This graph shows the population density in the federal state of Texas from 1960 to 2018. In 2018, the population density of Texas stood at 109.9 residents per square mile of land area.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined because of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard Census Bureau geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous.

The 2022 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

These are the data for the Demographic Profiles displayed on austintexas.gov/demographics. These profiles were published in 2025, but display data from 2023 and 2024. Most data are from the 2023 American Community Survey (the most recent available at the time of publication), but some data have other sources. All data come from the American Community Survey estimates except for: Total Population - City of Austin Planning Department (2023) (City and Council Districts only) Population Low-Moderate Income - Dept. of Housing and Urban Development LMISD Summary Data (5 year 2016-2020) Occupied Housing Units - City of Austin Planning Department (2023) (City and Council Districts only) Median Home Closing Price - Austin Board of Realtors (2024) Average Monthly Rent - ApartmentTrends.com by Austin Investor Interests (Q4 2024) Income Restricted Units - City of Austin Affordable Housing Inventory (March 2025) Housing Units - City of Austin Planning Department (2023)(City only) Population Density - Esri Updated Demographics (2024) (County, MSA, Council Districts) Daytime Population Density - Esri Updated Demographics (2024) (County, MSA, Council Districts) Population Density - Calculation derived from 2023 Population Estimates, City of Austin Demographics & Data Division (City only) Daytime Population Density - 2023 Population Estimates, City of Austin Demographics & Data Division (City only) Selected Land Use Percentages - City of Austin Land Use Inventory (2024) Transit Stops - Capital Metro (January 2025) City, County, and MSA data are 1-Year ACS estimates. Council Districts are 5-year ACS estimates. Some datapoints may not be available for all geographies. More information and links to these alternate sources, when available, can be found at austintexas.gov/demographics. These profiles are updated annually. City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

Urban areas comprise a densely-settled core of census blocks that meet minimum housing unit density and/or population density requirements. This includes adjacent territory containing non-residential urban land uses. To qualify as an urban area, the territory identified according to criteria must encompass at least 2,000 housing units or a population of at least 5,000. These areas were delineated by the U.S. Census Bureau following the 2020 Decennial Census. For additional information, see: https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html. For FAQs see: https://www2.census.gov/geo/pdfs/reference/ua/Census_UA_2020FAQs.pdf.

This database contains populations of argon 4p state population densities in a glow discharge plasma at a range of pressures and discharge voltages. Median as well as 5th and 95th percentile values are given.

These are the data for displayed in the Demographic Profiles displayed on austintexas.gov/demographics. These profiles were published in 2024, but display data from 2022 and 2023.

Most data are from the 2022 American Community Survey (the most recent available at the time of publication), but some data have other sources. All data come from the American Community Survey estimates except for:

Total Population - City of Austin Planning Department (2023) Population Low-Moderate Income - Dept. of Housing and Urban Development LMISD Summary Data (2022) Occupied Housing Units - City of Austin Planning Department (2023) Median Home Closing Price - Austin Board of Realtors (2023) Average Monthly Rent - Austin Investor Interests (Q4 2023) Income Restricted Units - City of Austin Affordable Housing Inventory Housing Units-City of Austin Planning Department (2023) Population Density - Esri Updated Demographics Daytime Population Density - Esri Updated Demographics Selected Land Use Percentages - City of Austin Land Use Inventory Transit Stops - Capital Metro (2023)

City, County, and MSA data are 1-Year ACS estimates. Council Districts are 5-year ACS estimates.

More information and links to these alternate sources, when available, can be found at austintexas.gov/demographics.

These profiles are updated annually.

City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

Vector polygon map data of city limits from across the State of Texas containing 2142 features.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

We conducted population and habitat assessments for Mountain Plovers in Texas during winters of 2019 and 2020. We used roadside surveys and distance-sampling to estimate bird density and calculate population totals for the study area, which included parts of five ecoregions (Chihuahuan Deserts, High Plains, Central Great Plains, Southern Texas Plains, Texas Blackland Prairies, and Western Gulf Coastal Plain). In 2019, we surveyed 103 transects along 3,032 km (1,884 mi) and, in 2020, we surveyed 152 transects along 4,985 km (3,098 mi). When driving along transects, we stopped every 3.2 km (2 mi) to assess habitat conditions (vegetation height, vegetation density, etc.) and land cover (National Land Cover Database categories).

This data set represents the average population density, in number of people per square kilometer multiplied by 10 for the year 2000, compiled for every MRB_E2RF1 catchment of selected Major River Basins (MRBs, Crawford and others, 2006). The source data set is the 2000 Population Density by Block Group for the Conterminous United States (Hitt, 2003).

The MRB_E2RF1 catchments are based on a modified version of the U.S. Environmental Protection Agency's (USEPA) RF1_2 and include enhancements to support national and regional-scale surface-water quality modeling (Nolan and others, 2002; Brakebill and others, 2011).

Data were compiled for every MRB_E2RF1 catchment for the conterminous United States covering covering New England and Mid-Atlantic (MRB1), South Atlantic-Gulf and Tennessee (MRB2), the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy (MRB3), the Missouri (MRB4), the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf (MRB5), the Rio Grande, Colorado, and the Great basin (MRB6), the Pacific Northwest (MRB7) river basins, and California (MRB8).

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Vector polygon map data of city limits from Houston, Texas containing 731 features.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

Model state variables and parameters.

This dataset consists of growth and yield data for each year when maize (Zea mays, L., also known as corn in the United States) was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Maize was grown for grain on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four square fields are themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Irrigation was by linear move sprinkler system in 1989, 1990, and 1994. In 2013, 2016, and 2018, two lysimeters and their respective fields (NE and SE) were irrigated using subsurface drip irrigation (SDI), and two lysimeters and their respective fields (NW and SW) were irrigated by a linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. The growth and yield data include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, ear mass (when present), kernel number, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on maize ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used by the Agricultural Model Intercomparison and Improvement Project (AgMIP), by OPENET, and by many others for testing, and calibrating models of ET that use satellite and/or weather data.Resources in this dataset:Resource Title: 1989 Bushland, TX, east maize growth and yield data. File Name: 1989_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: This dataset consists of growth and yield data for one of the seasons when maize was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Maize was grown for grain on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four square fields are themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Irrigation was by linear move sprinkler system in 1989, 1990, and 1994. In 2013, 2016, and 2018, two lysimeters and their respective fields (NE and SE) were irrigated using subsurface drip irrigation (SDI), and two lysimeters and their respective fields (NW and SW) were irrigated by a linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. The growth and yield data include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, ear mass (when present), kernel number, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. There are separate spreadsheets for the east (NE and SE) lysimeters and fields, and for the west (NW and SW) lysimeters and fields. The spreadsheets contain tabs for data and corresponding tabs for data dictionaries. Typically there are separate data tabs and corresponding dictionaries for plant growth during the season, crop growth stage, plant population, manual harvest from replicate plots in each field and from lysimeter surfaces, and machine (combine) harvest, An Introduction tab explains the tab names and contents, lists the authors, explains conventions, and lists some relevant references.Resource Title: 1990 Bushland, TX, east maize growth and yield data. File Name: 1990_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1990 East.Resource Title: 1994 Bushland, TX, east maize growth and yield data. File Name: 1994_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1994 East.Resource Title: 1994 Bushland, TX, west maize growth and yield data. File Name: 1994_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1994 West.Resource Title: 2013 Bushland, TX, west maize growth and yield data. File Name: 2013_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2013 West.Resource Title: 2016 Bushland, TX, east maize growth and yield data. File Name: 2016_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2016 East.Resource Title: 2016 Bushland, TX, west maize growth and yield data. File Name: 2016_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2016 West.Resource Title: 2018 Bushland, TX, west maize growth and yield data. File Name: 2018_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2018 West.Resource Title: 2013 Bushland, TX, east maize growth and yield data. File Name: 2013_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2013 East.Resource Title: 2018 Bushland, TX, east maize growth and yield data. File Name: 2018_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2018 East.

The Family Violence Prevention and Services Act of 1984 (FVPSA) provided funding, through the Office of Victims of Crime in the United States Department of Justice, for 23 law enforcement training projects across the nation from 1986 to 1992. FVPSA was enacted to assist states in (1) developing and maintaining programs for the prevention of family violence and for the provision of shelter to victims and their dependents and (2) providing training and technical assistance for personnel who provide services for victims of family violence. The National Institute of Justice awarded a grant to the Urban Institute in late 1992 to evaluate the police training projects. One of the program evaluation methods the Urban Institute used was to conduct surveys of victims in New York and Texas. The primary objectives of the survey were to find out, from victims who had contact with law enforcement officers in the pre-training period and/or in the post-training period, what their experiences and evaluations of law enforcement services were, how police interventions had changed over time, and how the quality of services and changes related to the police training funded under the FVPSA. Following the conclusion of training, victims of domestic assault in New York and Texas were surveyed through victim service programs across each state. Similar, but not identical, instruments were used at the two sites. Service providers were asked to distribute the questionnaires to victims of physical or sexual abuse who had contact with law enforcement officers. The survey instruments were developed to obtain information and victim perceptions of the following key subject areas: history of abuse, characteristics of the victim-abuser relationship, demographic characteristics of the abuser and the victim, history of law enforcement contacts, services received from law enforcement officers, and victims' evaluations of these services. Variables on history of abuse include types of abuse experienced, first and last time physically or sexually abused, and frequency of abuse. Characteristics of the victim-abuser relationship include length of involvement with the abuser, living arrangement and relationship status at time of last abuse, number of children the victim had, and number of children at home at the time of last abuse. Demographic variables provide age, race/ethnicity, employment status, and education level of the abuser and the victim. Variables on the history of law enforcement contacts and services received include number of times law enforcement officers were called because of assaults on the victim, number of times law enforcement officers actually came to the scene, first and last time officers came to the scene, number of times officers were involved because of assaults on the victim, number of times officers were involved in the last 12 months, and type of law enforcement agencies the officers were from. Data are also included on city size by population, city median household income, county population density, county crime rate, and region of state of the responding law enforcement agencies. Over 30 variables record the victims' evaluations of the officers' responsiveness, helpfulness, and attitudes.

Climate often drives ungulate population dynamics, and as climates change, some areas may become unsuitable for species persistence. Unraveling the relationships between climate and population dynamics, and projecting them across time, advances ecological understanding that informs and steers sustainable conservation for species. Using pronghorn (Antilocapra americana) as an ecological model, we used a Bayesian approach to analyze long-term population, precipitation, and temperature data from 18 subpopulations in the southwestern United States. We determined which long-term (12 and 24 months) or short-term (gestation trimester and lactation period) climatic conditions best predicted annual rate of population growth (λ). We used these predictions to project population trends through 2090. Projections incorporated downscaled climatic data matched to pronghorn range for each population, given a high and a lower atmospheric CO2 concentration scenario. Since the 1990s, 15 of the pronghorn subpopulations declined in abundance. Sixteen subpopulations demonstrated a significant relationship between precipitation and λ, and in 13 of these, temperature was also significant. Precipitation predictors of λ were highly seasonal, with lactation being the most important period, followed by early and late gestation. The influence of temperature on λ was less seasonal than precipitation, and lacked a clear temporal pattern. The climatic projections indicated that all of these pronghorn subpopulations would experience increased temperatures, while the direction and magnitude of precipitation had high subpopulation-specific variation. Models predicted that nine subpopulations would be extirpated or approaching extirpation by 2090. Results were consistent across both atmospheric CO2 concentration scenarios, indicating robustness of trends irrespective of climatic severity. In the southwestern United States, the climate underpinning pronghorn subpopulations is shifting, making conditions increasingly inhospitable to pronghorn persistence. This realization informs and steers conservation and management decisions for pronghorn in North America, while exemplifying how similar research can aid ungulates inhabiting arid regions and confronting similar circumstances elsewhere. Long-term data from annual aerial surveys of pronghorn subpopulations in Utah, Arizona, New Mexico, and western Texas were used to calculate annual rates of population growth (λ). When subpopulation-specific harvest and translocation data were available, population estimates for calculating λ were adjusted according to the following equation: λt = Nt/(Nt-1 - h - r + a), where λt is population change from time t-1 to t, Nt and Nt-1 are population estimates from current and previous surveys, respectively, h is number of pronghorn harvested, and r and a are number of individuals removed from and released into the population, respectively, through translocations. Only population estimates from surveys conducted in consecutive years were used to calculate λ. If λ = 2, the associated surveys were removed from analyses because λ would be considered to be derived from unreliable or unstandardized population estimates, resulting in biologically unrealistic population growth rates. Monthly climate data (precipitation [mm/day] and mean temperature [degrees C]) were from 14 x 14 km cells from pronghorn range in each subpopulation in Utah, Arizona, New Mexico, and western Texas. Means across grids were calculated to obtain monthly values of precipitation and temperature. Two realistic future global climate scenarios were compared; a lower (Representative Concentrations Pathways 4.5) and a high (Representative Concentrations Pathways 8.5) atmospheric CO2 concentration scenario. Standardized precipitation index for 3-, 6-, 12-, and 24-month periods were calculated from all available monthly precipitation data using program SPI SL 6 (National Drought Mitigation Center 2014). Monthly mean temperature, total precipitation, and mean SPI (3-, 6-, and 12-month periods) were summarized by important periods in an adult female pronghorn's annual reproductive cycle relative to peak fawning (i.e., early, mid-, and late gestation [3 months each] and lactation [4 months]). Mean temperature and total precipitation were also calculated for 12 and 24 months preceding each population survey. Historic pronghorn population trends in relation to temperature and precipitation were assessed using integrated Bayesian population models. All models included a covariate for density effect (i.e., population in the previous year). Precipitation and temperature model comparison sets were run separately, and each model set included a null model (i.e., only density covariate, no climate covariates). These top individual precipitation and temperature covariates were then combined in models (i.e., one precipitation and temperature covariate per model), and these combined models were run including a term for the interaction between precipitation and temperature using the following equation: ln(λt) = Alpha + Beta1XN[t-1] + Beta2Xprec + Beta3Xtemp + Beta4Xprec*temp. Projected climate data for each pronghorn subpopulation was used to predict λt for each year to 2090. An integrated modeling approach was used, whereby the best performing model climatic predictors from historic population trends for each pronghorn subpopulation was embedded in that subpopulation pronghorn population projection model.

This dataset consists of growth and yield data for each season when upland cotton [Gossympium hirsutum (L.)] was grown for lint and seed at the USDA-ARS Conservation and Production Research Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). In the 2000 through 2004, 2008, 2010, 2012, and 2020 seasons, cotton was grown on from one to four large, precision weighing lysimeters, each in the center of a 4.44 ha square field also planted to cotton. The square fields were themselves arranged in a larger square with four fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field were thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Cotton was grown on different combinations of fields in different years. When irrigated, irrigation was by linear move sprinkler system years before 2014, and by both sprinkler and subsurface drip irrigation in 2020. Irrigation protocols described as full were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Irrigation protocols described as deficit typically involved irrigation at rates established as percentages of full irrigation ranging from 33% to 75% depending on the year. The growth and yield data typically include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, boll mass (when present), lint mass, seed mass, final yield, and lint quality. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from only manual sampling on replicate plots in each field and lysimeters. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on cotton ET, crop coefficients, crop water productivity, and simulation modeling of crop water use, growth, and yield. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used for testing, and calibrating models of ET that use satellite and/or weather data. See the README for descriptions of each data file.

The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

This dataset consists of growth and yield data for each season when sorghum [Sorghum bicolor (L.)] was grown at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). In the 1988, 1991, 1993, 1997, 1998, 1999, 2003 through 2007, 2014, and 2015 seasons (13 years), sorghum was grown on from one to four large, precision weighing lysimeters, each in the center of a 4.44 ha square field also planted to sorghum. The square fields were themselves arranged in a larger square with four fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field were thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Sorghum was grown on different combinations of fields in different years. When irrigated, irrigation was by linear move sprinkler system years before 2014, and by both sprinkler and subsurface drip irrigation in 2014 and 2015. Irrigation protocols described as full were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Irrigation protocols described as deficit typically involved irrigation at rates established as percentages of full irrigation ranging from 33% to 75% depending on the year. The growth and yield data include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, head mass (when present), seed mass, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. Machine harvest yields are commonly smaller than hand harvest yields due to combine losses. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on sorghum ET, crop coefficients, crop water productivity, and simulation modeling of crop water use, growth, and yield. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used for testing, and calibrating models of ET that use satellite and/or weather data. See the README for descriptions of each data file.

This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Texas. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of Texas. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Texas. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7RV0KQH