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

This data is part of the Regional Environmental Vulnerability Assessment (ReVA) in USEPA Region 3. This variable was created as part of a set of indicators that demonstrate links between the condition of natural areas and human concerns and that quantify dependencies on resources. More information about these resources, including the variables used in this study, may be found here: https://edg.epa.gov/data/Public/ORD/NERL/ReVA/ReVA_Data.zip.

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.

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.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Virginia Coast Reserve (VCR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Virginia Coast Reserve (VCR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

The City needed geographical area definitions that were homogenous and non-political. The preexisting Neighborhoods feature class was defined to maintain homogenous areas of the City, but they were determined to be too discrete and numerous. Pertaining to size as well, it was believed that the geographical areas should not be so large, as to group together areas of the City that were dissimilar in character. Of particular importance, it was also a requirement that NSA geography was designed to permit analysis using Census data.It was decided by the Planning Dept that adhering to Census Block Groups was the best approach. It was also determined that attempts to approximate the Planning Districts would also be beneficial. The approach to defining the NSAs was as follows: a) 2010 Census Block Groups were merged together to create each individual NSA, b) they were grouped in ways to maximize the ability to share boundaries with existing Planning Districts were ever possible. While most NSAs lie almost entirely within one Planning District, some NSAs are pretty equally split between two planning districts (notably D-1). In the case of D-1, PDR arbitrarily decided to put it with the other ‘Downtown’ NSAs.The identification/naming of the NSAs was based upon the Planning Districts they most corresponded to, along with a sequential numbering assignment. Most NSA lie almost entirely within one Planning District, and where named from that Planning District. Names starting with "NO" are mostly in the North planning district; "NW" are mostly in the Near West planning district; "BR" are mostly in the Broad Rock planning district, etc... There’s no significance for the number following the planning district lettering used by NSAs (NO-1, NO-2, NO-3, etc). The number was just randomly assigned to further uniquely define the area subdivided within the Planning District, and has no relationship in terms to square area, population density, or anything.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Coweeta (CWT) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

Step by step instructions to calculating most probable absolute population density and its 95% confidence bounds.

The 2016 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.

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 generalized boundaries for counties and equivalent entities are as of January 1, 2010.

EPA Intelligent Dasymetric Mapping (IDM) ToolboxThe Intelligent Dasymetric Mapping (IDM) Toolbox is available to download based on the version of ArcGIS software implemented.The IDM Toolbox uses ArcPy and arcpy.da functionality. This version requires ArcGIS 10.3 or higher. The ArcPy toolbox contains a number of scripts that assist preparing vector population and raster ancillary datasets for intelligent dasymetric mapping, performs the dasymetric calculations, and then generates a floating point output raster of revised population density. Please see the documentation in the zip file for more information on the individual tools.You may find more information by the EPA about this data and the toolbox here: https://www.epa.gov/enviroatlas/dasymetric-toolboxTO DOWNLOAD: simply click on the "Open" button at the top right to start the 2GB download of the zip file. Or, you may go directly to the EPA's FTP download site here: https://edg.epa.gov/data/public/ORD/EnviroAtlas/National/ConterminousUS/ and download the "dasymetric_us_20160208.zip" file.

The 2015 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. 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.

"The Virginia Geographic Information Network (VGIN) acquired the Virginia Base Mapping Program (VBMP) aerial photography through funding provided by the E911 Services Board. The photography was captured in the spring 00 (during the leaf-off season). This is a statewide product. The aerial photography was initially captured at 1- or -foot resolution (contingent on local population density) in true color. In addition, some localities opted up for a 6-inch-resolution product. The data set provided on the DVD is a 1-meter resampled product. It is available in Virginia Lambert Conformal Conic (a customized projection developed by VDOT; see projection information in Appendix A). Note that areas associated with military bases and other points of national interest have been resampled at 5-meter resolution. The imagery is stored in tiles that measure ~3 miles on each side. Additional information on the VBMP aerial photography program and other VBMP data products available in www.vgin.virginia. gov/VBMP/VBMPHandbook_r2.pdfFor more information on this data refer to the supplemental metadata pdf found at: https://secure-archive.gis.vt.edu/gisdata/public/UnitedStates/Virginia/VCE_2002_metadata/METADATA.pdf "

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Coweeta (CWT) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Virginia. 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 Virginia. 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 Virginia. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7CJ8BG0; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Virginia. 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 Virginia. 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 Virginia. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7CJ8BG0

This series measures the prevalence and correlates of drug use in the United States. The surveys are designed to provide quarterly, as well as annual, estimates. Information is provided on the use of illicit drugs, alcohol, anabolic steroids, and tobacco among members of United States households aged 12 and older. Questions include age at first use, as well as lifetime, annual, and past-month usage for the following drug classes: marijuana, inhalants, cocaine, hallucinogens, heroin, alcohol, tobacco, and nonmedical use of psychotherapeutics. Respondents were also asked about problems resulting from their use of drugs, alcohol, and tobacco, their perceptions of the risks involved, insurance coverage, and personal and family income sources and amounts. Demographic data include sex, race, ethnicity, educational level, job status, income level, household composition, and population density. This study has 1 Data Set.

Human development has major implications for wildlife populations. Urban-exploiter species can benefit from human subsidized resources, whereas urban-avoider species can vanish from wildlife communities in highly developed areas. Therefore, understanding how the density of different species varies in response to landcover changes associated with human development can provide important insight into how wildlife communities are likely to change and provide a starting point for predicting the consequences of those changes. Here, we estimated the population density of five common mesocarnivore species (coyote (Canis latrans), bobcat (Lynx rufus), red fox (Vulpes vulpes), raccoon (Procyon lotor), and Virginia opossum (Didelphis virginiana)) along an urban to rural gradient in the greater Fayetteville Area, Northwest Arkansas, USA between November 2021, and March 2022. At each study site, we applied the Random Encounter Model (REM) to data from motion-triggered cameras to calculate the density of our five focal species. Coyote density ranged from 0–3.47 with a mean of 0.4 individuals/km2. Raccoon density ranged from 0–93.26 with a mean of 4.2 individuals/ km2. Bobcat density ranged from 0–8.87 with a mean of 0.33 individuals/km2. Opossum density ranged from 0–27.35 with a mean of 0.76 individuals/km2. Red fox density ranged from 0–0.73, with a mean of 0.02 individuals/km2. We used generalized linear models to evaluate the density of each species against environmental and anthropogenic variables. Coyotes and raccoons occurred in the greatest densities in areas with high anthropogenic noise levels, suggesting that both species are synanthropic and able to co-exist in areas of high human activity. Alternatively, Virginia opossum and red fox densities were greatest in open, developed areas (lawns, golf courses, cemeteries, and parks) and were absent (red fox) or rare (opossum) in natural areas. We found no evidence that bobcat density varied along the urban to rural gradient studied, but this lack of evidence may have been driven by the small spatial scale of many of our sites in relation to space needs of this wide-ranging species. The density estimates we report based on game camera data of unmarked animals were consistent with reports from the literature for these same species derived from traditional methods, providing additional support to the REM as a viable, non-invasive method to calculate density of unmarked species. Methods This data was collected through camera traps set between November 1, 2021, and March 14, 2022. Cameras were set at 12 study sites in the Ozark Mountain Ecoregion, Northwest Arkansas, USA. Sites were chosen to represent a continuum of human activity and ranged from 2km to 60km from downtown Fayetteville, Arkansas. Camera trap images were sorted using Timelapse 2.0 software. Detections were sorted into 5-minute "episodes", and each episode was treated as a single detection to avoid double counting individuals. To estimate the density (D) of our five focal species from game camera detections, we applied the Random Encounter Model (REM) equation, where y refers to the total detections of each animal per camera, and t is the total trap nights in hours (measure of trapping effort). V is the day range of each species, referring to how far an animal travels in a 24-hour period. We used published day range estimates for each species and used the median day range value for each species from all reported estimates to parameterize our models. Values for the detection radius (r), and detection angle (θ) were collected for each camera in the field through walk tests. A walk test entailed walking directly towards each camera to calculate detection radius and from each side at 5m from the camera to calculate detection angle in degrees. Detection was determined by whether or not the detection light was triggered on the camera during each walk test. The detection angle was later converted to radians for density calculations. To assess which landcover variables most influenced the density of each focal species, we used an iterative approach to assemble 31 Generalized Linear Mixed Models (GLMM) with additive effects using r programming and the “lme4” and “AICcmodavg” packages for five predictor variables: HUD, noise, distance to water, and developed open, including a global model (all variables with random effect) and a null model (only random effect). We used study site as a random effect in each model. The zero inflation in the data was accounted for by using a gamma distribution in all models. We then used AICc selection criteria with an a priori cutoff of two for the ∆AIC delta value.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Coweeta (CWT) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

This study investigates the population density of meiofauna on the intertidal mudflats of the Tamar estuary in Cornwall. Monthly core samples were collected and analysed for meiofaunal specimens. The dataset contains a species list of the organisms found and comparisons in the size of the organisms and the density they were found at the various sample sites along the estuary.

Background Gene expression is affected by population density. Cell density is a potent negative regulator of cell cycle time during exponential growth. Here, we asked whether SV40 large T antigen (Tag) levels, driven by two different promoters, changed in a predictable and regular manner during exponential growth in clonal astrocyte cell lines, immortalized and dependent on Tag.

   Results
   Expression and cell cycle phase fractions were measured and correlated using flow cytometry. T antigen levels did not change or increased during exponential growth as a function of the G1 fraction and increasing cell density when Tag was transcribed from the Moloney Murine Leukemia virus (MoMuLV) long terminal repeat (LTR). When an Rb-binding mutant T antigen transcribed from the LTR was tested, levels decreased. When transcribed from the herpes thymidine kinase promoter, Tag levels decreased. The directions of change and the rates of change in Tag expression were unrelated to the average T antigen levels (i.e., the expression potential).


   Conclusions
   These data show that Tag expression potential in these lines varies depending on the vector and clonal variation, but that the observed level depends on cell density and cell cycle transit time. The hypothetical terms, expression at zero cell density and expression at minimum G1 phase fraction, were introduced to simplify measures of expression potential.