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.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows the density of the Canadian population for 1951. The first map display Western provinces, while the second map concentrates on southern Ontario and the Maritimes. Only the most populous areas are covered. Population density is illustrated by denoting the number of inhabitants per square mile. It shows a significant difference in the population distribution across Canada, mainly in urban and metropolitan areas. The cities with greater inhabitants are clusters within Capital cities, and a even larger concentration south, near the U.S. border, in particular along ocean or inland coastlines.

This data set represents "water input," the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter, in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in ground water used for drinking. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 14 data sets (1 output data set and 13 input data sets) associated with the GWAVA-DW model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 13 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for > nitrate in ground water used for drinking (GWAVA-DW) > and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-dw_ffer > 2 confined manure (kg/hectare) gwava-dw_conf > 3 orchards/vineyards (percent) gwava-dw_orvi > 4 population density (people/km2) gwava-dw_popd > >Transport to Aquifer Factors > 5 water input (km2/cm) gwava-dw_wtin > 6 glacial till (yes/no) gwava-dw_gtil > 7 semiconsolidated sand aquifers gwava-dw_semc > (yes/no) > 8 sandstone and carbonate rocks gwava-dw_sscb > (yes/no) > 9 drainage ditch (km2) gwava-dw_ddit > 10 Hortonian overland flow gwava-dw_hor > (percent of streamflow) > >Attenuation Factors > 11 fresh surface water withdrawal gwava-dw_swus > for irrigation (megaliters/day) > 12 irrigation tailwater recovery (km2) gwava-dw_twre > 13 Dunne overland flow gwava-dw_dun > (percent of streamflow) > 14 well depth (meters) - "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Semiconsolidated sand aquifers" is the presence or absence of semiconsolidated sand aquifers. "Sandstone and carbonate rocks" is the presence or absence of sandstone and carbonate rock aquifers. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Hortonian overland flow" is infiltration excess overland flow estimated by TOPMODEL, in percent of streamflow. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Dunne overland flow" is saturation overland flow estimated by TOPMODEL, in percent of streamflow. "Well depth" is the depth of the well, in meters. Well depth was not compiled as a spatial data set. Well depth equals 50 meters for the model simulation being presented. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

Canada, with 3.33 people per square kilometre, has one of the lowest population densities in the world. In 2001, most of Canada's population of 30,007,094 lived within 200 kilometres of the United States (along Canada's south). In fact, the inhabitants of our three biggest cities -- Toronto, Montréal and Vancouver -- can drive to the border in less than two hours. Thousands of kilometres to the north, our polar region -- the Yukon, the Northwest Territories and Nunavut -- is relatively empty, embracing 41% of our land mass but only 0.3% of our population. An inset map shows in greater detail the Windsor-Québec Corridor where a high concentration of Canadians live.

This data set represents 1990 block group population density, in people per square kilometer, in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for nitrate in shallow > ground water (GWAVA-S) and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-s_ffer > 2 confined manure (kg/hectare) gwava-s_conf > 3 orchards/vineyards (percent) gwava-s_orvi > 4 population density (people/km2) gwava-s_popd > 5 cropland/pasture/fallow (percent) gwava-s_crpa > >Transport to Aquifer Factors > 6 water input (km2/cm) gwava-s_wtin > 7 carbonate rocks (yes/no) gwava-s_crox > 8 basalt and volcanic rocks (yes/no) gwava-s_vrox > 9 drainage ditch (km2) gwava-s_ddit > 10 slope (percent x 1000) gwava-s_slop > 11 glacial till (yes/no) gwava-s_gtil > 12 clay sediment (percent x 1000) gwava-s_clay > >Attenuation Factors > 13 fresh surface water withdrawal gwava-s_swus > for irrigation (megaliters/day) > 14 irrigation tailwater recovery (km2) gwava-s_twre > 15 histosol soil type (percent) gwava-s_hist > 16 wetlands (percent) gwava-s_wetl "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks. "Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Slope" is the soil surface slope, in percent times 1000. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Clay sediment" is the amount of clay sediment in the soil, in percent times 1000. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Histosol soil type" is the amount of histosols soil taxonomic order, in percent. "Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

Relationship Between Population Density of Individual Species and Patch Size.

This data set represents "water input," the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter, in the conterminous United States.

The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water.

Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation.

One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking.

This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006).

For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1).

Table 1.-- Parameters of nonlinear regression model for nitrate in shallow ground water (GWAVA-S) and corresponding input spatial data sets. [kg, kilograms; km2, square kilometers.]

Nitrogen Source Factors Data Set Name 1 farm fertilizer (kg/hectare) gwava-s_ffer 2 confined manure (kg/hectare) gwava-s_conf 3 orchards/vineyards (percent) gwava-s_orvi 4 population density (people/km2) gwava-s_popd 5 cropland/pasture/fallow (percent) gwava-s_crpa

Transport to Aquifer Factors 6 water input (km2/cm) gwava-s_wtin 7 carbonate rocks (yes/no) gwava-s_crox 8 basalt and volcanic rocks (yes/no) gwava-s_vrox 9 drainage ditch (km2) gwava-s_ddit 10 slope (percent x 1000) gwava-s_slop 11 glacial till (yes/no) gwava-s_gtil 12 clay sediment (percent x 1000) gwava-s_clay

Attenuation Factors 13 fresh surface water withdrawal gwava-s_swus for irrigation (megaliters/day) 14 irrigation tailwater recovery (km2) gwava-s_twre 15 histosol soil type (percent) gwava-s_hist 16 wetlands (percent) gwava-s_wetl

"Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare.

"Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare.

"Orchards/vineyards" is the percent of orchards/vineyards land cover classification.

"Population density" is 1990 block group population density, in people per square kilometer.

"Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications.

"Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter.

"Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks.

"Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks.

"Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers.

"Slope" is the soil surface slope, in percent times 1000.

"Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains.

"Clay sediment" is the amount of clay sediment in the soil, in percent times 1000.

"Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day.

"Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers.

"Histosol soil type" is the amount of histosols soil taxonomic order, in percent.

"Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications.

Reference cited:

Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

Whereas inhalation exposure to organic contaminants can negatively impact human health, knowledge of their spatial variability in the ambient atmosphere remains limited. We analyzed the extracts of passive air samplers deployed at 119 unique sites in Southern Canada between 2019 and 2022 for 353 organic vapors. Hierarchical clustering of the obtained data set revealed four archetypes of spatial concentration variability in the outdoor atmosphere, which are indicative of common sources and similar atmospheric dispersion behavior. “Point Source” signatures are characterized by elevated concentration in the vicinity of major release locations. A “Population” signature applies to compounds whose air concentrations are highly correlated with population density, and is associated with emissions from consumer products. The “Water Source” signature applies to substances with elevated levels in the vicinity of water bodies from which they evaporate. Another group of compounds displays a “Uniform” signature, indicative of a lack of major sources within the study area. We illustrate how such a data set, and the derived spatial patterns, can be applied to support the identification of sources, the quantification of atmospheric emissions, the modeling of air quality, and the investigation of potential inequities in inhalation exposure.

Based on the background of urbanization in China, we used the dynamic spatial panel Durbin model to study the driving mechanism of ozone pollution empirically. We also analyzed the spatial distribution of ozone driving factors using the GTWR. The results show that: i) The average annual increase of ozone concentration in ambient air in China from 2015 to 2019 was 1.68μg/m3, and 8.39μg/m3 elevated the year 2019 compared with 2015. ii) The Moran’s I value of ozone in ambient air was 0.027 in 2015 and 0.209 in 2019, showing the spatial distribution characteristics of "east heavy and west light" and "south low and north high". iii) Per capita GDP industrial structure, population density, land expansion, and urbanization rate have significant spillover effects on ozone concentration, and the regional spillover effect is greater than the local effect. R&D intensity and education level have a significant negative impact on ozone concentration. iv) There is a decreasing trend in the inhibitory effect of educational attainment and R&D intensity on ozone concentration, and an increasing trend in the promotional effect of population urbanization rate, land expansion, and economic development on ozone concentration. Empirical results suggest a twofold policy meaning: i) to explore the causes behind the distribution of ozone from the new perspective of urbanization, and to further the atmospheric environmental protection system and ii) to eliminate the adverse impacts of ozone pollution on nature and harmonious social development.

In 2008, the Quality Deer Management Association (QDMA) developed a map of white-tailed deer density with information obtained from state wildlife agencies. The map contains information from 2001 to 2005, with noticeable changes since the development of the first deer density map made by QDMA in 2001. The University of Minnesota, Forest Ecosystem Health Lab and the US Department of Agriculture, Forest Service-Northern Research Station have digitized the deer density map to provide information on the status and trends of forest health across the eastern United States. The QDMA spatial map depicting deer density (deer per square mile) was digitized across the eastern United States. Estimates of deer density were: White = rare, absent, or urban area with unknown population, Green = less than 15 deer per square mile, Yellow = 15 to 30 deer per square mile, Orange = 30 to 40 deer per square mile, or Red = greater than 45 deer per square mile. These categories represent coarse deer density levels as identified in the QDMA report in 2009 and should not be used to represent current or future deer densities across the study region. Sponsorship: Quality Deer Management Association; US Department of Agriculture, Forest Service-Northern Research Station; Minnesota Agricultural Experiment Station. Resources in this dataset:Resource Title: Link to DRUM catalog record. File Name: Web Page, url: https://conservancy.umn.edu/handle/11299/178246

Geospatial Analysis of Population Demographics and Traffic Density in MinneapolisIntroductionThis interactive web map provides a geospatial analysis of population distribution and traffic density for the city of Minneapolis, Minnesota. By integrating demographic data at the census tract level with real-time traffic information, the application serves as a critical tool for urban planning, transportation management, and sociological research.Data Visualization and SymbologyThe map employs distinct color schemes to represent the core datasets, allowing for intuitive visual analysis: Traffic Density: The city's road network is symbolized using a color gradient to indicate traffic volume. Segments rendered in deep red represent a high traffic density index, signifying areas of significant vehicular congestion. This transitions to a light yellow for segments experiencing lower traffic flow. Population Density: The demographic landscape is visualized using a green color ramp applied to census tract polygons. Dark green shades correspond to areas with a high population concentration, whereas lighter green shades denote regions with a lower population density. Analytical Utility and ApplicationsThe juxtaposition of these datasets reveals spatial correlations between residential density and transportation bottlenecks. This allows for data-driven inquiry into key urban challenges. The patterns visualized can help city planners and transportation authorities identify specific corridors where infrastructure investment could be most effective. Strategic improvements in these areas have the potential to optimize traffic flow, reduce commuter travel times, and decrease vehicle fuel consumption and emissions, thereby enhancing the overall sustainability and livability of Minneapolis.Interactive Features and Data ExplorationUsers are encouraged to engage with the map's interactive features for a deeper understanding of the data: Layers and Legend: Utilize the "Layers" and "Legend" tools to deconstruct the map's composition and understand the specific values associated with the color symbology. Pop-up Information: Click on individual census tracts or road segments to activate pop-up windows. These provide detailed attribute information, such as total population counts, demographic breakdowns, household income statistics, and spatial relationship metrics like nearest neighbor analysis. This application is built upon a foundational demographic data layer for Minneapolis and is enhanced by the integration of a dynamic traffic layer from the ArcGIS Living Atlas of the World.

These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number. The datasets are organized in the files by instrument type: PTA (particle tracking analysis), RMM (resonant mass measurement), ESZ (electrical sensing zone), and OTH (other techniques not covered in the three largest groups, including holographic particle characterization, laser diffraction, flow imaging, and flow cytometry). In the OTH group, the specific instrument type for each dataset is noted. Each instrument type (PTA, RMM, ESZ, OTH) has a dedicated file. Included in the data files for each dataset are: (1) the cumulative particle number concentration (PNC, (1/mL)); (2) the concentration distribution density (CDD, (1/mL·nm)) based upon five bins centered at each particle population peak diameter; (3) the CDD in higher resolution, varied-width bins. The lower-diameter bin edge (µm) is given for (2) and (3). Additionally, the PTA, RMM, and ESZ files each contain unweighted mean cumulative particle number concentrations and concentration distribution densities calculated from all datasets reporting values. The associated standard deviations and standard errors of the mean are also given. In the OTH file, the means and standard deviations were calculated using only data from one of the sub-groups (holographic particle characterization) that had n = 3 paired datasets. Where necessary, datasets not using the common bin resolutions are noted (PTA, OTH groups). The data contained here are presented and discussed in a manuscript to be submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.

Little research has been conducted on how internal migration of different ethnic groups, in and out of areas, contributes to population patterns. Research that has explored migration by ethnicity has compared 'white' with 'non-white' people to discuss patterns of segregation, ethnic concentration and majority population 'ghettos' or 'enclaves'. However, it is likely that there will be variations within the minority ethnic group that will offer important insights to these debates. This research will explore patterns of residential migration within different ethnic groups within England and Wales. Specifically, whether there is a tendency for people from different ethnic groups to move into or out of areas where their own ethnic group has a high or low density. These research questions will be explored through secondary analysis of the 2001 census using a specially commissioned table containing information about migration and ethnicity. Inflows and outflows of individual ethnic groups (as defined by the 2001 census) will be separately measured at the ward level (to assess local level migration). Patterns will be mapped using GIS software. This detailed analysis will help to establish if patterns of internal migration for different ethnic groups are related to the densities of their own and other groups. N/A - secondary data analysis of the census 2001

The mass segregation of stellar clusters could be primordial rather than dynamical. Despite the abundance of studies of mass segregation for stellar clusters, those for stellar progenitors are still scarce, so the question on the origin and evolution of mass segregation is still open. Our goal is to characterize the structure of the NGC 2264 molecular cloud and compare the populations of clumps and young stellar objects (YSOs) in this region whose rich YSO population has shown evidence of sequential star formation. We separated the Herschel column density map of NGC 2264 in three subregions and compared their cloud power spectra using a multiscale segmentation technique. We identified in the whole NGC 2264 cloud a population of 256 clumps with typical sizes of ~0.1pc and masses ranging from 0.08M_{sun} to 53M{sun}. Although clumps have been detected all over the cloud, the central subregion of NGC 2264 concentrates most of the massive, bound clumps. The local surface density and the mass segregation ratio indeed indicate a strong degree of mass segregation for the 15 most massive clumps, with a median Sigma_6 _three time that of the whole clumps population and Lambda_MSR about 8. We showed that this cluster of massive clumps is forming within a high-density cloud ridge, itself formed and probably still fed by the high concentration of gas observed on larger scales in the central subregion. The time sequence obtained from the combined study of the clump and YSO populations in NGC 2264 suggests that the star formation started in the northern subregion, that it is now actively developing at the center and will soon start in the southern subregion. Taken together, the cloud structure and the clump and YSO populations in NGC 2264 argue for a dynamical scenario of star formation.

This data set represents the presence or absence of basalt and volcanic rocks in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for nitrate in shallow > ground water (GWAVA-S) and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-s_ffer > 2 confined manure (kg/hectare) gwava-s_conf > 3 orchards/vineyards (percent) gwava-s_orvi > 4 population density (people/km2) gwava-s_popd > 5 cropland/pasture/fallow (percent) gwava-s_crpa > >Transport to Aquifer Factors > 6 water input (km2/cm) gwava-s_wtin > 7 carbonate rocks (yes/no) gwava-s_crox > 8 basalt and volcanic rocks (yes/no) gwava-s_vrox > 9 drainage ditch (km2) gwava-s_ddit > 10 slope (percent x 1000) gwava-s_slop > 11 glacial till (yes/no) gwava-s_gtil > 12 clay sediment (percent x 1000) gwava-s_clay > >Attenuation Factors > 13 fresh surface water withdrawal gwava-s_swus > for irrigation (megaliters/day) > 14 irrigation tailwater recovery (km2) gwava-s_twre > 15 histosol soil type (percent) gwava-s_hist > 16 wetlands (percent) gwava-s_wetl "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks. "Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Slope" is the soil surface slope, in percent times 1000. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Clay sediment" is the amount of clay sediment in the soil, in percent times 1000. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Histosol soil type" is the amount of histosols soil taxonomic order, in percent. "Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

From bacteria to metazoans, higher-density populations have lower per capita metabolic rates than lower-density populations. This relationship between density and metabolism was actually first proposed 100 years ago and was focused on spermatozoa but contemporary studies of sperm metabolism specifically assume that sperm concentration has no effect on metabolism. We did a systematic review to estimate the relationship between sperm aerobic metabolism and sperm concentration, for 203 estimates spanning 49 species, from protostomes to humans from 89 studies. We found strong evidence that per capita metabolic rates are concentration-dependent: both within- and among-species, sperm have lower metabolisms in dense ejaculates but increase their metabolism when diluted. On average, a 10-fold decrease in sperm concentration increased per capita metabolic rate by ~60%. Metabolic plasticity in sperm may be an adaptive response but this requires further testing., , , Manuscript Title: Per capita sperm metabolism is density-dependent Authors: Ashley Potter, Craig White, Dustin Marshall Date updated: Tue 28 February 2023

This file contains information for all the datasets in the manuscript and can be used with 'RDE-Data-Accessibility.Rmd' and 'Supplemental_24_02.docx'

NOTE: For all datasets, NA's indicate no data in the cell

Please contact Ashley Potter if there are any other questions or concerns ashley.ashpot.potter (at) gmail.com

Title of Dataset1: Density-dependent sperm metabolism

Csv name: RDE_data.csv

This dataset contains data on density-dependent sperm metabolism - how sperm metabolism changes across density

Description of the Data and file structure

Column names and descriptions

• Species
• Diluent_cat2: Diluent used to dilute sperm to a known concentration
• Phyla
• Class
• Order
• Family
• LNConc: actual sperm concentration (sperm per ml) that was used when metabolism was measured - [natural log transfor...

This dataset summarizes the apatite fission-track (AFT) data for 24 samples and zircon fission-track (ZFT) data for 11 samples. Single-grain data for AFT and ZFT are presented in Table S4 and Table S5, respectively. AFT and ZFT data were acquired between 2020 and 2021 using the external detector and zeta-calibration techniques at the University of Tübingen, Germany. a: Sample name with a=apatite or z=zirconb: Apatite fission-track (AFT) or zircon fission-track (ZFT)c: Number of grains dated for the sampled: Ns: number of spontaneous tracks, Ni: number of induced trackse: Mean uranium concentration of the sample with 1 standard deviation (1SD)f: Chi-squared probability (if >0.05, the sample passes the chi-squared test and grain ages are considered to be of the same kinetic population)g: Effective track density for dosimeter glass (with uranium concentration of 15 ppm for AFT and 50 ppm for ZFT) with 1 standard error (1SE)h: Number of tracks counted for 𝜌di: Zeta factor with 1 standard error (1SE) j: Mean sample Dpar (maximum diameter of fission-track etch figure parallel to the c-axis) with 1 standard deviation (1SD)

Temperature and food density are the most important factors influencing the population dynamics of rotifers. In the present study, the effects of temperature and food concentration on the developmental durations, egg ratio, and life-table demography in Brachionus angularis and Keratella valga were studied at four temperatures (15 °C, 20 °C, 25 °C and 30 °C) and four food levels (0.5 × 106, 1.0 × 106, 2.0 × 106 and 3.0 × 106 cells/mL Scenedesmus obliquus). The results showed significant effects of both temperature and food concentration, independently and interactively on the embryonic development (ED), juvenile period (JP), average lifespan (LS), generation time (T) and intrinsic rate of population increase (rm) in B. angularis, while the ED, life expectancy at hatching (e0), LS, T and rm in K. valga. In all conditions, the number of eggs per female and rm in B. angularis were higher than those in K. valga. These results suggested that B. angularis might be more suitable to mass culture in aquaculture than K. valga, and a potential prey for fish larvae in freshwater aquaculture.

This data release contains 15 datasets and associated metadata of watershed characteristics and data related to stream water quality and constituent load estimation for 15 study watersheds in Gwinnett County, Georgia. Dataset periods vary but range within 2000 to 2021. The 15 datasets are organized as individual child items. The data release includes three Geographic Information System shapefiles: (1) 01: Watersheds shapefile for the 15 study watersheds in Gwinnett County, Georgia; (2) 02: Stormwater drainage areas shapefile for the 15 study watersheds in Gwinnett County, Georgia, August 2021; and (3) 03: 200-foot stream buffer shapefiles for 15 watersheds in Gwinnett County, Georgia. The data release includes four comma separated value (csv) format datasets related to watershed characteristics: (1) 04: National Land Cover Database land cover at 15 watersheds in Gwinnett County, Georgia from 2001 to 2019; (2) 05: Impervious areas within 15 watersheds in Gwinnett County, Georgia from 2000 to 2020; (3) 06: Property parcel building construction dates and densities in 15 watersheds in Gwinnett County, Georgia in 2021; and (4) 07: Population density in 15 watersheds in Gwinnett County, Georgia from 2000 to 2020. The data release also includes the following eight csv format datasets related to stream water quality and constituent load estimation: (1) 08: Daily average stream base flow at 14 watersheds in Gwinnett County, Georgia for water years 2002-2020; (2) 09: Streamwater quality assurance sample results for 19 water-quality constituents in 15 watersheds in Gwinnett County, Georgia for years 2000-2020; (3) 10: Laboratory standard reference samples for the Gwinnett County, Georgia study for years 2014-2020; (4) 11: Streamwater sample constituent concentration outliers from 15 watersheds in Gwinnett County, Georgia for water years 2003-2020; (5) 12: Model calibration data for fitting regression models used to estimate streamwater loads for 12 constituents in 13 watersheds in Gwinnett County, Georgia for water years 2003-2020; (6) 13: Models coefficients and statistics for regression models used to estimate streamwater loads for 12 water-quality constituents in 13 watersheds in Gwinnett County, Georgia for water years 2003-2020 (includes 488 portable document format files (pdf) with reports and plots for evaluating model fits); (7) 14: LOADEST estimation dataset used to estimate streamwater loads for 12 constituents in 13 watersheds in Gwinnett County, Georgia for water years 2003-2020; and (8) 15: Streamwater load and yield estimates for 12 constituents in 13 watersheds in Gwinnett County, Georgia for water years 2003-2020.

This dataset contains simulation output from seven fire-enabled vegetation models from a historical reference simulation and sensitivity simulations with individual forcing factors kept constant. These forcing variables are: climate, lightning, population density, land use, and atmospheric CO2 concentration. The dataset also includes the processing and analysis scripts. This work is part of the fire model intercomparison project (FireMIP).