We quantified the relative abundance of greater sage-grouse (Centrocercus urophasianus) populations within Bureau of Land Management (BLM) Sagebrush Focal Areas (SFAs).

We quantified the relative abundance of greater sage-grouse (Centrocercus urophasianus) populations within Bureau of Land Management (BLM) Sagebrush Focal Areas (SFAs).

Sagebrush (Artemisia spp.) ecosystems provide critical habitat for the near-threatened Greater sage-grouse (Centrocercus urophasianus), and future loss of sagebrush habitat because of land use change and global climate change is of concern. We used a dynamic additive spatio-temporal model to estimate effects of climate (spring-summer temperatures and precipitation) on sagebrush cover dynamics at 32 sage-grouse management (core) areas in Wyoming, 1985-2018. We then use the fitted models to make probabilistic projections of sagebrush cover in each core area across three time intervals (2018-2040, 2041-2070, 2071-2100) and under three climate change scenarios and weighted averages of 18 Global Circulation Models (ssp126, ssp245, and ssp585), producing 351 netCDF files (USGS_SageCastWY.zip).

Sagebrush (Artemisia spp.) ecosystems provide critical habitat for the near-threatened Greater sage-grouse (Centrocercus urophasianus), and future loss of sagebrush habitat because of land use change and global climate change is of concern. We used a dynamic additive spatio-temporal model to estimate effects of climate (spring-summer temperatures and precipitation) on sagebrush cover dynamics at 32 sage-grouse management (core) areas in Wyoming, 1985-2018. We then use the fitted models to make probabilistic projections of sagebrush cover in each core area across three time intervals (2018-2040, 2041-2070, 2071-2100) and under three climate change scenarios and weighted averages of 18 Global Circulation Models (ssp126, ssp245, and ssp585), producing 351 netCDF files (USGS_SageCastWY.zip).

The distance within which populations respond to features in a landscape (scale of effect) can indicate how disturbance and management may affect wildlife. Using annual counts of male Greater Sage-grouse (Centrocercus urophasianus) attending 584 leks in southwest Wyoming (2003-2019) and estimates of sagebrush cover from the Rangeland Condition Monitoring Assessment and Projection (RCMAP), we used a scale selection approach to jointly estimate the scale of effect and the effect of sagebrush cover in the surrounding landscape for sage-grouse population trends. We estimated these parameters using a state-space model fit with a Bayesian approach. Data formatting necessary for this analysis produced data stored in two lists, one for model constants (nimbleconstants_sg_wlci.txt, including number of years, number of sites [leks], number of scales, number of visits, indicators for site and year, and number of detection parameters) and one for model data (nimbledata_sg_wlci.txt, including lek counts/surveys in both long- and array-format, a matrix for detection covariates, an array for sagebrush cover [scaled], and unscaled arrays for sagebrush, ordinal date, and time since sunrise).

The distance within which populations respond to features in a landscape (scale of effect) can indicate how disturbance and management may affect wildlife. Using annual counts of male Greater Sage-grouse (Centrocercus urophasianus) attending 584 leks in southwest Wyoming (2003-2019) and estimates of sagebrush cover from the Rangeland Condition Monitoring Assessment and Projection (RCMAP), we used a scale selection approach to jointly estimate the scale of effect and the effect of sagebrush cover in the surrounding landscape for sage-grouse population trends. We estimated these parameters using a state-space model fit with a Bayesian approach. Data formatting necessary for this analysis produced data stored in two lists, one for model constants (nimbleconstants_sg_wlci.txt, including number of years, number of sites [leks], number of scales, number of visits, indicators for site and year, and number of detection parameters) and one for model data (nimbledata_sg_wlci.txt, including lek counts/surveys in both long- and array-format, a matrix for detection covariates, an array for sagebrush cover [scaled], and unscaled arrays for sagebrush, ordinal date, and time since sunrise).

Identifying ecologically relevant reference sites is important for evaluating ecosystem recovery, but the relevance of references that are temporally static is unclear in the context of vast landscapes with varying disturbance and environmental contexts over space and time. This question is pertinent for landscapes dominated by sagebrush (Artemisia spp.) which face a suite of threats from disturbance and development but also have lengthy recovery times. Here, we applied a dynamic reference approach to studying and projecting recovery of sagebrush on former oil and gas well pads in southwestern Wyoming, USA using over 3 decades of remote sensing data (1985–2018). We also used quantile regression to evaluate factors that may affect recovery including soils, weather, elevation, and well pad characteristics. We then created projections for percent recovery and years to recovery (relative to thresholds for greater sage-grouse [Centrocercus urophasianus] habitat) in areas identified as either nesting or summer (brood-rearing) habitat, resulting in 4 rasters, each with 5 bands representing 5 quantiles.

Identifying ecologically relevant reference sites is important for evaluating ecosystem recovery, but the relevance of references that are temporally static is unclear in the context of vast landscapes with varying disturbance and environmental contexts over space and time. This question is pertinent for landscapes dominated by sagebrush (Artemisia spp.) which face a suite of threats from disturbance and development but also have lengthy recovery times. Here, we applied a dynamic reference approach to studying and projecting recovery of sagebrush on former oil and gas well pads in southwestern Wyoming, USA using over 3 decades of remote sensing data (1985–2018). We also used quantile regression to evaluate factors that may affect recovery including soils, weather, elevation, and well pad characteristics. We then created projections for percent recovery and years to recovery (relative to thresholds for greater sage-grouse [Centrocercus urophasianus] habitat) in areas identified as either nesting or summer (brood-rearing) habitat, resulting in 4 rasters, each with 5 bands representing 5 quantiles.

Map of sage thrasher density (birds/ha) in relation to vegetation, abiotic, and anthropogenic features.

Map of sage thrasher density (birds/ha) in relation to vegetation, abiotic, and anthropogenic features.