The file 'ssm_data_3.25.csv' contains data necessary for analyzing state-space models for male greater sage-grouse (Centrocercus urophasianus) populations in response to grazing level (relative grazing index), timing, and NDVI (Normalized Difference Vegetation Index) in Wyoming, USA. In this case, all covariates were measured within 3.25 km of lek sites.

The file 'ssm_data.csv' contains data necessary for analyzing state-space models for male greater sage-grouse (Centrocercus urophasianus) populations in response to grazing level (relative grazing index), timing, and NDVI (Normalized Difference Vegetation Index) in Wyoming, USA, and then to compare models with 10-fold cross validation scores (Monroe et al. 2017). Literature Cited: Monroe, A. P., C. L. Aldridge, T. J. Assal, K. E. Veblen, D. A. Pyke, and M. L. Casazza. 2017. Patterns in Greater Sage-grouse Population Dynamics Correspond with Public Grazing Records at Broad Scales. Ecological Applications. doi: 10.1002/eap.1512.

The file 'ssm_data.csv' contains data necessary for analyzing state-space models for male greater sage-grouse (Centrocercus urophasianus) populations in response to grazing level (relative grazing index), timing, and NDVI (Normalized Difference Vegetation Index) in Wyoming, USA, and then to compare models with 10-fold cross validation scores (Monroe et al. 2017). Literature Cited: Monroe, A. P., C. L. Aldridge, T. J. Assal, K. E. Veblen, D. A. Pyke, and M. L. Casazza. 2017. Patterns in Greater Sage-grouse Population Dynamics Correspond with Public Grazing Records at Broad Scales. Ecological Applications. doi: 10.1002/eap.1512.

In 'Broad-scale analysis of greater sage-grouse population trends in response to grazing records in Wyoming, USA (2004-2014)', we provide data and R code necessary for analyzing state-space models for male greater sage-grouse (Centrocercus urophasianus) populations in response to grazing level, timing, and NDVI in Wyoming, USA, and then to compare models with 10-fold cross validation scores (Monroe et al. 2017). In 'Analysis of Land Health Standard failure among allotments in Wyoming, USA (2001-2009)', we provide data and R code necessary for logistic regression analyzing effects of grazing level and timing on the probability of an allotment failing one or more Land Health Standard (LHS) the previous year (Monroe et al. 2017). Relative predictive ability of models are then compared with a 10-fold cross-validation score. In 'Data to evaluate sensitivity of model results to scale and allotment overlap threshold', we provide data used to evaluate the sensitivity of our results to our choice of scale (6.44 km around lek sites) and the overlap threshold for allotments with grazing data (>75%). Literature Cited: Monroe, A. P., C. L. Aldridge, T. J. Assal, K. E. Veblen, D. A. Pyke, and M. L. Casazza. 2017. Patterns in Greater Sage-grouse Population Dynamics Correspond with Public Grazing Records at Broad Scales. Ecological Applications. doi: 10.1002/eap.1512.

In 'Broad-scale analysis of greater sage-grouse population trends in response to grazing records in Wyoming, USA (2004-2014)', we provide data and R code necessary for analyzing state-space models for male greater sage-grouse (Centrocercus urophasianus) populations in response to grazing level, timing, and NDVI in Wyoming, USA, and then to compare models with 10-fold cross validation scores (Monroe et al. 2017). In 'Analysis of Land Health Standard failure among allotments in Wyoming, USA (2001-2009)', we provide data and R code necessary for logistic regression analyzing effects of grazing level and timing on the probability of an allotment failing one or more Land Health Standard (LHS) the previous year (Monroe et al. 2017). Relative predictive ability of models are then compared with a 10-fold cross-validation score. In 'Data to evaluate sensitivity of model results to scale and allotment overlap threshold', we provide data used to evaluate the sensitivity of our results to our choice of scale (6.44 km around lek sites) and the overlap threshold for allotments with grazing data (>75%). Literature Cited: Monroe, A. P., C. L. Aldridge, T. J. Assal, K. E. Veblen, D. A. Pyke, and M. L. Casazza. 2017. Patterns in Greater Sage-grouse Population Dynamics Correspond with Public Grazing Records at Broad Scales. Ecological Applications. doi: 10.1002/eap.1512.

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).

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. 2018. Designing hierarchically nested and biologically relevant monitoring frameworks to study populations across scales. Ecosphere

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. 2018. Designing hierarchically nested and biologically relevant monitoring frameworks to study populations across scales. Ecosphere

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. 2018. Designing hierarchically nested and biologically relevant monitoring frameworks to study populations across scales. Ecosphere