Environmental Analysis Data: These data were compiled to investigate the complex interactions between environmental gradients and geographic distance across the Intermountain West of the western United States. Due to complex topography, physiographic heterogeneity, and complicated relationships with large bodies of water, spatial autocorrelation of environmental similarity may be expected. We provide an R script (VarioAnalysis.R) that uses four associated data files (annualprecip.csv, annualSWA.csv, annualtemp.csv, key.csv) to reproduce Figure 3 in Massatti et al. 2020 (see Larger Work Citation). The data files contain information on yearly soil water availability, temperature, and precipitation, which are summed or averaged and used to test autocorrelations using semi variograms. There is also a shapefile (see Source Data) and raster (RasterbySiteID.tif) that ties all of the site-specific information together and places data into a spatial context. The script and data were developed, extracted, and/or compiled by R.K. Shriver. Genetic Analysis Data: These data were compiled to assess the relationship between genetic differentiation and geographic distance in the Intermountain West of the western United States. Included are 14 files: 13 tab-delimited text files that detail species-specific data and one R script (czi.R) that uses data within the 13 files to reproduce Figures 1 and 2 in Massatti et al. 2020 (see Larger Work Citation). Species-specific files include site names, location information (latitude/longitude), and information on which genetic population each site belongs to according to the original publication document (see Table 1 in the Larger Work Citation). The R script is annotated to provide important information regarding how the analyses work and how they can be modified if users want to tailor analyses to other geographic regions. The script and data were developed, extracted, and/or compiled by R. Massatti.

These data were compiled to investigate the evolutionary history of Graham's beardtongue (Penstemon grahamii). Objective(s) of our study were to determine the evolutionary history of P. grahamii, including ancestral population sizes, the history of population divergences, and historical connectivity. In addition, we characterized population structure, genetic diversity summary statistics, and landscape factors influencing differentiation. These data represent anonymous loci sequenced from throughout the P. grahamii genome (specifically, .vcf and .structure files). Data in these files were manipulated to represent site frequency spectra between population pairs (.data files). These data were collected in 2019 from across the P. grahamii distribution, which is located in northeastern Utah and western Colorado. Specifically, plants are located at lower elevations on the northern slope of the Book Cliffs, which forms the southern side of the Uinta Basin. These data were collected by employees of the U.S. Geological Survey. Known occurrences of P. grahamii were visited and leaf samples from individual plants were collected from across the species’ distribution. These data can be used to further investigate the genetic differentiation, genetic diversity, and evolutionary history of P. grahamii.

These data were compiled to investigate the evolutionary history of Graham's beardtongue (Penstemon grahamii). Objective(s) of our study were to determine the evolutionary history of P. grahamii, including ancestral population sizes, the history of population divergences, and historical connectivity. In addition, we characterized population structure, genetic diversity summary statistics, and landscape factors influencing differentiation. These data represent anonymous loci sequenced from throughout the P. grahamii genome (specifically, .vcf and .structure files). Data in these files were manipulated to represent site frequency spectra between population pairs (.data files). These data were collected in 2019 from across the P. grahamii distribution, which is located in northeastern Utah and western Colorado. Specifically, plants are located at lower elevations on the northern slope of the Book Cliffs, which forms the southern side of the Uinta Basin. These data were collected by employees of the U.S. Geological Survey. Known occurrences of P. grahamii were visited and leaf samples from individual plants were collected from across the species’ distribution. These data can be used to further investigate the genetic differentiation, genetic diversity, and evolutionary history of P. grahamii.

Genetic variation is a well-known indicator of population fitness yet is not typically included in monitoring programs for sensitive species. Additionally, most programs monitor populations at one scale, which can lead to potential mismatches with ecological processes critical to species’ conservation. Recently developed methods generating hierarchically nested population units (i.e., clusters of varying scales) for greater sage-grouse (Centrocercus urophasianus) have identified population trend declines across spatiotemporal scales to help managers target areas for conservation. The same clusters used as a proxy for spatial scale can alert managers to local units (i.e., fine-scale) with low genetic diversity relative to regional units (i.e., coarse-scale), further facilitating identification of management targets. We developed a genetic warning system utilizing previously developed hierarchical population units to identify management-relevant areas with low genetic diversity within the greater sage-grouse range. Within this warning system we characterized conservation concern thresholds based on values of genetic diversity for hierarchically nested populations. We developed a spatial data set to display genetic diversity values and conservation concern information from a Genetic Warning System (GWS) for population monitoring of greater sage-grouse, as described in Zimmerman et al. (2022). Here we added the genetic diversity estimates (allelic richness and expected heterozygosity) and GWS information as attributes to the relevant fine-scale (level 2) and coarse-scale (level 13) previously developed hierarchically nested population clusters (O’Donnell et al. 2019, O’Donnell et al. 2022). The GWS incorporates population trend decline watches and warnings from the Targeted Annual Warning System (TAWS) for greater sage-grouse as reported in Coates et al. (2021) to further refine degree of conservation concern.

Genetic variation is a well-known indicator of population fitness yet is not typically included in monitoring programs for sensitive species. Additionally, most programs monitor populations at one scale, which can lead to potential mismatches with ecological processes critical to species’ conservation. Recently developed methods generating hierarchically nested population units (i.e., clusters of varying scales) for greater sage-grouse (Centrocercus urophasianus) have identified population trend declines across spatiotemporal scales to help managers target areas for conservation. The same clusters used as a proxy for spatial scale can alert managers to local units (i.e., fine-scale) with low genetic diversity relative to regional units (i.e., coarse-scale), further facilitating identification of management targets. We developed a genetic warning system utilizing previously developed hierarchical population units to identify management-relevant areas with low genetic diversity within the greater sage-grouse range. Within this warning system we characterized conservation concern thresholds based on values of genetic diversity for hierarchically nested populations. We developed a spatial data set to display genetic diversity values and conservation concern information from a Genetic Warning System (GWS) for population monitoring of greater sage-grouse, as described in Zimmerman et al. (2022). Here we added the genetic diversity estimates (allelic richness and expected heterozygosity) and GWS information as attributes to the relevant fine-scale (level 2) and coarse-scale (level 13) previously developed hierarchically nested population clusters (O’Donnell et al. 2019, O’Donnell et al. 2022). The GWS incorporates population trend decline watches and warnings from the Targeted Annual Warning System (TAWS) for greater sage-grouse as reported in Coates et al. (2021) to further refine degree of conservation concern.

These data were compiled to investigate the demographic, phylogeographic, and adaptation history of Hilaria jamesii. The data release consists of three tab delimited text files that may be used to infer population structure or putative adaptive loci (hija_adaptation_dataset.stru), relationships among sampling localities (hija_phylogeny_dataset.phylip), or genetic diversity statistics (hija_diversity_stats.vcf). All files record genetic variation on an individual (.stru and .vcf) or sampling locality (.phylip) level. The .vcf file contains all of the information contained in the other files, but the file structures vary based on the programs used for analysis. These files may be opened and edited in a text editor program, such as Notepad ++ (PC) or BBEdit (Mac). The .vcf file can be loaded into the Stacks population program (Catchen et al. 2013) to calculate genetic diversity statistics. The .phylip file can be uploaded to phyML to generate a tree-based visualization of relationships ( http://www.atgc-montpellier.fr/phyml/). The .stru file can be used in the STRUCTURE program (Falush et al. 2007) to estimate population structure.

These data were compiled to investigate the demographic, phylogeographic, and adaptation history of Hilaria jamesii. The data release consists of three tab delimited text files that may be used to infer population structure or putative adaptive loci (hija_adaptation_dataset.stru), relationships among sampling localities (hija_phylogeny_dataset.phylip), or genetic diversity statistics (hija_diversity_stats.vcf). All files record genetic variation on an individual (.stru and .vcf) or sampling locality (.phylip) level. The .vcf file contains all of the information contained in the other files, but the file structures vary based on the programs used for analysis. These files may be opened and edited in a text editor program, such as Notepad ++ (PC) or BBEdit (Mac). The .vcf file can be loaded into the Stacks population program (Catchen et al. 2013) to calculate genetic diversity statistics. The .phylip file can be uploaded to phyML to generate a tree-based visualization of relationships ( http://www.atgc-montpellier.fr/phyml/). The .stru file can be used in the STRUCTURE program (Falush et al. 2007) to estimate population structure.

These data represent nuclear microsatellite data collected from four riparian plant species that occur in and around Grand Canyon National Park: Populus fremontii (POFR), Salix gooddingii (SAGO), Salix exigua (SAEX), and Prosopis glandulosa (PRGL). These data were collected for population genetic analysis to help inform native plant material development in Grand Canyon National Park. Leaf samples were collected at sites spanning 470 km of the Colorado River between Glen Canyon Dam and Lake Mead and in tributaries. Known revegetation areas were not sampled. We aimed to collect leaf tissue from at least 15 individuals at each sample site. If there were fewer than 15 individuals per species at a site, leaf tissue was collected from every individual. Leaves were immediately dried and stored in silica gel. For P. fremontii, S. gooddingii, and P. glandulosa, total genomic DNA was extracted from dried leaf tissue using a high-molecular weight protocol with modifications. For S. exigua, DNeasy Plant Minikits were used. We amplified 9, 9, 11, and 13 loci for P. fremontii, S. gooddingii, S. exigua, and P. glandulosa, respectively. All loci were amplified using polymerase chain reaction (PCR) and fragment analysis processed on an ABI 3730xl Genetic Analyzer with GeneScan LIZ500 internal size standard. Allele fragment sizes were scored using GeneMarker v2.2.0. Loci that were missing more than 5% of values, were not polymorphic, or could not be reliably scored were omitted. Nine loci were included for P. fremontii, six for S. gooddingii, eight for P. glandulosa, and nine for S. exigua. All species are diploid, so each microsatellite locus has two values. Associated with the microsatellite data are labels delineating what collection site each individual came from, which genetic group each individual was assigned to during statistical analyses, and information about those sites. Specifically, sites are noted as being on the Colorado River or on a tributary to it, and inside or outside of the canyon rims.

These data represent nuclear microsatellite data collected from four riparian plant species that occur in and around Grand Canyon National Park: Populus fremontii (POFR), Salix gooddingii (SAGO), Salix exigua (SAEX), and Prosopis glandulosa (PRGL). These data were collected for population genetic analysis to help inform native plant material development in Grand Canyon National Park. Leaf samples were collected at sites spanning 470 km of the Colorado River between Glen Canyon Dam and Lake Mead and in tributaries. Known revegetation areas were not sampled. We aimed to collect leaf tissue from at least 15 individuals at each sample site. If there were fewer than 15 individuals per species at a site, leaf tissue was collected from every individual. Leaves were immediately dried and stored in silica gel. For P. fremontii, S. gooddingii, and P. glandulosa, total genomic DNA was extracted from dried leaf tissue using a high-molecular weight protocol with modifications. For S. exigua, DNeasy Plant Minikits were used. We amplified 9, 9, 11, and 13 loci for P. fremontii, S. gooddingii, S. exigua, and P. glandulosa, respectively. All loci were amplified using polymerase chain reaction (PCR) and fragment analysis processed on an ABI 3730xl Genetic Analyzer with GeneScan LIZ500 internal size standard. Allele fragment sizes were scored using GeneMarker v2.2.0. Loci that were missing more than 5% of values, were not polymorphic, or could not be reliably scored were omitted. Nine loci were included for P. fremontii, six for S. gooddingii, eight for P. glandulosa, and nine for S. exigua. All species are diploid, so each microsatellite locus has two values. Associated with the microsatellite data are labels delineating what collection site each individual came from, which genetic group each individual was assigned to during statistical analyses, and information about those sites. Specifically, sites are noted as being on the Colorado River or on a tributary to it, and inside or outside of the canyon rims.

These data were compiled to investigate the demographic and phylogeographic of Carex specuicola. Objectives of our study were to understand the demographic and dispersal history of Carex specuicola across hanging gardens, the hybridization history between Carex specuicola and Carex utahensis, and the population structure of Carex specuicola across its distribution. The data release consists of three tab delimited text files that may be used to infer population structure and diversity (CASP.stru), relationships among sampling localities Carex.phylip), or genetic diversity statistics and demographic history (Carex.snps.vcf). These data represent genetic variation on an individual (.stru and .vcf) or sampling locality (.phylip) level. The .vcf file contains all of the information contained in the other files, but the file structures vary based on the programs used for analysis. These files may be opened and edited in a text editor program, such as Notepad ++ (PC) or BBEdit (Mac). The .vcf file can be loaded into the Stacks population program (Catchen et al. 2013) to calculate genetic diversity statistics. The .phylip file can be uploaded to phyML to generate a tree-based visualization of relationships (http://www.atgc-montpellier.fr/phyml/). The .stru file can be used in the STRUCTURE program (Falush et al. 2007) to estimate population structure. These data were collected in 2020 using leaf samples collected from hanging gardens in the Four Corners region of Arizona and Utah. These data were collected by U.S. Geological Survey and Deaver Herbarium researchers who visited hanging garden sites and sampled leaf tissues from individual plants.