In small isolated populations, genetic drift is expected to increase chance fixation of partly recessive, mildly deleterious mutations, reducing mean fitness and inbreeding depression within populations and increasing heterosis in outcrosses between populations. We estimated relative effective sizes and migration among populations and compared mean fitness, heterosis, and inbreeding depression for eight large and eight small populations of a perennial plant on the basis of fitness of progeny produced by hand pollinations within and between populations. Migration was limited, and, consistent with expectations for drift, mean fitness was 68% lower in small populations; heterosis was significantly greater for small (mean = 70%, SE = 14) than for large populations (mean = 7%, SE = 27); and inbreeding depression was lower, although not significantly so, in small (mean = )0.29%, SE = 28) than in large (mean = 0.28%, SE = 23) populations. Genetic drift promotes fixation of deleterious mutations in small populations, which could threaten their persistence. Limited migration will exacerbate drift, but data on migration and effective population sizes in natural populations are scarce. Theory incorporating realistic vari- ation in population size and patterns of migration could better predict genetic threats to small population persistence.

This map contains the 2020 Vulnerable Population Index along with the component demographic layers. The following seven populations were determined to be vulnerable based on an understanding of both federal requirements and regional demographics: 1) Low-Income Population (below 200% of poverty level) 2) Non-Hispanic Minority Population 3) Hispanic or Latino Population (all races) 4) Population with Limited English Proficiency (LEP) 5) Population with Disabilities 6) Elderly Population (age 75 and up) 7) Households with No CarFor each of these populations, Census tracts with concentrations above the regional mean concentration are divided into two categories above the regional mean. These categories are calculated by dividing the range of values between the regional mean and the regional maximum into two equal-sized intervals. Tracts in the lower interval are given a score of 1 and tracts in the upper interval are given a score of 2 for that demographic variable. The scores are totaled from the seven individual demographic variables to yield the Vulnerable Population Index (VPI). The VPI can range from zero to fourteen (0 to 14). A lower VPI indicates a less vulnerable area, while a higher VPI indicates a more vulnerable area.FIELDSP_PovL100: Percent Below 100% of the Poverty Level, P_PovL200: Percent Below 200% of the Poverty Level, P_Minrty: Percent Minority (non-White, non-Hispanic), P_Hisp: Percent Hispanic, P_LEP: Percent Limited English Proficiency (speak English "not well" or "not at all"), P_Disabld: Percent with Disabilities, P_Elderly: Percent Elderly (age 75 and over), P_NoCarHH: Percent Households with No Vehicle, RG_PovL100: Regional Average (Mean) of Percent Below 100% of the Poverty Level, RG_PovL200: Regional Average (Mean) of Percent Below 200% of the Poverty Level, RG_Minrty: Regional Average (Mean) of Percent Minority (non-White, non-Hispanic), RG_Hisp: Regional Average (Mean) of Percent Hispanic, RG_LEP: Regional Average (Mean) of Percent Limited English Proficiency (speak English "not well" or "not at all"), RG_Disabld: Regional Average (Mean) of Percent with Disabilities, RG_Elderly: Regional Average (Mean) of Percent Elderly (age 75 and over), RG_NoCarHH: Regional Average (Mean) of Percent Households with No Vehicle, [NO SC_PovL100: Note: Percent Below 100% of the Poverty Level not used in VPI 2020 calculation],SC_PovL200: VPI Score for Below 200% of the Poverty Level (Values: 0, 1, or 2),SC_Minrty: VPI Score for Minority (non-White, non-Hispanic) (Values: 0, 1, or 2),SC_Hisp: VPI Score for Hispanic (Values: 0, 1, or 2),SC_LEP: VPI Score for Limited English Proficiency (speak English "not well" or "not at all") (Values: 0, 1, or 2),SC_Disabld: VPI Score for Disabilities (Values: 0, 1, or 2),SC_Elderly: VPI Score for Elderly (age 75 and over) (Values: 0, 1, or 2),SC_NoCarHH: VPI Score for Households with No Vehicle (Values: 0, 1, or 2),VPI_2020: Total VPI Score (0 minimum to 14 maximum).Additional information on equity planning at BMC can be found here.Sources: Baltimore Metropolitan Council, U.S. Census Bureau 2016–2020 American Community Survey 5-Year Estimates. Margins of error are not shown.Updated: April 2022

Population with limited English proficiency is defined as persons over the age of 5 years that speak English less than very well.Individuals with limited English proficiency can face significant language barriers, which can make it difficult for them to navigate various social systems, such as educational institutions, or access essential services, such as health insurance, healthcare, or food assistance programs.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

A rising trend in catches of non-targeted species has recently been observed in major fisheries including tuna longline fisheries, yet most of these species are unmanaged. Given their importance to local economies and sustainable livelihoods in many coastal countries, there is a need to provide plans for their management. However, most non-targeted species are data-limited which hampers the use of conventional assessment methods. This study applied a novel data-limited length-based Bayesian biomass estimator (LBB) method to assess the stocks of five species from the Atlantic and Pacific Oceans. Estimates of growth, length at first capture and present relative biomass (B/B0, B/BMSY) of these species were gotten from length-frequency (LF) data. Of the ten populations (5 species from two regions) assessed, one has collapsed, one grossly overfished, and three overfished. Six populations had the ratio of mean lengths at first capture (Lc) on the mean length at first capture, which maximizes the catch and biomass (Lc_opt) greater than unity, indicating the presence of large-sized specimens in the populations. Two species faced intense fishing pressure in the Atlantic while one population collapsed in the Pacific Ocean. Our results indicate that even non-targeted pelagic can be prone to over-exploitation. Therefore, there is an urgent need for stakeholders and fisheries managers to focus on improving fishery statistics and to conduct periodic monitoring of stock status indicators for non-target species.

Sites were stratified by DAF in Africans or DAF in Europeans into three bins: nearly fixed ancestral (DAF < = 5%), non-fixed (DAF 5–95%), and nearly fixed derived (DAF > = 95%). This was done for unascertained data (all sites), for random ascertainment (AT/GC sites), and for four non-random ascertainment schemes as indicated in the leftmost column The number and proportion of f4-informative sites falling into each DAF bin are shown. Mean DAF in four populations, mean differences in DAF between populations 1 and 2, populations 3 and 4, mean products of the DAF differences (i.e., f4-statistics) and their Z-scores are shown for these frequency bins. (XLSX)

1. Effective population size should be positively related to census size and density, and is expected to influence the strength of genetic drift, inbreeding and response to selection, and thus the distribution of the genetic load across populations.
2. We examined if census population size and density predicts the strength of inbreeding depression, heterosis and population mean fitness at the seed stage in the terrestrial orchid Gymnadenia conopsea by conducting controlled crosses (self, outcross within and between populations) in 20 populations of varying size (7-30,000 individuals) and density (1-12.8 individuals/m2). In the largest population, we also examined how local density affects occurrence of self-pollination with a pollen staining experiment.
3. The majority of populations expressed strong inbreeding depression at the seed stage (mean δID: min-max = 0.26: -0.53–0.51), consistent with a mainly outcrossing mating system and substantial genetic load. The effect of between-population crosses varied from strong outbreeding depression to heterosis (mean δOD: min-max= 0.05: -0.22–0.92), indicating varying influence of drift and selection among populations.
4. Census population size did not significantly predict the strength of inbreeding depression, heterosis or population mean fitness. However, inbreeding depression was positively and heterosis negatively correlated with population density. The proportion of self-massulae deposition was three times higher in sparse patches compared to dense ones (41% vs. 14%).
5. Combined effects of density-dependent pollinator behavior and limited seed dispersal may cause stronger genetic sub-structuring in sparse populations and reduce the strength of the correlation between census and effective population size. The results point to the importance of considering population density in addition to size when evaluating the distribution of recessive deleterious alleles across populations.
6. Synthesis and application: Management plans for threatened species often involve crosses between populations to restore genetic variation, a process termed genetic rescue. Such conservation efforts should be more succesful if designed on the basis of population density in addition to population size.03-Apr-2020 Methods Results of the controlled crossings conducted in 20 populations of Gymnadenia conopsea on Öland.

In Austria a population census takes place every 10 years; this census contains a program of important statistical data on population and employment. They roughly corresponds to the information in the Mikrozensus standard survey but are more detailed (for instance with question on the connection of the place of residence and the workplace, questions on education, confession, etc.) Population and Mikrozensus are closely linked which the name already implies: Mikrozensus means a small-scale population census; this should demonstrate that what the population census reports only every 10 years, the Mikrozensus reports through the method of ongoing sampling. These ongoing sample are also collected in the years of the population census. The Mikrozensus however is far more detailed than the survey program of the population census because the Mikrozensus special surveys offer the possibility of asking questions which are fare beyond the scope of the population census. This complementary function of Mikrozensus and population census becomes especially obvious in the June-survey: certain questions that could not be posed in the population census due to the limited program were answered in the Mikrozensus via sampling. These were the topics: questions on the social stratification of the population questions on fertility and succession of birth questions on the silent Human Resources

Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.55 billion inhabitants on the continent at the beginning of 2025, the number of inhabitants is expected to reach 3.81 billion by 2100. In total, the global population is expected to reach nearly 10.18 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2024. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

Sites were stratified by DAF in Africans or DAF in Europeans into three bins: nearly fixed ancestral (DAF < = 5%), non-fixed (DAF 5–95%), and nearly fixed derived (DAF > = 95%). This was done for unascertained data (all sites), for random ascertainment (AT/GC sites), and for four non-random ascertainment schemes as indicated in the leftmost column. The number and proportion of f4-informative sites falling into each DAF bin are shown. Mean DAF in four populations, mean differences in DAF between populations 1 and 2, populations 3 and 4, mean products of the DAF differences (i.e., f4-statistics) and their Z-scores are shown for these frequency bins. (XLSX)

Title VI of the Civil Rights Act and the Executive Order on Environmental Justice (#12898) do not provide specific guidance to evaluate EJ issues within a region's transportation planning process. Therefore, MPOs must devise their own methods for ensuring that EJ issues are investigated and evaluated in transportation decision-making. In 2001, DVRPC developed an EJ technical assessment to identify direct and disparate impacts of its plans, programs, and planning process on defined population groups in the Delaware Valley region. This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:

Youth

Older Adults

Female

Racial Minority

Ethnic Minority

Foreign-Born

Disabled

Limited English Proficiency

Low-Income Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates Using U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group. Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates. For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/ For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipd Source of tract boundaries: 2020 US Census Bureau, TIGER/Line Shapefiles Note: Tracts with null values should be symbolized as "Insufficient or No Data". Data Dictionary for Attributes: (Source = DVRPC indicates a calculated field)

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..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical 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..Source: U.S. Census Bureau, 2018 American Community Survey 1-Year Estimates.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 .ACS Technical Documentation..). The effect of nonsampling error is not represented in these tables..The 65 years and over column of data refers to the age of the householder for the estimates of households, occupied housing units, owner-occupied housing units, and renter-occupied housing units lines..The age specified on the population 15 years and over, population 25 years and over, population 30 years and over, civilian population 18 years and over, civilian population 5 years and over, population 1 years and over, population 5 years and over, and population 16 years and over lines refer to the data shown in the "Total" column while the second column is limited to the population 65 years and over..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the .Evaluation Report Covering Disability....Telephone service data are not available for certain geographic areas due to problems with data collection of this question that occurred in 2015 and 2016. Both ACS 1-year and ACS 5-year files were affected. It may take several years in the ACS 5-year files until the estimates are available for the geographic areas affected..While the 2018 American Community Survey (ACS) data generally reflect the July 2015 Office of Management and Budget (OMB) delineations 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 delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..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, or the margin of error associated with a median was larger than the median itself..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....

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.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..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..A "limited English speaking household" is one in which no member 14 years old and over (1) speaks only English or (2) speaks a non-English language and speaks English "very well." In other words, all members 14 years old and over have at least some difficulty with English. By definition, English-only households cannot belong to this group. Previous Census Bureau data products have referred to these households as "linguistically isolated" and "Households in which no one 14 and over speaks English only or speaks a language other than English at home and speaks English 'very well'." This table is directly comparable to tables from earlier years that used these labels..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Sagittal Craniosynostosis (SC) is a congenital condition whereby the newborn skull develops abnormally due to premature ossification of the sagittal suture. Spring-assisted cranioplasty (SAC) is a minimally invasive surgical technique to treat SC where metallic distractors are used to reshape the newborn’s head. Although safe and effective, SAC outcomes remain uncertain due to the limited understanding of skull-distractor interaction and limited information provided by the analysis of single surgical cases. Hereby, an SC population average skull model was created to simulate spring insertion by means of finite element analysis.

Abstract

This research is an Indicator Survey conducted in Samoa from May 25 to Oct. 9, 2009, as part of the Enterprise Survey initiative. An Indicator Survey, which is similar to an Enterprise Survey, is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

Questionnaire topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, land and permits, taxation, business-government relations, and performance measures.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Samoa was selected using stratified random sampling. Two levels of stratification were used in this country: industry and establishment size.

Industry stratification was designed in the way that follows: the universe was stratified into 23 manufacturing industries, and one services sector.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification did not take place as only the island of Upolu, containing the capital city of Apia, was surveyed. Of the two islands that make up the majority of Samoa, Upolu has the largest population.

Due to limited data sources available in Samoa on registered businesses, the final sample frame was obtained from a combined dataset obtained from the Samoa National Provident Fund (SNPF). The list provided by the SNPF was limited to including information on the sector and location of enterprises, with no details on the number of employees. Therefore, original sample counts were not able to be stratified by enterprise size. The combined sample frame was than reviewed and duplicate establishments or establishments with ineligible characteristics (industry sector, number of employees, geographic location) removed from the list. The modified sample frame was used to select the sample of establishments for the full survey. This database contained the following information: -Name of the firm -Contact details -Location -ISIC code.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 50% (416 out of 835 establishments). Breaking down by industry, the following numbers of establishments were surveyed: Manufacturing - 24, Services - 85.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Services Questionnaire - Manufacturing Questionnaire - Screener Questionnaire.

The Services Questionnaire is administered to the establishments in the services sector. The Manufacturing Questionnaire is built upon the Services Questionnaire and adds specific questions relevant to manufacturing.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Samoa Implementation 2009" in "Technical Documents" folder.

Abstract

The main objective of the survey (TLSS-03) was to measure the level of living of the people of Turkmenistan with respect to various social and economic indicators and produce comparable statistics to the TLSS-98. The survey results formed an important database for building a system of monitoring of the living standards in the country.

The survey will focus on income level and expenditure pattern of households along with their social opportunity and access to public services. The survey will integrate the social and economic aspects of living standards and reveal the social strata that need more attention and protection from state. The survey will analyse the different factors affecting the living standards and will produce valuable information required in development planning and policy making.

A wide range of information collected from the survey was analysed to reveal the major socio-economic factors affecting the level of living. The basic survey approach and the questionnaire was designed to ensure the comparability of statistics with TLSS-98, so that data analysis can be made in cross-statistics as well as in time series.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Like in 1998, the survey was designed as a two-stage stratified cluster sampling. The principle of stratification into urban and rural for each 5 regions (Velayats) also remains unchanged. It created 11 independent strata (10 from 5 regions plus one stratum of Ashgabad). Primary sampling units (psu) were clusters formed of enumeration area units as described above. Households were listed in the selected clusters and sub-sampled by field staffs from the listing sheets.

TLSS-03 had a self-weighting design and samples were spread out over the wide area of the country. For this purpose, psu's were arranged in the order of geographical location across the different Etraps. Selection of PSU's was made systematically probability proportional to the number of households in clusters.

A fixed sample of 20 households was selected from each cluster using simple random sampling method. Selection of psu's by pps method at first stage and inversely proportional to the number of households at second stage resulted in a self-weighting sample, which was very important for this survey, especially because a large number of indicators are means and proportions. In a self-weighting design, sample means and sample proportions are unbiased estimators of population means and population proportions.

See detail sampling information in "Turkmenistan Living Standards Survey 2003 Technical Report" document.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was collected using two type of questionnaires: - Household Questionnaire - Community Questionnaire

Cleaning operations

Prior to the data entry, questionnaires filled and returned from the field were checked and edited especially with regard to household identification numbers and data items. The questionnaire included, household listing form, household questionnaire and the community questionnaire. To facilitate the smooth data entry, the community questionnaires were folioed by Oblast, while the household questionnaires were folioed by the survey block. Each folio was provided with appropriate folio cover, which included the household identification and indicators to determine the status of every folio during machine processing. The total folios produced were as follows. - Community Questionnaire, 6 folios - Household Questionnare, 120 folios

The data entry programme was developed in CS Pro 2.3. The screen format for data entry was designed to make its look as similar as possible to the questionnaire. The form labels were made in both English and Russian versions. The programme also included the necessary control mechanism to ensure validity of entries. As mentioned above, there were two levels of questionnaires, so programme files were developed separately for community and household questionnaires.

Several department of TMH housed the data entry process. However, it was not felt necessary to install a network due to the relatively smaller size of the data load. An additional computer was designated for batch editing, form receipts and control and the monitoring purposes. The data entry was conducted from 4 January to 7 February 2004.

CSPro 2.3 was also used for editing. A batch edit program was developed to control the quality of data. Range checks were done on every data item. Additional consistency checks between data items were included in the edit programme. The program generated a list of errors for all questionnaires belonging to a particular household. The data items with error were manually compared with the corresponding questionnaire for verification. All necessary corrections were recorded in the error list and were later used for data correction. Since this is a sample based survey, automatic imputations were not done to preserve reliability of data.

Sampling error estimates

Estimation of the standard error was made based on the Balanced Repeated Replicates (BRR method). The method required exactly two psu’s per stratum. It takes half sample from each stratum and as many complements. The squared differences of two estimates provide an unbiased estimate of variance.

See detail estimation of the standard error and design effect information in "Turkmenistan Living Standards Survey 2003 Technical Report" document.

Data appraisal

Limitations of the survey Although, the utmost attention was paid to ensure the quality of survey results, TLSS had some limitations. Users are strongly recommended to take these limitations into considerations while using the data of this survey. The limitations of the survey are broadly described below.

The survey frame 1. The main limitation of the survey was the quality of the frame used in the survey design. The last population census in Turkmenistan was conducted in 1995. Since then, a lot of demographic changes were observed mainly due the emigration of the Russian speaking population and internal replacement caused by massive housing reconstruction. Despite of all possible attempts directed to improve the frame, it must be recognised that the baseline data still came from the last census.

1. While the last population census results are no more a valid database for any kind of plausible statistical investigations, it is unfortunate that the upcoming Population census in 2005 has now been cancelled, which will be replaced by a “Mini-census of 5%”. Such census may produce the population figures, however, it will not provide so acutely required data for household surveys. Therefore, the problem of the frame is most likely to affect adversely also the quality of other household surveys to be conducted in future.

2. The problem of the frame is related also to the lack of maps of enumeration blocks used in the survey. The size of the earlier blocks in terms of the number of households has significantly changed, so new boundaries were fixed for this survey. However, there was no map available to show the recent changes. Field staffs prepared a new map by themselves for the selected blocks based on the list of households. However, the quality of such map could affect the accuracy of the size of blocks due to the omission or duplication that could occur in the absence of good map. In the absence of the decennial census, maps throughout the country are not updated in terms of the boundaries of enumeration blocks and the number of households. Again, it could also create difficulties in conducting other surveys in future.

Training and the fieldwork 4. During the data editing and consistency checking, several mistakes of field staffs were found in filling the questionnaire. These mistakes actually were the result of insufficient training of the field staffs. The supervisor’s training in the centre was limited only to those from TMH. Field staffs recruited from the centre and from the regional offices did not get the sufficient time of interaction on the various conceptual issues of the questionnaire, so could not sufficiently address much of the expected problems of the survey.

1. The effect of the poor training could have been minimised by an intensive and close supervision of the survey staffs. However, the number of supervisors deployed in the field was often below the initially planned number due to the constraints of time and manpower. There was no coordinated supervision of the fieldwork because the core survey staffs themselves were involved in data collection.

Total survey error 6. Although, sampling error of major variables of interest were at the accepted level, non-sampling errors of the survey were relatively high due to the poor quality of the frame, lack of sufficient training of the field staffs and weak supervision of data collection. Non-sampling error was also caused by measurement and non-response problem as mentioned in the earlier chapter. Therefore, the total margin of error of major estimates was higher, often substantially, than the estimated value of sampling error.

Profile of the living standard 7. The analysis of the living standards requires a statistically viable baseline that allows the results of the survey for comparison over time and territory. In international practice, such baseline is the subsistence minimum, which serves as an objective criterion of measuring the level of living of population. In Turkmenistan, the subsistence minimum is not used for living standard analysis

When resources are limited, mean fitness is constrained and competition can cause genes and phenotypes to enhance an individual’s own fitness while reducing the fitness of their competitors. Negative social effects on fitness have the potential to constrain adaptation, but the interplay between ecological opportunity and social constraints on adaptation remains poorly studied in nature. Here, we tested for evidence of phenotypic social effects on annual fitness (survival and reproductive success) in a long-term study of wild North American red squirrels (Tamiasciurus hudsonicus) under conditions of both resource limitation and super-abundant food resources. When resources were limited, populations remained stable or declined, and there were strong negative social effects on annual survival and reproductive success. That is, mean fitness was constrained and individuals had lower fitness when other nearby individuals had higher fitness. In contrast, when food resources were super-abundant...

Species’ geographic range limits interest biologists and resource managers alike; however, scientists lack strong mechanistic understanding of the factors that set geographic range limits in the field, especially for animals. There exists a clear need for detailed case studies that link mechanisms to spatial dynamics and boundaries because such mechanisms allow us to predict whether climate change is likely to change a species’ geographic range and, if so, how abundance in marginal populations compares to the core. The bagworm Thyridopteryx ephemeraeformis (Lepidoptera: Psychidae) is a major native pest of cedars, arborvitae, junipers, and other landscape trees throughout much of North America. Across dozens of bagworm populations spread over six degrees of latitude in the American Midwest, we find latitudinal declines in fecundity and egg and pupal survivorship as you proceed towards the northern range boundary. A spatial gradient of bagworm reproductive success emerges, which is associated with a progressive decline in local abundance and an increase in the risk of local population extinction near the species’ geographic range boundary. We develop a mathematical model, completely constrained by empirically estimated parameters, to explore the relative roles of reproductive asynchrony and stage-specific survivorship in generating the range limit for this species. We find that overwinter egg mortality is the biggest constraint on bagworm persistence beyond their northern range limit. Overwinter egg mortality is directly related to winter temperatures that fall below the bagworm eggs’ physiological limit. This threshold, in conjunction with latitudinal declines in fecundity and pupal survivorship, creates a non-linear response to climate extremes that sets the geographic boundary and provides a path for predicting northward range expansion under altered climate conditions. Our mechanistic modeling approach demonstrates how species’ sensitivity to climate extremes can create population tipping points not reflected in demographic responses to climate means, a distinction that is critical to successful ecological forecasting.

Context

Seagrasses provide numerous ecosystem services for coastal and estuarine environments, such as nursery functions, erosion protection, pollution filtration, and carbon sequestration. Zostera marina (common name "eelgrass") is one of the seagrass bed-forming species distributed widely in the northern hemisphere, including the Korean Peninsula. Recently, however, there has been a drastic decline in the population size of Z. marina worldwide, including Korea. We examined the current population genetic status of this species on the southern coast of Korea by estimating the levels of genetic diversity and genetic structure of 10 geographic populations using eight nuclear microsatellite markers. The level of genetic diversity was found to be significantly lower for populations on Jeju Island [mean allelic richness (AR) = 1.92, clonal diversity (R) = 0.51], which is located approximately 155 km off the southernmost region of the Korean Peninsula, than for those in the South Sea (mean AR = 2.69, R = 0.82), which is on the southern coast of the mainland. South Korean eelgrass populations were substantially genetically divergent from one another (FST = 0.061-0.573), suggesting that limited contemporary gene flow has been taking place among populations. We also found weak but detectable temporal variation in genetic structure within a site over 10 years. In additional depth comparisons, statistically significant genetic differentiation was observed between shallow (or middle) and deep zones in two of three sites tested. Depleted genetic diversity, small effective population sizes (Ne) and limited connectivity for populations on Jeju Island indicate that these populations may be vulnerable to local extinction under changing environmental conditions, especially given that Jeju Island is one of the fastest warming regions around the world. Overall, our work will inform conservation and restoration efforts, including transplantation for eelgrass populations at the southern tip of the Korean Peninsula, for this ecologically important species.

Content

Raw dataset of eight microsatellite loci for the 16 populations_Ramet Raw dataset (ramets sampled) of eight microsatellite loci for the 16 populations from Jeju Island and the South Sea in Korea

Raw dataset of eight microsatellite loci for the 16 populations_Genet Raw dataset (genets) of eight microsatellite loci for the 16 populations from Jeju Island and the South Sea in Korea

Acknowledgements

Kim, Jae Hwan et al. (2018), Data from: Population genetic structure of eelgrass (Zostera marina) on the Korean coast: current status and conservation implications for future management, Dryad, Dataset, https://doi.org/10.5061/dryad.v25c2

Sites were stratified by DAF in Africans or DAF in Europeans into three bins: nearly fixed ancestral (DAF < = 5%), non-fixed (DAF 5–95%), and nearly fixed derived (DAF > = 95%). This was done for unascertained data (all sites), for random ascertainment (AT/GC sites), and for four non-random ascertainment schemes as indicated in the leftmost column. The number and proportion of f4-informative sites falling into each DAF bin are shown. Mean DAF in four populations, mean differences in DAF between populations 1 and 2, populations 3 and 4, mean products of the DAF differences (i.e., f4-statistics) and their Z-scores are shown for these frequency bins. (XLSX)