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.

About this dataset

Context

The dataset tabulates the Day County population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Day County across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Day County was 5,451, a 0.44% decrease year-by-year from 2022. Previously, in 2022, Day County population was 5,475, an increase of 0.90% compared to a population of 5,426 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Day County decreased by 756. In this period, the peak population was 6,207 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Day County is shown in this column.
• Year on Year Change: This column displays the change in Day County population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Day County Population by Year. You can refer the same here

In 2023, about 17.7 percent of the American population was 65 years old or over; an increase from the last few years and a figure which is expected to reach 22.8 percent by 2050. This is a significant increase from 1950, when only eight percent of the population was 65 or over. A rapidly aging population In recent years, the aging population of the United States has come into focus as a cause for concern, as the nature of work and retirement is expected to change to keep up. If a population is expected to live longer than the generations before, the economy will have to change as well to fulfill the needs of the citizens. In addition, the birth rate in the U.S. has been falling over the last 20 years, meaning that there are not as many young people to replace the individuals leaving the workforce. The future population It’s not only the American population that is aging -- the global population is, too. By 2025, the median age of the global workforce is expected to be 39.6 years, up from 33.8 years in 1990. Additionally, it is projected that there will be over three million people worldwide aged 100 years and over by 2050.

About this dataset

Context

The dataset tabulates the Day town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Day town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Day town was 820, a 0.61% decrease year-by-year from 2022. Previously, in 2022, Day town population was 825, a decline of 0.36% compared to a population of 828 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Day town decreased by 84. In this period, the peak population was 923 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Day town is shown in this column.
• Year on Year Change: This column displays the change in Day town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Day town Population by Year. You can refer the same here

Brazil and the United States are the two most populous countries in the Americas today. In 1500, the year that Pedro Álvares Cabral made landfall in present-day Brazil and claimed it for the Portuguese crown, it is estimated that there were roughly one million people living in the region. Some estimates for the present-day United States give a population of two million in the year 1500, although estimates vary greatly. By 1820, the population of the U.S. was still roughly double that of Brazil, but rapid growth in the 19th century would see it grow 4.5 times larger by 1890, before the difference shrunk during the 20th century. In 2024, the U.S. has a population over 340 million people, making it the third most populous country in the world, while Brazil has a population of almost 218 million and is the sixth most populous. Looking to the future, population growth is expected to be lower in Brazil than in the U.S. in the coming decades, as Brazil's fertility rates are already lower, and migration rates into the United States will be much higher. Historical development The indigenous peoples of present-day Brazil and the U.S. were highly susceptible to diseases brought from the Old World; combined with mass displacement and violence, their population growth rates were generally low, therefore migration from Europe and the import of enslaved Africans drove population growth in both regions. In absolute numbers, more Europeans migrated to North America than Brazil, whereas more slaves were transported to Brazil than the U.S., but European migration to Brazil increased significantly in the early 1900s. The U.S. also underwent its demographic transition much earlier than in Brazil, therefore its peak period of population growth was almost a century earlier than Brazil. Impact of ethnicity The demographics of these countries are often compared, not only because of their size, location, and historical development, but also due to the role played by ethnicity. In the mid-1800s, these countries had the largest slave societies in the world, but a major difference between the two was the attitude towards interracial procreation. In Brazil, relationships between people of different ethnic groups were more common and less stigmatized than in the U.S., where anti-miscegenation laws prohibited interracial relationships in many states until the 1960s. Racial classification was also more rigid in the U.S., and those of mixed ethnicity were usually classified by their non-white background. In contrast, as Brazil has a higher degree of mixing between those of ethnic African, American, and European heritage, classification is less obvious, and factors such as physical appearance or societal background were often used to determine racial standing. For most of the 20th century, Brazil's government promoted the idea that race was a non-issue and that Brazil was racially harmonious, but most now acknowledge that this actually ignored inequality and hindered progress. Racial inequality has been a prevalent problem in both countries since their founding, and today, whites generally fare better in terms of education, income, political representation, and even life expectancy. Despite this adversity, significant progress has been made in recent decades, as public awareness of inequality has increased, and authorities in both countries have made steps to tackle disparities in areas such as education, housing, and employment.

About this dataset

Context

The dataset tabulates the data for the Day, New York population pyramid, which represents the Day town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Day, New York, is 11.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Day, New York, is 64.3.
• Total dependency ratio for Day, New York is 75.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Day, New York is 1.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Day town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Day town for the selected age group is shown in the following column.
• Population (Female): The female population in the Day town for the selected age group is shown in the following column.
• Total Population: The total population of the Day town for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Day town Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the John Day population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of John Day across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of John Day was 1,659, a 0.24% decrease year-by-year from 2022. Previously, in 2022, John Day population was 1,663, a decline of 0.78% compared to a population of 1,676 in 2021. Over the last 20 plus years, between 2000 and 2023, population of John Day decreased by 176. In this period, the peak population was 1,835 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the John Day is shown in this column.
• Year on Year Change: This column displays the change in John Day population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for John Day Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Day town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Day town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Day town was 1,047, a 0% decrease year-by-year from 2022. Previously, in 2022, Day town population was 1,047, a decline of 0.57% compared to a population of 1,053 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Day town increased by 29. In this period, the peak population was 1,149 in the year 2017. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Day town is shown in this column.
• Year on Year Change: This column displays the change in Day town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Day town Population by Year. You can refer the same here

The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

This statistic shows the 20 countries with the highest population growth rate in 2024. In SouthSudan, the population grew by about 4.65 percent compared to the previous year, making it the country with the highest population growth rate in 2024. The global population Today, the global population amounts to around 7 billion people, i.e. the total number of living humans on Earth. More than half of the global population is living in Asia, while one quarter of the global population resides in Africa. High fertility rates in Africa and Asia, a decline in the mortality rates and an increase in the median age of the world population all contribute to the global population growth. Statistics show that the global population is subject to increase by almost 4 billion people by 2100. The global population growth is a direct result of people living longer because of better living conditions and a healthier nutrition. Three out of five of the most populous countries in the world are located in Asia. Ultimately the highest population growth rate is also found there, the country with the highest population growth rate is Syria. This could be due to a low infant mortality rate in Syria or the ever -expanding tourism sector.

Mobile media usage in the United States is set to increase to **** hours and ** minutes per day between in 2022. In 2020, the adult U.S. population spent an average of ** daily minutes on social networking. Mobile social network usage is projected to increase to ** minutes per day in 2023.

How many people are on social media? Social media usage is one of the most popular online activities and in 2021, ** percent of the population in the United States had a social networking profile, representing a *** percent increase from the ** percent usage reach in the previous year. This equals approximately 223 million U.S. social media users as of 2020. Global social media accessAccording to estimates, the number of worldwide social media users reached *** billion in January 2021. The overall most popular social network based on active users is the American market leader Facebook. In January 2021, Facebook had some **** billion accounts, followed by YouTube and WhatsApp with roughly *** billion and *** billion users respectively. The regions with the highest penetration of social media users are Western and Northern Europe. Social media audiences in the United StatesAlthough knowing how many people use social media is a powerful indicator of the tremendous influence such websites and apps have in our day to day life, how people are using them and who these users are is also telling. A report on social media usage released in 2019 shows that among Americans, younger online audiences were more likely to use social networks than older generations. Social media users in the United States use different social networks for a wide range of purposes. In a February 2019 survey, Instagram was the top social network for viewing photos whereas Facebook was more popular for sharing content.

In 2023, it was found that 22.4 percent of men in the United States participated in sports, exercise, and recreational activities daily, compared to only 19.9 percent of women. These statistics highlight a notable difference in the daily engagement of different genders in sporting activities. Other factors influencing this participation include socioeconomic status, age, disability, ethnicity, geography, personal interests, and societal expectations. These barriers can prevent individuals from having equal access to, and opportunities for, sport participation. What role does gender play in sports participation? Historically, many sports have been segregated by gender, with men and women participating in separate leagues and competitions. This segregation has led to a lack of opportunities for women and girls to participate in sports at the same level as men and boys. Additionally, societal attitudes and stereotypes about gender can discourage women and girls from participating in sports or limit their access to resources and support for their athletic pursuits. This often results in fewer women and girls participating in sports and a lack of representation of women and girls in leadership roles within the sports industry. However, in recent years, there has been an increased focus on promoting gender equality in sports and providing equal opportunities for men and women to participate in sports. This includes initiatives to increase funding and support for women's sports, as well as efforts to challenge gender stereotypes and discrimination in the athletic world. Impact of the COVID-19 pandemic on sports participation The COVID-19 pandemic led to many people spending more time at home due to lockdowns, remote work, and school closures. This resulted in many people having more time to engage in sports and other physical activities, as seen in the share of the U.S. population engaged in sports and exercise peaking in 2020. With gyms and sports facilities closed or with limited access, many people turned to home-based workouts and other activities. This included activities such as running, cycling, and strength training that could all be done at home with minimal equipment. Online classes and streaming services also saw an increase in usage during the pandemic, providing people with access to a wide range of workout options and fitness programs.

According to the forecast, Africa's total population would reach nearly 2.5 billion by 2050. In 2025, the continent had around 1.55 billion inhabitants, with Nigeria, Ethiopia, and Egypt as the most populous countries. In the coming years, Africa will experience significant population growth and will close the gap significantly with the Asian population by 2100. Rapid population growth In Africa, the annual growth rate of the population followed an overall increasing trend up to 2013, reaching nearly 2.63 percent. This was followed by a drop to 2.32 percent by 2023. Although population growth was slowing down, it was still growing faster than in all other regions. The reasons behind this rapid growth are various. One factor is the high fertility rate registered in African countries. In 2023, a woman in Somalia, Chad, Niger, the Democratic Republic of Congo, and the Central African Republic had an average of over six children in her reproductive years, the highest rate on the continent. High fertility resulted in a large young population and partly compensated for the high mortality rate in Africa, leading to fast-paced population growth. High poverty levels Africa’s population is concerned with widespread poverty. In 2025, over 438 million people on the continent are extremely poor and live with less than 2.15 U.S. dollars per day. Globally, Africa is the continent hosting the highest poverty rate. In 2025, the countries of Nigeria and the Democratic Republic of the Congo account for over 23 percent of the world's population living in extreme poverty. Nevertheless, the share of the population living in poverty in Africa is forecast to decrease in the coming years.

Identifying the specific environmental features and associated density-dependent processes that limit population growth is central to both ecology and conservation. Comparative assessments of sympatric species allow for inference into how ecologically similar species differentially respond to their shared environment, which can be used to inform community-level conservation strategies. Comparative assessments can nevertheless be complicated by interactions and feedback loops among the species in question. We developed an integrated population model based on sixty-one years of ecological data describing the demographic histories of Canvasbacks (Aythya valisineria) and Redheads (Aythya americana), two species of migratory diving ducks that utilize similar breeding habitats and affect each other’s demography through interspecific nest parasitism. We combined this model with a transient life table response experiment to determine the extent that demographic rates, and their contributions to population growth, were similar between these two species. We found that demographic rates and, to a lesser extent, their contributions to population growth covaried between Canvasbacks and Redheads, but the trajectories of population abundances widely diverged between the two species during the end of the 20th century due to inherent differences between the species life-histories and sensitivities to both environmental variation and harvest pressure. We found that annual survival of both species increased during years of restrictive harvest regulations; however, recent harvest pressure on female Canvasbacks may be contributing to population declines. Despite periodic, and often dramatic, increases in breeding abundance during wet years, the number of breeding Canvasbacks declined by 13% whereas the number of breeding Redheads has increased by 37% since 1961. Reductions in harvest pressure and improvements in submerged aquatic vegetation throughout the wintering grounds have mediated the extent to which populations of both species contracted during dry years in the Prairie Pothole Region. However, continued degradation of breeding habitats through climate-related shifts in wetland hydrology and agricultural conversion of surrounding grassland habitats may have exceeded the capacity for demographic compensation during the non-breeding season. Methods DATA COLLECTION We combined a series of long-term data sets into a single integrated population model that provided insights into how variation in seasonal survival (band releases and recoveries) and offspring production (harvest age-ratios) contributed to fluctuations in population growth (breeding survey, harvest estimates) for Canvasbacks and Redheads from 1961–2021. Banding Data – Information regarding the banding and subsequent harvest of ducks was acquired from the GameBirds Database CD (Bird Banding Lab, USGS Patuxent Wildlife Research Center, Laurel MD, USA, version August 2022). Male and female Canvasbacks and Redheads were captured following breeding but prior to the hunting season (Pre-Hunting) as ducklings (Local) or hatch year (HY; fledged juvenile) individuals as well as after hatch year (AHY; adult) individuals or following the hunting season (Post-Hunting) as an undifferentiated mixture of second year (SY) and after second year (ASY) individuals captured and released across North America from 1961–2022. We limited the pre-harvest banding data for both species to include all individuals banded and released alive in areas within the Canadian provinces of Alberta, Manitoba, Saskatchewan, as well as the states of Minnesota, Montana, North Dakota, and South Dakota within the USA (Fig. 1). For the pre-hunting banding group, we retained individuals captured between 1961–2021 during the late summer (Jul 15th – Sep 15th) with a known sex (M or F) and age-class (local, HY or AHY) that were released without any additional markers considered to meaningfully affect survival of an individual (e.g., nasal saddles or dual banding were permissible but telemetered individuals were excluded; Lameris & Kleyheeg, 2017). For post-hunting banding, we limited the spatial boundary of banding efforts to only consider individuals released from the Atlantic, Central, or Mississippi Flyways (Fig. 1). We followed the same data selection procedures, but limited releases to occur between Jan. 1st – March 15th from 1962–2022. Because too few banders differentiated SY from ASY at time of banding, we treated all post-hunting samples as AHY adults. Individuals banded during this period that were reported to be harvested during the winter they were originally banded were censored from the analysis, as the underlying model assumption was that this cohort of individuals had already survived the current hunting season. For both seasonal banding efforts, we only included recoveries of hunter-shot individuals harvested between September and February in which a known year-of-death could be ascertained. In addition to self-reported recoveries (i.e., reported by the hunter), we included hunter-harvested individuals that were instead reported by federal, state, or provincial entities (e.g., outcomes of hunter check stations or other forms of solicitation). We limited the dataset to only include recoveries of hunter-harvested individuals killed within 15 years of initial banding, which represented > 99% of pre-hunting and post-hunting recoveries. This cut-off was arbitrarily selected but did not meaningfully bias parameter estimation while vastly improving computational efficiency by bypassing the estimation of hundreds of zero-equivalent cell probabilities (personal communication S. Bonner). Harvest Intensity – We used the average number of Canvasbacks or Redheads allowed to be harvested per day (i.e., bag limit; (Appendix S1: Tables S1a-b) across the U.S. portions of the Atlantic, Mississippi, Central, and Pacific flyways during each year of the study as an index of harvest regulatory pressure. Annual harvest restrictions were acquired from the published literature (Péron et al., 2012), the annual release of the Late-Season Migratory Bird Hunting Regulations (e.g., USFWS 2022), and direct requests to the U.S. Fish and Wildlife Service. For these species, liberal harvest regulations were bag limits of two (Canvasbacks) and two to four (Redheads) allowable harvest per day, whereas conservative harvest regulations were either a bag limit of one individual per day or total closure. Harvest Composition – Data describing the age and sex structure of the harvested Canvasback and Redhead populations were derived from the annual Parts Collection surveys conducted by the U.S. Fish and Wildlife Service (USFWS) where a subset of hunters submit a wing from every duck they harvested (Pearse et al. 2014). These data were acquired through a direct request to the U.S. Fish and Wildlife Service. Additionally, estimates of the total number of Canvasbacks and Redheads harvested in the United States and Canada were derived from the Harvest Information Program (Steeg et al., 2002) and Canadian National Harvest Survey (Smith et al., 2022), respectively. Breeding Duck and Pond Densities – The relative number of breeding Canvasbacks and Redheads, as well as the relative amount of their breeding habitat (i.e., flooded ponds) within the Prairies were calculated using count data from the USFWS Waterfowl Breeding Population and Habitat Survey (hereafter BPOP; Smith, 1995), which has conducted an annual survey of breeding waterfowl and their habitats throughout the core part of these species’ breeding ranges (i.e., central Canada and the north-central United States) during the spring from 1961 through 2022 (U.S. Fish and Wildlife Service, 2022). However, BPOP surveys did not occur during 2020 and 2021. For the purposes of this study, we limited the spatial extent of BPOP survey to only include transects flown within Alberta, Manitoba, Saskatchewan, Montana, North Dakota, and South Dakota. Agriculture Development – The amounts of active cropland in the Prairies during each year of the study were estimated from Canada and United States Agriculture Census data (see Buderman et al., 2020). Annual estimates of active cropland acreages were summarized to represent an index of agricultural development during 1961–2021. Although agricultural development is predicted to have greater impact on upland-nesting dabbling ducks (Duncan and Devries 2018), it also impacts the wetland habitats in which Canvasbacks and Redheads forage and nest, as well as the predator communities that can access overwater nesting pochards (Sargeant et al. 1993, Bartzen et al. 2010). Winter Habitat – Winter habitat conditions were assumed to be related to submerged aquatic vegetation (SAV) within the Chesapeake for Canvasbacks and environmental salinity (TDS; total dissolved solids) in the Laguna Madre for Redheads. Although Redheads likely respond to variation in SAV, time series data describing SAV were not available for the Laguna Madre. Therefore, we assumed that annual fluctuations in salinity were an informative proxy of both SAV conditions and osmotic constraints (Quammen and Onuf 1993, Moore 2009), which in turn was representative of winter habitat conditions that simultaneously influenced Redhead food availability and harvest risk (Ballard et al. 2021).. Climate Data – We used the average Pacific/North American (PNA; Leathers et al., 1991) teleconnection pattern from April–July as an index of drought severity or environmental stress during the breeding season throughout the Prairies, and average sea-surface temperatures (SST) from September–March in the Chesapeake and Laguna Madre as an index of winter severity for Canvasbacks and Redheads, respectively (see Data Availability statement).

About this dataset

Context

The dataset tabulates the data for the John Day, OR population pyramid, which represents the John Day population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for John Day, OR, is 21.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for John Day, OR, is 28.1.
• Total dependency ratio for John Day, OR is 49.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for John Day, OR is 3.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the John Day population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the John Day for the selected age group is shown in the following column.
• Population (Female): The female population in the John Day for the selected age group is shown in the following column.
• Total Population: The total population of the John Day for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for John Day Population by Age. You can refer the same here

When comparing residential water use as GPCD (gallons per capita per day) in the U.S., western states tend to have higher rates of water usage based on 2015 USGS data. For instance, the U.S. average GPCD was 83 and the state of Arizona was substantially higher at 146 GPCD. While USGS provides state and county water use data, this data layer records GPCD for Sample Cities in the Colorado River Basin. Cities were selected based on attributes from US Census Bureau population statistics for census-designated places. Sample City status determined by: 1) a population base under 300,000, and 2) average population growth rate over 8 percent between 2008-2015.

After decades of turmoil and instability, a period of calm and progress evolved in Guinea-Bissau in 2009. A military coup in April 2012 interrupted it. A fresh start is needed to alter the dynamics that kept Guinea-Bissau poor. In 2013, Gross National Income per capita was US$590. Average economic growth barely kept pace with population growth. In 2010, poverty at the national poverty line of US$2 a day was 70 percent; extreme poverty at US$1 a day was 33 percent. These numbers have increased from their 2002 levels and they are estimated to have increased further since 2010. It is time to make a fresh start and turn the page on anemic growth and poverty. Guinea-Bissau’s elections of May and June 2014 are described by many observers as the freest and fairest in the country’s history. Voter registration and turnout were at record-levels. The conditions for progress and stability are favorable. Guinea-Bissau is a rural economy, almost entirely dependent on a single cash crop: cashew. It is the main source of income for most of the country’s poor. Cashew nuts are Guinea-Bissau’s main export, accounting for 85 to 90 percent of the country’s total exports. The balance of payments is dominated by cashew, on the export side, and food and fuel, among imports. The economy is open, with exports and imports by land and sea amounting to more than 70 percent of GDP. Shocks to cashew, rice and oil prices have a considerable effect on the current account balance. Official Development Assistance (ODA) makes a critical contribution to supporting the state budget. In 2011, Guinea-Bissau ranked 20th among the world’s most aid dependent countries. Recently, policy mistakes aggravated an already dire situation. However, the 2014 cashew campaign was been better than the 2013 campaign, and the prospects for a pick-up in growth have improved.

Climate often drives ungulate population dynamics, and as climates change, some areas may become unsuitable for species persistence. Unraveling the relationships between climate and population dynamics, and projecting them across time, advances ecological understanding that informs and steers sustainable conservation for species. Using pronghorn (Antilocapra americana) as an ecological model, we used a Bayesian approach to analyze long-term population, precipitation, and temperature data from 18 subpopulations in the southwestern United States. We determined which long-term (12 and 24 months) or short-term (gestation trimester and lactation period) climatic conditions best predicted annual rate of population growth (λ). We used these predictions to project population trends through 2090. Projections incorporated downscaled climatic data matched to pronghorn range for each population, given a high and a lower atmospheric CO2 concentration scenario. Since the 1990s, 15 of the pronghorn subpopulations declined in abundance. Sixteen subpopulations demonstrated a significant relationship between precipitation and λ, and in 13 of these, temperature was also significant. Precipitation predictors of λ were highly seasonal, with lactation being the most important period, followed by early and late gestation. The influence of temperature on λ was less seasonal than precipitation, and lacked a clear temporal pattern. The climatic projections indicated that all of these pronghorn subpopulations would experience increased temperatures, while the direction and magnitude of precipitation had high subpopulation-specific variation. Models predicted that nine subpopulations would be extirpated or approaching extirpation by 2090. Results were consistent across both atmospheric CO2 concentration scenarios, indicating robustness of trends irrespective of climatic severity. In the southwestern United States, the climate underpinning pronghorn subpopulations is shifting, making conditions increasingly inhospitable to pronghorn persistence. This realization informs and steers conservation and management decisions for pronghorn in North America, while exemplifying how similar research can aid ungulates inhabiting arid regions and confronting similar circumstances elsewhere. Long-term data from annual aerial surveys of pronghorn subpopulations in Utah, Arizona, New Mexico, and western Texas were used to calculate annual rates of population growth (λ). When subpopulation-specific harvest and translocation data were available, population estimates for calculating λ were adjusted according to the following equation: λt = Nt/(Nt-1 - h - r + a), where λt is population change from time t-1 to t, Nt and Nt-1 are population estimates from current and previous surveys, respectively, h is number of pronghorn harvested, and r and a are number of individuals removed from and released into the population, respectively, through translocations. Only population estimates from surveys conducted in consecutive years were used to calculate λ. If λ = 2, the associated surveys were removed from analyses because λ would be considered to be derived from unreliable or unstandardized population estimates, resulting in biologically unrealistic population growth rates. Monthly climate data (precipitation [mm/day] and mean temperature [degrees C]) were from 14 x 14 km cells from pronghorn range in each subpopulation in Utah, Arizona, New Mexico, and western Texas. Means across grids were calculated to obtain monthly values of precipitation and temperature. Two realistic future global climate scenarios were compared; a lower (Representative Concentrations Pathways 4.5) and a high (Representative Concentrations Pathways 8.5) atmospheric CO2 concentration scenario. Standardized precipitation index for 3-, 6-, 12-, and 24-month periods were calculated from all available monthly precipitation data using program SPI SL 6 (National Drought Mitigation Center 2014). Monthly mean temperature, total precipitation, and mean SPI (3-, 6-, and 12-month periods) were summarized by important periods in an adult female pronghorn's annual reproductive cycle relative to peak fawning (i.e., early, mid-, and late gestation [3 months each] and lactation [4 months]). Mean temperature and total precipitation were also calculated for 12 and 24 months preceding each population survey. Historic pronghorn population trends in relation to temperature and precipitation were assessed using integrated Bayesian population models. All models included a covariate for density effect (i.e., population in the previous year). Precipitation and temperature model comparison sets were run separately, and each model set included a null model (i.e., only density covariate, no climate covariates). These top individual precipitation and temperature covariates were then combined in models (i.e., one precipitation and temperature covariate per model), and these combined models were run including a term for the interaction between precipitation and temperature using the following equation: ln(λt) = Alpha + Beta1XN[t-1] + Beta2Xprec + Beta3Xtemp + Beta4Xprec*temp. Projected climate data for each pronghorn subpopulation was used to predict λt for each year to 2090. An integrated modeling approach was used, whereby the best performing model climatic predictors from historic population trends for each pronghorn subpopulation was embedded in that subpopulation pronghorn population projection model.

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