A user-friendly decision support tool for monitoring and managing greater sage-grouse populations Active
Researchers within the U.S. Geological Survey (USGS) and Colorado State University (CSU) worked with BLM and State Wildlife Agencies to develop a hierarchical population monitoring framework for managing greater sage-grouse (Centrocercus urophasianus) populations and the sagebrush ecosystems that they depend upon for survival and reproduction. This hierarchical population monitoring strategy now serves as a foundation for future investigations into the relationships between landscape alterations and sage-grouse population performance. The products from this effort have been synthesized into a user-friendly decision support tool that allows managers to access 1) the most recent data on sage-grouse trends, and 2) a targeted annual warning system that identifies where and when sage-grouse populations may benefit from management intervention.
Background
The greater sage-grouse hierarchical population monitoring framework arose out of a need to increase the accuracy of sage-grouse monitoring across its entire range. To develop the framework, scientists from the USGS and CSU worked closely with state agencies to develop a unified lek database from long-term sage-grouse breeding site (lek) monitoring data (1953-2021), and model population trends at three key spatial scales: (1) leks, (2) ‘neighborhoods’ of leks clustered together, and (3) clusters of neighborhoods, termed ‘climate’ clusters. They found that changes in trends at the climate cluster scale are driven by abiotic factors, while changes at the neighborhood and lek scale are more likely driven by causes that can be mitigated with management actions. Long-term trend estimation provides key information on broad-scale effectiveness of management outcomes. The framework further characterizes population trends to identify when populations are declining in a way that warrants a signal, either for additional monitoring (that is, a “watch”) or management intervention (that is, a “warning”). This is the foundation of the Targeted Annual Warning System (TAWS). The TAWS serves as a guide for directing management activities in a more targeted and localized manner.
Research Objective
We aimed to develop user-friendly, decision support software for the range-wide hierarchical monitoring framework for targeted and adaptive management of sagebrush ecosystems (Figure 1). This software can be used by federal and state agencies to identify or assess the following:
- where and when conservation actions are needed to reverse unexpected declines in sage-grouse populations.
- when population declines are driven by local disturbances that may be mitigated with management actions
- persistent population declines
- effectiveness of management outcomes to reverse population declines.
The Web Tool
To promote rapid and effective application of results from the hierarchical population monitoring framework, the USGS and CSU developed a web-based R Shiny application that allows users to explore and visualize sage-grouse trends and implement the TAWs within user-defined areas of interest (Figure 2). The web-based application facilitates the return of customized outputs that directly inform a user’s specific decision-making requirements. The application is composed of two separate tools: 1) the trends tool that produces maps, figures, and tables with estimated long-term sage-grouse trends within a user-defined area and timeframe of interest (Figure 3), and 2) the TAWS tool, which allows users to review the status of different neighborhoods or leks within their area and timeframe of interest through maps and tables. The results of the trends tool help managers identify long-term population declines that may be driven by chronic disturbance. The outputs from the TAWS tool provides a real-time snapshot of where and when local populations are declining. The map outputs identify specific neighborhood or lek clusters that have signaled a watch or warning to help managers target their efforts where they will be most effective. Additionally, the tool allows the user to review multiple years of signals at a single glance. Seeing repeat signals can help managers identify areas impacted by long-term disturbance, and monitoring changes in signals following management actions can inform adaptive management.
Results and Implications
The application allows practitioners to identify where and when sage-grouse populations may benefit from management intervention. It also provides a framework for adaptive management, allowing managers to track changes in populations due to recent disturbance (for example, wildfire) or management actions (for example, conifer removal). The application provides easy access and summaries of trends (Figure 3) and TAWS results within a user-defined area and can be applied to help inform many different decision-making processes. This web-based application tool will likely be very valuable to various forms of analyses carried out by federal and state agencies (for example, environmental analyses and impact statements completed under the National Environmental Policy Act, and various forms of mitigation strategies).
Future Co-production
We continue working with all collaborators to improve sage-grouse management tools. Each year, a new standardized database is developed to include newly digitized historical data and improve data quality using critical quality control methods. These data are incorporated into the trends and TAWS models to produce results that are delivered in time for annual agency decision making. During this process, the decision support software is updated with the latest data and released to federal and state agency partners. In addition, we will continue to promote technology transfer through web-based materials and training modules.
Data Restrictions
State wildlife agencies collect and manage lek databases. Because sage-grouse are a species of conservation concern and sensitive to activities during breeding, these data are available only after acquiring data-sharing agreements with individual states.
Funders
U.S. Geological Survey (Ecosystem Mission Area, Land Management Research Program and Species Management Research Program, Wyoming Landscape Conservation Initiative) and Bureau of Land Management.
Partners
State Wildlife Agencies (California Department of Fish and Wildlife; Colorado Parks and Wildlife; Idaho Department of Fish and Game; Montana Fish, Wildlife & Parks; Nevada Department of Wildlife; North Dakota Game and Fish Department; Oregon Department of Fish and Wildlife; South Dakota Department of Game, Fish and Parks; Utah Division of Wildlife Resources; Wyoming Game and Fish Department; Washington Department of Fish and Wildlife), Colorado State University, BLM, US Fish and Wildlife Service, US Forest Service, researchers who provided field data to evaluate results.
Data Harmonization for Greater Sage-Grouse Populations
Hierarchical Units of Greater Sage-Grouse Populations Informing Wildlife Management
Researchers within the U.S. Geological Survey (USGS) and Colorado State University (CSU) worked with BLM and State Wildlife Agencies to develop a hierarchical population monitoring framework for managing greater sage-grouse (Centrocercus urophasianus) populations and the sagebrush ecosystems that they depend upon for survival and reproduction. This hierarchical population monitoring strategy now serves as a foundation for future investigations into the relationships between landscape alterations and sage-grouse population performance. The products from this effort have been synthesized into a user-friendly decision support tool that allows managers to access 1) the most recent data on sage-grouse trends, and 2) a targeted annual warning system that identifies where and when sage-grouse populations may benefit from management intervention.
Background
The greater sage-grouse hierarchical population monitoring framework arose out of a need to increase the accuracy of sage-grouse monitoring across its entire range. To develop the framework, scientists from the USGS and CSU worked closely with state agencies to develop a unified lek database from long-term sage-grouse breeding site (lek) monitoring data (1953-2021), and model population trends at three key spatial scales: (1) leks, (2) ‘neighborhoods’ of leks clustered together, and (3) clusters of neighborhoods, termed ‘climate’ clusters. They found that changes in trends at the climate cluster scale are driven by abiotic factors, while changes at the neighborhood and lek scale are more likely driven by causes that can be mitigated with management actions. Long-term trend estimation provides key information on broad-scale effectiveness of management outcomes. The framework further characterizes population trends to identify when populations are declining in a way that warrants a signal, either for additional monitoring (that is, a “watch”) or management intervention (that is, a “warning”). This is the foundation of the Targeted Annual Warning System (TAWS). The TAWS serves as a guide for directing management activities in a more targeted and localized manner.
Research Objective
We aimed to develop user-friendly, decision support software for the range-wide hierarchical monitoring framework for targeted and adaptive management of sagebrush ecosystems (Figure 1). This software can be used by federal and state agencies to identify or assess the following:
- where and when conservation actions are needed to reverse unexpected declines in sage-grouse populations.
- when population declines are driven by local disturbances that may be mitigated with management actions
- persistent population declines
- effectiveness of management outcomes to reverse population declines.
The Web Tool
To promote rapid and effective application of results from the hierarchical population monitoring framework, the USGS and CSU developed a web-based R Shiny application that allows users to explore and visualize sage-grouse trends and implement the TAWs within user-defined areas of interest (Figure 2). The web-based application facilitates the return of customized outputs that directly inform a user’s specific decision-making requirements. The application is composed of two separate tools: 1) the trends tool that produces maps, figures, and tables with estimated long-term sage-grouse trends within a user-defined area and timeframe of interest (Figure 3), and 2) the TAWS tool, which allows users to review the status of different neighborhoods or leks within their area and timeframe of interest through maps and tables. The results of the trends tool help managers identify long-term population declines that may be driven by chronic disturbance. The outputs from the TAWS tool provides a real-time snapshot of where and when local populations are declining. The map outputs identify specific neighborhood or lek clusters that have signaled a watch or warning to help managers target their efforts where they will be most effective. Additionally, the tool allows the user to review multiple years of signals at a single glance. Seeing repeat signals can help managers identify areas impacted by long-term disturbance, and monitoring changes in signals following management actions can inform adaptive management.
Results and Implications
The application allows practitioners to identify where and when sage-grouse populations may benefit from management intervention. It also provides a framework for adaptive management, allowing managers to track changes in populations due to recent disturbance (for example, wildfire) or management actions (for example, conifer removal). The application provides easy access and summaries of trends (Figure 3) and TAWS results within a user-defined area and can be applied to help inform many different decision-making processes. This web-based application tool will likely be very valuable to various forms of analyses carried out by federal and state agencies (for example, environmental analyses and impact statements completed under the National Environmental Policy Act, and various forms of mitigation strategies).
Future Co-production
We continue working with all collaborators to improve sage-grouse management tools. Each year, a new standardized database is developed to include newly digitized historical data and improve data quality using critical quality control methods. These data are incorporated into the trends and TAWS models to produce results that are delivered in time for annual agency decision making. During this process, the decision support software is updated with the latest data and released to federal and state agency partners. In addition, we will continue to promote technology transfer through web-based materials and training modules.
Data Restrictions
State wildlife agencies collect and manage lek databases. Because sage-grouse are a species of conservation concern and sensitive to activities during breeding, these data are available only after acquiring data-sharing agreements with individual states.
Funders
U.S. Geological Survey (Ecosystem Mission Area, Land Management Research Program and Species Management Research Program, Wyoming Landscape Conservation Initiative) and Bureau of Land Management.
Partners
State Wildlife Agencies (California Department of Fish and Wildlife; Colorado Parks and Wildlife; Idaho Department of Fish and Game; Montana Fish, Wildlife & Parks; Nevada Department of Wildlife; North Dakota Game and Fish Department; Oregon Department of Fish and Wildlife; South Dakota Department of Game, Fish and Parks; Utah Division of Wildlife Resources; Wyoming Game and Fish Department; Washington Department of Fish and Wildlife), Colorado State University, BLM, US Fish and Wildlife Service, US Forest Service, researchers who provided field data to evaluate results.