These data contain selected census tract level demographic indicators (estimates) from the 2015-2019 American Community Survey representing the population density by square mile (land area).

This graph shows the population density in the federal state of Colorado from 1960 to 2018. In 2018, the population density of Colorado stood at ** 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.

We investigated population dynamics in chorus frogs (Pseudacris maculata) relative to extrinsic (air temperatures and snowpack) and intrinsic (density dependence) characteristics at 2 sites in Colorado, USA. We used capture--mark-recapture (cmr) data (i.e., 1 or 0, provided here) and a Bayesian model framework to assess our a priori hypotheses about interactions among covariates and chorus frog survival and population growth rates. Files include: Cameron_Lily_cmr_NOV2020.csv, Cameron_Matthews_cmr_NOV2020.csv, and Cameron_covariates_NOV2020.csv. Data associated with paper by Kissel et al. 2021.

Feature class representing retail alcohol outlet density at the census tract level developed directly from address information from liquor licensee lists that were obtained from the Colorado Department of Revenue-Liquor Enforcement Division (DOR-LED). This file was developed for use in activities and exercises within the Colorado Department of Public Health and Environment (CDPHE), including the Alcohol Outlet Density StoryMap. CDPHE nor DOR-LED are responsible for data products made using this publicly available data. It should be stated that neither agency is acting as an active data steward of this map service/geospatial data layer at this point in time. This dataset is representative of Colorado licensing data gathered in January 2024. The data file contains the following attributes:FIPSTract Name Tract FIPS StateCountyLand Area Square Miles (Area of Land in Square Miles)Water Area SquareMiles (Area of Water in Square Miles)Population Total (Total Population as estimated in ACS 2018-2022)Percent Race White (Percent of population identifying as White as estimated in ACS 2018-2022) Percent Race African American Percent (Percent of population identifying as African American as estimated in ACS 2018-2022)Race American Indian Alaskan Native (Percent of population identifying as American Indian or Alaskan Native as estimated in ACS 2018-2022)Percent Race Asian (Percent of population identifying as Asian as estimated in ACS 2018-2022)Percent Race NHawaiian OPI (Percent of population identifying as Native Hawaiian or Pacific Islander as estimated in 2018-2022)Percent Race Other (Percent of population identifying as another race as estimated in 2018-2022)Percent Ethnicity Hispanic Latino (Percent of population identifying as Hispanic or Latino as estimated in 2018-2022)Percent Ethnicity Not Hispanic or Latino (Percent of population identifying as not Hispanic or Latino as estimated in 2018-2022)Percent Race Minority Race or Hispanic Latino (Percent of population made up of a Race and/or Ethnicity other than White, Non-Hispanic)Percent Population over 24 Years No HS Diploma (Percent of population over 24 years old without a High School Diploma as estimated in 2018-2022)Frequency All Retail Outlets 2024 (All retail alcohol outlets from January 2024)Average Distance Between Outlets in Meters (Average distance in Meters between an alcohol outlet and its nearest neighboring outlet)Frequency Off Premises Outlets 2024 (All Off-premises retail alcohol outlets from January 2024)Frequency On Premises Outlets 2024 (All On-premises retail alcohol outlets from January 2024)Rate Total Outlets per Square Mile (Rate of all retail alcohol outlets per square mile)Rate Total Outlets per 1,000 Residents (Rate of all retail alcohol outlets per 1,000 residents)Rate On Premises Outlets per Square Mile (Rate of On-premises retail alcohol outlets per square mile)Rate Off Premises Outlets per Square Mile (Rate of On-premises retail alcohol outlets per square mile)Rate On Premises Outlets per 1,000 Residents (Rate of on-premises retail alcohol outlets per 1,000 residents)Rate Off Premises Outlets per 1,000 Residents (Rate of off-premises retail alcohol outlets per 1,000 residents)Average Distance Between Outlets in Miles (Average distance in Miles between an alcohol outlet and its nearest neighboring outlet)

Map containing historical census data from 1900 - 2000 throughout the western United States at the county level. Data includes total population, population density, and percent population change by decade for each county. Population data was obtained from the US Census Bureau and joined to 1:2,000,000 scale National Atlas counties shapefile.

This HYDROSHARE link contains the data used for the research entitled: "Integrated Water Management Under Different Water Rights Institutions and Population Patterns: Methodology and Application". In this article, we develop a methodology to evaluate how population location under alternative water institutions and climate scenarios impacts water demands, shortages, and derived economic values. We apply this methodology to the South Platte River Basin (SPRB) in Northeastern Colorado under three scenarios with ~1,800 simulations. Results suggest that while water rights institutions have a negligible impact on total volumetric shortages relative to climate change, they have substantial distributional and economic implications. Results also suggest that continuous population growth in upstream cities yields the lowest water shortages if per capita use decreases with urbanization. However, if we assume that per capita demands do not decrease with population density, an equal distribution of population to upstream and downstream regions yields the lowest water shortage and highest economic value. These findings indicate the need that planning efforts must account for return flows and development patterns throughout a watershed in order to reduce water shortages and promote economic prosperity. Questions should be directed to "ahmed.gharib24@alumni.colostate.edu".

Future county population was based on projections for 2100 from the Spatially Explicit Regional Growth Model (SERGoM; Theobald 2005). SERGoM simulates population based on existing patterns of growth by census block, groundwater well and road density, and transportation distance to urban areas, while constraining the pattern of development to areas outside of protected areas and urban areas (Theobald 2005). The dataset here is a projection for a “baseline” growth scenario that assumes a similar trajectory to that of current urban growth (Bierwagen et al. 2010). SERGoM accuracy is estimated as 79–99% when compared to 1990 and 2000 census data, with the accuracy varying by urban/exurban/rural categories and increasing slightly with coarser resolution (Theobald 2005). The accuracy of future model predictions with different economic scenarios is most sensitive to fertility rates, which are subject to cultural change, economic recessions, and the current pattern of lands protected from development (Bierwagen et al. 2010). Bierwagen, B. G., D. M. Theobald, C. R. Pyke, A. Choate, P. Groth, J. V. Thomas, and P. Morefield. 2010. National housing and impervious surface scenarios for integrated climate impact assessments. Proceedings of the National Academy of Sciences of the United States of America 107:20887-20892. Theobald, D. M. 2005. Landscape patterns of exurban growth in the USA from 1980 to 2020. Ecology and Society 10: article 32.

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 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.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83392 Carnivores are among the most conspicuous, charismatic and economically important mammals in shortgrass steppe, yet relatively is little is known about their populations or of the ecological factors that determine their distribution and abundance, in part because densities tend to be low. Mammalian carnivores represent the top predators in grassland food webs, consuming rodents, rabbits, young ungulates and other small vertebrates. In addition, shortgrass steppe is the primary habitat of the swift fox (Vulpes velox), a species of special conservation concern throughout most of its range. Fox populations are thought to be limited by predation from coyotes (Canis latrans), the most common carnivore in these grasslands and a species of interest, both for its ecological roles and well as a target species for human exploitation, ie hunting and predator control. In 1994, we implemented a low-intensity sampling scheme to monitor long-term changes in relative abundance of mammalian carnivores and help us examine interactions between these predators and their small mammal prey, including rodents and rabbits. We estimated relative abundance of carnivores using scat surveys along a fixed route. Four times each year (January, April, July, October), we drove a 32-km route consisting of pasture two-track and gravel roads on the CPER. We first drove the route to remove all scats (‘PRE-census’); we then returned ~14 d later and counted the number of scats deposited on the route (‘CENSUS’). We recorded the species that deposited the scat and estimated the scat age based on external appearance (4 categories). Beginning in 1997, we recorded the vegetation (habitat) type and topographic position of all scat locations to describe habitat use. Latrines are indicated by locations containing multiple scats. We used the ‘CENSUS’ data to calculate a scat index, defined as the number of scats deposited per km of road per night. The scat index can be used to estimate population density using equations for coyotes (Knowlton 1982) and swift foxes (Schauster et al. 2002) that described the rate of scat deposition from surveys where density was known. To estimate density and compare trends among seasons and years, we omitted scats collected along the 8.3 km of the route that occurred on gravel county roads. These roads are graded sporadically, sometimes between pre-census and census surveys, which tended to remove scats. (NOTE: these observations are NOT omitted in the dataset). Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=135 Webpage with information and links to data files for download

This data set includes riparian woody stem counts, stem densities and landscape variables collected at 28 sites along the South Platte River, Colorado, United States from 2011-2016. Riparian woody stem densities were collected in the field during 2011, 2015, and 2016, and include the species Ulmus pumila, Populus deltoides, Salix amygdaloides, Fraxinus pennsylvanica, and Elaeagnus angustifolia. For Ulmus pumila, data are included for total stem density and stem densities of three size classes: saplings, medium, and large trees. Landscape variables at each site include: farmstead density, population density, upland Ulmus pumila density, bridge density, road density, active river channel, floodplain area, upstream cumulative drainage area, and mean annual river flow.

Tabular and raster data containing spatial capture recapture, GPS telemetry, ground observations, and genetic records for male and female Rocky Mountain bighorn sheep (Ovis canadensis canadensis) in Dinosaur National Monument from 2006 to 2022. Includes associated tabular data files required for analysis of data with spatial capture models and resource selection models, and the raster data describing the output from these analyses. Associated tables and rasters include details for trap locations and genetic captures, the state space required for modeling with associated landscape covariates, and rasters describing population density and habitat use.

Ecological factors often shape demography through multiple mechanisms, making it difficult to identify the sources of demographic variation. In particular, conspecific density can influence both the strength of competition and the predation rate, but density-dependent competition has received more attention, particularly among terrestrial vertebrates and in island populations. A better understanding of how both competition and predation contribute to density-dependent variation in fecundity can be gained by partitioning the effects of density on offspring number from its effects on reproductive failure, while also evaluating how biotic and abiotic factors jointly shape demography. We examined the effects of population density and precipitation on fecundity, nest survival, and adult survival in an insular population of orange-crowned warblers (Oreothlypis celata) that breeds at high densities and exhibits a suite of traits suggesting strong intraspecific competition. Breeding density had a negative influence on fecundity, but it acted by increasing the probability of reproductive failure through nest predation, rather than through competition, which was predicted to reduce the number of offspring produced by successful individuals. Our results demonstrate that density-dependent nest predation can underlie the relationship between population density and fecundity even in a high-density, insular population where intraspecific competition should be strong.

Detecting contemporary evolution requires demonstrating that genetic change has occurred. Mixed-effects models allow estimation of quantitative genetic parameters and are widely used to study evolution in wild populations. However, predictions of evolution based on these parameters frequently fail to match observations. Furthermore, such studies often lack an independent measure of evolutionary change against which to verify predictions. Here, we applied three commonly used quantitative genetic approaches to predict the evolution of size at maturity in a wild population of Trinidadian guppies. Crucially, we tested our predictions against evolutionary change observed in common garden experiments performed on samples from the same population. We show that standard quantitative genetic models underestimated or failed to detect the cryptic evolution of this trait as demonstrated by the common garden experiments. The models failed because: 1) size at maturity and fitness both decreased with increases in population density, 2) offspring experienced higher population densities than their parents, and 3) selection on size was strongest at high densities. When we accounted for environmental change, predictions better matched observations in the common garden experiments, although substantial uncertainty remained. Our results demonstrate that predictions of evolution are unreliable if environmental change is not appropriately captured in models. Methods This dataset includes:

guppy_data.csv - individual phenotypic, fitness and environmental data from the study population guppy_ped.csv - pedigree for the study population common_garden.csv - phenotypic data from the common garden experiments

In addition, we include code for performing the analyses described in the manuscript as well as model output objects.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Six sites approximately 6 km apart were selected at the Central Plains Experimental Range in 1997. Within each site, there was a pair of adjacent ungrazed and moderately summer grazed (40-60% removal of annual aboveground production by cattle) locations. Grazed locations had been grazed from 1939 to present and ungrazed locations had been protected from 1991 to present by the establishment of exclosures. Within grazed and ungrazed locations, all tillers and root crowns of B. gracilis were removed from two treatment plots (3 m x 3 m) with all other vegetation undisturbed. Two control plots were established adjacent to the treatment plots. Plant density was measured annually by species in a fixed 1m x 1m quadrat in the center of treatment and control plots. For clonal species, an individual plant was defined as a group of tillers connected by a crown Coffin & Lauenroth 1988, Fair et al. 1999). Seedlings were counted as separate individuals. In the same quadrat, basal cover by species, bare soil, and litter were estimated annually using a point frame. A total of 40 points were read from four locations halfway between the center point and corners of the 1m x 1m quadrat. Density was measured from 1998 to 2005 and cover from 1997 to 2006. All measurements were taken in late June/early July. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=702 Webpage with information and links to data files for download

Six sites approximately 6 km apart were selected at the Central Plains Experimental Range in 1997. Within each site, there was a pair of adjacent ungrazed and moderately summer grazed (40-60% removal of annual aboveground production by cattle) locations. Grazed locations had been grazed from 1939 to present and ungrazed locations had been protected from 1991 to present by the establishment of exclosures. Within grazed and ungrazed locations, all tillers and root crowns of B. gracilis were removed from two treatment plots (3 m x 3 m) with all other vegetation undisturbed. Two control plots were established adjacent to the treatment plots. Plant density was measured annually by species in a fixed 1m x 1m quadrat in the center of treatment and control plots. For clonal species, an individual plant was defined as a group of tillers connected by a crown (Coffin & Lauenroth 1988, Fair et al. 1999). Seedlings were counted as separate individuals. In the same quadrat, basal cover by species, bare soil, and litter were estimated annually using a point frame. A total of 40 points were read from four locations halfway between the center point and corners of the 1m x 1m quadrat. Density was measured from 1998 to 2005 and cover from 1997 to 2006. All measurements were taken in late June/early July.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/85547. In a 10-year study, we assessed the influence of five carbon (C) treatments on the labile C and nitrogen (N) pools of historically N enriched plots on the Shortgrass Steppe Long Term Ecological Research site located in northeastern Colorado. For eight years, we applied sawdust, sugar, industrial lignin, sawdust + sugar, and lignin + sugar to plots that had received N and water additions in the early 1970s. Previous work showed that past water and N additions altered plant species composition and enhanced rates of nutrient cycling; these effects were still apparent 25 years later. We hypothesized that labile C amendments would stimulate microbial activity and suppress rates of N mineralization, whereas complex forms of carbon (sawdust and lignin) could enhance humification and lead to longer-term reductions in N availability. Results indicated that of the five carbon treatments, sugar, sawdust, and sawdust + sugar suppressed N availability, with sawdust + sugar being the most effective treatment to reduce N availability. The year after treatments stopped, N availability remained less in the sawdust + sugar treatment plots than in the high-N control plots. Three years after treatments ended, reductions in N availability were smaller (40-60%). Our results suggest that highly labile forms of carbon generate strong short- term N sinks, but these effects dissipate within one year of application, and that more recalcitrant forms reduce N longer. Sawdust + sugar was the most effective treatment to decrease exotic species canopy cover and increase native species density over the long term. Labile carbon had neither short- nor long-term effects on exotic species. Even though the organic amendments did not contribute to recovery of the dominant native species Bouteloua gracilis, they were effective in increasing another native species, Carex eleocharis. These results indicate that organic amendments may be a useful tool for restoring some native species in the shortgrass steppe. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=541 Webpage with information and links to data files for download

This interactive webmap allows the user to toggle between layers and determine areas that are susceptible to flooding and flood damage in Boulder Colorado due population density and poverty.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. When the CPER was established in 1939, researchers constructed a .5-1 ha grazing exclosure in each of the pastures. These areas have remained protected from grazing for the past 70 years. The remaining areas have been grazed for the past 20+ years. This collection of pastures and exclosures provided an extraordinary opportunity to reinitiate grazing and protection, and evaluate the balance between degradation and aggradation. We proposed to rearrange fences and expose areas to grazing that have been protected for 50 years, and protect areas from grazing that had been grazed for 50 years. The combinations of grazing conditions were: 1. Long-term protection 2. Long-term grazing (moderate) 3. 50 years of protection followed by grazing 4. 50 years of grazing followed by protection Net primary production, nitrogen dynamics, cattle utilization, and community dynamics of vegetation were measured. Additional information and referenced materials can be found: http://hdl.handle.net/10217/85596. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=529 Webpage with information and links to data files for download