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."

In 2024, the UK population grew by approximately ******* people, with the vast majority of this population growth due to international migration, at *******, with natural change (births minus deaths) at just ******.

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.

The data collection contains population projections for UK ethnic groups and all local area by age (single year of age up to 100+) and sex. Included in the data set are also input data to the cohort component model that was used to project populations into the future-fertility rates, mortality rates, international migration flows and internal migration probabilities. Also included in data set are output data: Number of deaths, births and internal migrants. All data included are for the years 2011 to 2061. We have produced two ethnic population projections for UK local authorities, based on information on 2011 Census ethnic populations and 2010-2011-2012 ethnic components. Both projections align fertility and mortality assumptions to ONS assumptions. Where they differ is in the migration assumptions. In LEEDS L1 we employ internal migration rates for 2001 to 2011, including periods of boom and bust. We use a new assumption about international migration anticipating that the UK may leave the EU (BREXIT). In LEEDS L2 we use average internal migration rates for the 5 year period 2006-11 and the official international migration flow assumptions with a long term balance of +185 thousand per annum.

This project aims to understand and to forecast the ethnic transition in the United Kingdom's population at national and sub-national levels. The ethnic transition is the change in population composition from one dominated by the White British to much greater diversity. In the decade 2001-2011 the UK population grew strongly as a result of high immigration, increased fertility and reduced mortality. Both the Office for National Statistics (ONS) and Leeds University estimated the growth or decline in the sixteen ethnic groups making up the UK's population in 2001. The 2011 Census results revealed that both teams had over-estimated the growth of the White British population and under-estimated the growth of the ethnic minority populations. The wide variation between our local authority projected populations in 2011 and the Census suggested inaccurate forecasting of internal migration. We propose to develop, working closely with ONS as our first external partner, fresh estimates of mid-year ethnic populations and their components of change using new data on the later years of the decade and new methods to ensure the estimates agree in 2011 with the Census. This will involve using population accounting theory and an adjustment technique known as iterative proportional fitting to generate a fully consistent set of ethnic population estimates between 2001 and 2011.

We will study, at national and local scales, the development of demographic rates for ethnic group populations (fertility, mortality, internal migration and international migration). The ten year time series of component summary indicators and age-specific rates will provide a basis for modelling future assumptions for projections. We will, in our main projection, align the assumptions to the ONS 2012-based principal projection. The national assumptions will need conversion to ethnic groups and to local scale. The ten years of revised ethnic-specific component rates will enable us to study the relationships between national and local demographic trends. In addition, we will analyse a consistent time series of local authority internal migration. We cannot be sure, at this stage, how the national-local relationships for each ethnic group will be modelled but we will be able to test our models using the time series.

Of course, all future projections of the population are uncertain. We will therefore work to measure the uncertainty of component rates. The error distributions can be used to construct probability distributions of future populations via stochastic projections so that we can define confidence intervals around our projections. Users of projections are always interested in the impact of the component assumptions on future populations. We will run a set of reference projections to estimate the magnitude and direction of impact of international migrations assumptions (net effect of immigration less emigration), of internal migration assumptions (the net effect of in-migration less out-migration), of fertility assumptions compared with replacement level, of mortality assumptions compared with no change and finally the effect of the initial age distribution (i.e. demographic potential).

The outputs from the project will be a set of technical reports on each aspect of the research, journal papers submitted for peer review and a database of projection inputs and outputs available to users via the web. The demographic inputs will be subject to quality assurance by Edge Analytics, our second external partner. They will also help in disseminating these inputs to local government users who want to use them in their own ethnic projections. In sum, the project will show how a wide range of secondary data sources can be used in theoretically refined demographic models to provide us with a more reliable picture of how the UK population is going to change in ethnic composition.

Components of population growth, annual: births, deaths, immigrants, emigrants, returning emigrants, net temporary emigrants, net interprovincial migration, net non-permanent residents, residual deviation.

Population structure in genotype data has been extensively studied, and is revealed by looking at the principal components of the genotype matrix. However, no similar analysis of population structure in gene expression data has been conducted, in part because a naïve principal components analysis of the gene expression matrix does not cluster by population. We identify a linear projection that reveals population structure in gene expression data. Our approach relies on the coupling of the principal components of genotype to the principal components of gene expression via canonical correlation analysis. Our method is able to determine the significance of the variance in the canonical correlation projection explained by each gene. We identify 3,571 significant genes, only 837 of which had been previously reported to have an associated eQTL in the GEUVADIS results. We show that our projections are not primarily driven by differences in allele frequency at known cis-eQTLs and that similar projections can be recovered using only several hundred randomly selected genes and SNPs. Finally, we present preliminary work on the consequences for eQTL analysis. We observe that using our projection co-ordinates as covariates results in the discovery of slightly fewer genes with eQTLs, but that these genes replicate in GTEx matched tissue at a slightly higher rate.

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.

Data and scripts required to replicate the analyses in Thia (2022) "Guidelines for standardising the application of discriminant analysis of principal components to genotype data" in Molecular Ecology. This study aimed to address methodological misunderstandings and misuse of the DAPC method in population genetics. The analyses are used to illustrate that for genotype data comprising k effective populations, there are only k−1 PC axes that describe populations structure, and that are biologically informative. These PC axes are the only suitable axes for modelling the among-population differences with a DA. Use of many more than k−1 PC axes leads to decreasing biological relevancy of the final DA solution, with implications for misinterpretations of population structure. Methods Metapopulations with different migrations rates and levels of genetic differentaition were simualted using fastsimcoal v2.7. Simulated individuals were imported into R v4.1.2 for further downstream analysis.

Subnational population projections indicate the future size and age-sex structure of the population of New Zealand's 16 regional council areas, 67 territorial authority areas, and 21 Auckland local board areas based on different combinations of fertility, mortality, and migration assumptions, and current policy settings.

Statistics Canada has published five sets of population projections for Canada, provinces and territories since 1974, with the last report in 1994. The projections issued on a regular basis ensure methodologically and numerically consistent and comparable population projections at the national and provincial/territorial level. This report contains Statistics Canada's first population projections to the year 2026. It also describes the methodology and the assumptions and provides a brief analysis of the results. The projections in this report use the 2000 preliminary population estimates as their base which are based on the 1996 Census. They take into account emerging demographic trends, primarily based on recent changes in the components of population growth. These include the notable changes in immigration target levels, a further reduction in fertility level, a continued increase in life expectancy, and significant changes in interprovincial migration trends, especially the reduction of out-migration trends in the Atlantic provinces.There has also been a significant upward revision in emigration estimates since 1996. The new projections take into consideration the impact of this change on the dynamics of future population growth. For current population projections for Canada, provinces, and territories data refer to Statistics Canada Access data here

Components of population change by census metropolitan area and census agglomeration, single year of age, five-year age group and gender for the period from July 1 to June 30, annual, based on the Standard Geographical Classification (SGC) 2021. The components include births, deaths, immigrants, net emigration, emigrants, returning emigrants, net temporary emigration, net interprovincial migration, net intraprovincial migration, net non-permanent residents and residual deviation.

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

Part 1: Non-technical description This is a continuation of a long-term population dynamics study (1978-present) using an intensive mark-recapture tagging of Weddell seals in Erebus Bay, Antarctica. Past work has become a global model for population studies of large animals. Results have documented strong annual variation in reproduction, abundance, and population composition. This program will add components to evaluate the demographic role of immigrant mothers, evaluate possible drivers of annual variation in overall population dynamics, assess genetic differences between immigrant and locally born mothers, and document patterns of gene flow among seal colonies in the Ross Sea region. These new aspects will focus on understanding of population structure, function, and genetics and provide key information for predicting how the seal population will respond to environmental change. The addition of genetic approaches will advance available data for multiple groups in multiple countries working on Weddell Seals. This work includes an early career scientists training program for faculty university graduate and undergraduate students and well as a defined program for data sharing. The research is paired with active education and outreach programs, social media, websites, educational resources, videos and high-profile public lecture activities. The informal science education program will expand on the project’s successful efforts at producing and delivering short-form videos that have been viewed over 1.6 million times to date. In addition, the education program will add new topics such as learning about seals using genomics and how seals respond to a changing world to a multimedia-enhanced electronic book about the project’s long-term research on Weddell seals, which will be freely available to the public early in the project. Part 2: Technical description Reliable predictions are needed for how populations of wild species, especially those at high latitudes, will respond to future environmental conditions. This study will use a strategic extension of the long-term demographic research program that has been conducted annually on the Erebus Bay population of Weddell seals since 1978 to help meet that need. Recent analyses of the study population indicate strong annual variation in reproduction, abundance, and population composition. The number of new immigrant mothers that join the population each year has recently grown such that most new mothers are now immigrants. Despite the growing number of immigrants, the demographic importance and geographic origins of immigrants are unknown. The research will (1) add new information on drivers of annual variation in immigrant numbers, (2) compare and combine information on the vital rates and demographic role of immigrant females and their offspring with that of locally born females, and (3) add genomic analyses that will quantify levels of genetic variation in and gene flow among the study population and other populations in the Ross Sea. The project will continue the long-term monitoring of the population at Erebus Bay and characterize population dynamics and the role of immigration using a combination of mark-recapture analyses, stochastic population modeling, and genomic analyses. The study will continue to provide detailed data on individual seals to other science teams, educate and mentor individuals in the next generation of ecologists, introduce two early-career, female scientists to Antarctic research, and add genomics approaches to the long-term population study of Erebus Bay Weddell seals. The research will be complemented with a robust program of training and an informal science education program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

columns are individual-id, sex and population

EUROPOP2019 are the latest Eurostat population projections produced at national and subnational levels for 31 countries: all 27 European Union (EU) Member States and four European Free Trade Association (EFTA) countries, covering the time horizon from 2019 to 2100.

Population projections are 'what-if scenario' that aim to show the hypothetically developments of the population size and its structure based on a sets of assumptions for fertility, mortality and net migration; they are presented for a long time period that covers more than a half-century (50 years).

The datasets at national level are composed by the baseline population projections and five sensitivity tests, namely:

• no migration – it is assumed that the net migration is set to zero in each year of the entire horizon of projections;
• lower migration – it is assumed that the net migration is 33% lower than in the baseline assumptions, in each year of the entire horizon of projections;
• higher migration – it is assumed that the net migration is 33% higher than in the baseline assumptions, in each year of the entire horizon of projections;
• lower fertility - it is assumed that the fertility rates are lower 20% than in the baseline assumptions, in each year of the entire horizon of projections;
• lower mortality - it is assumed that the mortality rates are decreased such that the life expectancy at birth will increase of about two years by 2070 when compared with the baseline assumptions.

Data are available by single year time interval, as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected life expectancy by age (in completed years) and sex.

Moreover, the demographic balances and indicators are available for the baseline projections and the five sensitive variants:

• Total numbers of the projected live births and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The dataset at regional level is composed by the baseline population projections and covers all 1169 regions classified as NUTS level 3 corresponding to the NUTS-2016 classification (the Nomenclature of Territorial Units for Statistics) and the 47 Statistical Regions (SR) agreed between European Commission and EFTA countries. Statistical regions are defined according to principles similar to those used in the establishment of the NUTS classification.

For all 1216 regions NUTS-3 level, data are available by single year time interval as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected deaths by age and sex;
• Projected life expectancy by age (reached during the year) and sex, which is computed according to the method described in the https://ec.europa.eu/eurostat/cache/metadata/Annexes/proj_19n_esms_an_24.pdf" target="_self">Technical note - Alternative life table (with annex)

In addition to the baseline projections, datasets on projected population at regional level are available for two sensitivity tests:

• no migration - it is assumed that migration is zero for both international and internal components in each year of the entire horizon of projections;
• no inter-regional migration - it is assumed that only internal migration is zero in each year of the entire horizon of projections.

Moreover, the demographic balances and indicators are available for the baseline projections and the two sensitive variants:

• Total numbers of the projected live births by sex and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The additional dataset called ‘Short-term update of the projected population (2022-2032)’ [proj_stp22] was published on 28 September 2022. While EUROPOP2019 remain the main set of reference for population projections, this new dataset includes updates of baseline projections for the total population, population in the age group 15 to 74 years (considered as the population in the working-age group), and its share in the total population. In addition, two sensitivity tests are carried out – high and very high number of refugees – by introducing in the baseline projections a shock due to the mass-influx of refugees fleeing the war in Ukraine, and who have received temporary protection in the EU countries.

The updated EUROPOP2019 projections were constructed from cumulative sums of weighted averages of annual population changes of two series: the original EUROPOP2019 projection and a new short-term population projection computed from the latest available data over the period of 10 years.

The two sensitivity tests were built on the following assumptions:

• High number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 only, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 10% of the total influx in 2022;
• Very high number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 and 2023, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 15% of the cumulated influx in 2022 and 2023.

The provide detailed statistical tables for 18 scenarios by single year of the projection period (2001 to 2017). For each of the scenarios, data are available for persons who identify with each of the following three groups: the North American Indian population, the Métis or the Inuit. All three groups were projected separately for each of the ten provinces and three territories. However, the subprovincial and subterritorial level shown for the three groups varies as it depends on the groups' size. For the North American Indians, future numbers were calculated for the urban parts of all census metropolitan areas (CMAs), urban areas outside CMAs, rural areas and reserves. For the Métis, places of residence were grouped into urban parts of CMAs, urban areas outside CMAs and rural areas, which also include reserves. Because of their relatively small size, the Inuit population was projected separately for urban and rural locations only. This information is further broken down by age and sex. The 18 scenarios, as well as scenario-specific assumptions on the future trend in fertility and internal migration, are presented in the table below. In addition to these two components of population growth, all scenarios assumed declining mortality and negligible importance of international migration to the change of the size of three Aboriginal groups. The statistical tables of this CD-ROM are organized into three sections: Aboriginal groups - The projected population by Aboriginal group, type of residence, province/territory and sex for the 18 scenarios by single year from 2001 to 2017; Age and sex - The projected population by Aboriginal group, type of residence, age group and sex for the 18 scenarios by single year from 2001 to 2017; and Province/territory - The projected total Aboriginal population by province/territory, age group, sex and type of residence for the 18 scenarios for 2001 and 2017. The statistical tables are supplementary to the publication Projections of the Aboriginal populations, Canada, provinces and territories: 2001 to 2017 (catalogue no. 91-547).

The Southern Resident killer whale (SRKW) population is threatened by a number of identified risk factors including prey availability, contaminants, vessel noise and disturbance, and small population size. However, the population may also be subject to internal factors that limit population growth. Continued assessment of the discreetness of this population through morphological and genetic characteristics is important to maintaining ESA status. In addition, an annual census provides important information that allows demographic analyses of this population to be conducted in order to assess population viability. The components of the project represent a significant level of investment of base funds over many years and these data and analyses provide the foundation of information on the population against which all research and management actions are measured that are attempting to address key risk factors of the SRKW population as protected under the ESA and MMPA. Data taken seasonally.

EUROPOP2019 are the latest Eurostat population projections produced at national and subnational levels for 31 countries: all 27 European Union (EU) Member States and four European Free Trade Association (EFTA) countries, covering the time horizon from 2019 to 2100.

Population projections are 'what-if scenario' that aim to show the hypothetically developments of the population size and its structure based on a sets of assumptions for fertility, mortality and net migration; they are presented for a long time period that covers more than a half-century (50 years).

The datasets at national level are composed by the baseline population projections and five sensitivity tests, namely:

• no migration – it is assumed that the net migration is set to zero in each year of the entire horizon of projections;
• lower migration – it is assumed that the net migration is 33% lower than in the baseline assumptions, in each year of the entire horizon of projections;
• higher migration – it is assumed that the net migration is 33% higher than in the baseline assumptions, in each year of the entire horizon of projections;
• lower fertility - it is assumed that the fertility rates are lower 20% than in the baseline assumptions, in each year of the entire horizon of projections;
• lower mortality - it is assumed that the mortality rates are decreased such that the life expectancy at birth will increase of about two years by 2070 when compared with the baseline assumptions.

Data are available by single year time interval, as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected life expectancy by age (in completed years) and sex.

Moreover, the demographic balances and indicators are available for the baseline projections and the five sensitive variants:

• Total numbers of the projected live births and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The dataset at regional level is composed by the baseline population projections and covers all 1169 regions classified as NUTS level 3 corresponding to the NUTS-2016 classification (the Nomenclature of Territorial Units for Statistics) and the 47 Statistical Regions (SR) agreed between European Commission and EFTA countries. Statistical regions are defined according to principles similar to those used in the establishment of the NUTS classification.

For all 1216 regions NUTS-3 level, data are available by single year time interval as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected deaths by age and sex;
• Projected life expectancy by age (reached during the year) and sex, which is computed according to the method described in the https://ec.europa.eu/eurostat/cache/metadata/Annexes/proj_19n_esms_an_24.pdf" target="_self">Technical note - Alternative life table (with annex)

In addition to the baseline projections, datasets on projected population at regional level are available for two sensitivity tests:

• no migration - it is assumed that migration is zero for both international and internal components in each year of the entire horizon of projections;
• no inter-regional migration - it is assumed that only internal migration is zero in each year of the entire horizon of projections.

Moreover, the demographic balances and indicators are available for the baseline projections and the two sensitive variants:

• Total numbers of the projected live births by sex and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The additional dataset called ‘Short-term update of the projected population (2022-2032)’ [proj_stp22] was published on 28 September 2022. While EUROPOP2019 remain the main set of reference for population projections, this new dataset includes updates of baseline projections for the total population, population in the age group 15 to 74 years (considered as the population in the working-age group), and its share in the total population. In addition, two sensitivity tests are carried out – high and very high number of refugees – by introducing in the baseline projections a shock due to the mass-influx of refugees fleeing the war in Ukraine, and who have received temporary protection in the EU countries.

The updated EUROPOP2019 projections were constructed from cumulative sums of weighted averages of annual population changes of two series: the original EUROPOP2019 projection and a new short-term population projection computed from the latest available data over the period of 10 years.

The two sensitivity tests were built on the following assumptions:

• High number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 only, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 10% of the total influx in 2022;
• Very high number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 and 2023, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 15% of the cumulated influx in 2022 and 2023.

Heterogeneity in life courses among individuals of a population influences the speed of adaptive evolutionary processes, but it is less clear how biotic and abiotic environmental fluctuations influence such heterogeneity. We investigate principal drivers of variability in sequence of stages during an individual's life in a stage-structured population. We quantify heterogeneity by measuring population entropy of a Markov chain, which computes the rate of diversification of individual life courses. Using individual data of a primate population, we show that density regulates the stage composition of the population, but its entropy and the generating moments of heterogeneity are independent of density. This lack of influence of density on heterogeneity is neither due to low year-to-year variation in entropy nor due to differences in survival among stages, but due to differences in stage transitions. Our analysis thus shows that well-known classical ecological selective forces, such as density regulation, are not linked to potential selective forces governing heterogeneity through underlying stage dynamics. Despite evolution acting heavily on individual variability in fitness components, our understanding is poor whether observed heterogeneity is adaptive and how it evolves and is maintained. Our analysis illustrates how entropy represents a more integrated measure of diversity compared to the population structural composition, giving us new insights about the underlying drivers of individual heterogeneity within populations and potential evolutionary mechanisms.