searched the ISI Web of Science in March 2017 for comparative studies that included data on phenotypic traits and/or neutral genetic diversity of populations on true islands and on mainland sites in any taxonomic group. Search terms were 'island' and ('mainland' or 'continental') and 'population*' and ('demograph*' or 'fitness' or 'survival' or 'growth' or 'reproduc*' or 'density' or 'abundance' or 'size' or 'genetic diversity' or 'genetic structure' or 'population genetics') and ('plant*' or 'tree*' or 'shrub*or 'animal*' or 'bird*' or 'amphibian*' or 'mammal*' or 'reptile*' or 'lizard*' or 'snake*' or 'fish'), subsequently refined to the Web of Science categories 'Ecology' or 'Evolutionary Biology' or 'Zoology' or 'Genetics Heredity' or 'Biodiversity Conservation' or 'Marine Freshwater Biology' or 'Plant Sciences' or 'Geography Physical' or 'Ornithology' or 'Biochemistry Molecular Biology' or 'Multidisciplinary Sciences' or 'Environmental Sciences' or 'Fisheries' or 'Oceanography' or 'Biology' or 'Forestry' or 'Reproductive Biology' or 'Behavioral Sciences'. The search included the whole text including abstract and title, but only abstracts and titles were searchable for older papers depending on the journal. The search returned 1237 papers which were distributed among coauthors for further scrutiny. First paper filter To be useful, the papers must have met the following criteria: Overall study design criteria: Include at least two separate islands and two mainland populations; Eliminate studies comparing populations on several islands where there were no clear mainland vs. island comparisons; Present primary research data (e.g., meta-analyses were discarded); Include a field study (e.g., experimental studies and ex situ populations were discarded); Can include data from sub-populations pooled within an island or within a mainland population (but not between islands or between mainland sites); Island criteria: Island populations situated on separate islands (papers where all information on island populations originated from a single island were discarded); Can include multiple populations recorded on the same island, if there is more than one island in the study; While we accepted the authors' judgement about island vs. mainland status, in 19 papers we made our own judgement based on the relative size of the island or position relative to the mainland (e.g. Honshu Island of Japan, sized 227 960 km² was interpreted as mainland relative to islands less than 91 km²); Include islands surrounded by sea water but not islands in a lake or big river; Include islands regardless of origin (continental shelf, volcanic); Taxonomic criteria: Include any taxonomic group; The paper must compare populations within a single species; Do not include marine species (including coastline organisms); Databases used to check species delimitation: Handbook of Birds of the World (www.hbw.com/); International Plant Names Index (https://www.ipni.org/); Plants of the World Online(https://powo.science.kew.org/); Handbook of the Mammals of the World; Global Biodiversity Information Facility (https://www.gbif.org/); Biogeographic criteria: Include all continents, as well as studies on multiple continents; Do not include papers regarding migratory species; Only include old / historical invasions to islands (>50 yrs); do not include recent invasions; Response criteria: Do not include studies which report community-level responses such as species richness; Include genetic diversity measures and/or individual and population-level phenotypic trait responses; The first paper filter resulted in 235 papers which were randomly reassigned for a second round of filtering. Second paper filter In the second filter, we excluded papers that did not provide population geographic coordinates and population-level quantitative data, unless data were provided upon contacting the authors or could be obtained from figures using DataThief (Tummers 2006). We visually inspected maps plotted for each study separately and we made minor adjustments to the GPS coordinates when the coordinates placed the focal population off the island or mainland. For this study, we included only responses measured at the individual level, therefore we removed papers referring to demographic performance and traits such as immunity, behaviour and diet that are heavily reliant on ecosystem context. We extracted data on population-level mean for two broad categories of response: i) broad phenotypic measures, which included traits (size, weight and morphology of entire body or body parts), metabolism products, physiology, vital rates (growth, survival, reproduction) and mean age of sampled mature individuals; and ii) genetic diversity, which included heterozygosity,allelic richness, number of alleles per locus etc. The final dataset includes 112 studies and 108 species. Methods for processing the data: We made minor adjustments to the GPS location of some populations upon visual inspection on Google Maps of the correct overlay of the data point with the indicated island body or mainland. For each population we extracted four climate variables reflecting mean and variation in temperature and precipitation available in CliMond V1.2 (Kritikos et al. 2012) at 10 minutes resolution: mean annual temperature (Bio1), annual precipitation (Bio12), temperature seasonality (CV) (Bio4) and precipitation seasonality (CV) (Bio15) using the "prcomp function" in the stats package in R. For populations where climate variables were not available on the global climate maps mostly due to small island size not captured in CliMond, we extracted data from the geographically closest grid cell with available climate values, which was available within 3.5 km away from the focal grid cell for all localities. We normalised the four climate variables using the "normalizer" package in R (Vilela 2020), and we performed a Principal Component Analysis (PCA) using the psych package in R (Revelle 2018). We saved the loadings of the axes for further analyses. References:

Bruno Vilela (2020). normalizer: Making data normal again.. R package version 0.1.0. Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J., et al.(2012). CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol., 3, 53--64. Revelle, W. (2018) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 1.8.12. Tummers, B. (2006). DataThief III. https://datathief.org/

How many cattle are in the world? The global live cattle population amounted to about 1.57 billion heads in 2023, up from approximately 1.51 billion in 2021. Cows as livestock The domestication of cattle began as early as 10,000 to 5,000 years ago. From ancient times up to the present, cattle are bred to provide meat and dairy. Cattle are also employed as draft animals to plow the fields or transport heavy objects. Cattle hide is used for the production of leather, and dung for fuel and agricultural fertilizer. In 2022, India was home to the highest number of milk cows in the world. Cattle farming in the United States Cattle meat such as beef and veal is one of the most widely consumed types of meat across the globe, and is particularly popular in the United States. The United States is the top producer of beef and veal of any country worldwide. In 2021, beef production in the United States reached 12.6 million metric tons. Beef production appears to be following a positive trend in the United States. More than 33.07 million cattle were slaughtered both commercially and in farms annually in the United States in 2019, up from 33 million in the previous year.

In 2024, more than 27,000 flowering plants were considered to be threatened species. Flowering plants, more colloquially known as flowers, are the most diverse group of land plants. They are also the largest group within the plant kingdom regarding the number of described species. Endangered and threatened Species Threatened species and organisms are those that are vulnerable to being endangered in the future. The population growth rate is one way to determine whether a species is going to become endangered. In the United States, plants were the most endangered wildlife and plant species. In Latin America, Ecuador had the highest number of threatened living species on the IUCN Red List. The International Union for Conservation of Nature The International Union for Conservation of Nature and Natural Resources (IUCN) is an international organization that works to protect the environment and its inhabitants. Founded in 1948, it is headquartered in Gland, Switzerland. The IUCN’s mission is to make sure that the entire world works to conserve the environment and nature. They also want to make sure that the natural resources that are being used are renewable sources. Their Red List is the most all-inclusive status of global conservation of the earth’s species. In 2023, they listed plants as the most threatened species worldwide.

A global high resolution data base of animal population densities and associated methane (CH4) emissions has been developed at the NASA Goddard Institute for Space Studies (NASA/GISS). The animal population statistics were based primarily on the Food and Agriculture Organization (FAO) compilations and other sources. The animals were distributed using a 1-degree by 1-degree resolution data base of countries and land-use (Matthews, 1983). The animals included are cattle and dairy cows, water buffalo, sheep, goats, camels, pigs, horses, and caribou. Estimates of methane production from each animal type were applied to the animal populations to yield a global distribution of methane emissions by animals. About 55% of the global annual emissions was concentrated between 25 N and 55 N. Estimates of methane emissions from animals were based on the research by Crutzen et al. (1986).

The understanding of the structure of free-roaming dog populations is of extreme importance for the planning and monitoring of populational control strategies and animal welfare. The methods used to estimate the abundance of this group of dogs are more complex than the ones used with domiciled owned dogs. In this systematic review, we analyze the techniques and the results obtained in studies that seek to estimate the size of free-ranging dog populations. Twenty-six studies were reviewed regarding the quality of execution and their capacity to generate valid estimates. Seven of the eight publications that take a simple count of the animal population did not consider the different probabilities of animal detection; only one study used methods based on distances; twelve relied on capture-recapture models for closed populations without considering heterogeneities in capture probabilities; six studies applied their own methods with different potential and limitations. Potential sources of bias in the studies were related to the inadequate description or implementation of animal capturing or viewing procedures and to inadequacies in the identification and registration of dogs. Thus, there was a predominance of estimates with low validity. Abundance and density estimates carried high variability, and all studies identified a greater number of male dogs. We point to enhancements necessary for the implementation of future studies and to potential updates and revisions to the recommendations of the World Health Organization with respect to the estimation of free-ranging dog populations.

These satellite tag data were collected on three species of Mobulid Ray (Mobula mobular, Mobula thurstoni, Mobula munkiana) in the southern Gulf of California between 2004 and 2014 to describe their movement patterns and habitat use. Wildlife Computers PAT tags (Mk10, MiniPAT, PAT4) were deployed. Data collected include: 1) date, time and location of PAT tag deployment; 2) date, time, and location of PAT tag pop-off; 3) light geolocation of PAT tag while at liberty; and 4) depth and temperature time series and histograms from PAT tags. Data provided in CSV and proprietary Wildlife Computers formats.

The HMAP database (http://www.hull.ac.uk/hmap) is an open access facility that currently comprises time series of commercial catches covering the period 1611-2000. It is a growing resource and extends more that 240,000 records and more than 100 species. Data are mostly recovered from archives, tax records, custom records or surveys. The facility includes a web guide to the database (the Data Directory) and a web library of dataset downloads (the Data Library), while users can create customized datasets through the HMAP Portal, which is an interactive facility for searching the database. A significant proportion of these holdings are currently available through OBIS. HMAP is a distributed data contributor and the constituent datasets have been mapped to the OBIS schema using DiGIR since 2004.

The HMAP program (http://www.hmapcoml.org) is the historical component of the Census of Marine Life (CoML). It is a multidisciplinary, collaborative project which aims to enhance knowledge and understanding of how and why the diversity, distribution and abundance of marine life in the world's oceans changes over the long term. The HMAP program is currently composed of 9 datasets, 3 of which focus on trawl records from Southeast Australia, one on world whaling, 2 on Northwest Atlantic, and 3 on catch data from Norwegian and North and Baltic seas.

Wildlife Health Data Platforms Market Outlook

According to our latest research, the global wildlife health data platforms market size reached USD 1.48 billion in 2024, reflecting a robust expansion driven by technological advancements and increasing awareness regarding wildlife health monitoring. The market is projected to grow at a CAGR of 10.2% from 2025 to 2033, with the forecasted market size expected to reach USD 3.56 billion by 2033. This growth trajectory is primarily attributed to the rising necessity for real-time disease surveillance, data-driven conservation strategies, and the integration of digital solutions in wildlife health management. As per our latest research, the marketÂ’s upward momentum is underscored by increasing government and non-governmental initiatives focused on biodiversity preservation and the prevention of zoonotic diseases.

The growth of the wildlife health data platforms market is fundamentally propelled by the escalating demand for advanced disease surveillance systems. In recent years, the emergence of zoonotic diseases such as avian flu, Ebola, and COVID-19 has heightened global scrutiny on the intersection of wildlife and public health. Governments and conservation organizations are increasingly investing in robust data infrastructures capable of early detection and response to wildlife health threats. The integration of artificial intelligence, machine learning, and IoT-based sensors into these platforms enables the aggregation and analysis of vast datasets, providing actionable insights for disease prevention and control. These technological advancements are not only enhancing the efficacy of surveillance but are also fostering cross-sector collaborations between public health, veterinary, and environmental agencies.

Another significant growth driver is the expansion of population monitoring and conservation management initiatives worldwide. With biodiversity loss accelerating at an unprecedented rate, there is a pressing need for data-driven approaches to monitor wildlife populations and ecosystem health. Wildlife health data platforms serve as critical tools for tracking animal migrations, population dynamics, and habitat utilization patterns, thereby informing targeted conservation interventions. These platforms facilitate the seamless sharing of data among stakeholders, including government agencies, research institutions, and non-profit organizations. The ability to centralize and analyze diverse data streams is empowering conservationists to make informed decisions, prioritize resource allocation, and measure the impact of their efforts with greater precision.

Additionally, the growing emphasis on research and academia is fueling market growth. Academic institutions and research centers are increasingly leveraging wildlife health data platforms to conduct epidemiological studies, assess environmental impacts, and develop predictive models for disease outbreaks. The availability of comprehensive, high-quality datasets is accelerating scientific discoveries and fostering innovation in wildlife health management. Furthermore, the rising adoption of cloud-based platforms is democratizing access to data, enabling researchers from diverse geographic locations to collaborate and contribute to global knowledge repositories. This trend is expected to further catalyze market expansion as data sharing and interoperability become central to wildlife health research.

In the realm of wildlife conservation, innovative funding mechanisms are gaining traction, with Wildlife Token Donation emerging as a novel approach. This method leverages blockchain technology to facilitate transparent and traceable donations, empowering individuals and organizations to contribute directly to wildlife conservation efforts. By utilizing digital tokens, donors can ensure that their contributions are allocated efficiently and effectively, supporting initiatives such as habitat restoration, anti-poaching measures, and species recovery programs. The integration of Wildlife Token Donation into existing conservation frameworks is enhancing accountability and fostering greater public engagement in biodiversity preservation. As this trend continues to evolve, it holds the potential to revolutionize the way conservation projects are funded and managed, providing a sustainable financial model for protecting the planet's most vulnerable species.

See "01_ASDN_readme.txt" (under "Download Data" tab) for data author and contact information. Field data on shorebird ecology and environmental conditions were collected from 1993-2014 at 16 field sites in Alaska, Canada, and Russia. Data were not collected in every year at all sites. Studies of the population ecology of these birds included nest-monitoring to determine timing of reproduction and reproductive success; live capture of birds to collect blood samples, feathers, and fecal samples for investigations of population structure and pathogens; banding of birds to determine annual survival rates; resighting of color-banded birds to determine space use and site fidelity; and use of light-sensitive geolocators to investigate migratory movements. Data on climatic conditions, prey abundance, and predators were also collected. Environmental data included weather stations that recorded daily climatic conditions, surveys of seasonal snowmelt, weekly sampling of terrestrial and aquatic invertebrates that are prey of shorebirds, live trapping of small mammals (alternate prey for shorebird predators), and daily counts of potential predators (jaegers, falcons, foxes). Detailed field methods for each year are available in the ASDN_protocol_201X.pdf files. All research was conducted under permits from relevant federal, state and university authorities.

Potential users of these data should first contact the relevant data author(s), listed below. This will enable coordination in terms of updates/corrections to the data and ongoing analyses. Key analyses of the data are in progress and will be included in the theses and dissertations of graduate students who collected these field data.

Please acknowledge this dataset and the authors in any analysis, publication, presentation, or other output that uses these data. If you use the full dataset, we suggest you cite it as:
Lanctot, RB, SC Brown, and BK Sandercock. 2017. Arctic Shorebird Demographics Network. NSF Arctic Data Center. doi: INSERT HERE. If you use data from only one or a few sites, we suggest you cite data for each site as per this example:
Lanctot, RB and ST Saalfeld. 2017. Barrow, 2014. Arctic Shorebird Demographics Network. NSF Arctic Data Center. doi: INSERT HERE. Note that each updated version of the dataset has its own unique DOI.

Disclaimers: The dataset is distributed “as is” and with absolutely no warranty. The data providers have invested considerable effort to ensure that the data are of highest quality, but it is possible that undetected errors remain. Data have been processed with several steps for quality assurance, but the data providers accept no liability or guarantee that the data are up-to-date, correct, or complete. Access to data is provided on the understanding that the data providers are not responsible for any damages from inaccuracies in the data.

Abstract of paperRapid human-driven environmental changes are impacting animal populations around the world. Currently, land-use and climate change are two of the biggest pressures facing biodiversity. However, studies investigating the impacts of these pressures on population trends often do not consider potential interactions between climate and land-use change. Further, a population’s climatic position (how close the ambient temperature and precipitation conditions are to the species’ climatic tolerance limits) is known to influence responses to climate change but has yet to be investigated with regard to its influence on land-use change responses over time. Consequently, important variation across species’ ranges in responses to environmental changes may be being overlooked. Here, we combine data from the Living Planet and BioTIME databases to carry out a global analysis exploring the impacts of land use, habitat loss, climatic position, climate change, and the interactions between these variables, on vertebrate population trends. By bringing these datasets together, we analyse over 7,000 populations across 42 countries. We find that land-use change is interacting with climate change and a population’s climatic position to influence rates of population change. Moreover, features of a population’s local landscape (such as surrounding land cover) play important roles in these interactions. For example, populations in agricultural land uses where maximum temperatures were closer to their hot thermal limit, declined at faster rates when there had also been rapid losses in surrounding semi-natural habitat. The complex interactions between these variables on populations highlights the importance of taking intraspecific variation and interactions between local and global pressures into account. Understanding how drivers of change are interacting and impacting populations, and how this varies spatially, is critical if we are to identify populations at risk, predict species’ responses to future environmental changes and produce suitable conservation strategies.Information on data and code 'Code_to_run_models_JJW_GCB.R' contains the R script to run all the candidate models and then compare them.'Data_JJW_GCB.rds' contains the data used to run the candidate models that investigated how rate of population change was affected by land-use type and change, a population’s climatic position, and the rate of climate change experienced.MetadataBinomial = Species name (HIDDEN if classed as confidential within the Living Planet Index database)Class = The vertebrate Classloc_id = ID based on the population's location (latitude and longitude)data = The database the population’s time-series data was acquired from, either the Living Planet Index (LPI) or BioTIME databaseBaselineLU2 = Starting land-use typeTmax_rate, Tmin_rate, Ppmax_rate, Ppmin_rate = The average annual rate of change in maximum temperature of the warmest month, minimum temperature of the coldest month, precipitation of the wettest month and precipitation of the driest month, respectively, over the length of the population time-seriesPercentPVV2_rate = The average annual rate of change in the percentage of semi-natural habitat within a 1-km radius of the population, over the length of the population time-seriesstand_dist = The standardised distance of a population from their species’ geographic range edgeStart_Tmax_Pos, Start_Tmin_Pos, Start_Ppmax_Pos, Start_Ppmin_Pos = The maximum temperature of the warmest month (Tmax), minimum temperature of the coldest month (Tmin), precipitation of the wettest month (Ppmax), and precipitation of the driest month (Ppmin), a population experienced in the first year they were measured, relative to the species-level upper and lower realised thermal (for Tmax and Tmin) or precipitation (for Ppmax and Ppmin) tolerance limitslambda_mean = The average logged annual rate of population change

Records of fauna (animals), and environmental change derived from animal fossils. Parameter keywords describe what was measured in this data set. Additional summary information can be found in the abstracts of papers listed in the data set citations. For details please see: http://www.ncdc.noaa.gov/paleo/fauna.html

This dataset provides an extensive overview of the top 100 most endangered species across the globe, aiming to raise awareness and support conservation efforts. Each entry includes key details to help understand the challenges these species face and the importance of preserving biodiversity.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20369779%2F302887c259c8f5d351c49370245e5aee%2Fa-friendly-and-colorful-vector-style-illustration-showing-various-endangered-animals-include-a-beng.jpeg?generation=1729143878618409&alt=media" alt=""> Key Features: - Species Name: Both common and scientific names of the species. - Habitat: The natural environments these species inhabit (e.g., forests, oceans, grasslands). - Conservation Status: The IUCN Red List classification, indicating the severity of their endangered status (e.g., Critically - Endangered, Endangered). - Estimated Population: An approximation of the remaining individuals in the wild. - Threats: Major factors contributing to their endangered status, such as habitat loss, poaching, and climate change. - Conservation Efforts: Initiatives and programs aimed at protecting and rehabilitating these species.

This dataset is designed for researchers, educators, conservationists, and anyone interested in understanding and addressing the plight of endangered wildlife. By utilizing this data, we can work towards impactful conservation strategies to safeguard our planet's biodiversity. 🌿🌍

Global Animal Health Incident Reports (2005–2025)

Overview

This dataset contains two decades of global animal health incident reports, sourced from the World Organisation for Animal Health (WOAH, formerly OIE) via the WAHIS platform. It captures country-submitted disease reports for domestic and wild animal populations, standardized across time and geography for analysis.

The data spans from 2005 to 2025, covering more than 100 countries, and includes detailed reporting on: - Diseases like Avian Influenza, African Swine Fever, Foot and Mouth Disease, and Anthrax - Affected animal species (e.g., poultry, swine, cattle) - Report frequency over time and space - Outbreak context and resolution status

What's Inside

Why This Matters

This dataset provides a high-value lens into how global systems detect, report, and respond to animal health threats. These events impact not only food security and animal welfare, but also economic stability and zoonotic spillover risk.

It’s useful for: - Epidemiological modeling - Time series forecasting - Species vulnerability analysis - Regional disease burden tracking - Biosecurity planning and investment

Example Insights from the Notebook (Included)

• Monthly report volumes have more than tripled since 2010.
• Poultry is the most frequently affected species globally.
• Russia, Ukraine, and Germany are top reporters — possibly due to policy, proximity to outbreaks, and agri-intensity.
• HPAI and ASF have seen persistent growth across continents since the mid-2010s.
• Many reports lack species tagging, highlighting an opportunity for structured improvements.

📎 Companion Files

• animal_health_incidents.csv — Full dataset
• data_dictionary.csv — Column definitions
• README.md — Dataset background and methodology
• Global_Animal_Health_Incident_Reports.ipynb — Full exploratory notebook (optional)

Source

Data collected and cleaned from:
🌐 WOAH WAHIS Platform

Use responsibly under public reporting guidelines.

License

This dataset is released under CC0 Public Domain. Use, remix, and cite freely.

Ideas for Exploration

• Track disease emergence across time and space
• Compare reporting behavior between continents or income levels
• Build predictive models for disease outbreaks
• Integrate with climate, trade, or migration data
• Assess underreported diseases/species

Wildlife ecologists throughout the world strive to monitor trends in population abundance to help manage wildlife populations and conserve species at risk. Spatial capture-recapture studies are the gold standard for monitoring density, yet they can be difficult to apply because researchers must be able to distinguish all detected individuals. Spatial mark-resight (SMR) models only require a subset of the population to be marked and identifiable. Recent advances in SMR models with radio-collared animals required a two-staged analysis. We developed a one-stage generalized SMR (gSMR) model that used detection histories of marked and unmarked animals in a single analysis. We used simulations to assess the performance of one- and two-stage gSMR models. We then applied the one-stage gSMR with telemetry and remote camera data to estimate grizzly bear (Ursus arctos) abundance from 2012 to 2023 within the Canadian Rocky Mountains. We estimated abundance trends for the population and reproductive females (females with cubs of the year). Simulations suggest one- and two-stage models performed equally well. One-stage models are more dependable as they use exact likelihoods whereas two-stage models have shorter computation times for large datasets. Both methods had > 95% credible interval coverage and minimal bias. Increasing the number of marked animals increased the accuracy and precision of abundance estimates and > 10 marked animals were required to obtain coefficients of variation < 20% in most scenarios. The grizzly bear population increased slightly (growth rate λmean = 1.02) to a 2023 density of 10.4 grizzly bears/1000 km2. Reproductive female abundance had high interannual variability and increased to 1.0 bears/1000 km2. Population density was highest within protected areas, within high quality habitat and far from paved roads. The density of activity centers declined near paved roads over time. Mechanisms of decline may have included direct mortality and shifting activity centers to avoid human activity. Our study demonstrates the influence of human activity on localized density and importance of protected areas for carnivore conservation. Finally, our study highlights the widespread utility of remote camera and telemetry-based spatial mark-resight models for monitoring spatiotemporal trends in abundance. Methods The gSMR models combine remote camera detections of marked animals, unmarked animals, and telemetry data to estimate the baseline detection rate, home range scale parameter, and spatially explicit estimates of density. Our study area encompassed 15,483 km2 and included Banff, Kootenay, and Yoho Nation Parks and the Ya Ha Tinda ecosystem within the Rocky Mountains of Canada. The remote camera data contains detection histories from 25 marked, radio-collared grizzly bears and detections of unmarked grizzly bears recorded at 625 remote cameras from 2012 to 2021. Telemetry data contains daily global positioning system (GPS) locations from fifteen female and ten male grizzly bears. We provide source code to estimate spatial and temporal trends in grizzly bear density as well as the density of female grizzly bears with cubs of the year. We describe each data set and associated attributes in tbl_DataDescription_2023-11-13.csv.