This graph shows the population of the U.S. by race and ethnic group from 2000 to 2023. In 2023, there were around 21.39 million people of Asian origin living in the United States. A ranking of the most spoken languages across the world can be accessed here. U.S. populationCurrently, the white population makes up the vast majority of the United States’ population, accounting for some 252.07 million people in 2023. This ethnicity group contributes to the highest share of the population in every region, but is especially noticeable in the Midwestern region. The Black or African American resident population totaled 45.76 million people in the same year. The overall population in the United States is expected to increase annually from 2022, with the 320.92 million people in 2015 expected to rise to 341.69 million people by 2027. Thus, population densities have also increased, totaling 36.3 inhabitants per square kilometer as of 2021. Despite being one of the most populous countries in the world, following China and India, the United States is not even among the top 150 most densely populated countries due to its large land mass. Monaco is the most densely populated country in the world and has a population density of 24,621.5 inhabitants per square kilometer as of 2021. As population numbers in the U.S. continues to grow, the Hispanic population has also seen a similar trend from 35.7 million inhabitants in the country in 2000 to some 62.65 million inhabitants in 2021. This growing population group is a significant source of population growth in the country due to both high immigration and birth rates. The United States is one of the most racially diverse countries in the world.

LifeTablesLife tables for 24 species of terrestrial vertebrates.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Morristown by race. It includes the distribution of the Non-Hispanic population of Morristown across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Morristown across relevant racial categories.

Key observations

Of the Non-Hispanic population in Morristown, the largest racial group is White alone with a population of 367 (92.21% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/morristown-ny-population-by-race-and-ethnicity.jpeg" alt="Morristown Non-Hispanic population by race">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Morristown
• Population: The population of the racial category (for Non-Hispanic) in the Morristown is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Morristown total Non-Hispanic 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 Morristown Population by Race & Ethnicity. You can refer the same here

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

How racially diverse are residents in Massachusetts? This topic shows the demographic breakdown of residents by race/ethnicity and the increases in the Non-white population since 2010.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Colchester town by race. It includes the distribution of the Non-Hispanic population of Colchester town across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Colchester town across relevant racial categories.

Key observations

Of the Non-Hispanic population in Colchester town, the largest racial group is White alone with a population of 13,566 (91.84% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/colchester-ct-population-by-race-and-ethnicity.jpeg" alt="Colchester town Non-Hispanic population by race">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Colchester town
• Population: The population of the racial category (for Non-Hispanic) in the Colchester town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Colchester town total Non-Hispanic 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 Colchester town Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Salem town by race. It includes the distribution of the Non-Hispanic population of Salem town across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Salem town across relevant racial categories.

Key observations

Of the Non-Hispanic population in Salem town, the largest racial group is White alone with a population of 3,675 (93.30% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/salem-ct-population-by-race-and-ethnicity.jpeg" alt="Salem town Non-Hispanic population by race">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Salem town
• Population: The population of the racial category (for Non-Hispanic) in the Salem town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Salem town total Non-Hispanic 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 Salem town Population by Race & Ethnicity. You can refer the same here

Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics

Diversity index information by neighborhoods in Johns Creek, GA.Neighborhood boundaries are created and maintained by Johns Creek, GA.Demographics data is from Esri GeoEnrichment Services.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Pioneer by race. It includes the distribution of the Non-Hispanic population of Pioneer across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Pioneer across relevant racial categories.

Key observations

With a zero Hispanic population, Pioneer is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 19 (100% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/pioneer-ia-population-by-race-and-ethnicity.jpeg" alt="Pioneer Non-Hispanic population by race">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Pioneer
• Population: The population of the racial category (for Non-Hispanic) in the Pioneer is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Pioneer total Non-Hispanic 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 Pioneer Population by Race & Ethnicity. You can refer the same here

This map shows a comparison of diversity and median household income in the US by tract. Esri's Diversity Index measures the likelihood that two persons, chosen at random from the same area, belong to different races or ethnic groups. In theory, the index ranges from 0 (no diversity) to 100 (complete diversity). If an area's entire population is divided evenly into two race groups and one ethnic group, then the diversity index equals 50. As more race groups are evenly represented in the population, the diversity index increases. Minorities accounted for 30.9 percent of the population in 2000 and are expected to make up 42.3 percent of the population by 2023. Vintage of data: 2023Areas in a darker orange are less diverse than light blue areas with higher diversity. Median household income is symbolized by size. The national median household income is $58,100 and any household below the national value has the smallest symbol size. The largest size has a median household income over $100,000 per year. Esri Updated Demographics represent the suite of annually updated U.S. demographic data that provides current-year and five-year forecasts for more than two thousand demographic and socioeconomic characteristics, a subset of which is included in this layer. Included are a host of tables covering key characteristics of the population, households, housing, age, race, income, and much more. Esri's Updated Demographics data consists of point estimates, representing July 1 of the current and forecast years.Esri Updated Demographics DocumentationMethodologyUnderstanding Esri’s Updated Demographics portfolioEssential Esri Demographics vocabularyThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. This layer requires an ArcGIS Online subscription and does not consume credits. Please cite Esri when using this data.

We used this dataset to assess the strength of isolation due to geographic and macroclimatic distance across island and mainland systems, comparing published measurements of phenotypic traits and neutral genetic diversity for populations of plants and animals worldwide. The dataset includes 112 studies of 108 species (72 animals and 36 plants) in 868 island populations and 760 mainland populations, with population-level taxonomic and biogeographic information, totalling 7438 records. Methods Description of methods used for collection/generation of data: We searched the ISI Web of Science in March 2017 for comparative studies that included data on phenotypic traits and/or neutral genetic diversity of populations on true islands and on mainland sites in any taxonomic group. Search terms were 'island' and ('mainland' or 'continental') and 'population*' and ('demograph*' or 'fitness' or 'survival' or 'growth' or 'reproduc*' or 'density' or 'abundance' or 'size' or 'genetic diversity' or 'genetic structure' or 'population genetics') and ('plant*' or 'tree*' or 'shrub*or 'animal*' or 'bird*' or 'amphibian*' or 'mammal*' or 'reptile*' or 'lizard*' or 'snake*' or 'fish'), subsequently refined to the Web of Science categories 'Ecology' or 'Evolutionary Biology' or 'Zoology' or 'Genetics Heredity' or 'Biodiversity Conservation' or 'Marine Freshwater Biology' or 'Plant Sciences' or 'Geography Physical' or 'Ornithology' or 'Biochemistry Molecular Biology' or 'Multidisciplinary Sciences' or 'Environmental Sciences' or 'Fisheries' or 'Oceanography' or 'Biology' or 'Forestry' or 'Reproductive Biology' or 'Behavioral Sciences'. The search included the whole text including abstract and title, but only abstracts and titles were searchable for older papers depending on the journal. The search returned 1237 papers which were distributed among coauthors for further scrutiny. First paper filter To be useful, the papers must have met the following criteria: Overall study design criteria: Include at least two separate islands and two mainland populations; Eliminate studies comparing populations on several islands where there were no clear mainland vs. island comparisons; Present primary research data (e.g., meta-analyses were discarded); Include a field study (e.g., experimental studies and ex situ populations were discarded); Can include data from sub-populations pooled within an island or within a mainland population (but not between islands or between mainland sites); Island criteria: Island populations situated on separate islands (papers where all information on island populations originated from a single island were discarded); Can include multiple populations recorded on the same island, if there is more than one island in the study; While we accepted the authors' judgement about island vs. mainland status, in 19 papers we made our own judgement based on the relative size of the island or position relative to the mainland (e.g. Honshu Island of Japan, sized 227 960 km² was interpreted as mainland relative to islands less than 91 km²); Include islands surrounded by sea water but not islands in a lake or big river; Include islands regardless of origin (continental shelf, volcanic); Taxonomic criteria: Include any taxonomic group; The paper must compare populations within a single species; Do not include marine species (including coastline organisms); Databases used to check species delimitation: Handbook of Birds of the World (www.hbw.com/); International Plant Names Index (https://www.ipni.org/); Plants of the World Online(https://powo.science.kew.org/); Handbook of the Mammals of the World; Global Biodiversity Information Facility (https://www.gbif.org/); Biogeographic criteria: Include all continents, as well as studies on multiple continents; Do not include papers regarding migratory species; Only include old / historical invasions to islands (>50 yrs); do not include recent invasions; Response criteria: Do not include studies which report community-level responses such as species richness; Include genetic diversity measures and/or individual and population-level phenotypic trait responses; The first paper filter resulted in 235 papers which were randomly reassigned for a second round of filtering. Second paper filter In the second filter, we excluded papers that did not provide population geographic coordinates and population-level quantitative data, unless data were provided upon contacting the authors or could be obtained from figures using DataThief (Tummers 2006). We visually inspected maps plotted for each study separately and we made minor adjustments to the GPS coordinates when the coordinates placed the focal population off the island or mainland. For this study, we included only responses measured at the individual level, therefore we removed papers referring to demographic performance and traits such as immunity, behaviour and diet that are heavily reliant on ecosystem context. We extracted data on population-level mean for two broad categories of response: i) broad phenotypic measures, which included traits (size, weight and morphology of entire body or body parts), metabolism products, physiology, vital rates (growth, survival, reproduction) and mean age of sampled mature individuals; and ii) genetic diversity, which included heterozygosity,allelic richness, number of alleles per locus etc. The final dataset includes 112 studies and 108 species. Methods for processing the data: We made minor adjustments to the GPS location of some populations upon visual inspection on Google Maps of the correct overlay of the data point with the indicated island body or mainland. For each population we extracted four climate variables reflecting mean and variation in temperature and precipitation available in CliMond V1.2 (Kritikos et al. 2012) at 10 minutes resolution: mean annual temperature (Bio1), annual precipitation (Bio12), temperature seasonality (CV) (Bio4) and precipitation seasonality (CV) (Bio15) using the "prcomp function" in the stats package in R. For populations where climate variables were not available on the global climate maps mostly due to small island size not captured in CliMond, we extracted data from the geographically closest grid cell with available climate values, which was available within 3.5 km away from the focal grid cell for all localities. We normalised the four climate variables using the "normalizer" package in R (Vilela 2020), and we performed a Principal Component Analysis (PCA) using the psych package in R (Revelle 2018). We saved the loadings of the axes for further analyses. References:

Bruno Vilela (2020). normalizer: Making data normal again.. R package version 0.1.0. Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J., et al.(2012). CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol., 3, 53--64. Revelle, W. (2018) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 1.8.12. Tummers, B. (2006). DataThief III. https://datathief.org/

Demographic Data - Diversity Efforts

Summary of several social indicators of the populations in counties around the Appalachian Trail in 2019. The dashboard includes maps of median household income in the last 12 months, the population diversity as the proportion of non-whites plus Hispanic ethnicity (independently of race) and the trend of population aging. Data is based on American Community Survey of 2019 and change rates refers to increases since Decennial Census 2000. Chart of the evolution of income (inflation corrected) compares household's revenue in Decennial Census 2000, 2010 and ACS2019-5yrs respectively.

We assessed the local and global epidemiology, genetic diversity, and population structure of the rice-infecting bacterium Xanthomonas oryzae pv. oryzicola using MLVA and whole-genome sequencing.

Sacramento River Fall-run ProductionThis data set contains yearly production values (estimated escapement abundances plus in-river and ocean harvests) of Sacramento River Fall-run Chinook salmon for 1952-2010. The Sacramento River Fall-run Chinook is an aggregate stock consisting of five populations associated with different tributaries of the Sacramento River: Battle Creek, the Sacramento River mainstem, Feather River, Yuba River, and American River. Data were previously available as part of the Central Valley ChinookProd data set, maintained by the US Fish and Wildlife Service Anadromous Fish Restoration Program (https://www.fws.gov/lodi/anadromous_fish_restoration/afrp_index.htm). These specific data are no longer available online, but are presented here in the format used for analyses in the manuscript. Note that analyzed data includes years 1957-2010.Sacramento_Fall_Production_1952_2010.csv

Comprehensive demographic data including income distribution, education levels, age distribution, and household composition

A territory mapping method was used in 2015 to assess the population density of coraciiform species breeding in a riparian forest on the Zambezi River near Katima Mulilo, NE Namibia. The forest, c. 280 ha in surface, was partly transformed by human settlement and arable grounds. A total of 13 species and 42 breeding pairs were recorded.

Detecting recent demographic changes is a crucial component of species conservation and management, as many natural populations face declines due to anthropogenic habitat alteration and climate change. Genetic methods allow researchers to detect changes in effective population size (Ne) from sampling at a single timepoint. However, in species with long lifespans, there is a lag between the start of a decline in a population and the resulting decrease in genetic diversity. This lag slows the rate at which diversity is lost, and therefore makes it difficult to detect recent declines using genetic data. However, the genomes of old individuals can provide a window into the past, and can be compared to those of younger individuals, a contrast that may help reveal recent demographic declines. To test whether comparing the genomes of young and old individuals can help infer recent demographic bottlenecks, we use forward-time, individual-based simulations with varying mean individual lifespans and extents of generational overlap. We find that age information can be used to aid in the detection of demographic declines when the decline has been severe. When average lifespan is long, comparing young and old individuals from a single timepoint has greater power to detect a recent (within the last 50 years) bottleneck event than comparing individuals sampled at different points in time. Our results demonstrate how longevity and generational overlap can be both a hindrance and a boon to detecting recent demographic declines from population genomic data. Methods All data for this publication were generated via evolutionary simulations in SLiM. Here, we archive all scripts necesarily to generate, analyze, and visualize the results presented in Clark et al. 2024. First, we performed simulations in SLiM using a perennial and annual model for a variety of average lifespans (for the perennial model), and bottleneck severities. The output of these simulations is (1) a .tree file contain the geneological history of the population, from which we will extract information about genetic diversity, (2) individual-based metadata for all individuls alive during the simulation sampling time: the generation number, individual pedigree id and the individual's age, (3) Census population size information about the population at each generation in the sampling period. Second, we used tskit, msprime, and pyslim to load and process .tree files as tree sequences. We then loop through focal sampling points in the tree sequence, and sampling individuals to perform age and temporal comparisons. Genetic diversity data from the sampled bins is exported as .txt files. Finally, genetic diversity data is loaded in R, permutation tests are performed to test for significant differences in genetic diversity between bins, and figures are created.