In 2022, about 17.3 percent of the American population was 65 years old or over; an increase from the last few years and a figure which is expected to reach 22 percent by 2050. This is a significant increase from 1950, when only eight percent of the population was 65 or over.

A rapidly aging population

In recent years, the aging population of the United States has come into focus as a cause for concern, as the nature of work and retirement is expected to change in order to keep up. If a population is expected to live longer than the generations before, the economy will have to change as well in order to fulfill the needs of the citizens. In addition, the birth rate in the U.S. has been falling over the last 20 years, meaning that there are not as many young people to replace the individuals leaving the workforce.

The future population

It’s not only the American population that is aging -- the global population is, too. By 2025, the median age of the global workforce is expected to be 39.6 years, up from 33.8 years in 1990. Additionally, it is projected that there will be over three million people worldwide aged 100 years and over by 2050.

The total population of Germany was estimated at over 84.4 million inhabitants in 2025, although it is projected to drop in the coming years and fall below 80 million in 2043. Germany is the most populous country located entirely in Europe, and is third largest when Russia and Turkey are included. Germany's prosperous economy makes it a popular destination for immigrants of all backgrounds, which has kept its population above 80 million for several decades. Population growth and stability has depended on immigration In every year since 1972, Germany has had a higher death rate than its birth rate, meaning its population is in natural decline. However, Germany's population has rarely dropped below its 1972 figure of 78.6 million, and, in fact, peaked at 84.7 million in 2024, all due to its high net immigration rate. Over the past 75 years, the periods that saw the highest population growth rates were; the 1960s, due to the second wave of the post-WWII baby boom; the 1990s, due to post-reunification immigration; and since the 2010s, due to high arrivals of refugees from conflict zones in Afghanistan, Syria, and Ukraine. Does falling population = economic decline? Current projections predict that Germany's population will fall to almost 70 million by the next century. Germany's fertility rate currently sits around 1.5 births per woman, which is well below the repacement rate of 2.1 births per woman. Population aging and decline present a major challenge economies, as more resources must be invested in elderly care, while the workforce shrinks and there are fewer taxpayers contributing to social security. Countries such as Germany have introduced more generous child benefits and family friendly policies, although these are yet to prove effective in creating a cultural shift. Instead, labor shortages are being combatted via automation and immigration, however, both these solutions are met with resistance among large sections of the population and have become defining political issues of our time.

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin; for the United States, States, Counties; and for Puerto Rico and its Municipios: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

PurposeThe present study examines how the coronavirus disease 2019 (COVID-19) experience affected values and priorities.MethodsThis cross-sectional study collected data between January and April 2023, from 1,197 individuals who are chronically ill or part of a general population sample. Using open-ended prompts and closed-ended questions, we investigated individuals’ perceptions about COVID-19-induced changes in what quality of life means to them, what and who are important, life focus, and changes in norms and stressors. Data analyses included content and psychometric analysis, leading to latent profile analysis (LPA) to characterize distinct groups, and analysis of variance and chi-squared to compare profile groups’ demographic characteristics.ResultsAbout 75% of the study sample noted changes in values and/or priorities, particularly in the greater prominence of family and friends. LPA yielded a four-profile model that fit the data well. Profile 1 (Index group; 64% of the sample) had relatively average scores on all indicators. Profile 2 (COVID-Specific Health & Resignation to Isolation Attributable to COVID-19; 5%) represented COVID-19-specific preventive health behaviors along with noting the requisite isolation and disengagement entailed in the social distancing necessary for COVID-19 prevention. Profile 3 (High Stress, Low Trust; 25%) represented high multi-domain stress, with the most elevated scores both on focusing on being true to themselves and perceiving people to be increasingly uncivil. Profile 4 (Active in the World, Low Trust; 6%) was focused on returning to work and finding greater meaning in their activities. These groups differed on race, marital status, difficulty paying bills, employment status, number of times they reported having had COVID-19, number of COVID-19 boosters received, whether they had Long COVID, age, BMI, and number of comorbidities.ConclusionThree years after the beginning of the worldwide COVID-19 pandemic, its subjective impact is notable on most study participants’ conceptualization of quality of life, priorities, perspectives on social norms, and perceived stressors. The four profile groups reflected distinct ways of dealing with the long-term effects of COVID-19.

This statistic shows the degree of urbanization in Thailand from 2013 to 2023. Urbanization means the share of urban population in the total population of a country. In 2023, 53.61 percent of Thailand's total population lived in urban areas and cities. The migration of the Thai population to metropolises and urban areas Thailand is in the midst of transforming itself from a predominantly rural country to an increasingly urban one. Today, over half the population lives in urban areas, which is much higher than most bordering countries. While Thailand's urbanization rates are still low compared to more developed nations - which can reach levels over 90 percent, this transformation in Thailand is still significant, especially as most of this growth occurs and is expected to occur in the Krung Thep area, better known as Bangkok, capital and largest city in Thailand. Krung Thep is now home to more than 5.6 million people. The number of tourists and overnight visitors to the city is also on the rise, and Bangkok is usually among the ten most visited cities in each year, with over 20 million visitors in 2023. This development will place increasing demands on urban infrastructure, as the city grows and grows. The second largest city in Thailand is Nonthaburi, but it only has around one quarter of a million inhabitants, a significant difference. Despite the country’s rural but shifting population, Thailand's fertility rate is low and well below the natural replacement rate, and population growth in general is thus only minimal. Interestingly, despite this migration, agriculture has retained a stable share in GDP generation, actually increasing slightly over time, while the contributions of industry and services to GDP have also remained relatively the same.

Climate change-induced phenological shifts are ubiquitous and have the potential to disrupt natural communities by changing the timing of species interactions. Shifts in first and/or mean phenological date are well documented, but recent studies indicate that shifts in synchrony (individual variation around these metrics) can be just as common. However, we know little about how both types of phenological shifts interact to affect species interactions and natural communities. Here, we experimentally manipulated the hatching phenologies of two competing species of larval amphibians to address this conceptual gap. Specifically, we manipulated the relative mean hatching time (early, same, or late relative to competitor) and population synchrony (high, medium, or low levels of variation around the mean) in a full 3x3 factorial design to measure independent and interactive effects of phenological mean and population phenological synchrony on competitive outcomes. Our results indicate that phenological synchrony within a population strongly influences intraspecific competition by changing the density of individuals and relative strength of early vs. late arriving individuals. Individuals from high synchrony populations competed symmetrically while individuals from low synchrony populations competed asymmetrically. At the community scale, shifts in population phenological synchrony interact with shifts in phenological mean to strongly affect key demographic rates (survival, biomass export, per capita mass, and emergence timing). Furthermore, changes in mean timing of species interactions altered phenological synchrony within a population at the next life stage, and phenological synchrony at one life stage altered the mean timing of the next life stage. Thus, shifts in phenological synchrony within populations can not only alter species interactions but species interactions in turn can also drive shifts in phenology.

The median age of Germans in 2020 was 44.9 years, meaning that half the German population was younger, half older. This number decreased slightly from 1950 to 1970, likely due to the baby boom after World War II, then began increasing. It is expected to slowly increase to 47.4 by 2100. Aging in Germany This shift in the age makeup of Germany is driven by having fewer young people and more old people. While it has increased slightly in the last decade, the German fertility rate remains low. Fewer young people lead to a higher median age. Simultaneously, the life expectancy has increased, having the opposite effect. Regional and global trends The entire European Union, due to higher levels of development, shows an upward shift in its age distribution. While this shift is occurring globally, the level of Germany’s median age is a European phenomenon. In other parts of the world, the proportion of young and old inhabitants is skewed sharply toward the young, pulling the median age lower.

Questionnaires are among the most basic and widespread tools to assess the mental health of a population in epidemiological and public health studies. Their most obvious advantage (firsthand self-report) is also the source of their main problems: the raw data requires interpretation, and are a snapshot of the specific sample’s status at a given time. Efforts to deal with both issues created a bi-dimensional space defined by two orthogonal axes, in which most of the quantitative mental health research can be located. Methods aimed to assure that mental health diagnoses are solidly grounded on existing raw data are part of the individual validity axis. Tools allowing the generalization of the results across the entire population compose the collective validity axis. This paper raises a different question. Since one goal of mental health assessments is to obtain results that can be generalized to some extent, an important question is how robust is a questionnaire result when applied to a different population or to the same population at a different time. In this case, there is deep uncertainty, without any a priori probabilistic information. The main claim of this paper is that this task requires the development of a new robustness to deep uncertainty axis, defining a three-dimensional research space. We demonstrate the analysis of deep uncertainty using the concept of robustness in info-gap decision theory. Based on data from questionnaires collected before and during the Covid-19 pandemic, we first locate a mental health assessment in the space defined by the individual validity axis and the collective validity axis. Then we develop a model of info-gap robustness to uncertainty in mental health assessment, showing how the robustness to deep uncertainty axis interacts with the other two axes, highlighting the contributions and the limitations of this approach. The ability to measure robustness to deep uncertainty in the mental health realm is important particularly in troubled and changing times. In this paper, we provide the basic methodological building blocks of the suggested approach using the outbreak of Covid-19 as a recent example.

Financial Wellness Benefits Market is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024 to 2031.

Global Financial Wellness Benefits Market Drivers

The market drivers for the Financial Wellness Benefits Market can be influenced by various factors. These may include:

Initiatives for Employee Well-Being: Companies are realising the value of promoting their workers’ overall wellbeing, including their financial stability. Providing financial wellness benefits can increase job satisfaction, productivity, and retention rates while also showing a commitment to the health of your workforce.
Increasing Financial Stress: Debt, insufficient savings, and economic instability are some of the major issues that contribute to financial stress, which is a global problem affecting people. Financial wellness benefits are becoming more widely available as part of larger employee assistance programmes as a result of employers realising the negative effects that financial stress has on worker performance and morale.
The retirement landscape: is changing as defined contribution retirement plans gain popularity and traditional pension plans shrink, placing more responsibility on individuals to save for their retirement. Employees may better manage the complexity of retirement planning and safeguard their financial future with the support of financial wellness benefits like savings matching programmes and retirement planning guidance.
Changing Workforce Demographics: A variety of generations are working together in the workplace, each with their own set of financial difficulties, and the workforce is growing more diverse. Benefits related to financial wellbeing can be customised to meet the demands of various demographic groups, such as Baby Boomers approaching retirement, Gen Xers balancing work and family obligations, and Millennials struggling with student loan debt.
Demand for Comprehensive Benefits Packages: Modern workers look to their employers for more than just a paycheck; they want benefits that cover all aspects of their health, including their financial well-being. Companies that provide comprehensive benefits for financial wellness have an advantage over their competitors in luring and keeping top talent.
Growing Recognition and Education: The demand for financial wellness benefits is driven by a growing recognition of the significance of financial literacy and education. In order to provide their staff the financial knowledge they need to make wise decisions, employers are funding programmes that teach them about debt management, investing, saving, and budgeting.
Healthcare Cost Containment: Employers and employees are under pressure as a result of growing healthcare expenses. Financial wellness benefits, such flexible spending accounts (FSAs) and health savings accounts (HSAs), give companies cost-saving options while assisting employees in managing their healthcare costs.
Regulatory Requirements: Employers are encouraged to give financial wellness efforts top priority by regulatory developments, such as the addition of financial wellness benefits to retirement plan requirements and fiduciary standards. The implementation of comprehensive financial wellness programmes is driven by regulatory compliance.
Remote Work and Flexible Work Schedules: The COVID-19 epidemic has expedited the transition to remote work and flexible work schedules, which emphasises the value of anytime, anywhere digital financial wellness solutions. In order to provide financial wellness services to remote and dispersed workforces, employers are investing in mobile apps and web platforms.
Corporate Social Responsibility (CSR): CSR programmes focus on the welfare of employees in addition to environmental sustainability. Providing financial wellness benefits strengthens the employer brand, attracts investors and consumers who care about social issues, and is in line with CSR goals.

This statistic shows the degree of urbanization in Germany from 2013 to 2023. Urbanization means the share of urban population in the total population of a country. In 2023, 77.77 percent of Germany's total population lived in urban areas and cities. Urbanization in Germany Currently, about three quarter of the German population live in urban areas and cities, which is more than in most nations around the world. Urbanization, as it can be seen in this graph, refers to the number of people living in an urban area and has nothing to do with the actual geographical size or footprint of an area or country. A country which is significantly bigger than Germany could have a similar degree of urbanization, just because not all areas in the country are inhabitable, for example. One example for this is Russia, where urbanization has reached comparable figures to Germany, even though its geographical size is significantly bigger. However, Germany’s level of urbanization does not make the list of the top 30 most urbanized nations in the world, where urbanization rates are higher than 83 percent. Also, while 25 percent of the population in Germany still lives in rural areas, rural livelihoods are not dependent on agriculture, as only 0.75 percent of GDP came from the agricultural sector in 2014. So while Germany's urbanization rate is growing, a significant percentage of the population is still living in rural areas. Furthermore, Germany has a number of shrinking cities which are located to the east and in older industrial regions around the country. Considering that population growth in Germany is on the decline, because of low fertility rates, and that a number of cities are shrinking, the urban population is likely shifting to bigger cities which have more economic opportunities than smaller ones.

Economically Active Population Survey: Employed by sex, professional situation and types of working conditions. conditions. National. Employed persons by type of work shift, sex and professional situation.

1.Mortality and recruitment rates are fundamental measures of population dynamics. Ecologists and others have defined and estimated these vital rates in various ways. We review these alternatives focusing on tree population census data in fixed area plots, though many aspects have wider application when similar data characteristics and assumptions apply: our goal is to guide choices and facilitate comparisons.

2.We divide our estimates into 'instantaneous' and 'annual' rates, corresponding to continuous- or discrete-time dynamics respectively. In each case, vital rate estimates can be further divided into those based on population density ('per-capita' rates) and those based on census area ('per-area' rates). We also examine how all such rate estimates relate to each other and can thus be interconverted and compared.

3.In a heterogeneous population (e.g., trees in forest stand) comprising subpopulations (e.g., species, locations, exposure classes), estimates of vital rates that assume homogeneity (equal likelihood of mortality and equal likelihood of recruitment for all individuals) are biased towards lower vital rates in stable mixed populations (due to survivorship bias) and towards lower absolute values of population change rate (due to changing-frequency bias).

4.We describe and illustrate an individual-based Bayesian procedure for estimating vital rates that reduces biases by accounting for demographic heterogeneity and sampling errors among and within subpopulations.

With the migration of sex workers to online advertising in Canada, a substantial body of research has emerged on how they communicate with prospective clients. However, given the enormous quantity of archival material available, finding representative ways to identify what sex workers say is a difficult task. Numerical analysis of commonly used phrases allows for the analysis of large numbers of documents potentially identifying themes that may be missed using other techniques. This study considers how Canadian sex workers communicate by examining how the word “no” was used by online advertisers over a 15-year period. Source materials consisted of three collections of online classified advertising containing over 4.2 million ads collected between 2007 and 2022 representing 214456 advertisers. Advertisers and demographic variables were extracted from ad metadata. Common terms surrounding the word “no” were used to identify themes. The word “no” was used by 115127 advertisers. Five major themes were identified: client reassurance (54084 advertisers), communication (47130 advertisers), client race (32612 advertisers), client behavior (23863 advertisers), and service restrictions (8545 advertisers). The probability of there being an association between an advertiser and a major theme was found to vary in response to several variables, including: time period, region, advertiser gender, and advertiser ethnicity. Results are compared with previous work on race and risk messaging in sex work advertising and factors influencing client race restrictions are considered. Over time, the restriction related themes of client behavior, service restrictions, and client race became more prominent. Collectives, multi-regional, cis-female, and Black or Mixed ancestry advertisers were more likely to use restrictions.

Strawberry guava (waiawī, Psidium cattleyanum O. Deg., Myrtaceae) is a small tree invasive on oceanic islands where it may alter forest ecosystem processes and community structure. To better understand the dynamics of its invasion in Hawaiian rainforests in anticipation of the release of a biocontrol agent, we measured growth and abundance of vertical stems >= 0.5 cm DBH for 16 years (2005-2020) in an intact Metrosideros-Cibotium rainforest on windward Hawai'i Island. Specifically, we compared the growth and abundance of both shoots (originating from seed or from the root mat) and sprouts (originating above ground from established stems) in four replicate study sites. Mean stem density increased from 9562 stems/ha in 2005 to 26,595 stems/ha in 2020, the majority of which were stems < 2 cm DBH. Mean annual rates of population growth (lambda) varied between 1.03 and 1.17. Early in the invasion, both density and per capita recruitment of shoots was greater than that of sprouts, but as overall stem density increased over time, sprout abundance and recruitment came to surpass that of shoots. Relative growth rates among small stems < 2 cm DBH declined over time for both shoots and sprouts, but relative growth rates of sprouts were consistently greater than that of shoots after the first 3 years. The capacity of strawberry guava to recruit from both rooted shoots and vegetative sprouts contributes to the facility with which it can invade intact rainforest, persist in the forest understory, and respond to canopy opening. Strawberry guava thus poses a considerable risk of stand replacement for Hawaiian rainforests. Stand management will require perpetual efforts of guava control at high priority sites as extreme weather events associated with climate change bring canopy-opening events due to storms, drought and pathogens. Methods Sites: We measured guava stem diameters annually between 2005 and 2020 at each of four replicate study plots selected to represent early stages of strawberry guava invasions in intact Metrosideros-Cibotium rainforest on windward Hawai'i Island (Juvik and Juvik 1998). Wet forests in Hawai'i are high priority conservation areas because of the biological diversity they harbor and their importance in the water economy of the islands (Jacobi and Warshauer 1992, Tunison 1992). Our study plots were established in the following conservation areas: Kahauale'a Natural Area Reserve (KAH, 19o10'N, 155o10'W), Pu'u Maka'ala Natural Area Reserve (MAK, 19o34'N, 155o11'W), Ola'a Forest Reserve (OLA, 19o27'N, 155o11'W), and Upper Waiakea Forest Reserve (WAI, 19o35'N, 155o12'W). All sites are at approximately 900 m elevation and distances between sites are 2 to 17 km. Estimated annual rainfall is 3000-4000 mm at OLA and KAH and 4000-5000 mm at WAI and MAK (Giambelluca et al. 1996). Projected mean annual temperature based on adiabatic lapse rates is 17-17.5° C for the elevation range of the four study sites (Giambelluca and Schroeder 1998). All sites are on relatively young tholeiitic basalt lava flows that formed 200-1500 years BP (Wolfe and Morris 1996). The forests resemble native lowland (100-1200m elevation) wet forests with an 'ōhi'a lehua (Metrosideros polymorpha Gaud) overstory and an understory dominated by tree fern hāpu'u (Cibotium spp.) as described by Gagne and Cuddihy (1999) and Juvik and Juvik (1998). All areas are under conservation protection by the State of Hawai'i. Species: Strawberry guava (waiawī, Psidium cattleyanum O. Deg.) is a small tree, 2-8 m tall. The yellow-fruited form (P. cattleyanum f. lucidum), dominant in the forests studied here, is one of three forms common across Hawai'i (Wagner et al. 1999) occurring in similar habitats. Strawberry guava produces 2-3 cm diameter berries with multiple 5 mm long seeds (Wagner et al. 1999) via both sexual reproduction and apomixis. In the wet forests of Hawai'i, seeds germinate within a year and do not accumulate in a soil seed bank (Uowolo and Denslow 2008). In Hawai'i seeds are dispersed by birds, rodents, and pigs as well as humans.
Strawberry guava also reproduces vegetatively from both above-ground stems and from the root mat. For the purposes of this study, sprouts are defined as arising above-ground from established leaning or vertical stems. Such sprouts may overtake a leaning mother stem, obscuring the origin of older stems. Alternatively rooted shoots may arise via seed germination or directly from the root mat. In this study we measured and tracked vertical stems standing more than 45 degrees from horizontal and greater than 0.5 cm at breast height (1.37 m, DBH). The population thus contained both shoots, apparently originating from seed or roots, and sprouts, originating as branches from older shoots or sprouts. We were unable to distinguish root sprouts from seedlings non-destructively and thus identified stems with an obvious above-ground connection to a mother stem as sprouts; shoots arising from the soil with no obvious above-ground connection to an existing stem were assumed to have originated from seeds or roots. Huenneke and Vitousek (1990), working in forests in the same area found that the proportion of rooted stems arising from seeds versus from roots varied widely. Thus, our study population is narrowly defined as vertical stems arising directly from the soil (shoots) or vegetatively from previously established stems (sprouts); leaning stems were excluded. Surveys: At the start of the study (2005) all four sites had established populations of strawberry guava with a range of stem diameters represented. With one exception (OLA), we established one 0.25 ha plot at each site. The study plot at OLA, with an initially higher-density guava population, was 0.15 ha. All vertical stems at least 2 cm DBH were tagged in each plot and their diameters measured. In addition, we tagged and measured all vertical stems >= 0.5 cm DBH and < 2 cm DBH in a stratified random set of 5 x 5 m subplots at each site (KAH: 6 subplots; MAK: 5 subplots; OLA: 5 subplots; WAI: 11 subplots). Diameter was re-measured annually, and new recruits tagged. Stems dying and leaning to less than 45 deg from horizontal were noted and not included in the study population going forward. The population of strawberry guava at each site reported here thus comprised only vertical stems. Analyses: We calculated basal area and yearly relative growth rates (RGR=log (BA t+1/BA t) based on basal area for individual stems. Density (stems/ha) was calculated from sample plots to allow comparisons among sites with different sample areas; estimates of total population density was based on the sum of the density of stems >=2 cm DBH from the entire plot plus the estimate of density of small stems (>=0.5 cm DBH and < 2cm DBH) from the subplots. Thus, total estimated density comprised all vertical stems >=0.5 cm DBH for each site. Total basal area per hectare was calculated similarly. Lambda (N(t+1)/ N(t)) was calculated from total population densities. To better understand the pattern differences in shoots and sprouts, we focused on sources of variation among small stems < 2 cm DBH, which comprised the majority of the population. Per capita annual recruitment and per capita stem death plus initial leaning of stems were calculated for both shoots and sprouts as a function of the total number of stems of all sizes present in the previous year at each site. To determine whether lambda varied over time, we used linear mixed effects models using the lme() function in the nlme package (Pinheiro et al. 2023) in R (R Core Team, 2022). Year coded as a factor) was the fixed effect and site was the random effect. We accounted for temporal autocorrelation using AR1() auto correlation structure. We used a likelihood ratio test to assess whether the random effect of site was significant. To determine how shoots and sprouts differed over time in their densities, relative abundances, total basal area per ha, relative growth rates of stems < 2cm DBH, per capita recruitment, and per capita dying/leaning, we used linear mixed effects models using the lme() function in the nlme package.. For all models, we included stem type (shoot, sprout), year, and the stem type x year interaction as fixed effects (with year coded as a factor) and site as a random effect. Additionally, for the relative growth rate model we included a random effect of a stem ID nested within site. All models accounted for temporal autocorrelation using AR1() autocorrelation structure. When needed, we also accounted for heteroscedasticity by fitting different variances for each stem type in each year using varIdent(). Type III ANOVAs were run on the models to test significance of fixed effects. Post hoc tests were used to test for the difference between stem types in each year using the multcomp package (Hothorn et al. 2008). Marginal means and standard errors for plotting relative growth rates were calculated using the emmeans package (Lenth 2023). ANOVA results are provided in the figure captions. In addition, we estimated the ages of shoots in the population based on annual basal area increments. For this estimate we pooled data from the shoots at all four study sites under the assumption that the sites were samples of a forest-wide population of strawberry guava. Only shoot growth was used in this estimate because sprout growth is in part dependent on the mother stem. Shoot growth rates vary as a function of their DBH as well as a function of light availability and other microsite characteristics; thus, we used four estimates of annual growth increment to provide a range of age estimates. Excluding stems with zero or negative relative growth rates, we estimated smallest, largest, mean and median basal area increments of shoots in 1 cm DBH size-classes using data for the year 2019-2020. For the few stems larger than 12 cm DBH we pooled data for all remaining large stems to estimate the increment. For each 1 cm growth

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

This table contains data on race, age, sex, and marital status from the American Community Survey 2006-2010 database for tracts. The American Community Survey (ACS) is a household survey conducted by the U.S. Census Bureau that currently has an annual sample size of about 3.5 million addresses. ACS estimates provides communities with the current information they need to plan investments and services. Information from the survey generates estimates that help determine how more than $400 billion in federal and state funds are distributed annually. Each year the survey produces data that cover the periods of 1-year, 3-year, and 5-year estimates for geographic areas in the United States and Puerto Rico, ranging from neighborhoods to Congressional districts to the entire nation. This table also has a companion table (Same table name with MOE Suffix) with the margin of error (MOE) values for each estimated element. MOE is expressed as a measure value for each estimated element. So a value of 25 and an MOE of 5 means 25 +/- 5 (or statistical certainty between 20 and 30). There are also special cases of MOE. An MOE of -1 means the associated estimates do not have a measured error. An MOE of 0 means that error calculation is not appropriate for the associated value. An MOE of 109 is set whenever an estimate value is 0. The MOEs of aggregated elements and percentages must be calculated. This process means using standard error calculations as described in "American Community Survey Multiyear Accuracy of the Data (3-year 2008-2010 and 5-year 2006-2010)". Also, following Census guidelines, aggregated MOEs do not use more than 1 0-element MOE (109) to prevent over estimation of the error. Due to the complexity of the calculations, some percentage MOEs cannot be calculated (these are set to null in the summary-level MOE tables).

The name for table 'ACS10POPTRMOE' was added as a prefix to all field names imported from that table. Be sure to turn off 'Show Field Aliases' to see complete field names in the Attribute Table of this feature layer. This can be done in the 'Table Options' drop-down menu in the Attribute Table or with key sequence '[CTRL]+[SHIFT]+N'. Due to database restrictions, the prefix may have been abbreviated if the field name exceded the maximum allowed characters.

Abstract1. One hypothesis explaining extra-pair reproduction is that socially monogamous females mate with extra-pair males to adjust the coefficient of inbreeding (f) of extra-pair offspring (EPO) relative to that of within-pair offspring (WPO) they would produce with their socially paired male. Such adjustment of offspring f requires non-random extra-pair reproduction with respect to relatedness, which is in turn often assumed to require some mechanism of explicit pre-copulatory or post-copulatory kin discrimination. 2. We propose three demographic processes that could potentially cause mean f to differ between individual females' EPO and WPO given random extra-pair reproduction with available males without necessarily requiring explicit kin discrimination. Specifically, such a difference could arise if social pairings formed non-randomly with respect to relatedness or persisted non-randomly with respect to relatedness, or if the distribution of relatedness between females and their sets of potential mates changed during the period through which social pairings persisted. 3. We used comprehensive pedigree and pairing data from free-living song sparrows (Melospiza melodia) to quantify these three processes and hence investigate how individual females could adjust mean offspring f through instantaneously random extra-pair reproduction. 4. Female song sparrows tended to form social pairings with unrelated or distantly related males slightly less frequently than expected given random pairing within the defined set of available males. Furthermore, social pairings between more closely related mates tended to be more likely to persist across years than social pairings between less closely related mates. However, these effects were small and the mean relatedness between females and their sets of potential extra-pair males did not change substantially across the years through which social pairings persisted. 5. Our framework and analyses illustrate how demographic and social structuring within populations might allow females to adjust mean f of offspring through random extra-pair reproduction without necessarily requiring explicit kin discrimination, implying that adjustment of offspring f might be an inevitable consequence of extra-pair reproduction. New theoretical and empirical studies are required to explore the general magnitude of such effects and quantify the degree to which they could facilitate or constrain long-term evolution of extra-pair reproduction. Usage notesPairsData_DryadThis file contains data required for the basic descriptive analysis of social pairing in relation to kinship.NewMalesPairings_DryadThis file contains data required for the analysis of social pairing in relation to kinship between females and the 'new males' set of potential mates.AllMalesPairings_DryadThis file contains data required for the analysis of social pairing in relation to kinship between females and the 'all males' set of potential mates.PairPersistence_DryadThis file contains data required for the analysis of social pair persistence in relation to kinship.ChangeMeanK_DryadThis file contains data required for the analysis of changing mean kinship within the duration of females' social pairings.

Economically Active Population Survey: Employees by sex,, type of contract or labour relation and types of working conditions. National. Employees by type of shift, sex and type of contract or labour relation.

In 2024, 34.59 percent of all households in the United States were two person households. In 1970, this figure was at 28.92 percent. Single households Single mother households are usually the most common households with children under 18 years old found in the United States. As of 2021, the District of Columbia and North Dakota had the highest share of single-person households in the United States. Household size in the United States has decreased over the past century, due to customs and traditions changing. Families are typically more nuclear, whereas in the past, multigenerational households were more common. Furthermore, fertility rates have also decreased, meaning that women do not have as many children as they used to. Average households in Utah Out of all states in the U.S., Utah was reported to have the largest average household size. This predominately Mormon state has about three million inhabitants. The Church of the Latter-Day Saints, or Mormonism, plays a large role in Utah, and can contribute to the high birth rate and household size in Utah. The Church of Latter-Day Saints promotes having many children and tight-knit families. Furthermore, Utah has a relatively young population, due to Mormons typically marrying and starting large families younger than those in other states.

Ambiguous bigrams are followed by the most common succeeding terms in parentheses.

The real values appear without parentheses, while the values obtained using enlarged samples are in parentheses.