This study is part of a series of studies assembled by an interdisciplinary research team led by Myron Gutmann of the University of Michigan between 1995 and 2004, as part of a research project funded by the National Institute of Child Health and Human Development (Grant Number R01HD033554 to the University of Michigan). The goal of the project was to amass information about approximately 500 counties in 12 states of the Great Plains of the United States, and then to analyze those data in order to understand the relationships between population and environment that existed between the years of 1860 and 2003. The data distributed as part of this series are all data about counties. They fall into four broad categories: information about the counties, about agriculture, about demographic and social conditions, and about the environment. The information about counties (name, area, identification code, and whether the project classified the county as part of the Great Plains in a given year) is embedded in each of the other data files, so that there will be three series of data (agriculture, demographic and social conditions, and environment), containing individual data files for each year for which data are available. Specifically, this study contains environmental data and is meant to aid the modeling of the biogeochemical effects of cropping in the Great Plains region. These data were generated by the Daycent ecosystem model, which has been used extensively to simulate soil biogeochemical dynamics from agricultural systems throughout the United States. Variables include information on above-ground production, soil and system carbon, evaporation and transpiration data, soil temperature, nitrogen mineralization, and fluxes of various chemical compounds.

Heading date in wheat (Triticum aestivum L.) and other small grain cereals is affected by the vernalization and photoperiod pathways. The reduced-height loci also have an effect on growth and development. Heading date, which occurs just prior to anthesis, was evaluated in a population of 299 hard winter wheat entries representative of the U.S. Great Plains region, grown in nine environments during 2011–2012 and 2012–2013. The germplasm was evaluated for candidate genes at vernalization (Vrn-A1, Vrn-B1, and Vrn-D1), photoperiod (Ppd-A1, Ppd-B1 and Ppd-D1), and reduced-height (Rht-B1 and Rht-D1) loci using polymerase chain reaction (PCR) and Kompetitive Allele Specific PCR (KASP) assays. Our objectives were to determine allelic variants known to affect flowering time, assess the effect of allelic variants on heading date, and investigate changes in the geographic and temporal distribution of alleles and haplotypes. Our analyses enhanced understanding of the roles developmental genes have on the timing of heading date in wheat under varying environmental conditions, which could be used by breeding programs to improve breeding strategies under current and future climate scenarios. The significant main effects and two-way interactions between the candidate genes explained an average of 44% of variability in heading date at each environment. Among the loci we evaluated, most of the variation in heading date was explained by Ppd-D1, Ppd-B1, and their interaction. The prevalence of the photoperiod sensitive alleles Ppd-A1b, Ppd-B1b, and Ppd-D1b has gradually decreased in U.S. Great Plains germplasm over the past century. There is also geographic variation for photoperiod sensitive and reduced-height alleles, with germplasm from breeding programs in the northern Great Plains having greater incidences of the photoperiod sensitive alleles and lower incidence of the semi-dwarf alleles than germplasm from breeding programs in the central or southern plains.

Resident Population in the Plains BEA Region was 21977.41300 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in the Plains BEA Region reached a record high of 21977.41300 in January of 2024 and a record low of 10357.00000 in January of 1900. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in the Plains BEA Region - last updated from the United States Federal Reserve on June of 2025.

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.

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.

The decline of biodiversity from anthropogenic landscape modification is among the most pressing conservation problems world-wide. In North America, long-term population declines have elevated the recovery of the grassland avifauna to among the highest conservation priorities. Because the vast majority of grasslands of the Great Plains are privately owned, the recovery of these ecosystems and bird populations within them depend on landscape-scale conservation strategies that integrate social, economic, and biodiversity objectives. The Conservation Reserve Program (CRP) is a voluntary program for private agricultural producers administered by the United States Department of Agriculture that provides financial incentives to take cropland out of production and restore perennial grassland. We investigated spatial patterns of grassland availability and restoration to inform landscape-scale conservation for a comprehensive community of grassland birds in the Great Plains. The research ob...

description: Recovery efforts for the Northern Great Plains Population of the piping plover (Charadrius melodus) focus on achieving recovery goals established by the Great Lakes and Northern Great Plains Piping Plover Recovery Plan (1988). The recovery goals established by the 1988 plan called for birds in the Northern Great Plains to increase to 1.300 pairs and remain stable for 15 years. Recommended recovery actions to reach those goals included determining population trends and habitat requirements: protect. enhance. and increase populations during breeding, migration, and wintering periods: develop management plans for use and protection of various habitat types and develop public education and awareness programs about the piping plover. In response to these recommended actions the Fish and Wildlife Service Mountain-Prairie Region (Region 6) has moved forward with management efforts directed at recovery of the Northern Great Plains Population of the piping plover. This report summarizes surveying, monitoring efforts, management programs and partnerships, research, permit monitoring, Section 6 Projects and Section 7 consultations for piping plovers in North Dakota, Montana, Nebraska, South Dakota, Kansas, Colorado.; abstract: Recovery efforts for the Northern Great Plains Population of the piping plover (Charadrius melodus) focus on achieving recovery goals established by the Great Lakes and Northern Great Plains Piping Plover Recovery Plan (1988). The recovery goals established by the 1988 plan called for birds in the Northern Great Plains to increase to 1.300 pairs and remain stable for 15 years. Recommended recovery actions to reach those goals included determining population trends and habitat requirements: protect. enhance. and increase populations during breeding, migration, and wintering periods: develop management plans for use and protection of various habitat types and develop public education and awareness programs about the piping plover. In response to these recommended actions the Fish and Wildlife Service Mountain-Prairie Region (Region 6) has moved forward with management efforts directed at recovery of the Northern Great Plains Population of the piping plover. This report summarizes surveying, monitoring efforts, management programs and partnerships, research, permit monitoring, Section 6 Projects and Section 7 consultations for piping plovers in North Dakota, Montana, Nebraska, South Dakota, Kansas, Colorado.

description: Whooping cranes (Grus americana) of the Aransas- Wood Buffalo population migrate twice each year through the Great Plains in North America. Recovery activities for this endangered species include providing adequate places to stop and rest during migration, which are generally referred to as stopover sites. To assist in recovery efforts, initial estimates of stopover site use intensity are presented, which provide opportunity to identify areas across the migration range used more intensively by whooping cranes. We used location data acquired from 58 unique individuals fitted with platform transmitting terminals that collected global position system locations. Radio-tagged birds provided 2,158 stopover sites over 10 migrations and 5 years (201014). Using a grid-based approach, we identified 1,095 20-square-kilometer grid cells that contained stopover sites. We categorized occupied grid cells based on density of stopover sites and the amount of time cranes spent in the area. This assessment resulted in four categories of stopover site use: unoccupied, low intensity, core intensity, and extended-use core intensity. Although provisional, this evaluation of stopover site use intensity offers the U.S. Fish and Wildlife Service and partners a tool to identify landscapes that may be of greater conservation significance to migrating whooping cranes. Initially, the tool will be used by the U.S. Fish and Wildlife Service and other interested parties in evaluating the Great Plains Wind Energy Habitat Conservation Plan.; abstract: Whooping cranes (Grus americana) of the Aransas- Wood Buffalo population migrate twice each year through the Great Plains in North America. Recovery activities for this endangered species include providing adequate places to stop and rest during migration, which are generally referred to as stopover sites. To assist in recovery efforts, initial estimates of stopover site use intensity are presented, which provide opportunity to identify areas across the migration range used more intensively by whooping cranes. We used location data acquired from 58 unique individuals fitted with platform transmitting terminals that collected global position system locations. Radio-tagged birds provided 2,158 stopover sites over 10 migrations and 5 years (201014). Using a grid-based approach, we identified 1,095 20-square-kilometer grid cells that contained stopover sites. We categorized occupied grid cells based on density of stopover sites and the amount of time cranes spent in the area. This assessment resulted in four categories of stopover site use: unoccupied, low intensity, core intensity, and extended-use core intensity. Although provisional, this evaluation of stopover site use intensity offers the U.S. Fish and Wildlife Service and partners a tool to identify landscapes that may be of greater conservation significance to migrating whooping cranes. Initially, the tool will be used by the U.S. Fish and Wildlife Service and other interested parties in evaluating the Great Plains Wind Energy Habitat Conservation Plan.

Unpublished data product not for circulation Persistent Poverty tracts*Persistent poverty area and enduring poverty area measures with reference year 2015-2019 are research measures only. The ERS offical measures are updated every ten years. The next updates will use 1960 through 2000 Decennial Census data and 2007-2011 and 2017-2021 5-year ACS estimates. The updates will take place following the Census Bureau release of the 2017-2021 estimates (anticipated December 2022).A reliability index is calculated for each poverty rate (PctPoor) derived using poverty count estimates and published margins of error from the 5-yr ACS. If the poverty rate estimate has low reliability (=3) AND the upper (PctPoor + derived MOE) or lower (PctPoor - derived MOE) bounds of the MOE adjusted poverty rate would change the poverty status of the estimate (high = 20.0% or more; extreme = 40.0% or more) then the county/tract type is coded as "N/A". If looking at metrics named "PerPov0711" and PerPov1519" ERS says: The official measure ending in 2007-11 included data from 1980. The research measure ending in 2015-19 drops 1980 and begins instead with 1990. There were huge differences in geographic coverage of census tracts and data quality between 1980 and 1990, namely "because tract geography wasn’t assigned to all areas of the country until the 1990 Decennial Census. Last date edited 9/1/2022Variable NamesVariable Labels and ValuesNotesGeographic VariablesGEO_ID_CTCensus download GEOID when downloading county and tract data togetherSTUSABState Postal AbbreviationfipsCounty FIPS code, in numericCountyNameArea Name (county, state)TractNameArea Name (tract, county, state)TractCensus Tract numberRegionCensus region numeric code 1 = Northeast 2 = Midwest 3 = South 4 = Westsubreg3ERS subregions 1 = Northeast and Great Lakes 2 = Eastern Metropolitan Belt 3 = Eastern and Interior Uplands 4 = Corn Belt 5 = Southeastern Coast 6 = Southern Coastal Plain 7 = Great Plains 8 = Rio Grande and Southwest 9 = West, Alaska and HawaiiMetNonmet2013Metro and nonmetro county code 0 = nonmetro county 1 = metro countyBeale2013ERS Rural-urban Continuum Code 2013 (counties) 1 = counties in metro area of 1 million population or more 2 = counties in metro area of 250,000 to 1 million population 3 = counties in metro area of fewer than 250,000 population 4 = urban population of 20,000 or more, adjacent to a metro area 5 = urban population of 20,000 or more, not adjacent to a metro area 6 = urban population of 2,500 to 19,999, adjacent to a metro area 7 = urban population of 2,500 to 19,999, not adjacent to a metro area 8 = completely rural or less than 2,500, adjacent to a metro area 9 = completely rural or less than 2,500, not adjacent to a metro areaRUCA_2010Rural Urban Commuting Areas, primary code (census tracts) 1 = Metropolitan area core: primary flow within an urbanized area (UA) 2 = Metropolitan area high commuting: primary flow 30% or more to a UA 3 = Metropolitan area low commuting: primary flow 10% to 30% to a UA 4 = Micropolitan area core: primary flow within an Urban Cluster of 10,000 to 49,999 (large UC) 5 = Micropolitan high commuting: primary flow 30% or more to a large UC 6 = Micropolitan low commuting: primary flow 10% to 30% to a large UC 7 = Small town core: primary flow within an Urban Cluster of 2,500 to 9,999 (small UC) 8 = Small town high commuting: primary flow 30% or more to a small UC 9 = Small town low commuting: primary flow 10% to 30% to a small UC 10 = Rural areas: primary flow to a tract outside a UA or UC 99 = Not coded: Census tract has zero population and no rural-urban identifier informationBNA01Census tract represents block numbering areas; BNAs are small statistical subdivisions of a county for numbering and grouping blocks in nonmetropolitan counties where local committees have not established tracts. 0 = not a BNA tract 1 = BNA tractPoverty Areas MeasuresHiPov60Poverty Rate greater than or equal to 20.0% 1960 (counties only) -1 = N/A 0 = PctPoor60 < 20.0% 1 = PctPoor60 >= 20.0%HiPov70Poverty Rate greater than or equal to 20.0% 1970 -1 = N/A 0 = PctPoor70 < 20.0% 1 = PctPoor70 >= 20.0%HiPov80Poverty Rate greater than or equal to 20.0% 1980 -1 = N/A 0 = PctPoor80 < 20.0% 1 = PctPoor80 >= 20.0%HiPov90Poverty Rate greater than or equal to 20.0% 1990 -1 = N/A 0 = PctPoor90 < 20.0% 1 = PctPoor90 >= 20.0%HiPov00Poverty Rate greater than or equal to 20.0% 2000 -1 = N/A 0 = PctPoor00 < 20.0% 1 = PctPoor00 >= 20.0%HiPov0711Poverty Rate greater than or equal to 20.0% 2007-11 ACS -1 = N/A 0 = PctPoor0711 < 20.0% 1 = PctPoor0711 >= 20.0%HiPov1519Poverty Rate greater than or equal to 20.0% 2015-19 ACS -1 = N/A 0 = PctPoor1519 < 20.0% 1 = PctPoor1519 >= 20.0%ExtPov60Poverty Rate greater than or equal to 40.0% 1960 (counties only) -1 = N/A 0 = PctPoor60 < 40.0% 1 = PctPoor60 >= 40.0%ExtPov70Poverty Rate greater than or equal to 40.0% 1970 -1 = N/A 0 = PctPoor70 < 40.0% 1 = PctPoor70 >= 40.0%ExtPov80Poverty Rate greater than or equal to 40.0% 1980 -1 = N/A 0 = PctPoor80 < 40.0% 1 = PctPoor80 >= 40.0%ExtPov90Poverty Rate greater than or equal to 40.0% 1990 -1 = N/A 0 = PctPoor90 < 40.0% 1 = PctPoor90 >= 40.0%ExtPov00Poverty Rate greater than or equal to 40.0% 2000 -1 = N/A 0 = PctPoor00 < 40.0% 1 = PctPoor00 >= 40.0%ExtPov0711Poverty Rate greater than or equal to 40.0% 2007-11 ACS -1 = N/A 0 = PctPoor0711 < 40.0% 1 = PctPoor0711 >= 40.0%ExtPov1519Poverty Rate greater than or equal to 40.0% 2015-19 ACS -1 = N/A 0 = PctPoor1519 < 40.0% 1 = PctPoor1519 >= 40.0%PerPov90Official ERS Measure: Persistent Poverty 1990: poverty rate >= 20.0% in 1960, 1970, 1980, and 1990 (counties only) May not match previously published versions due to changes in geographic normalization procedures. -1 = N/A 0 = poverty rate not >= 20.0% in 1960, 1970, 1980, and 1990 1 = poverty rate >= 20.0% in 1960, 1970, 1980, and 1990PerPov00Official ERS Measure: Persistent Poverty 2000: poverty rate >= 20.0% in 1970, 1980, 1990, and 2000May not match previously published versions due to changes in geographic normalization procedures. -1 = N/A 0 = poverty rate not >= 20.0% in 1970, 1980, 1990, and 2000 1 = poverty rate >= 20.0% in 1970, 1980, 1990, and 2000PerPov0711Official ERS Measure: Persistent Poverty 2007-11: poverty rate >= 20.0% in 1980, 1990, 2000, and 2007-11May not match previously published versions due to changes in geographic normalization procedures and -1 = N/A application of reliability criteria. 0 = poverty rate not >= 20.0% in 1980, 1990, 2000, and 2007-11 1 = poverty rate >= 20.0% in 1980, 1990, 2000, and 2007-11PerPov1519Research Measure Only: Persistent Poverty 2015-19: poverty rate >= 20.0% in 1990, 2000, 2007-11, and 2015May not match previously published versions due to changes in geographic normalization procedures and -1 = N/A application of reliability criteria. 0 = poverty rate not >= 20.0% in 1990, 2000, 2007-11, and 2015-19 1 = poverty rate >= 20.0% in 1990, 2000, 2007-11, and 2015-19EndurePov0711Official ERS Measure: Enduring Poverty 2007-11: poverty rate >= 20.0% for at least 5 consecutive time periods up-to and including 2007-11 -1 = N/A 0 = Poverty Rate not >=20.0% in 1970, 1980, 1990, 2000, and 2007-11 1 = poverty rate >= 20.0% in 1970, 1980, 1990, 2000, and 2007-11 2 = poverty rate >=20.0% in 1960, 1970, 1980, 1990, 2000, and 2007-11 (counties only)EndurePov1519Research Measure Only: Enduring Poverty 2015-19: poverty rate >= 20.0% for at least 5 consecutive time periods, up-to and including 2015-19 -1 = N/A 0 = Poverty Rate not >=20.0% in 1980, 1990, 2000, 2007-11, and 2015-19 1 = poverty rate >= 20.0% in 1980, 1990, 2000, 2007-11, and 2015-19 2 = poverty rate >= 20.0% in 1970, 1980, 1990, 2000, 2007-11, and 2015-19 3 = poverty rate >=20.0% in 1960, 1970, 1980, 1990, 2000, 2007-11, and 2015-19 (counties only)Additional Notes: *In the combined data tab each variable ends with a 'C' for county and a 'T' for tractThe spreadsheet was joined to Esri's Living Atlas Social Vulnerability Tract Data (CDC) and therefore contains the following information as well: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and TransportationThis feature layer visualizes the 2018 overall SVI for U.S. counties and tracts. Social Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. county and tract.15 social factors grouped into four major themes | Index value calculated for each county for the 15 social factors, four major themes, and the overall rank

Ice nucleating particles (INPs) are rare particles that initiate primary ice formation, a critical step required for subsequent important cloud microphysical processes that ultimately govern cloud phase and cloud radiative properties. Laboratory studies have found that organic-rich dusts, such as those found in soils, are more efficient INPs compared to mineral dust. However, the atmospheric relevance of these organic-rich dusts are not well understood, particularly in regions with significant agricultural activity. The Agricultural Ice nuclei at the Southern Great Plains field campaign (AGINSGP) was conducted in rural Oklahoma to investigate how soil dusts contribute to INP populations in the Great Plains. We present chemical characterization of ambient and ice crystal residual particles from a single day of sampling using single particle mass spectrometry (SPMS) and scanning microscopy. Ambient particle concentrations were primarily carbonaceous or secondary aerosol, while dust particles were enhanced in the residual particles. Dust particles measured during residual sampling contained elevated signals for phosphate (6363PO−22− and 7979PO−33−) and lead (208208Pb+22+). Nitrate was slightly depleted relative to ambient dust, and strong sulfate signals were not seen in the residual particles measured by the miniSPLAT. The residual and ambient particles measured by the miniSPLAT were approximately the same size, though this may be a reflection of the SPMS size-dependent transmission efficiency and not that of physical truth. This study shows that organic-rich soils may be important contributors to the ambient INP population in agricultural regions.

We present one tabular data file to evaluate piping plover (Charadrius melodus) seasonal (breeding and nonbreeding) adult survival. These data were part of a study to examine adult (n = 3474) survival during 2012-2019 at breeding regions within the Northern Great Plains and nonbreeding regions in the Gulf and southern Atlantic Coasts of North America. This file includes USGS-funded data and not the full data used in the Larger Work. The seasonal adult survival data includes a multistate model encounter history.

The ambient concentration data of ice-nucleating particles and cloud condensation nuclei was collected at two contrasting sites, one in the Southern Great Plains region of the United States with a substantial terrestrially influenced aerosol population, and one in the Eastern North Atlantic area with a primarily marine-influenced aerosol population. The 6-hour time-averaged data was collected from each site, the first from October 1 - November 15, 2019, in Oklahoma, USA, and the second from October 1, 2020 - November 30, 2020, on Graciosa Island, Azores. These datasets were collected to comprehensively understand the difference or similarity in the particle source(s) as well as aerosol-cloud interactions in the atmosphere in association with other ambient observations at these sites. For both sites, the abundance data was taken with online monitors and complementary measurements of ice-nucleating particles by means of offline droplet freezing assay. A total of 14 tabular datasets is provided (the metadata and data matrices are separated). An individual method-oriented data abstract is available in each metadata.

description: This three-band, 30-m resolution raster contains sagebrush vegetation types, soil temperature/moisture regime classes, and large fire frequencies across greater sage-grouse population areas within the Great Plains sage-grouse management zone. Sagebrush vegetation types were defined by grouping together similar vegetation types from the LANDFIRE biophysical settings layer. Soil moisture and temperature regimes were from an USDA-NRCS analysis of soil types across the greater sage-grouse range. Fire frequencies were derived from fire severity rasters created by the Monitoring Trends in Burn Severity program. The area of analysis included the greater sage-grouse populations areas within specific management zones. Methods used to derive these data are detailed in the report [Brooks, M.L., Matchett, J.R., Shinneman, D.J., and Coates, P.S., 2015, Fire patterns in the range of greater sage-grouse, 1984-2013; Implications for conservation and management: U.S. Geological Survey Open-File Report 2015-1167, 66 p., http://dx.doi.org/10.3133/ofr20151167]; abstract: This three-band, 30-m resolution raster contains sagebrush vegetation types, soil temperature/moisture regime classes, and large fire frequencies across greater sage-grouse population areas within the Great Plains sage-grouse management zone. Sagebrush vegetation types were defined by grouping together similar vegetation types from the LANDFIRE biophysical settings layer. Soil moisture and temperature regimes were from an USDA-NRCS analysis of soil types across the greater sage-grouse range. Fire frequencies were derived from fire severity rasters created by the Monitoring Trends in Burn Severity program. The area of analysis included the greater sage-grouse populations areas within specific management zones. Methods used to derive these data are detailed in the report [Brooks, M.L., Matchett, J.R., Shinneman, D.J., and Coates, P.S., 2015, Fire patterns in the range of greater sage-grouse, 1984-2013; Implications for conservation and management: U.S. Geological Survey Open-File Report 2015-1167, 66 p., http://dx.doi.org/10.3133/ofr20151167]

description: 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; abstract: 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

This statistic shows the degree of urbanization in Morocco from 2013 to 2023. Urbanization means the share of urban population in the total population of a country. In 2023, 65.12 percent of Morocco's total population lived in urban areas and cities. Urbanization in Morocco Like many countries around the world, Morocco is reporting growing urbanization figures, which means that an increasing number of Moroccans are moving to the cities and urban areas. In 2005, Morocco’s population was around 55.13 percent urban, however, over the course of ten years, the urban population had increased by 5 percent to 60.2 percent as of 2015. There are three major environmental zones in Morocco, and the majority of the population lives in the region which includes the coastal plains and plateaus. The desert and mountainous regions of the country are less populated. The largest city is Casablanca, which is located on the Atlantic Coast on the Chawiya Plain. Casablanca has about 3.3 million inhabitants. The country's capital, Rabat, is also locatzed on the Atlantic Coast, but much smaller with only about 600,000 inhabitants. The population of Morocco is growing at a faster rate than most developed nations, butthe growth rate is still significantly lower than that of the rest of Africa. Currently, Morocco has a population growth rate of around 1.39 percent, and the fertility rate is at around 2.5 children per woman. In total, Morocco has around 35 million inhabitants. Life expectancy is also slightly lower than average at around 74 years of age.

The ambient concentration data of ice-nucleating particles and cloud condensation nuclei was collected at two contrasting sites, one in the Southern Great Plains region of the United States with a substantial terrestrially influenced aerosol population, and one in the Eastern North Atlantic area with a primarily marine-influenced aerosol population. The 6-hour time-averaged data was collected from each site, the first from October 1 - November 15, 2019, in Oklahoma, USA, and the second from October 1, 2020 - November 30, 2020, on Graciosa Island, Azores. These datasets were collected to comprehensively understand the difference or similarity in the particle source(s) as well as aerosol-cloud interactions in the atmosphere in association with other ambient observations at these sites. For both sites, the abundance data was taken with online monitors and complementary measurements of ice-nucleating particles by means of offline droplet freezing assay. A total of 14 tabular datasets is provided (the metadata and data matrices are separated). An individual method-oriented data abstract is available in each metadata.

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An average of roughly 10,000 ha of grasslands, primarily northern mixed-grass prairie, is treated annually with prescribed fire on the U.S. Department of the Interior’s National Wildlife Refuges (NWRs) in the Dakotas and eastern Montana. This management continues despite sparse information on effects of fire on wildlife, introduced and native plants, and wildlife-habitat relationships in the northern mixed-grass prairie ecosystem. To address basic information gaps, we assessed direct and indirect, short and long term impacts of fire or fire suppression on vegetation and wildlife population dynamics at 4 NWRs in northwestern and north central North Dakota during 1997-2003; most work was conducted at Des Lacs NWR and J. Clark Salyer NWR. Funding from the Joint Fire Science Program during the final 2 years of our work helped us expand the inferential value of our studies while giving land managers a novel chance to more clearly identify opportunities and limitations with prescribed burning in relation to the mission and goals of their respective NWRs. Our chief goals were to document effects of prescribed burning of northern mixed-grass prairie on the abundance, productivity, and nest site selection of migratory birds especially grassland songbirds; measure influences of major sources of woody fuels and habitat edges (e.g., woodland, cropland, wetland) on occurrences and productivity of common bird species; and assess relationships between fire history and vegetation composition and structure on several spatial and temporal scales. Our study area lies within a cool-season (C3)-dominated, needlegrass-wheatgrass (Stipa-Agropyron) association. However, the contemporary prairie we studied on the NWRs is invaded by introduced, cool-season grasses and native shrubs and trees, as are most other prairie tracts managed by the U.S. Fish and Wildlife Service and other conservation agencies in the northern Great Plains region. We used 2 basic approaches to examine fire effects on vegetation and wildlife. First, we designed short-term (<10 years) field experiments to test specific hypotheses regarding fire effects on vegetation structure, plant community composition, and wildlife abundance and productivity. Secondly, we assessed long-term (60-100 years) changes in plant communities associated with changes in fire disturbance regimes during and after settlement of the region by persons of European descent. To address study objectives, we used standard methods to collect, analyze, and report data.

Abstract

The Republic of Sierra Leone is a small coastal West African country bordered by Guinea and Liberia. Sierra Leone has an area of 71,620 square kilometers (about 28,000 square miles). The country is divided into four major Administrative Areas namely, The Western Area, Northern Province, Southern Province and Eastern Province.

The Provinces are divided into twelve districts and the districts are divided into one hundred and forty nine chiefdoms. Western Area is divided into (Western Urban) Freetown and Western Rural Areas. Freetown is divided into wards.

There are five Physical Regions in Sierra Leone: (i) The Central Plains, (ii) The Northern Woodlands Savannah, (iii) The South Western Upland, (iv) The Western Coastal Swamps and (v) the Western Peninsula Upland Region.

The country is mountainous; about 50% of the terrain is covered by mountains including the Capital, Freetown. Agriculture is the main occupation for the people of Sierra Leone; especially rice farming in which about 60% of the people are engaged through the practice of shifting cultivation.

Sierra Leone has a tropical climate with two very different seasons - the Dry Season, traditionally from November to April and the Rainy Season from May to October with July and August being the wettest months of the year. In 2004, the census was taken in December.

This is the final report of the 1985 National Population and Housing Census. It is an analytical report and provides a detailed picture of the demographic, socio-economic and household-housing situation in the country. The entire exercise was carried out by local analysts. Finally whereas the total population counted was 3,515,812, some characteristics like education, economic activity, fertility etc. are tabulated and therefore analysed for a total population of 3,222,901.

A number of volumes have preceded this final report. These volumes have presented data on various aspects of the country's population and the general housing conditions. These include data on the demographic, social and economic characteristics of the population; the analysis of age and sex structure; fertility and mortality; migration and housing. Furthermore, detailed and small-area statistics will be available on request at the Central Statistics Office.

The ultimate objective of the census was to enhance national capacity in planning by providing estimates of total population and its growth rates, fertility, mortality, and other related socio-economic indicators. Data collection was completed within the first two weeks of December 1985 and the provisional results submitted and accepted by Government in January 1986. Thereafter, machine processing of the data was carried out until April 1990 when the final statistical tables were produced. The results were finally endorsed by the Government of Sierra Leone in May, 1992 and a National Seminar for dissemination of the results was held in that same month.

A national undertaking of this magnitude depends for its accomplishment on a great number of factors. Adequate financial resources, technical know-how, national and unflinching public co-operation are among the most important ingredients for success.

In presenting this final report, the Central Statistics Office would again like to take this opportunity to acknowledge the valuable contributions made to the success of the project by various national and international organizations, government agencies and institutions and the general public. Financial assistance, material and human resources for the census project were provided by the Sierra Leone Government, the United Nations Population Fund (UNFPA), the United Nations Development Programme (UNDP), the Federal Republic of Germany and the Economic Commission for Africa (ECA).

Special mention must be made of the authors who worked on areas of speciality and who inspite of all the odds continued to support every stage of the census up to its final conclusion. The final editing of this report was jointly concluded by the Census Analyst, Professor H.B.S. Kandeh and UNFPA/Country Support Team (CST) Regional Adviser Dr. K. V. Ramachandran of the Economic Commission for Africa, whose dedication is greatly appreciated.

Finally, the resourcefulness and dedication demonstrated by Dr. Peter L. Tucker, Census Commissioner, staff of the National Population Secretariat and the Central Statistics Office have been acknowledged by all.

This publication marks the successful conclusion of the 1985 census and I now look forward to your continued support as the Government prepares for the 1996 National Population and Housing Census.

Geographic coverage

Country-Wide

Analysis unit

Household and individuals

Universe

Everybody that slept within the boundaries of Sierra Leone on Census Night (2nd - 3rd December 1985)

Kind of data

Census/enumeration data [cen]

Sampling procedure

There was no sampling procedures as it was a national survey

Mode of data collection

Face-to-face [f2f]

Research instrument

The scope of a census as finally determined is reflected in the questionnaire which contains the topics to be investigated in the census. The selection of topics for the 1985 census was based on a balanced consideration of all the major factors involved, such as the requests for data submitted by the various Government Ministries; Local and International Organizations; the ability of the Enumerators to ask questions correctly and the respondents to furnish reasonably accurate answers; the need to keep the questionnaire of reasonable length and so on.

Because of the wide variety of data sought, the possibility of collecting some of the data by means of sampling methods was considered. The idea was, however, abandoned, because it was feared that this might introduce too many complications into the processing and yield results of doubtful quality. It was decided that the entire population was to be treated uniformly during the enumeration. The proposed questionnaire for the census was fully tested in the Pilot Census and the results provided the basis for the preparation of the final questionnaire (Appendix 1.1).

The questions on relationship within household, sex, age, nationality and place of birth are standard questions in African Censuses. In view of the great need for information on fertility and mortality, questions on children born and survival of parents were also included; data from which would yield reasonable estimates of these parameters by the use of special well-known techniques, since information on fertility and mortality had been collected in the 1974 Census. The inclusion of questions on housing represented a significant improvement over the 1963 and 1974 censuses. Questions on level of education and school attendance asked in 1963 and 1974 were repeated. No question was included on literacy, as experience had shown that this topic usually poses problems under enumeration conditions as there are other ways of obtaining reasonable estimates of literacy level.

Questions on the economic characteristics of the population are also regarded as basic in any census, although these topics are amongst the most difficult to investigate properly in African censuses. Much consideration was therefore given to the economic questions which were included in the questionnaire. There was a great demand for data on employment status and on the distribution of the working population by occupation and industry.

In the interest of ease of handling and economy, it was decided after the Pilot Census experience that the questionnaires should be bound up into pads of 50 questionnaires each consisting of 10 lines. Since the vast majority of household comprised less than 10 persons, the arrangement was very convenient as it allowed all the particulars for a household to be accommodated on one page in the majority of cases. This made for easier cross-checking of answering to questions pertaining to the members of the same household and promoted more accurate reporting.

Cleaning operations

Data editing took place at a number of stages through the processing, including:

• Office editing and coding
• Secondary editing

Detailed documentation of the editing of data can be found in the "Census Coding Schedule" document provided as an external resource

Sampling error estimates

Not Applicable. Entire population was covered country-wide

Data appraisal

A Post-Enumeration survey was conducted to assess the reliability of data or any observations regarding data quality.

Gene flow can have rapid effects on adaptation and is an important evolutionary tool available when undertaking biological conservation and restoration. This tool is underused partly because of the perceived risk of outbreeding depression and loss of mean fitness when different populations are crossed. In this article we briefly review some theory and empirical findings on how genetic variation is distributed across species ranges, describe known patterns of gene flow in nature with respect to environmental gradients, and highlight the effects of gene flow on adaptation in small or stressed populations in challenging environments (e.g., at species range limits). We then present a case study involving crosses at varying spatial scales among mountain populations of a trigger plant (Stylidium armeria: Stylidiaceae) in the Australian Alps to highlight how some issues around gene flow effects can be evaluated. We found evidence of outbreeding depression in seed production at greater geographic distances. Nevertheless, we found no evidence of maladaptive gene flow effects in likelihood of germination, plant performance (size), and performance variance, suggesting that gene flow at all spatial scales produces many offspring with high adaptive potential. This case study demonstrates a path to evaluating how increasing sources of gene flow in managed wild and restored populations could identify some offspring with high fitness that could bolster the ability of populations to adapt to future environmental changes. We suggest further ways in which managers and researchers can act to understand and consider adaptive gene flow in natural and conservation contexts under rapidly changing conditions. Methods Study system We examined F1 hybrid performance of the thrift-leaved trigger plant, Stylidium armeria, a species common throughout the montane and high elevation woodland areas of southeastern Australia (with the current focus on the Australian Alps) (Figure 3). The alpine areas in Australia form a rare ecosystem, with treeless alpine vegetation covering ~0.15% of the continent, and like other alpine environments around the world, they are highly vulnerable to the effects of climate change (Hughes, 2003). Cuttings from wild plants were harvested from various sites throughout the Victorian and New South Wales high country. Outcrossing of the different populations was performed and the F1 progeny of these outcrossings were germinated under controlled nursery conditions. Stylidium armeria is morphologically variable throughout its distribution, with differences thought to be related to surrounding vegetation, soil type and climatic factors (Raulings & Ladiges, 2001). The pollination unit is a zygomorphic flower, which is characterized by the fusion of staminate and pistillate tissues into a motile, protandrous column, which is “triggered” when pollinators, usually native bees, land on the corolla (Armbruster & Muchhala, 2009). Twelve populations were sourced, 4 from each of 3 mountain regions within the Australian Alps (Bogong High Plains, Victoria; Mount Buller region, Victoria; and Kosciuszko region, New South Wales) to include in the experimental crosses (Table 1, Figure 4). The 3 regions vary in distance from each other between ca. 50-200 km. Stylidium armeria is genetically highly differentiated between Victorian alpine regions based on pooled SNP data, comparable to differentiation seen for alpine herbaceous plants, which tend to be more differentiated than alpine shrubs (Bell et al., 2018). Within each region, the four populations differ in elevation by 174-227 m and distance by 3-15 km from each other. Within each population, at least 30 plants were collected as cuttings from rhizomes, each including a basal rosette of leaves. Each collection spanned at least a 100 m2 area. Plants were first transferred in Fall 2011 after collection into planting tubes with a “native mix” soil medium used at Burnley Campus (University of Melbourne) glasshouses, which consists of pine bark, peat and sand. After winter, plants were transferred individually into 7-inch pots. After pollinations were initiated some plants died due to lack of soil drainage, which may have contributed to some failed pollinations.

Experimental crosses Experimental pollinations between all populations, including within population crosses, were conducted to examine outcrossing effects of three broad spatial categories of gene flow: within site/population (WP), the site of collection; within range (WR), between sites within a mountain range; and between mountain ranges (BR). Crosses were completed during summer and fall of 2012 (443 total crosses). Within each population, sire plants were chosen randomly during flowering and were mated with up to 2 dams from each population, including their originating population. Dams were chosen randomly within each population, with replacement. Plants within each population were used only once as pollen donors (sires) to individuals in their population and to all other populations, but could also serve as pollen receivers (dams) to other sires in the study. Two replicate flower pollinations were made for each cross. Crosses were completed with equal directionality between populations; that is to say, each population served as both pollen donor and pollen receiver to each other population. This design produced more possible crossing combinations in increasing order of geographic scale: WP = 12 possible types; WR = 18 possible types; BR = 48 possible types. We used this design to increase sampling size and variety in anticipation of outbreeding depression from longer-distance gene flow crosses, and we considered WP crosses to serve as a natural control against which to compare WR and BR crosses. To test for background or contamination pollination, 69 individual flowers, randomly chosen, were marked to test for unintended seed set rate. Thirty flowers were randomly chosen and self-pollinated to test for self-incompatibility. Of the total pollinations observed (including crosses, self-pollinations, and tests of inadvertent pollination), 262 yielded seeds (59.1% of 443). Some crosses may have been unsuccessful due to plant stress that occurred from lack of soil drainage, but this effect was independent of population of origin. Capsules were harvested when mature and dried, and the resulting seeds were later counted and the seed lots weighed to produce an average seed weight (number of seeds divided by total weight). F1 seeds were sowed into 7 x 8-cell seedling trays during autumn of 2014. The cells were partially filled with the native mix and covered with a layer of “seed raising mix,” which consisted of 5 parts medium-grade pine bark, 5 parts fine pine bark, 1 part coarse sand, and 1 part sieved peat, including the additives Saturaid (1500 g/m2) and dolomite (750 g/m2). Each replicate included three seeds sown per cell. Depending on seed availability, 1-15 replicates were sown per seed family and all were completely randomized across 50 trays included in the study, for a total of 2,800 replicates sowed. Following the completion of sowing, trays were treated with Regen Smokemaster 2000, a smoke water solution applied at a rate of 100 mL per 1L of water, sprayed per square meter in order to trigger earlier germination. Trays were randomly rotated once weekly, during which trays were monitored for evidence of germination and survival. Seedlings (520 total) were transferred into larger pots with new potting material after several months’ growth. Plants were grown for 235 days between the first germinant and the end of the growth trial. Plant sizes were estimated by multiplying the longest aboveground height and width measurements of surviving plants (493 in total). Plant size was also standardized by dividing the final plant size by the number of days since germination. Percent germinated plants, seed number, average seed weight, final plant size, and the variance in final plant size were all compared to assess the outcomes of outcrossing among the three general spatial categories, WP, WR, and BR.

Statistics Our main question was, what is the effect of crossing type among the three spatial categories? We used means comparison methods (ANOVA–and non-parametric Kruskal-Wallis tests by ranks) to test for differences among crossing types. We also used generalized linear models (GLM) to model additional effects, including the variation of specific crosses within WP, WR, and BR categories. Models incorporating all cross types simultaneously could not run due to too many missing rows of data (e.g., a row with a WP cross type cannot have a BR cross type, and vice versa). However, we did test these effects in reduced models, separating WP and WR effects from BR effects (see Supplemental Information). Plant size data were square root-transformed to meet parametric assumptions and analysis of variance (ANOVA) was used to detect significant differences among crossing categories. Because transformations of seed number and average seed weight data still failed to meet parametric assumptions, we instead used Kruskal-Wallis tests to test for differences among crossing types for these two variables. Chi-squared analysis was used to test for differences in germination. Levene’s test of equality of variances was used to test for significant variance differences among crossing types in plant size. Finally, we tested for the effect of spatial distance of plant crosses using the estimated Haversine distance between populations on the above seed and plant size variables. Crosses within populations were assigned a distance of 0 km. Because crossing distance data failed to meet assumptions of parametric analyses, we used Spearman’s rho (ρ) rank correlations to test the effect of crossing distance on seed and plant size traits. All analyses were carried out in the JMP statistical package (version 16.0.0, SAS Institute, Inc.,