Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

In the past four centuries, the population of the Thirteen Colonies and United States of America has grown from a recorded 350 people around the Jamestown colony in Virginia in 1610, to an estimated 346 million in 2025. While the fertility rate has now dropped well below replacement level, and the population is on track to go into a natural decline in the 2040s, projected high net immigration rates mean the population will continue growing well into the next century, crossing the 400 million mark in the 2070s. Indigenous population Early population figures for the Thirteen Colonies and United States come with certain caveats. Official records excluded the indigenous population, and they generally remained excluded until the late 1800s. In 1500, in the first decade of European colonization of the Americas, the native population living within the modern U.S. borders was believed to be around 1.9 million people. The spread of Old World diseases, such as smallpox, measles, and influenza, to biologically defenseless populations in the New World then wreaked havoc across the continent, often wiping out large portions of the population in areas that had not yet made contact with Europeans. By the time of Jamestown's founding in 1607, it is believed the native population within current U.S. borders had dropped by almost 60 percent. As the U.S. expanded, indigenous populations were largely still excluded from population figures as they were driven westward, however taxpaying Natives were included in the census from 1870 to 1890, before all were included thereafter. It should be noted that estimates for indigenous populations in the Americas vary significantly by source and time period. Migration and expansion fuels population growth The arrival of European settlers and African slaves was the key driver of population growth in North America in the 17th century. Settlers from Britain were the dominant group in the Thirteen Colonies, before settlers from elsewhere in Europe, particularly Germany and Ireland, made a large impact in the mid-19th century. By the end of the 19th century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. It is also estimated that almost 400,000 African slaves were transported directly across the Atlantic to mainland North America between 1500 and 1866 (although the importation of slaves was abolished in 1808). Blacks made up a much larger share of the population before slavery's abolition. Twentieth and twenty-first century The U.S. population has grown steadily since 1900, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. Since WWII, the U.S. has established itself as the world's foremost superpower, with the world's largest economy, and most powerful military. This growth in prosperity has been accompanied by increases in living standards, particularly through medical advances, infrastructure improvements, clean water accessibility. These have all contributed to higher infant and child survival rates, as well as an increase in life expectancy (doubling from roughly 40 to 80 years in the past 150 years), which have also played a large part in population growth. As fertility rates decline and increases in life expectancy slows, migration remains the largest factor in population growth. Since the 1960s, Latin America has now become the most common origin for migrants in the U.S., while immigration rates from Asia have also increased significantly. It remains to be seen how immigration restrictions of the current administration affect long-term population projections for the United States.

In 1800, the population of the region of present-day India was approximately 169 million. The population would grow gradually throughout the 19th century, rising to over 240 million by 1900. Population growth would begin to increase in the 1920s, as a result of falling mortality rates, due to improvements in health, sanitation and infrastructure. However, the population of India would see it’s largest rate of growth in the years following the country’s independence from the British Empire in 1948, where the population would rise from 358 million to over one billion by the turn of the century, making India the second country to pass the billion person milestone. While the rate of growth has slowed somewhat as India begins a demographics shift, the country’s population has continued to grow dramatically throughout the 21st century, and in 2020, India is estimated to have a population of just under 1.4 billion, well over a billion more people than one century previously. Today, approximately 18% of the Earth’s population lives in India, and it is estimated that India will overtake China to become the most populous country in the world within the next five years.

The population of Europe was estimated to be 745 million in 2024, an increase of around 4 million when compared with 2012. Over 35 years between 1950 and 1985, the population of Europe grew by approximately 157.8 million. But 35 years after 1985 it was estimated to have only increased by around 38.7 million. Since the 1960s, population growth in Europe has fallen quite significantly and was even negative during the mid-1990s. While population growth has increased slightly since the low of -0.07 percent in 1998, the growth rate for 2020 was just 0.04 percent. Which European country has the biggest population? As of 2024, the population of Russia was estimated to be approximately 144.8 million and was by far Europe's largest country in terms of population, with Turkey being the second-largest at over 87 million. While these two countries both have territory in Europe, however, they are both only partially in Europe, with the majority of their landmasses being in Asia. In terms of countries wholly located on the European continent, Germany had the highest population at 84.5 million, and was followed by the United Kingdom and France at 69.1 million and 66.5 million respectively. Characteristics of Europe's population There are approximately 384.6 million females in Europe, compared with 359.5 million males, a difference of around 25 million. In 1950, however, the male population has grown faster than the female one, with the male population growing by 104.7 million, and the female one by 93.6 million. As of 2024, the single year of age with the highest population was 37, at 10.6 million, while in the same year there were estimated to be around 136 thousand people aged 100 or over.

Like many polar animals, emperor penguin populations are challenging to monitor because of the species’ life history and remoteness. Consequently, it has been difficult to establish its global status, a subject important to resolve as polar environments change. To advance our understanding of emperor penguins, we combined remote sensing, validation surveys, and using Bayesian modeling we estimated a comprehensive population trajectory over a recent 10-year period, encompassing the entirety of the species’ range. Reported as indices of abundance, our study indicates with 81% probability that the global population of adult emperor penguins declined between 2009 and 2018, with a posterior median decrease of 9.6% (95% credible interval (CI) -26.4% to +9.4%). The global population trend was -1.3% per year over this period (95% CI = -3.3% to +1.0%) and declines likely occurred in four of eight fast ice regions, irrespective of habitat conditions. Thus far, explanations have yet to be identifi..., During the 2018 Antarctic field season, under permit #2019-006 granted by the National Science Foundation, our US-based team conducted aerial photography at emperor penguin colonies in the Ross Sea to add to robust validation of imagery. Our efforts included one flight via fixed wing aircraft over colonies distant from McMurdo Station and five flights via helicopter to a single colony (Cape Crozier) near the station. The five flights to Cape Crozier, 24 October to 15 November, were used to better understand population fluctuation through a single season. Our fixed wing survey took place on 31 October 2018, flying in the vicinity of Beaufort Island (ASPA 105), Franklin Island, Cape Washington (ASPA 173), Coulman Island, and Cape Roget. At each location (both by fixed wing and helicopter), we circled the colony 1-4 times, maintaining a minimum of 500 m horizontal distance from the periphery of the colony and a minimum altitude of 500 m. No behavioral disturbance to birds (e.g., rapid move..., , # LaRue et al. (2024): Advances in remote sensing of emperor penguins: first multi-year time series documenting global population change

10.5061/dryad.m63xsj48v

Overview

This repository contains data, code, and model output associated with the global-scale analysis of Emperor penguin population dynamics described in LaRue et al. (2024), based on integrating raw data from aerial surveys with time series of circumpolar satellite surveys of known emperor penguin colonies.

The model is used to estimate an annual index of abundance at every known Emperor penguin colony in Antarctica (as of 2018), for every year between 2008 and 2018. Regional and global population indices are then calculated by summing colony-level estimates, according to regional colony membership.

Simulations are also performed to evaluate the ability of the model to accurately detect population trends, if they exist.

File structure and code description

• analysis/
  • **ou...

This statistic shows the total population of the European Union from 2010 to 2023. The population is based on data from the most recent census adjusted by the components of population change produced since the last census, or based on population registers. At the beginning of 2023, the total population of the European Union amounted to approximately 448.38 million inhabitants. See figures for the total population by continent here. The global population The global population is rapidly increasing. Between 1990 and 2015, the global population has increased by around 2 billion people, and it is estimated to have increased by another 1 billion people by 2030. Asia is the continent in the world with the largest population, followed by Africa and Europe. Asia has the two most populous nations in the world: China and India. In 2014, the combined population in China and India amounted to more than 2.6 billion people. The total population in Europe is around 741 million people. As of 2014, about 10.2 percent of the global population lived in Europe. Europe is the continent with the second highest life expectancy at birth in the world. Born in 2013, the average European was estimated to live for around 78 years. Stable economies as well as developing and emerging markets in Europe provide for good living conditions for foreign nationals; seven of the top twenty countries in the world with the largest gross domestic product in 2024 are located in Europe.

Effective population size (Ne) is a particularly useful metric for conservation as it affects genetic drift, inbreeding and adaptive potential within populations. Current guidelines recommend a minimum Ne of 50 and 500 to avoid short-term inbreeding and to preserve long-term adaptive potential, respectively. However, the extent to which wild populations reach these thresholds globally has not been investigated, nor has the relationship between Ne and human activities. Through a quantitative review, we generated a dataset with 4610 georeferenced Ne estimates from 3829 unique populations, extracted from 723 articles. These data show that certain taxonomic groups are less likely to meet 50/500 thresholds and are disproportionately impacted by human activities; plant, mammal, and amphibian populations had a <54% probability of reaching = 50 and a <9% probability of reaching = 500. Populations listed as being of conservation concern according to the IUCN Red List had a smaller median than unlisted populations, and this was consistent across all taxonomic groups. was reduced in areas with a greater Global Human Footprint, especially for amphibians, birds, and mammals, however relationships varied between taxa. We also highlight several considerations for future works, including the role that gene flow and subpopulation structure plays in the estimation of in wild populations, and the need for finer-scale taxonomic analyses. Our findings provide guidance for more specific thresholds based on Ne and help prioritize assessment of populations from taxa most at risk of failing to meet conservation thresholds. Methods Literature search, screening, and data extraction A primary literature search was conducted using ISI Web of Science Core Collection and any articles that referenced two popular single-sample Ne estimation software packages: LDNe (Waples & Do, 2008), and NeEstimator v2 (Do et al., 2014). The initial search included 4513 articles published up to the search date of May 26, 2020. Articles were screened for relevance in two steps, first based on title and abstract, and then based on the full text. For each step, a consistency check was performed using 100 articles to ensure they were screened consistently between reviewers (n = 6). We required a kappa score (Collaboration for Environmental Evidence, 2020) of ³ 0.6 in order to proceed with screening of the remaining articles. Articles were screened based on three criteria: (1) Is an estimate of Ne or Nb reported; (2) for a wild animal or plant population; (3) using a single-sample genetic estimation method. Further details on the literature search and article screening are found in the Supplementary Material (Fig. S1). We extracted data from all studies retained after both screening steps (title and abstract; full text). Each line of data entered in the database represents a single estimate from a population. Some populations had multiple estimates over several years, or from different estimation methods (see Table S1), and each of these was entered on a unique row in the database. Data on N̂e, N̂b, or N̂c were extracted from tables and figures using WebPlotDigitizer software version 4.3 (Rohatgi, 2020). A full list of data extracted is found in Table S2. Data Filtering After the initial data collation, correction, and organization, there was a total of 8971 Ne estimates (Fig. S1). We used regression analyses to compare Ne estimates on the same populations, using different estimation methods (LD, Sibship, and Bayesian), and found that the R2 values were very low (R2 values of <0.1; Fig. S2 and Fig. S3). Given this inconsistency, and the fact that LD is the most frequently used method in the literature (74% of our database), we proceeded with only using the LD estimates for our analyses. We further filtered the data to remove estimates where no sample size was reported or no bias correction (Waples, 2006) was applied (see Fig. S6 for more details). Ne is sometimes estimated to be infinity or negative within a population, which may reflect that a population is very large (i.e., where the drift signal-to-noise ratio is very low), and/or that there is low precision with the data due to small sample size or limited genetic marker resolution (Gilbert & Whitlock, 2015; Waples & Do, 2008; Waples & Do, 2010) We retained infinite and negative estimates only if they reported a positive lower confidence interval (LCI), and we used the LCI in place of a point estimate of Ne or Nb. We chose to use the LCI as a conservative proxy for in cases where a point estimate could not be generated, given its relevance for conservation (Fraser et al., 2007; Hare et al., 2011; Waples & Do 2008; Waples 2023). We also compared results using the LCI to a dataset where infinite or negative values were all assumed to reflect very large populations and replaced the estimate with an arbitrary large value of 9,999 (for reference in the LCI dataset only 51 estimates, or 0.9%, had an or > 9999). Using this 9999 dataset, we found that the main conclusions from the analyses remained the same as when using the LCI dataset, with the exception of the HFI analysis (see discussion in supplementary material; Table S3, Table S4 Fig. S4, S5). We also note that point estimates with an upper confidence interval of infinity (n = 1358) were larger on average (mean = 1380.82, compared to 689.44 and 571.64, for estimates with no CIs or with an upper boundary, respectively). Nevertheless, we chose to retain point estimates with an upper confidence interval of infinity because accounting for them in the analyses did not alter the main conclusions of our study and would have significantly decreased our sample size (Fig. S7, Table S5). We also retained estimates from populations that were reintroduced or translocated from a wild source (n = 309), whereas those from captive sources were excluded during article screening (see above). In exploratory analyses, the removal of these data did not influence our results, and many of these populations are relevant to real-world conservation efforts, as reintroductions and translocations are used to re-establish or support small, at-risk populations. We removed estimates based on duplication of markers (keeping estimates generated from SNPs when studies used both SNPs and microsatellites), and duplication of software (keeping estimates from NeEstimator v2 when studies used it alongside LDNe). Spatial and temporal replication were addressed with two separate datasets (see Table S6 for more information): the full dataset included spatially and temporally replicated samples, while these two types of replication were removed from the non-replicated dataset. Finally, for all populations included in our final datasets, we manually extracted their protection status according to the IUCN Red List of Threatened Species. Taxa were categorized as “Threatened” (Vulnerable, Endangered, Critically Endangered), “Nonthreatened” (Least Concern, Near Threatened), or “N/A” (Data Deficient, Not Evaluated). Mapping and Human Footprint Index (HFI) All populations were mapped in QGIS using the coordinates extracted from articles. The maps were created using a World Behrmann equal area projection. For the summary maps, estimates were grouped into grid cells with an area of 250,000 km2 (roughly 500 km x 500 km, but the dimensions of each cell vary due to distortions from the projection). Within each cell, we generated the count and median of Ne. We used the Global Human Footprint dataset (WCS & CIESIN, 2005) to generate a value of human influence (HFI) for each population at its geographic coordinates. The footprint ranges from zero (no human influence) to 100 (maximum human influence). Values were available in 1 km x 1 km grid cell size and were projected over the point estimates to assign a value of human footprint to each population. The human footprint values were extracted from the map into a spreadsheet to be used for statistical analyses. Not all geographic coordinates had a human footprint value associated with them (i.e., in the oceans and other large bodies of water), therefore marine fishes were not included in our HFI analysis. Overall, 3610 Ne estimates in our final dataset had an associated footprint value.

Understanding how bottom-up and top-down forces affect resource selection can inform restoration efforts. With a global population size of <500 individuals, the hirola Beatragus hunteri is the world's most endangered antelope, with a declining population since the 1970s. While the underlying mechanisms are unclear, some combination of habitat loss and predation are thought to be responsible for low abundances of contemporary populations. Efforts to conserve hirola are hindered by a lack of understanding as to why population density remains low, despite eradication of the viral disease, rinderpest. To elucidate factors underlying chronically low numbers, we examined resource selection and landscape change within the hirola's native range. Because hirola are grazers, we hypothesized that the availability of open areas would be linked both to forage and safety from predators. We quantified: (1) changes in tree cover across the hirola's historical range in eastern Kenya over the past 27 ...

During the eighteenth century, it is estimated that France's population grew by roughly fifty percent, from 19.7 million in 1700, to 29 million by 1800. In France itself, the 1700s are remembered for the end of King Louis XIV's reign in 1715, the Age of Enlightenment, and the French Revolution. During this century, the scientific and ideological advances made in France and across Europe challenged the leadership structures of the time, and questioned the relationship between monarchial, religious and political institutions and their subjects. France was arguably the most powerful nation in the world in these early years, with the second largest population in Europe (after Russia); however, this century was defined by a number of costly, large-scale conflicts across Europe and in the new North American theater, which saw the loss of most overseas territories (particularly in North America) and almost bankrupted the French crown. A combination of regressive taxation, food shortages and enlightenment ideologies ultimately culminated in the French Revolution in 1789, which brought an end to the Ancien Régime, and set in motion a period of self-actualization.

War and peace

After a volatile and tumultuous decade, in which tens of thousands were executed by the state (most infamously: guillotined), relative stability was restored within France as Napoleon Bonaparte seized power in 1799, and the policies of the revolution became enforced. Beyond France's borders, the country was involved in a series of large scale wars for two almost decades, and the First French Empire eventually covered half of Europe by 1812. In 1815, Napoleon was defeated outright, the empire was dissolved, and the monarchy was restored to France; nonetheless, a large number of revolutionary and Napoleonic reforms remained in effect afterwards, and the ideas had a long-term impact across the globe. France experienced a century of comparative peace in the aftermath of the Napoleonic Wars; there were some notable uprisings and conflicts, and the monarchy was abolished yet again, but nothing on the scale of what had preceded or what was to follow. A new overseas colonial empire was also established in the late 1800s, particularly across Africa and Southeast Asia. Through most of the eighteenth and nineteenth century, France had the second largest population in Europe (after Russia), however political instability and the economic prioritization of Paris meant that the entire country did not urbanize or industrialize at the same rate as the other European powers. Because of this, Germany and Britain entered the twentieth century with larger populations, and other regions, such as Austria or Belgium, had overtaken France in terms of industrialization; the German annexation of Alsace-Lorraine in the Franco-Prussian War was also a major contributor to this.

World Wars and contemporary France

Coming into the 1900s, France had a population of approximately forty million people (officially 38 million* due to to territorial changes), and there was relatively little growth in the first half of the century. France was comparatively unprepared for a large scale war, however it became one of the most active theaters of the First World War when Germany invaded via Belgium in 1914, with the ability to mobilize over eight million men. By the war's end in 1918, France had lost almost 1.4 million in the conflict, and approximately 300,000 in the Spanish Flu pandemic that followed. Germany invaded France again during the Second World War, and occupied the country from 1940, until the Allied counter-invasion liberated the country during the summer of 1944. France lost around 600,000 people in the course of the war, over half of which were civilians. Following the war's end, the country experienced a baby boom, and the population grew by approximately twenty million people in the next fifty years (compared to just one million in the previous fifty years). Since the 1950s, France's economy quickly grew to be one of the strongest in the world, despite losing the vast majority of its overseas colonial empire by the 1970s. A wave of migration, especially from these former colonies, has greatly contributed to the growth and diversity of France's population today, which stands at over 65 million people in 2020.

AbstractUnderstanding how bottom-up and top-down forces affect resource selection can inform restoration efforts. With a global population size of <500 individuals, the hirola Beatragus hunteri is the world's most endangered antelope, with a declining population since the 1970s. While the underlying mechanisms are unclear, some combination of habitat loss and predation are thought to be responsible for low abundances of contemporary populations. Efforts to conserve hirola are hindered by a lack of understanding as to why population density remains low, despite eradication of the viral disease, rinderpest. To elucidate factors underlying chronically low numbers, we examined resource selection and landscape change within the hirola's native range. Because hirola are grazers, we hypothesized that the availability of open areas would be linked both to forage and safety from predators. We quantified: (1) changes in tree cover across the hirola's historical range in eastern Kenya over the past 27 years; (2) how tree cover has influenced resource selection by hirola; and (3) interactions between tree cover and predation. Between 1985 and 2012, tree cover increased by 251% across the historical range of hirola. Tree encroachment was associated with a 98% decline of hirola and elephant Loxodonta africana populations, a 74% decline in cattle Bos indicus, an increase in browsing livestock by 327%, and a reduction in rainfall. Although hirola avoided tree cover, we found no evidence that predation on hirola increased with increasing tree cover. Synthesis and applications. Hirola may qualify as a refugee species, in which contemporary populations are restricted to suboptimal habitat and exhibit low survival, reproduction, or both. The extinction of hirola would be the first of a mammalian genus on the African continent in modern history. We conclude that contemporary low numbers of hirola are due at least partly to habitat loss via tree encroachment, triggered by some combination of elephant extirpation, overgrazing, drought, and perhaps fire suppression. We recommend a combination of rangeland restoration efforts (including conservation of elephants, manual clearing of trees, and grass seeding), increased enforcement of an existing protected area (Arawale National Reserve), and reintroductions to enhance recovery for this endangered species. These efforts will rely on enhanced support from the international conservation community and the cooperation of pastoralist communities with which the hirola coexist. Usage notesLivestock, Cattle, elephants abundance in hirola's range_77_2011This is aerial survey data from the Department of Resource Survey and Remote Sensing Kenya from 1977 to 2011 to give historical estimates wildlife and livestock abundances. We estimated the trends of livestock, hirola, elephants abundances from this data.Masalani_daily rainfallThe rainfall data file contains daily rainfall observations for Masalani station in the centre of hirola's geographic range for the period from 1970-2009.Hirolacollardatamay2nd 2015The hirola collar data contains the telemetry data of collared hirola individuals within the hirola's geographic range. Note: due to the sensitive nature of the data, GPS location information for the critically endangered Beatragus hunteri have been removed from the dataset.Imagery analysisP166R61zip contains details of the imagery analysis (1985 and 2012) for the hirola's geographic range.P166R61_zip.zipHirolakillsites_2015Hirola kill sites data contains observed hirola kill sites collected by network of scouts within the hirola's geographic range. Note: due to the sensitive nature of the data, GPS location information for the critically endangered Beatragus hunteri have been removed from the dataset.

The raster dataset consists of a 500 m score grid for slaughterhouse industry facilities, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location.

The analysis is based on goat production intensification potential defined using crop production, livestock production systems, and goat density.

The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, livestock distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility)

It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.2) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.3) + (”Goat Intensification” * 0.3)

Data publication: 2021-10-15

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Justeen De Ocampo

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets: 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2) 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zipped TIF raster file for slaughterhouse location score(Rwanda - ~ 500m)

Important Note: This item is in mature support as of June 2023 and will retire in December 2025. A new version of this item is available for your use.The layers going from 1:1 to 1:1.5M present the 2010 Census Urbanized Areas (UA) and Urban Clusters (UC). A UA consists of contiguous, densely settled census block groups (BGs) and census blocks that meet minimum population density requirements (1000 people per square mile (ppsm) / 500 ppsm), along with adjacent densely settled census blocks that together encompass a population of at least 50,000 people. A UC consists of contiguous, densely settled census BGs and census blocks that meet minimum population density requirements, along with adjacent densely settled census blocks that together encompass a population of at least 2,500 people, but fewer than 50,000 people. The dataset covers the 50 States plus the District of Columbia within United States. The layer going over 1:1.5M presents the urban areas in the United States derived from the urban areas layer of the Digital Chart of the World (DCW). It provides information about the locations, names, and populations of urbanized areas for conducting geographic analysis on national and large regional scales. To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to USA Census Urban Areas.

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Movements of individuals within and among populations help to maintain genetic variability and population viability. Therefore, understanding landscape connectivity is vital for effective species conservation. The snow leopard is endemic to mountainous areas of Central Asia and occurs within 12 countries. We assess potential connectivity across the species’ range to highlight corridors for dispersal and genetic flow between populations, prioritizing research and conservation action for this wide-ranging, endangered top-predator. We used resistant kernel modeling to assess snow leopard population connectivity across its global range. We developed an expert-based resistance surface that predicted cost of movement as functions of topographical complexity and land cover. The distribution of individuals was simulated as a uniform density of points throughout the currently accepted global range. We modeled population connectivity from these source points across the resistance surface using three different dispersal scenarios that likely bracket the lifetime movements of individual snow leopard: 100km, 500km and 1000km. The resistant kernel models produced predictive surfaces of dispersal frequency across the snow leopard range for each distance scenario. We evaluated the pattern of connectivity in each of these scenarios and identified potentially important movement corridors and areas where connectivity might be impeded. The models predicted two regional populations, in the north and south of the species range respectively, and revealed a number of potentially important connecting areas. Discrepancies between model outputs and observations highlight unsurveyed areas of connected habitat that urgently require surveying to improve understanding of the global distribution and ecology of snow leopard, and target land management actions to prevent population isolation. The connectivity maps provide a strong basis for directed research and conservation action, and usefully direct the attention of policy makers.

In 1800, the population of Japan was just over 30 million, a figure which would grow by just two million in the first half of the 19th century. However, with the fall of the Tokugawa shogunate and the restoration of the emperor in the Meiji Restoration of 1868, Japan would begin transforming from an isolated feudal island, to a modernized empire built on Western models. The Meiji period would see a rapid rise in the population of Japan, as industrialization and advancements in healthcare lead to a significant reduction in child mortality rates, while the creation overseas colonies would lead to a strong economic boom. However, this growth would slow beginning in 1937, as Japan entered a prolonged war with the Republic of China, which later grew into a major theater of the Second World War. The war was eventually brought to Japan's home front, with the escalation of Allied air raids on Japanese urban centers from 1944 onwards (Tokyo was the most-bombed city of the Second World War). By the war's end in 1945 and the subsequent occupation of the island by the Allied military, Japan had suffered over two and a half million military fatalities, and over one million civilian deaths.

The population figures of Japan were quick to recover, as the post-war “economic miracle” would see an unprecedented expansion of the Japanese economy, and would lead to the country becoming one of the first fully industrialized nations in East Asia. As living standards rose, the population of Japan would increase from 77 million in 1945, to over 127 million by the end of the century. However, growth would begin to slow in the late 1980s, as birth rates and migration rates fell, and Japan eventually grew to have one of the oldest populations in the world. The population would peak in 2008 at just over 128 million, but has consistently fallen each year since then, as the fertility rate of the country remains below replacement level (despite government initiatives to counter this) and the country's immigrant population remains relatively stable. The population of Japan is expected to continue its decline in the coming years, and in 2020, it is estimated that approximately 126 million people inhabit the island country.

Throughout the early modern period, the largest city in Italy was Naples. The middle ages saw many metropolitan areas along the Mediterranean grow to become the largest in Europe, as they developed into meeting ports for merchants travelling between the three continents. Italy, throughout this time, was not a unified country, but rather a collection of smaller states that had many cultural similarities, and political control of these cities regularly shifted over the given period. Across this time, the population of each city generally grew between each century, but a series of plague outbreaks in the 1600s devastated the populations of Italy's metropolitan areas, which can be observed here. Naples At the beginning of the 1500s, the Kingdom of Naples was taken under the control of the Spanish crown, where its capital grew to become the largest city in the newly-expanding Spanish Empire. Prosperity then grew in the 16th and 17th centuries, before the city's international importance declined in the 18th century. There is also a noticeable dip in Naples' population size between 1600 and 1700, due to an outbreak of plague in 1656 that almost halved the population. Today, Naples is just the third largest city in Italy, behind Rome and Milan. Rome Over 2,000 years ago, Rome became the first city in the world to have a population of more than one million people, and in 2021, it was Italy's largest city with a population of 2.8 million; however it did go through a period of great decline in the middle ages. After the Fall of the Western Roman Empire in 476CE, Rome's population dropped rapidly, below 100,000 inhabitants in 500CE. 1,000 years later, Rome was an important city in Europe as it was the seat of the Catholic Church, and it had a powerful banking sector, but its population was just 55,000 people as it did not have the same appeal for merchants or migrants held by the other port cities. A series of reforms by the Papacy in the late-1500s then saw significant improvements to infrastructure, housing, and sanitation, and living standards rose greatly. Over the following centuries, the Papacy consolidated its power in the center of the Italian peninsula, which brought stability to the region, and the city of Rome became a cultural center. Across this period, Rome's population grew almost three times larger, which was the highest level of growth of these cities.