This dataset includes data on 25 transitions of a matrix demographic model of the invasive species Vincetoxicum nigrum (L.) Moench (black swallow-wort or black dog-strangling vine) and Vincetoxicum rossicum (Kleopow) Barb. (pale swallow-wort or dog-strangling vine) (Apocynaceae, subfamily Asclepiadoideae), two invasive perennial vines in the northeastern U.S.A. and southeastern Canada. The matrix model was developed for projecting population growth rates as a result of changes to lower-level vital rates from biological control although the model is generalizable to any control tactic. Transitions occurred among the five life stages of seeds, seedlings, vegetative juveniles (defined as being in at least their second season of growth), small flowering plants (having 1–2 stems), and large flowering plants (having 3 or more stems). Transition values were calculated using deterministic equations and data from 20 lower-level vital rates collected from 2009-2012 from two open field and two forest understory populations of V. rossicum (43°51’N, 76°17’W; 42°48'N, 76°40'W) and two open field populations of V. nigrum (41°46’N, 73°44’W; 41°18’N, 73°58’W) in New York State. Sites varied in plant densities, soil depth, and light levels (forest populations). Detailed descriptions of vital rate data collection may be found in: Milbrath et al. 2017. Northeastern Naturalist 24(1):37-53. Five replicate sets of transition data obtained from five separate spatial regions of a particular infestation were produced for each of the six populations. Note: Added new excel file of vital rate data on 12/7/2018. Resources in this dataset:Resource Title: Matrix model transition data for Vincetoxicum species. File Name: Matrix_model_transition_data.csvResource Description: This data set includes data on 25 transitions of a matrix demographic model of two invasive Vincetoxicum species from six field and forest populations in New York State.Resource Title: Variable definitions. File Name: Matrix_model_metadata.csvResource Description: Definitions of variables including equations for each transition and definitions of the lower-level vital rates in the equationsResource Title: Vital Rate definitions. File Name: Vital_Rate.csvResource Description: Vital Rate definitions of lower-level vital rates used in transition equations - to be substituted into the Data Dictionary for full definition of each transition equation.Resource Title: Data Dictionary. File Name: Matrix_Model_transition_data_DD.csvResource Description: See Vital Rate resource for definitions of lower-level vital rates used in transition equations where noted.Resource Title: Matrix model vital rate data for Vincetoxicum species. File Name: Matrix_model_vital rate_data.csvResource Description: This data set includes data on 20 lower-level vital rates used in the calculation of transitions of a matrix demographic model of two invasive Vincetoxicum species in New York State as well as definitions of the vital rates. (File added on 12/7/2018)Resource Software Recommended: Microsoft Excel,url: https://office.microsoft.com/excel/

The methodology used to produce these projections differs from ICLUS v2.0 (https://cfpub.epa.gov/ncea/iclus/recordisplay.cfm?deid=322479). The demographic components of change (i.e., rates of fertility and mortality) for ICLUS v2.1 were taken directly from the Wittgenstein Centre Data Explorer (http://witt.null2.net/shiny/wic/). These projections were produced more recently than the Census projections used in ICLUS v2.0, and incorporate more recent observations of population change. SSP2 is a “middle-of-the-road” projection, where social, economic and technological trends do not shift markedly from historical patterns, resulting in a U.S. population of 455 million people by 2100. Domestic migration trends remain largely consistent with the recent past, however the amenity value of local climate (average precipitation and temperature for summer and winter) is used in ICLUS v2.1.1 to influence migration patterns. The name of the climate model used as the source of future climate patterns is included at the end of the file name (e.g., "GISS-E2-R" or "HadGEM2-ES"). The approach for incorporating climate change into the migration model is described in the ICLUS v2.0 documentation. The SSP5 narrative describes a rapidly growing and flourishing global economy that remains heavily dependent on fossil fuels, and a U.S. population that exceeds 730 million by 2100. ICLUS v2.1 land use projections under SSP5 result in a considerably larger expansion of developed lands relative to SSP2. The the amenity value of local climate (average precipitation and temperature for summer and winter) is used in ICLUS v2.1.1 to influence migration patterns. The name of the climate model used as the source of future climate patterns is included at the end of the file name (e.g., "GISS-E2-R" or "HadGEM2-ES"). The approach for incorporating climate change into the migration model is described in the ICLUS v2.0 documentation. RCP4.5 assumes that global greenhoue gas emissions increase into the latter part of the century, before leveling off and eventually stabilizing by 2100 as a result of various climate change policies. RCP8.5 assumes that global greenhoue gas emissions increase through the year 2100.

The U.S. landscape has undergone substantial changes since Europeans first arrived. Many land use changes are attributable to human activity. Historical data concerning these changes are frequently limited and often difficult to develop. Modeling historical land use changes may be necessary. We develop annual population series from first European settlement to 1999 for all 50 states and Washington D.C. for use in modeling land use trends. Extensive research went into developing the historical data. Linear interpolation was used to complete the series after critically evaluating the appropriateness of linear interpolation versus exponential interpolation.Our objective was to develop an annual population data series from the first nonindigenous settlements to 1999 for each present day state that could be used to model landscape change presumed to be a direct result of activities associated with the settlement of nonindigenous people.

Introduction

This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends.

Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change – birth, death and net migration – is projected separately for each five-year birth cohort and sex. The cohort – component method employs the following basic demographic balancing equation: P1 = P0 + B – D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration – Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is “survived” to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years).

Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates.

Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The base special population by age and sex from the 2010 population census was used for this purpose with the assumption that this population will remain the same throughout each projection period.

Mortality Future deaths were projected by applying age and sex specific survival rates to each age and sex specific base population. The assumptions on survival rates were developed on the basis of trends of mortality rates in the individual life tables constructed for each level of geography for 1989-1991, 1999-2001 and 2009-2011. The application of five-year survival rates provides a projection of the number of persons from the initial population expected to be alive in five years. Resident deaths data by age and sex from 1989 to 2011 were provided by the Illinois Center for Health Statistics (ICHS), Illinois Department of Public Health.

Fertility Total fertility rates (TFRs) were first computed for each county. For most counties, the projected 2015 TFRs were computed as the average of the 2000 and 2010 TFRs. 2010 or 2015 rates were retained for 2020 projections, depending on the birth trend of each county. The age-specific birth rates (ASBR) were next computed for each county by multiplying the 2010 ASBR by each projected TFR. Total births were then projected for each county by applying age-specific birth rates to the projected female population of reproductive ages (15 to 49 years). The total births were broken down by sex, using an assumed sex-ratio at birth. These births were survived five years applying assumed survival ratios to get the projected population for the age group 0-4. For the special counties, special populations by age and sex were taken out before computing age-specific birth rates. The resident birth data used to compute age-specific birth rates for 1989-1991, 1999-2001 and 2009-2011 came from ICHS. Births to females younger than 15 years of age were added to those of the 15-19 age group and births to women older than 49 years of age were added to the 45-49 age group.

Net Migration Migration is the major component of population change in Illinois, Chicago and Illinois counties. The state is experiencing a significant loss of population through internal (domestic migration within the U.S.) net migration. Unlike data on births and deaths, migration data based on administrative records are not available on a regular basis. Most data on migration are collected through surveys or indirectly from administrative records (IRS individual tax returns). For this report, net migration trends have been reviewed using data from different sources and methods (such as residual method) from the University of Wisconsin, Madison, Illinois Department of Public Health, individual exemptions data from the Internal Revenue Service, and survey data from the U.S. Census Bureau. On the basis of knowledge gained through this review and of levels of net migration from different sources, assumptions have been made that Illinois will have annual net migrants of -40, 000, -35,000 and -30,000 during 2010-2015, 2015-2020 and 2020-2025, respectively. These figures have been distributed among the counties, using age and sex distribution of net migrants during 1995-2000. The 2000 population census was the last decennial census, which included the question “Where did you live five years ago?” The age and sex distribution of the net migrants was derived, using answers to this question. The net migration for Chicago has been derived independently, using census survival method for 1990-2000 and 2000-2010 under the assumption that the annual net migration for Chicago will be -40,000, -30,000 and -25,000 for 2010-2015, 2015-2020 and 2020-2025, respectively. The age and sex distribution from the 2000-2010 net migration was used to distribute the net migrants for the projection periods.

Conclusion These projections were prepared for use by the Certificate of Need (CON) Program; they are produced using evidence-based techniques, reasonable assumptions and the best available input data. However, as assumptions of future demographic trends may contain errors, the resulting projections are unlikely to be free of errors. In general, projections of small areas are less reliable than those for larger areas, and the farther in the future projections are made, the less reliable they may become. When possible, these projections should be regularly reviewed and updated, using more recent birth, death and migration data.

One of the central puzzles in the study of sociocultural evolution is how and why transitions from small-scale human groups to large-scale, hierarchically more complex ones occurred. Here we develop a spatially explicit agent-based model as a first step towards understanding the ecological dynamics of small and large-scale human groups. By analogy with the interactions between single-celled and multicellular organisms, we build a theory of group lifecycles as an emergent property of single cell demographic and expansion behaviours. We find that once the transition from small-scale to large-scale groups occurs, a few large-scale groups continue expanding while small-scale groups gradually become scarcer, and large-scale groups become larger in size and fewer in number over time. Demographic and expansion behaviours of groups are largely influenced by the distribution and availability of resources. Our results conform to a pattern of human political change in which religions and nation states come to be represented by a few large units and many smaller ones. Future enhancements of the model should include decision-making rules and probabilities of fragmentation for large-scale societies. We suggest that the synthesis of population ecology and social evolution will generate increasingly plausible models of human group dynamics.

These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across the distribution of five common pinyon-juniper species across the US Southwest. These data were collected by the US Forest service in their monitoring program, which is a systematic survey of forested regions across the entire US. Our data is from western US states, including AZ, CA, CO, ID, MT, NM, ND, NV, OR, SD, TX, UT, and was collected between 2000-2007, depending on state census collection times. These data were collected by the Forest Inventory and Analysis program of the USDA US Forest Service. Within each established plot, all adult trees greater than 12.7 cm (5 in.) diameter at breast height (DBH) are assigned unique tags and tracked within four, 7.32 m (24 ft.) radius subplots. All saplings <12.7 cm & > 2.54 cm (1 in.) DBH are assigned unique tags and tracked within four, 2.07 m (6.8 ft.) radius microplots within the larger adult plots. Finally, seedlings <2.54 cm DBH are counted within the same microplots as the saplings. Two censuses were conducted 10 years apart in each plot. These data can be used to inform how tree species have unique responses to changing climate conditions and how management actions, like tree density reduction, may effectively resist transformation away from pinyon-juniper woodland to other ecosystem types.

The methodology used to produce these projections differs from ICLUS v2.0 (https://cfpub.epa.gov/ncea/iclus/recordisplay.cfm?deid=322479). The demographic components of change (i.e., rates of fertility and mortality) for ICLUS v2.1 were taken directly from the Wittgenstein Centre Data Explorer (http://witt.null2.net/shiny/wic/). These projections were produced more recently than the Census projections used in ICLUS v2.0, and incorporate more recent observations of population change. SSP2 is a “middle-of-the-road” projection, where social, economic and technological trends do not shift markedly from historical patterns, resulting in a U.S. population of 455 million people by 2100. Domestic migration trends remain largely consistent with the recent past, however the amenity value of local climate (average precipitation and temperature for summer and winter) is used in ICLUS v2.1.1 to influence migration patterns. The name of the climate model used as the source of future climate patterns is included at the end of the file name (e.g., "GISS-E2-R" or "HadGEM2-ES"). The approach for incorporating climate change into the migration model is described in the ICLUS v2.0 documentation. RCP4.5 assumes that global greenhouse gas emissions increase into the latter part of the century, before leveling off and eventually stabilizing by 2100 as a result of various climate change policies.

Adult female blue-winged teal (n = 112,639) were captured in traps and nets prior to the hunting season (July-September) in the prairie potholes and aspen parklands of the North American midcontinent from 1973 to 2016 (Figure 1). Teal were ringed with uniquely engraved metal markers, and some marked individuals were killed by hunters. A portion of these markers were retrieved and reported to the USGS Bird Banding Lab (n = 2,518; USGS Patuxent Wildlife Research Center). From 1974-2016, waterfowl breeding population and habitat surveys were flown at the beginning of the breeding season over the same area by the U.S. Fish and Wildlife Service and the Canadian Wildlife Service to estimate the total number of breeding pairs of teal (y_n,t) and other ducks, and the number of ponds (y_p,t), a landscape scale measure of habitat suitability for breeding waterfowl (Walker et al. 2013, U.S. Fish & Wildlife Service 2018). We downloaded the ringing and recovery data from the GameBirds Database CD (Bird Banding Lab, USGS Patuxent Wildlife Research Center), and the Waterfowl Breeding Population and Habitat Survey data from the USFWS Migratory Birds Data Center. We retained females marked in Canada and the United States in Waterfowl Breeding Population and Habitat Survey strata 20-49 (U.S. Fish & Wildlife Service 2018), and we restricted re-encounters to harvested individuals recovered and reported by hunters in the United States and Canada from September through early February, with half of all reported hunting mortality occurring in September. We excluded recoveries in Mexico, Central and South America, and the Carribean (n = 316) due to the inclusion of band reporting probabilities (r = r_1973, ... , r_2016) in our analyses, which were not available for Latin America. Mark-recovery data were downloaded from the USGS Bird Banding Lab Celis-Murillo et al. 2020. We accessed estimates of teal abundance and pond abundance from the Waterfowl Breeding Population and Habitat Survey (U.S. Fish & Wildlife Service 2018), as well as data on federal duck stamp sales, which are required to hunt for waterfowl in the United States. Third party data were used for this study, collection of which followed appropriate ethical guidelines. No additional ethical approval was required from our respective insitutions. We formatted the capture-recovery data into a multinomial array to reduce computational requirements. Please contact the authors for additional information about data processing. 1. Harvest of wild organisms is an important component of human culture, economy, and recreation, but can also put species at risk of extinction. Decisions that guide successful management actions therefore rely on the ability of researchers to link changes in demographic processes to the anthropogenic actions or environmental changes that underlie variation in demographic parameters. 2. Ecologists often use population models or maximum sustained yield curves to estimate the impacts of harvest on wildlife and fish populations. Applications of these models usually focus exclusively on the impact of harvest and often fail to consider adequately other potential, often collinear, mechanistic drivers of the observed relationships between harvest and demographic rates. In this study, we used an integrated population model and long-term data (1973-2016) to examine the relationships among hunting and natural mortality, the number of hunters, habitat conditions, and population size of blue-winged teal (Spatula discors), an abundant North American dabbling duck with a relatively fast-paced life history strategy. 3. Over the last two and a half decades of the study, teal abundance tripled, hunting mortality probability increased slightly (< 0.02), and natural mortality probability increased substantially (> 0.1) at greater population densities. We demonstrate strong density-dependent effects on natural mortality and fecundity as population density increased, indicative of compensatory harvest mortality and compensatory natality. Critically, an analysis that only assessed the relationship between survival and hunting mortality would spuriously indicate depensatory hunting mortality due to multicollinearity between abundance, natural mortality, and hunting mortality. 4. Our findings demonstrate that models that only consider the direct effect of hunting on survival or natural mortality can fail to accurately assess the mechanistic impact of hunting on population dynamics due to multicollinearity among demographic drivers. This multicollinearity limits inference and may have strong impacts on applied management actions globally. The open-source programs R and JAGS are required to run the integrated population model described in this manuscript.

Immigration and demographic change have become highly salient in American politics, partly because of the 2016 campaign of Donald Trump. Previous research indicates that local influxes of immigrants or unfamiliar ethnic groups can generate threatened responses, but has either focused on non-electoral outcomes or has analyzed elections in large geographic units such as counties. Here, we examine whether demographic changes at low levels of aggregation were associated with vote shifts toward an anti-immigration presidential candidate between 2012 and 2016. To do so, we compile a novel, precinct-level data set of election results and demographic measures for almost 32,000 precincts in the states of Florida, Georgia, Michigan, Nevada, Ohio, Pennsylvania, and Washington. We employ regression analyses varying model specifications and measures of demographic change. Our estimates uncover little evidence that influxes of Hispanics or non-citizen immigrants benefited Trump relative to past Republicans, instead consistently showing that such changes were associated with shifts to Trump's opponent.

Listed are the variable, transition between states, the equation for the vital rate, the value, and references that inform parameterization. Note that some values may differ slightly from those presented in the original source and that if left blank the variable was designated specifically for this model.

Abstract

In the early-mid 1990s, Albania entered a new phase of major changes, moving from a totalitarian to a democratic system and shifting gradually to the free market economy. This process led, naturally, to changes in various demographic and health characteristics of the Albanian society.

The 2008-09 Albania Demographic and Health Survey (ADHS) is a nationally representative study aimed at collecting and providing information on population, demographic, and health characteristics of the country. Population-based studies of this magnitude are a major undertaking that provide information on important indicators which measure the progress of a country.

The ADHS results help provide the necessary information to assess, measure, and evaluate the existing programs in the country. They also provide crucial information to policy-makers when drafting new policies and strategies related to the health sector and health services in Albania.

The information collected in the 2008-09 Albania Demographic and Health Survey will be used not only by local decision-makers and programme managers, but also by partners and foreign donors involved in various development areas in Albania, as well as by academic institutions to do further analysis with the collected data.

The 2008-09 Albania Demographic and Health Survey (ADHS) was implemented by the Institute of Statistics (INSTAT) and the Institute of Public Health (IPH), of the Ministry of Health. ICF Macro provided technical assistance to the ADHS through funding from the United Nations Children’s Fund (UNICEF) and the United State Agency for International Development (USAID)-funded MEASURE DHS programme. Local costs of the survey were supported by USAID, the Swiss Cooperation Office in Albania (SCO-A), UNICEF, the United Nations Population Fund (UNFPA), and the World Health Organization (WHO).

Data collection was conducted from 28 October, 2008 to 26 April, 2009 using a nationally representative sample of almost 9,000 households. All women age 15-49 in these households and all men age 15-49 in half of the households were eligible to be individually interviewed. In addition to the data collected through interviews with these women and men, capillary blood samples were collected from all children age 6-59 months and all eligible women and men age 15-49 for anaemia testing. All children under five years of age and eligible women and men age 15-49 were weighed and measured to assess their nutritional status. Finally, blood pressure (BP) was measured for eligible women and men in the households selected for the men’s interview to estimate the prevalence of hypertension in the adult population.

The 2008-09 ADHS is designed to provide data to monitor the population and health situation in Albania. Specifically, the 2008-09 ADHS collected information on fertility levels, marriage, sexual activity, fertility preferences, knowledge and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood mortality, maternal and child health, and awareness and behaviour regarding AIDS and other sexually transmitted infections. Additional features of the 2008-09 ADHS include the collection of information on migration (out-migration, returning migrants and internal migration), haemoglobin testing to detect the presence of anaemia, blood pressure (BP) measurements among the adult population, and questions related to accessibility and affordability of health services. The information collected in the 2008-09 ADHS provides updated estimates of an array of demographic and health indicators that will assist in the development of appropriate policies and programmes to address the most important health issues in Albania.

Geographic coverage

National

Analysis unit

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

Universe

All women age 15-49 in the total sample of households, and all men age 15-49 in the subsample of half of the households, who were either usual residents of the households or visitors present in the household on the night before the survey were eligible to be interviewed.

Kind of data

Sample survey data

Sampling procedure

The 2008-09 Albania Demographic and Health Survey is based on a representative probability sample of almost 9,000 households. This sample was selected in such a manner as to allow separate urban and rural, as well as regional-level estimates for key population and health indicators, e.g., fertility, contraceptive prevalence, and infant mortality for children under five.

The 2008-09 ADHS utilized a two-stage sample design. The first stage involved selection of a sample of primary sampling units (PSUs) from the PSUs used for the 2008 Living Standards Measurement Study (LSMS). In total, 450 PSUs were selected for the ADHS sample, including 245 urban PSUs and 205 rural PSUs, covering 4 geographic domains-mountains, central, coastal, and urban Tirana. A listing of each of the selected PSUs was carried out in preparation for the LSMS. The ADHS survey selected 20 households from the updated household listing in each PSU, excluding those households selected for the LSMS. In two PSUs, numbers 27 (13 households) and 172 (17 households), there were less than 20 households in the re-listed PSU-all households were selected in those cases. In a further 6 PSUs there were less than 20 households after the LSMS households were excluded. In these PSUs some of the households from the LSMS sample were included to bring the number of households selected up to 20. After selection of the households, the sample selection forms were printed and the list of selected households was adapted for use in a Personal Digital Assistant (PDA).

All women age 15-49 in the total sample of households, and all men age 15-49 in the subsample of half of the households, who were either usual residents of the households or visitors present in the household on the night before the survey were eligible to be interviewed.

Note: See detailed description of sample implementation in APPENDIX A of the survey final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used for the 2008-09 ADHS: the Household Questionnaire, the Women’s Questionnaire and the Men’s Questionnaire. The content of these questionnaires was based on model questionnaires developed by the MEASURE DHS programme.

Consultations with partners were held in Tirana to obtain input from various national and international experts on a broad array of issues. Based on these consultations, the DHS model questionnaires were modified to reflect issues relevant in Albania concerning population, women and children’s health, family planning, and other health issues. After approval of the final content by the Steering and the Technical Committees, the questionnaires were translated from English into Albanian.

The Household Questionnaire was used to list all the usual members and visitors in the selected households and to identify women and men who were eligible for the individual interview. Basic information was collected on the characteristics of each person listed, including their age, sex, education, and relationship to the head of the household. In addition, a separate listing and basic information on former household members who had emigrated abroad was collected. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor and roof of the house, and ownership of various durable goods. A module was included to obtain information about methods used in the household for disciplining children; the information was gathered concerning one selected child in the age range 2-14 years. Finally, height and weight measurements, and the results of haemoglobin measurements for consenting women and men age 15-49 years and children age 6 to 59 months were recorded in the Household Questionnaire. The haemoglobin testing procedures are described in detail in the next section.

The Women’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following topics: - Background characteristics (education, residential history, media exposure, etc.); - Reproductive history; - Knowledge and use of family planning methods; - Fertility preferences; - Antenatal and delivery care; - Breastfeeding and infant feeding practices; - Vaccinations and childhood illnesses; - Marriage and sexual activity; - Woman’s work and husband’s background characteristics; - Infant and child feeding practices; - Childhood mortality; and - Awareness and behaviour about AIDS and other sexually transmitted infections (STIs).

The Women’s Questionnaire had a number of important additions not present in the DHS model questionnaire. First, the BP readings were taken for all women age 15-49 that lived in the households selected for the men’s survey. Secondly, a vaccination module was added for each child under the age of five years to be completed at the local health clinic or centre. As indicated by the 2005 MICS survey findings and according to child health experts, immunization information in Albania is more frequently kept at the health clinics or centres than on an immunization card or child health book in the mother’s possession. The purpose of this module was, therefore, to collect information on immunizations from the local health clinics or centres in addition to that collected during the woman’s interview. The vaccination module provides better quality immunization indicators because

Climate often drives ungulate population dynamics, and as climates change, some areas may become unsuitable for species persistence. Unraveling the relationships between climate and population dynamics, and projecting them across time, advances ecological understanding that informs and steers sustainable conservation for species. Using pronghorn (Antilocapra americana) as an ecological model, we used a Bayesian approach to analyze long-term population, precipitation, and temperature data from 18 subpopulations in the southwestern United States. We determined which long-term (12 and 24 months) or short-term (gestation trimester and lactation period) climatic conditions best predicted annual rate of population growth (λ). We used these predictions to project population trends through 2090. Projections incorporated downscaled climatic data matched to pronghorn range for each population, given a high and a lower atmospheric CO2 concentration scenario. Since the 1990s, 15 of the pronghorn subpopulations declined in abundance. Sixteen subpopulations demonstrated a significant relationship between precipitation and λ, and in 13 of these, temperature was also significant. Precipitation predictors of λ were highly seasonal, with lactation being the most important period, followed by early and late gestation. The influence of temperature on λ was less seasonal than precipitation, and lacked a clear temporal pattern. The climatic projections indicated that all of these pronghorn subpopulations would experience increased temperatures, while the direction and magnitude of precipitation had high subpopulation-specific variation. Models predicted that nine subpopulations would be extirpated or approaching extirpation by 2090. Results were consistent across both atmospheric CO2 concentration scenarios, indicating robustness of trends irrespective of climatic severity. In the southwestern United States, the climate underpinning pronghorn subpopulations is shifting, making conditions increasingly inhospitable to pronghorn persistence. This realization informs and steers conservation and management decisions for pronghorn in North America, while exemplifying how similar research can aid ungulates inhabiting arid regions and confronting similar circumstances elsewhere. Long-term data from annual aerial surveys of pronghorn subpopulations in Utah, Arizona, New Mexico, and western Texas were used to calculate annual rates of population growth (λ). When subpopulation-specific harvest and translocation data were available, population estimates for calculating λ were adjusted according to the following equation: λt = Nt/(Nt-1 - h - r + a), where λt is population change from time t-1 to t, Nt and Nt-1 are population estimates from current and previous surveys, respectively, h is number of pronghorn harvested, and r and a are number of individuals removed from and released into the population, respectively, through translocations. Only population estimates from surveys conducted in consecutive years were used to calculate λ. If λ = 2, the associated surveys were removed from analyses because λ would be considered to be derived from unreliable or unstandardized population estimates, resulting in biologically unrealistic population growth rates. Monthly climate data (precipitation [mm/day] and mean temperature [degrees C]) were from 14 x 14 km cells from pronghorn range in each subpopulation in Utah, Arizona, New Mexico, and western Texas. Means across grids were calculated to obtain monthly values of precipitation and temperature. Two realistic future global climate scenarios were compared; a lower (Representative Concentrations Pathways 4.5) and a high (Representative Concentrations Pathways 8.5) atmospheric CO2 concentration scenario. Standardized precipitation index for 3-, 6-, 12-, and 24-month periods were calculated from all available monthly precipitation data using program SPI SL 6 (National Drought Mitigation Center 2014). Monthly mean temperature, total precipitation, and mean SPI (3-, 6-, and 12-month periods) were summarized by important periods in an adult female pronghorn's annual reproductive cycle relative to peak fawning (i.e., early, mid-, and late gestation [3 months each] and lactation [4 months]). Mean temperature and total precipitation were also calculated for 12 and 24 months preceding each population survey. Historic pronghorn population trends in relation to temperature and precipitation were assessed using integrated Bayesian population models. All models included a covariate for density effect (i.e., population in the previous year). Precipitation and temperature model comparison sets were run separately, and each model set included a null model (i.e., only density covariate, no climate covariates). These top individual precipitation and temperature covariates were then combined in models (i.e., one precipitation and temperature covariate per model), and these combined models were run including a term for the interaction between precipitation and temperature using the following equation: ln(λt) = Alpha + Beta1XN[t-1] + Beta2Xprec + Beta3Xtemp + Beta4Xprec*temp. Projected climate data for each pronghorn subpopulation was used to predict λt for each year to 2090. An integrated modeling approach was used, whereby the best performing model climatic predictors from historic population trends for each pronghorn subpopulation was embedded in that subpopulation pronghorn population projection model.

How will worldwide changes in population affect pressures for international migration in the future? We examine the past three decades, during which population pressures contributed to substantial labor flows from neighboring countries into the United States and Europe, and contrast them with the coming three decades, which will see sharp reductions in labor-supply growth in Latin America but not in Africa or much of the Middle East. Using a gravity-style empirical model, we examine the contribution of changes in relative labor-supply to bilateral migration in the 2000s and then apply this model to project future bilateral flows based on long-run UN forecasts of working-age populations in sending and receiving countries. Because the Americas are entering an era of uniformly low population growth, labor flows across the Rio Grande are projected to slow markedly. Europe, in contrast, will face substantial demographically driven migration pressures from across the Mediterranean for decades to come. Although these projected inflows would triple the first-generation immigrant stocks of larger European countries between 2010 and 2040, they would still absorb only a small fraction of the 800-million-person increase in the working-age population of Sub-Saharan Africa that is projected to occur over this period.

ARC’s long-range, regional control forecasts were developed using the Regional Economic Models, Incorporated (REMI) econometric model. Additionally, the ARC regional forecast was steered and endorsed by a Technical Advisory Committee (TAC). The TAC comprised of 26 regional economists and policy advisors who reviewed and recommended model calibration, modeling assumptions, and forecast outcomes through 2050.The forecasting model relates change in the region’s population and jobs to national expectations. The model is carefully calibrated based on historical data for the Atlanta Region and the nation. The forecast series is developed from a 2015 baseline and runs to a 2050 horizon year. Methods, inputs, and outputs are reviewed not only by the TAC but also by an internal planning advisory group. The internal planning advisory group consists of 7 members. Members of the group bring specific specialties with experiences and knowledge in natural resources, community development and planning, workforce solutions, transportation, and aging and independent services to collaborate on the preliminary forecast providing feedback from a regional planning perspective. Within any forecast model there are key assumptions underlying the forecasts. ARC and the TAC made assumptions concerning survival rates, birth rates, labor force, employment base forecast, unemployment rates, special populations, and migration patterns. Incorporation of these assumptions insures that the resulting regional forecast is consistent with reasonable expectations of change in the entire Region and United States economy.Additional details: Employment refers to the number of jobs available in a geographic area. Households and Population are derived from two different universes, and therefore it is not possible to use this dataset to determine residents per household.For more information, visit https://atlantaregional.org/atlanta-region/population-employment-forecasts/.

The life-cycle age groups are:under 15 years15 to 29 years30 to 64 years65 years and over.Map shows the percentage change in the census usually resident population count for life-cycle age groups between the 2018 and 2023 Censuses.Download lookup file from Stats NZ ArcGIS Online or Stats NZ geographic data service.FootnotesGeographical boundariesStatistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.Subnational census usually resident populationThe census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. Caution using time seriesTime series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).About the 2023 Census datasetFor information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings. Data qualityThe quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.Quality rating of a variableThe quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable. Age concept quality ratingAge is rated as very high quality. Age – 2023 Census: Information by concept has more information, for example, definitions and data quality.Using data for goodStats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga".ConfidentialityThe 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically, the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for high oil and gas development, low population size, and with effects of climate change under an RCP 8.5 scenario (2050) . The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

Climate and land-use change are major components of global environmental change with feedbacks between these components. The consequences of these interactions show that land use may exacerbate or alleviate climate change effects. Based on these findings it is important to use land-use scenarios that are consistent with the specific assumptions underlying climate-change scenarios. The Integrated Climate and Land-Use Scenarios (ICLUS) project developed land-use outputs that are based on a downscaled version of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and demographic storylines. ICLUS outputs are derived from a pair of models. A demographic model generates county-level population estimates that are distributed by a spatial allocation model (SERGoM v3) as housing density across the landscape. Land-use outputs were developed for the four main SRES storylines and a baseline ("base case"). The model is run for the conterminous USA and output is semi-decadally for each scenario to 2100. In addition to housing density at a 1 hectare spatial resolution, this project also generated estimates of impervious surface at a resolution of 1 square kilometer.

File Name: Matrix_model_transition_data.csvResource Description: This data set includes data on 25 transitions of a matrix demographic model of two invasive Vincetoxicum species from six field and forest populations in New York State.Resource Title: Variable definitions. File Name: Matrix_model_metadata.csvResource Description: Definitions of variables including equations for each transition and definitions of the lower-level vital rates in the equationsResource Title: Vital Rate definitions. File Name: Vital_Rate.csvResource Description: Vital Rate definitions of lower-level vital rates used in transition equations - to be substituted into the Data Dictionary for full definition of each transition equation.Resource Title: Data Dictionary. File Name: Matrix_Model_transition_data_DD.csvResource Description: See Vital Rate resource for definitions of lower-level vital rates used in transition equations where noted.Resource Title: Matrix model vital rate data for Vincetoxicum species. File Name: Matrix_model_vital rate_data.csvResource Description: This data set includes data on 20 lower-level vital rates used in the calculation of transitions of a matrix demographic model of two invasive Vincetoxicum species in New York State as well as definitions of the vital rates. (File added on 12/7/2018)Resource Software Recommended: Microsoft Excel,url: https://office.microsoft.com/excel/

Mean and standard deviation for demographic attributes of the 200 Metropolitan Statistical Areas (MSAs) considered in our study.Statistics of metropolitan statistical areas.

The data presented in this repository include the following datasets used in the article Multisite integrated population model reveals diverging drivers of population dynamics in a highly mobile species, were we studied three neighbouring populations (Catalonia -CAT-, Aragon -ARA- and Valencian Community -VAL-) of the Eurasian griffon vulture (Gyps fulvus) in the northeastern Iberian Peninsula and assessed site-specific population dynamics over 14 years, while accounting for dispersal across these populations using a Bayesian multisite integrated population model.Below is a detailed description of each file:"Productivity and count data.xlsx": An Excel file containing data on reproductive pair counts and productivity (number of fledglings per monitored pair) across the three studied regions."CH.csv": Capture-recapture history data used for the multi-state model."msmodel.txt" and "msIPMscript.txt": These files contain the code for the Bayesian multi-state model and the multisite Integrated Population Model, respectively, written in JAGS language using the jagsui package."States-matrix-multistatemodel.xlsx": The states transition matrix of the multi-state model.For further details on the methods and modeling, please refer to the original article and supplementary material.