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 1803, and reach eight billion in 2023, and will peak at almost 11 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 live 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 decade 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

In the past four centuries, the population of the United States has grown from a recorded 350 people around the Jamestown colony of Virginia in 1610, to an estimated 331 million people in 2020. The pre-colonization populations of the indigenous peoples of the Americas have proven difficult for historians to estimate, as their numbers decreased rapidly following the introduction of European diseases (namely smallpox, plague and influenza). Native Americans were also omitted from most censuses conducted before the twentieth century, therefore the actual population of what we now know as the United States would have been much higher than the official census data from before 1800, but it is unclear by how much. Population growth in the colonies throughout the eighteenth century has primarily been attributed to migration from the British Isles and the Transatlantic slave trade; however it is also difficult to assert the ethnic-makeup of the population in these years as accurate migration records were not kept until after the 1820s, at which point the importation of slaves had also been illegalized. Nineteenth century In the year 1800, it is estimated that the population across the present-day United States was around six million people, with the population in the 16 admitted states numbering at 5.3 million. Migration to the United States began to happen on a large scale in the mid-nineteenth century, with the first major waves coming from Ireland, Britain and Germany. In some aspects, this wave of mass migration balanced out the demographic impacts of the American Civil War, which was the deadliest war in U.S. history with approximately 620 thousand fatalities between 1861 and 1865. The civil war also resulted in the emancipation of around four million slaves across the south; many of whose ancestors would take part in the Great Northern Migration in the early 1900s, which saw around six million black Americans migrate away from the south in one of the largest demographic shifts in U.S. history. By the end of the nineteenth 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. Twentieth and twenty-first century The U.S. population has grown steadily throughout the past 120 years, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. In the past century, the U.S. established itself as a global superpower, with the world's largest economy (by nominal GDP) and most powerful military. Involvement in foreign wars has resulted in over 620,000 further U.S. fatalities since the Civil War, and migration fell drastically during the World Wars and Great Depression; however the population continuously grew in these years as the total fertility rate remained above two births per woman, and life expectancy increased (except during the Spanish Flu pandemic of 1918).

Since the Second World War, Latin America has replaced Europe as the most common point of origin for migrants, with Hispanic populations growing rapidly across the south and border states. Because of this, the proportion of non-Hispanic whites, which has been the most dominant ethnicity in the U.S. since records began, has dropped more rapidly in recent decades. Ethnic minorities also have a much higher birth rate than non-Hispanic whites, further contributing to this decline, and the share of non-Hispanic whites is expected to fall below fifty percent of the U.S. population by the mid-2000s. In 2020, the United States has the third-largest population in the world (after China and India), and the population is expected to reach four hundred million in the 2050s.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

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

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.

Historical chart and dataset showing total population for Russia by year from 1950 to 2025.

Climate variations on seasonal-to-decadal (S2D) timescales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales an invaluable tool for policymakers and stakeholders. Such variations modulate the likelihood and intensity of extreme weather events including, tropical cyclones (TCs), heat waves, winter storms, atmospheric rivers (ARs), and floods, which have all been associated with (1) increases in human morbidity and mortality rates; (2) severe impacts on agriculture, energy use, and industrial activity; and (3) economic costs in the billions of dollars. Changes in prevailing climate patterns are also responsible for prolonged droughts, which can have profoundly negative effects on large segments of the world population. Enhancing our foreknowledge of climate variability on S2D time scales and understanding its influence on extreme weather events could help mitigate negative impacts on human and biological populations, making climate predictions an exceptionally important climate and social science frontier. Over the past six years, our research team consisting of scientists at Texas A and M University (TAMU) and the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR) has made major breakthroughs in advancing high-resolution global climate modeling and prediction. We have completed an unprecedented 10-member ensemble of Community Earth System Model (CESM) historical and future climate simulations at a TC-permitting and ocean-eddy-rich resolution (hereafter simply referred to as CESM-HR). This CESM-HR ensemble was completed as part of our NSF-funded project entitled "Understanding the Role of MESoscale Atmosphere-Ocean Interactions in Seasonal-to-Decadal CLImate Prediction (MESACLIP)". This ensemble is particularly timely, following the April 2023 report entitled "Extreme Weather Risk in a Changing Climate: Enhancing Prediction and Protecting Communities" from the U.S....

Climate variations on seasonal-to-decadal (S2D) timescales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales an invaluable tool for policymakers and stakeholders. Such variations modulate the likelihood and intensity of extreme weather events including, tropical cyclones (TCs), heat waves, winter storms, atmospheric rivers (ARs), and floods, which have all been associated with (1) increases in human morbidity and mortality rates; (2) severe impacts on agriculture, energy use, and industrial activity; and (3) economic costs in the billions of dollars. Changes in prevailing climate patterns are also responsible for prolonged droughts, which can have profoundly negative effects on large segments of the world population. Enhancing our foreknowledge of climate variability on S2D time scales and understanding its influence on extreme weather events could help mitigate negative impacts on human and biological populations, making climate predictions an exceptionally important climate and social science frontier.

Over the past six years, our research team consisting of scientists at Texas A&M University (TAMU) and the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR) has made major breakthroughs in advancing high-resolution global climate modeling and prediction. We have completed an unprecedented 10-member ensemble of Community Earth System Model (CESM) historical and future climate simulations at a tropical cyclone-permitting and ocean-eddy-rich resolution (hereafter simply referred to as CESM-HR). This CESM-HR ensemble was completed as part of our NSF-funded project entitled "Understanding the Role of MESoscale Atmosphere-Ocean Interactions in Seasonal-to-Decadal CLImate Prediction (MESACLIP)". This ensemble is particularly timely, following the April 2023 report entitled "Extreme Weather Risk in a Changing Climate: Enhancing Prediction and Protecting Communities" from the U.S. President's Council of Advisors on Science and Technology (PCAST). Indeed, this report made several recommendations on how climate science can support the provision of information about future risks from extreme weather and highlight the urgent need for high-resolution simulations to improve predictions of extreme weather events and guide risk management strategies. More specifically, the report recognized that high-resolution simulations, in the range of 10 to 25 km horizontal resolution, would capture extreme events more accurately than typical low-resolution (approximately 100 km) climate projections, and it goes on to recommend "a focused federal effort to provide estimates of the risk that a weather event of a given severity will occur in any location and year between now and mid-century". Our 10-member CESM-HR ensemble is able to meet some of the key aspects of this PCAST report.

The CESM-HR configuration is based on an earlier CESM version, CESM1.3, with many additional modifications and improvements. CESM-HR uses a 0.25 degree grid in the atmosphere and land components and a 0.1 degree grid in the ocean and sea-ice components. The primary reason for using an older model version, instead of the latest CESM2, is that CESM2 does not support a high-resolution version per the decision by the CESM Scientific Steering Committee. The component models within CESM1.3 are the Community Atmosphere Model version 5 (CAM5; Neale et al., 2012), the Parallel Ocean Program version 2 (POP2; Danabasoglu et al., 2012; Smith et al., 2010), the Community Ice Code version 4 (CICE4; Hunke & Lipscomb, 2008), and the Community Land Model version 4 (CLM4; Lawrence et al., 2011).

The CESM-HR ensemble experimental design follows a similar approach as the CESM LENS1 large ensemble. We started with a 500-year preindustrial control (PI-CTRL) simulation forced by a perpetual climate forcing that corresponds to the year 1850 conditions. The first ensemble member is branched at year 250 of the PI-CTRL simulation and then integrated forward from year 1850 to 2100 (Figure 1 [https://rda.ucar.edu/OS/web/datasets/d651009/docs/Figure1_RDA_d651009.png]). Ensemble members 2-10 are subsequently started from the year 1920 of ensemble member 1 and integrated forward to 2100 (Figure 1 [https://rda.ucar.edu/OS/web/datasets/d651009/docs/Figure1_RDA_d651009.png]). Spread in the ensemble is generated by applying round-off level perturbations in the atmospheric potential temperature initial conditions for members 2-10. All 10 members use the same specified external climate forcing. Following the CMIP5 protocol for the Coupled Model Intercomparison Project phase 5 (CMIP5) experiments, historical forcing is used from 1920 to 2005 followed by the representative concentration pathway 8.5 (RCP 8.5) forcing from 2006 to 2100. RCP 8.5 is a high-emissions scenario and is frequently referred to as the "business as usual" scenario. It refers to the concentration of carbon that delivers global warming at an average of 8.5 W/m^2 across the planet by 2100. All 10 members produce a warming of approximately 4.5K at the end of 2100 in response to the applied historical and RCP 8.5 external forcing (Figure 1 [https://rda.ucar.edu/OS/web/datasets/d651009/docs/Figure1_RDA_d651009.png]). The warming produced by CESM-HR is consistent with the warming from the standard low-resolution (approximately 1 degree) version of the model. The rate of warming simulated by CESM-HR over the observed period agrees very well with the observed rate of warming derived from the Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (Figure 1 [https://rda.ucar.edu/OS/web/datasets/d651009/docs/Figure1_RDA_d651009.png]).

Citation: The two papers linked below are the most appropriate references for the CESM-HR ensemble. To cite the dataset, use Chang et al. (2025). We ask that you also cite the dataset itself using the reference Castruccio et al [https://rda.ucar.edu/datasets/d651009/citation/]. (2024) in any documents or publications using these data. Chang et al. (2020) describes the initial CESM-HR simulations, including the 500-year pre- industrial control simulation and the first 250-year historical and future climate simulation from 1850 to 2100. We would also appreciate receiving a copy of the relevant publications. This will help us to justify keeping the data freely available online in the future. Thank you!

The historical seal populations at King George Island, Antarctica, for the past 1,500 years, have been estimated from the seal-hair abundance, bio-element concentrations, total organic carbon (TOC) and total nitrogen (TN) in one terrestrial sediment sequence influenced by seal excrement. Prior to human interference, the seal populations exhibited dramatic fluctuations with two peaks during 750–500 and 1400–1100 years before present (yr B.P.) and two troughs during 1100–750 and 500– 200 yr B.P. A tentative comparison of the seal populations and historical climates in the Antarctic Peninsula region suggests that the seal populations may be linked to climate- related factors such as sea-ice coverage and atmospheric temperature

US Census American Community Survey (ACS) 2020, 5-year estimates of the key social characteristics of Cities/Places geographic level in Orange County, California. The data contains 500 fields for the variable groups S01: Households by type (universe: total households, table X11, 17 fields); S02: Relationship (universe: population in households, table X9, 19 fields); S03: Marital status (universe: population 15 years and over, table X12, 13 fields); S04: Fertility (universe: women 15-50 years who had birth in the past 12 months, table X13, 11 fields); S05: Grandparents (universe: grandparents living or responsible for own grandchildren under 18 years, table X10, 18 fields); S06: School enrollment (universe: population 3 years old and over enrolled in school, table X14, 17 fields); S07: Educational attainment (universe: population 25 years and over, table X15, 25 fields); S08: Veteran status (universe: civilian population 18 years and over, table X21, 2 fields); S09: Disability status and type by sex and age (universe: total civilian non-institutionalized population, table X18, 77 fields); S10: Disability status by age and health insurance coverage (universe: civilian non-institutionalized population, table X18, 16 fields); S11: Residence 1 year ago (universe: population 1 year and over, table X7, 6 fields); S12: Place of birth (universe: total population, table X5, 27 fields); S13: Citizenship status by nativity in the US (universe: total population, table X5, 6 fields); S14: Year of entry (universe: population born outside the US, table X5, 21 fields); S15: World region of birth of foreign born population (universe: foreign born population, excluding population born at sea, table X5, 25 fields); S16: Language spoken in households (universe: total households, table X16, 6 fields); S17: Language spoken at home (universe: population 5 years and over, table X16, 67 fields); S18: Ancestry (universe: total population reporting ancestry, table X4, 114 fields), and; S19: Computers and internet use (universe: total population in households and total households, table X28, 13 fields). The US Census geodemographic data are based on the 2020 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project GitHub page (https://github.com/ktalexan/OCACS-Geodemographics).

US Census American Community Survey (ACS) 2021, 5-year estimates of the key social characteristics of Census Tract geographic level in Orange County, California. The data contains 500 fields for the variable groups S01: Households by type (universe: total households, table X11, 17 fields); S02: Relationship (universe: population in households, table X9, 19 fields); S03: Marital status (universe: population 15 years and over, table X12, 13 fields); S04: Fertility (universe: women 15-50 years who had birth in the past 12 months, table X13, 11 fields); S05: Grandparents (universe: grandparents living or responsible for own grandchildren under 18 years, table X10, 18 fields); S06: School enrollment (universe: population 3 years old and over enrolled in school, table X14, 17 fields); S07: Educational attainment (universe: population 25 years and over, table X15, 25 fields); S08: Veteran status (universe: civilian population 18 years and over, table X21, 2 fields); S09: Disability status and type by sex and age (universe: total civilian non-institutionalized population, table X18, 77 fields); S10: Disability status by age and health insurance coverage (universe: civilian non-institutionalized population, table X18, 16 fields); S11: Residence 1 year ago (universe: population 1 year and over, table X7, 6 fields); S12: Place of birth (universe: total population, table X5, 27 fields); S13: Citizenship status by nativity in the US (universe: total population, table X5, 6 fields); S14: Year of entry (universe: population born outside the US, table X5, 21 fields); S15: World region of birth of foreign born population (universe: foreign born population, excluding population born at sea, table X5, 25 fields); S16: Language spoken in households (universe: total households, table X16, 6 fields); S17: Language spoken at home (universe: population 5 years and over, table X16, 67 fields); S18: Ancestry (universe: total population reporting ancestry, table X4, 114 fields), and; S19: Computers and internet use (universe: total population in households and total households, table X28, 13 fields). The US Census geodemographic data are based on the 2021 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project GitHub page (https://github.com/ktalexan/OCACS-Geodemographics).

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...

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.

US Census American Community Survey (ACS) 2017, 5-year estimates of the key social characteristics for Orange County, California. The data contains 500 fields for the variable groups S01: Households by type (universe: total households, table X11, 17 fields); S02: Relationship (universe: population in households, table X9, 19 fields); S03: Marital status (universe: population 15 years and over, table X12, 13 fields); S04: Fertility (universe: women 15-50 years who had birth in the past 12 months, table X13, 11 fields); S05: Grandparents (universe: grandparents living or responsible for own grandchildren under 18 years, table X10, 18 fields); S06: School enrollment (universe: population 3 years old and over enrolled in school, table X14, 17 fields); S07: Educational attainment (universe: population 25 years and over, table X15, 25 fields); S08: Veteran status (universe: civilian population 18 years and over, table X21, 2 fields); S09: Disability status and type by sex and age (universe: total civilian non-institutionalized population, table X18, 77 fields); S10: Disability status by age and health insurance coverage (universe: civilian non-institutionalized population, table X18, 16 fields); S11: Residence 1 year ago (universe: population 1 year and over, table X7, 6 fields); S12: Place of birth (universe: total population, table X5, 27 fields); S13: Citizenship status by nativity in the US (universe: total population, table X5, 6 fields); S14: Year of entry (universe: population born outside the US, table X5, 21 fields); S15: World region of birth of foreign born population (universe: foreign born population, excluding population born at sea, table X5, 25 fields); S16: Language spoken in households (universe: total households, table X16, 6 fields); S17: Language spoken at home (universe: population 5 years and over, table X16, 67 fields); S18: Ancestry (universe: total population reporting ancestry, table X4, 114 fields), and; S19: Computers and internet use (universe: total population in households and total households, table X28, 13 fields). The US Census geodemographic data are based on the 2017 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

US Census American Community Survey (ACS) 2020, 5-year estimates of the key social characteristics of ZIP Code Tabulation Areas geographic level in Orange County, California. The data contains 500 fields for the variable groups S01: Households by type (universe: total households, table X11, 17 fields); S02: Relationship (universe: population in households, table X9, 19 fields); S03: Marital status (universe: population 15 years and over, table X12, 13 fields); S04: Fertility (universe: women 15-50 years who had birth in the past 12 months, table X13, 11 fields); S05: Grandparents (universe: grandparents living or responsible for own grandchildren under 18 years, table X10, 18 fields); S06: School enrollment (universe: population 3 years old and over enrolled in school, table X14, 17 fields); S07: Educational attainment (universe: population 25 years and over, table X15, 25 fields); S08: Veteran status (universe: civilian population 18 years and over, table X21, 2 fields); S09: Disability status and type by sex and age (universe: total civilian non-institutionalized population, table X18, 77 fields); S10: Disability status by age and health insurance coverage (universe: civilian non-institutionalized population, table X18, 16 fields); S11: Residence 1 year ago (universe: population 1 year and over, table X7, 6 fields); S12: Place of birth (universe: total population, table X5, 27 fields); S13: Citizenship status by nativity in the US (universe: total population, table X5, 6 fields); S14: Year of entry (universe: population born outside the US, table X5, 21 fields); S15: World region of birth of foreign born population (universe: foreign born population, excluding population born at sea, table X5, 25 fields); S16: Language spoken in households (universe: total households, table X16, 6 fields); S17: Language spoken at home (universe: population 5 years and over, table X16, 67 fields); S18: Ancestry (universe: total population reporting ancestry, table X4, 114 fields), and; S19: Computers and internet use (universe: total population in households and total households, table X28, 13 fields). The US Census geodemographic data are based on the 2020 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project GitHub page (https://github.com/ktalexan/OCACS-Geodemographics).

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.

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 ...