How does the association between gender attitudes and housework share vary across countries and time? We examine the second demographic transition as it unmasks in the association between gender attitudes and housework participation. Using data of the 2002 and 2012 International Social Survey Programme (ISSP) for 24 countries, we find that the association between gender attitudes and housework share became stronger over time in most countries, signifying that the Second Demographic Transition was in place. The results also show that the association varied across the 24 countries, reaching an equilibrium in many but at different stages. Our findings suggest that equilibria in the domestic division of labour take various forms and paces in the ISSP countries.

Abstract Consensual unions have increased greatly in Brazil over the last few decades. Initially, restricted to less-educated groups, they have now been observed in all educational groups, leading some to suggest a diffusion of the Second Demographic Transition (SDT) in the country. In this paper, we examine the characteristics of women choosing consensual unions in Brazil between 1980 and 2010, with a focus on differentials by education. The results show that higher educated women, when compared to the least educated group, prefer marriage over consensual union both in 1980 and 2010. In addition, we show a growing difference between educational groups over time for choosing informal unions, as the probabilities for higher educated women to choose this type of union have increased less than for lower educated ones. For women with high educational levels in 2010, the likelihood of being in a consensual union is greater than among those from lower socioeconomic groups and among blacks, browns, and Catholics. Our results question the explanations given by the SDT for the expansion of consensual unions in upper socioeconomic groups in Brazil.

Abstract

The Sahel Women Empowerment and Demographic Dividend (P150080) project is a regional project aiming to accelerate the demographic transition by addressing both supply- and demand-side constraints to family planning and reproductive and sexual health. To achieve its objective, the project targets adolescent girls and young women mainly between the ages of 8 and 24, who are vulnerable to early marriage, teenage pregnancy, and early school drop-out. The project targeted 9 countries of the Sahel and Western Africa (Benin, Burkina Faso, Cameroon, Chad, Côte d’Ivoire, Guinea, Mali, Mauritania, and Niger) and is expanding in other African countries. The SWEDD is structured into three main components: component 1 seeks to generate demand for reproductive, maternal, neonatal, child health and nutrition products and services; component 2 seeks to improve supply of these products and qualified personnel; and component 3 seeks to strengthen national capacity and policy dialogue.

The World Bank Africa Gender Innovation Lab and its partners are conducting rigorous impact evaluations of key interventions under component 1 to assess their effects on child marriage, fertility, and adolescent girls and young women’s empowerment. The interventions were a set of activities targeting adolescent girls and their communities, designed in collaboration with the government of Côte d’Ivoire. These were (i) safe spaces to empower girls through the provision of life skills and SRH education; (ii) support to income-generating activities (IGA) with the provision of grants and entrepreneurship training; (iii) husbands’ and future husbands’ clubs, providing boys of the community with life skills and SRH education; and finally (iv) community sensitization by religious and village leaders. The latter two have the objective to change restrictive social norms and create an enabling environment for girls’ empowerment.

These data represent the first round of data collection (baseline) for the impact evaluation.

Geographic coverage

Mali, Regions of Kayes, Ségou and Sikasso

Analysis unit

Households, individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The baseline sample comprises 8776 households and 7463 girls living in the regions of Kayes, Sikasso and Ségou in Mali. To define the sample, we partnered with INSTAT Mali. At first, INSTAT conducted a census of the population living in the areas around the 49 schools selected by the education focal point that will all benefit from the SWEDD program. Therefore, census activities were concentrated in 287 villages located within a radius of 10/15km around these schools. Eventually, 10 villages had to be dropped due to security reasons. Keeping with the eligibility criteria of surveying villages where there were at least 10 households with a girl aged between 12 and 24 years old, 270 villages were eventually sampled. Households were surveyed before randomization into groups assigned to receive the SWEDD program.

The objective of the baseline survey was to build a comprehensive dataset, which would serve as a reference point for the entire sample, before treatment and control assignment and program implementation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire administrated to girls contains the following sections: 1. Education 2. Marriage and children 3. Aspirations 4. Health and family planning 5. Knowledge of HIV/AIDS 6. Women's empowerment 7. Gender-based violence 8. Income-generating activities 9. Savings and credit 10. Personal relationships and social networks 11. Committee members and community participation

The household questionnaire was administered to the head of the household or to an authorized person capable of answering questions about all individuals in the household. The adolescent questionnaire was administered to an eligible pre-selected girl within the household. Considering the modules of the adolescent questionnaire, it was only administered by female enumerators. The questionnaires were written in French, translated into Bambara, and programmed on tablets in French using the CAPI program.

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/

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.

In 2025, 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.

Whites have become decreasingly likely to support the Democratic Party. I show this shift is being driven by two mechanisms. The first mechanism is the process of ideological sorting. The Democratic Party has lost support among conservative whites because the relationships between partisanship, voting behavior, and policy orientations have strengthened. The second mechanism relates to demographic changes. The growth of liberal minority populations has shifted the median position on economic issues to the left and away from the median white citizen’s position. The parties have responded to these changes by shifting their positions and whites have become less likely to support the Democratic Party as a result. I test these explanations using 40 years of ANES and DW-NOMINATE data. I find that whites have become 7.7-points more likely vote for the Republican Party and mean white partisanship has shifted .25 points in favor of the Republicans as a combined result of both mechanisms.

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

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'

About this dataset

Context

The dataset tabulates the Two Harbors 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 Harbors 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 Harbors was 3,531, a 1.34% decrease year-by-year from 2022. Previously, in 2022, Two Harbors population was 3,579, a decline of 1.51% compared to a population of 3,634 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Two Harbors decreased by 100. In this period, the peak population was 3,739 in the year 2010. 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 Harbors is shown in this column.
• Year on Year Change: This column displays the change in Two Harbors 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 Harbors Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Two Buttes 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 Buttes 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 Buttes was 32, a 3.03% decrease year-by-year from 2022. Previously, in 2022, Two Buttes population was 33, a decline of 2.94% compared to a population of 34 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Two Buttes decreased by 35. In this period, the peak population was 67 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 Two Buttes is shown in this column.
• Year on Year Change: This column displays the change in Two Buttes 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 Buttes Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Two Rivers 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 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 was 11,169, a 0.01% decrease year-by-year from 2022. Previously, in 2022, Two Rivers population was 11,170, a decline of 0.76% compared to a population of 11,255 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Two Rivers decreased by 1,438. In this period, the peak population was 12,607 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 Two Rivers is shown in this column.
• Year on Year Change: This column displays the change in Two Rivers 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 Population by Year. You can refer the same here

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

Identifying mechanisms of population change is fundamental for conserving small and declining populations and determining effective management strategies. Few studies, however, have measured the demographic components of population change for small populations of mammals (< 50 individuals). We estimated vital rates and trends in two adjacent but genetically distinct, threatened brown bear (Ursus arctos) populations in British Columbia, Canada, following the cessation of hunting. One population had approximately 45 resident bears but had some genetic and geographic connectivity to neighbouring populations, while the other population had < 25 individuals and was isolated.

We estimated population-specific vital rates by monitoring survival and reproduction of telemetered female bears and their dependent offspring from 2005 to 2018. In the larger, connected population, independent female survival was 1.00 (95% CI: 0.96-1.00) and the survival of cubs in their first year was 0.85 (95% CI: 0.62-0.95). In the smaller, isolated population, independent female survival was 0.81 (95% CI: 0.64-0.93) and first-year cub survival was 0.33 (95% CI: 0.11-0.67). Reproductive rates did not differ between populations. The large differences in age-specific survival estimates resulted in a projected population increase in the larger population (λ = 1.09; 95% CI: 1.04-1.13) and population decrease in the smaller population (λ = 0.84; 95% CI: 0.72-0.95). Low female survival in the smaller population was the result of both continued human-caused mortality and an unusually high rate of natural mortality. Low cub survival may have been due to inbreeding and the loss of genetic diversity common in small populations, or to limited resources. In a systematic literature review, we compared our population trend estimates with those reported for other small populations (< 300 individuals) of brown bears. Results suggest that once brown bear populations become small and isolated, populations rarely increase and, even with intensive management, recovery remains challenging.

Methods ch_survival.txt: This dataset is the monthly survival and monitoring data of independent (not with mother) female grizzly bears. It is formatted for processing in Rmark with monthly intervals for 7 months and one 5 month interval for winter. Each year from 2005-2018.

transition_reproductive_state.txt: Transitions between reproductive state for collared adult female grizzly bears in two adjacent populations. AA -alone to alone; AC - alone to with cubs; CY- with cubs to with yearlings; CA - with cubs to alone; Y-A yearling to alone; Y-T yearling to with two-year-old offspring; T-A with two-year-old offspring to alone or older offspring.

coy_survial.txt: Dependent offspring survival in the first year of life and litter membership by population.

yearling_survival.txt: Dependent offspring survival in the second year of life by population.

primiparity.txt: Monitoring and reproduction of primiparous female grizzly bears by population. For each age, data show whether they had their first litter or not and whether they were removed from the sample by mortality or because their reproductive fate was unknown (i.e. radio-collar dropped).

interbirth_interval.txt: Interbirth interval of parous female grizzly bears by population. For each year following a reproductive event whether another reproductive event was observed until the female reproduced or they were removed from the sample by mortality or because their reproductive fate was unknown (i.e. radio-collar dropped).

Understanding the demography of species over recent history (e.g., < 100 years) is critical in studies of ecology and evolution, but records of population history are rarely available. Surveying genetic variation is a potential alternative to census-based estimates of population size, and can yield insight into the demography of a population. However, to assess the performance of genetic methods it is important to compare their estimates of population history to known demography. Here, we leveraged the exceptional resources from a wetland with 37 years of amphibian mark-recapture data to study the utility of genetically-based demographic inference on salamander species with documented population declines (Ambystoma talpoideum) and expansions (A. opacum); patterns that have been shown to be correlated with changes in wetland hydroperiod. We generated ddRAD data from two temporally sampled populations of A. opacum (1993, 2013) and A. talpoideum (1984, 2011) and used coalescent-based demographic inference to compare alternate evolutionary models. For both species, demographic model inference supported population size changes that corroborated mark-recapture data. Parameter estimation in A. talpoideum was robust to our variations in analytical approach, while estimates for A. opacum were highly inconsistent, tempering our confidence in detecting a demographic trend in this species. Overall, our robust results in A. talpoideum suggest that genome-based demographic inference has utility on an ecological scale, but researchers should also be cognizant that these methods may not work in all systems and evolutionary scenarios. Demographic inference may be an important tool for population monitoring and conservation management planning.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

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.

C++ simulation programCode to simulate populations with varying size in which self-fertilisation can evolve.cycle.tar