These data were developed by the Research & Analytics Department at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.For a deep dive into the data model including every specific metric, see the ACS 2019-2023. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2019-2023). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin: April 1, 2010 to July 1, 2018 // 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. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // 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. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) 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 Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

BackgroundAn increase in the average age of dengue hemorrhagic fever (DHF) cases has been reported in Thailand. The cause of this increase is not known. Possible explanations include a reduction in transmission due to declining mosquito populations, declining contact between human and mosquito, and changes in reporting. We propose that a demographic shift toward lower birth and death rates has reduced dengue transmission and lengthened the interval between large epidemics.Methods and FindingsUsing data from each of the 72 provinces of Thailand, we looked for associations between force of infection (a measure of hazard, defined as the rate per capita at which susceptible individuals become infected) and demographic and climactic variables. We estimated the force of infection from the age distribution of cases from 1985 to 2005. We find that the force of infection has declined by 2% each year since a peak in the late 1970s and early 1980s. Contrary to recent findings suggesting that the incidence of DHF has increased in Thailand, we find a small but statistically significant decline in DHF incidence since 1985 in a majority of provinces. The strongest predictor of the change in force of infection and the mean force of infection is the median age of the population. Using mathematical simulations of dengue transmission we show that a reduced birth rate and a shift in the population's age structure can explain the shift in the age distribution of cases, reduction of the force of infection, and increase in the periodicity of multiannual oscillations of DHF incidence in the absence of other changes.ConclusionsLower birth and death rates decrease the flow of susceptible individuals into the population and increase the longevity of immune individuals. The increase in the proportion of the population that is immune increases the likelihood that an infectious mosquito will feed on an immune individual, reducing the force of infection. Though the force of infection has decreased by half, we find that the critical vaccination fraction has not changed significantly, declining from an average of 85% to 80%. Clinical guidelines should consider the impact of continued increases in the age of dengue cases in Thailand. Countries in the region lagging behind Thailand in the demographic transition may experience the same increase as their population ages. The impact of demographic changes on the force of infection has been hypothesized for other diseases, but, to our knowledge, this is the first observation of this phenomenon.Please see later in the article for the Editors' Summary

Climate change is projected to cause extensive plant range shifts, and in many cases such shifts already are underway. Most long-term studies of range shifts measure emergent changes in species distributions but not the underlying demographic patterns that shape them. To better understand species’ elevational range shifts and their underlying demographic processes, we use the powerful approach of rephotography, comparing historical (1978-82) and modern (2015-16) photographs taken along a 1000 m elevational gradient in theColorado Desert of Southern California. This approach allowed us to track demographic outcomes for 4263 individual plants of 11 long-lived, perennial species over the past ~36 years. All species showed an upward shift in mean elevation (average = 45 m), consistent with observed increasing temperature and severe drought in the region. We found that varying demographic processes underlaid these elevational shifts, with some species showing higher recruitment and some showing higher survival with increasing elevation. Species with faster life history rates (higher background recruitment and mortality rates) underwent larger elevational shifts. Our findings emphasize the importance of demography and life history in shaping range shift responses and future community composition, as well as the sensitivity of desert systems to climate change despite the typical ‘slow motion’ population dynamics of perennial desert plants. Methods We utilized photos originally taken by Dr. Wilbur Mayhew between 1977 and 1982 (Mayhew 1981), which we digitized from 35 mm slides stored at Philip L. Boyd Deep Canyon Desert Research Center (doi:10.21973/N3V66D). We relocated permanently marked sites where historical photos had been taken and rephotographed them using a Canon 5D Mark II camera and tripod in 2015 and 2016. We took one additional set of photos in April 2017 after the end of a multi-year drought, so that we could distinguish dormant from dead individuals of two drought-deciduous species (brittlebush, Encelia farinosa and white bursage, Ambrosia dumosa). We approximated the original view of the original photos as closely as possible in modern photos. For each photo view, we chose a single historical and modern photo for analysis based on resolution, contrast and coloration. The mean timespan between paired historical and modern photos was 36 years. We perfected the alignment between the paired historical and modern photos in Photoshop by making one photo semi-transparent, then rotating and resizing it while maintaining original aspect ratios. Data extraction We extracted data on 11 perennial species that appeared in 5+ sites. We extracted data from the photos in ArcGIS, arranging the paired photos as map layers. We created polygons to delimit a survey area close enough to the camera to identify species; these polygons serve as the “sites” in our subsequent analysis. In some cases, we collected data on larger-bodied or particularly conspicuous species, such as ocotillo (Fouquieria splendens) and creosote (Larrea tridentata), in a larger area including locations farther from the camera than for smaller, less conspicuous species. We recorded whether each plant underwent recruitment (absent historical, alive modern), mortality (alive historical, dead modern) or survival (alive both). We excluded plants that were dead in the historical period or with main stems outside the site polygon. In some cases we consulted other historical and modern photos of the same site to determine species identity or assess whether an individual was alive. We counted and measured clusters of agave (Agave deserti) and Mojave yucca (Yucca schidigera) as single individuals. Rarely, we may have misidentified pygmy cedar (Peucephyllum schottii) for creosote where these species co-occur on steep slopes, since they have similar morphology and are difficult to distinguish from a distance. We measured individual relative change in plant size by measuring the height (perpendicular to the ground) and width (the largest horizontal extent of the plant perpendicular to the camera, i.e. canopy width) of surviving plants in both time periods, using the ruler tool in ArcGIS and focusing on woody biomass. When dead agave rosettes were surrounded by live rosettes, we did not include the width that was dead if it was >20% the total width. We calculated the relative change in height of each plant as (H1–H0) / H0, where H indicates plant height and the subscripts 0 and 1 indicate the historical and modern period, respectively. We used an equivalent equation for relative change in width. For some species at some sites, we could not track the fate of individuals between the two time periods. This most often occurred for narrow-bodied and relatively short-lived species (e.g. teddy bear cholla, Cylindropuntia bigelovii) in photo pairs that were difficult to perfectly align, thereby making it difficult to tell whether plants either survived, or died and were replaced by recruits. It also occurred when a large plant died and a new plant “appeared” in a spot that was previously hidden, such that we were unable to determine whether the second plant was a recruit or a surviving plant. We therefore designated two site types for each species: “trackable” sites – those where we could track the fate of at least one third of individuals of a given species over time, and “count-only” sites – those where we could track fewer than one third of individuals, and instead only counted individuals. Count-only sites were retained for analyses of mean elevation shifts but not demographic rates. Geophysical data We used Google Earth Pro “ground level view” to draw polygons matching the extent of the site polygons outlined in the photos. To do so, we first “stood” at the camera’s locality and angle, then used corresponding features (e.g. washes, large creosote, hills) to find the exact site, and finally dropped pins to mark polygon vertices. We used these polygons to extract data on each site’s size, as well as its mean elevation, aspect, slope and annual solar radiation (“insolation”) using USGS NED Contiguous US 1/3 arc-second digital elevation model (2013) in ArcGIS. We took the cosine of aspect to create linear values ranging from -1 (South) to 1 (North). Additional details Additional details on how these data were collected and processed can be found in the Methods and Supplementary Materials of Skikne et al. 2024. Contrasting demographic processes underlie uphill shifts in a desert ecosystem.

this graph was created in R,PowerBi and Tableau:

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The elderly population refers to the portion of a country's inhabitants who are aged 65 and older. This demographic plays a crucial role in various economic and social analyses, especially when it comes to determining the dependent population. The dependent population consists of those individuals who do not actively participate in the workforce and, as a result, rely on others for essential goods and services. This group primarily includes both the elderly and the youth (typically under 15 years of age).

The concept of the elderly dependency ratio is a significant measure used to understand the burden on the working-age population, which consists of those between the ages of 15 and 64. This ratio is calculated by comparing the number of elderly people to those of working age. A higher elderly dependency ratio indicates a larger proportion of elderly individuals relative to those who are contributing economically, leading to increased demands on social systems such as healthcare, pensions, and other support services.

These demographic shifts have widespread implications for both government policies and private sectors. As the elderly population increases, so too does the pressure on pension systems and healthcare services, necessitating reforms to ensure sustainability. Additionally, the aging population affects broader economic growth and welfare, as fewer people of working age contribute to economic productivity, potentially slowing overall economic expansion.

This indicator, often measured as a percentage of the total population, provides valuable insights into the aging trends within a society and their potential impact on the economy, welfare, and social structures. Understanding these trends is essential for shaping future policies that address the needs of an aging population while maintaining economic stability and growth.

Guaranteed Income Product Market Outlook

According to our latest research, the global guaranteed income product market size reached USD 2.13 trillion in 2024, with a robust compound annual growth rate (CAGR) of 7.4% projected through the forecast period. By 2033, the market is expected to reach USD 4.06 trillion, driven by rising demand for financial security, demographic shifts, and product innovation. The guaranteed income product market is experiencing notable expansion, fueled by increased consumer awareness, evolving regulatory frameworks, and a growing emphasis on retirement planning and wealth protection.

The guaranteed income product market is witnessing significant growth due to the heightened need for financial stability in an increasingly volatile global economic environment. As individuals and organizations seek to mitigate risks associated with market fluctuations and uncertain income streams, demand for products like annuities, structured settlements, and pension plans has surged. Furthermore, the ongoing shift from defined benefit to defined contribution retirement schemes has placed greater responsibility on individuals to secure their post-retirement income, amplifying the appeal of guaranteed income solutions. The market is also benefiting from the proliferation of product options that cater to diverse risk profiles and financial goals, making guaranteed income products more accessible and customizable than ever before.

Another key growth factor for the guaranteed income product market is the rapid digital transformation within the financial services sector. The integration of advanced technologies such as artificial intelligence, machine learning, and blockchain has streamlined the product distribution process, enhanced customer engagement, and improved the transparency and efficiency of transactions. Online platforms are playing a pivotal role in educating consumers, offering tailored advice, and simplifying the purchase journey. This digital evolution has not only broadened the reach of guaranteed income products but has also enabled providers to develop innovative solutions that address the unique needs of a diverse clientele, including younger demographics who are increasingly prioritizing long-term financial planning.

Demographic trends, particularly the aging global population, are significantly influencing the guaranteed income product market. As life expectancy rises and birth rates decline in many developed and emerging economies, the proportion of retirees seeking stable income streams is increasing rapidly. Governments and employers are also encouraging private retirement savings, further boosting the adoption of guaranteed income products. Additionally, heightened awareness of longevity risk—the risk of outliving one’s savings—has prompted both individuals and institutions to explore these products as a means of securing predictable, lifelong income. This demographic shift is particularly pronounced in regions such as North America, Europe, and parts of Asia Pacific, where pension reforms and social security limitations are reinforcing the importance of personal retirement planning.

Regionally, the guaranteed income product market exhibits strong growth potential across Asia Pacific, North America, and Europe, with each region displaying unique drivers and challenges. North America remains the largest market, supported by a mature insurance and pension landscape, while Asia Pacific is witnessing the fastest growth due to rapid urbanization, rising middle-class affluence, and proactive government initiatives to promote retirement savings. Europe, on the other hand, is characterized by a sophisticated regulatory environment and high levels of consumer awareness. Latin America and the Middle East & Africa are emerging markets, gradually increasing their market shares as financial literacy improves and new distribution channels are developed. The global marketÂ’s expansion is thus underpinned by both mature and emerging economies, each contributing to the sectorÂ’s dynamic growth trajectory.

Annuities, as a financial instrument, have long been a cornerstone of retirement planning, offering individuals a reliable means to secure a steady income stream during their post-worki

In 2023, the median age of the population of the United States was 39.2 years. While this may seem quite young, the median age in 1960 was even younger, at 29.5 years. The aging population in the United States means that society is going to have to find a way to adapt to the larger numbers of older people. Everything from Social Security to employment to the age of retirement will have to change if the population is expected to age more while having fewer children. The world is getting older It’s not only the United States that is facing this particular demographic dilemma. In 1950, the global median age was 23.6 years. This number is projected to increase to 41.9 years by the year 2100. This means that not only the U.S., but the rest of the world will also have to find ways to adapt to the aging population.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

These summary metadata refer to the first results on the main demographic developments in the year of reference.

Member States send to Eurostat the first results on the main demographic developments in the year of reference (T), containing the total population figure on 31 December of year T (further published by Eurostat as Population on 1 January of year T+1), total births and total deaths during year T. This data collection is defined under http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32013R1260&from=EN" target="_blank">Regulation 1260/2013 on European demographic statistics. Countries may also transmit to Eurostat, on voluntary basis, provisional data on total immigration, emigration and net migration during the year (T).

Eurostat's data collection on the above figures is called DEMOBAL and it is carried out in June of each year. Eurostat publishes these first demographic estimates in July of each year in the online database, in the table Population change - Demographic balance and crude rates (demo_gind).

These first demographic estimates may either be confirmed or updated by Eurostat's demographic data collection taking place in December each year (called Unidemo), whereby countries submit detailed breakdowns (e.g. by age and sex) of their yearly population data, including data on migration, both at national and at regional level. The online table Population change - Demographic balance and crude rates (demo-gind) will be accordingly updated. This table includes the latest updates on total population, births and deaths reported by the countries, while the detailed breakdowns by various characteristics included in the rest of the tables of the Eurostat database (Demography domain and Migration, for example the Population by citizenship and by country of birth table) may be transmitted to Eurostat at a subsequent date.

The online table Population change - Demographic balance and crude rates (demo-gind) contains time series going back to 1960; data before 2013 were collected by Eurostat from the national statistical offices on voluntary basis.

The individual metadata files reported by the countries are attached to this metadata file.

Please note this page provides neighborhood demographic data using 2010 Census tracts. For updated Neighborhood Demographics using 2020 Census tracts consistently across historical years, please refer to the Planning Department Research Division's Exploring Neighborhood Change Tool. The tool visualizes demographic, economic, and housing data for Boston's tracts and neighborhoods from 1950 to 2025 (with projections to 2035) using the most up-to-date 2020 Census tract-based Neighborhood boundaries.

Boston is a city defined by the unique character of its many neighborhoods. The historical tables created by the BPDA Research Division from U.S. Census Decennial data describe demographic changes in Boston’s neighborhoods from 1950 through 2010 using consistent tract-based geographies. For more analysis of these data, please see Historical Trends in Boston's Neighborhoods. The most recent available neighborhood demographic data come from the 5-year American Community Survey (ACS). The ACS tables also present demographic data for Census-tract approximations of Boston’s neighborhoods. For pdf versions of the data presented here plus earlier versions of the analysis, please see Boston in Context.

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 in the United States reached 39.2 years in 2024. This was up from 28.1 in 1970, reflecting a significant aging of the population. Over the coming decades, the number of retirees is projected to rise by about 40 percent by 2050. This demographic shift will present new challenges to American society, reshaping patterns of consumption, work and public policy in the decades ahead. Can an older America balance the books? Social Security spending is set to rise as America grows older. The program, which is the government’s main pillar of support for retirees, already absorbs about five percent of GDP. This could reach six percent by 2035. That trajectory will keep pressure on policymakers to balance promises to pensioners with broader fiscal constraints. A world growing older The aging trend is not unique to the U.S. The global median age reached 30.9 in 2025, up from 20.3 in 1970. By 2050, China, Japan and South Korea are expected to rank among the countries with the largest shares of people aged 65 and over. The change will oblige policymakers to adapt long-standing arrangements to societies where a larger share of people are in later life.

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.

Abstract

The Thai Demographic and Health Survey (TDHS) was a nationally representative sample survey conducted from March through June 1988 to collect data on fertility, family planning, and child and maternal health. A total of 9,045 households and 6,775 ever-married women aged 15 to 49 were interviewed. Thai Demographic and Health Survey (TDHS) is carried out by the Institute of Population Studies (IPS) of Chulalongkorn University with the financial support from USAID through the Institute for Resource Development (IRD) at Westinghouse. The Institute of Population Studies was responsible for the overall implementation of the survey including sample design, preparation of field work, data collection and processing, and analysis of data. IPS has made available its personnel and office facilities to the project throughout the project duration. It serves as the headquarters for the survey.

The Thai Demographic and Health Survey (TDHS) was undertaken for the main purpose of providing data concerning fertility, family planning and maternal and child health to program managers and policy makers to facilitate their evaluation and planning of programs, and to population and health researchers to assist in their efforts to document and analyze the demographic and health situation. It is intended to provide information both on topics for which comparable data is not available from previous nationally representative surveys as well as to update trends with respect to a number of indicators available from previous surveys, in particular the Longitudinal Study of Social Economic and Demographic Change in 1969-73, the Survey of Fertility in Thailand in 1975, the National Survey of Family Planning Practices, Fertility and Mortality in 1979, and the three Contraceptive Prevalence Surveys in 1978/79, 1981 and 1984.

Geographic coverage

National

Analysis unit

• Household
• Women age 15-49

Universe

The population covered by the 1987 THADHS is defined as the universe of all women Ever-married women in the reproductive ages (i.e., women 15-49). This covered women in private households on the basis of a de facto coverage definition. Visitors and usual residents who were in the household the night before the first visit or before any subsequent visit during the few days the interviewing team was in the area were eligible. Excluded were the small number of married women aged under 15 and women not present in private households.

Kind of data

Sample survey data

Sampling procedure

SAMPLE SIZE AND ALLOCATION

The objective of the survey was to provide reliable estimates for major domains of the country. This consisted of two overlapping sets of reporting domains: (a) Five regions of the country namely Bangkok, north, northeast, central region (excluding Bangkok), and south; (b) Bangkok versus all provincial urban and all rural areas of the country. These requirements could be met by defining six non-overlapping sampling domains (Bangkok, provincial urban, and rural areas of each of the remaining 4 regions), and allocating approximately equal sample sizes to them. On the basis of past experience, available budget and overall reporting requirement, the target sample size was fixed at 7,000 interviews of ever-married women aged 15-49, expected to be found in around 9,000 households. Table A.I shows the actual number of households as well as eligible women selected and interviewed, by sampling domain (see Table i.I for reporting domains).

THE FRAME AND SAMPLE SELECTION

The frame for selecting the sample for urban areas, was provided by the National Statistical Office of Thailand and by the Ministry of the Interior for rural areas. It consisted of information on population size of various levels of administrative and census units, down to blocks in urban areas and villages in rural areas. The frame also included adequate maps and descriptions to identify these units. The extent to which the data were up-to-date as well as the quality of the data varied somewhat in different parts of the frame. Basically, the multi-stage stratified sampling design involved the following procedure. A specified number of sample areas were selected systematically from geographically/administratively ordered lists with probabilities proportional to the best available measure of size (PPS). Within selected areas (blocks or villages) new lists of households were prepared and systematic samples of households were selected. In principle, the sampling interval for the selection of households from lists was determined so as to yield a self weighting sample of households within each domain. However, in the absence of good measures of population size for all areas, these sampling intervals often required adjustments in the interest of controlling the size of the resulting sample. Variations in selection probabilities introduced due to such adjustment, where required, were compensated for by appropriate weighting of sample cases at the tabulation stage.

SAMPLE OUTCOME

The final sample of households was selected from lists prepared in the sample areas. The time interval between household listing and enumeration was generally very short, except to some extent in Bangkok where the listing itself took more time. In principle, the units of listing were the same as the ultimate units of sampling, namely households. However in a small proportion of cases, the former differed from the latter in several respects, identified at the stage of final enumeration: a) Some units listed actually contained more than one household each b) Some units were "blanks", that is, were demolished or not found to contain any eligible households at the time of enumeration. c) Some units were doubtful cases in as much as the household was reported as "not found" by the interviewer, but may in fact have existed.

Mode of data collection

Face-to-face

Research instrument

The DHS core questionnaires (Household, Eligible Women Respondent, and Community) were translated into Thai. A number of modifications were made largely to adapt them for use with an ever- married woman sample and to add a number of questions in areas that are of special interest to the Thai investigators but which were not covered in the standard core. Examples of such modifications included adding marital status and educational attainment to the household schedule, elaboration on questions in the individual questionnaire on educational attainment to take account of changes in the educational system during recent years, elaboration on questions on postnuptial residence, and adaptation of the questionnaire to take into account that only ever-married women are being interviewed rather than all women. More generally, attention was given to the wording of questions in Thai to ensure that the intent of the original English-language version was preserved.

a) Household questionnaire

The household questionnaire was used to list every member of the household who usually lives in the household and as well as visitors who slept in the household the night before the interviewer's visit. Information contained in the household questionnaire are age, sex, marital status, and education for each member (the last two items were asked only to members aged 13 and over). The head of the household or the spouse of the head of the household was the preferred respondent for the household questionnaire. However, if neither was available for interview, any adult member of the household was accepted as the respondent. Information from the household questionnaire was used to identify eligible women for the individual interview. To be eligible, a respondent had to be an ever-married woman aged 15-49 years old who had slept in the household 'the previous night'.

Prior evidence has indicated that when asked about current age, Thais are as likely to report age at next birthday as age at last birthday (the usual demographic definition of age). Since the birth date of each household number was not asked in the household questionnaire, it was not possible to calculate age at last birthday from the birthdate. Therefore a special procedure was followed to ensure that eligible women just under the higher boundary for eligible ages (i.e. 49 years old) were not mistakenly excluded from the eligible woman sample because of an overstated age. Ever-married women whose reported age was between 50-52 years old and who slept in the household the night before birthdate of the woman, it was discovered that these women (or any others being interviewed) were not actually within the eligible age range of 15-49, the interview was terminated and the case disqualified. This attempt recovered 69 eligible women who otherwise would have been missed because their reported age was over 50 years old or over.

b) Individual questionnaire

The questionnaire administered to eligible women was based on the DHS Model A Questionnaire for high contraceptive prevalence countries. The individual questionnaire has 8 sections: - Respondent's background - Reproduction - Contraception - Health and breastfeeding - Marriage - Fertility preference - Husband's background and woman's work - Heights and weights of children and mothers

The questionnaire was modified to suit the Thai context. As noted above, several questions were added to the standard DHS core questionnaire not only to meet the interest of IPS researchers hut also because of their relevance to the current demographic situation in Thailand. The supplemental questions are marked with an asterisk in the individual questionnaire. Questions concerning the following items were added in the individual questionnaire: - Did the respondent ever

These data identify the mean annual population growth rate and ratio change in abundance of common raven (Corvus corax; ravens) populations from 1966 through 2018.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2000 data. Boundaries for urban areas have not been updated since Census 2000. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2008-2012 American Community Survey (ACS) data generally reflect the December 2009 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..For more information on understanding race and Hispanic origin data, please see the Census 2010 Brief entitled, Overview of Race and Hispanic Origin: 2010, issued March 2011. (pdf format).The ACS questions on Hispanic origin and race were revised in 2008 to make them consistent with the Census 2010 question wording. Any changes in estimates for 2008 and beyond may be due to demographic changes, as well as factors including questionnaire changes, differences in ACS population controls, and methodological differences in the population estimates, and therefore should be used with caution. For a summary of questionnaire changes see http://www.census.gov/acs/www/methodology/questionnaire_changes/. For more information about changes in the estimates see http://www.census.gov/population/hispanic/files/acs08researchnote.pdf..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2008-2012 American Community Survey

Tropical mountains are global hotspots for birdlife. However, there is a dearth of baseline avifaunal data along eleva-tional gradients, particularly in Africa, limiting our ability to observe and assess changes over time in tropical montane avian communities. In this study, we undertook a multi-year assessment of understory birds along a 1,750 m elevational gradient (1,430-3,186 m) in an Afrotropical moist evergreen montane forest within Ethiopia's Bale Mountains. Analyzing 6 years of systematic bird-banding data from 5 sites, we describe the patterns of species richness, abundance, community composition, and demographic rates over space and time. We found bimodal patterns in observed and estimated species richness across the elevational gradient (peaking at 1,430 and 2,388 m), although no sites reached asymptotic species richness throughout the study. Species turnover was high across the gradient, though forested sites at mid-elevations resembled each other in species composition. We found significant variation across sites in bird abundance in some of the dietary and habitat guilds. However, we did not find any significant trends in species richness or guild abundances over time. For the majority of analyzed species, capture rates did not change over time and there were no changes in species' mean elevations. Population growth rates, recruitment rates, and apparent survival rates averaged 1.02, 0.52, and 0.51 respectively, and there were no elevational patterns in demographic rates. This study establishes a multi-year baseline for Afrotropical birds along an elevational gradient in an under-studied international biodiversity hotspot. These data will be critical in assessing the long-term responses of tropical montane birdlife to climate change and habitat degradation.

Methods Statistical Analyses

Community-level Analyses

To test whether our survey effort had adequately surveyed the local bird community, we calculated rarified species accumulation curves across sampling days for each site, based on observed and expected (sample-based rarefaction) species richness (Colwell et al. 2012) using the “exact” method of the specaccum function from the R package VEGAN (Oksanen et al. 2019). Since our species accumulation curves did not reach asymptotes for species richness, observed species richness likely does not capture true species richness. We, therefore, used sample-size-based rarefaction and extrapolation (R/E) of Hill numbers (the effective number of species, which integrates species richness and relative abundances; Chao et al. 2014). Sample-size-based rarefaction and extrapolation of Hill numbers is an emerging approach used to standardize and compare estimates of diversity between samples (see Cox et al. 2017, Fair et al. 2018, Baumel et al. 2018, Chao et al. 2019, Debela et al. 2020). Specifically, we used this framework to estimate two values of Hill number 0 (i.e. estimated species richness). First, we calculated standardized species richness. We used the function iNEXT from the R package iNEXT (Hsieh et al. 2016) to calculate R/E curves, standardizing our curve parameters to a maximum of 1,000 individual bird captures (endpoint = 1,000), knots = 500, and a bootstrap replication of 1,000 (nboot = 1,000). From these curves, we provide standardized estimates of species richness based on the sampling of 1,000 individuals at each site. We also estimated asymptotic species richness using the function ChaoRichness from the package iNEXT (Hsieh et al. 2016). Although the asymptotic species richness is an estimate of true species richness, in practice, reaching an asymptote can take a long time and a lot of sampling. We then plotted the R/E curves of standardized species richness (i.e. over 1,000 individuals) for each site as a function of sample size using the function ggiNEXT (Hsieh et al. 2016). We also visualized asymptotic species richness by setting the endpoint of the iNEXT function to 10,000 individuals.

Next, we assessed the spatial and temporal patterns in observed species richness and guild-specific captures. For guild-specific captures, we identified the primary diet and habitat association of each species using a global dataset of avian ecological traits (Table 1; see Şekercioğlu et al. 2004, 2019 for a description of the dataset) and summed captures for each separate guild based on either primary diet or habitat. We restricted our analyses to guilds that had ≥40 captures and ≥5 species over the study period and modeled each guild independently. We chose a ≥40 capture threshold as our cutoff between infrequently and frequently encountered species. Most species above this threshold were recorded each year and more than once or twice in each year (the few species that were not recorded each year were recorded multiple times in the other years), whereas individuals under this threshold tended to have few captures across more than one year. We chose a ≥5 species threshold for the guild models to ensure that results for these metrics represented more than a few species.

We constructed models comparing each response variable (observed species richness, dietary, and habitat guild-specific captures) as a function of the site, and included the number of survey days per site and year (Table 2) as a covariate to control for the variation in the sampling effort. We used generalized linear models (GLMs) for species richness and guild-specific captures, as these represent count data. Within the GLMs, we used a Poisson error structure for species richness, and for guild-specific captures, we used a quasi-Poisson error structure to account for over-dispersion in the count data. To assess changes in the bird community over time, we ran an additional model for each response variable that contained year and site, with a year * site interaction (error structures were applied as above). We tested the significance of the explanatory variables in the GLMs with an analysis of deviance.

We assessed species dissimilarity between sites along the elevational gradient by calculating the Sørenson dissimilarity index (S8) for pairs of sites adjacent to each other along the elevational gradient, as well as for Chiri-1430 and Dinsho-3186 at either end of the gradient. S8 can range from complete dissimilarity (S8 = 1) to complete similarity (S8 = 0). This dissimilarity can be further decomposed into turnover and nestedness, which we calculated using the function beta.pair in the package betapart (Baselga et al. 2020). Finally, to compare community composition (captures of different species, weighted by abundance), we ran a Principal Coordinate Analysis (PCoA) based on a Bray-Curtis dissimilarity matrix (Legendre and Legendre 2012). A PCoA extracts the greatest orthogonal axes of variation in community composition, plotting them in multidimensional space such that more similar communities are closer to each other in Euclidean space. We extracted the first two axes from the PCoA that represent the greatest variation in community composition.

Species-level Analyses

As a proxy for species abundance (Dulle et al. 2016), we calculated species-specific captures (the number of captured and recaptured individuals of a particular species) per site and year for the most frequently-captured species (≥ 40 captures over the study period). To assess the variation in species’ elevational distributions, we calculated the mean elevation at which each species was detected each year (hereafter “mean elevation”) for frequently-captured species that were detected at least once in every year of the study. Smaller range shifts in tropical birds are more detectable when analyzing mean elevational occurrence rather than the changes in upper or lower range boundaries, as the position of range boundaries is strongly dependent on the sampling effort (Shoo et al. 2006).

We regressed both species-specific captures (in a GLM with a quasi-Poisson error structure) and mean elevation (in a simple linear model) against year. Since the Dinsho-3186 site was located far from the other sites, we decided to re-run the species-level analyses with Dinsho-3186 data removed. The results remained similar with Dinsho-3186 excluded (Supplemental Material Tables S2 and S3) and, therefore, we retained Dinsho-3186 data in the analyses to increase our statistical power. Additionally, we compared our elevational records for banded birds with those reported in the literature for Ethiopia and the Horn of Africa (Ash and Atkins 2009, Dowsett and Dowsett-Lemaire 2015, Rannestad 2016) in order to assess whether any species were detected outside of their recorded elevational distributions. We used an elevational difference of at least 150 m to indicate whether a species had clearly been recorded in our study higher or lower than previously reported in Ethiopia, a distance previously used to signify extralimital records of birds in Ethiopia (Dowsett and Dowsett-Lemaire 2015). A difference of <150 m could result from chance, whereas a difference >150 m is more likely to result from a systematic change in the elevational range.

At the population level, we used Pradel models (Pradel 1996) implemented with the package RMark (Laake and Rexstad 2012) to estimate the rates of apparent survival (φ), recruitment (F), and realized population growth (λ) while controlling for encounter probabilities (p). φ is the rate at which individuals remain in the population; F is the rate at which new individuals join the population via birth or immigration; and λ is the combined effect of survival and recruitment. A population does not change in size when λ = 1, declines when λ <1, and grows when λ >1. These mark-recapture models cannot distinguish movement in and out of a study area (immigration/emigration) from true birth and survival. However, birds living in tropical mountains are known to have small range sizes (Orme et al. 2006), and tropical

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2000 data. Boundaries for urban areas have not been updated since Census 2000. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2012 American Community Survey (ACS) data generally reflect the December 2009 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..The ACS questions on Hispanic origin and race were revised in 2008 to make them consistent with the Census 2010 question wording. Any changes in estimates for 2008 and beyond may be due to demographic changes, as well as factors including questionnaire changes, differences in ACS population controls, and methodological differences in the population estimates, and therefore should be used with caution. For a summary of questionnaire changes see http://www.census.gov/acs/www/methodology/questionnaire_changes/. For more information about changes in the estimates see http://www.census.gov/population/hispanic/files/acs08researchnote.pdf..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2012 American Community Survey

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

This indicator is no longer maintained, and is considered OBSOLETE.

INDICATOR DEFINITION
Demographic parameters for the Adelie penguin at Bechervaise Island near Mawson.

TYPE OF INDICATOR
There are three types of indicators used in this report:
1.Describes the CONDITION of important elements of a system;
2.Show the extent of the major PRESSURES exerted on a system;
3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: CONDITION

RATIONALE FOR INDICATOR SELECTION
The Adelie penguin is a relatively long lived sea bird dependent on krill. It is expected that major changes in the availability of food (krill) to sea birds will be reflected ultimately in recruitment into the breeding population. Causes of changes in the availability of krill relate directly to changes in both the biological and physical environment brought about by man made or natural means. Ageing populations may give an outward appearance of stability in terms of numbers at a breeding colony but such a condition may mask a decline in recruitment. To determine whether there are environmental influences on the population it is necessary to undertake detailed demographic studies.

Demographic studies carried out over many years on animal populations comprising known age cohorts are required to determine those factors responsible for any observed changes in recruitment and/or mortality. Population reconstruction techniques provide estimates of recruitment and mortality and relate these functions to population size and/or population trends. These studies may alert us to possible changes in the ecosystem particularly related to the availability of food to the penguins or changes to the physical environment. The identification of the cause of changes must come from detailed investigations of food availability and the environment carried out at the same time.

Annual breeding success at Bechervaise Island (eggs laid to chicks fledged) varies enormously from 0 in catastrophic years to above 1 for good seasons. The population at Bechervaise Island near Mawson has been monitored since 1990 as part of the CCAMLR Ecosystem Monitoring Program. Chicks and adults have been tagged annually. The number of breeding pairs has increased slightly between 1990-2001, but changes in the non -breeding population are unknown. Demographic studies based on the return rate of birds tagged as chicks provide information on trends in the overall population and the net rate of recruitment. Since it is intended that this program be undertaken indefinitely it makes this population an excellent subject for monitoring in the context of the SOE.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM
Spatial Scale: Restricted to the Mawson region. Similar studies are carried out by other national research programs at Terra Nova Bay (Italy) and on the Antarctic Peninsula (USA).

Frequency: Annual

Measurement Technique: The Adelie penguin population at Bechervaise Island consists of approximately 1800 breeding pairs. Each breeding season since 1990/91 in excess of 250 chicks have been given implanted electronic identification tags. The return of birds to their natal colony has been detected automatically by the Automated Penguin Monitoring System (APMS)or by checking all birds with a hand held tag reader. Additional and associated biological data as prescribed by CCAMLR (1997 are collected to aid interpretation of demographic and other trends. To detect trends in the population size and in demographic parameters, particularly of recruitment, it will be necessary to maintain an annual tagging program of chicks and recording of all tagged birds.

RESEARCH ISSUES
comprehensive analysis of the data collected over the duration of this study is required to determine natural variation and potential anthropogenic influences affecting Adelie penguin population dynamics.

LINKS TO OTHER INDICATORS
Sea-ice extent and concentration.