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.

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 used 7 microsatellites to characterize the invasion genetics of 528 American bullfrogs sampled from the Yellowstone River floodplain (Montana). American bullfrog samples were collected from a 140 km section of the Yellowstone River near Billings, MT in 2013 - 2017.

We used 7 microsatellites to characterize the invasion genetics of 528 American bullfrogs sampled from the Yellowstone River floodplain (Montana). American bullfrog samples were collected from a 140 km section of the Yellowstone River near Billings, MT in 2013 - 2017.

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 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.

Collection of microsatellite genetic data from multiple projects involving southern mule deer (Odocoileus hemionus fuliginatus) in San Diego County, California. Samples were collected 2005-2007, 2010, 2012-2013, 2015, and 2018-2020.

Collection of microsatellite genetic data from multiple projects involving southern mule deer (Odocoileus hemionus fuliginatus) in San Diego County, California. Samples were collected 2005-2007, 2010, 2012-2013, 2015, and 2018-2020.