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/

In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species’ range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of Trichocentrum undulatum, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (? and ?s) among sites. Methods Field methods Censuses took place between 2013 and 2021. The longest census period was that of the Peripheral population with a total of nine years (2013–2021). All four populations in Cuba used in demographic modeling that were censused more than once: Core 1 site (2016–2019, four years), Core 2 site (2018–2019, two years), Core 3 (2016 and 2018 two years), and Core 4 (2018–2019, two years) (Appendix S1: Table S1). In November 2017, Hurricane Irma hit parts of Cuba and southern Florida, impacting the Peripheral population. The Core 5 population (censused on 2016 and 2018) was small (N=17) with low survival on the second census due to logging. Three additional populations in Cuba were visited only once, Core 6, Core 7, and Core 8 (Table 1). Sites with one census or with a small sample size (Core 5) were not included in the life history and matrix model analyses of this paper due to the lack of population transition information, but they were included in the analysis on the correlation between herbivory and fruit rate, as well as the use of mortality observations from logging for modeling. All Cuban sites were located between Western and Central Cuba, spanning four provinces: Mayabeque (Core 1), Pinar del Rio (Core 2 and Core 6), Matanzas (Core 3 and Core 5), and Sancti Spiritus (Core 4, Core 7, Core 8). At each population of T. undulatum presented in this study, individuals were studied within ~1-km strips where T. undulatum occurrence was deemed representative of the site, mostly occurring along informal forest trails. Once an individual of T. undulatum was located, all trees within a 5-m radius were searched for additional individuals. Since tagging was not permitted, we used a combination of information to track individual plants for the repeated censuses. These include the host species, height of the orchid, DBH of the host tree, and hand-drawn maps. Individual plants were also marked by GPS at the Everglades Peripheral site. If a host tree was found bearing more than one T. undulatum, then we systematically recorded the orchids in order from the lowest to highest as well as used the previous years’ observations in future censuses for individualized notes and size records. We recorded plant size and reproductive variables during each census including: the number of leaves, length of the longest leaf (cm), number of inflorescence stalks, number of flowers, and the number of mature fruits. We also noted any presence of herbivory, such as signs of being bored by M. miamensis, and whether an inflorescence was partially or completely affected by the fly, and whether there was other herbivory, such as D. boisduvalii on leaves. We used logistic regression analysis to examine the effects of year (at the Peripheral site) and sites (all sites) on the presence or absence of inflorescence herbivory at all the sites. Cross tabulation and chi-square analysis were done to examine the associations between whether a plant was able to fruit and the presence of floral herbivory by M. miamensis. The herbivory was scored as either complete or partial. During the orchid population scouting expeditions, we came across a small population in the Matanzas province (Core 5, within 10 km of the Core 3 site) and recorded the demographic information. Although the sampled population was small (N = 17), we were able to observe logging impacts at the site and recorded logging-associated mortality on the subsequent return to the site. Matrix modeling Definition of size-stage classes To assess the life stage transitions and population structures for each plant for each population’s census period we first defined the stage classes for the species. The categorization for each plant’s stage class depended on both its size and reproductive capabilities, a method deemed appropriate for plants (Lefkovitch 1965, Cochran and Ellner 1992). A size index score was calculated for each plant by taking the total number of observed leaves and adding the length of the longest leaf, an indication of accumulated biomass (Borrero et al. 2016). The smallest plant size that attempted to produce an inflorescence is considered the minimum size for an adult plant. Plants were classified by stage based on their size index and flowering capacity as the following: (1) seedlings (or new recruits), i.e., new and small plants with a size index score of less than 6, (2) juveniles, i.e., plants with a size index score of less than 15 with no observed history of flowering, (3) adults, plants with size index scores of 15 or greater. Adult plants of this size or larger are capable of flowering but may not produce an inflorescence in a given year. The orchid’s population matrix models were constructed based on these stages. In general, orchid seedlings are notoriously difficult to observe and easily overlooked in the field due to the small size of protocorms. A newly found juvenile on a subsequent site visit (not the first year) may therefore be considered having previously been a seedling in the preceding year. In this study, we use the discovered “seedlings” as indicatory of recruitment for the populations. Adult plants are able to shrink or transition into the smaller juvenile stage class, but a juvenile cannot shrink to the seedling stage. Matrix elements and population vital rates calculations Annual transition probabilities for every stage class were calculated. A total of 16 site- and year-specific matrices were constructed. When seedling or juvenile sample sizes were < 9, the transitions were estimated using the nearest year or site matrix elements as a proxy. Due to the length of the study and variety of vegetation types with a generally large population size at each site, transition substitutions were made with the average stage transition from all years at the site as priors. If the sample size of the averaged stage was still too small, the averaged transition from a different population located at the same vegetation type was used. We avoided using transition values from populations found in different vegetation types to conserve potential environmental differences. A total of 20% (27/135) of the matrix elements were estimated in this fashion, the majority being seedling stage transitions (19/27) and noted in the Appendices alongside population size (Appendix S1: Table S1). The fertility element transitions from reproductive adults to seedlings were calculated as the number of seedlings produced (and that survived to the census) per adult plant. Deterministic modeling analysis We used integral projection models (IPM) to project the long-term population growth rates for each time period and population. The finite population growth rate (?), stochastic long-term growth rate (?s), and the elasticity were projected for each matrices using R Popbio Package 2.4.4 (Stubben and Milligan 2007, Caswell 2001). The elasticity matrices were summarized by placing each element into one of three categories: fecundity (transition from reproductive adults to seedling stage), growth (all transitions to new and more advanced stage, excluding the fecundity), and stasis (plants that transitioned into the same or a less advanced stage on subsequent census) (Liu et al. 2005). Life table response experiments (LTREs) were conducted to identify the stage transitions that had the greatest effects on observed differences in population growth between select sites and years (i.e., pre-post hurricane impact and site comparisons of same vegetation type). Due to the frequent disturbances that epiphytes in general experience as well as our species’ distribution in hurricane-prone areas, we ran transient dynamic models that assume that the populations censused were not at stable stage distributions (Stott et al. 2011). We calculated three indices for short-term transient dynamics to capture the variation during a 15-year transition period: reactivity, maximum amplification, and amplified inertia. Reactivity measures a population’s growth in a single measured timestep relative to the stable-stage growth, during the simulated transition period. Maximum amplification and amplified inertia are the maximum of future population density and the maximum long-term population density, respectively, relative to a stable-stage population that began at the same initial density (Stott et al. 2011). For these analyses, we used a mean matrix for Core 1, Core 2 Core 3, and Core 4 sites and the population structure of their last census. For the Peripheral site, we averaged the last three matrices post-hurricane disturbance and used the most-recent population structure. We standardized the indices across sites with the assumption of initial population density equal to 1 (Stott et al. 2011). Analysis was done using R Popdemo version 1.3-0 (Stott et al. 2012b). Stochastic simulation We created matrices to simulate the effects of episodic recruitment, hurricane impacts, herbivory, and logging (Appendix S1: Table S2). The Peripheral population is the longest-running site with nine years of censuses (eight

Throughout most of human history, global population growth was very low; between 10,000BCE and 1700CE, the average annual increase was just 0.04 percent. Therefore, it took several thousand years for the global population to reach one billion people, doing so in 1803. However, this period marked the beginning of a global phenomenon known as the demographic transition, from which point population growth skyrocketed. With the introduction of modern medicines (especially vaccination), as well as improvements in water sanitation, food supply, and infrastructure, child mortality fell drastically and life expectancy increased, causing the population to grow. This process is linked to economic and technological development, and did not take place concurrently across the globe; it mostly began in Europe and other industrialized regions in the 19thcentury, before spreading across Asia and Latin America in the 20th century. As the most populous societies in the world are found in Asia, the demographic transition in this region coincided with the fastest period of global population growth. Today, Sub-Saharan Africa is the region at the earliest stage of this transition. As population growth slows across the other continents, with the populations of the Americas, Asia, and Europe expected to be in decline by the 2070s, Africa's population is expected to grow by three billion people by the end of the 21st century.

The aims of the project were to examine and analyse demographic processes of fertility, nuptiality, marital fertility, mortality and migration during periods encompassing the demographic transition in England and Wales. In particular, the goal was to reveal underlying relationships between demographic processes in the context of changing socio-economic conditions. With this goal in mind, population, occupational, and education data were compilated, and demographic and statistical models were employed to estimate key measures and indicators of demographic change. The large majority of the data and estimates were compiled and made at the registration district level for the period 1851-1911. In addition decennial inter-county migration flows were estimated for the period 1851-1911.

Abstract

The Sahel Women Empowerment and Demographic Dividend (P150080) project in Burkina Faso focuses on advancing women's empowerment to spur demographic transition and mitigate gender disparities. This project seeks to empower young women by promoting entrepreneurship through business skills training and grants, and by enhancing access to reproductive health information and contraception, thereby aiming to lower fertility rates.

The World Bank Africa Gender Innovation Lab, along with its partners, is conducting detailed impact evaluations of the SWEDD program’s key initiatives to gauge their effects on child marriage, fertility, and the empowerment of adolescent girls and young women.

This data represents the first round of data collection (baseline) for the impact evaluation and include a household and community level surveys. The household level sample comprises 9857 households, 70,169 individuals and 9382 adolescent girls and young wives aged 24 living in the Boucle du Mouhoun and the East regions of Burkina Faso. The community level sample includes 175 villages.

The insights derived from this survey could help policymakers develop strategies to: - Reduce fertility and child marriage by enhancing access to contraceptives and broadening reproductive health education. - Promote women’s empowerment by increasing their participation in economic activities

This data is valuable for planners who focus on improving living standards, particularly for women. The Ministry of Women, National Solidarity, Family, and Humanitarian Action of Burkina Faso, along with District Authorities, Research Institutions, NGOs, and the general public, stand to benefit from this survey data.

Geographic coverage

Burkina Faso, Regions of Boucle du Mouhoun and East

Analysis unit

The unit of analysis is adolescent girls for the adolescent survey and households for the household survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

We randomly selected 200 villages from the 11 provinces in the two regions of the Boucle du Mouhoun and the East. The 200 villages were selected proportionally, based on the formula (Np/N)*200, where Np represents the number of eligible villages in the province and N the total number of eligible villages. 25 villages were later dropped because of lack of safety.

A census was first administered in each village to identify eligible girls and young wives, as well as households with these eligible individuals. All households with at least one eligible person then constituted the universe from which the survey sample was drawn. In total 9857 households and 9382 girls and young wives were sampled. A village-level questionnaire was also administered.

The objective of the baseline survey was to build a comprehensive dataset, which would serve as a reference point for the entire sample, before treatment and control assignment and program implementation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The data consists of responses from households to questions pertaining to: 1. List of household members 2. Education of household members 3. Occupations of household members 4. Characteristics of housing and durable goods 5. Food security 6. Household head's aspirations, as well as those of a boy aged 12 to 24 7. Opinions on women's empowerment and gender equality

The questionnaire administrated to girls contains the following sections: 1. Education 2. Marriage and children 3. Aspirations 4. Health and family planning 5. Knowledge of HIV/AIDS 6. Women's empowerment 7. Gender-based violence 8. Income-generating activities 9. Savings and credit 10. Personal relationships and social networks 11. Committee members and community participation

The questionnaire administered at the village-level contains the following sections: 1. Social norms (marriage norms) 2. Ethnic and religious compositions 3. Economic infrastructures (markets and roads) 4. Social services a. Health b. Education

The household questionnaire was administered to the head of the household or to an authorized person capable of answering questions about all individuals in the household. The adolescent questionnaire was administered to each eligible pre-selected individual within the household. Considering the modules of the adolescent questionnaire, it was only administered by female enumerators. The village-level questionnaire was administered to a group of three to five village leaders with enough knowledge of the village. The enumerators were instructed to include women in this group whenever possible. The questionnaires were written in French, translated into the local languages, and programmed on tablets in French using the CAPI program.

Cleaning operations

Data was anonymized through decoding and local suppression.

Environmental change continually perturbs populations from a stable state, leading to transient dynamics that can last multiple generations. Several long-term studies have reported changes in trait distributions along with demographic response to environmental change. Here we conducted an experimental study on soil mites and investigated the interaction between demography and an individual trait over a period of nonstationary dynamics. By following individual fates and body sizes at each life-history stage, we investigated how body size and population density influenced demographic rates. By comparing the ability of two alternative approaches, a matrix projection model and an integral projection model, we investigated whether consideration of trait-based demography enhances our ability to predict transient dynamics. By utilizing a prospective perturbation analysis, we addressed which stage-specific demographic or trait-transition rate had the greatest influence on population dynamics. Both body size and population density had important effects on most rates; however, these effects differed substantially among life-history stages. Considering the observed trait-demography relationships resulted in better predictions of a population’s response to perturbations, which highlights the role of phenotypic plasticity in transient dynamics. Although the perturbation analyses provided comparable predictions of stage-specific elasticities between the matrix and integral projection models, the order of importance of the life-history stages differed between the two analyses. In conclusion, we demonstrate how a trait-based demographic approach provides further insight into transient population dynamics. Daily sampling of individual mitesday: day of the study (day t) | no: individual ID for each day | surv: survival to day t+1? | stage: life-history stage at day t | stage1: life-history stage at day t+1 | trns: transition to next stage at day t+1? | tsex: transition to female stage at day t+1? | dens: weighted population density at day t | size: log(body size) at day t | size1: log(body size) at day t+1 | rep: produced eggs at day t+1? | rec: number of eggs produced on day t+1 | day2: number of eggs hatched on day t+2 | day3: number of eggs hatched on day t+3 | day4: number of eggs hatched on day t+4 | day5: number of eggs hatched on day t+5 | day6: number of eggs hatched on day t+6 | day7: number of eggs hatched after day t+6 | eggsurv: proportion of eggs hatched | hrate: daily hatching rate | eggsize: average log(egg size)ind_data.csvAdditional experiment measuring egg-to-larva size transitioneggSize: log(egg size) | larvaSize: log(larva size)egg_data.csvDaily population censusday: day of the study (day t) | e: number of eggs | l: number of larvae | p: number of protonymphs | t: number of tritonymphs | f: number of female adults | m: number of male adults | group: (c)ontrol or (s)ample group? | dens: weighted population densitypop_census.csv

ObjectivesThe burden and highest regional prevalence of anaemia is reported in sub-Saharan Africa (SSA). The study evaluated changes in anaemia prevalence across the Demographic Health Surveys (DHS) periods in SSA and reported factors influencing observed changes in the trend.MethodThe study was implemented by a two-stage cross-sectional stratified sampling approach. The study involved women of reproductive age (15–49 years) in sub-Saharan Africa countries (Ghana, Sierra Leone, Mali, and Benin) using two different periods of their demographic health surveys (DHS) data. The study adopted both descriptive and inferential statistical methods. The chi-square test was used to determine the existence of a statistically significant relationship between the outcome and predictor variables and test the observed changes in anaemia. Multivariable logistic regression analyses were conducted on each survey year and the pooled dataset for eligible study countries. Multivariate decomposition analysis was performed to explain how compositional changes and behavioural effects of women characteristics affected the changes in anaemia prevalence. The study reported frequencies, percentages and odds ratios along with their 95% confidence intervals (CI).ResultsGhana and Sierra Leone experienced 17.07% [95% CI: 14.76–19.37, p  0.05] of anaemia decrease from period 1 to period 2, respectively, while Mali and Benin experienced 11% [95% CI: 9.14–12.90, p 

Footnotes: 1 Population estimates based on the Standard Geographical Classification (SGC) 2016 as delineated in the 2016 Census. 2 A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program. If the population of the core of a CA falls below 10,000, the CA is retired from the next census. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA that are not population centres are rural areas. When a CA has a core of at least 50,000, based on data from the previous Census of Population, it is subdivided into census tracts. Census tracts are maintained for the CA even if the population of the core subsequently falls below 50,000. All CMAs are subdivided into census tracts (2016 Census Dictionary, catalogue number 98-301-X2016001). 3 An area outside census metropolitan areas and census agglomerations is made up of all areas (within a province or territory) unallocated to a census metropolitan area (CMA) or census agglomeration (CA). 4 The population growth, which is used to calculate population estimates of census metropolitan areas and census agglomerations (table 17100135), is comprised of the components of population growth (table 17100136). 5 This table replaces table 17100079. 6 The components of population growth for census metropolitan areas (CMAs) and census agglomerations (CAs) sometimes had to be calculated using information at the census division level, using the geographic conversion method. This method involves using the population component calculated at the level of the CD(s) in which the CMA or CA is located and applying a ratio corresponding to the proportion of the CMA or CA population included in the corresponding CD(s). For periods prior to 2005/2006, all demographic components for all CMAs and CAs were calculated using geographic conversions. For the periods from 2005/2006 to 2010/2011 inclusively, emigration and internal migration components for areas that were not CMAs according to the 2011 SGC were calculated using geographic conversions. For the periods 2011/2012 to 2015/2016 inclusively, the emigration and internal migration components of regions that were not CMAs or CAs according to the 2011 SGC were calculated using geographic conversions. For the relevant demographic components, trends should be interpreted with caution where the method of calculation has changed over time. This caveat applies particularly to the intraprovincial migration component, for which the assumptions of the geographic conversion method are more at risk of not being met. 7 Period from July 1 to June 30. 8 Age on July 1. 9 The estimates for deaths are preliminary for 2020/2021, updated for 2019/2020 and final up to 2018/2019. Preliminary and updated estimates of deaths were produced by Demography Division, Statistics Canada (see definitions, data sources and methods record number 3601 and 3608) with the exception of Quebec's data which are taken from the estimates of "l'Institut de la statistique du Québec" (ISQ) and then adjusted to Statistics Canada's provincial estimates. Final data were produced by Health Statistics Division Statistics Canada (see definitions data sources and methods record number 3233). However before 2011 the final estimates may differ from the data released by the Health Statistics Division due to the imputation of certain unknown values. In addition for estimates of deaths the age represents age at the beginning of the period (July 1st) and not the age at the time of occurrence as with the Health Statistics Division data."

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

Abstract copyright UK Data Service and data collection copyright owner.

This study provides estimates of births, deaths and net-migration, by ethnic group, for each electoral ward (England and Wales) and local authority area (England, Wales and Scotland), for the period July 1st, 1991 –June 30th, 2001.

The study uses the eight-category classification of ethnic group: White, Caribbean, African, Indian, Pakistani, Bangladeshi and Other. Ethnic group is not included in civil registration of births and deaths in the UK. These estimates are based on estimates of fertility of each ethnic group in each locality, based on local child/woman ratios, common schedules of mortality, and estimates of ethnic group population consistent with the latest estimates of mid-year population for 1991 and 2001 by the Office for National Statistics (ONS) and the General Register Office. Net migration is estimated indirectly as the residual after births and deaths are deducted from population change during the period 1991-2001, using standard methods of applied demography described in Simpson, Finney and Lomax (2008). There are no other estimates of demographic components of change for this period.

The eight ethnic group categories are known to be more stable between the two censuses of 1991 and 2001 than other possible classifications that amalgamate the 10 ethnic group categories of 1991 with the 16 ethnic group categories of 2001. The least stable categories across this time are Caribbean, African, and Other.

Further information is available on the Ethnic Group Population Change and Integration: a Demographic Approach to Small Area Ethnic Geographies ESRC Award web page.

Main Topics:

The study includes seven files for the 8,797 electoral wards of England and Wales (as constituted January 2003) and seven files for the 408 local authority districts, unitary authorities and council areas of Great Britain (as constituted January 2003).

Each set of seven files includes input data and final detailed estimates of births, deaths and net-migration, by ethnic group.

The demographic dataset of Cayo Santiago rhesus macaques was shared by the Caribbean Primate Research Center, University of Puerto Rico.

The American black bear (Ursus americanus) has one of the broadest geographic distributions of any mammalian carnivore in North America. Populations occur from high to low elevations and from mesic to arid environments, and their demographic traits have been documented in a wide variety of environments. However, the demography of American black bears in semiarid environments, which comprise a significant portion of the geographic range, is poorly documented. To fill this gap in understanding, we used data from a long-term mark-recapture study of black bears in the semiarid environment of eastern Utah, USA. Cub and yearling survival were low and adult survival was high relative to other populations. Adult life stages had the highest reproductive value, comprised the largest proportion of the population, and exhibited the highest elasticity contribution to the population growth rate (i.e., λ). Vital rates of black bears in this semiarid environment are skewed toward higher survival of adults, and lower survival of cubs compared to other populations. Methods Mark-Recapture study We estimated survival rates from long-term mark-recapture data gathered as part of a 27-year study on American black bears of the East Tavaputs Plateau. During the first 12 years of the study (June to August 1991-2003) female bears were captured and radio-collared, and all bears were tagged in the ear, except for cubs and yearlings. For the entire study (1992 – 2019), collared females were visited in their dens annually during their winter hibernation to count newborn cubs and surviving yearlings. Age of individual bears was determined by 2 methods: (1) direct observation of cubs or yearlings (i.e., year of birth was known) or (2) cementum annuli analysis of a cross-section of the root of an extracted premolar (Palochak, 2004; Willey, 1974). The data we used to derive survival and fecundity rates consisted of the ID_number, cohort (cub, yearling, subadult, prime-aged adult, and old adult), age in years, sex (female, male, unknown), number of cubs, number of yearlings, first observation of individual, last observation of individual, days from last observation, and survival status. We did not include subadult and adult male bears in the analysis. Survival rates To determine the average survival rates for each life stage, we used a Cox proportional hazards model in program R (Team, 2022). This model accommodates staggered entries, where individuals enter the study at different times, and censoring, where the event of interest (e.g., mortality) is not observed for all individuals due to the inability to follow-up or the study ending before the event occurs. These features allow for a more accurate representation of survival over time, even with incomplete data (Cox, 1972). The Cox model is a semi-parametric approach that examines how covariates, such as age and environmental factors, influence the risk of death at any given point in time. Unlike fully parametric models, which require defining the baseline hazard function (the risk of death when all covariates are at baseline levels), the Cox model does not require this step, making it highly flexible and suitable for diverse data and applications (Zhang, 2016). The hazard function in this context refers to the rate or likelihood of an event (e.g., death) occurring at a specific moment, given that the individual has survived up to that time. The Cox model is expressed as follows: h(t|X) = h0(t) exp(β1X1 + β2X2 +...+ βpXp) where h(t|X) is the hazard function at time t given covariates X, h0(t) is the baseline hazard function β1, β2, …, βp are the coefficients for the predictor variables X1, X2, …, Xp. The model assumes proportional hazards, meaning the relative risk of death (the hazard ratio) between two groups remains constant over time (Zhang, 2016). The advantage of the Cox model is its ability to handle censored data, common in survival analysis. Censoring occurs when some individuals have not experienced mortality by the end of the study, so we only know that they survived up to that point. Moreover, the Cox model can incorporate time-dependent covariates, enabling a dynamic analysis of how risk factors influence survival over time (Therneau & Grambsch, 2000). For our analysis, we formulated four Cox proportional hazards models as follows: 1) constant survival, 2) a model with the effect of maternal age, 3) a model with the effect of cohort, and 4) a model with the combined effect of age and cohort. We compared these models using Akaike’s Information Criterion (AIC) to identify the best fit and then evaluate the effect sizes of covariates based on the β coefficients from the top-performing model (Burnham et al., 2011; Symonds & Moussalli, 2011). When there was uncertainty in model selection, we used model averaging to estimate effect sizes and β coefficients. Each model was also checked for uninformative parameters (Arnold, 2010). We reviewed the model summaries to assess the estimated effects of covariates (constant survival, maternal age, cohort, and the combination of age and cohort) on survival outcomes. Fecundity rates To determine fecundity rates, we used females monitored through the use of radio-collars. All females that were ≥ four years old were counted in the breeding pool. We removed any female ≥ 25 years of age from the breeding pool (Noyce, 2010). We classified old adults as ≥ 15 years old and prime-aged adults as 4-14 years of age. We visited dens of females to observe whether they were alone or accompanied by cubs or yearlings as well as the sexes of their offspring. At the height of the study, we had 15 prime-aged adult females, along with a few old-adult females. There was variation in the number of adult females and old-adult females throughout the study period and we had at least two old-adult females in each year for 12 years during the study. Matrix Transition Model and Analysis We developed a transition matrix model based on adult females and their offspring to estimate population growth and additional demographic parameters. In the model, we assumed every cub was born on January 1st and survived through the full year if they were alive through the 15th of October. We assumed density of males does not affect breeding success (Lewis et al., 2014). We divided the population into five age-based stages: cub (0–1 year-old); yearling (1–2 years old), subadult (2–4 years old), prime-aged adult (4–14 years old), and old adult (15+). We used the term sm to indicate the probability of surviving and transitioning to a new stage (matrix sub diagonal), and the term ss indicated the probability of surviving and staying in the same stage (matrix diagonal). We used f to indicate fecundity or reproduction (matrix upper right corner; Fig. 1A, 1B). We used the software Unified Life Models (ULM; (Legendre & Clobert, 1995) to evaluate the matrix model and to calculate population growth rate, stable age distribution, reproductive value, and sensitivity and elasticity matrices. We summed elasticity values across all stages for the three demographic processes: fecundity (f), growth (sm, transition from one age stage to another), and stasis (ss, survival without transitioning). Our matrix transition model differed from the matrix transition model generated by Beston (2011), which used nine life stages. To ensure an accurate comparison between the two models, we combined the nine life stages from the matrix transition model in the meta-analysis (Beston, 2011) into five broader stages: cub, yearling, subadult, adult, and old adult. We selected five life stages due to the assumption that age might influence reproductive output, a pattern supported by research on other mammals (Hilderbrand et al., 2019; Nussey et al., 2008; Promislow & Harvey, 1990).

Abstract (en): Comparative Cities is a teaching package designed to introduce students to analysis of manuscript schedules of the nineteenth century census for social, urban, family, and demographic history. The files are designed for use with SPSS. It was initially developed at Brown University with assistance of a project grant from the National Endowment for the Humanities. The file is organized to illustrate contrasts among cities at different stages of industrialization and the demographic transition in Europe and America: Pisa, Italy (1841), Amiens, France (1851), Stockport, England (1841 and 1851), and Providence, R.I. (1850, 1865, and 1880). The rural district around Pisa and part of Providence County are also included. There are approximately 1400 cases with information for individuals in each of eleven subfiles. These are random samples from the original 1:10 house samples for the four places made to permit flexible and economical student use. Summaries imbedded in the file permit analysis at the individual, household, or nuclear unit level. There are 142 variables for each individual. The package also contains a coursebook with explanation of each variable, a dictionary with occupational titles that appear in the censuses, course syllabus, and other instructions for use. The files are being used in the separate ongoing research of the two principal investigators and should be used for instructional purposes only. This teaching package can be supplied as two card-image data files, two files of SPSS instruction cards, and associated printed documentation. The package has also been updated with several files designed to be used with microcomputers. Included in the updated materials are four text files (Contents of Tape, Coursebook, Explanatory Materials, and Dictionary of Occupational Titles and Codes), a file of SPSSx data definition statements for use with PC-SPSSx, and a file of data definition statements for use with the Consortium's ABC statistical analysis package. Nine separate sub-files, each derived from the original census data and designed for analysis on micro-computers which are equipped with PC-SPSSx or ABC, are also provided. Finally, the package includes two mainframe SPSSx "Export" files which contain all of the data collected for each city. While these latter files duplicate the SPSS files contained in the earlier Comparative Cities package, they have been modified for use with SPSSx. The original Comparative Cities Teaching Package files can still be supplied as well. These files are oriented towards use of SPSS Version 9 on mainframe computers. 2006-01-12 All files were removed from dataset 20 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 20 and flagged as study-level files, so that they will accompany all downloads.

Abstract

The transition from socialism to a market economy has transformed the lives of many people. What are people's perceptions and attitudes to transition? What are the current attitudes to market reforms and political institutions?

To analyze these issues, the EBRD and the World Bank have jointly conducted the comprehensive, region-wide "Life in Transition Survey" (LiTS), which combines traditional household survey features with questions about respondents' attitudes and is carried out through two-stage sampling with a random selection of households and respondents.

The LiTS assesses the impact of transition on people through their personal and professional experiences during the first 15 years of transition. LiTS attempts to understand how these personal experiences of transition relate to people’s attitudes toward market and political reforms, as well as their priorities for the future.

The main objective of the LiTS was to build on existing studies to provide a comprehensive assessment of relationships among life satisfaction and living standards, poverty and inequality, trust in state institutions, satisfaction with public services, attitudes to a market economy and democracy and to provide valuable insights into how transition has affected the lives of people across a region comprising 16 countries in Central and Eastern Europe (“CEE”) and 11 in the Commonwealth of Independent State (“CIS”). Turkey and Mongolia were also included in the survey.

Geographic coverage

The LITS was to be implemented in the following 29 countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former Yugoslav Republic of Macedonia (FYROM), Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Mongolia, Poland, Romania, Russia, Serbia and Montenegro, Slovak Republic, Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine and Uzbekistan.

Kind of data

Sample survey data [ssd]

Sampling procedure

A total of 1,000 face-to-face household interviews per country were to be conducted, with adult (18 years and over) occupants and with no upper limit for age. The sample was to be nationally representative. The EBRD’s preferred procedure was a two stage sampling method, with census enumeration areas (CEA) as primary sampling units and households as secondary sampling units. To the extent possible, the EBRD wished the sampling procedure to apply no more than 2 stages.

The first stage of selection was to use as a sampling frame the list of CEA's generated by the most recent census. Ideally, 50 primary sampling units (PSU's) were to be selected from that sample frame, with probability proportional to size (PPS), using as a measure of size either the population, or the number of households.

The second sampling stage was to select households within each of the primary sampling units, using as a sampling frame a specially developed list of all households in each of the selected PSU's defined above. Households to be interviewed were to be selected from that list by systematic, equal probability sampling. Twenty households were to be selected in each of the 50 PSU's.

The individuals to be interviewed in each household were to be selected at random, within each of the selected households, with no substitution if possible.

ESTABLISHMENT OF THE SAMPLE FRAME OF PSU’s

In each country we established the most recent sample frame of PSU’s which would best serve the purposes of the LITS sampling methodology. Details of the PSU sample frames in each country are shown in table 1 (page 10) of the survey report.

In the cases of Armenia, Azerbaijan, Kazakhstan, Serbia and Uzbekistan, CEA’s were used. In Croatia we also used CEA’s but in this case, because the CEA’s were very small and we would not have been able to complete the targeted number of interviews within each PSU, we merged together adjoining CEA’s and constructed a sample of 1,732 Merged Enumeration Areas. The same was the case in Montenegro.

In Estonia, Hungary, Lithuania, Poland and the Slovak Republic we used Eurostat’s NUTS area classification system.

[NOTE: The NUTS (from the French "Nomenclature des territoriales statistiques" or in English ("Nomenclature of territorial units for statistics"), is a uniform and consistent system that runs on five different NUTS levels and is widely used for EU surveys including the Eurobarometer (a comparable survey to the Life in Transition). As a hierarchical system, NUTS subdivides the territory of the country into a defined number of regions on NUTS 1 level (population 3-7 million), NUTS 2 level (800,000-3 million) and NUTS 3 level (150,000-800,000). At a more detailed level NUTS 3 is subdivided into smaller units (districts and municipalities). These are called "Local Administrative Units" (LAU). The LAU is further divided into upper LAU (LAU1 - formerly NUTS 4) and LAU 2 (formerly NUTS 5).]

Albania, Bulgaria, the Czech Republic, Georgia, Moldova and Romania used the electoral register as the basis for the PSU sample frame. In the other cases, the PSU sample frame was chosen using either local geographical or administrative and territorial classification systems. The total number of PSU sample frames per country varied from 182 in the case of Mongolia to over 48,000 in the case of Turkey. To ensure the safety of our fieldworkers, we excluded from the sample frame PSU’s territories (in countries such as Georgia, Azerbaijan, Moldova, Russia, etc) in which there was conflict and political instability. We have also excluded areas which were not easily accessible due to their terrain or were sparsely populated.

In the majority of cases, the source for this information was the national statistical body for the country in question, or the relevant central electoral committee. In establishing the sample frames and to the extent possible, we tried to maintain a uniform measure of size namely, the population aged 18 years and over which was of more pertinence to the LITS methodology. Where the PSU was based on CEA’s, the measure was usually the total population, whereas the electoral register provided data on the population aged 18 years old and above, the normal voting age in all sampled countries. Although the NUTS classification provided data on the total population, we filtered, where possible, the information and used as a measure of size the population aged 18 and above. The other classification systems used usually measure the total population of a country. However, in the case of Azerbaijan, which used CEA’s, and Slovenia, where a classification system based on administrative and territorial areas was employed, the measure of size was the number of households in each PSU.

The accuracy of the PSU information was dependent, to a large extent, on how recently the data has been collected. Where the data were collected recently then the information could be considered as relatively accurate. However, in some countries we believed that more recent information was available, but because the relevant authorities were not prepared to share this with us citing secrecy reasons, we had no alternative than to use less up to date data. In some countries the age of the data available makes the figures less certain. An obvious case in point is Bosnia and Herzegovina, where the latest available figures date back to 1991, before the Balkan wars. The population figures available take no account of the casualties suffered among the civilian population, resulting displacement and subsequent migration of people.

Equally there have been cases where countries have experienced economic migration in recent years, as in the case of those countries that acceded to the European Union in May, 2004, such as Hungary, Poland and the Baltic states, or to other countries within the region e.g. Armenians to Russia, Albanians to Greece and Italy; the available figures may not accurately reflect this. And, as most economic migrants tend to be men, the actual proportion of females in a population was, in many cases, higher than the available statistics would suggest. People migration in recent years has also occurred from rural to urban areas in Albania and the majority of the Asian Republics, as well as in Mongolia on a continuous basis but in this case, because of the nomadic population of the country.

SAMPLING METHODOLOGY

Brief Overview

In broad terms the following sampling methodology was employed: · From the sample frame of PSU’s we selected 50 units · Within each selected PSU, we sampled 20 households, resulting in 1,000 interviews per country · Within each household we sampled 1 and sometimes 2 respondents The sampling procedures were designed to leave no free choice to the interviewers. Details on each of the above steps as well as country specific procedures adapted to suit the availability, depth and quality of the PSU information and local operational issues are described in the following sections.

Selection of PSU’s

The PSU’s of each country (all in electronic format) were sorted first into metropolitan, urban and rural areas (in that order), and within each of these categories by region/oblast/province in alphabetical order. This ensured a consistent sorting methodology across all countries and also that the randomness of the selection process could be supervised.

To select the 50 PSU’s from the sample frame of PSU’s, we employed implicit stratification and sampling was done with PPS. Implicit stratification ensured that the sample of PSU’s was spread across the primary categories of explicit variables and a better representation of the population, without actually stratifying the PSU’s thus, avoiding difficulties in calculating the sampling errors at a later stage. In brief, the PPS involved the

The dataset describes 150 patients with the following demographic characteristics : sex, age, HOMA-IR , systolic and diastolic blood pressure , and LDL-Cholesterol . These patients were followed for 28 years . The characteristics are the mean of the following measurement each year. In each year a liver biopsy was taken to record the stage of fibrosis and then the count of transition among stages is recorded in the columns called ( lambda ij) where the i,j represents the stages where the patients move between them . In other word , for each patient , there is a column for the transition count this patient had made from stage 0 to stage 1 , then there is another column for the transition counts he made from stage 1 to stage 2 in this follow up 28 years , and so on , that is to mean there are 9 columns for the count of each transition for each patient as there are 9 transitions that could be made : from 0 to 1 , from 1 to 2 , from 2 to 3 , from 3 to 4 , from 1 to 0 , from 2 to 1 , from 3 to 2 , from 2 to 0 and from 3 to 1 . these transition counts are the dependent or the response variable , while the demographic characteristics are the predictors or the independent variables . Using Poisson regression model is used to relate these counts with the risk factors for NAFLD .

Footnotes: 1 Population estimates based on the Standard Geographical Classification (SGC) 2016 as delineated in the 2016 Census. 2 A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program. If the population of the core of a CA falls below 10,000, the CA is retired from the next census. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA that are not population centres are rural areas. When a CA has a core of at least 50,000, based on data from the previous Census of Population, it is subdivided into census tracts. Census tracts are maintained for the CA even if the population of the core subsequently falls below 50,000. All CMAs are subdivided into census tracts (2016 Census Dictionary, catalogue number 98-301-X2016001). 3 An area outside census metropolitan areas and census agglomerations is made up of all areas (within a province or territory) unallocated to a census metropolitan area (CMA) or census agglomeration (CA). 4 The population growth, which is used to calculate population estimates of census metropolitan areas and census agglomerations (table 17100135), is comprised of the components of population growth (table 17100136). 5 This table replaces table 17100079. 6 The components of population growth for census metropolitan areas (CMAs) and census agglomerations (CAs) sometimes had to be calculated using information at the census division level, using the geographic conversion method. This method involves using the population component calculated at the level of the CD(s) in which the CMA or CA is located and applying a ratio corresponding to the proportion of the CMA or CA population included in the corresponding CD(s). For periods prior to 2005/2006, all demographic components for all CMAs and CAs were calculated using geographic conversions. For the periods from 2005/2006 to 2010/2011 inclusively, emigration and internal migration components for areas that were not CMAs according to the 2011 SGC were calculated using geographic conversions. For the periods 2011/2012 to 2015/2016 inclusively, the emigration and internal migration components of regions that were not CMAs or CAs according to the 2011 SGC were calculated using geographic conversions. For the relevant demographic components, trends should be interpreted with caution where the method of calculation has changed over time. This caveat applies particularly to the intraprovincial migration component, for which the assumptions of the geographic conversion method are more at risk of not being met. 7 Period from July 1 to June 30. 8 Age on July 1. 9 The estimates for immigrants are preliminary for 2020/2021 and final up to 2019/2020.

Footnotes: 1 Population estimates based on the Standard Geographical Classification (SGC) 2016 as delineated in the 2016 Census.2 A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program. If the population of the core of a CA falls below 10,000, the CA is retired from the next census. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA that are not population centres are rural areas. When a CA has a core of at least 50,000, based on data from the previous Census of Population, it is subdivided into census tracts. Census tracts are maintained for the CA even if the population of the core subsequently falls below 50,000. All CMAs are subdivided into census tracts (2016 Census Dictionary, catalogue number 98-301-X2016001).3 An area outside census metropolitan areas and census agglomerations is made up of all areas (within a province or territory) unallocated to a census metropolitan area (CMA) or census agglomeration (CA).4 The population growth, which is used to calculate population estimates of census metropolitan areas and census agglomerations (table 17100135), is comprised of the components of population growth (table 17100136).5 This table replaces table 17100079.6 The components of population growth for census metropolitan areas (CMAs) and census agglomerations (CAs) sometimes had to be calculated using information at the census division level, using the geographic conversion method. This method involves using the population component calculated at the level of the CD(s) in which the CMA or CA is located and applying a ratio corresponding to the proportion of the CMA or CA population included in the corresponding CD(s). For periods prior to 2005/2006, all demographic components for all CMAs and CAs were calculated using geographic conversions. For the periods from 2005/2006 to 2010/2011 inclusively, emigration and internal migration components for areas that were not CMAs according to the 2011 SGC were calculated using geographic conversions. For the periods 2011/2012 to 2015/2016 inclusively, the emigration and internal migration components of regions that were not CMAs or CAs according to the 2011 SGC were calculated using geographic conversions. For the relevant demographic components, trends should be interpreted with caution where the method of calculation has changed over time. This caveat applies particularly to the intraprovincial migration component, for which the assumptions of the geographic conversion method are more at risk of not being met.7 Period from July 1 to June 30.8 Age on July 1.9 The estimates for immigrants are preliminary for 2020/2021 and final up to 2019/2020.

Approximately 25% of mammals are currently threatened with extinction, a risk that is amplified under climate change. Species persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development, reproduction), and hence, population dynamics. Thus, to quantify which species and regions on Earth are most vulnerable to climate-driven extinction, a global understanding of how different demographic rates respond to climate is urgently needed. Here, we perform a systematic review of literature on demographic responses to climate, focusing on terrestrial mammals, for which extensive demographic data are available. To assess the full spectrum of responses, we synthesize information from studies that quantitatively link climate to multiple demographic rates. We find only 106 such studies, corresponding to 87 mammal species. These 87 species constitute < 1% of all terrestrial mammals. Our synthesis reveals a strong mismatch between the locations of demographic studies and the regions and taxa currently recognized as most vulnerable to climate change. Surprisingly, for most mammals and regions sensitive to climate change, holistic demographic responses to climate remain unknown. At the same time, we reveal that filling this knowledge gap is critical as the effects of climate change will operate via complex demographic mechanisms: a vast majority of mammal populations display projected increases in some demographic rates but declines in others, often depending on the specific environmental context, complicating simple projections of population fates. Assessments of population viability under climate change are in critical need to gather data that account for multiple demographic responses, and coordinated actions to assess demography holistically should be prioritized for mammals and other taxa.

Methods For each mammal species i with available life-history information, we searched SCOPUS for studies (published before 2018) where the title, abstract, or keywords contained the following search terms:

Scientific species namei AND (demograph* OR population OR life-history OR "life history" OR model) AND (climat* OR precipitation OR rain* OR temperature OR weather) AND (surv* OR reprod* OR recruit* OR brood OR breed* OR mass OR weight OR size OR grow* OR offspring OR litter OR lambda OR birth OR mortality OR body OR hatch* OR fledg* OR productiv* OR age OR inherit* OR sex OR nest* OR fecund* OR progression OR pregnan* OR newborn OR longevity).

We used the R package taxize (Chamberlain and Szöcs 2013) to resolve discrepancies in scientific names or taxonomic identifiers and, where applicable, searched SCOPUS using all scientific names associated with a species in the Integrated Taxonomic Information System (ITIS; http://www.itis.gov).

We did not extract information on demographic-rate-climate relationships if:

A study reported on single age or stage-specific demographic rates (e.g., Albon et al. 2002; Rézoiki et al. 2016)
A study used an experimental design to link demographic rates to climate variation (e.g., Cain et al. 2008)
A study considered the effects of climate only indirectly or qualitatively. In most cases, this occurred when demographic rates differed between seasons (e.g., dry vs. wet season) but were not linked explicitly to climatic factors (e.g., varying precipitation amount between seasons) driving these differences (e.g., de Silva et al. 2013; Gaillard et al. 2013).

We included several studies of the same population as different studies assessed different climatic variables or demographic rates or spanned different years (e.g., for Rangifer tarandus platyrhynchus, Albon et al. 2017; Douhard et al. 2016).

We note that we can miss a potentially relevant study if our search terms were not mentioned in the title, abstract, or keywords. To our knowledge, this occurred only once, for Mastomys natalensis (we included the relevant study [Leirs et al. 1997] into our review after we were made aware that it assesses climate-demography relationships in the main text).

Lastly, we checked for potential database bias by running the search terms for a subset of nine species in Web of Science. The subset included three species with > three climate-demography studies published and available in SCOPUS (Rangifer tarandus, Cervus elaphus, Myocastor coypus); three species with only one climate-demography study obtained from SCOPUS (Oryx gazella, Macropus rufus, Rhabdomys pumilio); and another three species where SCOPUS did not return any published study (Calcochloris obtusirostris, Cynomops greenhalli, Suncus remyi). Species in the three subcategories were randomly chosen. Web of Science did not return additional studies for the three species where SCOPUS also failed to return a potentially suitable study. For the remaining six species, the total number of studies returned by Web of Science differed, but the same studies used for this review were returned, and we could not find any additional studies that adhered to our extraction criteria.

Description of key collected data

From all studies quantitatively assessing climate-demography relationships, we extracted the following information:

Geographic location - The center of the study area was always used. If coordinates were not provided in a study, we assigned coordinates based on the study descriptions of field sites and data collection.
Terrestrial biome - The study population was assigned to one of 14 terrestrial biomes (Olson et al. 2001) corresponding to the center of the study area. As this review is focused on general climatic patterns affecting demographic rates, specific microhabitat conditions described for any study population were not considered.
Climatic driver - Drivers linked to demographic rates were grouped as either local/regional precipitation & temperature values or derived indices (e.g., ENSO, NAO). The temporal extent (e.g., monthly, seasonal, annual, etc.) and aggregation type (e.g., minimum, maximum, mean, etc.) of drivers was also noted.
Demographic rate modeled - To facilitate comparisons, we grouped the demographic rates into either survival, reproductive success (i.e., whether or not reproduction occurre, reproductive output (i.e., number or rate of offspring production), growth (including stage transitions), or condition that determines development (i.e., mass or size). 
Stage or sex modeled - We retrieved information on responses of demographic rates to climate for each age class, stage, or sex modeled in a given study.
Driver effect - We grouped effects of drivers as positive (i.e., increased demographic rates), negative (i.e., reduced demographic rate), no effect, or context-dependent (e.g., positive effects at low population densities and now effect at high densities). We initially also considered nonlinear effects (e.g., positive effects at intermediate values and negative at extremes of a driver), but only 4 studies explicitly tested for nonlinear effects, by modelling squared or cubic climatic drivers in combination with driver interactions. We therefore considered nonlinear demographic effects as context dependent.  
Driver interactions - We noted any density dependence modeled and any non-climatic covariates included (as additive or interactive effects) in the demographic-rate models assessing climatic effects.
Future projections of climatic driver - In studies that indicated projections of drivers under climate change, we noted whether drivers were projected to increase, decrease, or show context-dependent trends. For studies that provided no information on climatic projections, we quantified projections as described in Detailed description of climate-change projections below (see also climate_change_analyses_mammal_review.R).

Footnotes: 1 Population estimates based on the Standard Geographical Classification (SGC) 2016 as delineated in the 2016 Census. 2 A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program. If the population of the core of a CA falls below 10,000, the CA is retired from the next census. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA that are not population centres are rural areas. When a CA has a core of at least 50,000, based on data from the previous Census of Population, it is subdivided into census tracts. Census tracts are maintained for the CA even if the population of the core subsequently falls below 50,000. All CMAs are subdivided into census tracts (2016 Census Dictionary, catalogue number 98-301-X2016001). 3 An area outside census metropolitan areas and census agglomerations is made up of all areas (within a province or territory) unallocated to a census metropolitan area (CMA) or census agglomeration (CA). 4 The population growth, which is used to calculate population estimates of census metropolitan areas and census agglomerations (table 17100135), is comprised of the components of population growth (table 17100136). 5 This table replaces table 17100079. 6 The components of population growth for census metropolitan areas (CMAs) and census agglomerations (CAs) sometimes had to be calculated using information at the census division level, using the geographic conversion method. This method involves using the population component calculated at the level of the CD(s) in which the CMA or CA is located and applying a ratio corresponding to the proportion of the CMA or CA population included in the corresponding CD(s). For periods prior to 2005/2006, all demographic components for all CMAs and CAs were calculated using geographic conversions. For the periods from 2005/2006 to 2010/2011 inclusively, emigration and internal migration components for areas that were not CMAs according to the 2011 SGC were calculated using geographic conversions. For the periods 2011/2012 to 2015/2016 inclusively, the emigration and internal migration components of regions that were not CMAs or CAs according to the 2011 SGC were calculated using geographic conversions. For the relevant demographic components, trends should be interpreted with caution where the method of calculation has changed over time. This caveat applies particularly to the intraprovincial migration component, for which the assumptions of the geographic conversion method are more at risk of not being met. 7 Period from July 1 to June 30. 8 Age on July 1. 9 The estimates for net non-permanent residents are preliminary for 2020/2021, updated for 2019/2020 final up to 2018/2019. 10 Non-permanent residents (NPRs) are persons who are lawfully in Canada on a temporary basis under the authority of a temporary resident permit, along with members of their family living with them. NPRs include foreign workers, foreign students, the humanitarian population and other temporary residents. The humanitarian population includes refugee claimants and temporary residents who are allowed to remain in Canada on humanitarian grounds and are not categorized as either foreign workers or foreign students.

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.