This dataset includes data on 25 transitions of a matrix demographic model of the invasive species Vincetoxicum nigrum (L.) Moench (black swallow-wort or black dog-strangling vine) and Vincetoxicum rossicum (Kleopow) Barb. (pale swallow-wort or dog-strangling vine) (Apocynaceae, subfamily Asclepiadoideae), two invasive perennial vines in the northeastern U.S.A. and southeastern Canada. The matrix model was developed for projecting population growth rates as a result of changes to lower-level vital rates from biological control although the model is generalizable to any control tactic. Transitions occurred among the five life stages of seeds, seedlings, vegetative juveniles (defined as being in at least their second season of growth), small flowering plants (having 1–2 stems), and large flowering plants (having 3 or more stems). Transition values were calculated using deterministic equations and data from 20 lower-level vital rates collected from 2009-2012 from two open field and two forest understory populations of V. rossicum (43°51’N, 76°17’W; 42°48'N, 76°40'W) and two open field populations of V. nigrum (41°46’N, 73°44’W; 41°18’N, 73°58’W) in New York State. Sites varied in plant densities, soil depth, and light levels (forest populations). Detailed descriptions of vital rate data collection may be found in: Milbrath et al. 2017. Northeastern Naturalist 24(1):37-53. Five replicate sets of transition data obtained from five separate spatial regions of a particular infestation were produced for each of the six populations. Note: Added new excel file of vital rate data on 12/7/2018. Resources in this dataset:Resource Title: Matrix model transition data for Vincetoxicum species. File Name: Matrix_model_transition_data.csvResource Description: This data set includes data on 25 transitions of a matrix demographic model of two invasive Vincetoxicum species from six field and forest populations in New York State.Resource Title: Variable definitions. File Name: Matrix_model_metadata.csvResource Description: Definitions of variables including equations for each transition and definitions of the lower-level vital rates in the equationsResource Title: Vital Rate definitions. File Name: Vital_Rate.csvResource Description: Vital Rate definitions of lower-level vital rates used in transition equations - to be substituted into the Data Dictionary for full definition of each transition equation.Resource Title: Data Dictionary. File Name: Matrix_Model_transition_data_DD.csvResource Description: See Vital Rate resource for definitions of lower-level vital rates used in transition equations where noted.Resource Title: Matrix model vital rate data for Vincetoxicum species. File Name: Matrix_model_vital rate_data.csvResource Description: This data set includes data on 20 lower-level vital rates used in the calculation of transitions of a matrix demographic model of two invasive Vincetoxicum species in New York State as well as definitions of the vital rates. (File added on 12/7/2018)Resource Software Recommended: Microsoft Excel,url: https://office.microsoft.com/excel/

In 2022, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth

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.

This data collection includes 'life story' interviews with Russian-speaking women from Russia, Ukraine, and Belarus who have married Chinese citizens and moved for their married lives to the People's Republic of China. Most of the recorded interviews were transcribed verbatim in Russian. Some of the non-recorded conversations are summarised in English. The topics covered in the interviews include the women's journeys to China, their experiences of family, social, and working lives, the challenges of legal, socio-cultural and emotional adaptation, and the questions of citizenship and immigration status for women and their children.

The growth of mega-cities and more generally rapid urbanization in China not only include hundreds of millions internal migrants, but an increasing number of foreign (including Taiwanese and returning ethnic Chinese) migrants as well. At present, foreign migrants fill relatively small and specific skills and knowledge gaps, but also include marriage migrants, traders, investors, retirees and unskilled workers. However as China's population growth levels off, population ageing sets in. China's working age population is set to decline, slowly at first but increasingly rapidly, especially roughly after 2025. Moreover, the population's sex imbalance will become ever more pronounced and China will face an increasing shortage of marriageable and working age people. Although international migration is set to make an important contribution to these increasing demographic and labour market shortages in China, little research has as yet been done. Our project will provide estimates and projections of the role of international and internal migration on population dynamics in China. The central focus of our project is on the impact of the second demographic transition in China, including family changes, ageing, migration and regional population changes. We will collect vital data on the interaction between labour markets and population dynamics, the consequences of migration, integration policies in China, EU-China mobility, and shifting patterns of inequality and the cultural division of labour. The project therefore speaks directly to the issues under the theme Understanding Population Change of the Europe - China call for collaborative research.

The molecular clock hypothesis is fundamental in evolutionary biology as by assuming constancy of the molecular rate it provides a time frame for evolution. However, increasing evidence shows time dependence of inferred molecular rates with inflated values obtained using recent calibrations. As recent demographic calibrations are virtually non-existent in most species, older phylogenetic calibration points (>1 Ma) are commonly used, which overestimate demographic parameters. To obtain more reliable rates of molecular evolution for population studies, I propose the Calibration of Demographic Transition (CDT) method, which uses the timing of climatic changes over the late glacial warming period to calibrate expansions in various species. Simulation approaches and empirical datasets from a diversity of species (from mollusk to humans) confirm that, when compared to other genealogy-based calibration methods, the CDT provides a robust and broadly applicable clock for population genetics. The resulting CDT rates of molecular evolution also confirm rate heterogeneity over time and among taxa. Comparisons of expansion dates with ecological evidence confirm the inaccuracy of phylogenetically derived divergence rates when dating population-level events. The CDT method opens opportunities for addressing issues such as demographic responses to past climate change and the origin of rate heterogeneity related to taxa, genes, time and genetic information content.

In this paper, we test the hypothesis of the Neolithic Demographic Transition in the Central Balkan Early Neolithic (6250–5300 BC) by applying the method of summed calibrated probability distributions to the set of more than 200 new radiocarbon dates from Serbia. The results suggest that there was an increase in population size after the first farmers arrived to the study area around 6250 BC. This increase lasted for approximately 250 years and was followed by a decrease of the population size proxy after 6000 BC, reaching its minimum around 5800 BC. This was followed by another episode of growth until 5600 BC when population size proxy rapidly declined, reaching its minimum around 5500 BC. The reconstructed intrinsic growth rate value indicates that the first episode of growth might have been fuelled both by high fertility and migrations, potentially related to the effects of the 8.2 ky event. The second episode of population growth after 5800 BC was probably due to the high fertility alone. It remains unclear what caused the population to decrease episodes.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography'

For most of the past two centuries, falling birth rates have been associated with societal progress. During the demographic transition, where pre-industrial societies modernize in terms of fertility and mortality, falling death rates, especially among infants and children, are the first major change. In response, as more children survive into adulthood, women have fewer children as the need to compensate for child mortality declines. This transition has happened at different times across the world and is an ongoing process, with early industrial countries being the first to transition, and Sub-Saharan African countries being the most recent to do so. Additionally, some Asian countries (particularly China through government policy) have gone through their demographic transitions at a much faster pace than those deemed more developed. Today, in countries such as Japan, Italy, and Germany, birth rates have fallen well below death rates; this is no longer considered a positive demographic trend, as it leads to natural population decline, and may create an over-aged population that could place a burden on healthcare systems.

About this dataset

Context

The dataset tabulates the Two Creeks 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 Two Creeks 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 Two Creeks town was 389, a 0.78% increase year-by-year from 2022. Previously, in 2022, Two Creeks town population was 386, a decline of 1.03% compared to a population of 390 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Two Creeks town decreased by 161. In this period, the peak population was 551 in the year 2001. 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 Two Creeks town is shown in this column.
• Year on Year Change: This column displays the change in Two Creeks 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 Two Creeks town Population by Year. You can refer the same here

Russian estimates suggest that the total population of the Soviet Union in 1941 was 195.4 million people, before it fell to 170.5 million in 1946 due to the devastation of the Second World War. Not only did the USSR's population fall as a consequence of the war, but fertility and birth rates also dropped due to the disruption. Hypothetical estimates suggest that, had the war not happened and had fertility rates remained on their pre-war trajectory, then the USSR's population in 1946 would have been 39 million higher than in reality. Gender differences When it comes to gender differences, the Soviet male population fell from 94 million in 1941, to 74 million in 1946, and the female population fell from 102 to 96 million. While the male and female population fell by 19 and 5.5 million respectively, hypothetical estimates suggest that both populations would have grown by seven million each had there been no war. In actual figures, adult males saw the largest change in population due to the war, as a drop of 18 to 21 percent was observed across the three age groups. In contrast, the adult female population actually grew between 1941 and 1946, although the population under 16 years fell by a number similar to that observed in the male population due to the war's impact on fertility.

Abstract

The transition from socialism to a market economy has transformed the lives of many people. What are people's perceptions and attitudes to transition? What are the current attitudes to market reforms and political institutions?

To analyze these issues, the EBRD and the World Bank have jointly conducted the comprehensive, region-wide "Life in Transition Survey" (LiTS), which combines traditional household survey features with questions about respondents' attitudes and is carried out through two-stage sampling with a random selection of households and respondents.

The LiTS assesses the impact of transition on people through their personal and professional experiences during the first 15 years of transition. LiTS attempts to understand how these personal experiences of transition relate to people’s attitudes toward market and political reforms, as well as their priorities for the future.

The main objective of the LiTS was to build on existing studies to provide a comprehensive assessment of relationships among life satisfaction and living standards, poverty and inequality, trust in state institutions, satisfaction with public services, attitudes to a market economy and democracy and to provide valuable insights into how transition has affected the lives of people across a region comprising 16 countries in Central and Eastern Europe (“CEE”) and 11 in the Commonwealth of Independent State (“CIS”). Turkey and Mongolia were also included in the survey.

Geographic coverage

The LITS was to be implemented in the following 29 countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former Yugoslav Republic of Macedonia (FYROM), Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Mongolia, Poland, Romania, Russia, Serbia and Montenegro, Slovak Republic, Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine and Uzbekistan.

Kind of data

Sample survey data [ssd]

Sampling procedure

A total of 1,000 face-to-face household interviews per country were to be conducted, with adult (18 years and over) occupants and with no upper limit for age. The sample was to be nationally representative. The EBRD’s preferred procedure was a two stage sampling method, with census enumeration areas (CEA) as primary sampling units and households as secondary sampling units. To the extent possible, the EBRD wished the sampling procedure to apply no more than 2 stages.

The first stage of selection was to use as a sampling frame the list of CEA's generated by the most recent census. Ideally, 50 primary sampling units (PSU's) were to be selected from that sample frame, with probability proportional to size (PPS), using as a measure of size either the population, or the number of households.

The second sampling stage was to select households within each of the primary sampling units, using as a sampling frame a specially developed list of all households in each of the selected PSU's defined above. Households to be interviewed were to be selected from that list by systematic, equal probability sampling. Twenty households were to be selected in each of the 50 PSU's.

The individuals to be interviewed in each household were to be selected at random, within each of the selected households, with no substitution if possible.

ESTABLISHMENT OF THE SAMPLE FRAME OF PSU’s

In each country we established the most recent sample frame of PSU’s which would best serve the purposes of the LITS sampling methodology. Details of the PSU sample frames in each country are shown in table 1 (page 10) of the survey report.

In the cases of Armenia, Azerbaijan, Kazakhstan, Serbia and Uzbekistan, CEA’s were used. In Croatia we also used CEA’s but in this case, because the CEA’s were very small and we would not have been able to complete the targeted number of interviews within each PSU, we merged together adjoining CEA’s and constructed a sample of 1,732 Merged Enumeration Areas. The same was the case in Montenegro.

In Estonia, Hungary, Lithuania, Poland and the Slovak Republic we used Eurostat’s NUTS area classification system.

[NOTE: The NUTS (from the French "Nomenclature des territoriales statistiques" or in English ("Nomenclature of territorial units for statistics"), is a uniform and consistent system that runs on five different NUTS levels and is widely used for EU surveys including the Eurobarometer (a comparable survey to the Life in Transition). As a hierarchical system, NUTS subdivides the territory of the country into a defined number of regions on NUTS 1 level (population 3-7 million), NUTS 2 level (800,000-3 million) and NUTS 3 level (150,000-800,000). At a more detailed level NUTS 3 is subdivided into smaller units (districts and municipalities). These are called "Local Administrative Units" (LAU). The LAU is further divided into upper LAU (LAU1 - formerly NUTS 4) and LAU 2 (formerly NUTS 5).]

Albania, Bulgaria, the Czech Republic, Georgia, Moldova and Romania used the electoral register as the basis for the PSU sample frame. In the other cases, the PSU sample frame was chosen using either local geographical or administrative and territorial classification systems. The total number of PSU sample frames per country varied from 182 in the case of Mongolia to over 48,000 in the case of Turkey. To ensure the safety of our fieldworkers, we excluded from the sample frame PSU’s territories (in countries such as Georgia, Azerbaijan, Moldova, Russia, etc) in which there was conflict and political instability. We have also excluded areas which were not easily accessible due to their terrain or were sparsely populated.

In the majority of cases, the source for this information was the national statistical body for the country in question, or the relevant central electoral committee. In establishing the sample frames and to the extent possible, we tried to maintain a uniform measure of size namely, the population aged 18 years and over which was of more pertinence to the LITS methodology. Where the PSU was based on CEA’s, the measure was usually the total population, whereas the electoral register provided data on the population aged 18 years old and above, the normal voting age in all sampled countries. Although the NUTS classification provided data on the total population, we filtered, where possible, the information and used as a measure of size the population aged 18 and above. The other classification systems used usually measure the total population of a country. However, in the case of Azerbaijan, which used CEA’s, and Slovenia, where a classification system based on administrative and territorial areas was employed, the measure of size was the number of households in each PSU.

The accuracy of the PSU information was dependent, to a large extent, on how recently the data has been collected. Where the data were collected recently then the information could be considered as relatively accurate. However, in some countries we believed that more recent information was available, but because the relevant authorities were not prepared to share this with us citing secrecy reasons, we had no alternative than to use less up to date data. In some countries the age of the data available makes the figures less certain. An obvious case in point is Bosnia and Herzegovina, where the latest available figures date back to 1991, before the Balkan wars. The population figures available take no account of the casualties suffered among the civilian population, resulting displacement and subsequent migration of people.

Equally there have been cases where countries have experienced economic migration in recent years, as in the case of those countries that acceded to the European Union in May, 2004, such as Hungary, Poland and the Baltic states, or to other countries within the region e.g. Armenians to Russia, Albanians to Greece and Italy; the available figures may not accurately reflect this. And, as most economic migrants tend to be men, the actual proportion of females in a population was, in many cases, higher than the available statistics would suggest. People migration in recent years has also occurred from rural to urban areas in Albania and the majority of the Asian Republics, as well as in Mongolia on a continuous basis but in this case, because of the nomadic population of the country.

SAMPLING METHODOLOGY

Brief Overview

In broad terms the following sampling methodology was employed: · From the sample frame of PSU’s we selected 50 units · Within each selected PSU, we sampled 20 households, resulting in 1,000 interviews per country · Within each household we sampled 1 and sometimes 2 respondents The sampling procedures were designed to leave no free choice to the interviewers. Details on each of the above steps as well as country specific procedures adapted to suit the availability, depth and quality of the PSU information and local operational issues are described in the following sections.

Selection of PSU’s

The PSU’s of each country (all in electronic format) were sorted first into metropolitan, urban and rural areas (in that order), and within each of these categories by region/oblast/province in alphabetical order. This ensured a consistent sorting methodology across all countries and also that the randomness of the selection process could be supervised.

To select the 50 PSU’s from the sample frame of PSU’s, we employed implicit stratification and sampling was done with PPS. Implicit stratification ensured that the sample of PSU’s was spread across the primary categories of explicit variables and a better representation of the population, without actually stratifying the PSU’s thus, avoiding difficulties in calculating the sampling errors at a later stage. In brief, the PPS involved the

About this dataset

Context

The dataset tabulates the Two Rivers 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 Two Rivers 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 Two Rivers town was 1,685, a 0.54% increase year-by-year from 2022. Previously, in 2022, Two Rivers town population was 1,676, a decline of 0.36% compared to a population of 1,682 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Two Rivers town decreased by 242. In this period, the peak population was 1,928 in the year 2001. 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 Two Rivers town is shown in this column.
• Year on Year Change: This column displays the change in Two Rivers 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 Two Rivers town Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Iowa 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 Iowa 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 2024, the population of Iowa was 3.24 million, a 0.72% increase year-by-year from 2023. Previously, in 2023, Iowa population was 3.22 million, an increase of 0.49% compared to a population of 3.2 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of Iowa increased by 313,297. In this period, the peak population was 3.24 million in the year 2024. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. 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 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the Iowa is shown in this column.
• Year on Year Change: This column displays the change in Iowa 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 Iowa 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.

Longitudinal studies of marked animals provide an opportunity to assess the relative contributions of survival and reproductive output to population dynamics and change. Cassin's auklets are a long-lived seabird that maximizes annual reproductive effort in resource-rich years through a behavior called double brooding, the initiation of a second breeding attempt following the success of the first during the same season. Our objective was to explore whether double brooding influenced population change by contributing a greater number of future recruits. We fit temporal symmetry models to 32-years of mark-recapture data of Cassin's auklets to infer the mechanisms underlying the observed variability in per capita recruitment rates. We found that periodic peaks in recruitment were explained by an increase in available nest sites, the proportion of the population double brooding 4 years prior, and spring upwelling conditions. Estimates of population change suggests a relatively stable population throughout the time series, attributable to a "floating" demographic class of sexually mature individuals excluded from breeding by competition which quickly fill vacant sites following periods of low adult survival. Our results highlight the importance of recruitment in maintaining the population of a long-lived seabird periodically impacted by adverse environmental conditions.

Although the pervasiveness of intraspecific wing-size polymorphism and transitions to flightlessness have long captivated biologists, the demographic outcomes of shifts in dispersal ability are not yet well understood and have been seldom studied at early stages of diversification. Here, we use genomic data to infer the consequences of dispersal-related trait variation in the taxonomically controversial short-winged (Chorthippus corsicus corsicus) and long-winged (Chorthippus corsicus pascuorum) Corsican grasshoppers. Our analyses revealed lack of contemporary hybridization between sympatric long- and short-winged forms and phylogenomic reconstructions supported their taxonomic distinctiveness, rejecting the hypothesis of intraspecific wing polymorphism. Statistical evaluation of alternative models of speciation strongly supported a scenario of Pleistocene divergence (<1.5 Ma) with ancestral gene flow. According to neutral expectations from differences in dispersal capacity, historical effective migration rates from the long- to the short-winged taxon were three-fold higher than in the opposite direction. Although populations of the two taxa present a marked genetic structure and have experienced parallel demographic histories, our coalescent-based analyses suggest that reduced dispersal has fueled diversification in the short-winged C. c. corsicus. Collectively, our study illustrates how dispersal reduction can speed up geographical diversification and increase the opportunity for allopatric speciation in topographically complex landscapes.

Methods Genomic library preparation We used NucleoSpin Tissue (Macherey-Nagel, Düren, Germany) kits to extract and purify DNA from a hind leg of each individual. We processed genomic DNA into one genomic library using the double-digestion restriction-site associated DNA sequencing procedure (ddRAD-seq) described in Peterson et al. (2012). In brief, we digested DNA with the restriction enzymes MseI and EcoRI (New England Biolabs, Ipswich, MA, USA) and ligated Illumina adaptors including unique 7-bp barcodes to the digested fragments of each individual. We pooled ligation products and size-selected them between 475-580 bp with a Pippin Prep instrument (Sage Science, Beverly, MA, USA). We amplified the fragments by PCR with 12 cycles using the iProofTM High-Fidelity DNA Polymerase (BIO-RAD, Veenendaal, Netherlands) and sequenced the library in a single-read 150-bp lane on an Illumina HiSeq2500 platform at The Centre for Applied Genomics (Toronto, ON, Canada). Genomic data assembling and filtering Raw sequences were demultiplexed and preprocessed using stacks v. 1.35 (Catchen et al., 2013) and assembled using pyrad v. 3.0.66 (Eaton, 2014). Libraries were demultiplexed and filtered for overall quality using process_radtags (Catchen et al., 2011, 2013), retaining reads with a Phred score > 10 (using a sliding window of 15%), no adaptor contamination, and that had an unambiguous barcode and restriction cut site. Raw sequence data quality was checked in fastqc v. 0.11.5 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and sequences were trimmed to 129 bp using seqtk (Heng Li, https://github.com/lh3/seqtk) in order to remove barcodes and low-quality reads near the 3´ ends. We assembled our sequences into de novo loci using pyrad v. 3.0.66 (Eaton, 2014). Briefly, reads retained after process_radtags were further quality-filtered with pyrad to convert base calls with a Phred score <20 into Ns and discard reads with >2 Ns. Retained reads were clustered within- and across samples considering a threshold of sequence similarity (Wclust) of 85% and clusters with a coverage depth <5 were discarded. Loci containing one or more heterozygous sites across >15% of individuals were excluded, as we expect that this represents a fixed difference among clustered paralogs rather than a true polymorphism (Eaton, 2014). Unless otherwise indicated, all downstream analyses were performed using datasets of unlinked SNPs (i.e., a single SNP per RAD locus) obtained with pyrad considering a clustering threshold of sequence similarity of 0.85 (Wclust = 0.85) and discarding loci that were not present in at least 50 % individuals (minCov = 50 %). References Catchen, J. M., A. Amores, P. Hohenlohe, W. Cresko, and J. H. Postlethwait. 2011. stacks: Building and genotyping loci de novo from short-read sequences. G3-Genes Genom. Genet. 1:171-182. Catchen, J., P. A. Hohenlohe, S. Bassham, A. Amores, and W. A. Cresko. 2013. stacks: an analysis tool set for population genomics. Mol. Ecol. 22:3124-3140. Eaton, D. A. R. 2014. pyrad: assembly of de novo RADseq loci for phylogenetic analyses. Bioinformatics 30:1844-1849. Peterson, B. K., J. N. Weber, E. H. Kay, H. S. Fisher, and H. E. Hoekstra. 2012. Double digest RADseq: An inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS One 7:e37135.

There are approximately 8.16 billion people living in the world today, a figure that shows a dramatic increase since the beginning of the Common Era. Since the 1970s, the global population has also more than doubled in size. It is estimated that the world's population will reach and surpass 10 billion people by 2060 and plateau at around 10.3 billion in the 2080s, before it then begins to fall. Asia When it comes to number of inhabitants per continent, Asia is the most populous continent in the world by a significant margin, with roughly 60 percent of the world's population living there. Similar to other global regions, a quarter of inhabitants in Asia are under 15 years of age. The most populous nations in the world are India and China respectively; each inhabit more than three times the amount of people than the third-ranked United States. 10 of the 20 most populous countries in the world are found in Asia. Africa Interestingly, the top 20 countries with highest population growth rate are mainly countries in Africa. This is due to the present stage of Sub-Saharan Africa's demographic transition, where mortality rates are falling significantly, although fertility rates are yet to drop and match this. As much of Asia is nearing the end of its demographic transition, population growth is predicted to be much slower in this century than in the previous; in contrast, Africa's population is expected to reach almost four billion by the year 2100. Unlike demographic transitions in other continents, Africa's population development is being influenced by climate change on a scale unseen by most other global regions. Rising temperatures are exacerbating challenges such as poor sanitation, lack of infrastructure, and political instability, which have historically hindered societal progress. It remains to be seen how Africa and the world at large adapts to this crisis as it continues to cause drought, desertification, natural disasters, and climate migration across the region.

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.

Spatial prisoner’s dilemma (SPD) has attracted researchers’ attention as a model of conflict for players. In SPD, players have two different strategies, namely, defectors and cooperators. A defector earns a high payoff from an opponent co-operator while getting nothing from an opponent defector. On the contrary, cooperators promote a win–win relationship between the two cooperators. These mechanisms influence population dynamics in SPD, and many SPD models have been developed. However, little is known about the emergence of an unstable or unpredictable evolution in population dynamics using an SPD model, which may be observed in living systems. In addressing this issue, two SPD models were proposed. In both models, players change the neighborhood definition in accordance with their strategies and sometimes select the rule for this change using probability or local information. Result showed that our models generated characteristic population patterns that may be linked to a self-organized criticality (SOC), a term referring to many systems of interconnected, nonlinear elements that evolve over time into a critical state. In fact, the second model could be spontaneously close to the critical point using local information.

About this dataset

Context

The dataset tabulates the Two Rivers township 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 Two Rivers township 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 Two Rivers township was 787, a 0% decrease year-by-year from 2022. Previously, in 2022, Two Rivers township population was 787, an increase of 0.51% compared to a population of 783 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Two Rivers township increased by 203. In this period, the peak population was 787 in the year 2022. 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 Two Rivers township is shown in this column.
• Year on Year Change: This column displays the change in Two Rivers township 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 Two Rivers township Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Two Inlets township 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 Two Inlets township 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 Two Inlets township was 208, a 0.48% decrease year-by-year from 2022. Previously, in 2022, Two Inlets township population was 209, a decline of 0% compared to a population of 209 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Two Inlets township decreased by 31. In this period, the peak population was 269 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 Two Inlets township is shown in this column.
• Year on Year Change: This column displays the change in Two Inlets township 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 Two Inlets township Population by Year. You can refer the same here