This document summarizes the potential sagebrush vegetation as well as the 2012 - 2019 sagebrush vegetation availability estimates on Greater Sage-Grouse Priority and Important Habitat Management Areas (PHMA and IHMA, respectively) within the Biologically Significant Units (BSUs) identified in the 2015 Greater Sage-Grouse Land Use Plans as maintained through 2021. BSUs are grouped by State and, along with PHMA and IHMA datasets, were provided by each individual planning area between May 2015 and February 2021. Sagebrush potential and availability were derived from LANDFIRE Biophysical Setting (BpS) and Existing Vegetation Type (EVT) data products, respectively, as described in the Greater Sage-Grouse Monitoring Framework. Updates to the EVT product from 2013 to 2015 were also performed as outlined in the Greater Sage-Grouse Monitoring Framework. All analyses were completed by the BLM’s Wildlife Habitat Spatial Analysis Lab at the National Operations Center.

We produced 13 hierarchically nested cluster levels that reflect the results from developing a hierarchical monitoring framework for greater sage-grouse across the western United States. Polygons (clusters) within each cluster level group a population of sage-grouse leks (sage-grouse breeding grounds) and each level increasingly groups lek clusters from previous levels. We developed the hierarchical clustering approach by identifying biologically relevant population units aimed to use a statistical and repeatable approach and include biologically relevant landscape and habitat characteristics. We desired a framework that was spatially hierarchical, discretized the landscape while capturing connectivity (habitat and movements), and supported management questions at different 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 organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates at local scales affected by changes in habitat quality compared to larger scaled processes affecting growth rates, such as regional climate/vegetation communities. Therefore, the use of multiple scales (hierarchical cluster levels) that group demographic data can provide information driving population changes at different spatial scales, thereby providing a tool for population monitoring and adaptive management.

We produced 13 hierarchically nested cluster levels that reflect the results from developing a hierarchical monitoring framework for greater sage-grouse across the western United States. Polygons (clusters) within each cluster level group a population of sage-grouse leks (sage-grouse breeding grounds) and each level increasingly groups lek clusters from previous levels. We developed the hierarchical clustering approach by identifying biologically relevant population units aimed to use a statistical and repeatable approach and include biologically relevant landscape and habitat characteristics. We desired a framework that was spatially hierarchical, discretized the landscape while capturing connectivity (habitat and movements), and supported management questions at different 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 organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates at local scales affected by changes in habitat quality compared to larger scaled processes affecting growth rates, such as regional climate/vegetation communities. Therefore, the use of multiple scales (hierarchical cluster levels) that group demographic data can provide information driving population changes at different spatial scales, thereby providing a tool for population monitoring and adaptive management.

The 2017 Sage-grouse Habitat Planning Map update incorporates wildfire data and other edits relevant to the 2017 field and fire season as per typical processes used during the past decade. Additional areas of non-habitat, such as paved highways, municipal boundaries, water bodies etc., were also removed. However, the map extent for sage-grouse key habitat and potential restoration areas should be considered provisional, contingent on the final outcome of additional analyses currently underway in association with the national Greater Sage-grouse Conservation Strategy. This data set contains simple, landscape-scale greater sage-grouse habitat types for Idaho and constitutes a current approximation of sage-grouse habitat in the state. The data can be used for general conservation and restoration planning purposes, but additional data or field verification are needed for applications at finer scales. The habitat types include: (1) key sage-grouse habitat areas and (2) four habitat restoration types: (a) R1 - perennial native and non-native grasslands with high restoration potential; (b) R2 - annual grass dominated areas (either shrubland or grassland) with low restoration potential; (c) R3 - conifer encroachment areas with high restoration potential and (d) RB - areas that have recently burned and the type of habitat that is coming back and its restoration potential has not yet been determined. Beginning in 2015, the NA class was included to track areas that were previously identified as habitat but were removed due to vegetation type or terrain (eg. stands of existing timber on North-facing slopes). A new data set will be developed annually to update the planning map and chart landscape-level changes in sage-grouse habitat over time. Edits may also document refinements discovered through inventory, monitoring and modeling. This data covers all of Idaho and a small area in Nevada that is managed by Idaho BLM. Intended scale of use is 1:100,000. This dataset is not synonymous with BLM's "Preliminary Priority Habitat" or "Preliminary General Habitat" mapping efforts, as those incorporate additional habitat and sage-grouse population data or models. For more information contact us at blm_id_stateoffice@blm.gov.

The 2017 Sage-grouse Habitat Planning Map update incorporates wildfire data and other edits relevant to the 2017 field and fire season as per typical processes used during the past decade. Additional areas of non-habitat, such as paved highways, municipal boundaries, water bodies etc., were also removed. However, the map extent for sage-grouse key habitat and potential restoration areas should be considered provisional, contingent on the final outcome of additional analyses currently underway in association with the national Greater Sage-grouse Conservation Strategy. This data set contains simple, landscape-scale greater sage-grouse habitat types for Idaho and constitutes a current approximation of sage-grouse habitat in the state. The data can be used for general conservation and restoration planning purposes, but additional data or field verification are needed for applications at finer scales. The habitat types include: (1) key sage-grouse habitat areas and (2) four habitat restoration types: (a) R1 - perennial native and non-native grasslands with high restoration potential; (b) R2 - annual grass dominated areas (either shrubland or grassland) with low restoration potential; (c) R3 - conifer encroachment areas with high restoration potential and (d) RB - areas that have recently burned and the type of habitat that is coming back and its restoration potential has not yet been determined. Beginning in 2015, the NA class was included to track areas that were previously identified as habitat but were removed due to vegetation type or terrain (eg. stands of existing timber on North-facing slopes). A new data set will be developed annually to update the planning map and chart landscape-level changes in sage-grouse habitat over time. Edits may also document refinements discovered through inventory, monitoring and modeling. This data covers all of Idaho and a small area in Nevada that is managed by Idaho BLM. Intended scale of use is 1:100,000. This dataset is not synonymous with BLM's "Preliminary Priority Habitat" or "Preliminary General Habitat" mapping efforts, as those incorporate additional habitat and sage-grouse population data or models. For more information contact us at blm_id_stateoffice@blm.gov.

nv_lvl1_finescale: Nevada 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 population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates, indicating the appropriate location and spatial scale of a management action. The data presented here reflect the results from developing a hierarchical monitoring framework and then applying these methods to Greater Sage-grouse in Nevada and Wyoming, US. When using these data for evaluating population changes or when identifying a spatially balanced sampling protocol, all cluster levels are designed to work together and therefore we recommend evaluating multiple cluster levels prior to selecting a single cluster level, if a single scale is desired, when analyzing population growth rates or other analyses, as these data are intended for multi-scale efforts. In other words, let your data decide which scale(s) are appropriate for the given species. These cluster levels are specific to Greater Sage-grouse but they may be appropriate for other sagebrush obligate species, but the user will need to make this determination. The products from this study aim to support multiple research and management needs. However, these data represent an interim data product because there may be errors associated with clusters along the edges of the state boundaries (due to the lack of lek data in neighboring states). We are planning to release new data that we will develop for the Greater sage-grouse range. We recommend using the new data products once available instead of these data products. These data will remain online as they are associated with the following citation, which provides a detailed explanation of the methods used to develop these data: O’Donnell, Michael S., David R. Edmunds, Cameron L. Aldridge, Julie A. Heinrichs, Peter S. Coates, Brian G. Prochazka, and Steve E. Hanser. 2019. Designing multi-scale hierarchical monitoring frameworks for wildlife with high site fidelity to support conservation: a sage-grouse case study. Ecosphere

nv_lvl1_finescale: Nevada 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 population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates, indicating the appropriate location and spatial scale of a management action. The data presented here reflect the results from developing a hierarchical monitoring framework and then applying these methods to Greater Sage-grouse in Nevada and Wyoming, US. When using these data for evaluating population changes or when identifying a spatially balanced sampling protocol, all cluster levels are designed to work together and therefore we recommend evaluating multiple cluster levels prior to selecting a single cluster level, if a single scale is desired, when analyzing population growth rates or other analyses, as these data are intended for multi-scale efforts. In other words, let your data decide which scale(s) are appropriate for the given species. These cluster levels are specific to Greater Sage-grouse but they may be appropriate for other sagebrush obligate species, but the user will need to make this determination. The products from this study aim to support multiple research and management needs. However, these data represent an interim data product because there may be errors associated with clusters along the edges of the state boundaries (due to the lack of lek data in neighboring states). We are planning to release new data that we will develop for the Greater sage-grouse range. We recommend using the new data products once available instead of these data products. These data will remain online as they are associated with the following citation, which provides a detailed explanation of the methods used to develop these data: O’Donnell, Michael S., David R. Edmunds, Cameron L. Aldridge, Julie A. Heinrichs, Peter S. Coates, Brian G. Prochazka, and Steve E. Hanser. 2019. Designing multi-scale hierarchical monitoring frameworks for wildlife with high site fidelity to support conservation: a sage-grouse case study. Ecosphere

nv_lvl1_finescale: Nevada 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 population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates, indicating the appropriate location and spatial scale of a management action. The data presented here reflect the results from developing a hierarchical monitoring framework and then applying these methods to Greater Sage-grouse in Nevada and Wyoming, US. When using these data for evaluating population changes or when identifying a spatially balanced sampling protocol, all cluster levels are designed to work together and therefore we recommend evaluating multiple cluster levels prior to selecting a single cluster level, if a single scale is desired, when analyzing population growth rates or other analyses, as these data are intended for multi-scale efforts. In other words, let your data decide which scale(s) are appropriate for the given species. These cluster levels are specific to Greater Sage-grouse but they may be appropriate for other sagebrush obligate species, but the user will need to make this determination. The products from this study aim to support multiple research and management needs. However, these data represent an interim data product because there may be errors associated with clusters along the edges of the state boundaries (due to the lack of lek data in neighboring states). We are planning to release new data that we will develop for the Greater sage-grouse range. We recommend using the new data products once available instead of these data products. These data will remain online as they are associated with the following citation, which provides a detailed explanation of the methods used to develop these data: O’Donnell, Michael S., David R. Edmunds, Cameron L. Aldridge, Julie A. Heinrichs, Peter S. Coates, Brian G. Prochazka, and Steve E. Hanser. 2019. Designing multi-scale hierarchical monitoring frameworks for wildlife with high site fidelity to support conservation: a sage-grouse case study. Ecosphere

The 2015 Sage-grouse Habitat Planning Map update incorporates wildfire data and other edits relevant to the 2015 field and fire season as per typical processes used during the past decade. Additional areas of non-habitat, such as paved highways, municipal boundaries, water bodies etc., were also removed. However, the map extent for sage-grouse key habitat and potential restoration areas should be considered provisional, contingent on the final outcome of additional analyses currently underway in association with the national Greater Sage-grouse Conservation Strategy. This data set contains simple, landscape-scale greater sage-grouse habitat types for Idaho and constitutes a current approximation of sage-grouse habitat in the state. The data can be used for general conservation and restoration planning purposes, but4 additional data or field verification are needed for applications at finer scales. The habitat types include: (1) key sage-grouse habitat areas and (2) four habitat restoration types: (a) R1 - perennial native and non-native grasslands with high restoration potential; (b) R2 - annual grass dominated areas (either shrubland or grassland) with low restoration potential; (c) R3 - conifer encroachment areas with high restoration potential and (d) RB - areas that have recently burned and the type of habitat that is coming back and its restoration potential has not yet been determined. Beginning in 2015, the NA class was included to track areas that were previously identified as habitat but were removed due to vegetation type (eg. stands of existing timber on North-facing slopes) A new data set will be developed annually to update the planning map and chart landscape-level changes in sage-grouse habitat over time. Edits may also document refinements discovered through inventory, monitoring and modeling. This data covers all of Idaho and a small area in Nevada that is managed by Idaho BLM. Intended scale of use is 1:100,000. This dataset is not synonymous with BLM's "Preliminary Priority Habitat" or "Preliminary General Habitat" mapping efforts, as those incorporate additional habitat and sage-grouse population data or models. For more information contact us at blm_id_stateoffice@blm.gov.

The 2015 Sage-grouse Habitat Planning Map update incorporates wildfire data and other edits relevant to the 2015 field and fire season as per typical processes used during the past decade. Additional areas of non-habitat, such as paved highways, municipal boundaries, water bodies etc., were also removed. However, the map extent for sage-grouse key habitat and potential restoration areas should be considered provisional, contingent on the final outcome of additional analyses currently underway in association with the national Greater Sage-grouse Conservation Strategy. This data set contains simple, landscape-scale greater sage-grouse habitat types for Idaho and constitutes a current approximation of sage-grouse habitat in the state. The data can be used for general conservation and restoration planning purposes, but4 additional data or field verification are needed for applications at finer scales. The habitat types include: (1) key sage-grouse habitat areas and (2) four habitat restoration types: (a) R1 - perennial native and non-native grasslands with high restoration potential; (b) R2 - annual grass dominated areas (either shrubland or grassland) with low restoration potential; (c) R3 - conifer encroachment areas with high restoration potential and (d) RB - areas that have recently burned and the type of habitat that is coming back and its restoration potential has not yet been determined. Beginning in 2015, the NA class was included to track areas that were previously identified as habitat but were removed due to vegetation type (eg. stands of existing timber on North-facing slopes) A new data set will be developed annually to update the planning map and chart landscape-level changes in sage-grouse habitat over time. Edits may also document refinements discovered through inventory, monitoring and modeling. This data covers all of Idaho and a small area in Nevada that is managed by Idaho BLM. Intended scale of use is 1:100,000. This dataset is not synonymous with BLM's "Preliminary Priority Habitat" or "Preliminary General Habitat" mapping efforts, as those incorporate additional habitat and sage-grouse population data or models. For more information contact us at blm_id_stateoffice@blm.gov.