We developed and report a microsatellite data set composed of 52 microsatellite loci for 2305 individuals from 20 bison conservation herds (17 US federal, 1 tribal, 2 Canadian) and a single nucleotide polymorphism (SNP) data set composed of 5013 biallic loci for 376 individuals from 16 bison conservation herds that were used as part of a broader study. We also developed an algorithm to select a subset of SNPs that captures the genetic variation present in the full SNP data set. Human expansion is a major driver of both declining wildlife species abundance and the contraction of species’ distributions, increasing the risk of genetic erosion and the need for genetic monitoring. Rapidly advancing technology has expanded the types of genetic data that are available for wildlife conservation. However, the use of different genetic markers could result in different management decisions and, thus, must be considered carefully. Rebounding from near extinction in the early 1900s, the majority of plains bison (Bison bison bison) are managed as small and isolated herds. Microsatellite-based analyses are currently used to inform management of the US federal bison conservation herds. Transitioning from monitoring with tens of multiallelic loci (e.g., microsatellite loci) to thousands of biallelic loci (e.g., SNP loci) could increase genotyping efficiency and improve the precision of population genetic inference but would require an understanding of the inferential differences between genetic marker types.

We developed and report a microsatellite data set composed of 52 microsatellite loci for 2305 individuals from 20 bison conservation herds (17 US federal, 1 tribal, 2 Canadian) and a single nucleotide polymorphism (SNP) data set composed of 5013 biallic loci for 376 individuals from 16 bison conservation herds that were used as part of a broader study. We also developed an algorithm to select a subset of SNPs that captures the genetic variation present in the full SNP data set. Human expansion is a major driver of both declining wildlife species abundance and the contraction of species’ distributions, increasing the risk of genetic erosion and the need for genetic monitoring. Rapidly advancing technology has expanded the types of genetic data that are available for wildlife conservation. However, the use of different genetic markers could result in different management decisions and, thus, must be considered carefully. Rebounding from near extinction in the early 1900s, the majority of plains bison (Bison bison bison) are managed as small and isolated herds. Microsatellite-based analyses are currently used to inform management of the US federal bison conservation herds. Transitioning from monitoring with tens of multiallelic loci (e.g., microsatellite loci) to thousands of biallelic loci (e.g., SNP loci) could increase genotyping efficiency and improve the precision of population genetic inference but would require an understanding of the inferential differences between genetic marker types.

This GIS shapefile, "quantile_bands," is derived from a parent shapefile, "density_contours," which describes 1) a bison study area at Tallgrass Prairie National Preserve, Kansas, and 2) 25%, 50%, 75%, and 99% kernel density contours for locations of bison marked with GPS collars during 2021-2023. Percentages associated with parent contours describe nominal coverage, i.e., proportions of observations they are expected to encompass, and approximate the distribution of bison activity within the study area. Each contour encompasses lower-coverage contours: the 75% contour, for example, encompasses the 50% and 25% contours but not the 99% contour. Quantile bands correspond with intervals between contours in "density_contours." Each of the first 4 bands encompasses approximately 25% of bison locations. Percentages associated with contours describe nominal coverage, i.e., quantiles the bands are expected to encompass. Bands comprise a partition of the bison study area and do not overlap. Users are advised that ground conditions within the study area may change over time, leading to changes in bison distribution.

,This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83465. Thirteen colonies of black-tailed prairie dogs were studied within a 264-km2 area of the Central Plains Experimental Range and the Pawnee National Grasslands in Weld County, Colorado. Tissue Collection, DNA Extraction, and microsatellite genotype scoring was performed.,,

Monitoring change in genetic diversity in wildlife populations across multiple scales could facilitate prioritization of conservation efforts. We used microsatellite genotypes from 7,080 previously collected genetic samples from across the greater sage-grouse (Centrocercus urophasianus) range to develop a modelling framework for estimating genetic diversity within a recently developed hierarchically nested monitoring framework (clusters). The majority of these genetic samples (n=6560) were used in previous research (Oyler-McCance et al. 2014; Cross et. al 2018; Row et. al. 2018). Genetic diversity values associated with clusters across multiple scales could facilitate the identification of areas with low genetic diversity and inform the potential management or conservation priority and response. We also report the data used to define genetic diversity thresholds of conservation concern and a full reporting of the genetic diversity estimates associated with the evaluated clusters.

Monitoring change in genetic diversity in wildlife populations across multiple scales could facilitate prioritization of conservation efforts. We used microsatellite genotypes from 7,080 previously collected genetic samples from across the greater sage-grouse (Centrocercus urophasianus) range to develop a modelling framework for estimating genetic diversity within a recently developed hierarchically nested monitoring framework (clusters). The majority of these genetic samples (n=6560) were used in previous research (Oyler-McCance et al. 2014; Cross et. al 2018; Row et. al. 2018). Genetic diversity values associated with clusters across multiple scales could facilitate the identification of areas with low genetic diversity and inform the potential management or conservation priority and response. We also report the data used to define genetic diversity thresholds of conservation concern and a full reporting of the genetic diversity estimates associated with the evaluated clusters.

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

This GIS shapefile, "quantile_bands," is derived from a parent shapefile, "density_contours," which describes 1) a bison study area in the North Unit of Theodore Roosevelt National Park, North Dakota and 2) 25%, 50%, 75%, and 99% kernel density contours for locations of bison marked with GPS collars during 2017-2020. Percentages associated with contours describe nominal coverage, i.e., proportions of observations they are expected to encompass, and approximate the distribution of bison activity within the study area. Each contour encompasses lower-coverage contours: the 75% contour, for example, encompasses the 50% and 25% contours but not the 99% contour. Quantile bands correspond with intervals between contours in "density_contours." Each of the first 4 bands encompasses approximately 25% of bison locations. Percentages associated with contours describe nominal coverage, i.e., quantiles the bands are expected to encompass. Bands comprise a partition of the bison study area and do not overlap. Users are advised that ground conditions within the study area may change over time, leading to changes in bison distribution.

The GIS shapefile "bison_locations" provides 132996 locations of 5 adult female bison marked with global positioning system (GPS) collars at Tallgrass Prairie National Preserve during February 2021 to November 2023. We provide locations computed at 15-min and 1-h intervals for individual bison and the date and time of acquisition for each location. Users are advised that ground conditions within the study area may change over time, leading to changes in bison distribution.