A user-friendly decision support tool for monitoring and managing greater sage-grouse populations

Background

The Greater Sage-Grouse Population Monitoring Framework arose from a need to improve 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-present), and to 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 scales are more likely driven by factors that can be mitigated through 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.

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Research Objective

Researchers aimed to develop user-friendly decision-support software for the range-wide Greater Sage-Grouse Population Monitoring Framework to support 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 address unexpected declines in sage-grouse populations;
• when population declines are driven by local disturbances that may be mitigated with management actions;
• persistent population declines; and
• the effectiveness of management outcomes to address population declines.

The Web Tool

To promote rapid and effective application of results from the greater sage-grouse 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 delivery 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 TAWS signals (such as, watch, warning, chronic warning) 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 persistent disturbance. The outputs from the TAWS tool provide a real-time snapshot of where and when local populations are declining. The map outputs identify specific neighborhoods 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.

Media

Sources/Usage: Public Domain. View Media Details

Media

Sources/Usage: Public Domain. View Media Details

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 resulting from recent disturbances (for example, wildfire) or management actions (for example, conifer removal). The application provides easy access to and summaries of trends (Figure 3) and TAWS results within a user-defined area and can help inform many different decision-making processes. This web-based application tool will likely be highly 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, as well as various mitigation strategies)

Co-production 

We continue working with all collaborators to improve sage-grouse management tools. Each year, we develop a new standardized database to incorporate newly digitized historical data and to improve data quality through rigorous 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 are sensitive to activities during breeding, these data are available only after formal data-sharing agreements are in place 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 the 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.