Millennials were the largest generation group in the United States in 2024, with an estimated population of ***** million. Born between 1981 and 1996, Millennials recently surpassed Baby Boomers as the biggest group, and they will continue to be a major part of the population for many years. The rise of Generation Alpha Generation Alpha is the most recent to have been named, and many group members will not be able to remember a time before smartphones and social media. As of 2024, the oldest Generation Alpha members were still only aging into adolescents. However, the group already makes up around ***** percent of the U.S. population, and they are said to be the most racially and ethnically diverse of all the generation groups. Boomers vs. Millennials The number of Baby Boomers, whose generation was defined by the boom in births following the Second World War, has fallen by around ***** million since 2010. However, they remain the second-largest generation group, and aging Boomers are contributing to steady increases in the median age of the population. Meanwhile, the Millennial generation continues to grow, and one reason for this is the increasing number of young immigrants arriving in the United States.

In 2024, Millennials were the largest generation group in the United States, making up about 21.81 percent of the population. However, Generation Z was not far behind, with Gen Z accounting for around 20.81 percent of the population in that year.

This map layer shows the prevalent generations that make up the population of the United States using multiple scales. As of 2018, the most predominant generations in the U.S. are Baby Boomers (born 1946-1964), Millennials (born 1981-1998), and Generation Z (born 1999-2016). Currently, Millennials are the most predominant population in the U.S.A generation represents a group of people who are born around the same time and experience world events and trends during the same stage of life through similar mediums (for example, online, television, print, or radio). Because of this, people born in the same generation are expected to have been exposed to similar values and developmental experiences, which may cause them to exhibit similar traits or behaviors over their lifetimes. Generations provide scientists and government officials the opportunity to measure public attitudes on important issues by people’s current position in life and document those differences across demographic groups and geographic regions. Generational cohorts also give researchers the ability to understand how different developmental experiences, such as technological, political, economic, and social changes, influence people’s opinions and personalities. Studying people in generational groups is significant because an individual’s age is a conventional predictor for understanding cultural and political gaps within the U.S. population.Though there is no exact equation to determine generational cutoff points, it is understood that we designate generational spans based on a 15- to 20-year gap. The only generational period officially designated by the U.S. Census Bureau is based on the surge of births after World War II in 1946 and a significant decline in birth rates after 1964 (Baby Boomers). From that point, generational gaps have been determined by significant political, economic, and social changes that define one’s formative years (for example, Generation Z is considered to be marked by children who were directly affected by the al Qaeda attacks of September 11, 2001).In this map layer, we visualize six active generations in the U.S., each marked by significant changes in American history:The Greatest Generation (born 1901-1924): Tom Brokaw’s 1998 book, The Greatest Generation, coined the term ‘the Greatest Generation” to describe Americans who lived through the Great Depression and later fought in WWII. This generation had significant job and education opportunities as the war ended and the postwar economic booms impacted America.The Silent Generation (born 1925-1945): The title “Silent Generation” originated from a 1951 essay published in Time magazine that proposed the idea that people born during this period were more cautious than their parents. Conflict from the Cold War and the potential for nuclear war led to widespread levels of discomfort and uncertainty throughout the generation.Baby Boomers (born 1946-1964): Baby Boomers were named after a significant increase in births after World War II. During this 20-year span, life was dramatically different for those born at the beginning of the generation than those born at the tail end of the generation. The first 10 years of Baby Boomers (Baby Boomers I) grew up in an era defined by the civil rights movement and the Vietnam War, in which a lot of this generation either fought in or protested against the war. Baby Boomers I tended to have great economic opportunities and were optimistic about the future of America. In contrast, the last 10 years of Baby Boomers (Baby Boomers II) had fewer job opportunities and available housing than their Boomer I counterparts. The effects of the Vietnam War and the Watergate scandal led a lot of second-wave boomers to lose trust in the American government. Generation X (born 1965-1980): The label “Generation X” comes from Douglas Coupland’s 1991 book, Generation X: Tales for An Accelerated Culture. This generation was notoriously exposed to more hands-off parenting, out-of-home childcare, and higher rates of divorce than other generations. As a result, many Gen X parents today are concerned about avoiding broken homes with their own kids.Millennials (born 1981-1998): During the adolescence of Millennials, America underwent a technological revolution with the emergence of the internet. Because of this, Millennials are generally characterized by older generations to be technologically savvy.Generation Z (born 1999-2016): Generation Z or “Zoomers” represent a generation raised on the internet and social media. Gen Z makes up the most ethnically diverse and largest generation in American history. Like Millennials, Gen Z is recognized by older generations to be very familiar with and/or addicted to technology.Questions to ask when you look at this mapDo you notice any trends with the predominant generations located in big cities? Suburbs? Rural areas?Where do you see big clusters of the same generation living in the same area?Which areas do you see the most diversity in generations?Look on the map for where you, your parents, aunts, uncles, and grandparents live. Do they live in areas where their generation is the most predominant?

In 2024, Generation Z represented 24.6 percent of the global population, making them the largest generation group in the world, according to the source. In 2030, Millennials were forecast to represent 21.6 percent of the population worldwide.

The statistic shows the number of people in the U.S. in 2011 and 2030, by generation. By 2030, the Millennial generation will have 78 million people whereas the Boomer generation will only have 56 million people in the United States.

In 2023, there were about **** million Millennials estimated to be living in the United States, making them the largest generation group in the country. In comparison, there were ***** million Gen Z and ***** million Gen X estimated to be in the United States in that year.

About this dataset

Context

The dataset tabulates the United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.

Key observations

The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the United States is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of United States total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States Population by Age. You can refer the same here

Washington, D.C. had the highest net migration for 18 to 24-year-olds in 2021, making it the most attractive city among the generation Z population. The number of Zoomers who moved in less the number of Zoomers who moved out of Washington stood at ******. Columbia, SC, and Boston, MA, were the two other cities where this figure where the net migration exceeded 10,000.

In 2023, there were approximately ***** million millennials in the United Kingdom, making it the largest generational cohort at that time. Millennials surpassed the Baby Boomer generation as the largest generation for the first time in 2019. The two youngest generations, Gen Z and Gen Alpha, numbered approximately **** million, and *** million respectively. Gen X are, as of the most recent year, the second-largest generation in the UK at ***** million people, with their parent's generation, the Silent Generation, numbering around *** million people in the same year. There were estimated to be ****** people who belonged to the Greatest Generation, the parents of the Baby Boomer generation, who lived through major events such as the Great Depression and World War Two. Post-War Baby Boom The baby boomer generation was the largest generation for much of this period due to the spike in births that happened after the Second World War. In 1947, for example, there were over *** million live births in the United Kingdom, compared with just ******* live births just thirty years later in 1977. Members of this generation are typically the parents of millennials, and were the driving force behind the countercultural movement of the 1960s, due to their large numbers relative to older generations at the time. The next generational cohort after Boomers are Generation X, born between 1965 and 1980. This generation had fewer members than the Boomer generation for most of its existence, and only became larger than it in 2021. Millennials and Gen Z As of 2022, the most common single year of age in the United Kingdom in 2020 was 34, with approximately ******* people this age. Furthermore, people aged between 30 and 34 were the most numerous age group in this year, at approximately 4.67 million people. As of 2022, people in this age group were Millennials, the large generation who came of age in the late 1990s and early 2000s. Many members of this generation entered the workforce following the 2008 financial crash, and suffered through high levels of unemployment during the early 2010s. The generation that followed Millennials, Generation Z, have also experienced tough socio-economic conditions recently, with key formative years dominated by the COVID-19 pandemic, climate change, and an increasingly unstable geopolitical situation.

In 2023, half of Generation Z in the United States were white. In comparison, 48 percent of Gen Alpha were white in that year, making it the first generation that does not have a majority white population in the United States.

EN:

Preface

I would like to begin this work by offering a few introductory words. This is the first time I am writing this type of work, and I want to emphasize that I am open to any comments and suggestions regarding my work. I know that there is always room for improvement, and I would gladly take advantage of your advice to become better at what I do.

github with Dashboard and python file: https://github.com/Dzynekz/Poland-s-population-by-voivodeship-2002-2021-

Thank you in advance for your time and I wish you a pleasant reading.

Introduction

The aim of the study is to approximate the trends and changes in selected demographic data describing the population of Poland from 2002 to 2021. The collected data allows for analysis, taking into account the administrative division into voivodeships, age groups and gender. The study focuses on answering the following research questions: 1. How has the population of Poland changed? 2. Does the introduction of the "500+" program in 2016 have a positive impact on increasing the number of births? 3. How have economic age groups changed over the years?

Data acquisition

One of the key tools used during the acquisition of reliable data was the API of the Central Statistical Office, which allowed me to access a huge database containing, among other things, information about the population in Poland from 2002 to 2021. Through analysis of the open API documentation of the CSO and the use of provided methods, I selected the most interesting ranges of information about the population, divided by voivodeships, age groups, and gender. I downloaded the complete set of statistical data using self-developed Python code, which, based on defined parameters, automated the necessary API method calls, conversion, and saving of the received data in CSV format. Having the data in the selected format, I was able to easily and efficiently import, process, and analyze the collected information using chosen tools. Without access to the open API of the CSO and the ability to use it, collecting data on population changes over the years would have been much more difficult and time-consuming. Thanks to widely used API interfaces in today's times, we can effectively acquire, gather, and process valuable data that can be used for analysis, forecasting trends, creating long-term strategies, or making daily decisions in many aspects of our daily lives (economy, finance, economics, etc.).

Visualization

Below I present a visualization that illustrates changes in the population of Poland over the years: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14257214%2Fb7b5b7b2d92cfc75b225df87a9fd004f%2FDashboard.png?generation=1681134675762237&alt=media" alt="">

How has the population changed over the years?

Overall population of the country

Analyzing the data on the population of Poland from 2002 to 2021, we can see that it underwent interesting changes. From 2002 to 2006, the population slightly decreased and amounted to: 38.21 million, 38.18 million, 38.17 million, 38.15 million, and 38.13 million, respectively. Then, from 2007 to 2011, the population strongly increased, reaching a peak of 38.53 million in 2011. In the following years, the population began to slightly decrease until 2019, to the level of 38.38 million. The largest decrease in population was recorded in 2020-2021, reaching a level of 37.9 million people, most likely due to the COVID-19 pandemic. Overall, over the entire period under investigation, the population in Poland decreased by about 1.3%.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14257214%2F1a9ca78c8df280505efccfddb4d73cb5%2Fobraz_2023-04-10_155200671.png?generation=1681134723226009&alt=media" alt="">

Population by voivodeships

The changes in the population of residents in individual voivodeships are very interesting. The largest increase in population was recorded in the Mazowieckie voivodeship and amounted to 380 thousand. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14257214%2F6de93256154094f2462b9f3c27bcba06%2Fobraz_2023-04-10_155258708.png?generation=1681134780752830&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14257214%2Fad4b988567ac7a915f2ed4461c5b9c82%2Fobraz_2023-04-10_155318168.png?generation=1681134799959003&alt=media" alt="">

The largest population growth was recorded in the Mazowieckie, Małopolskie, Wielkopolskie and Pomorskie voivodeships. At the same time, the trend in the Śląskie and Lubelskie voivodeships was the opposite, with the population decreasing.

Furthermore, the data shows that in the remaining voivodeships of Poland, the number of inhabitants decreased. The largest decrease was recorded in the Śląskie voivodeship, which amounted to 350,000, and the...

Context

This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

Content

There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

Acknowledgements

Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

Inspiration

Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch. This course material is maintained by the health data science sandbox. This webpage shows the latest version of the course material.

1. Data.tar.gz Contains the datasets and executable files for some of the softwares
   You can unpack by simply doing
   tar -zxf Data.tar.gz -C ./
   This will create a folder called Data with the uncompressed material inside
2. Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes (Note this environment is created on Ubuntu 22.10). You do this in the command line by
   1. creating the folder Course_Env: mkdir Course_Env
   2. untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env
   3. Activate the environment: conda activate ./Course_Env
   4. Run the unpacking script (it can take quite some time to get it done): conda-unpack
3. Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the version above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.
4. environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands:
   1. conda env create -f environment_with_args.yml -p ./Course_Env
   2. conda activate ./Course_Env

The data is connected to the following repository: https://github.com/hds-sandbox/Popgen_course_aarhus. The original course material from Prof Kasper Munch is at https://github.com/kaspermunch/PopulationGenomicsCourse.

Description

The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.

The participants must at the end of the course be able to:

• Identify an experimental platform relevant to a population genomic analysis.
• Apply commonly used population genomic methods.
• Explain the theory behind common population genomic methods.
• Reflect on strengths and limitations of population genomic methods.
• Interpret and analyze results of population genomic inference.
• Formulate population genetics hypotheses based on data

The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.

Curriculum

The curriculum for each week is listed below. "Coop" refers to a set of lecture notes by Graham Coop that we will use throughout the course.

Course plan

1. Course intro and overview:
   • Coop chapters 1, 2, 3, Paper: Genome Diversity Project
2. Drift and the coalescent:
   • Coop chapter 4; Paper: Platypus
   • Exercise: Read mapping and base calling
3. Recombination:
   • Lecture: Review: Recombination in eukaryotes, Review: Recombination rate estimation
   • Exercise: Phasing and recombination rate
4. Population strucure and incomplete lineage sorting:
   • Lecture: Coop chapter 6, Review: Incomplete lineage sorting
   • Exercise: Working with VCF files
5. Hidden Markov models:
   • Lecture: Durbin chapter 3, Paper: population structure
   • Exercise: Inference of population structure and admixture
6. Ancestral recombination graphs:
   • Lecture: Paper: Approximating the ARG, Paper: Tree inference
   • Exercise: ARG dashboard exercises + Inference of trees along sequence
7. Past population demography:
   • Lecture: Coop chapter 4, Paper: PSMC, revisit Paper: Tree inference
   • Exercise: Inferring historical populations
8. Direct and linked selection:
   • Lecture: Coop chapters 12, 13, revisit Paper: Tree inference
9. Admixture:
   • Lecture: Review: Admixture, Paper: Admixture inference
   • Exercise: Detecting archaic ancestry in modern humans
10. Genome-wide association study (GWAS):
    • Lecture: Coop lecture notes 99-120
    • Exercise: GWAS quality control
11. Heritability:
    • Lecture: Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)
    • Exercise: Association testing
12. Evolution and disease:
    • Lecture: Coop Lecture notes Sec. 11.0.1 (p217-221)
    • Exercise: Estimating heritability

The count and description of the families of the Basque Country is done using various statistical sources. On the one hand, families are counted through the Population and Housing Census; on the other hand, the operation Demographic Survey (ED), of an intercensal and five-year periodicity, offers information on the formation of families, family size, preferences on the number and spacing of children, as well as numerous other characteristics, using the method of retrospective approximation to demographic phenomena. The count and description of the families of the Basque Country is done using various statistical sources. On the one hand, families are counted through the Population and Housing Census; on the other hand, the operation Demographic Survey (ED), of an intercensal and five-year periodicity, offers information on the formation of families, family size, preferences on the number and spacing of children, as well as numerous other characteristics, using the method of retrospective approximation to demographic phenomena.

Globally, many species are threatened by population decline because of anthropogenic changes leading to population fragmentation, genetic isolation, and inbreeding depression. Genetic rescue, the controlled introduction of genetic variation, is a method used to relieve such effects in small populations. However, without understanding how the characteristics of rescuers impact rescue attempts interventions run the risk of being sub-optimal, or even counterproductive. We use the Red Flour Beetle (Tribolium castaneum) to test the impact of rescuer sex, and sexual selection background, on population productivity. We record the impact of genetic rescue on population productivity in 24 and 36 replicated populations for ten generations following intervention. We find little or no impact of rescuer sex on the efficacy of rescue but show that a background of elevated sexual selection makes individuals more effective rescuers. In both experiments, rescue effects diminish 6-10 generations after the rescue. Our results confirm that the efficacy of genetic rescue can be influenced by characteristics of the rescuers and that the level of sexual selection in the rescuing population is an important factor. We show that any increase in fitness associated with rescue may last for a limited number of generations, suggesting implications for conservation policy and practice. Methods Husbandry T. castaneum were kept in a controlled environment at 30°C and 60% humidity with a 12:12 light-dark cycle. Populations were kept on standard fodder consisting of 90% organic white flour, 10% brewer’s yeast and a layer of oats for traction unless otherwise stated. During the husbandry cycle, 2mm and 850µm sieves were used to remove pupae and adults from fodder. The following cycle was started by a set number of adults (line dependent, see below) being placed into containers with fresh standard fodder. The oviposition phase: populations were given seven days to mate and lay eggs before adults were removed by sieving to prevent overlapping generations. The fodder containing eggs was returned to the container. The development phase: eggs were kept in the containers for 35 days to allow the eggs to develop into mature adults. Around day 21 of the development phase, pupae were collected to obtain known-sex virgin individuals which were then used to start the next generation. The pupae were kept as virgins in single-sex groups of 20 for 10 days to allow them to complete development. Once mature, the cycle began again with those beetles going into fresh fodder to form a population of males and females. Tribolium castaneum lines Krakow Super Strain (KSS): was created by mixing fourteen laboratory strains to maximise genetic diversity in a single strain (Laskowski et al., 2015). This was used as the outbred treatment in the genetic rescue experiments. Inbred Lines: Founded from KSS and inbred through three single-pair bottlenecks in the first, fifth and seventh generations. Between bottlenecks, the lines were maintained at a maximum population size of 100 randomly selected adults. Of the initial 30 lines, 24 survived the inbreeding treatment and 12 lines were maintained and used for experiments. Sexual Selection Lines: polyandrous and monogamous lines were created from the Georgia 1 stock (Haliscak and Beeman, 1983; Lumley et al., 2015). Each polyandrous line (n=3) was maintained each generation in twelve groups each consisting of five males and one female. Following oviposition, the eggs from all groups in a line are mixed to form one population from which the next generation’s groups will be sourced. For each monogamous line (n=3) twenty separate mating pairs are bred. Following oviposition, the eggs from all pairs are mixed and the next pairs are sourced from this population to maintain that line. The number of groups and pairs in each regime results in a theoretical Ne = 40 in each treatment (Godwin et al., 2020). These regimes had been maintained for 150 generations when rescuers were taken. The polyandrous lines are hereafter referred to as sexual selection lines, and monogamous as no sexual selection. Genetic rescue protocol Replicate experimental inbred populations were created from the inbred lines to serve as populations to be rescued. Pupae were sexed and placed into plastic dishes with lids, containing 10ml standard fodder in single-sex groups. 10±2 days after eclosion, ten males and ten females from a given line were placed in a 125ml tub with 70ml of standard fodder creating populations each containing twenty adult beetles at a 1:1 sex ratio for the oviposition phase. On day 20±1 of the development phase, pupae were again taken from the populations using the method outlined above to create the next non-overlapping generation. Populations were maintained using twenty reproducing adults per generation, not allowing population growth. This allowed us to maintain a roughly constant population density during offspring development across generations, avoiding the confounding influence of negative density-dependence on offspring production (Duval et al., 1939; King and Dawson, 1972; Janus, 1989). Each experimental population was randomly assigned an ID number, to avoid bias when handling. After being established at the experimental size, the populations were maintained in experimental conditions for one generation to avoid transgenerational density effects affecting the genetic rescue results (Đukić et al., 2021). The rescue treatments were applied in the second generation under experimental conditions. In each population, a single beetle was replaced with a rescuer thus maintaining the 1:1 sex ratio and population size, avoiding any increase in productivity due to a demographic rescue. Rescuers taken from their source populations as pupae were age-matched as closely as possible to individuals in experimental populations. On day 37 of the development phase experimental populations were frozen at -6°C and mature offspring were counted as a measure of productivity (our metric for population fitness). If a population was removed from the experiment because of slow development (pupae were not available to establish the next generation), that population was analysed as part of all generations prior but excluded henceforth. The sex of the rescuer in genetic rescue Due to logistic issues with ventilation, four out of the 12 experimental inbred populations failed to produce offspring in generation 0. From each of the remaining eight inbred lines, three replicate populations were created and assigned to one of three treatments; No Rescue control (ten inbred line males, ten inbred line females); Male Rescue (nine inbred line males, one KSS male, ten inbred line females); and Female Rescue (ten inbred line males, nine inbred line females, one KSS female; Figure 1). Populations were maintained for ten, non-overlapping generations. Sexual selection and genetic rescue We investigated the impact of a rescuer’s sexual selection history on the effectiveness of genetic rescue. From 12 inbred lines, three replicate populations were created and assigned to one of three treatments; No Rescue Control (ten inbred line males, ten inbred line females); Sexual Selection Rescue (nine inbred line males, one polyandrous male and, ten inbred line females); No Sexual Selection Rescue (nine inbred line males, one monogamous male, ten inbred line females; Figure 2). A single polyandrous and single monandrous line were used as the source for rescuers. Populations were maintained for nine generations. Stressful conditions To test rescue under stress conditions, duplicate rescue populations were established from each rescued line at generation five in the ‘sex’ experiment, and generation six in the ‘sexual selection’ experiment. These were maintained as in the main experiments (until generation ten and nine respectively), but with a reduction in the yeast content of the fodder, which is the main source of protein for the experimental populations. This reduction generates nutrient stress in T. castaneum (Godwin et al., 2020). In the ‘sex’ experiment fodder contained 0% yeast and 1% yeast in the ‘sexual selection’ experiment (because of low survival with zero yeast). Statistical analyses Statistical analyses were carried out in R V4.4.1 (R Core Team, 2024) utilising R studio version 2024.04.2+764 (Posit team, 2024). Tidyverse (Wickham et al., 2019), stats (R Core Team, 2024), Rmisc (Hope, 2022)and googlesheets4 (Bryan, 2023) were used for data management and exploration. Plots were created using ggplot2 (Wickham, 2016). The distribution of data was checked using the shapiro.test function (R Core Team, 2024). Generalised Linear Mixed Models (GLMMs) were fitted to test for differences in productivity between the experimental treatments using glmmTMB (Brooks et al., 2017). Model fit was checked using DHARMa (Hartig, 2022). Model parameters were checked for collinearity using variance inflation factor (Vif) scores with the check_collinearity function from performance (Lüdecke et al., 2021). There were no issues with overdispersion or collinearity (VIF: <3 for all variables) in any models. R2 was determined using the r.squaredGLMM function in MuMIn (Bartoń, 2024). Post-hoc pairwise Tukey tests were carried out using multcomp (Hothorn et al., 2008). Within each experiment, we fitted GLMMs with the same model structure, using a negative binomial distribution to model productivity counts, which provided better model fit than a Poisson distribution. Productivity was the response variable, with treatment, generation and generation2 as fixed effects. Inbred line of origin and experimental population ID were included as random effects, with ID nested within inbred line. Interaction terms (treatment x generation, treatment x generation2) were initially included but removed from the model if not significant. The generation2 factor was not significant in the

Population who worked mainly full-time for most of the weeks during the reference year by visible minority and selected characteristics (age group, gender, first official language spoken, immigrant status, period of immigration, generation status and highest certificate, diploma or degree), for the population aged 15 years and over in private households in Canada, geographical regions of Canada, provinces and territories and census metropolitan areas with parts.

Data on visible minority by income, generation status, highest certificate, diploma or degree, age and gender for the population aged 15 years and over in private households in Canada, provinces and territories, census metropolitan areas, census agglomerations and parts.

The ability to predict when an individual will die can be extremely useful for many research problems in aging. A technique for predicting death in the model organism, Drosophila melanogaster , has been proposed which relies on an increase in the permeability of the fly intestinal system, allowing dyes from the diet to permeate the body of the fly shortly before death. In this study we sought to verify this claim in a large cohort study using different populations of D. melanogaster and different dyes. We found that only about 50% of the individuals showed a visible distribution of dye before death. This number did not vary substantially with the dye used. Most flies that did turn a blue color before death did so within 24 hours of death. There was also a measurable effect of the dye on the fly mean longevity. These results would tend to limit the utility of this method depending on the application the method was intended for.

Methods Populations

Five large independent populations of Drosophila melanogaster were used in this experiment. Two of these populations, ACO and CO, are large, outbred populations that have been maintained on different age-at-reproduction schedules for hundreds of generations. The ACO population was maintained on 9 day discrete generation cycles. The CO population was kept on 28 day discrete generation cycles. The remaining populations, S93, A4 3852 and Canton S (CAS), were inbred lines raised on three week cycles in the Long lab at the University of California, Irvine. All populations were raised in identical conditions of temperature, food, cultures and density for three generations prior to these experiments .

Mortality Assay

Adult, 14 day old (from egg) flies were knocked out with CO2 gas and placed into individual plastic straws about 4 inches in length and capped with plastic pipette tips on both ends (Fig. 1). During anesthetization, a steady supply of CO2was flowing through a semi-porous plate. The flies were placed on the plate and separated by gender and each fly was gently swept into the plastic straw using a fine painters brush. An equal number of females and males were used per population. Food was provided to each fly at one end of the straw. Each fly was transferred to a new straw with new food and new pipette tips every 3 days to maintain a clean environment. The straw length and girth permitted individuals to fly from one end to the other.

The process of transferring the flies, as well as daily checking of the flies, required a light tapping of the fly into the pipette tip. Cohorts of about 56 adult flies, equal numbers of males and females from each of the five populations were exposed to either control food or food with one of six dyes (table 1) added to their food. Substantial replication was used. Thus, the original dye, SPS Alfachem, was replicated in 5 different populations, and each population was replicated in 6 different dye environments. The use of different FDA FD&C Blue dye #1’s permitted us to determine if the development of the SMURF phenotype was sensitive to the particular dye used. By using a combination of different populations of D. melanogaster,which varied in levels of inbreeding, we could determine if the development of the SMURF phenotype was limited to inbred populations.

The flies were exposed to the blue dyes from day 14 (from egg) continuously to their death. Each fly was individually checked underneath a microscope and light to see if it had become a ‘smurf’. Smurf status required that the entire body changed to any variation of a blue color. This was an important distinction as all the Drosophilaflies fed food with a blue dye would have visible blue coloring in only the gut portion when they weren’t a Smurf. Some of the dyes resulted in a slight variation in blue color in the Smurfs. Every day under a microscope with a light we looked for any change of color in the fly thorax, head and abdomen. If the fly was any shade of blue in all three sections, it was marked as a Smurf and was then checked daily to see when it died. We did not limit our observations to individual sections of the fly, such as only the thorax, for our evaluation of when a fly became a Smurf.

Tapping

We did the tapping experiment to see if the physical disruption, the process of tapping the fly into the pipette tip, affected the mean longevity and lifespan of the fly. A total of 164 ACO flies were chosen for this assay – 83 males and 81 females. The 164 flies were placed into regular food straws with no dye. A total of 84 flies (42 male and 42 female) were tapped 5 times daily, mimicking the checking that occurred in the original experiment, and the other 81 (41 males and 39 females) flies were not tapped. The flies were transferred to new straws, with fresh food and new pipette tips every 3 days. Each fly was checked daily for movement and if no movement was detected, the fly was classified as deceased on that day. Only ACO flies were used as the purpose of the Tapping experiment was to see if our methods for checking for Smurf flies would affect the mean longevity of the fly.

Food & Dyes

Flies were provided with banana-molasses food with one of the dyes added. The control flies received only banana molasses food in their respective straws. The recipe for the banana molasses food used in the lab, as well as the experiment, can be found in the Supplemental Portion. Food with dye was prepared by mixing 2.5 grams of each dye to create a 100 ml solution of the banana molasses food mixed with the dye (2.5% wt/vol). Food was always prepared the day before it was needed and stored in a refrigerator until it was used. The dyes were kept separate and carefully handled so no cross-contamination occurred during the preparation and food blending process.

Statistical Analysis

To analyze the effects of dye, sex and population on longevity we let yijklbe the age at death of the lth individual of sex-i(i=1 (female), 2 (male)), treatment-j (j=1,,..,7 (see table 1 of paper, 7=control)), and population-k(k=1,..,5 (see table 1 of paper)), whereds=0 if s=1, and 1 otherwise, eijklis an error term assumed to have normal distribution with mean 0 and variance s2. An initial test showed no significant differences between sexes so the final model tested did not include the bparameter. These tests were run with R (version 3.4.3, R Core team, 2017) and the lmfunction. Pairwise tests with Bonferroni corrections for simultaneous tests were conducted with the R emmeansfunction.

    At the time of death each fly was classified according to their sex, population, treatment, and Smurf status (blue: yes or no). Using hierarchical log-linear models (loglmfunction in the R MASS package) we tested in succession whether sex, treatment, and population would have an effect on Smurf status at the time of death. 

A t-test was performed on the Tapping Experiment results, comparing the mean longevity of the tapped flies versus the non-tapped flies to see if the mechanical disruption would affect their mean longevity.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.