In terms of population size, the sex ratio in the United States favors females, although the gender gap is remaining stable. In 2010, there were around 5.17 million more women, with the difference projected to decrease to around 3 million by 2027.

Gender ratios by U.S. state In the United States, the resident population was estimated to be around 331.89 million in 2021. The gender distribution of the nation has remained steady for several years, with women accounting for approximately 51.1 percent of the population since 2013. Females outnumbered males in the majority of states across the country in 2020, and there were eleven states where the gender ratio favored men.

Metro areas by population National differences between male and female populations can also be analyzed by metropolitan areas. In general, a metropolitan area is a region with a main city at its center and adjacent communities that are all connected by social and economic factors. The largest metro areas in the U.S. are New York, Los Angeles, and Chicago. In 2019, there were more women than men in all three of those areas, but Jackson, Missouri was the metro area with the highest share of female population.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

Public Law 94-171, enacted in 1975, requires the Census Bureau to provide redistricting data in a format requested by state governments. Within one year following the 1990 decennial Census (by April 1, 1991), the Census Bureau provided the governor and legislature of each state with the population data needed to redraw legislative districts. This collection contains the same substantive and geographic variables as the original Public Law 94-171 files [see CENSUS OF POPULATION AND HOUSING, 1990 [UNITED STATES]: PUBLIC LAW (P.L.) 94-171 DATA (ICPSR 9516)] but with the population counts adjusted for undernumeration. Adjusted Public Law 94-171 counts are supplied for a sample of one-half of blocks in the United States and a complete selection of areas with 1,000 or more persons. Each state file provides data for the state and its subareas in the following order: state, county, voting district, county subdivision, place, and block. Additionally, complete summaries are provided for the following geographic areas: county subdivision, place, consolidated city, state portion of American Indian and Alaska Native area, and county portion of American Indian and Alaska Native area. Area characteristics such as land area, water area, latitude, and longitude are provided. Summary statistics are provided for all persons, for persons 18 years old and over, and for housing units in the geographic areas. Counts by race and by Hispanic and non-Hispanic origin are also recorded.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME
Population estimates

LAST UPDATED
February 2023

DESCRIPTION
Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCE
California Department of Finance: Population and Housing Estimates - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/
Table E-6: County Population Estimates (1960-1970)
Table E-4: Population Estimates for Counties and State (1970-2021)
Table E-8: Historical Population and Housing Estimates (1990-2010)
Table E-5: Population and Housing Estimates (2010-2021)

Bay Area Jurisdiction Centroids (2020) - https://data.bayareametro.gov/Boundaries/Bay-Area-Jurisdiction-Centroids-2020-/56ar-t6bs
Computed using 2020 US Census TIGER boundaries

U.S. Census Bureau: Decennial Census Population Estimates - http://www.s4.brown.edu/us2010/index.htm- via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University
1970-2020

U.S. Census Bureau: American Community Survey (5-year rolling average; tract) - https://data.census.gov/
2011-2021
Form B01003

Priority Development Areas (Plan Bay Area 2050) - https://opendata.mtc.ca.gov/datasets/MTC::priority-development-areas-plan-bay-area-2050/about

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
All historical data reported for Census geographies (metropolitan areas, county, city and tract) use current legal boundaries and names. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of December 2022.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area tracts and PDAs are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Population estimates for PDAs are allocated from tract-level Census population counts using an area ratio. For example, if a quarter of a Census tract lies with in a PDA, a quarter of its population will be allocated to that PDA. Estimates of population density for PDAs use gross acres as the denominator. Note that the population densities between PDAs reported in previous iterations of Vital Signs are mostly not comparable due to minor differences and an updated set of PDAs (previous iterations reported Plan Bay Area 2040 PDAs, whereas current iterations report Plan Bay Area 2050 PDAs).

The following is a list of cities and towns by geographical area:

Big Three: San Jose, San Francisco, Oakland

Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside

Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville

Unincorporated: all unincorporated towns

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

IntroductionThe National Institutes of Health (NIH) is the primary federal agency in the United States (US) that supports biomedical research, training, and clinical trials. NIH funding creates patents and jobs and thus helps the regional and national economy grow. Therefore, NIH funding would be expected to flow equitably to all 50 US states. However, there is a significant geographic disparity in the level of NIH funding received by various states. To that end, in 1993, authorized by Congress, NIH initiated a funding program called the Institutional Development Award (IDeA) to support states, called IDeA states, which received low levels of NIH funding. However, whether this approach has helped reduce the geographic disparity in NIH funding is unclear.MethodsIn the current study, we analyzed data on various NIH funding mechanisms awarded to 23 IDeA states vs. 27 non-IDeA states, as identified by NIH. We compared these data to the population size, federal taxes paid, and the number of PhDs and Post-doctoral Fellows(PDFs) trained in IDeA vs. non-IDeA states.ResultsThe non-IDeA states received 93.6% of the total NIH funding, whereas IDeA states received only 6.4%. On average, one Institutional Training Grant was received for every 24 PhDs trained in non-IDeA states, while IDeA states received one such grant for every 46 PhDs trained. The non-IDeA states comprised 84.3% of the US population, whereas IDeA states comprised 15.7%. Thus, on a per capita basis, non-IDeA states received $120 from NIH, whereas IDeA states received $45 per person. For every million dollars contributed by the non-IDeA states toward federal taxes, they received $7,903 in NIH funding, while the IDeA States received only $4,617. For FY 2022, the NIH funding created an economic activity of $90.6 Billion in non-IDeA states and only $6.3 billion in IDeA states. When total NIH funding to the states was analyzed for the years 1992, 2002, 2012, and 2022, IDeA states received 4.7% of the total NIH funding in 1992, which increased to 7.2% in 2002 but dropped to 6.8% in 2012 and 6.5% in 2022. This demonstrated that IDeA states’ share of NIH funding remained relatively unchanged for the past 20 years.DiscussionEliminating the geographic disparity in NIH funding is crucial for achieving equitable health outcomes across the US, and for the IDeA states to successfully train future generations of physicians and scientists, as well as grow the regional economy. Although the NIH IDeA programs have helped enhance the research capacity in IDeA states, the funding currently constitutes less than 1% of the total NIH budget. Thus, it is critical to increase NIH funding to IDeA states to improve health outcomes for all Americans.

Prepared by the Inter-university Consortium for Political and Social Research, this data collection consists of selected subsets extracted from the Census of Population and Housing, 2000, Summary File 3 (SF3). The SF3 data contain information compiled from the questions asked of a sample of persons and housing units enumerated in Census 2000. Population items include sex, age, race, Hispanic or Latino origin, household relationship, marital status, caregiving by grandparents, language and ability to speak English, ancestry, place of birth, citizenship status and year of entry to the United States, migration, place of work, journey to work, school enrollment, educational attainment, veteran status, disability, employment status, industry, occupation, class of worker, income, and poverty status. Housing items include housing unit vacancy status, housing unit tenure (owner/renter), number of rooms, number of bedrooms, year moved into unit, occupants per room, units in structure, year structure built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, rent, and shelter costs. The information in SF3 is presented in 813 tables, one variable per table cell, plus additional variables with geographic information. Cases in the summary file data are classified by levels of observation, known as "summary levels" in the Census Bureau's nomenclature, which served as the selection criteria for the subsets generated by ICPSR. Each subset comprises all of the cases in one of 10 summary levels: the nation (summary level 010), states (summary level 040), Metropolitan Statistical Areas (MSA)/Consolidated Metropolitan Statistical Areas (CMSA) (summary level 380), Primary Metropolitan Statistical Areas (PMSA) (summary level 385), places (summary level 160), counties (summary level 050), county subdivisions (summary level 060), whole census tracts (summary level 140), census tracts in places (summary level 158), and 5-Digit ZIP Code Tabulation Areas (ZCTA) (summary level 860). Four files are supplied for the summary level 860 subset: a single file that contains all of the SF3 tables, plus three smaller files, each of which contains about one third of the tables. Five files are supplied for each of the summary level 010, 040, 380, 385, 160, and 050 subsets: a single file that contains all of the SF3 tables, plus four smaller files, each of which contains approximately one quarter of the tables. Fifteen files are provided for each of the summary level 140 and 158 subsets. There is a national file with all of the SF3 tables, plus two smaller national files, each of which contains approximately one half of the tables. Additionally, there are three files for each of the four census regions (Northeast, Midwest, South, and West): a file with all tables and two smaller files each containing about one half of the tables. Twenty files are supplied for summary level 060. There is a national file with all tables, plus three smaller national files, each of which contains approximately one third of the tables. In addition, there are four files for each of the four census regions: a file with all tables and three smaller files each containing about one third of the tables.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED October 2019

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

The Public Use Microdata Samples (PUMS) contain person- and household-level information from the "long-form" questionnaires distributed to a sample of the population enumerated in the 1980 Census. The C Sample, containing 1 percent data, identifies census regions, divisions, 27 individual states, and the District of Columbia. Four types of areas are shown: inside central cities, urban fringe, other urban, and rural. The C Sample separately identifies every urbanized area with a total population over 800,000, and roughly half of the urbanized areas between 200,000 and 800,000. Household-level variables include housing tenure, year structure was built, number and types of rooms in dwelling, plumbing facilities, heating equipment, taxes and mortgage costs, number of children, and household and family income. Person-level variables include sex, age, marital status, race, Spanish origin, income, occupation, transportation to work, and education.

Lake County, Illinois Demographic Data. Explanation of field attributes: Total Population – The entire population of Lake County. White – Individuals who are of Caucasian race. This is a percent.African American – Individuals who are of African American race. This is a percent.Asian – Individuals who are of Asian race. This is a percent. Hispanic – Individuals who are of Hispanic ethnicity. This is a percent. Does not Speak English- Individuals who speak a language other than English in their household. This is a percent. Under 5 years of age – Individuals who are under 5 years of age. This is a percent. Under 18 years of age – Individuals who are under 18 years of age. This is a percent. 18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent. 65 years of age and older – Individuals who are 65 years old or older. This is a percent. Male – Individuals who are male in gender. This is a percent. Female – Individuals who are female in gender. This is a percent. High School Degree – Individuals who have obtained a high school degree. This is a percent. Associate Degree – Individuals who have obtained an associate degree. This is a percent. Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent. Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent. Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount. No High School – Individuals who have not obtained a high school degree. This is a percent. Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.

Disclaimer: These data are updated by the author and are not an official product of the Federal Reserve Bank of Cleveland.This project provides two sets of migration estimates for the major US metro areas. The first series measures net migration of people to and from the urban neighborhoods of the metro areas. The second series covers all neighborhoods but breaks down net migration to other regions by four region types: (1) high-cost metros, (2) affordable, large metros, (3) midsized metros, and (4) small metros and rural areas. These series were introduced in a Cleveland Fed District Data Brief entitled “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?"The migration estimates in this project are created with data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP). The CCP is a 5 percent random sample of the credit histories maintained by Equifax. The CCP reports the census block of residence for over 10 million individuals each quarter. Each month, Equifax receives individuals’ addresses, along with reports of debt balances and payments, from creditors (mortgage lenders, credit card issuers, student loan servicers, etc.). An algorithm maintained by Equifax considers all of the addresses reported for an individual and identifies the individual’s most likely current address. Equifax anonymizes the data before they are added to the CCP, removing names, addresses, and Social Security numbers (SSNs). In lieu of mailing addresses, the census block of the address is added to the CCP. Equifax creates a unique, anonymous identifier to enable researchers to build individuals’ panels. The panel nature of the data allows us to observe when someone has migrated and is living in a census block different from the one they lived in at the end of the preceding quarter. For more details about the CCP and its use in measuring migration, see Lee and Van der Klaauw (2010) and DeWaard, Johnson and Whitaker (2019). DefinitionsMetropolitan areaThe metropolitan areas in these data are combined statistical areas. This is the most aggregate definition of metro areas, and it combines Washington DC with Baltimore, San Jose with San Francisco, Akron with Cleveland, etc. Metro areas are combinations of counties that are tightly linked by worker commutes and other economic activity. All counties outside of metropolitan areas are tracked as parts of a rural commuting zone (CZ). CZs are also groups of counties linked by commuting, but CZ definitions cover all counties, both metropolitan and non-metropolitan. High-cost metropolitan areasHigh-cost metro areas are those where the median list price for a house was more than $200 per square foot on average between April 2017 and April 2022. These areas include San Francisco-San Jose, New York, San Diego, Los Angeles, Seattle, Boston, Miami, Sacramento, Denver, Salt Lake City, Portland, and Washington-Baltimore. Other Types of RegionsMetro areas with populations above 2 million and house price averages below $200 per square foot are categorized as affordable, large metros. Metro areas with populations between 500,000 and 2 million are categorized as mid-sized metros, regardless of house prices. All remaining counties are in the small metro and rural category.To obtain a metro area's total net migration, sum the four net migration values for the the four types of regions.Urban neighborhoodCensus tracts are designated as urban if they have a population density above 7,000 people per square mile. High density neighborhoods can support walkable retail districts and high-frequency public transportation. They are more likely to have the “street life” that people associate with living in an urban rather than a suburban area. The threshold of 7,000 people per square mile was selected because it was the average density in the largest US cities in the 1930 census. Before World War II, workplaces, shopping, schools and parks had to be accessible on foot. Tracts are also designated as urban if more than half of their housing units were built before WWII and they have a population density above 2,000 people per square mile. The lower population density threshold for the pre-war neighborhoods recognizes that many urban tracts have lost population since the 1960s. While the street grids usually remain, the area also needs su

By 2030, the middle-class population in Asia-Pacific is expected to increase from **** billion people in 2015 to **** billion people. In comparison, the middle-class population of sub-Saharan Africa is expected to increase from *** million in 2015 to *** million in 2030. Worldwide wealth While the middle-class has been on the rise, there is still a huge disparity in global wealth and income. The United States had the highest number of individuals belonging to the top one percent of wealth holders, and the value of global wealth is only expected to increase over the coming years. Around ** percent of the world’s population had assets valued at less than 10,000 U.S. dollars, while less than *** percent had assets of more than one million U.S. dollars. Asia had the highest percentage of investable assets in the world in 2018, whereas Oceania had the highest percentage of non-investable assets. The middle-class The middle class is the group of people whose income falls in the middle of the scale. China accounted for over half of the global population for middle-class wealth in 2017. In the United States, the debate about the middle class “disappearing” has been a popular topic due to the increase in wealth among the top billionaires in the nation. Due to this, there have been arguments to increase taxes on the rich to help support the middle class.

This geopackage contains sixteen layers representing data from a variety of landscape metrics used to analyze the landscape dynamics of the Greater Yellowstone Area. Their names, descriptions and categorization are as follows: Study Area - GYCC_GYA_AreaOfAnalysis. This polygon, as defined by the Greater Yellowstone Coordinating Committee (GYCC), represents the Greater Yellowstone Area (GYA) and serves as the primary area of analysis for most of the indicators described in the report. SonoranInstitute_AreaOfAnalysis. These polygons represent the 34 counties considered most influential in terms of regional land use characteristics and serve as a second area of analysis. Produced by the Sonoran Institute. Conservation Status - PADUS1_2. This feature layer includes polygons from the Protected Areas Database of the United States (PADUS) version 1.2, which originates from the US Geological Survey (USGS) Gap Analysis Program (GAP). The PAD-US is a geodatabase that illustrates and describes public land ownership, management and conservation lands nationally, including voluntarily provided privately protected areas. The lands included in PAD-US are assigned conservation measures that qualify their intent to manage lands for the preservation of biological diversity and to other natural, recreational and cultural uses; managed for these purposes through legal or other effective means. Population - Hydro_features. The polygons in this feature layer represent the water feature areas (for example, bays, glaciers, lakes, and swamps) of the Western half of the United States as determined by the National Atlas of the United States and the United States Geological Survey (USGS). CensusBlockGroups_1990 This feature class contains block group polygons, population totals and calculated population attributes for the 1990 Census. Population values come from the U.S. Census Bureau. CensusBlockGroups_2000. his feature class contains block group polygons, population totals and calculated population attributes for the 2000 Census. Population values come from the U.S. Census Bureau. CensusTracts_GYA_2010. This feature layer contains polygons representing census tracts in the GYA based on U.S. Census Bureau data from 2010. Source data, TIGER/Line Shapefile, 2010, 2010 state, Idaho, 2010 Census Tract State-based, originates from the U.S. Department of Commerce, U.S. Census Bureau, Geography Division. CensusBlockGroups_GYA_2010. This feature layer contains polygons representing census block groups in the GYA based on U.S. Census Bureau data from 2010. Source data, TIGER/Line Shapefile, 2010, 2010 state, Idaho, 2010 Census Block Group State-based, originates from the U.S. Department of Commerce, U.S. Census Bureau, Geography Division. Roads - Traffic_Points. This feature layer contains points representing locations of Automatic Traffic Count Stations (ATR) and annual average daily traffic counts using data from the Federal Highway Administration's (FHWA) Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS). Streets. This feature layer contains polylines that represent detailed streets, interstate highways, and major roads in and around the Greater Yellowstone Area. This data set is from the 2003 Tele Atlas Dynamap Transportation version 5.2 product. Major_Roads. This feature layer contains polylines that represent the major roads of the United States and Canada. These include interstates, inter-metropolitan area, and intra-state highways and major roads. Wildlife - MuleDeerHabitat. This feature layer contains polygons representing habitat ranges for mule deer within the GYA. GrizzlyHabitat_50sqkm. This feature layer contains polygons representing potential grizzly bear core areas at least 50 square kilmeters in size. The area of each polygon is included in the attributes table. Data originates from modeled grizzly bear core habitat as part of a wildlife movement corridor models by the Craighead Institute. GrizzlyHabitat_250sqkm. This feature layer contains polygons representing potential grizzly bear core areas at least 250 square kilometers in size. These core areas were used for the connectivity portion of the analysis. WolverineHabitat. This feature layer contains polygons representing wolverine habitat. UngulateMigration. This feature layer contains polylines representing large mammal migration routes for five ungulate species (elk, mule deer, bighorn sheep, moose, and pronghorn) in the Greater Yellowstone Ecosystem, as compiled from GIS data on migration route locations for Wyoming, Montana, and Idaho. Estimates of distribution were originally made by the Wildlife Conservation Society.

The data contains the total population for U.S. counties with a population of over 65,000 as reported in the U.S. Census American Community Survey 1-year Summary (B01001_001). Data will generally reflect the latest survey data available. Additional information includes fields with True/False flags indicating whether the county has a population over 1 million and half a million and whether the (Georgia) county is part of the Atlanta metropolitan statistical area, part of the core metropolitan area, a member of the Atlanta Regional Commission or part of the Metropolitan North Georgia Water Planning District.

This layer represents USDA Food Access Research Atlas data at the census tract geography. Low Income is defined as tracts with a poverty rate of 20% or higher, or tracts with median family income less than 80% of median family income of the state or metropolitan area. Low Access is defined as tracts where a significant number or share of residents is more than 1 mile (urban) or 10 miles (rural) from the nearest supermarket.http://www.ers.usda.gov/data-products/food-access-research-atlas/go-to-the-atlas.aspxFood accessLimited access to supermarkets, supercenters, grocery stores, or other sources of healthy and affordable food may make it harder for some Americans to eat a healthy diet. There are many ways to measure food store access for individuals and for neighborhoods, and many ways to define which areas are food deserts—neighborhoods that lack healthy food sources. Most measures and definitions take into account at least some of the following indicators of access:Accessibility to sources of healthy food, as measured by distance to a store or by the number of stores in an area.Individual-level resources that may affect accessibility, such as family income or vehicle availability.Neighborhood-level indicators of resources, such as the average income of the neighborhood and the availability of public transportation.In the Food Access Research Atlas, several indicators are available to measure food access along these dimensions. For example, users can choose alternative distance markers to measure low access in a neighborhood, such as the number and share of people more than half a mile to a supermarket or 1 mile to a supermarket. Users can also view other census-tract-level characteristics that provide context on food access in neighborhoods, such as whether the tract has a high percentage of households far from supermarkets and without vehicles, individuals with low income, or people residing in group quarters.Low-income neighborhoodsThe criteria for identifying a census tract as low income are from the Department of Treasury’s New Markets Tax Credit (NMTC) program. This program defines a low-income census tract as any tract where:The tract’s poverty rate is 20 percent or greater; orThe tract’s median family income is less than or equal to 80 percent of the State-wide median family income; orThe tract is in a metropolitan area and has a median family income less than or equal to 80 percent of the metropolitan area's median family income.Low-access census tractsIn the Food Access Research Atlas, low access to healthy food is defined as being far from a supermarket, supercenter, or large grocery store ("supermarket" for short). A census tract is considered to have low access if a significant number or share of individuals in the tract is far from a supermarket.In the original Food Desert Locator, low access was measured as living far from a supermarket, where 1 mile was used in urban areas and 10 miles was used in rural areas to demarcate those who are far from a supermarket. In urban areas, about 70 percent of the population was within 1 mile of a supermarket, while in rural areas over 90 percent of the population was within 10 miles (see Access to Affordable and Nutritious Food: Updated Estimates of Distance to Supermarkets Using 2010 Data). Updating the original 1- and 10-mile low-access measure shows that an estimated 18.3 million people in these low-income and low-access census tracts were far from a supermarket in 2010.Three additional measures of food access based on distance to a supermarket are provided in the Atlas:One additional measure applies a 0.5-mile demarcation in urban areas and a 10-mile distance in rural areas. Using this measure, an estimated 52.5 million people, or 17 percent of the U.S. population, have low access to a supermarket;A second measure applies a 1.0-mile demarcation in urban areas and a 20-mile distance in rural areas. Under this measure, an estimated 16.5 million people, or 5.3 percent of the U.S. population, have low access to a supermarket; andA slightly more complex measure incorporates vehicle access directly into the measure, delineating low-income tracts in which a significant number of households are located far from a supermarket and do not have access to a vehicle. This measure also includes census tracts with populations that are so remote, that, even with a vehicle, driving to a supermarket may be considered a burden due to the great distance. Using this measure, an estimated 2.1 million households, or 1.8 percent of all households, in low-income census tracts are far from a supermarket and do not have a vehicle. An additional 0.3 million people are more than 20 miles from a supermarket.For each of the first three measures that are based solely on distance, a tract is designated as low access if the aggregate number of people in the census tract with low access is at least 500 or the percentage of people in the census tract with low access is at least 33 percent. For the final measure using vehicle availability, a tract is designated as having low vehicle access if at least one of the following is true:at least 100 households are more than ½ mile from the nearest supermarket and have no access to a vehicle; orat least 500 people or 33 percent of the population live more than 20 miles from the nearest supermarket, regardless of vehicle access.Methods used to assess distance to the nearest supermarket are the same for each of these measures. First, the entire country is divided into ½-km square grids, and data on the population are aerially allocated to these grids (see Access to Affordable and Nutritious Food: Updated Estimates of Distance to Supermarkets Using 2010 Data). Then, distance to the nearest supermarket is measured for each grid cell by calculating the distance between the geographic center of the ½-km square grid that contains estimates of the population (number of people and other subgroup characteristics) and the center of the grid with the nearest supermarket.Once the distance to the nearest supermarket is calculated for each grid cell, the estimated number of people or housing units that are more than 1 mile from a supermarket in urban tracts, or 10 miles in rural census tracts, is aggregated at the census-tract level (and similarly for the alternative distance markers). A census tract is considered rural if the population-weighted centroid of that tract is located in an area with a population of less than 2,500; all other tracts are considered urban tracts.Food desertsThe Food Access Research Atlas maps census tracts that are both low income (li) and low access (la), as measured by the different distance demarcations. This tool provides researchers and other users multiple ways to understand the characteristics that can contribute to food deserts, including income level, distance to supermarkets, and vehicle access.Additional tract-level indicators of accessVehicle availabilityA tract is identified as having low vehicle availability if more than 100 households in the tract report having no vehicle available and are more than 0.5 miles from the nearest supermarket. This corresponds closely to the 80th percentile of the distribution of the number of housing units in a census tract without vehicles at least 0.5 miles from a supermarket (the 80th percentile value was 106 housing units). This means that about 20 percent of all census tracts had more than 100 housing units that were 0.5 miles from a supermarket and without a vehicle. This indicator was applied to both urban and rural census tracts.Overall, 8.8 percent of all housing units in the United States do not have a vehicle, and 4.2 percent of all housing units are at least 0.5 mile from a store and without a vehicle. Vehicle availability is defined in the American Community Survey as the number of passenger cars, vans, or trucks with a capacity of 1-ton or less kept at the home and available for use by household members. The number of available vehicles includes those vehicles leased or rented for at least 1 month, as well as company, police, or government vehicles that are kept at home and available for non-business use.Whether a vehicle is available to a household for private use is an important additional indicator of access to healthy and affordable food. For households living far from a supermarket or large grocery store, access to a private vehicle may make accessing these retailers easier than relying on public or alternative means of transportation.Group quarters populationUsers may be interested in highlighting tracts with large shares of people living in group quarters. Group quarters are residential arrangements where an entity or organization owns and provides housing (and often services) for individuals residing in these buildings. This includes college dormitories, military quarters, correctional facilities, homeless shelters, residential treatment centers, and assisted living or skilled nursing facilities. These living arrangements frequently provide dining and food retail solely for their residents. While individuals living in these areas may appear to be far from a supermarket or grocery store, they may not truly experience difficulty accessing healthy and affordable food. Tracts in which 67 percent of individuals or more live in group quarters are highlighted.General tract characteristicsPopulation, tract totalGeographic level: census tractYear of data: 2010Definition: Total number of individuals residing in a tract.Data sources: Data are from the 2012 report, Access to Affordable and Nutritious Food: Updated Estimates of Distances to Supermarkets Using 2010 Data. Population data are reported at the block level from the 2010 Census of Population and Housing. These data were aerially allocated down to ½-kilometer-square grids across the United States.Low-income tractGeographic level: census tractYear of data: 2010Definition: A tract with either a poverty rate of 20

Abstract

The 1998 Turkish Demographic and Health Survey (TDHS-98) is a nationally representative sample survey designed to provide information on fertility levels and trends, infant and child mortality, family planning, and maternal and child health. Survey results are presented at the national level, by urban and rural residence and for each of the five regions in the country.

The survey was fielded between August and November 1998. Hacettepe University Institute of Population Studies (HIPS) carried out the TDHS-98 in collaboration with the General Directorate of Mother and Child Health and Family Planning, Ministry of Health. Funding for the TDHS-98 was provided both by the U.S. Agency for International Development through the MEASURE/DHS+ program and United Nations Population Fund.

Interviews were carried out in 8,059 households, with 8,576 women, and with 1,971 husbands. All women at ages 15-49 who were present in the household on the night before the interview or who generally live in that household were eligible for the survey. In half of the selected households for women interview, husbands (of currently married eligible women), who were present in the household on the night before the interview or who generally live in that particular household were eligible husbands for the survey.

The 1998 Turkish Demographic and Health Survey (TDHS-98) is the latest in a series of national- level population and health surveys that have been conducted during the last thirty years in Turkey. The primary objective of the TDHS-98 is to provide data on fertility and mortality, family planning, materaal and child health, and reproductive health. The survey obtained detailed information on these issues from a sample of women in the reproductive ages (15-49) and from fl~e husbands of cun'ently married eligible women.

More specifically, the objectives of the TDHS were to: - Collect data at the national level that allow the calculation of demographic rates, particularly fertility and childhood mortality rates; Obtain information on direct and indirect factors that determine levels and trends in fertility and childhood mortality; - Measure the level of contraceptive knowledge and practice by method, region, and urban- rural residence; - Collect data on mother and child health, including innnunisations, prevalence and treatment of diarrhoea among children under five, antenatal care, assistance at delivery, and breastfeeding; - Measure the nutritional status of children under five and of their mothers using anthropometric measurements.

Geographic coverage

The 1998 Turkish Demographic and Health Survey (TDHS-98) is a nationally representative sample survey. Results are also presented by urban and rural residence and for each of the five regions in the country (West, South, Central, North and East).

Analysis unit

• Household
• Women age 15-49
• Children under five

Universe

The population covered by the 1998 DHS is defined as the universe of all women at ages 15-49 who were present in the household on the night before the interview were eligible for the survey. In half of the selected households for women interview, husbands of currently married eligible women, who were present in the household on the night before the interview or who usually lived in the household were eligible for the husband survey.

Kind of data

Sample survey data

Sampling procedure

The sample for tile TDHS-98 was designed to provide estimates of population and health indicators including fertility and mortality rates for the nation as a who/e, for urban and rural areas, and for tile five major regions of tile country (West, South, Central, North and East). A weighted, multi-stage, stratified cluster sampling approach was used in tile selection of the TDHS-98 sample.

The optimal distribution with a target sample size of I0,000 selected households was based on the provisional results of the 1997 General Population Count. Selection of the TDHS-98 sample was undertaken in three stages. Tile sampling units at tile first stage were tile settlements stratified by population size. The ti'ame for the selection of the primary sampling units (PSU) was prepared using the provisional results of the 1997 Population Count. The fi'ame was divided into two groups, one including those settlements with populations of more than 10,000 and the other including settlements with populations less than 10,000. The selection of the settlement in each group was carried out with probability proportional to size (1997 poptdatiou).

The second stage of selection required the selection of the assigned nnmber of clusters in each selected settlement. For the majority of the settlements (340 clusters), the selection of clusters was based on the household lists that were available from the 1995 Structure Schedules. The State Institute of Statistics (SIS) selected the clusters and provided to Hacettepe Institute of Population Studies a description of each selected cluster. Each cluster included approximately 100 households. For those settlements where SIS was not able to provide information (140 clusters), the lists of households were prepared in the field.

Following the selection of the secondary sampling units (SSUs), a household listing was prepared or updated for each SSU by the TDHS-98 listing teams. Using the household lists, a systematic random sample of fixed number of households (25 in clusters located in settlements over 10,000 and 15 in those less than 10,000) was chosen within each cluster for the TDHS-98. All women at ages 15-49 who were present in the household on the night before the interview were eligible for the survey. In half of the selected households for women interview, husbands of currently married eligible women, who were present in the household on the night before the interview or who usually lived in the household were eligible for the husband survey.

SAMPLE FRAME

Different criteria have been used to describe "urban" and "rural" settlements in Turkey. In the demographic surveys of the 1970s a population size of 2,000 was used to differentiate between urban and rural settlements. In the 1980s, this was increased to 10,00O and, in some surveys in the 1990s, to 20,000. A number of surveys used the administrative status of settlements in combination with population size for the purpose of differentiation.

The urban frame of the 1998 TDHS consisted of a list of provincial centres, district centres, and other settlements with populations larger than 10,000, regardless of administrative status. In turn, the rural frame consists of all district centres, subdistricts and villages not included iF the urban fi'ame. Initial information on these settlements was obtained from the preliminary results of 1997 Population Count. The preliminary results of 1997 Population Count provided a computerized list of all settlements (provincial and district centres, , subdistricts and villages) and their population. The population counts were taken from the cumulative enumeration forms for settlements, which were filled by supervisors during the Population Count.

STRATIFICATION

Currently Turkey is divided administratively into 80 provinces. This figure was 67 for a long time, with new provinces formed since the late 1980s, For purposes of selection in prior surveys in Turkey, these provinces have been grouped into five regions, as described in Chapter 1. This regional breakdown has been popularised as a powerful variable for understanding the demographic, social, cultural, and economic differences between different parts of the country. The five regions, West, South, Central, North, and East regions, include varying numbers of provinces.

One of tile priorities of the TDHS was to produce a sample design that was methodologically and conceptually consistent with the designs of previous demographic surveys carried out by the Hacettepe Institute of Population Studies. In surveys prior to the 1993, the five-region division of the country was used for stratification. In the 1993 TDHS, a more detailed stratification taking into account subregions was employed to obtain a better dispersion of file sample. The criteria for subdividing the five major regions into subregions were the infant mortality rates &each province, estimated from the 1990 Population Census using indirect techniques? Using the infant mortality estimates as well as geographic proximity, the provinces in each region were grouped into 14 subregions at the time of the 1993 TDHS. The sub-regional division developed during the 1993 TDHS was used in the 1998 survey.

SAMPLE ALLOCATION

The target sample size of 10,000 households was allocated among the five major divisions using the sampling error estimates from the TDHS-93 in combination with the power allocation technique with the ex- pectation that the target sample size would provide about 8,000 completed individual interviews. During the power allocation calculations, the aim was to keep the allocation as similar as possible to the 1993 TDHS. The optimal distribution (with power 0.4) among the five major regions is shown in Table B.I. For purposes of comparison, Table B.I also shows the allocation of the TDHS-93 sample and the allocation if the TDHS-98 sample had been distributed proportional to the size of the population in each region. To have an adequate representation of clusters within each of the five major regions, it was decided to select 25 households per standard urban segments (each consisting of 100 households) and 15 households per standard rural segment. It was also determined that 70 percent of the 10,000 households would be located in urban settlements and 30 percent in rural settlements.

SAMPLE SELECTION - SELECTION PROCEDURES

The

In 2025, the degree of urbanization worldwide was at 58 percent. North America, Latin America, and the Caribbean were the regions with the highest level of urbanization, with over four-fifths of the population residing in urban areas. The degree of urbanization defines the share of the population living in areas defined as "cities". On the other hand, less than half of Africa's population lives in urban settlements. Globally, China accounts for over one-quarter of the built-up areas of more than 500,000 inhabitants. The definition of a city differs across various world regions - some countries count settlements with 100 houses or more as urban, while others only include the capital of a country or provincial capitals in their count. Largest agglomerations worldwideThough North America is the most urbanized continent, no U.S. city was among the top ten urban agglomerations worldwide in 2023. Tokyo-Yokohama in Japan was the largest urban area in the world that year, with 37.7 million inhabitants. New York ranked 13th, with 21.4 million inhabitants. Eight of the 10 most populous cities are located in Asia. ConnectivityIt may be hard to imagine how the reality will look in 2050, with 70 percent of the global population living in cities, but some statistics illustrate the ways urban living differs from suburban and rural living. American urbanites may lead more “connected” (i.e., internet-connected) lives than their rural and/or suburban counterparts. As of 2021, around 89 percent of people living in urban areas owned a smartphone. Internet usage was also higher in cities than in rural areas. On the other hand, rural areas always have, and always will, attract those who want to escape the rush of the city.

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

How many people are on social media? Social media usage is one of the most popular online activities and in 2021, ** percent of the population in the United States had a social networking profile, representing a *** percent increase from the ** percent usage reach in the previous year. This equals approximately 223 million U.S. social media users as of 2020. Global social media accessAccording to estimates, the number of worldwide social media users reached *** billion in January 2021. The overall most popular social network based on active users is the American market leader Facebook. In January 2021, Facebook had some **** billion accounts, followed by YouTube and WhatsApp with roughly *** billion and *** billion users respectively. The regions with the highest penetration of social media users are Western and Northern Europe. Social media audiences in the United StatesAlthough knowing how many people use social media is a powerful indicator of the tremendous influence such websites and apps have in our day to day life, how people are using them and who these users are is also telling. A report on social media usage released in 2019 shows that among Americans, younger online audiences were more likely to use social networks than older generations. Social media users in the United States use different social networks for a wide range of purposes. In a February 2019 survey, Instagram was the top social network for viewing photos whereas Facebook was more popular for sharing content.

BACKGROUND:

The diamondback moth (DBM), Plutella xylostella (Lepidoptera: Plutellidae), is a notorious pest of cruciferous plants. In temperate areas, annual populations of DBM originate from adult migrants. However, the source populations and migration trajectories of immigrants remain unclear. Here, we investigated migration trajectories of DBM in China with genome-wide single nucleotide polymorphisms (SNPs) genotyped using double-digest RAD (ddRAD) sequencing. We first analyzed patterns of spatial and temporal genetic structure among southern source and northern recipient populations, then inferred migration trajectories into northern regions using discriminant analysis of principal components (DAPC), assignment tests and spatial kinship patterns.

RESULTS:

Temporal genetic differentiation among populations was low, indicating sources of recipient populations and migration trajectories are stable. Spatial genetic structure indicated three genetic clusters in the southern source populations. Assignment tests linked northern populations to the Sichuan cluster, and central-eastern populations to the South and Yunnan clusters, indicating that Sichuan populations are sources of northern immigrants and South and Yunnan populations are sources of central-eastern populations. First-order (full-sib) and second-order (half-sib) kin pairs were always found within populations, but about 35-40% of third-order (cousin) pairs were found in different populations. Closely related individuals in different populations were in about 35-40% of cases found at distances of 900 to 1500 km, while some were separated by over 2000 km.

CONCLUSION:

This study unravels seasonal migration patterns in the DBM. We demonstrate how careful sampling and population genomic analyses can be combined to help understand cryptic migration patterns in insects.

Methods Specimen collection and DNA extraction DBM were sampled from potential source population locations in the annual breeding area of southern China. DBM were collected from cabbage and oilseed rape fields, and all sampling was completed before the first observations of DBM in northern China between March and May 1, 2. In order to reduce the likelihood of sampling siblings within populations, third- and fourth-instar larvae of DBM were collected from about 20 sites at each sampling location, each at least 10 m apart. Putative immigrant male adults were collected in northern China by sex pheromone trapping before the presence of first-generation larvae. Trapping of male DBM was conducted in unplanted fields with no greenhouses within 500 m, to reduce the likelihood of trapping individuals overwintering in protected conditions. The distance between traps was at least 50 m. The development of one generation of DBM takes about 30 days in early spring 3. This strategy therefore restricted sampling of genetically related individuals to within three generations between source and recipient populations, and reduced the influence of genomic admixture between immigrants from different sources. This sampling was conducted in 2017 and again in 2018, to examine annual variation in migratory trajectories and temporal variation in population genetic structure. In total, samples were collected from 16 locations in 2017 and 17 locations in 2018, and in 2018 four locations were sampled across multiple months (Fig. 1, Table 1). Twenty individuals from each population (specimens collected at different times from the same location were considered as different populations) were used for genotyping. Genomic DNA for library preparation was extracted from individual specimens using DNeasy Blood and Tissue Kit (Qiagen, Germany). SNP genotyping The ddRAD libraries were prepared following a published protocol 4 for identifying SNPs. Briefly, 120 ng of extracted genomic DNA from each sample was digested by the restriction enzymes NlaIII and AciI (New England Biolabs, USA) 5. The 50 μL digestion reaction was run for 3 hours at 37 °C, followed by DNA cleaning using 1.5× volume of AMPure XP beads (Beckman Coulter, USA) instead of a heat kill step. Next, we ligated each sample to adapters barcoded with a combinatorial index at 16 °C overnight in a 40 μL ligation reaction, labeling each population with a 6-bp index and each individual with a unique 9-bp barcode. After ligation, we pooled uniquely barcoded samples into multiplexed libraries. Fragments between 380-540 bp were selected using BluePippin and a 2% gel cassette (Sage Sciences, USA). Finally, the pooled libraries were enriched with 12 amplification cycles on a Mastercycler Nexus Thermal Cycler (Eppendorf, Germany). PCR products were cleaned with 0.8× volume of beads. We used Qubit 3.0 (Life Invitrogen, USA) and Agilent 2100 Bioanalyzer (Agilent Technology, USA) to check the concentration and size distribution of enriched libraries, respectively. Pooled libraries were sequenced on an Illumina HiSeq 2500 platform to obtain 150-bp paired-end reads, at BerryGenomics Company (Beijing, China). The Stacks v2.3 pipeline 6 was used to call SNPs, linking to the DBM genome (GenBank assembly accession: GCA_000330985.1) as reference 7. FastQC v 0.11.5 was employed to assess read quality and check for adapter contamination 8. Sequence data was demultiplexed and trimmed using process_radtags in Stacks v2.3 6, 9. Low quality reads with a Phred score below 20 were removed as well as any reads with an uncalled base. Reads were trimmed to 140 bp in length. The remaining paired-end reads were aligned to the DBM genome 7 using Bowtie v2.3.5 10. Output reads for all individuals were imported into Stacks pipeline ref_map.pl to call SNPs, requiring a minimum of three identical reads to create a stack. SNPs were called using a maximum likelihood statistical model. Finally, we obtained a catalog with all possible loci and alleles. The exported loci were present in all populations, and in at least 75% of individuals per population. The exported SNPs for populations that were collected in both years were further filtered using the R package vcfR 11 and VCFtools v0.1.16 12 with the following criteria: SNPs with sequencing depth ≤ 3 and in the highest 0.1% depth were removed, as were SNPs with missingness in all samples ≥ 0.05 and those with minimum minor allele count ≤ 20. An additional data matrix was generated by retaining only SNPs separated by at least 500 bp, to reduce linkage among SNPs. Genetic diversity, population structure and assignment tests Global population differentiation was estimated using Weir and Cockerham’s FST with 99% confidence intervals (1000 bootstraps) in diveRsity version 1.9.90. Pairwise FST for all population pairs was estimated using GenePop version 4.7.2 13. Discriminant analysis of principal components (DAPC) was performed in the R package adegenet v2.1.1 14, with the optimal number of clusters determined by the Akaike information criterion (AIC). Assignment tests were performed in assignPOP v1.1.7 15. Source groups of ST (south) and SW (southwest, this group was divided into YN and SC groups in 2018) (see Table 1 and Fig. 1 for locations) were trained using the support vector machine algorithm to build predictive models. For training, we used either 25, 28, or 32 random individuals (2017 samples) or 13, 15 or 17 random individuals (2018 samples) from each group, and loci with the highest 60%, 80% or 100% FST values. Monte-Carlo cross-validation was performed by resampling each training set combination 1000 times. The ratio of assignment probability between the most-likely and second most-likely assigned groups was calculated for each individual 16. When an individual showed an assignment ratio smaller than 2 in more than 30% of the resampling analysis, it was considered unstable and removed in subsequent training. This allowed us to remove individuals from source populations that are not similar enough to other individuals in that source population, thus leaving a set of source populations each comprised of individuals distinctive from those in other populations. Immigrants from the CE (central) and NT (north) regions (see Table 1 and Fig. 1 for locations) were assigned to the trained groups using the support vector machine algorithm. Kinship analysis As a complement to assignment tests (but focusing on the individual level rather than the population level), we investigated spatial patterns of kinship within and between populations. Related individuals were identified following the method of Jasper, Schmidt, Ahmad, Sinkins and Hoffmann 17. First, Loiselle’s K was calculated for all individual pairs using SPAGeDi 18 . Kinship coefficients represent the probability that any allele scored in both individuals is identical by descent, with theoretical mean K values for each kinship category as follows: full‐siblings = 0.25, half-siblings = 0.125, full‐cousins = 0.0625, half‐cousins = 0.0313, second-cousins = 0.0156 and unrelated = 0. To allocate pairs of individuals to relatedness categories across three orders of kinship, maximum‐likelihood estimation in the program ML‐Relate 19 was used to identify first‐order (full‐sibling) and second‐order (half‐sibling) pairs. The K scores of pairs within the full‐sibling and half-sibling data sets were used to calculate standard deviations for these categories. Using the theoretical means and standard deviations of K, we randomly sampled 100,000 simulated K scores from each kinship category. In the initial pool of 40755 pairings (2017) and 89676 pairings (2018), ML‐Relate identified 33 (2017) and 36 (2018) full‐sibling and half‐sibling pairs. Assuming that the data contained twice as many first cousin (full and half) pairings as sibling (full and half) pairings, and twice as many second cousin pairings as first cousin pairings, final sampling distributions were developed as follows: 100,000 unrelated, 320 second-cousins, 80 full‐cousins, 80 half‐cousins, 40