Abstract

The 2013 Turkey Demographic and Health Survey (TDHS-2013) is a nationally representative sample survey. The primary objective of the TDHS-2013 is to provide data on socioeconomic characteristics of households and women between ages 15-49, fertility, childhood mortality, marriage patterns, family planning, maternal and child health, nutritional status of women and children, and reproductive health. The survey obtained detailed information on these issues from a sample of women of reproductive age (15-49). The TDHS-2013 was designed to produce information in the field of demography and health that to a large extent cannot be obtained from other sources.

Specifically, the objectives of the TDHS-2013 included: - Collecting data at the national level that allows the calculation of some demographic and health indicators, particularly fertility rates and childhood mortality rates, - Obtaining information on direct and indirect factors that determine levels and trends in fertility and childhood mortality, - Measuring the level of contraceptive knowledge and practice by contraceptive method and some background characteristics, i.e., region and urban-rural residence, - Collecting data relative to maternal and child health, including immunizations, antenatal care, and postnatal care, assistance at delivery, and breastfeeding, - Measuring the nutritional status of children under five and women in the reproductive ages, - Collecting data on reproductive-age women about marriage, employment status, and social status

The TDHS-2013 information is intended to provide data to assist policy makers and administrators to evaluate existing programs and to design new strategies for improving demographic, social and health policies in Turkey. Another important purpose of the TDHS-2013 is to sustain the flow of information for the interested organizations in Turkey and abroad on the Turkish population structure in the absence of a reliable and sufficient vital registration system. Additionally, like the TDHS-2008, TDHS-2013 is accepted as a part of the Official Statistic Program.

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49
• Children under age of five

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years and women age 15-49 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design and sample size for the TDHS-2013 makes it possible to perform analyses for Turkey as a whole, for urban and rural areas, and for the five demographic regions of the country (West, South, Central, North, and East). The TDHS-2013 sample is of sufficient size to allow for analysis on some of the survey topics at the level of the 12 geographical regions (NUTS 1) which were adopted at the second half of the year 2002 within the context of Turkey’s move to join the European Union.

In the selection of the TDHS-2013 sample, a weighted, multi-stage, stratified cluster sampling approach was used. Sample selection for the TDHS-2013 was undertaken in two stages. The first stage of selection included the selection of blocks as primary sampling units from each strata and this task was requested from the TURKSTAT. The frame for the block selection was prepared using information on the population sizes of settlements obtained from the 2012 Address Based Population Registration System. Settlements with a population of 10,000 and more were defined as “urban”, while settlements with populations less than 10,000 were considered “rural” for purposes of the TDHS-2013 sample design. Systematic selection was used for selecting the blocks; thus settlements were given selection probabilities proportional to their sizes. Therefore more blocks were sampled from larger settlements.

The second stage of sample selection involved the systematic selection of a fixed number of households from each block, after block lists were obtained from TURKSTAT and were updated through a field operation; namely the listing and mapping fieldwork. Twentyfive households were selected as a cluster from urban blocks, and 18 were selected as a cluster from rural blocks. The total number of households selected in TDHS-2013 is 14,490.

The total number of clusters in the TDHS-2013 was set at 642. Block level household lists, each including approximately 100 households, were provided by TURKSTAT, using the National Address Database prepared for municipalities. The block lists provided by TURKSTAT were updated during the listing and mapping activities.

All women at ages 15-49 who usually live in the selected households and/or were present in the household the night before the interview were regarded as eligible for the Women’s Questionnaire and were interviewed. All analysis in this report is based on de facto women.

Note: A more technical and detailed description of the TDHS-2013 sample design, selection and implementation is presented in Appendix B of the final report of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two main types of questionnaires were used to collect the TDHS-2013 data: the Household Questionnaire and the Individual Questionnaire for all women of reproductive age. The contents of these questionnaires were based on the DHS core questionnaire. Additions, deletions and modifications were made to the DHS model questionnaire in order to collect information particularly relevant to Turkey. Attention also was paid to ensuring the comparability of the TDHS-2013 findings with previous demographic surveys carried out by the Hacettepe Institute of Population Studies. In the process of designing the TDHS-2013 questionnaires, national and international population and health agencies were consulted for their comments.

The questionnaires were developed in Turkish and translated into English.

Cleaning operations

TDHS-2013 questionnaires were returned to the Hacettepe University Institute of Population Studies by the fieldwork teams for data processing as soon as interviews were completed in a province. The office editing staff checked that the questionnaires for all selected households and eligible respondents were returned from the field. A total of 29 data entry staff were trained for data entry activities of the TDHS-2013. The data entry of the TDHS-2013 began in late September 2013 and was completed at the end of January 2014.

The data were entered and edited on microcomputers using the Census and Survey Processing System (CSPro) software. CSPro is designed to fulfill the census and survey data processing needs of data-producing organizations worldwide. CSPro is developed by MEASURE partners, the U.S. Bureau of the Census, ICF International’s DHS Program, and SerPro S.A. CSPro allows range, skip, and consistency errors to be detected and corrected at the data entry stage. During the data entry process, 100% verification was performed by entering each questionnaire twice using different data entry operators and comparing the entered data.

Response rate

In all, 14,490 households were selected for the TDHS-2013. At the time of the listing phase of the survey, 12,640 households were considered occupied and, thus, eligible for interview. Of the eligible households, 93 percent (11,794) households were successfully interviewed. The main reasons the field teams were unable to interview some households were because some dwelling units that had been listed were found to be vacant at the time of the interview or the household was away for an extended period.

In the interviewed 11,794 households, 10,840 women were identified as eligible for the individual interview, aged 15-49 and were present in the household on the night before the interview. Interviews were successfully completed with 9,746 of these women (90 percent). Among the eligible women not interviewed in the survey, the principal reason for nonresponse was the failure to find the women at home after repeated visits to the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TDHS-2013 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TDHS-2013 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

Understanding the ability of plants to spread is important for assessing conservation strategies, landscape dynamics, invasiveness and ability to cope with climate change. While long-distance seed dispersal is often viewed as a key process in population spread, the importance of inter-specific variation in demography is less explored. Indeed, the relative importance of demography vs seed dispersal in determining population spread is still little understood. We modelled species’ potential for population spread in terms of annual migration rates for a set of species inhabiting dry grasslands of central Europe. Simultaneously, we estimated the importance of demographic (population growth rate) vs long-distance dispersal (99th percentile dispersal distance) characteristics for among-species differences in modelled population spread. In addition, we assessed how well simple proxy measures related to demography (the number and survival of seedlings, the survival of flowering individuals) and dispersal (plant height, terminal velocity and wind speed during dispersal) predicted modelled spread rates. We found that species’ demographic rates were the more powerful predictors of species’ modelled potential to spread than dispersal. Furthermore, our simple proxies were correlated with modelled species spread rates and together their predictive power was high. Our findings highlight that for understanding variation among species in their potential for population spread, detailed information on local demography and dispersal might not always be necessary. Simple proxies or assumptions that are based primarily on species demography could be sufficient.

This project aimed to understand the public acceptability of a Low Emission Zone in the city of Bath, UK (formally known as the 'Clean Air Zone'). The dataset consists of socio-demographic, travel-related, and psychological variables, and a measure of Low Emission Zone acceptability.

Plant demography is a function of both the vital rate characteristics of a species (i.e., survival, growth, and reproduction) and the environmental factors that interact with them to create population dynamics. A more detailed understanding of how local-scale environmental factors and variation in individual vital rates shape population-level demographic patterns is needed to improve predictions of population responses to environmental change and implement successful plant conservation strategies. In this study, we examined how individual vital rates for Shortia galacifolia, an endangered, evergreen herb endemic to the southern Blue Ridge Mountains, USA, change as a function of individual size and resource availability and how that variation affects Shortia demography at four sites representing natural and introduced populations using integral projection models (IPMs). We found that Shortia population growth is positively related to individual size and soil moisture. Changes in soil moisture availability altered the importance of survival and growth in predicting Shortia demography but did not affect the contribution of asexual reproduction for most sites. Moreover, changes in vital rate contributions under a low soil moisture scenario were limited to introduced populations growing outside Shortia’s natural climate envelope. Our study underscores the importance of quantifying the influence of individual state characteristics and environmental variables on different vital rates among natural and introduced populations and demonstrates how the combination of these factors can contribute to the success or failure of rare plant populations.

The present study deals with demographic survey around Jaitapur Nuclear Power Plant proposed site which comes in Ratnagiri and part of Sindhudurg District. Demography study is important for creating base line data. The study area includes 121 villages which come into 30 km radial range around JNPP. The present study focuses on the demographic characteristics and socio-economic conditions of this region. The household survey was carried out around the proposed project site

Abstract

The Turkey Demographic and Health Survey (DHS) 2008 has been conducted by the Haccettepe University Institute of Population Studies in collaboration with the Ministry of health General Directorate of Mother and Child Health and Family Planning and Undersecretary of State Planning Organization. The Turkey Demographic and Health Survey 2008 has been financed the scientific and Technological research Council of Turkey (TUBITAK) under the support program for Research Projects of Public Institutions.

The primary objective of the Turkey DHS 2008 is to provide data on fertility, contraceptive methods, maternal and child health. Detailed information on these issues is obtained through questionnaires, filled by face-to face interviews with ever-married women in reproductive ages (15-49).

Another important objective of the survey, with aims to contribute to the knowledge on population and health as well, is to maintain the flow of information for the related organizations in Turkey on the Turkish demographic structure and change in the absence of reliable vital registration system and ascertain the continuity of data on demographic and health necessary for sustainable development in the absence of a reliable vital registration system. In terms of survey methodology and content, the Turkey DHS 2008 is comparable with the previous demographic surveys in Turkey (MEASURE DHS+).

Geographic coverage

National

Analysis unit

• Household
• Women age 15-49
• Children under age of five

Kind of data

Sample survey data

Mode of data collection

Face-to-face

Research instrument

Two main types of questionnaires were used to collect the TDHS-2008 data: a) The Household Questionnaire; b) The Individual Questionnaire for Ever-Married Women of Reproductive Ages.

The contents of these questionnaires were based on the DHS Model "A" Questionnaire, which was designed for the DHS program for use in countries with high contraceptive prevalence. Additions, deletions and modifications were made to the DHS model questionnaire in order to collect information particularly relevant to Turkey. Attention also was paid to ensuring the comparability of the DHS-2008 findings with previous demographic surveys carried out by the Hacettepe Institute of Population Studies. In the process of designing the TDHS-2003 questionnaires, national and international population and health agencies were consulted for their comments.

a) The Household Questionnaire was used to enumerate all usual members of and visitors to the selected households and to collect information relating to the socioeconomic position of the households. In the first part of the Household Questionnaire, basic information was collected on the age, sex, educational attainment, recent migration and residential mobility, employment, marital status, and relationship to the head of household of each person listed as a household member or visitor. The objective of the first part of the Household Questionnaire was to obtain the information needed to identify women who were eligible for the individual interview as well as to provide basic demographic data for Turkish households. The second part of the Household Questionnaire included questions on never married women age 15-49, with the objective of collecting information on basic background characteristics of women in this age group. The third section was used to collect information on the welfare of the elderly people. The final section of the Household Questionnaire was used to collect information on housing characteristics, such as the number of rooms, the flooring material, the source of water, and the type of toilet facilities, and on the household's ownership of a variety of consumer goods. This section also incorporated a module that was only administered in Istanbul metropolitan households, on house ownership, use of municipal facilities and the like, as well as a module that was used to collect information, from one-half of households, on salt iodization. In households where salt was present, test kits were used to test whether the salt used in the household was fortified with potassium iodine or potassium iodate, i.e. whether salt was iodized.

b) The Individual Questionnaire for ever-married women obtained information on the following subjects: - Background characteristics - Reproduction - Marriage - Knowledge and use of family planning - Maternal care and breastfeeding - Immunization and health - Fertility preferences - Husband's background
- Women's work and status - Sexually transmitted diseases and AIDS - Maternal and child anthropometry.

Cleaning operations

The questionnaires were returned to the Hacettepe Institute of Population Studies by the fieldwork teams for data processing as soon as interviews were completed in a province. The office editing staff checked that the questionnaires for all the selected households and eligible respondents were returned from the field.

A shift in scientific publishing from paper-based to knowledge-based practices promotes reproducibility, machine actionability and knowledge discovery. This is important for disciplines like social science, as study indicators are often social constructs such as race or education; hypothesis tests are challenging to compare in demographic research due to their limited temporal and spatial coverage; and natural language in research papers is often imprecise and ambiguous. Therefore, we present the MIRA-KG, consisting of: (1) an ontology for capturing social demography research, which links hypotheses and findings to evidence, (2) annotations of papers on health inequality in terms of the ontology, gathered by (i) prompting a Large Language Model to annotate paper abstracts using the ontology, (ii) mapping concepts to terms from NCBO BioPortal ontologies and GeoNames, and (iii) refining the final graph by a set of SHACL constraints, developed according to data quality criteria. The utility of the resource lies in its use for formally representing social demography research hypotheses, discovering research biases, discovery of knowledge, and the derivation of novel questions.This dataset was generated using the code available on Github at https://w3id.org/mira/ at version v1.0. It uses the following ontology: https://w3id.org/mira/ontology/.

Abstract

The Demographic Sample Survey 1986/87, shortly called as DSS 1986/87 is carried out by the Central Bureau of Statistics (CBS) with financial support from UNFPA and technical assistance from UNDTCD.

The major objectives of the DSS are to provide intercensal estimates of some important demographic parameters such as birth, death, migration, etc. The DSS 1986/87 not only provides these parameters but also examines the factors affecting fertility, mortality and migration in more details.

Geographic coverage

National Urban/ Rural areas Ecological Zones: Mountain, Hill, Terai

Analysis unit

Individual, Household

Universe

All private households

Kind of data

Sample survey data [ssd]

Sampling procedure

The DSS 1986/87 is a longitudinal study based on multi-stage national probability sample of 129 identifiable compact clusters known as ward/subwards. Ward/subwards (81 rural and 48 urban) were drawn from 35 districts (14 from Terai Zone and 18 and 3 from the Hill and Mountain zones respectively), out of a total of 75 districts in the country. The emphasis that the ultimate sampling units of DSS 1986/87 should be easily identifiable compact clusters is to ensure that the survey could be smoothly carried out in several successive rounds. The DSS 1986/87 drew samples from rural and urban areas separately in order to provide estimates of demographic and non-demographic parameters independently for each of the area.

Altogether 8640 households were eventually selected in the DSS 1986/87 for baseline and prospective study. The rural sample consisits of 6126 households while the urban sample accounts for 2514 households. The households selected in the Mountain, Hill and Terai are 675, 4179 and 3786 respectively. The urban households in the Hill and Terai are 1200 and 1314 respectively. In the Mountain there is no urban area. The sample consists of 35101 rural and 14412 urban population.

Refer to page 2 of "DSS Report" for a detailed description of the Sample Design.

Mode of data collection

Face-to-face [f2f]

Research instrument

The data at baseline survey were collected by using six different schedules:

1. Household schedule The household schedule was employed to collect information on some conventional socio-demographic measures of each usual/permanent member of the selected households.

2. In-migration schedule The In-migration schedule was used to collect detailed information on internal migrants and for immigrants.

3. Fertility and Mortality schedule The Fertility and Mortality schedule was used to collect the information on fertility anf mortality history of ever married worman in the household.

4. Out-migration schedule The Out-migration schedule was used to obtain detailed information on each out-migrant from the household which took place in the last five years preceding the survey.

5. Socio-economic status of the household schedule The Socio-economic status of the household schedule was used to obtain socio-economic characteristics of the households.

6. Migration survey-individual questionnaire The Migration survey-individual questionnaire was administered to internal migrants.

Refer to page 5 of "DSS Report" for detailed information on the types and contents of the questionnaires.

Resumo The demographic dividend has aroused interest among demographers and economists because it is seen as a window of oportunity for the economic development of countries that have experienced a demographic transition. There are reasons to question the sole virtuosity of the pure demographic dividend in economic growth. Crespo-Cuaresma et al. (2014) found that educational expansion has an important role in economic gains during the demographic dividend. To verify these results for the Brazilian case, we performed a decomposition exercise of economic support ratio (ESR), an alternative to demographic dependency ratio, to analyze the first demographic dividend. A simulation, applied for the period from 1970 to 2100 considering three scenarios of educational expansion, shows that educational expansion was and will be responsible for a big share of the economic gains of the Brazilian demographic dividend period, outperforming the change in age structure effect. In addition, an increase in a work-age population with post-secondary education appears to potentialize these results.

Background The aim of the present work was to investigate the relative importance of socio-demographic and physical health status factors for subjective functioning, as well as to examine the role of social support. Methods A cross-sectional health survey was carried out in a Greek municipality. 1356 adults of the general population were included in the study. Personal interviews were conducted with house-to-house visits. The response rate was 91.2%. Functioning has been measured by five indexes: 'The Social Roles and Mobility' scale (SORM), 'The Self-Care Restrictions' scale (SCR), 'The Serious Limitations' scale (SL), 'The Minor Self-care Limitations' scale (MSCR) and 'The Minor Limitations in Social Roles and Mobility' scale (MSORM). Results Among the two sets of independent variables, the socio-demographic ones had significant influence on the functional status, except for MSORM. Allowing for these variables, the physical health status indicators had also significant effects on all functioning scales. Living arrangements and marital status had significant effects on four out of five indexes, while arthritis, Parkinson's disease, past stroke and kidney stones had significant effects on the SCR and SL scales. Conclusions These results suggest that socio-demographic factors are as important as physical health variables in affecting a person's ability to function normally in their everyday life. Social support appears to play a significant role in explaining differences in subjective functioning: people living alone or only with the spouse, particularly the elderly, seem to be in greater risk for disability problems and should be targeted by preventive programs in the community.

This statistic shows the demographic changes having largest impact according to insurance companies in Africa in 2017. In 2017, ** percent of African insurers said that the growing black middle class would have a large impact on the insurance market in Africa, whereas only ** percent said the same about population growth.

This statistic displays the results of a survey of individuals in the United Kingdom (UK) in 2012, regarding their agreement with the statement, "It's important to continue learning new things throughout your life". Two percent of female respondents reported they somewhat disagreed with the statement. Agreement was strong across all demographic groups, but those aged 16 to 24 were most likely to express disagreement or neutrality to the statement.

Abstract

The 2003 Turkey Demographic and Health Survey (TDHS-2003) is a nationally representative sample survey designed to provide information on levels and trends on fertility, infant and child mortality, family planning and maternal and child health. Survey results are presented at the national level, by urban and rural residence, and for each of the five regions in the country. The TDHS2003 sample also allows analyses for some of the survey topics for the 12 geographical regions (NUTS1) which were adopted at the second half of 2002 within the context of Turkey's move to join the European Union.

Funding for the TDHS-2003 was provided initially by the Government of Turkey, as a project in the annual investment program of the State Planning Organization, and further funding was obtained from the European Union through the Turkey Reproductive Health Program implemented by the Ministry of Health.

The survey was fielded between December 2003 and May 2004. Interviews were completed with 10,836 households and with 8,075 ever-married women at reproductive ages (15-49). Ever-married women at ages 15-49 who were present in the household on the night before the interview or who usually live in that household were eligible for the survey.

The 2003 Turkish Demographic and Health Survey (TDHS-2003) is the latest in a series of national-level population and health surveys that have been conducted by the Hacettepe University Institute of Population Studies (HUIPS), in the last four decades. The primary objective of the TDHS-2003 is to provide data on socioeconomic characteristics of households and women, fertility, mortality, marriage patterns, family planning, maternal and child health, nutritional status of women and children, and reproductive health. The survey obtained detailed information on these issues from a sample of ever-married women in the reproductive ages (15-49). The TDHS-2003 was designed to produce information in the field of demography and health that to a large extent can not be obtained from other sources.

Specifically, the objectives of the TDHS-2003 included: - Collecting data at the national level that allows the calculation of demographic rates, particularly fertility and childhood mortality rates; - Obtaining information on direct and indirect factors that determine levels and trends in fertility and childhood mortality; - Measuring the level of contraceptive knowledge and practice by method, region, and urban-rural residence; - Collecting data relative to mother and child health, including immunizations, prevalence and treatment of acute respiratory tract infections among children under five, antenatal care, assistance at delivery, and breastfeeding; - Measuring the nutritional status of children under five and of their mothers; and - Collecting data at the national level on elderly welfare, knowledge of sexually transmitted diseases (STDs) and AIDS, and usage of iodide salt.

The TDHS-2003 information is intended to contribute data to assist policy makers and administrators to evaluate existing programs and to design new strategies for improving demographic, social and health policies in Turkey. Another important purpose of the TDHS2003 is to sustain the flow of information for the interested organizations in Turkey and abroad on the Turkish population structure in the absence of reliable and sufficient vital registration system.

SUMMARY OF FINDINGS

The results show that there have been important changes in various demographic and health indicators in a more positive direction than expected. The fertility data indicate that Turkey is achieving “replacement” fertility. The survey findings also document improvements in infant and child mortality and progress in mother and child health services.

Geographic coverage

The sample was designed to provide estimates for: - Turkey as a whole; - Urban and rural areas (each as a separate domain); - Each of the conventional major five regions of the country, namely the West, South, Central, North, and East regions - The 12 NUTS 13 regions, for selected indicators which are based on sufficient number of observations

Analysis unit

• Household
• Women age 15-49
• Children under five

Universe

The population covered by the 1998 DHS is defined as the universe of all ever-married women age 15-49 in the household who were identified as eligible in the household schedule were interviewed. In addition, some information was collected for households and women in a sub-sample of one-half of all households.

Kind of data

Sample survey data

Sampling procedure

A weighted, multistage, stratified cluster sampling approach was used in the selection of the TDHS-2003 sample. The sample was designed in this fashion because of the need to provide estimates for a variety of characteristics for various domains. These domains, which are frequently employed in the tabulation of major indicators from the survey, are: - Turkey as a whole; - Urban and rural areas (each as a separate domain); - Each of the conventional major five regions of the country, namely the West, South, Central, North, and East regions - The 12 NUTS 13 regions, for selected indicators which are based on sufficient number of observations

The major objective of the TDHS-2003 sample design was to ensure that the survey would provide estimates with acceptable precision for these domains for most of the important demographic characteristics, such as fertility, infant and child mortality, and contraceptive prevalence, as well as for the health indicators.

SAMPLE FRAME

Different criteria have been used to describe "urban" and "rural" settlements in Turkey. In the demographic surveys of the 1970s, a population size of 2,000 was used to differentiate between urban and rural settlements. In the 1980s, the cut-off point was increased to 10,000 and, in some surveys in the 1990s, to 20,000. A number of surveys used information on the administrative status of settlements in combination with population size for the purpose of differentiation. The urban frame of the TDHS-2003 consisted of a list of provincial centers, district centers, and other settlements with populations larger than 10,000, regardless of administrative status. The rural frame consisted of all district centers, sub-districts and villages not included in the urban frame. The urban-rural definitions of the TDHS-2003 are identical with those in the TDHS-1998.

Initial information on all settlements in Turkey was obtained from the 2000 General Population Census. The results of 2000 General Population Census provided a computerized list of all settlements (provincial and district centers, sub-districts and villages), their populations and the numbers of households.

STRATIFICATION

Currently Turkey is divided administratively into 81 provinces. For purposes of selection in prior surveys in Turkey, these provinces have been grouped into five regions. This regional breakdown has been popularized as a powerful variable for understanding the demographic, social, cultural, and economic differences between different parts of the country. The five regions, West, South, Central, North, and East regions, include varying numbers of provinces.

In addition to the conventional five geographic regions, a new system of regional breakdown was adopted in late 2002. In accordance with the accession process of Turkey to the European Union, the State Planning Office and the State Institute of Statistics constructed three levels of NUTS regions, which have since become official (Law No. 2002/4720). "NUTS" stands for "The Nomenclature of Territorial Units for Statistics". NUTS is a statistical region classification that is used by member countries of European Union (EU). The 81 provinces were designated as regions of NUTS 3 level; these were further aggregated into 26 regions to form the NUTS 2 regions. NUTS 1 regions were formed by aggregating NUTS 2 regions into 12 regions. Two of the NUTS 1 regions, Istanbul and the Southeastern Anatolia, were given special attention in the sample design process and a comparatively larger share of the total sample was allocated to these regions to ensure that statistically sound estimates for a larger number of indicators would be obtained than would be the case for the remaining 10 NUTS 1 regions. Policymakers, researchers and other concerned circles had voiced interest in information on demographic and health indicators for Istanbul and the Southeastern Anatolian regions in the past. Furthermore, as an add-on study, the Istanbul metropolitan area was designated by UN-Habitat as one of the mega-cities in their International Slum Survey series. In co-operation with UN-Habitat, HUIPS wished to be able to produce estimates for slum4 and non-slum areas within Istanbul; for this reason, the total sample size for Istanbul was kept at a relatively high magnitude.

One of the priorities of the TDHS-2003 was to produce a sample design that was methodologically and conceptually consistent with the designs of previous demographic surveys carried out by the Hacettepe Institute of Population Studies. In surveys prior to the TDHS-1993, the five-region breakdown of the country was used for stratification. In TDHS-1993, a more detailed stratification taking into account subregions was employed to obtain a better dispersion of the sample. The criteria for subdividing the five major regions into subregions were the infant mortality rates of each province, estimated from the 1990 Population Census using indirect techniques.5 Using the infant mortality estimates as well as geographic proximity, the provinces in each region were grouped into 14 subregions at the time of the TDHS-1993. The sub-regional division

1. Differences among individuals within a population are ubiquitous. Those differences are known to affect the entire life cycle with important consequences for all demographic rates and outcomes. One source of among-individual phenotypic variation that has received little attention from a demographic perspective is animal personality, which is defined as consistent and heritable behavioral differences between individuals. While many studies have shown that individual variation in individual personality can generate individual differences in survival and reproductive rates, the impact of personality on all demographic rates and outcomes remains to be assessed empirically.

   1. Here, we used a unique, long-term, dataset coupling demography and personality of wandering albatross (Diomedea exulans) in the Crozet Archipelago and a comprehensive analysis based on a suite of approaches (capture-mark-recapture statistical models, Markov chains models and structured matrix population models). We assessed the effect of boldness on annual demographic rates (survival, breeding probability, breeding success), life-history out-comes (life expectancy, lifetime reproductive outcome, occupancy times), and an integrative demographic outcome (population growth rate).

   2. We found that boldness had little impact on female demographic rates, but was very likely associated with lower breeding probabilities in males. By integrating the effects of boldness over the entire life cycle, we found that bolder males had slightly lower lifetime reproductive success compared to shyer males. Indeed, bolder males spent a greater proportion of their lifetime as non-breeders, which suggests longer inter-breeding intervals due to higher reproductive allocation.

   3. Our results reveal that the link between boldness and demography is more complex than anticipated by the pace-of-life literature and highlight the importance of considering the entire life cycle with a comprehensive approach when assessing the role of personality on individual performance and demography.

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.

Demographic correlations are pervasive in wildlife populations and can represent important secondary drivers of population growth. Empirical evidence suggests that correlations are in general positive for long-lived species, but little is known about the degree of variation among spatially segregated populations of the same species in relation to environmental conditions. We assessed the relative importance of two cross-season correlations in survival and productivity, for three Atlantic puffin (Fratercula arctica) populations with contrasting population trajectories and non-overlapping year-round distributions. The two correlations reflected either a relationship between adult survival prior to breeding on productivity or a relationship between productivity and adult survival in the subsequent year. Demographic rates and their correlations were estimated with an integrated population model, and their respective contributions to variation in population growth were calculated using a tra...

Diverse drivers such as climate, soil fertility, neighborhood competition, and functional traits all contribute to variation in tree stem demographic rates. However, these demographic drivers operate at different scales, making it difficult to compare the relative importance of each driver on tree demography. Using c. 20,000 stem records from New Zealand's temperate rain forests, we analyzed the growth, recruitment, and mortality rates of 48 tree species and determined the relative importance of demographic drivers in a multi-level modelling approach. Tree species’ maximum height emerged as the one most strongly associated with all demographic rates, with a positive association with growth rate and negative associations with recruitment and mortality rates. Climate, soil properties, neighborhood competition, stem size, and other functional traits also played significant roles in shaping demographic rates. Forest structure and functional composition were linked to climate and soil, with warm, dry climates and fertile soil associated with higher growth and recruitment rates. Neighborhood competition affected demographic rates depending on stem size, with smaller stems experiencing stronger negative effects, suggesting asymmetric competition where larger trees exert greater competitive effects on smaller trees. Our study emphasizes the importance of considering multiple drivers of demographic rates to better understand forest tree dynamics.

Model output of Ideal Free Distribution simulationsThe data file contains the model output of the Combined Model. Each row represents one simulation. The first four columns are model parameters that were varied in the simulations (see table A1 in the Appendix for parameter descriptions), and the fifth column is the recorded IFD slope of that simulation. All simulations were run in NetLogo.IFD dryad data Final.csv

Mutualisms play a central role in the origin and maintenance of biodiversity. Because many mutualisms have strong demographic effects, interspecific variation in partner quality could have important consequences for population dynamics. Nevertheless, few studies have quantified how a mutualist partner influences population growth rates, and still fewer have compared the demographic impacts of multiple partner species. We used integral projection models parameterized with three years of census data to compare the demographic effects of two ant species - Crematogaster laevis and Pheidole minutula - on populations of the Amazonian ant-plant Maieta guianensis. Estimated population growth rates were positive (i.e., λ>1) for all ant-plant combinations. However, populations with only Pheidole minutula had the highest asymptotic growth rate (λ=1.23), followed by those colonized by Crematogaster laevis (λ=1.16), and in which the partner ant alternated between C. laevis and P. minutula at least once during our study (λ=1.15). Our results indicate that the short-term superiority of a mutualist partner - in this system P. minutula is a better defender of plants against herbivores than C. laevis - can have long-term demographic consequences. Furthermore, the demographic effects of switching among alternative partners appear to be context-dependent, with no benefits to plants hosting C. laevis but a major cost of switching to plants hosting P. minutula. Our results underscore the importance of expanding the study of mutualisms beyond the study of pair-wise interactions to consider the demographic costs and benefits of interacting with different, and multiple, potential partners.