About this dataset

Context

The dataset tabulates the data for the Bison, KS population pyramid, which represents the Bison population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Bison, KS, is 20.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Bison, KS, is 27.2.
• Total dependency ratio for Bison, KS is 47.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Bison, KS is 3.7.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Bison population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Bison for the selected age group is shown in the following column.
• Population (Female): The female population in the Bison for the selected age group is shown in the following column.
• Total Population: The total population of the Bison for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Bison Population by Age. You can refer the same here

Summary population statistics of 14 U.S. Department of Interior and Parks Canada Agency bison herds, and all the herds assembled as a whole population.

The Wood Buffalo National Park (WBNP) Bison Abundance database is a data set that documents numbers of bison observed during bison population estimate surveys conducted in WBNP. Survey methodology was standardized from 2002 to present. A strip transect survey allows for both minimum counts and a population estimate with accompanying estimates of precision. Survey intervals have ranged from 2 to 5 years. The study area is based on the subpopulation boundaries outlined in Joly and Messier (2001), and is thought to include the entire ranges of the Delta, Garden River, Hay Camp and Nyarling subpopulations, as well as about half the range of the Little Buffalo subpopulation. Wood Buffalo National Park was created to protect habitat and prevent extinction of the Wood Bison (Soper 1941).

Habitat connectivity is important for the survival of species that occupy habitat patches too small to sustain an isolated population. A prominent example of such a species is the European bison (Bison bonasus), occurring only in small, isolated herds, and whose survival will depend on establishing larger, well-connected populations. Our goal here was to assess habitat connectivity of European bison in the Carpathians. We used an existing bison habitat suitability map and data on dispersal barriers to derive cost surfaces, representing the ability of bison to move across the landscape, and to delineate potential connections (as least-cost paths) between currently occupied and potential habitat patches. Graph theory tools were then employed to evaluate the connectivity of all potential habitat patches and their relative importance in the network. Our analysis showed that existing bison herds in Ukraine are isolated. However, we identified several groups of well-connected habitat patches in the Carpathians which could host a large population of European bison. Our analysis also located important dispersal corridors connecting existing herds, and several promising locations for future reintroductions (especially in the Eastern Carpathians) that should have a high priority for conservation efforts. In general, our approach indicates the most important elements within a landscape mosaic for providing and maintaining the overall connectivity of different habitat networks and thus offers a robust and powerful tool for conservation planning.

Stacked history timeline since 1800 CE of bison population, bison ownership, average carcass price, and bison system socio-ecological events. A) Log bison population (log10; gray bars (Hornaday 1889, Garretson 1918, 1926). B) Bison ownership shows the relative share of bison population by sectors of Tribal nations (white bars), US government (‘public’, black bars), Non-governmental organizations like zoos (‘NGO’, navy bars), and private entities (light blue bars). C) Average carcass price of bison (red line) with all prices corrected to 2018 equivalent dollars (U.S. Bureau of Labor Statistics 2020) since 1820 CE (Hornaday 1889, USDA 2020). D) Conservation events ranging across domains of natural (brown), social (yellow), economic (green), legislation about bison (authorization, blue), and recovery efforts (implementation, gray). All supporting data are availble here.

Habitat connectivity is important for the survival of species that occupy habitat patches too small to sustain an isolated population. A prominent example of such a species is the European bison (Bison bonasus), occurring only in small, isolated herds, and whose survival will depend on establishing larger, well-connected populations. Our goal here was to assess habitat connectivity of European bison in the Carpathians. We used an existing bison habitat suitability map and data on dispersal barriers to derive cost surfaces, representing the ability of bison to move across the landscape, and to delineate potential connections (as least-cost paths) between currently occupied and potential habitat patches. Graph theory tools were then employed to evaluate the connectivity of all potential habitat patches and their relative importance in the network. Our analysis showed that existing bison herds in Ukraine are isolated. However, we identified several groups of well-connected habitat patches in the Carpathians which could host a large population of European bison. Our analysis also located important dispersal corridors connecting existing herds, and several promising locations for future reintroductions (especially in the Eastern Carpathians) that should have a high priority for conservation efforts. In general, our approach indicates the most important elements within a landscape mosaic for providing and maintaining the overall connectivity of different habitat networks and thus offers a robust and powerful tool for conservation planning.

Bison distribution, season of habitat use, and habitat values are determined by local wildlife biologist relying on observations, surveys, and radio/satellite data. For use in large-scale planning and reporting.Habitat definitions:Crucial value - habitat on which the local population of a wildlife species depends for survival because there are no alternative ranges or habitats available. Crucial value habitat is essential to the life history requirements of a wildlife species. Degradation or unavailability of crucial habitat will lead to significant declines in carrying capacity and/or numbers of wildlife species in question.Substantial value - habitat used by a wildlife species but is not crucial for population survival. Degradation or unavailability of substantial value habitat will not lead to significant declines in carrying capacity and/or numbers of the wildlife species in question.

About this dataset

Context

The dataset tabulates the Bison population by age. The dataset can be utilized to understand the age distribution and demographics of Bison.

Content

The dataset constitues the following three datasets

• Bison, KS Age Group Population Dataset: A complete breakdown of Bison age demographics from 0 to 85 years, distributed across 18 age groups
• Bison, KS Age Cohorts Dataset: Children, Working Adults, and Seniors in Bison - Population and Percentage Analysis
• Bison, KS Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

NPSResponseToHalbert_etal_TableS1_JHered_Apr2012Breeding season ranges used by radio-collared, female bison in Yellowstone National Park during 2002-2011NPSResponseToHalbert_etal_TableS2_JHered_Apr2012Estimates of F-statistics and the number of migrants between central and northern breeding herds based on DNA extracted from fecal samples collected from male and female Yellowstone bison in both herds during the breeding seasons of 2006 and 2008 (Gardipee 2007).

Raster files related to the presence (value of 1) or absence (value of 0) of a species at risk distribution, for seven species/populations considered by the project to be 'unique' to the boreal caribou distribution. The definition of uniqueness was based on thresholding the proportion of the Canada-wide species distribution that falls within the boreal caribou distribution, and the proportion of the boreal caribou distribution that is covered by the species distribution. The file representing the presence of any of the seven species/population distributions is: bio_unique_species.tif Species / populations considered were:

Acipenser fulvescens (lake sturgeon), Western Hudson Bay population: bio_unique_species_Acipenser-fulvescens_WesternHudsonBay.tif Bison bison athabascae (wood bison): bio_unique_species_Bison-bison-bison.tif Bison bison bison (plains bison): bio_unique_species_Bison-bison-bison.tif Bucephala islandica (Barrow's goldeneye), Eastern population: bio_unique_species_Bucephala-islandica_Eastern.tif Grus americana (whooping crane): bio_unique_species_Grus-americana.tif Salmo salar (Atlantic salmon), Quebec Eastern North Shore population: bio_unique_species_Salmo-salar_QuebecEasternNorthShore.tif Salmo salar (Atlantic salmon), Quebec Western North Shore population: bio_unique_species_Salmo-salar_QuebecWesternNorthShore.tif

Sources Acipenser fulvescens (lake sturgeon), Western Hudson Bay population

The Committee on the Status of Endangered Wildlife in Canada. 2017. COSEWIC assessment and status report on the Lake Sturgeon Acipenser fulvescens, Western Hudson Bay populations, Saskatchewan-Nelson River populations, Southern Hudson Bay-James Bay populations and Great Lakes-Upper St. Lawrence populations in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. xxx + 153 pp. (https://wildlife-species.canada.ca/species-risk-registry/virtual_sara/files/cosewic/sr_Lake Sturgeon_2017_e.pdf) Distribution delineation is based on work units (sub-sub-drainage areas) from National Hydro Network, by Natural Resources Canada (https://open.canada.ca/data/en/dataset/a4b190fe-e090-4e6d-881e-b87956c07977). Sub-sub-drainage areas were assembled to match the designatable unit shown in Figure 2 (page 10) of the COSEWIC report, above.

Bison bison athabascae (wood bison)

The Committee on the Status of Endangered Wildlife in Canada. 2013. COSEWIC assessment and status report on the Plains Bison Bison bison bison and the Wood Bison Bison bison athabascae in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. xv + 109 pp. (https://wildlife-species.canada.ca/species-risk-registry/virtual_sara/files/cosewic/sr_Plains Bison and Wood Bison_2013_e.pdf) Distribution delineation is from the Envrinoment and Climate Change Canada (ECCC) 'Range Map extents - Species at Risk' data set (https://open.canada.ca/data/en/dataset/d00f8e8c-40c4-435a-b790-980339ce3121) which is compiled from a combination of NatureServe (https://www.natureserve.org/) data, species at risk recovery strategies, ECCC resources and COSEWIC status reports.

Bison bison bison (plains bison)

The Committee on the Status of Endangered Wildlife in Canada. 2013. COSEWIC assessment and status report on the Plains Bison Bison bison bison and the Wood Bison Bison bison athabascae in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. xv + 109 pp. (https://wildlife-species.canada.ca/species-risk-registry/virtual_sara/files/cosewic/sr_Plains Bison and Wood Bison_2013_e.pdf) Distribution delineation was digitized from Figure 6 (page 21) of the report above.

Bucephala islandica (Barrow's goldeneye), Eastern population

The Committee on the Status of Endangered Wildlife in Canada. 2000. COSEWIC assessment and status report on the Barrow’s Goldeneye Bucephala islandica, Eastern population, in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. vii + 65 pp. (https://wildlife-species.canada.ca/species-risk-registry/virtual_sara/files/cosewic/Barrow’s Goldeneye_2000_e.pdf) Distribution delineation is from the Environment and Climate Change Canada (ECCC) 'Range Map extents - Species at Risk' data set (https://open.canada.ca/data/en/dataset/d00f8e8c-40c4-435a-b790-980339ce3121) which is compiled from a combination of NatureServe (https://www.natureserve.org/) data, species at risk recovery strategies, ECCC resources and COSEWIC status reports.

Grus americana (whooping crane)

The Committee on the Status of Endangered Wildlife in Canada. 2010. COSEWIC assessment and status report on the Whooping Crane Grus americana in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. x + 36 pp. Distribution delineation is from the Envrinoment and Climate Change Canada (ECCC) 'Range Map extents - Species at Risk' data set (https://open.canada.ca/data/en/dataset/d00f8e8c-40c4-435a-b790-980339ce3121) which is compiled from a combination of NatureServe (https://www.natureserve.org/) data, species at risk recovery strategies, ECCC resources and COSEWIC status reports.

Salmo salar (Atlantic salmon), Quebec Eastern North Shore and Quebec Western North Shore populations

Department of Fisheries and Oceans and Québec Ministère des Ressources naturelles et de la Faune (DFO and MNRF). 2008. Conservation Status Report, Atlantic Salmon in Atlantic Canada and Quebec: PART I – Species Information. Can. MS Rep. Fish. Aquat. Sci. No. 2861, 208 p. (https://waves-vagues.dfo-mpo.gc.ca/Library/335625.pdf) Distribution delineation was digitized from Figure 9 (page 44) of the report above. The figure itself was adapted from the two reports below: O’Connell, M.F., J.B. Dempson, G. Chaput. 2006. Aspects of the life history, biology, and population dynamics of Atlantic salmon (Salmo salar L.) in Eastern Canada. (https://waves-vagues.dfo-mpo.gc.ca/Library/323204.pdf) Porter, T.R., M.C. Healey, M.F. O’Connell, E.T. Baum, A.T. Bielak, and Y. Côté. 1986. Implications of varying sea age at maturity of Atlantic salmon (Salmo salar) on yield to the fisheries. Pp. 110-117 In D. J. Meerburg [Ed.] Salmonid age at maturity. Can. Spec. Public. Fish. Aquat. Sci. 89, 118p. (https://waves-vagues.dfo-mpo.gc.ca/Library/35213.pdf)

This dataset contains all data on which the following publication below is based. Paper Citation: Risch, A.C., Frossard, A., Schütz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology doi: ... Methods Study area and study sites This study was conducted in YNP’s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163° N, 110.4169° W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995. For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951). The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter “days since kill”, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP’s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter “control”) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter “carcass”). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009). Ecosystem functions and soil properties We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 ºC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5 g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3–V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length >300 bp for 16S and >200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively. Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, Lüscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight). The other half of the soil sample was bagged in paper, dried to constant weight at 60°C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60°C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 – 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s. In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18’’, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20°C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (CTScontrol - CTSfinal/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties. Statistical analyses Univariate analyses for ecosystem functions, soil biotic and abiotic properties We tested whether individual ecosystem functions, soil biotic and abiotic properties differed

File List

   Koons_et_al_supplement.r (MD5: 7c626ca076aa6ab70d60cc7a908ce865)
  DescriptionKoons_et_al_supplement.r contains in-line R code for implementing a Gompertz state-space model of population dynamics with known extractions using the R2jags package that calls JAGS (which must be installed on your computer). On line 100, additional code is provided for estimating posterior model probabilities among a small set of hypothesized models.

This data is superseded by the MoBI 2024 data which can be found here.This map displays numbers of pollinators in the lower 48 United States that are protected by the Endangered Species Act and/or considered to be in danger of extinction. It is part of the Map of Biodiversity Importance (MoBI) data collection, a series of maps that identify areas of high importance for protecting species from extinction in the contiguous United States.Building on habitat suitability models for 2,216 of the nation’s most imperiled species, and information on range size and degree of protection derived from those models, the MoBI project provides a series of maps that can help inform conservation efforts. This map depicts richness of Critically Imperiled (categorized by NatureServe as “G1”), Imperiled (“G2”), and ESA-listed (i.e., species listed as Endangered or Threatened under the Endangered Species Act) pollinators (bumblebees, butterflies, and skippers; 43 species).High values identify areas where more imperiled pollinators are most likely to occur.Habitat models for most species were generated using the random forest algorithm. Data to train the models came from the NatureServe Network (e.g. state Natural Heritage Programs) supplemented by data from USGS BISON, and other sources of population and locality data. Environmental predictors used for the modeling include representations of terrain, climate, land cover, soils, and hydrology. The modeling resolution for terrestrial species was either 30 m (most species) or 330 m (some wide-ranging species). Models for aquatic species used the medium resolution National Hydrography Dataset (NHD) as the modeling unit. For species not amenable to random forest modeling, habitat maps were derived by buffering locality data and/or building simple deductive models based on habitat information. NatureServe converted habitat maps to a 990-m raster to provide a consistent unit of aggregation and avoid revealing the precise location of sensitive species. Richness values are simply a tally of the number of species with habitat overlapping a cell.These data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the NatureServe Network.For more information, see:Hamilton, H., Smyth, R.L., Young, B.E., Howard, T.G., Tracey, C., Breyer, S., Cameron, D.R., Chazal, A., Conley, A.K., Frye, C. and Schloss, C. (2022), Increasing taxonomic diversity and spatial resolution clarifies opportunities for protecting imperiled species in the U.S.. Ecological Applications. Accepted Author Manuscript e2534. https://doi.org/10.1002/eap.2534April 2021 Release Note: These data were updated with improved data. A minor issue with how the original data were snapped was fixed, ensuring that all species within all of the MOBI layers are aligned consistently, regardless of the layers to which a given species contributes. Results may thus differ somewhat from the February 2020 release.To download data as a layer package, navigate here.

This data is superseded by the MoBI 2024 data which can be found here.This map displays numbers of species in the lower 48 United States that are protected by the Endangered Species Act and/or considered to be in danger of extinction. It is part of the Map of Biodiversity Importance (MoBI) data collection, a series of maps that identify areas of high importance for protecting species from extinction in the contiguous United States.Building on habitat suitability models for 2,216 of the nation’s most imperiled species, and information on range size and degree of protection derived from those models, the MoBI project provides a series of maps that can help inform conservation efforts. This map depicts richness of Critically Imperiled (categorized by NatureServe as “G1”), Imperiled (“G2”), and ESA-listed (i.e., species listed as Endangered or Threatened under the Endangered Species Act) species in the following groups:Vertebrates (birds, mammals, amphibians, reptiles, freshwater fishes; 309 species) Freshwater invertebrates (mussels and crayfishes; 228 species) Pollinators (bumblebees, butterflies, and skippers; 43 species) Vascular plants (1,636 species)High values identify areas where more imperiled species are most likely to occur.Habitat models for most species were generated using the random forest algorithm. Data to train the models came from the NatureServe Network (e.g. state Natural Heritage Programs) supplemented by data from USGS BISON, and other sources of population and locality data. Environmental predictors used for the modeling include representations of terrain, climate, land cover, soils, and hydrology. The modeling resolution for terrestrial species was either 30 m (most species) or 330 m (some wide-ranging species). Models for aquatic species used the medium resolution National Hydrography Dataset (NHD) as the modeling unit. For species not amenable to random forest modeling, habitat maps were derived by buffering locality data and/or building simple deductive models based on habitat information. NatureServe converted habitat maps to a 990-m raster to provide a consistent unit of aggregation and avoid revealing the precise location of sensitive species. Richness values are simply a tally of the number of species with habitat overlapping a cell.These data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the NatureServe Network.For more information, see:Hamilton, H., Smyth, R.L., Young, B.E., Howard, T.G., Tracey, C., Breyer, S., Cameron, D.R., Chazal, A., Conley, A.K., Frye, C. and Schloss, C. (2022), Increasing taxonomic diversity and spatial resolution clarifies opportunities for protecting imperiled species in the U.S.. Ecological Applications. Accepted Author Manuscript e2534. https://doi.org/10.1002/eap.2534April 2021 Release Note: These data were updated with improved data. 33 species were added to the aggregate result that were previously erroneously excluded. In addition, a minor issue with how the original data were snapped was fixed, ensuring that all species within all of the MOBI layers are aligned consistently, regardless of the layers to which a given species contributes. Results may thus differ somewhat from the February 2020 release.To download data as a layer package, navigate here.

This data is superseded by the MoBI 2024 data which can be found here.This map displays numbers of freshwater invertebrates in the lower 48 United States that are protected by the Endangered Species Act and/or considered to be in danger of extinction. It is part of the Map of Biodiversity Importance (MoBI) data collection, a series of maps that identify areas of high importance for protecting species from extinction in the contiguous United States.Building on habitat suitability models for 2,216 of the nation’s most imperiled species, and information on range size and degree of protection derived from those models, the MoBI project provides a series of maps that can help inform conservation efforts. This map depicts richness of Critically Imperiled (categorized by NatureServe as “G1”), Imperiled (“G2”), and ESA-listed (i.e., species listed as Endangered or Threatened under the Endangered Species Act) freshwater invertebrates (mussels and crayfishes; 228 species).High values identify areas where more imperiled freshwater invertebrates are most likely to occur.Habitat models for most species were generated using the random forest algorithm. Data to train the models came from the NatureServe Network (e.g. state Natural Heritage Programs) supplemented by data from USGS BISON, and other sources of population and locality data. Environmental predictors used for the modeling include representations of terrain, climate, land cover, soils, and hydrology. The modeling resolution for terrestrial species was either 30 m (most species) or 330 m (some wide-ranging species). Models for aquatic species used the medium resolution National Hydrography Dataset (NHD) as the modeling unit. For species not amenable to random forest modeling, habitat maps were derived by buffering locality data and/or building simple deductive models based on habitat information. NatureServe converted habitat maps to a 990-m raster to provide a consistent unit of aggregation and avoid revealing the precise location of sensitive species. Richness values are simply a tally of the number of species with habitat overlapping a cell.These data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the NatureServe Network.For more information, see:Hamilton, H., Smyth, R.L., Young, B.E., Howard, T.G., Tracey, C., Breyer, S., Cameron, D.R., Chazal, A., Conley, A.K., Frye, C. and Schloss, C. (2022), Increasing taxonomic diversity and spatial resolution clarifies opportunities for protecting imperiled species in the U.S.. Ecological Applications. Accepted Author Manuscript e2534. https://doi.org/10.1002/eap.2534April 2021 Release Note: These data were updated with improved data. 1 species was added to the aggregate result that were previously erroneously excluded. In addition, a minor issue with how the original data were snapped was fixed, ensuring that all species within all of the MOBI layers are aligned consistently, regardless of the layers to which a given species contributes. Results may thus differ somewhat from the February 2020 release.To download data as a layer package, navigate here.

This data is superseded by the MoBI 2024 data which can be found here.This map displays numbers of vertebrates in the lower 48 United States that are protected by the Endangered Species Act and/or considered to be in danger of extinction. It is part of the Map of Biodiversity Importance (MoBI) data collection, a series of maps that identify areas of high importance for protecting species from extinction in the contiguous United States.Building on habitat suitability models for 2,216 of the nation’s most imperiled species, and information on range size and degree of protection derived from those models, the MoBI project provides a series of maps that can help inform conservation efforts. This map depicts richness of Critically Imperiled (categorized by NatureServe as “G1”), Imperiled (“G2”), and ESA-listed (i.e., species listed as Endangered or Threatened under the Endangered Species Act) vertebrates (birds, mammals, amphibians, reptiles, freshwater fishes; 309 species).High values identify areas where more imperiled vertebrates are most likely to occur.Habitat models for most species were generated using the random forest algorithm. Data to train the models came from the NatureServe Network (e.g. state Natural Heritage Programs) supplemented by data from USGS BISON, and other sources of population and locality data. Environmental predictors used for the modeling include representations of terrain, climate, land cover, soils, and hydrology. The modeling resolution for terrestrial species was either 30 m (most species) or 330 m (some wide-ranging species). Models for aquatic species used the medium resolution National Hydrography Dataset (NHD) as the modeling unit. For species not amenable to random forest modeling, habitat maps were derived by buffering locality data and/or building simple deductive models based on habitat information. NatureServe converted habitat maps to a 990-m raster to provide a consistent unit of aggregation and avoid revealing the precise location of sensitive species. Richness values are simply a tally of the number of species with habitat overlapping a cell.These data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the NatureServe Network.For more information, see:Hamilton, H., Smyth, R.L., Young, B.E., Howard, T.G., Tracey, C., Breyer, S., Cameron, D.R., Chazal, A., Conley, A.K., Frye, C. and Schloss, C. (2022), Increasing taxonomic diversity and spatial resolution clarifies opportunities for protecting imperiled species in the U.S.. Ecological Applications. Accepted Author Manuscript e2534. https://doi.org/10.1002/eap.2534April 2021 Release Note: These data were updated with improved data. 6 species were added to the aggregate result that were previously erroneously excluded. In addition, a minor issue with how the original data were snapped was fixed, ensuring that all species within all of the MOBI layers are aligned consistently, regardless of the layers to which a given species contributes. Results may thus differ somewhat from the February 2020 release.To download data as a layer package, navigate here.

Once thought to be extinct, running buffalo clover was rediscovered in West Virginia in the 1980s, when it was listed as endangered. Researchers at the Fernow Experimental Forest have monitored its populations over time and discovered the habitat characteristics that promote its growth and survival. As part of this monitoring work, researchers have found that running buffalo clover tends to grow following partial harvests and can be found along skid trails. This map, which shows the population counts of running buffalo clover over time alongside skid trails and the silvicultural treatment of each subcompartment, allows viewers to see the relationship between running buffalo clover population growth and light disturbance. Thanks in part to the research at the Fernow Experimental Forest, running buffalo clover is no longer listed as endangered. This map is featured in the 2025 Story Map "Bulldozers as Bison: Running Buffalo Clover Research and Management from the Fernow Experimental Forest"Primary Source: Thomas-Van Gundy, Melissa. 2022. Twenty-year trends in running buffalo clover (Trifolium stoloniferum Muhl. Ex A. Eaton; Fabaceae) on a managed forest in northeastern West Virginia. Natural Areas Journal. 42(1): 79-88. https://doi.org/10.3375/21-19.

About this dataset

Context

The dataset tabulates the Bison household income by age. The dataset can be utilized to understand the age-based income distribution of Bison income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Bison, KS annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Bison, KS household incomes: Comparative analysis across 16 income brackets

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Bison income distribution by age. You can refer the same here

This data is superseded by the MoBI 2024 data which can be found here.This map displays numbers of vascular plants in the lower 48 United States that are protected by the Endangered Species Act and/or considered to be in danger of extinction. It is part of the Map of Biodiversity Importance (MoBI) data collection, a series of maps that identify areas of high importance for protecting species from extinction in the contiguous United States.Building on habitat suitability models for 2,216 of the nation’s most imperiled species, and information on range size and degree of protection derived from those models, the MoBI project provides a series of maps that can help inform conservation efforts. This map depicts richness of Critically Imperiled (categorized by NatureServe as “G1”), Imperiled (“G2”), and ESA-listed (i.e., species listed as Endangered or Threatened under the Endangered Species Act) vascular plants (1,636 species).High values identify areas where more imperiled vascular plants are most likely to occur.Habitat models for most species were generated using the random forest algorithm. Data to train the models came from the NatureServe Network (e.g. state Natural Heritage Programs) supplemented by data from USGS BISON, and other sources of population and locality data. Environmental predictors used for the modeling include representations of terrain, climate, land cover, soils, and hydrology. The modeling resolution for terrestrial species was either 30 m (most species) or 330 m (some wide-ranging species). Models for aquatic species used the medium resolution National Hydrography Dataset (NHD) as the modeling unit. For species not amenable to random forest modeling, habitat maps were derived by buffering locality data and/or building simple deductive models based on habitat information. NatureServe converted habitat maps to a 990-m raster to provide a consistent unit of aggregation and avoid revealing the precise location of sensitive species. Richness values are simply a tally of the number of species with habitat overlapping a cell.These data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the NatureServe Network.For more information, see:Hamilton, H., Smyth, R.L., Young, B.E., Howard, T.G., Tracey, C., Breyer, S., Cameron, D.R., Chazal, A., Conley, A.K., Frye, C. and Schloss, C. (2022), Increasing taxonomic diversity and spatial resolution clarifies opportunities for protecting imperiled species in the U.S.. Ecological Applications. Accepted Author Manuscript e2534. https://doi.org/10.1002/eap.2534April 2021 Release Note: These data were updated with improved data. 26 species were added to the aggregate result that were previously erroneously excluded. In addition, a minor issue with how the original data were snapped was fixed, ensuring that all species within all of the MOBI layers are aligned consistently, regardless of the layers to which a given species contributes. Results may thus differ somewhat from the February 2020 release.To download data as a layer package, navigate here.

Census of Agriculture, 2021. Number of horses and ponies, donkeys and mules, goats, llamas and alpacas, bison, elk, deer, rabbits and mink.