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.

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]

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.

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...

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.

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.

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 ($^{63}$PO$_2^-$ and $^{79}$PO$_3^-$) and lead ($^{208}$Pb$_2^+$). 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 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.

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

The Aransas-Wood Buffalo population of whooping cranes migrates through the U.S. Great Plains twice annually, moving between wintering areas along coastal Texas and summering areas in and around Wood Buffalo National Park, Canada. These data support development of resource utilization functions that were used to predict wintering use of whooping cranes outside of their historic coastal wintering areas.

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 int...

This zip file contains data and R code used in Andreev et al., 2025. History and dynamics of an extensive plant hybrid zone on the Great Plains of North America.

description: The northern Great Plains population of Piping Plovers is declining at a rate of 5 - 13% annually (Ryan et al. 1993, Plissner and Haig 2000a, Plissner and Haig 2000b, Larson et al. 2002) due largely to inadequate reproductive success and alteration of breeding habitat. At this rate Piping Plovers could disappear from the region within 50 - 100 years (Ryan et al. 1993, Plissner and Haig 2000, Larson et al. 2002). Approximately 300 - 400 breeding pairs of the U.S. Great Plains population breeds annually within the U.S. Alkali Lakes Core Area (Core Area), which follows the Missouri Coteau landform from central North Dakota to northeastern Montana (Fig. 1; Plissner and Haig 2000a). Efforts to monitor and restore plovers on these lakes and wetlands were initiated during the mid-1980s; area-wide recovery activity has been ongoing since 1991. During the 2005 field season, 9 seasonal technicians and a Recovery Biologist worked to protect and monitor breeding pairs on private, federal, and state lands comprising the 8,000 mi2 Core Area. Support for this effort was provided by the U.S. Fish & Wildlife Service (USFWS); The Nature Conservancy (TNC); Montana Fish, Wildlife, & Parks (MTFWP); and the U.S. Army Corp of Engineers (USACOE). The goal of the recovery effort in the Core Area is to achieve an annual fledging rate of at least 1.24 1.44 chicks/breeding pair. This level of recruitment, if maintained in the Core Area as well as prairie Canada, should be sufficient to halt the decline in plover numbers (Larson et al. 2002). To achieve this goal, technicians 1) conduct an annual breeding census to track the status of the population and determine pair distribution, 2) search for nests and protect them with predator exclosures to enhance reproduction, and 3) monitor reproductive success to determine if management activities have been helpful.; abstract: The northern Great Plains population of Piping Plovers is declining at a rate of 5 - 13% annually (Ryan et al. 1993, Plissner and Haig 2000a, Plissner and Haig 2000b, Larson et al. 2002) due largely to inadequate reproductive success and alteration of breeding habitat. At this rate Piping Plovers could disappear from the region within 50 - 100 years (Ryan et al. 1993, Plissner and Haig 2000, Larson et al. 2002). Approximately 300 - 400 breeding pairs of the U.S. Great Plains population breeds annually within the U.S. Alkali Lakes Core Area (Core Area), which follows the Missouri Coteau landform from central North Dakota to northeastern Montana (Fig. 1; Plissner and Haig 2000a). Efforts to monitor and restore plovers on these lakes and wetlands were initiated during the mid-1980s; area-wide recovery activity has been ongoing since 1991. During the 2005 field season, 9 seasonal technicians and a Recovery Biologist worked to protect and monitor breeding pairs on private, federal, and state lands comprising the 8,000 mi2 Core Area. Support for this effort was provided by the U.S. Fish & Wildlife Service (USFWS); The Nature Conservancy (TNC); Montana Fish, Wildlife, & Parks (MTFWP); and the U.S. Army Corp of Engineers (USACOE). The goal of the recovery effort in the Core Area is to achieve an annual fledging rate of at least 1.24 1.44 chicks/breeding pair. This level of recruitment, if maintained in the Core Area as well as prairie Canada, should be sufficient to halt the decline in plover numbers (Larson et al. 2002). To achieve this goal, technicians 1) conduct an annual breeding census to track the status of the population and determine pair distribution, 2) search for nests and protect them with predator exclosures to enhance reproduction, and 3) monitor reproductive success to determine if management activities have been helpful.

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

Colombia has a livestock population of approximately 28.2 million heads, of which 20.4% is found in the eastern high plains of the Orinoquia region. The extensive beef cattle system predominates, whose diet is based on native and introduced pastures of the genus Urochloa sp. which is deficiently managed, affecting productive and reproductive indexes due to the low adoption of technology. We evaluated potential sustainable intensive systems for cattle production, which contribute to maintaining the provision of environmental services. Between 2011 and 2015 in Agrosavia’s Research Center at Carimagua (4° 37'N and 71° 19' W) environmental and animal production variables were monitored in the following systems: a) degraded pastures recovered with tillage and fertilization, b) annual crop rotation with pastures, c) forest arrangements in strips, and d) forest remnants in perimeter areas. Productive and reproductive variables were determined in animals such as weight gain, calving interval, among others while the pasture/crop productive variables included yield and forage quality. Regarding soil ecosystem services (ES) the macrofauna biodiversity, biogeochemical cycles, soil physical and chemical variables were considered. Estimation of indicators was carried out through principal components analysis for soil physical, chemical and macrofauna variables to extract the two main components that explain the variance. For climate regulation of ES, measurement of soil organic carbon (SOC) storage at a depth of 20 cm and the annual accumulated greenhouse gas (GHG) emissions were included. The systems that involved lime amendments and fertilizers increased the value in the year of application. Values in the water regulation indicator did not show significant differences among the options implemented during the years. Edaphic macrofauna biodiversity indicator value was sensitive to changes in management practices, with termites being the group with highest abundance. The indicator related to SOC was higher in the forest area compared to the pasture-crop system. Systems under the agroforestry schemes which integrated various practices such as managed and recovered pastures, crop-pasture rotation, patched areas and tree strips contributed to maintain or improve the ES, although each one of the strategies proposed provided improvement in at leas one ES.