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.

In 2023, California had the highest Hispanic population in the United States, with over 15.76 million people claiming Hispanic heritage. Texas, Florida, New York, and Illinois rounded out the top five states for Hispanic residents in that year. History of Hispanic people Hispanic people are those whose heritage stems from a former Spanish colony. The Spanish Empire colonized most of Central and Latin America in the 15th century, which began when Christopher Columbus arrived in the Americas in 1492. The Spanish Empire expanded its territory throughout Central America and South America, but the colonization of the United States did not include the Northeastern part of the United States. Despite the number of Hispanic people living in the United States having increased, the median income of Hispanic households has fluctuated slightly since 1990. Hispanic population in the United States Hispanic people are the second-largest ethnic group in the United States, making Spanish the second most common language spoken in the country. In 2021, about one-fifth of Hispanic households in the United States made between 50,000 to 74,999 U.S. dollars. The unemployment rate of Hispanic Americans has fluctuated significantly since 1990, but has been on the decline since 2010, with the exception of 2020 and 2021, due to the impact of the coronavirus (COVID-19) pandemic.

In modern ecosystems, regions of topographic heterogeneity, when compared with nearby topographically homogeneous regions, support high species densities of mammals and other groups. This biogeographic pattern could be explained by either greater diversification rates or greater accommodation of species in topographically complex regions. In this context, we assess the hypothesis that changes in landscape history have stimulated diversification in mammals. Landscape history includes tectonic and climatic processes that influence topographic complexity at regional scales. We evaluated the influence of changes in topographic complexity and climate on origination and extinction rates of rodents, the most diverse clade of mammals. We compared the Neogene records of rodent diversity for three regions in North America. The Columbia Basin of the Pacific Northwest (Region 1) and the northern Rocky Mountains (Region 2) were tectonically active over much of the Cenozoic and are characterized by h...

Eastern Asia (EA) and North America north of Mexico (NA) have comparable latitude, land area, and climate, but the overall plant diversity is much higher in EA than in NA. Despite intensive studies on disjunct taxa of the two regions, the temporal and spatial diversity patterns between the two floras remain unclear. Here we explore the floristic differences between EA and NA using the well-studied floras of China and the United States of America (USA) as exemplars, while also employing a newly generated dated phylogeny covering ~90% of the angiosperm genera of the two countries and comprehensive spatial distribution data. We find that China possesses both higher richness and phylogenetic diversity (PD) for angiosperm genera than the USA. Notably, most lineages contribute to the PD anomaly between the two floras, with 46 of 58 lineages having higher PD in China. Temporally, China has a higher proportion of genera that originated before the Miocene than are found in the USA (29.9% vs 23.2%). The eastern USA has more genera that originated during the Paleogene than does the western USA, but the reverse pattern is observed after the middle Miocene, with more genera originating in the west. Spatially, China shows a more distinct east-west deviation in diversity than the USA with eastern China possessing much higher generic richness and PD and more ancient lineages than western China. However, the eastern USA possesses lower generic richness, but higher PD and more ancient lineages than the western USA. Both the floras in China and the USA share a signature of an older east and a younger west, and this pattern may be largely driven by regional orogenic activities and climatic changes in the west of the two regions. Finally, our study indicates that more efforts are needed to enhance biodiversity conservation in southern China and the eastern USA by identifying and protecting phylogenetic diversity hotspots.

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What is the Size of Diversity And Inclusion Consulting Service Market?

The Diversity And Inclusion Consulting Service Market size is forecast to increase by USD 2.89 billion, at a CAGR of 12.7% between 2023 and 2028. The market is experiencing significant growth due to the increasing importance of fostering a sense of belonging and promoting social justice in the workplace. Companies are recognizing the value of diversity and inclusion as essential components of social responsibility and effective communication. Diversity strategy development, policy creation, and recruitment tools are becoming increasingly important for organizations seeking to hire and retain a diverse workforce. The integration of artificial intelligence (AI) into diversity and inclusion consulting services is also gaining traction, offering more efficient and effective solutions. However, the high cost associated with diversity and inclusion programs remains a challenge for some organizations. Remote work and gender equality are also key considerations in this market, as companies adapt to the changing work environment and strive for greater equality and inclusion. Effective diversity and inclusion initiatives can lead to increased loyalty among employees and a more productive workforce.

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Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Application

  Large enterprises
  Small and medium-sized enterprises


End-user

  Private sector
  Public sector
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India


  South America

    Brazil


  Middle East and Africa

Which is the Largest Segment Driving Market Growth?

The large enterprises segment is estimated to witness significant growth during the forecast period. Diversity and inclusion consulting services play a vital role in helping businesses establish and implement effective policies that promote equity and eliminate discrimination. In today's business landscape, regulatory pressures and customer expectations demand a commitment to diversity and inclusion (DEI). DEI consulting services assist organizations in addressing hiring practices, organizational culture, and training to ensure a workplace that values and respects all employees. By investing in DEI initiatives, companies can experience numerous benefits, including increased employee satisfaction, reduced turnover rates, and a more engaged workforce. A diverse workforce brings unique perspectives and ideas, fostering innovation and improving problem-solving capabilities.

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The large enterprises segment was valued at USD 1.24 billion in 2018. Furthermore, a strong DEI program enhances a company's reputation, making it more appealing to top talent and customers who prioritize social responsibility. Effective DEI policies not only benefit the organization but also contribute to a healthier, more inclusive society. As DEI consulting services continue to gain importance, businesses that prioritize these initiatives will be better positioned to compete in the market and maintain a positive brand image.

Which Region is Leading the Market?

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North America is estimated to contribute 42% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. In North America, the market is experiencing significant growth due to the increasing number of organizations recognizing the importance of inclusive business practices. The US, as part of North America, is a key contributor to this market, with over 7.6 million business entities as of Q1 2024. Approximately 83% of these entities operate in the service-providing sector, which includes industries such as finance, healthcare, and technology. These industries prioritize diversity and inclusion initiatives to attract and retain diverse talent, boost employee engagement, and enhance overall productivity.

To achieve measurable outcomes, diversity and inclusion consulting services employ various techniques, including inclusive leadership development and data-driven solutions. These approaches help organizations identify gaps and address them effectively. Seminars and training programs are also essential components of these services, providing tangible outcomes that contribute to lasting organizational change. By implementing these practices, businesses can foster an inclusive work environment, leading to a more productiv

The Central Appalachian region, USA, contains several high elevation-endemic woodland salamanders (genus Plethodon), which are thought to be particularly vulnerable to climate change due to their restricted distributions and low vagility. In West Virginia, there is a strong management focus on protection and recovery of the federally threatened Cheat Mountain salamander (Plethodon nettingi; CMS). To support this focus, there is a need for improved understanding of CMS occurrence-habitat relationships and spatially explicit projections of fine-scale contemporary and potential future habitat quality to inform management actions. In addition, there is concern among resource managers that climate change may increase habitat quality at high elevations for CMS competitors, particularly the eastern red-backed salamander (Plethodon cinereus; RBS), potentially resulting in increased competition pressure for CMS. To address these knowledge gaps, we created ecological niche models for CMS and RBS using the Random Forest classification algorithm and used the estimated occurrence-habitat relationships to assess ecological niche overlap between the species and project fine-scale contemporary and potential future habitat availability and quality. We estimated that the ecological niches of CMS and RBS were 80.5% similar, and habitat projections indicated the species would exhibit opposite responses to climate change in our region. For CMS, we estimated that amount of high-quality habitat will be reduced by mid-century and potentially lost by end-of-century, but that moderate and low-quality habitat will persist. For RBS, we estimated that amount of high-quality habitat will increase through end-of-century, and that high elevations will become more suitable for the species, indicating that competition pressure for CMS is likely to increase. This study improves understanding of important habitat characteristics for CMS and RBS, and our spatially explicit projections can assist natural resource managers with habitat protection actions, species monitoring efforts, and climate change adaptation strategies.

In the fiscal year of 2019, 21.39 percent of active-duty enlisted women were of Hispanic origin. The total number of active duty military personnel in 2019 amounted to 1.3 million people.

Ethnicities in the United States The United States is known around the world for the diversity of its population. The Census recognizes six different racial and ethnic categories: White American, Native American and Alaska Native, Asian American, Black or African American, Native Hawaiian and Other Pacific Islander. People of Hispanic or Latino origin are classified as a racially diverse ethnicity.

The largest part of the population, about 61.3 percent, is composed of White Americans. The largest minority in the country are Hispanics with a share of 17.8 percent of the population, followed by Black or African Americans with 13.3 percent. Life in the U.S. and ethnicity However, life in the United States seems to be rather different depending on the race or ethnicity that you belong to. For instance: In 2019, native Hawaiians and other Pacific Islanders had the highest birth rate of 58 per 1,000 women, while the birth rae of white alone, non Hispanic women was 49 children per 1,000 women.

The Black population living in the United States has the highest poverty rate with of all Census races and ethnicities in the United States. About 19.5 percent of the Black population was living with an income lower than the 2020 poverty threshold. The Asian population has the smallest poverty rate in the United States, with about 8.1 percent living in poverty.

The median annual family income in the United States in 2020 earned by Black families was about 57,476 U.S. dollars, while the average family income earned by the Asian population was about 109,448 U.S. dollars. This is more than 25,000 U.S. dollars higher than the U.S. average family income, which was 84,008 U.S. dollars.

description: Tree City USA is a national program that recognizes municipal commitment to community forestry. In return for meeting program requirements, Tree City USA participants expect social, economic, and/or environmental benefits. Understanding the geographic distribution and socioeconomic characteristics of Tree City USA communities at the national scale can offer insights into the motivations or barriers to program participation, and provide context for community forestry research at finer scales. In this study, researchers assessed patterns in Tree City USA participation for all U.S. communities with more than 2,500 people according to geography, community population size, and socioeconomic characteristics, such as income, education, and race. Nationally, 23.5% of communities studied were Tree City USA participants, and this accounted for 53.9% of the total population in these communities. Tree City USA participation rates varied substantially by U.S. region, but in each region participation rates were higher in larger communities, and long-term participants tended to be larger communities than more recent enrollees. In logistic regression models, owner occupancy rates were significant negative predictors of Tree City USA participation, education and percent white population were positive predictors in many U.S. regions, and inconsistent patterns were observed for income and population age. The findings indicate that communities with smaller populations, lower education levels, and higher minority populations are underserved regionally by Tree City USA, and future efforts should identify and overcome barriers to participation in these types of communities. This dataset is associated with the following publication: Berland , A., D. Herrmann , and M. Hopton. National Assessment of Tree City USA Participation According to Geography andSocioeconomic Characteristics. Arboriculture & Urban Forestry. International Society of Arboriculture, Champaign, IL, USA, 42(2): 120-130, (2016).; abstract: Tree City USA is a national program that recognizes municipal commitment to community forestry. In return for meeting program requirements, Tree City USA participants expect social, economic, and/or environmental benefits. Understanding the geographic distribution and socioeconomic characteristics of Tree City USA communities at the national scale can offer insights into the motivations or barriers to program participation, and provide context for community forestry research at finer scales. In this study, researchers assessed patterns in Tree City USA participation for all U.S. communities with more than 2,500 people according to geography, community population size, and socioeconomic characteristics, such as income, education, and race. Nationally, 23.5% of communities studied were Tree City USA participants, and this accounted for 53.9% of the total population in these communities. Tree City USA participation rates varied substantially by U.S. region, but in each region participation rates were higher in larger communities, and long-term participants tended to be larger communities than more recent enrollees. In logistic regression models, owner occupancy rates were significant negative predictors of Tree City USA participation, education and percent white population were positive predictors in many U.S. regions, and inconsistent patterns were observed for income and population age. The findings indicate that communities with smaller populations, lower education levels, and higher minority populations are underserved regionally by Tree City USA, and future efforts should identify and overcome barriers to participation in these types of communities. This dataset is associated with the following publication: Berland , A., D. Herrmann , and M. Hopton. National Assessment of Tree City USA Participation According to Geography andSocioeconomic Characteristics. Arboriculture & Urban Forestry. International Society of Arboriculture, Champaign, IL, USA, 42(2): 120-130, (2016).

Efforts to understand how pollinating insect diversity is distributed across large geographic areas are rare despite the importance of such work for conserving regional diversity. We sought to relate the diversity of bees (Hymenoptera: Apoidea), hoverflies (Diptera: Syrphidae), and butterflies (Lepidoptera) to the ecoregion, landscape context, canopy openness, and forest composition across southeastern U.S. forests. We established 5-7 plots in each experimental forest. In each, we sampled pollinators monthly (March-September) using colored pan traps, and collected data on local forest characteristics. We used the National Land Cover Database (NLCD) to quantify surrounding land cover at different spatial scales. Bee richness was negatively correlated with both the amount of conifer forest and the extent of wetlands in the surrounding landscape but was positively correlated with canopy openness. Hoverflies and butterflies were less sensitive to landscape context and stand conditions. Pollinator communities differed considerably among ecoregions, with those of the Central Appalachian and Coastal Plain ecoregions being particularly distinct. Bee richness and abundance peaked two months earlier in Central Appalachia than in the Coastal Plain and Southeastern Mixed Forest ecoregions. Our findings reveal ecoregional differences in pollinator communities across the southeastern U.S. and highlight the importance of landscape context and local forest conditions to this diverse fauna. The closed broadleaf forests of Appalachia and the open conifer-dominated forests of the Coastal Plain support particularly distinct pollinator communities with contrasting seasonality. Our results suggest pine forests may reduce pollinator diversity in regions historically dominated by broadleaf forests. However, efforts to create more open canopies can help improve conditions for pollinators in planted pine forests. Research exploring associations between forest pollinators and different broadleaf tree taxa is needed to better anticipate how they will be impacted by various management activities. Methods Pollinators (bees, butterflies, and hoverflies) were sampled using colored pan traps at 97 locations in 19 experimental forests across the southeastern United States. Data on canopy openness, tree composition, and basal area were collected at each plot. Landscape variables such as the amount of conifer forests and wetlands were based on National Land Cover Database data. This data file includes the five datasets that are used in the analysis. The "combined_matrix" includes species-level abundances by plot and was used in the community analysis (NMDS, adonis2, indicator species analysis). The file "merged_data_for_analysis" includes landscape and stand metrics as well as the richness of each pollinator taxa used in the diversity modeling. The last three datasets show the incidence of species by pollinator group used in the iNext analysis to compare gamma diversity among the three most-sampled ecoregions.

Aims: We investigate native and introduced populations of Solanum rostratum, an annual, self-compatible plant that has been introduced around the globe. This study is the first to compare the genetic diversity of Solanum rostratum between native and introduced populations. We aim to (1) determine the level of genetic diversity across the studied regions; (2) explore the likely origins of invasive populations in China; and (3) investigate whether there is the evidence of multiple introductions into China. Methods: We genotyped 329 individuals at 10 microsatellite loci to determine the levels of genetic diversity and to investigate population structure of native and introduced populations of S. rostratum. We studied five populations in each of three regions across two continents: Mexico, the U.S.A. and China. Important Findings: We found the highest genetic diversity among Mexican populations of S. rostratum. Genetic diversity was significantly lower in Chinese and U.S.A. populations, but we found no regional difference in inbreeding coefficients (FIS) or population differentiation (FST). Population structure analyses indicate that Chinese and U.S.A. populations are more closely related to each other than to sampled Mexican populations, revealing that introduced populations in China share an origin with the sampled U.S.A. populations. The distinctiveness between some introduced populations indicates multiple introductions of S. rostratum into China.

This map shows population density of the United States. Areas in darker magenta have much higher population per square mile than areas in orange or yellow. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico. From the Census:"Population density allows for broad comparison of settlement intensity across geographic areas. In the U.S., population density is typically expressed as the number of people per square mile of land area. The U.S. value is calculated by dividing the total U.S. population (316 million in 2013) by the total U.S. land area (3.5 million square miles).When comparing population density values for different geographic areas, then, it is helpful to keep in mind that the values are most useful for small areas, such as neighborhoods. For larger areas (especially at the state or country scale), overall population density values are less likely to provide a meaningful measure of the density levels at which people actually live, but can be useful for comparing settlement intensity across geographies of similar scale." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

Factors driving the spatial configuration of centres of endemism have long been a topic of broad interest and debate. Due to different eco-evolutionary processes, these highly biodiverse areas may harbour different amounts of ancient and recently diverged organisms (paleo- and neo-endemism, respectively). Patterns of endemism still need to be measured at distinct phylogenetic levels for most clades and, consequently, little is known about the distribution, the age and the causes of such patterns. Here we tested for the presence of centres with high Phylogenetic Endemism (PE) in the highly diverse Neotropical snakes, testing the age of these patterns (paleo- or neo-endemism), and the presence of PE centres with distinct phylogenetic composition. We then tested whether PE is predicted by topography, by climate (seasonality, stability, buffering and relictualness), or biome size. We found that most areas of high PE for Neotropical snakes present a combination of both ancient and recently diverged diversity, which is distributed mostly in the Caribbean region, Central America, the Andes, the Atlantic Forest and on scattered highlands in central Brazil. Turnover of lineages is higher across Central America, resulting in more phylogenetically distinct PE centres compared to South America, which presents a more phylogenetically uniform snake fauna. Finally, we found that elevational range (topographic roughness) is the main predictor of PE, especially for paleo-endemism, whereas low paleo-endemism levels coincide with areas of high climatic seasonality. Our study highlights the importance of mountain systems to both ancient and recent narrowly distributed diversity. Mountains are both museums and cradles of snake diversity in the Neotropics, which has important implications for conservation in this region.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Norwood Young America. The dataset can be utilized to gain insights into gender-based income distribution within the Norwood Young America population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Norwood Young America, among individuals aged 15 years and older with income, there were 1,348 men and 1,462 women in the workforce. Among them, 779 men were engaged in full-time, year-round employment, while 629 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.86% fell within the income range of under $24,999, while 4.13% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 12.32% of men in full-time roles earned incomes exceeding $100,000, while 5.56% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 Norwood Young America median household income by race. You can refer the same here

The structure and function of fungal communities in the coffee rhizosphere is shaped by crop environment. Because coffee can be grown along a management continuum from conventional application of pesticides and fertilizers in full sun to organic management in a shaded understory, we used coffee fields to hold host constant while comparing rhizosphere fungal communities in markedly different environmental conditions with regard to shade and inputs. We characterized the shade and soil environment in 25 fields under conventional, organic or transitional management in two regions of Costa Rica. We amplified the ITS2 region of fungal DNA from coffee roots in these fields and characterized the rhizosphere fungal community via high-throughput sequencing. Sequences were assigned to guilds to determine differences in functional diversity and trophic structure among coffee field environments. Organic fields had more shade, a greater richness of shade tree species, more leaf litter, and were less acidic, with lower soil nitrate availability and higher soil copper, calcium, and magnesium than conventionally-managed fields, although differences between organic and conventionally-managed fields in shade, calcium and magnesium depended on region. Differences in richness and community composition of rhizosphere fungi between organic and conventionally-managed fields were also correlated with shade, soil acidity, nitrate, and copper. Trophic structure differed with coffee field management. Saprotrophs, plant pathogens, and mycoparasites were more diverse and plant pathogens were more abundant in organic than in conventionally-managed fields, while saprotroph-plant pathogens were more abundant in conventionally-managed fields. These differences reflected environmental differences and depended on region.

IMPORTANCE

Rhizosphere fungi play key roles in ecosystems, as nutrient cyclers, pathogens, and mutualists, yet little is currently known about which environmental factors and how agricultural management shape rhizosphere fungal communities and their functional diversity. This field study of the coffee agroecosystem suggests that organic management not only fosters a greater overall diversity of fungi, but also maintains a greater richness of saprotrophic, plant pathogenic and mycoparasitic fungi that has implications for efficiency of nutrient cycling and regulation of plant pathogen populations in agricultural systems. As well as influencing community composition and richness of rhizosphere fungi, shade management and use of fungicides and synthetic fertilizers altered the trophic structure of the coffee agroecosystem.

Methods Site description and study design. Two coffee-growing regions of Costa Rica with a premontane wet forest climate were selected for this study, Monteverde (10⁰ 19'27.8" N, 084⁰ 50'30.1" W) and San Vito (08⁰ 52'41.1" N, 082⁰ 57'03.1" W). Soils in both Monteverde and San Vito are Andisols, a volcanic soil type with high organic matter, high leaching capacity and pH of 5.6 - 5.8. Monteverde experiences slightly lower rainfall on average (300 cm yr-1 vs. 400 cm yr-1 in San Vito.

Twenty-five coffee fields were included in this study. Thirteen fields were sampled in Monteverde, six between 25-28 May 2011 and seven between 1-4 June 2012. In San Vito, six fields each were sampled between 31 May-3 June 2011 and 7-11 June 2012. At each site, the farmer or farm manager was interviewed to determine types of herbicides, pesticides, fungicides, and fertilizers used on the field, as well as the cultivars present, age of the field and coffee plants, prior land use and pruning regimen. Fields were designated as ‘conventionally-managed’ if farmers reported using synthetic fertilizers and pesticides, as ‘organic’ if farmers reported that fields were certified organic or reported no use of synthetic fertilizers and pesticides in the previous five years, and as ‘minimal conventional’ if farmers reported that they were in the process of transitioning from conventional to organic management or had not used synthetic fertilizers or pesticides in the preceding 1-3 years.

Field sampling. For each field, species richness of shade trees, type of windbreak, and phenological status of coffee plants (vegetative, flowering, green or mature fruit) were recorded. All fields except one, in which plants were vegetative, were producing green (immature) or green and red (mature) fruits at the time of sampling. In each field, a 20 m × 20 m plot was established > 5 m from the edge and representative of the shade tree density of the field. Approximate elevation was recorded with a Garmin eTrex Venture HC® (Garmin Corp., Schaffhausen, Switzerland). Percent canopy cover at the center of the plot was calculated using a spherical densiometer with convex mirror (Forestry Suppliers, Jackson, Mississippi, USA) according to manufacturer’s instructions. Plot aspect was measured by compass; plot slope was measured qualitatively in 2011 and using a clinometer in 2012. Coffee plant density was estimated by averaging the distance between rows for five rows and the distance between plants within a row for five pairs of plants.

Within each plot, one coffee plant was sampled every 5 m along every other row, for a total of 20 plants per plot. At each plant, leaf litter depth was measured at the dripline, and a soil sample was taken using a 2 cm in diameter corer to a depth of approximately 20 cm. From every other sampled plant, root samples were taken at 1-15 cm of depth from 3-5 sections of fine roots and combined, for a total of 10 plants per plot. Soil samples within a field were pooled, air-dried in paper bags and stored at room temperature.

In the lab, each root sample was rinsed with tap water and divided in two. One subsample from each plant was stored in 1% KOH (w/v) for analysis of root colonization by AM fungi (Aldrich-Wolfe et al., in review), while the second was dried in the presence of Drierite (W.A. Hammond Company, Xenia, Ohio, USA) for DNA extraction. Drying roots results in no reduction in DNA yield relative to isolation from fresh or frozen samples, although it may reduce the yield of fungal DNA (86), and eliminates the risk of DNA degradation when frozen samples thaw in transit (87). At the end of each year’s sampling period, soils and dried root samples for DNA extraction were transported to the United States and stored at room temperature. Two-three soil subsamples from each field were analyzed for soil nutrient availability, pH in water, and organic matter by LOI at the Soils Testing Laboratory, North Dakota State University, Fargo, North Dakota, USA. Means per field were subsequently used for all statistical analyses.

Molecular detection of root fungi. Dried root samples were pulverized using six 2.33-mm in diameter chrome-steel beads (Biospec Products, Bartlesville, Oklahoma, USA) in a vortex adapter (Mo Bio Laboratories, Carlsbad, California, USA) on a Vortex-Genie® 2 Mixer for 1 h (Scientific Industries, Inc., Bohemia, New York, USA). DNA was isolated from 20 mg of each sample for 8-10 root samples per field using the Qiagen DNeasy Plant Mini Kit (Qiagen, Germantown, Maryland, USA), following the manufacturer’s protocol (with two elution volumes of 50 μL each) and stored at -20 °C.

The internal transcribed spacer region 2 (ITS2) was amplified by polymerase chain reaction (PCR) for each DNA extract using 12.5 μL of 2× Kapa HiFi Hotstart Ready Mix (Kapa Biosystems, Wilmington, Massachusetts, USA), 10 μL nuclease-free water, 0.8 μL each of 10 mM fungal-specific HPLC-purified primers 5.8SR and ITS4 (88), and 1 μL of DNA template for a total reaction volume of 25.1 μL. Each extract was amplified in triplicate using an Eppendorf Mastercycler (Hamburg, Germany) with 3 min activation at 95 °C, 30 cycles of denaturing at 98 °C for 20 s, annealing at 65.7 °C for 15 s and elongation at 72 °C for 45 s, and a final elongation at 72 °C for 5 min. PCR products were confirmed by electrophoresis in 1% agarose and 0.5× TBE followed by staining with ethidium bromide. Extracts which failed to produce PCR products were diluted tenfold and amplified using the above reaction conditions with an annealing temperature of 64.4 °C. PCR products were stored overnight at 4 °C and for longer periods at -20 °C.

Triplicate PCR products were pooled and purified using the Agencourt® Ampure® XP system (Beckman Coulter, Indianapolis, Indiana, USA) following the manufacturer’s protocol, with two washes with ethanol and elution in 10 mM Tris. Concentration of dsDNA in each sample was measured using a Qubit 2.0 fluorimeter (Invitrogen, Carlsbad, California, USA). Eight (2011) or ten (2012) samples per field were pooled at equal DNA concentration in 10 mM Tris, and 3-5 ng of DNA per field was shipped frozen on dry ice for sequencing at the University of Minnesota Genomics Center (UMGC, St. Paul, Minnesota, USA).

PCR products from each field were amplified using Nextera™ indexing primers (Illumina, San Diego, California, USA) and 10 cycles of denaturation at 98 °C for 20 s, annealing at 55 °C for 15 s, and elongation at 72 °C for 1 min. Indexed PCR products were denatured with 8 pM NaOH in Illumina HTI buffer (20% PhiX) at 96 °C for 2 min prior to loading and sequencing on an Illumina Miseq® using Reagent Kit v3 with separate index reads. Preliminary quality control (QC) and demultiplexing were conducted by the UMGC.

Sequence data processing. Sequences were processed with the PIPITS 1.4.0 pipeline (Gweon et al, 2015), which employs a number of different software packages, using the standard settings. Briefly, forward and reverse reads were merged using PEAR 0.9.8 (http://www.exelixis-lab.org/pear), followed by quality filtering using FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/), and extraction of the fungal-specific ITS2 region using ITSx 1.0.11 (90). Dereplication, removal

Reconstruction of historical relationships between geographic regions within a species' range can indicate dispersal patterns and help predict future responses to shifts in climate. Ascaphus truei (coastal tailed frog) is an indicator species of the health of forests and perennial streams in the Coastal and Cascade Mountains of the Pacific Northwest of North America. We used two genetic techniques — microsatellite and genotype-by-sequencing (GBS) — to compare the within region genetic diversity of populations near the northern extent of the species’ range (British Columbia, Canada) to two geographic regions in British Columbia and two in Washington, USA, moving towards the core of the range. Allelic richness and heterozygosity declined substantially as latitude increased. The northernmost region had the lowest mean expected heterozygosities for both techniques (microsatellite, M = 0.20, SE = 0.080; GBS, M = 0.025, SE = 0.0010) and the southernmost region had the highest (microsatellite, M = 0.88, SE = 0.054; GBS, M = 0.20, SE = 0.0029). The northernmost regions (NC and MC) clustered together in population structure models for both genetic techniques. Our discovery of reduced diversity may have important conservation and management implications for population connectivity and the response of A. truei to climate change. Methods We employed an opportunistic non-random sampling scheme for tissue collection, and streams were included based on accessibility. Larvae were caught using a dipnet while flipping over rocks. Tissue samples consisted of skin clipped from the posterior of the tail, and were preserved in 95% ethanol and stored at -80 °C. Sampling in British Columbia, Canada, was in 2014 and 2015. We recieved purified DNA from a colleague from two geographic regions in Washington, USA (see Spear & Storfer, 2008; Spear et al., 2012). DNA was extracted from tail clips using the DNeasy Blood and Tissue kit (Qiagen, Inc., Toronto, ON) following the manufacturer's instructions. This paragraph is in reference to the microsatellite dataset. We used ten polymorphic microsatellite DNA markers (Spear et al., 2008). PCR thermal cycling included an initial denaturation at 95 °C for 15 minutes, followed by 35 cycles at a locus specific annealing temperature for 30 seconds, an extension at 72 °C for 30 seconds, and a further denaturation at 95 °C for 30 seconds. The cycles were followed by an additional elongation at the annealing temperature for 60 seconds. One primer per pair was labeled with a fluorescent tag (FAM, PET, or VIC). Four amplicon pools were created based on size and generated multilocus genotypes using fragment analysis with the Applied Biosystems 3130xL (Burlington, ON). We scored microsatellite genotypes with GeneMapper (Applied Biosystems). The following 6 paragraphs are in reference to the genotype-by-sequencing dataset and the mtDNA dataset. We sent purified DNA for nextRAD library preparation, sequencing, and initial filtering to SNPsaurus, LLC (Eugene, OR). Fifteen samples were sent for sequencing in duplicate and triplicate to determine the efficacy of the genotyping method. SNPsaurus converted genomic DNA into nextRAD genotyping-by-sequencing libraries. Genomic DNA was randomly fragmented with Nextera reagent (Illumina, Inc), which also ligates short adapter sequences to the ends of the fragments. The Nextera reaction was scaled for fragmenting 25 ng of genomic DNA, although 50 ng of genomic DNA was used for input to compensate for any degraded DNA in the samples and to increase fragment sizes. Fragmented DNA was then amplified for 27 cycles with 74 °C extension, with one of the primers matching the adapter and extending 10 nucleotides into the genomic DNA with the selective sequence GTGTAGAGCC. Thus, only fragments starting with a sequence that could be hybridized by the selective sequence of the primer were efficiently amplified. SNPsaurus sequenced the nextRAD libraries on a HiSeq 4000 with two lanes of 150 base pair (bp), single reads (University of Oregon) and 20x depth of coverage. SNPsaurus LLC conducted the bioinformatic analysis of the raw reads to produce a vcf genotype file for population genetic analysis. Custom scripts that trimmed the sequence reads based on bbduk (BBMAP TOOLS): bash bbmap/bbduk.sh in=$file out=$outfile ktrim=r k=17 hdist=1 mink=8 ref=bbmap/resources/nextera.fa.gz minlen=100 ow=t qtrim=r trimq=10. A reference adapter was matched to the reads and all bases to the right were trimmed (as these were single-end reads), allowing for one mismatch. Reads were trimmed at bases with a quality score of 10 or less and reads shorter than 100 base pairs were removed. After trimming, an analysis of the reads (bbduk) shows 83.4% of bases had the highest-possible quality score and 1.4% had a quality score lower than Q20. Average quality declined slightly along the read length to a minimum of Q37.7 at nucleotide 150. A de novo reference was created by collecting 10 million reads in total, evenly from the samples, and excluding clusters that had counts fewer than 20 or more than 1000. The remaining clusters were then aligned to each other to identify allelic loci and collapse allelic haplotypes to a single representative. For each sample, all reads were mapped to the reference with an alignment identity threshold of 95% using bbmap (BBMAP TOOLS). Genotype calling was done using SAMTOOLS v1.8 and BCFTOOLS v1.8 (samtools mpileup -gu -Q 10 -t DP, DPR -f ref.fasta -b samples.txt | bcftools call -cv - > genotypes.vcf), generating a vcf file. The vcf file was filtered to remove alleles with a population frequency of less than 3% (referred to as minor allele frequency). Loci were removed that were heterozygous in all samples or had more than 2 alleles in a sample (suggesting collapsed paralogs). The presence of artifacts was checked by counting SNPs at each read nucleotide position and determining that SNP number did not increase with reduced base quality at the end of the read. From the vcf file provided, we removed loci that were variable due to base insertions or deletions using VCFTOOLS v0.1.14. We removed loci with greater than or equal to 40 % missing data in at least one geographic region and loci with an excessive heterozygosity p-value of less than or equal to 0.005 per region to further reduce the potential impact of paralogs (GENALEX). We used the previously published Ascaphus mitochondrial genome from GenBank (Gissi et al., 2006) with the nextRAD sequencing data to extract mtDNA reads. The mtDNA genome was indexed, each genotype was separated into individual files, and each file was aligned to the mtDNA genome with the Burrows-Wheeler Aligner algorithm BWA-MEM (BWA). Using SAMTOOLS v1.8, alignments were removed if they had a MAPQ score (−10 log10Pr {mapping position is wrong}) of ≤ 30. We removed duplicate sequences and merged files using PICARD, retaining a single sample from those sent in triplicate or duplicates for sequencing. Files were sorted and indexed using SAMTOOLS. SNPs were called using FREEBAYES v0.9.10. The ploidy was set to 1, and the defaults were used for all other settings. Loci that had more than 5% missing calls across haplotypes were not included in the analysis, and haplotypes with greater than 40% missing calls were removed.

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Brazil and the United States are the two most populous countries in the Americas today. In 1500, the year that Pedro Álvares Cabral made landfall in present-day Brazil and claimed it for the Portuguese crown, it is estimated that there were roughly one million people living in the region. Some estimates for the present-day United States give a population of two million in the year 1500, although estimates vary greatly. By 1820, the population of the U.S. was still roughly double that of Brazil, but rapid growth in the 19th century would see it grow 4.5 times larger by 1890, before the difference shrunk during the 20th century. In 2024, the U.S. has a population over 340 million people, making it the third most populous country in the world, while Brazil has a population of almost 218 million and is the sixth most populous. Looking to the future, population growth is expected to be lower in Brazil than in the U.S. in the coming decades, as Brazil's fertility rates are already lower, and migration rates into the United States will be much higher. Historical development The indigenous peoples of present-day Brazil and the U.S. were highly susceptible to diseases brought from the Old World; combined with mass displacement and violence, their population growth rates were generally low, therefore migration from Europe and the import of enslaved Africans drove population growth in both regions. In absolute numbers, more Europeans migrated to North America than Brazil, whereas more slaves were transported to Brazil than the U.S., but European migration to Brazil increased significantly in the early 1900s. The U.S. also underwent its demographic transition much earlier than in Brazil, therefore its peak period of population growth was almost a century earlier than Brazil. Impact of ethnicity The demographics of these countries are often compared, not only because of their size, location, and historical development, but also due to the role played by ethnicity. In the mid-1800s, these countries had the largest slave societies in the world, but a major difference between the two was the attitude towards interracial procreation. In Brazil, relationships between people of different ethnic groups were more common and less stigmatized than in the U.S., where anti-miscegenation laws prohibited interracial relationships in many states until the 1960s. Racial classification was also more rigid in the U.S., and those of mixed ethnicity were usually classified by their non-white background. In contrast, as Brazil has a higher degree of mixing between those of ethnic African, American, and European heritage, classification is less obvious, and factors such as physical appearance or societal background were often used to determine racial standing. For most of the 20th century, Brazil's government promoted the idea that race was a non-issue and that Brazil was racially harmonious, but most now acknowledge that this actually ignored inequality and hindered progress. Racial inequality has been a prevalent problem in both countries since their founding, and today, whites generally fare better in terms of education, income, political representation, and even life expectancy. Despite this adversity, significant progress has been made in recent decades, as public awareness of inequality has increased, and authorities in both countries have made steps to tackle disparities in areas such as education, housing, and employment.

The gopher tortoise (Gopherus polyphemus) has experienced dramatic population declines throughout its distribution in the southeastern United States and is federally listed as threatened in the area west of the Tombigbee-Mobile Rivers. While there is molecular support for recognizing the listed portion of the range as genetically distinct, other research has suggested that additional population structure exists at both range-wide and regional scales. In this study, we sought to comprehensively define structure at both spatial scales by doubling the data available in terms of the number of sampling sites, individuals and microsatellite loci compared to previously published work. Patterns of genetic diversity, gene flow and demographic history were also compared across the range. We collected 933 individuals from 47 sampling sites across the range and genotyped them for 20 microsatellite loci. Our range-wide analyses supported the recognition of five genetic groups (or regions) delineated by the Tombigbee-Mobile Rivers, Apalachicola-Chattahoochee Rivers, and the transitional areas between several physiographic province sections of the Coastal Plains (i.e., Eastern Gulf, Sea Island, and Floridian). Genetic admixture was found at sampling sites along the boundaries of these genetically defined groups. We detected some degree of additional genetic structure within each of the five regions. Notably, within the federally listed portion of the range, we found some support for two additional genetic groups loosely delineated by the Pascagoula-Chickasawhay Rivers, and we detected four more genetic groups within the Florida region that seemed to reflect the influence of the local physiography. Additionally, our range-wide analysis found the periphery of the range had lower levels of genetic diversity relative to the core. We suggest that the five main genetic groups delineated in our study warrant recognition as management units in terms of conservation planning. Intraregional population structure also points to the potential importance of other barriers to gene flow at finer spatial scales, although additional work is needed to better delineate these genetic groups.

Based on land area, Brazil is the largest country in Latin America by far, with a total area of over 8.5 million square kilometers. Argentina follows with almost 2.8 million square kilometers. Cuba, whose surface area extends over almost 111,000 square kilometers, is the Caribbean country with the largest territory.

Brazil: a country with a lot to offer

Brazil's borders reach nearly half of the South American subcontinent, making it the fifth-largest country in the world and the third-largest country in the Western Hemisphere. Along with its landmass, Brazil also boasts the largest population and economy in the region. Although Brasília is the capital, the most significant portion of the country's population is concentrated along its coastline in the cities of São Paulo and Rio de Janeiro.

South America: a region of extreme geographic variation

With the Andes mountain range in the West, the Amazon Rainforest in the East, the Equator in the North, and Cape Horn as the Southern-most continental tip, South America has some of the most diverse climatic and ecological terrains in the world. At its core, its biodiversity can largely be attributed to the Amazon, the world's largest tropical rainforest, and the Amazon river, the world's largest river. However, with this incredible wealth of ecology also comes great responsibility. In the past decade, roughly 80,000 square kilometers of the Brazilian Amazon were destroyed. And, as of late 2019, there were at least 1,000 threatened species in Brazil alone.