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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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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 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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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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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_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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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_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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This dataset contains two predictive lek (breeding site) persistence raster layers covering the U.S. greater sage-grouse distribution. In the United States, locations where males display and breed with females (i.e., leks) are often monitored annually by state wildlife agencies, providing valuable information on the persistence of birds in the surrounding areas. A U.S. range-wide lek database was recently compiled for greater sage-Grouse (O’Donnell et al. 2021), providing a standardized source of information to build statistical models to evaluate environmental characteristics associated with lek persistence. The compiled lek database classified a subset of leks as being either active (leks currently used for breeding...
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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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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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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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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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Wildfires are increasingly modifying wildlife habitat in the western United States and managers need ways to scope the pace and degree to which post-fire restoration actions can re-create habitat in dynamic landscapes. We simulated post-fire revegetation and greater sage-grouse (Centrocercus urophasianus) habitat restoration using a spatially explicit state-transition simulation model (STSM) developed for sagebrush ecosystems. The STSM represented the vegetation dynamics of the sagebrush ecosystem and included annual fires, annual grass invasion, conifer encroachment, and sagebrush revegetation restoration. We compared simulated vegetation output with sage-grouse perennial grass and sagebrush cover habitat needs...
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To assess the degree to which transplanting sagebrush (Artemisia spp.) could quickly restore former sage-grouse habitat and the strategies by which greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) habitat restoration is best accomplished, we linked vegetation transitions with habitat selection models to evaluate habitat recovery. Within our modeling extent (Tuscarora, Nevada), we simulated the fire-induced loss of habitat, planting of sagebrush seedlings, and the regrowth of sagebrush and other vegetation over 15 years. We used sagebrush growth equations and vegetation state transitions to return and grow vegetation within the burned and planted areas. Every year, we updated seasonal sage-grouse...
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In 'Simulation to evaluate response of population models to annual trends in detectability', we provide data and R code necessary to create simulation scenarios and estimate trends with different population models (Monroe et al. 2019). Literature cited: Monroe, A. P., G. T. Wann, C. L. Aldridge, and P. S. Coates. 2019. The importance of simulation assumptions when evaluating detectability in population models. Ecosphere 10(7):e02791. 10.1002/ecs2.2791, http://onlinelibrary.wiley.com/doi/10.1002/ecs2.2791/full.
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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_lvl3_moderatescale: Nevada hierarchical cluster level 3 (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 in...


map background search result map search result map 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 1 (Nevada), 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 3 (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 Simulation to evaluate response of population models to annual trends in detectability Greater sage-grouse habitat suitability 15-years post simulated fire event and sagebrush transplanting (2015-2030) Greater sage-grouse population structure and connectivity data to inform the development of hierarchical population units (western United States) U.S. range-wide spatial prediction layers of lek persistence probabilities for greater sage-grouse Greater sage-grouse genetic warning system, western United States (ver 1.1, January 2023) State-and-Transition Simulation Models to explore post-fire habitat restoration in three greater sage-grouse (Centrocercus urophasianus) Priority Areas for Conservation, USA (2018-2068) Greater sage-grouse habitat suitability 15-years post simulated fire event and sagebrush transplanting (2015-2030) State-and-Transition Simulation Models to explore post-fire habitat restoration in three greater sage-grouse (Centrocercus urophasianus) Priority Areas for Conservation, USA (2018-2068) 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 Simulation to evaluate response of population models to annual trends in detectability Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 1 (Nevada), 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 3 (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, Nevada and Wyoming, Interim U.S. range-wide spatial prediction layers of lek persistence probabilities for greater sage-grouse 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)