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wy_lvl7_coarsescale: Wyoming hierarchical cluster level 7 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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wy_lvl2_finescale: Wyoming hierarchical cluster level 2 (fine-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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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...
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We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different population growth rates among smaller clusters. Equally so, the spatial structure and ecological...
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nv_lvl6_coarsescale: Nevada hierarchical cluster level 6 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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wy_lvl8_coarsescale: Wyoming hierarchical cluster level 8 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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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...
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wy_lvl1_finescale: Wyoming hierarchical cluster level 1 (fine-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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wy_lvl4_moderatescale: Wyoming hierarchical cluster level 4 (moderate-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result...
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wy_lvl5_coarsescale: Wyoming hierarchical cluster level 5 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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nv_lvl7_coarsescale: Nevada hierarchical cluster level 7 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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nv_lvl2_finescale: Nevada hierarchical cluster level 2 (fine-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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wy_lvl6_coarsescale: Wyoming hierarchical cluster level 6 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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We monitored Greater Sage-Grouse (Centrocercus urophasianus; hereafter, Sage-Grouse) nests and various habitat characteristics at the nest locations near Susanville in northeastern California, crossing over into northwestern Nevada. We employed a before-after-control-impact (BACI) experimental design to account for spatiotemporal heterogeneity in the system and to derive estimates of relative change in survival parameters. Sage-Grouse nest survival decreased after the Rush Fire but decreased more in the burned area relative to the unburned area. Although female Sage-Grouse continued to occupy burned areas, nest survival was reduced from 52 percent to 19 percent. Using a BACI ratio approach we found that nest survival...
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Indices of habitat suitability and animal abundance provide useful proxy-based measures adaptive management (Coates et al. 2015a). Doherty et al. (in review) derived a range-wide population index model for sage-grouse using such indices that incorporated sage-grouse habitat suitability generated from Random Forest models (Evans et al. 2011), and spatially explicit abundance measures based on fixed kernel density functions informed by distributions of lek locations (lek locations defined by Western Association of Fish and Wildlife Agencies, see Coates et al. 2015b). The kernels were generated using two bandwidth distances representing the majority of breeding habitat in relation to leks (6.4 km) and seasonal movements...
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wy_lvl9_coarsescale: Wyoming hierarchical cluster level 9 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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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...
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Two different great basin perimeter files were intersected and dissolved to create the outer perimeter of the great basin for use modeling long-term wildfire effects on sage-grouse population growth, and development of sage-grouse concentration areas based on modeled habitat quality, lek density, and population abundance (Coates et al. 2015). These two perimeter files included a 1:1,000,000 map of hydrographic areas in the Great Basin) (Buto 2009), and vegetation characteristics (Karl et al. 2001). The resulting Modified Great Basin Extent represented a combination of hydrographic and floristic features best suited for the defining the spatial extent of the analyses. To ensure moving window analyses of habitat and...
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These data include encounter histories, nest attempts, hatched egg counts, brood counts, time-varying matrices, survival statistics, and lek counts, all used in an integrated population model (IPM) to determine the status of a population of translocated Columbian sharp-tailed grouse (Tympanuchus phasianellus columbianus; CSTG) in Nevada. Sharp-tailed grouse were translocated to a remote site in Nevada starting in 2013 through 2017. These data support the following publication: Mathews, S.R., ​Coates, P.S., Prochazka, B.G., Espinosa, S.P., and Delehanty, D.J., 2021, Offspring of translocated individuals drive the successful reintroduction of Columbian Sharp-tailed Grouse in Nevada, USA, Ornithological Applications,...
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These data are the results of a spatially interpolated integrated population model (SIIPM) fit to count and demographic data collected from populations of Greater Sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) located in Nevada, U.S.A. during 2013-2021. We used a novel framework, using integrated population models (IPMs), to express demographic relatedness among sampled and unsampled populations using geographic principles of spatial autocorrelation (Shepard, 1968; Tobler, 1970). Specifically, the framework pairs relatively inexpensive population count data with spatially interpolated demographic estimates. When conducted within a Bayesian framework, spatially interpolated demographic parameters...


map background search result map search result map Modifed Great Basin Extent (Buffered) Great Basin Sage-Grouse Concentration Areas Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Nevada and Wyoming, Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 2 (Nevada), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 6 (Nevada), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 7 (Nevada), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 1 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 2 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 4 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 5 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 6 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 7 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 8 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 9 (Wyoming), Interim Data to Inform an Integrated Population Model of Translocated Columbian Sharp-Tailed Grouse, Nevada 2013 - 2017 Greater sage-grouse population structure and connectivity data to inform the development of hierarchical population units (western United States) Genotypes and cluster definitions for a range-wide greater sage-grouse dataset collected 2005-2017 (ver 1.1, January 2023) Greater Sage-Grouse Nest Observations Before and After Wildfire Disturbance in Northeastern California (2007-2018) Greater sage-grouse genetic warning system, western United States (ver 1.1, January 2023) Spatially Explicit Estimates of Greater Sage-Grouse (Centrocercus urophasianus) Survival, Recruitment, and Rate of Population Change in Nevada, 2013-2021 Greater Sage-Grouse Nest Observations Before and After Wildfire Disturbance in Northeastern California (2007-2018) Data to Inform an Integrated Population Model of Translocated Columbian Sharp-Tailed Grouse, Nevada 2013 - 2017 Spatially Explicit Estimates of Greater Sage-Grouse (Centrocercus urophasianus) Survival, Recruitment, and Rate of Population Change in Nevada, 2013-2021 Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 1 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 2 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 4 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 5 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 6 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 7 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 8 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 9 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 2 (Nevada), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 6 (Nevada), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 7 (Nevada), Interim Great Basin Sage-Grouse Concentration Areas Modifed Great Basin Extent (Buffered) Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Nevada and Wyoming, Interim Genotypes and cluster definitions for a range-wide greater sage-grouse dataset collected 2005-2017 (ver 1.1, January 2023) Greater sage-grouse genetic warning system, western United States (ver 1.1, January 2023) Greater sage-grouse population structure and connectivity data to inform the development of hierarchical population units (western United States)