These data were estimated for use in the bioecomomic model simulation of the rainbow trout population in the Colorado River in Marble Canyon. The initial rainbow trout abundance is a vector (RBT_intN) representing the population of rainbow trout within each river segment (151 mile long sergments) along the mainstem of the Colorado River from Lees Ferry to 151 river miles downstream. The movement matrix (MMat) is a distribution that estimates the probability that a rainbow trout wil move to any one of the 151 river segments downstream of Lees Ferry.

These data were compiled to determine whether transient population dynamics substantially alter population growth rates of sagebrush after disturbance, impede resilience and restoration, and in turn drive ecosystem transformation. Data were collected from 2014-2016 on sagebrush population height distributions at 531 sites across the Great Basin that had burned and were subsequently reseeded by the BLM. These data include field data on sagebrush density in 6 size classes and site attributes (seeding year, sampling year, random site designation, elevation, seeding rate). Also included are modeled spring soil moisture data at each site from the year of seeding to sampling. This data release includes associated software code allows the inference of demographic rates (survival, reproduction, and individual growth) of sagebrush using Hamiltonian Monte Carlo approaches in Stan (https://mc-stan.org/).

These data were compiled to determine whether transient population dynamics substantially alter population growth rates of sagebrush after disturbance, impede resilience and restoration, and in turn drive ecosystem transformation. Data were collected from 2014-2016 on sagebrush population height distributions at 531 sites across the Great Basin that had burned and were subsequently reseeded by the BLM. These data include field data on sagebrush density in 6 size classes and site attributes (seeding year, sampling year, random site designation, elevation, seeding rate). Also included are modeled spring soil moisture data at each site from the year of seeding to sampling. This data release includes associated software code allows the inference of demographic rates (survival, reproduction, and individual growth) of sagebrush using Hamiltonian Monte Carlo approaches in Stan (https://mc-stan.org/).

These data were compiled to assess potential impacts of altered operations of Glen Canyon Dam on riparian plant resources. Objective(s) of our study were to quantify potential responses of specific vegetation metrics. These data represent predicted changes in vegetation metrics based on the data in the Interim Guidelines and LTEMP_SEIS folders. These data were collected in Grand Canyon and Glen Canyon below the dam from 2014-2019. These data were collected by USGS SBSC scientists through ground-based vegetation surveys. These data can be used to predict responses of vegetation metrics only to the specific alterations to dam operations that were simulated.

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.

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 accompany flow modeling work on Little Colorado river above the mouth (USGS gage 09402300). The data include example models in FaSTMECH and SToRM solvers in the iRIC framework, topographic data collected by LiDAR and total station in June 2017, and high water marks from nine historic floods. Other data also include location and other information for control points and gage structures. Topographic data include ground topography collected by LiDAR and channel bathymetry collected by total station survey of a 2500 meter reach of the Little Colorado River ending near the confluence with the Colorado River. High water mark data were collected by USGS personnel using total station surveys and are divided into nine distinct sets based on elevation profile.

We present five hierarchical demarcations of greater sage-grouse population structure, representing the spatial structure of populations which can exist due to differences in dispersal abilities, landscape configurations, and mating behavior. These demarcations represent Thiessen polygons of graph constructs (least-cost path [LCP] minimum spanning trees [MST; LCP-MST]) representing greater sage-grouse population structure. Because the graphs included locational information of sage-grouse breeding sites, we have provided polygons of the population structure. We also present two results using graph analytics representing node/connectivity importance based on our population structure. Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. We developed an approach to define hierarchical population structure (in other words, demarcation of subpopulations) using graph theory (in other words, connectivity) from an amalgamation of biological inferences encompassing dispersal capabilities based on movements and genetic flow, seasonal habitat conditions, and functional processes (for example, selection of habitat at multiple scales) affecting movements. We applied our approach to greater sage-grouse (Centrocercus urophasianus), an upland gamebird species of conservation concern in western United States. We defined sage-grouse population structure by creating a cost surface, informed from functional processes of habitat characteristics to account for the resistance of inter-patch movements, and developing least-cost paths between breeding habitat sites (leks). The least-cost paths were combined into a multi-path graph construct for which we then used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into structures of subpopulations.

We present five hierarchical demarcations of greater sage-grouse population structure, representing the spatial structure of populations which can exist due to differences in dispersal abilities, landscape configurations, and mating behavior. These demarcations represent Thiessen polygons of graph constructs (least-cost path [LCP] minimum spanning trees [MST; LCP-MST]) representing greater sage-grouse population structure. Because the graphs included locational information of sage-grouse breeding sites, we have provided polygons of the population structure. We also present two results using graph analytics representing node/connectivity importance based on our population structure. Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. We developed an approach to define hierarchical population structure (in other words, demarcation of subpopulations) using graph theory (in other words, connectivity) from an amalgamation of biological inferences encompassing dispersal capabilities based on movements and genetic flow, seasonal habitat conditions, and functional processes (for example, selection of habitat at multiple scales) affecting movements. We applied our approach to greater sage-grouse (Centrocercus urophasianus), an upland gamebird species of conservation concern in western United States. We defined sage-grouse population structure by creating a cost surface, informed from functional processes of habitat characteristics to account for the resistance of inter-patch movements, and developing least-cost paths between breeding habitat sites (leks). The least-cost paths were combined into a multi-path graph construct for which we then used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into structures of subpopulations.

These data were compiled to forecast climate exposure for 29 major plant communities in the southwestern United States to changing climate under two future climate change scenarios. An objective of our study was that species within plant communities have unique climate suitability signatures and forecast changes in climatic suitability will not be uniform within the species respective communities or among species within the community. We developed these spatial models where climate exposure is represented as a composite score of the climate exposure of characteristic plants for each community. Baseline climate exposure rasters represent a baseline climate change and were developed for current climate conditions (~1960-1990) from WordlClim v1.4 data. Climate exposure ratings are forecast for the period 2040-60 using the Community Climate System Model v4 (CCSM4) for Representative Concentration Pathways (RCP) 4.5 and 8.5. Climate exposure is indicated as a categorical score (1-5) that is a composite of climate suitability scores for characteristic plant species identified for each plant community and represents a range of climate exposure ratings from unfavorable to best climatic suitability. Plant communities are represented as mapped by the USGS – Core Science Analytics, Synthesis, and Library – Gap Analysis Project, 2011 (Gap Landcover).