Global Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995. Over 127,000 administrative units and population counts were collected and integrated from various sources to create the gridded data. In brief, GPW was created using the following steps:

* Population data were estimated for the product reference years, 1990 and 1995, either by the data source or by interpolating or extrapolating the given estimates for other years.
* Additional population estimates were created by adjusting the source population data to match UN national population estimates for the reference years.
* Borders and coastlines of the spatial data were matched to the Digital Chart of the World where appropriate and lakes from the Digital Chart of the World were added.
* The resulting data were then transformed into grids of UN-adjusted and unadjusted population counts for the reference years.
* Grids containing the area of administrative boundary data in each cell (net of lakes) were created and used with the count grids to produce population densities.

As with any global data set based on multiple data sources, the spatial and attribute precision of GPW is variable. The level of detail and accuracy, both in time and space, vary among the countries for which data were obtained.

The data sets in this directory were provided by Mr. Gregory Yetman and Drs. Stuart Gaffin and Deborah Balk from the Center for International Earth Science Information Network (CIESIN) at Columbia University. There are three data files at three spatial resolutions of 0.25, 0.5 and 1.0 degree in both latitude and longitude and for the reference year of 1990.

Estimates of Gross Domestic Product (GDP) are commonly given for nations as a single aggregated number. This data set generates estimates of GDP density distributed subnationally to facilitate the integration of GDP with other data at a sub-national level and to promote interdisciplinary studies that include socioeconomic aspects. This is one of two coarse resolution Socioeconomic data sets included in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection, the other being the Gridded Population of the World (GPW), also produced by CIESIN.

We examined phylogeographic structure in gray fox (Urocyon cinereoargenteus) across the United States to identify the location of secondary contact zone(s) between eastern and western lineages and investigate the possibility of additional cryptic intraspecific divergences. We generated and analyzed complete mitochondrial genome sequence data from 75 samples and partial control region mitochondrial DNA sequences from 378 samples to investigate levels of genetic diversity and structure through population- and individual-based analyses including estimates of divergence (FST and SAMOVA), median joining networks, and phylogenies. We used complete mitochondrial genomes to infer phylogenetic relationships and date divergence times of major lineages of Urocyon in the United States. Despite broad-scale sampling, we did not recover additional major lineages of Urocyon within the United States, but identified a deep east-west split (∼0.8 million years) with secondary contact at the Great Plains Suture Zone and confirmed the Channel Island fox (Urocyon littoralis) is nested within U. cinereoargenteus. Genetic diversity declined at northern latitudes in the eastern United States, a pattern concordant with post-glacial recolonization and range expansion. Beyond the east-west divergence, morphologically-based subspecies did not form monophyletic groups, though unique haplotypes were often geographically limited. Gray foxes in the United States displayed a deep, cryptic divergence suggesting taxonomic revision is needed. Secondary contact at a common phylogeographic break, the Great Plains Suture Zone, where environmental variables show a sharp cline, suggests ongoing evolutionary processes may reinforce this divergence. Follow-up study with nuclear markers should investigate whether hybridization is occurring along the suture zone and characterize contemporary population structure to help identify conservation units. Comparative work on other wide-ranging carnivores in the region should test whether similar evolutionary patterns and processes are occurring.

This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the Great Plains Fish Habitat Partnership. 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 boundary of the Great Plains Fish Habitat Partnership. 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 Great Plains Fish Habitat Partnership. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7TM7841

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

Plant populations are limited by resource availability and exhibit physiological trade-offs in resource acquisition strategies. These trade-offs may constrain the ability of populations to exhibit fast growth rates under water limitation and high cover of neighbors. However, traits that confer drought tolerance may also confer resistance to competition. It remains unclear how fitness responses to these abiotic conditions and biotic interactions combine to structure grassland communities and how this relationship may change along a gradient of water availability. To address these knowledge gaps, we estimated the low-density growth rates of populations in drought conditions with low neighbor cover and in ambient conditions with average neighbor cover for 82 species in six grassland communities across the Central Plains and Southwestern United States. We assessed the relationship between population tolerance to drought and resistance to competition and determined if this relationship was consistent across a precipitation gradient. We also tested whether population growth rates could be predicted using plant functional traits. Across six sites, we observed a positive correlation between low-density population growth rates in drought and in the presence of interspecific neighbors. This positive relationship was particularly strong in grasslands of the northern Great Plains but weak in the most xeric grasslands. High leaf dry matter content and low (more negative) leaf turgor loss point were associated with high population growth rates in drought and with neighbors in most grassland communities.

Synthesis: A better understanding of how both biotic and abiotic factors impact population fitness provides valuable insights into how grasslands will respond to extreme drought. Our results advance plant strategy theory by suggesting that drought tolerance increases population resistance to interspecific competition in grassland communities. However, this relationship is not evident in the driest grasslands where aboveground competition is likely less important. Leaf dry matter content and turgor loss point may help predict which populations will establish and persist based on local water availability and neighbor cover, and these predictions can be used to guide the conservation and restoration of biodiversity in grasslands.

Methods Cover data These data include a subset of 82 species (113 species-site combinations) that were monitored annually as part of the Extreme Drought in Grasslands Experiment (EDGE). Topographically unform and hydrologically isolated plots were set up across six grassland types (tallgrass prairie, southern mixed-grass prairie, northern mixed-grass prairie, northern shortgrass prairie, southern shortgrass prairie, and desert grassland) and absolute cover of all species in four 1 x 1 m quadrats was estimated yearly from 2012–2017. At each site, ten control plots at each site received ambient rainfall over the experimental period, and ten treatment plots experienced a 66% reduction in growing season precipitation (equivalent to roughly 40–50% over the whole year) using greenhouse rainout shelters equipped with strips of clear corrugated polycarbonate. Additional site and experimental design details are available in Griffin‐Nolan et al., (2019). Population growth rates Percent cover was used as a measure of population size for each species at the quadrat level. Population growth rate at time t was calculated as the total cover of a species in time t+1 divided by the total cover in time t. The natural logarithm of this value (intrinsic rate of increase) for a species in a quadrat describes whether the population increased (positive value) or decreased (negative value) in the transition from year t to t+1. Population growth rates were calculated for each species in each quadrat in each annual transition. Because we use species cover instead of counts of individuals to measure population size, intraspecific cover is equal to the cumulative cover of a species in a quadrat. Interspecific cover in each quadrat is defined as the cumulative cover of all non-focal species in a quadrat. We calculated low-density growth rates for populations of each species at each site to assess fitness in two different conditions: mean neighbor abundance under ambient rainfall and minimum neighbor abundance under extreme drought. Minimum and mean neighbor abundances were averaged across all five years of the experiment. To estimate these growth rates, we fit linear models predicting intrinsic rate of increase for each species in each grassland as a function of drought treatment, intraspecific neighborhood cover, and interspecific neighborhood cover across years. Functional traits Species-level trait data were assembled from several publications and trait databases and these eleven included leaf dry matter content (LDMC; g g-1), average individual leaf area (cm2), leaf turgor loss point (TLP; MPa), leaf nitrogen concentration (%), specific leaf area (SLA; cm2 g-1), leaf tissue density (LTD; cm3 g-1), root nitrogen (%), root tissue density (RTD; cm3 g-1), root diameter (mm), specific root length (SRL; m g-1), and average maximum height (mm). Trait data were compiled for each species at the site level where available (Table S1). Trait values measured at, or nearby, EDGE sites were considered the closest estimate for species traits. For this we used a mix of unpublished and open-access trait data from individual researchers (Blumenthal et al., 2020; Craine et al., 2011; Farrell, 2018; Laughlin et al., 2010; Stears et al., 2022; Tucker, 2010). Grassland communities that did not have data available at the local scale were filled in by progressively broader estimates using regional averages and eventually global estimates provided by the TRY database as needed (Kattge et al., 2019).

The typical statewide or county-wide red/blue map (shown at left) depicts presidential voting results on a winner-take-all basis, so they award an entire geographical area to the Republican or Democratic candidate no matter how close the actual vote tally The large map in the attachment factors in both the percentage of the popular vote won by each candidate as well as the population density of each county. So, the sparsely populated Great Plains and Rocky Mountain West are shown in a much lighter color than the Eastern Seaboard, and the map as a whole is more purple than either red or blue. Perhaps the United States is less divided than some maps would lead us to believe.

WISDEM simulates the variation in multi-species weed populations over time in response to crop rotation, tillage system, and specific weed management tactics and the consequent crop yield loss due to weed competition. Population dynamics of individual weed species are predicted from a limited number of parameters that can be derived from literature sources and expert opinion. Data to model the population dynamics and crop yield loss of multi-species weed populations is extremely limited as well as expensive and time-consuming to obtain. WISDEM simulates population dynamics of multi-species weed populations in response to crop rotation, tillage system, and specific weed management tactics as well as the resulting crop yield reduction from weed competition. The model uses an innovative structure for modeling weed population dynamics that requires only a small number of parameters and these can be readily derived from literature sources and regional surveys of weed experts. The structure is based on the general theory of density dependence of plant productivity and the extensive use of rectangular hyperbolic equations for describing crop yield as a function of weed density. Only two density-independent parameters are required for each species to represent differences in seed bank mortality, seedling emergence and maximum seed production. One equation is used to model crop yield loss and density-dependent weed seed production as a function of crop and weed density, relative time of weed and crop emergence and differences among species in competitive ability. WISDEM has been parameterized for 4 crops and 15 weeds of the Great Plains. A preliminary, limited evaluation provides evidence that predictions of yield loss from single species of weeds and the short term trajectories of changes in weed populations are biologically reasonable. We think the accuracy is sufficient for the goal of modeling general trends in population density accurately enough to highlight potential weed problems and solutions when comparing alternative crop management options for a field. Resources in this dataset:Resource Title: WISDEM. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=182&modecode=30-12-30-20 download page

Swainson’s hawks (Buteo swainsoni) are long-distance migratory raptors that nest primarily in isolated trees located in areas of high grassland density. In recent years, anthropogenic conversion of grassland habitat has raised concerns about the status of the breeding population in the northern Great Plains. In 2013, we initiated a study to investigate the influence of extrinsic factors influencing Swainson’s hawk nesting ecology in north-central South Dakota and south-central North Dakota. Using ground and aerial surveys, we located and monitored nesting Swainson’s hawk pairs: 73 in 2013 and 120 in 2014. We documented 98 successful breeding attempts that fledged 163 chicks; 1.52 and 1.72 fledglings per successful nest in 2013 and 2014, respectively. We used Program MARK to evaluate the influence of land cover on nest survival. The top model, SDist2Farm+%Hay, indicated that nest survival (fledging at least one chick) decreased as nests were located farther from farm sites and as the per...

We collected county-level cumulative COVID-19 confirmed cases and death from Mar 25 to Nov 12, 2020, across the contiguous United States from USAFacts (usafacts.org). We considered Mar 25 to Jun 3 as the “1st wave”, Jun 4 to Sep 2 as the “2nd wave”, and Sep 3 to Nov 12 as the “3rd wave” of COVID-19. For the 2nd and 3rd waves, we analyzed the targeted counties in the sunbelt region (including AL, AZ, AR, CA, FL, GA, KS, LA, MS, NV, NM, NC, OK, SC, TX, TN, and UT states) and great plains region (including IA, IL, IN, KS, MI, MO, MN, ND, NE, OH, SD, and WI states), respectively. MIR, as a proxy for survival rate, is calculated by dividing the number of confirmed deaths in each county by the confirmed cases in the same county at the same time-period multiplied by 100. MIR ranges from 0%-100%, 100% indicating the worst situation where all confirmed cases have died.

Thirty-eight potential risk factors (covariates), including county-level MR of comorbidities & disorders, demographics & social factors, and environmental factors, were retrieved from the University of Washington Global Health Data Exchange (http://ghdx.healthdata.org/us-data). Comorbidities and disorders include CVD, cardiomyopathy and myocarditis and myocarditis, hypertensive heart disease, peripheral vascular disease, atrial fibrillation, cerebrovascular disease, diabetes, hepatitis, HIV/AIDS, tuberculosis (TB), lower respiratory infection, interstitial lung disease and pulmonary sarcoidosis, asthma, COPD, ischemia, mesothelioma, tracheal cancer, leukemia, pancreatic cancer, rheumatic disease, drug use disorder, and alcohol use disorder. Demographics & social factors include age, female African American%, female white American%, male African American%, male white American%, Asian%, smokers%, unemployed%, income rate, food insecurity, fair/poor health, and uninsured%. Environmental factors include county population density, air quality index (AQI), temperature, and PM. A descriptive table, including all potential risk factors, is provided in Table S1).

Fine particulate matter (PM2.5) and near-surface ozone (O3) are the main atmospheric pollutants in China. Long-term exposure to high ozone concentrations adversely affects human health. It is of great significance to systematically analyze the spatiotemporal evolution mechanism and health effects of ozone pollution. Based on the ozone data of 91 monitoring stations in the Central Plains Urban Agglomeration from 2017 to 2020, the research used Kriging method and spatial autocorrelation analysis to investigate the spatiotemporal variations of ozone concentration. Additionally, the study assessed the health effects of ozone on the population using the population exposure risk model and exposure-response relationship model. The results indicated that: (1) The number of premature deaths caused by ozone pollution in the warm season were 37,053 at 95% confidence interval (95% CI: 28,190–45,930) in 2017, 37,685 (95% CI: 28,669–46,713) in 2018, and 37,655 (95% CI: 28,647–46,676) in 2019. (2) The ozone concentration of the Central Plains urban agglomeration showed a decreasing trend throughout the year and during the warm season from 2017 to 2020, there are two peaks monthly, one is June, and the other is September. (3) In the warm season, the high-risk areas of population exposure to ozone in the Central Plains Urban Agglomeration were mainly concentrated in urban areas. In general, the population exposure risk of the south is lower than that of the north. The number of premature deaths attributed to ozone concentration during the warm season has decreased, but some southern cities such as Xinyang and Zhumadian have also seen an increase in premature deaths. China has achieved significant results in air pollution control, but in areas with high ozone concentrations and high population density, the health burden caused by air pollution remains heavy, and stricter air pollution control policies need to be implemented.

1. Despite asymmetric competition and a wide array of functional similarities, two ecologically important C4 perennial grasses, Andropogon gerardii and Sorghastrum nutans, frequently codominate areas of the mesic tallgrass prairie of the US Great Plains. A subtle difference in their vegetative reproduction strategies may play a role in preventing exclusion of S. nutans, the presumed weaker competitor in this region.
2. While A. gerardii vegetative tiller densities peak in the early growing season and decline thereafter (determinate recruitment), those of S. nutans may continue to increase throughout the growing season (indeterminate recruitment), providing a potential avenue for recovery from more intensive early season competition. However, until now these patterns have only been informally observed in the field.
3. We examined the year-to-year consistency of growing season vegetative tiller dynamics (measured as seasonal change in tiller densities) of each grass species from at an intact tallgrass prairie in Kansas - a site within the core of both species’ distributions - over a period of 8 years. Then, to investigate environmental effects on these dynamics, we examined whether they differ across a Kansas landscape varying in topography, fire management regimes and the abundances of the study species. Finally, we expanded the investigation of environmental effects on growing season tiller dynamics by observing them at the periphery of the species’ distributions in central Colorado, where climatic conditions are dryer and the study species’ abundances are reduced.
4. Synthesis: We found that the tiller densities of A. gerardii decline within seasons with striking consistency regardless of spatio-temporal scale or environmental factors (topography and fire regimes). In contrast, we found the seasonal dynamics of S. nutans tiller densities were dependent on environmental factors, with seasonal tiller density increases occurring only within the Kansas populations but not consistently between years. These observations lay the groundwork for establishing differences in tiller recruitment determinacy as a potentially important yet underappreciated mechanism for promoting coexistence and codominance among perennial plant species.

This dataset contains estimated annual average Dall sheep (Ovis dalli dalli) lamb-to-ewe ratios for each year from 2000-2015 across the full species range in Alaska and Northwestern Canada. Sheep population data are from surveys conducted over the 14 major mountain ranges encompassing the range of Dall sheep. For this study, the mountain ranges were divided into 24 mountain units due to differing climate gradients. Estimated covariate environmental and climate data used to examine the relationship between environmental conditions and Dall sheep population performance (per mountain unit) are also provided and include precipitation, temperature, snow cover, elevation, and distance to the center of the range.