Introduction

As of the turn of the 21st century, sage-grouse occupied roughly half of their former historical range (Schroeder and others, 2004; Miller and others, 2011), and populations have subsequently experienced marked declines in many parts of their current range (Garton and others, 2011; Western Association of Fish and Wildlife Agencies, 2015). A 2021 study led by the U.S. Geological Survey (USGS), in cooperation with the Bureau of Land Management (BLM) and Western State wildlife agencies, revealed an approximately 3.1-percent annual average population decline range-wide (Coates and others, 2021). However, variation in trends was described across different spatial and temporal scales.

Decades of past research have attributed sage-grouse population declines to loss and fragmentation of sagebrush communities as well as to a suite of environmental stressors (Remington and others, 2021). Since 1999, sage-grouse have been petitioned for legal protection under the Endangered Species Act (ESA) on nine occasions, and actions to conserve and restore sage-grouse habitats are now central to guiding land-management actions and policies across most of the western United States. Specifically, the resource needs of sage-grouse have been used to help guide management and restoration actions aimed at improving conditions in sagebrush ecosystems, with resultant practices thought to benefit other sagebrush-dependent species (Rowland and others, 2006; Hanser and Knick, 2011; Dinkins and others, 2021). Sage-grouse are considered an indicator for the function of sagebrush ecosystems (Prochazka and others, 2023) and an umbrella for the protection of other sagebrush-obligate or sagebrush-dependent species because of their near-complete reliance on sagebrush ecosystems (Rich and Altman, 2001; Rich and others, 2005; Rowland and others, 2006; Hanser and Knick, 2011). Importantly, several Federal resource management plan amendments accompanying the U.S. Fish and Wildlife Service (USFWS) “not warranted” 2015 ESA listing determination called for greater integration of sage-grouse management into their land-use planning, specifically identifying how to implement adaptive management. An unprecedented level of conservation effort and planning among Federal (Bureau of Land Management, 2015; U.S. Department of Agriculture Forest Service, 2015a, b), State, and private stakeholders was identified as the primary driver for the USFWS decision in the most recent (2015) status assessment (U.S. Fish and Wildlife Service, 2015).

The purpose of this report is to provide updated results on sage-grouse population analyses (trends in abundance, extirpation probabilities, targeted annual warning system [TAWS]) across their geographical range in the western United States. This report reflects previous modeling methodologies (Coates and others, 2021, 2022a), describes updates to those methodologies, and includes additional data to inform population analyses through 2024. A detailed description of data collection and compilation, population clustering methods to identify spatial extents, and modeling methodologies was provided in the first three objectives described in Coates and others (2021). Additional methodologies developed since Coates and others (2021) are described in Coates and others (2022a) and in this report. The USGS, in cooperation with the BLM, are providing this scientific information to fulfill a prominent information gap, thus informing the adaptive management of sage-grouse populations, as well as monitoring and conservation management strategies. The ongoing collaborative effort between the USGS Western Ecological Research Center (WERC), Fort Collins Science Center (FORT), Colorado State University (CSU), BLM, USFWS, and Western State wildlife agencies aims to update and improve the hierarchical monitoring framework for sage-grouse within sagebrush ecosystems. These updates include a technical transfer of improved documentation (for example, frequently asked questions, numerous information sheets, and web pages), training, and regular meetings, all of which have added great value to improving the science that informs management.

Study Area

Our study extent consisted of the geographic range of sage-grouse in the United States and was described previously in Coates and others (2021). Briefly, this area represents the sagebrush biome occurring across western North America and extending east from the Sierra Nevada and Cascade Mountain ranges to the western regions of the Great Plains of the United States. The vegetation communities vary in precipitation, temperature, soils, topographic position, and elevation (Miller and others, 2011). The most abundant shrub species include sagebrush (Artemisia spp.), along with less abundant non-sagebrush species, such as rabbitbrush (Chrysothamnus spp.), horsebrush (Tetradymia spp.), greasewood (Sarcobatus spp.), common snowberry (Symphoricarpos spp.), serviceberry (Amelanchier spp.), fourwing saltbush (Atriplex spp.), and bitterbrush (Purshia spp.). The primary native herbaceous species include wheatgrass (Agropyron spp.), fescue (Festuca spp.), bluegrass (Poa spp.), needlegrass (Stipa spp.), and squirreltail (Elymus spp.), whereas less abundant forb species include phlox (Phlox spp.), milk-vetch (Astragalus spp.), and fleabane (Erigeron spp.). Non-native annual grasses, such as cheatgrass (Bromus tectorum), are prevalent across the range and can represent the dominant component of plant communities in many areas.

Data Compilation and Inputs

We developed a single unified database from all digitized field observations of sage-grouse lek counts completed since 1941 and compiled by all State wildlife agencies that monitored sage-grouse populations during that time. We worked with each State agency to ensure the fullest understanding of the data to maximize the number of appropriate records kept in the database. We addressed spatial errors, and we reviewed all data products with Western State wildlife agency biologists. Data compilation rules and the detailed methodology used in these analyses were published in Coates and others (2021, 2022a) and O’Donnell and others (2021). The additional years of data (through 2024) used to update these analyses followed the exact quality assessment and quality control (QA/QC) measures described in the previous publications and included additional updates described in this report. Using all the rules for selecting data appropriate for population modeling, we retained 112,699 observations (88,596; TAWS) across 5,514 leks (4,914; TAWS). The fewer number of observations and leks used in the TAWS stems from the more narrow and contemporary focus of that analysis, which aims to identify sustained, aberrant population declines that can be addressed now or in the near future. The sage-grouse lek data used in this update either are not publicly available or have limited availability owing to unique restrictions held by each State (data are managed by 11 Western States and are not public due to the sensitivity of the species and State regulations, policies, or laws). Individual Western State wildlife agencies (see the “Acknowledgments” section) can be contacted for additional information.

Changes in population abundance are affected by environmental factors that operate on multiple spatial and temporal scales, which follow ecological rather than geopolitical boundaries. Hence, we examined population trends across biologically relevant and hierarchically nested units to improve the detection of factors driving change across multiple spatial scales. We grouped sage-grouse lek sites into hierarchically nested clusters or populations using least-cost minimum spanning trees, a clustering algorithm, and a suite of relevant biotic and abiotic spatial products described in O’Donnell and others (2022). Briefly, we selected two cluster levels to represent a fine (neighborhood cluster [NC]) and a broad spatial scale (climate cluster [CC]). Model output included estimated apparent abundance ($\widehat{N}$; hereafter abundance) and intrinsic rates of population change ($\widehat{r}$) at the lek, NC, and CC levels. These parameters were used to calculate abundance indices and average annual rates of change in abundance ($\widehat{\lambda }$).

Range-Wide Sage-Grouse Population Model

Detailed formulation of the sage-grouse population model was described in Coates and others (2021). Coates and others (2022a) describe analytical updates that (1) identify population nadirs (two lowest points within a single complete population oscillation denoting the start and end of a single period) at spatial scales of the lek (breeding ground) and neighborhood cluster (group of leks) and (2) truncate prior probability distributions on rate of change in abundance parameters to more realistic boundaries for leks with missing data. Briefly, we used a state-space model (SSM) that relied on Markov chain Monte Carlo sampling, using Bayesian inference, to derive posterior probability distributions (PD) of $\widehat{N}$ and $\widehat{r}$ using lek count data across the sage-grouse geographical range. This approach allowed inferences for each lek, as well as higher-order, nested spatial extents, such as NC and CC, in each year of the time series. Advantages and assumptions inherent to the SSM approach were described in Coates and others (2021, 2022a).

Range-Wide Population Trends

Our model fit the observed data well (Bayesian p-value=0.50). Median $\widehat{N}$ from the SSM revealed seven distinct range-wide population nadirs across the 65 years of data, which were 1966, 1975, 1986, 1996, 2002, 2013, and 2021. The range-wide, across-years, mean male count was 17.5 per lek (95-percent confidence interval [CI]=17.4–17.6). The number of years per complete population oscillation (nadir-to-nadir) was relatively consistent across periods (average=9.1; 95-percent CI=4.9–15.0; table 1). Model estimates revealed evidence of range-wide decline, on average, from every historic abundance nadir to 2021 (fig. 1; table 2); for example, the average annual $\widehat{\lambda }$ for Period 5 (19 years, two oscillations), Period 3 (35 years, four oscillations), and Period 1 (55 years, six oscillations) was 0.971 (median; 95-percent credible interval [CRI]=0.969–0.973), 0.970 (median; 95-percent CRI=0.968–0.972), and 0.972 (median; 95-percent CRI=0.970–0.974), respectively. These trends imply declines of 41.2, 64.1, and 78.8 percent, relative to population sizes observed 19, 35, and 55 years earlier, respectively. In an earlier version of the model (Coates and others, 2021), we specified the final nadir for all populations using the final year of the dataset (2019), which was the lowest point of $\widehat{N}$ for most populations at that time. A more recent report provided an update of the original analysis and included years 2020–23 (Prochazka and others, 2024). In that report, researchers concluded that not all populations had reached a nadir in 2019. The additional year of count data (2024) provided in this report supports previous conclusions of 2021 being the most recent nadir for populations at the range-wide scale. Trends estimated at CC (fig. 2) and NC (fig. 3) scales are generally consistent with the previous report (Prochazka and others, 2024). We estimated median $\widehat{\lambda }$ to be less than 1.0 for 89.5, 92.3, and 97.7 percent of NCs in Period 5, Period 3, and Period 1, respectively, throughout the sage-grouse range.

Climate Cluster Population Trends

Climate cluster A (CC-A; Bi-State area) consisted of 11 NCs that encompassed 726,907 hectares (ha). Two NCs lacked sufficient lek count data to estimate trends. Climate cluster A consisted of 96 leks, representing approximately 1 percent of the range-wide database. After QA/QC, 61 leks met criteria for use in the SSM (table 2), totaling 1,788 field observations. Mean male count was 20.2 (95-percent confidence interval=18.9–21.5). For CC-A, we estimated six population abundance nadirs that dated back to 1960 and included nadirs of 1969, 1977, 1983, 1995, 2002, 2008, and 2019 (table 1). We estimated $\widehat{\lambda }$ for Period 5 (2002–19, two oscillations, 17 years), Period 3 (1983–2019, four oscillations, 36 years), and Period 1 (1969–2019, six oscillations, 50 years) as 0.975 (95-percent CRI=0.962–0.984), 0.987 (95-percent CRI=0.973–0.998), and 0.978 (95-percent CRI=0.967–0.987), respectively (table 2). In the past 17, 36, and 50 years, sage-grouse populations have experienced declines in abundance equal to 33.8, 36.6, and 67.1 percent, respectively (fig. 4A). We estimated median $\widehat{\lambda }$ to be less than 1.0 for all NCs in Period 5, Period 3, and Period 1, respectively.

Climate cluster B (CC-B; Washington area) consisted of four NCs that encompassed 1,139,954 ha. One NC lacked sufficient lek count data to estimate trends. Climate cluster B consisted of 54 leks, representing approximately 0.6 percent of the lek database. After QA/QC, 43 leks met criteria for use in the state-space trend model (table 2), totaling 1,237 field observations. Mean male count was 14.2 (95-percent confidence interval=13.3–15.0). For CC-B, we estimated six population abundance nadirs that dated back to 1960 and included 1964, 1976, 1986, 1994, 2008, 2017, and 2023 (table 1). We estimated $\widehat{\lambda }$ for Period 5 (2008–23, two oscillations, 15 years), Period 3 (1986–2023, four oscillations, 37 years), and Period 1 (1964–2023, six oscillations, 59 years) as 0.959 (95-percent CRI=0.947–0.971), 0.977 (95-percent CRI=0.967–0.984), and 0.972 (95-percent CRI=0.964–0.981), respectively (table 2). In the past 15, 37, and 59 years, sage-grouse populations have experienced declines in abundance equal to 44.3, 56.8, and 80.2 percent, respectively (fig. 4B). We estimated median $\widehat{\lambda }$to be less than 1.0 for all NCs in Period 5, Period 3, and Period 1, respectively.

Climate cluster C (CC-C; Jackson Hole, Wyoming area) consisted of two NCs that encompassed 66,733 ha. Climate cluster C consisted of 17 leks, representing approximately 0.2 percent of the lek database. After QA/QC, 14 leks met criteria for use in the SSM (table 2), totaling 352 field observations. Mean male count was 13.5 (95-percent confidence interval=11.9–15.2). For CC-C, we estimated population abundance nadirs in 1963, 1969, 1984, 1999, 2003, 2011, and 2019 (table 1). We estimated $\widehat{\lambda }$ for Period 5 (2003–19, two oscillations, 16 years), Period 3 (1984–2019, four oscillations, 35 years), and Period 1 (1963–2019, six oscillations, 56 years) as 0.965 (95-percent CRI=0.946–0.982), 0.970 (95-percent CRI=0.943–0.992), and 0.966 (95-percent CRI=0.949–0.986), respectively (table 2). In the past 16, 35, and 56 years, sage-grouse populations have experienced declines in abundance equal to 41.8, 64.6, and 85.2 percent, respectively (fig. 4C). We estimated median $\widehat{\lambda }$ to be less than 1.0 for all NCs across this temporal scale.

Climate cluster D (CC-D; Eastern area) consisted of 169 NCs that encompassed 25,920,530 ha. There were 23 NCs that lacked sufficient lek count data to estimate trends. Climate cluster D consisted of 3,096 leks, representing approximately 35.2 percent of the lek database. After QA/QC, 1,926 leks met criteria for use in the SSM (table 2) and totaled 38,565 field observations. Mean male count was 16.3 (95-percent confidence interval=16.0–16.5). For CC-D, we estimated six population abundance nadirs that dated back to 1960 and included 1966, 1981, 1986, 1997, 2004, 2013, and 2019 (table 1). We estimated $\widehat{\lambda }$ for Period 5 (2004–19, two oscillations, 15 years), Period 3 (1986–2019, four oscillations, 33 years), and Period 1 (1966–2019, six oscillations, 53 years) as 0.960 (95-percent CRI=0.956–0.963), 0.968 (95-percent CRI=0.963–0.972), and 0.967 (95-percent CRI=0.963–0.972), respectively (table 2). In the past 15, 33, and 53 years, sage-grouse populations have experienced declines in abundance equal to 43.6, 64.4, and 82.9 percent, respectively (fig. 4D). We estimated median $\widehat{\lambda }$ to be less than 1.0 for 91.4, 95.0, and 99.3 percent of NCs in Period 5, Period 3, and Period 1, respectively.

Climate cluster E (CC-E; Great Basin area) consisted of 241 NCs that encompassed 34,627,182 ha. There were 17 NCs that lacked sufficient lek count data to estimate trends. Climate cluster E consisted of 4,252 leks, representing approximately 48.3 percent of the lek database. After QA/QC, 2,452 leks met criteria for use in the SSM (table 2) and totaled 47,392 field observations. Mean male count was 16.0 (95-percent confidence interval=15.8–16.2). For CC-E, we estimated six population abundance nadirs that dated back to 1960 and included 1967, 1975, 1985, 1996, 2002, 2013, and 2021 (table 1). We estimated $\widehat{\lambda }$ for Period 5 (2002–21, two oscillations, 19 years), Period 3 (1985–2021, four oscillations, 36 years), and Period 1 (1967–2021, six oscillations, 54 years) as 0.967 (95-percent CRI=0.964–0.970), 0.974 (95-percent CRI=0.970–0.977), and 0.972 (95-percent CRI=0.970–0.974), respectively (table 2). In the past 19, 36, and 54 years, sage-grouse populations have experienced declines in abundance equal to 45.0, 60.2, and 77.6 percent, respectively (fig. 4E). We estimated median $\widehat{\lambda }$ to be less than 1.0 for 86.9, 89.6, and 97.3 percent of NCs in Period 5, Period 3, and Period 1, respectively.

Climate cluster F (CC-F; western Wyoming area) consisted of 56 NCs that encompassed 8,899,755 ha. Three NCs lacked sufficient lek count data to estimate trends. Climate cluster F consisted of 1,291 leks, representing approximately 14.7 percent of the lek database. After QA/QC, 1,018 leks met criteria for inclusion in the SSM (table 2) and totaled 23,365 field observations. Mean male count was 22.6 (95-percent confidence interval=22.2–23.0). For CC-F, we estimated six population abundance nadirs that dated back to 1960 and included 1966, 1975, 1987, 1996, 2002, 2013, and 2021 (table 1). We estimated $\widehat{\lambda }$ for Period 5 (2002–21, two oscillations, 19 years), Period 3 (1987–2021, four oscillations, 34 years), and Period 1 (1966–2021, six oscillations, 55 years) as 0.975 (95-percent CRI=0.971–0.980), 0.970 (95-percent CRI=0.965–0.974), and 0.975 (95-percent CRI=0.971–0.979), respectively (table 2). In the past 19, 35, and 55 years, sage-grouse populations have experienced declines in abundance equal to 36.5, 64.7, and 74.1 percent, respectively (fig. 4F). We estimated median $\widehat{\lambda }$ to be less than 1.0 for 92.5, 94.3, and 94.3 percent of NCs in Period 5, Period 3, and Period 1, respectively.

Neighborhood cluster trends for different temporal scales that are not listed in this report can be viewed in Coates and others (2022b). Coates and others (2022b) is the data release that accompanies this updated (1960–2024) report.

Probability of Future Extirpation

Expanding on the population analyses, we projected $\widehat{N}$ for each lek and NC across three temporal scales that reflected two, four, and six future oscillations using the same model and dataset. We used the mean duration of one complete oscillation (9.1 years) based on estimated $\widehat{N}$ from SSM results. We then calculated the proportion of the posterior probability distribution of $\widehat{N}$ that was less than two males (the minimum number to represent a lek) for the last prediction year of each temporal scale. Although this value is not true extirpation (zero birds), we refer to it as extirpation to align with State definitions of lek inactivity. Extirpation of leks within NCs was thought to reflect a loss of a metapopulation resulting from reduced demographic rates.

The mean extirpation probability calculated across all leks was 0.50 (standard deviation [SD]=0.32), 0.58 (SD=0.28), and 0.63 (SD=0.27) for two (fig. 5A), four (fig. 5B), and six (fig. 5C) future oscillations, respectively. However, distributions of predicted extirpation rates for leks appeared bimodal with peak probabilities near 0.08 and 0.93 (two oscillations, fig. 5A), 0.26 and 0.94 (four oscillations, fig. 5B), and 0.43 and 0.98 (six oscillations, fig. 5C). Approximately 51, 58, and 65 percent of leks possessed an extirpation probability greater than 0.5 for two, four, and six oscillations, respectively. The spatial distribution of lek extirpation probabilities demonstrated higher concentrations of higher probabilities at the edges of the species range (figs. 6A–C). The mean extirpation probability calculated across all NCs was 0.16 (SD=0.27), 0.24 (SD=0.30), and 0.32 (SD=0.31) for two (fig. 5D), four (fig. 5E), and six (fig. 5F) future oscillations, respectively. Approximately 13, 19, and 29 percent of NCs possessed an extirpation probability greater than 0.5 for two, four, and six oscillations, respectively. The highest probabilities of NC scale extirpation often occurred at the periphery of the species range (figs. 6D–F). Conversely, interior NCs consisted primarily of low extirpation probabilities ranging from 0 to 0.25.

Neighborhood cluster extirpation probabilities for different temporal scales that are not listed in this report can be viewed in Coates and others (2022b). Coates and others (2022b) is the data release that accompanies this updated (1960–2024) report.

Watches and Warnings from a Targeted Annual Warning System

The TAWS is a hierarchical monitoring strategy that contrasts estimates of $\widehat{r}$ across nested spatial scales on an annual basis. Comparisons can act as a powerful analytical tool to help target when and where to adjust monitoring efforts, or inform where to carry out management actions. Methodology of TAWS was described in detail in Coates and others (2021) and Prochazka and others (2023). The TAWS produces signals referred to as watches and warnings, which signify progressively greater degrees of evidence for aberrant decline. Evidence of aberrant decline is assessed using independent sets of standardized thresholds that seek to stabilize the range-wide population (range-wide thresholds) versus individual climate clusters (climate cluster thresholds). The original published report (Coates and others, 2021) describing TAWS results presented each set of thresholds to provide managers with multiple strategic options. Here, we chose to report TAWS results using CC thresholds based on feedback of implementation from State and Federal agency personnel. The primary reason for the preferential use of CC thresholds is the superior performance in targeting peripheral and sparsely distributed populations. Like the trend analysis, thousands of historic lek surveys underwent QA/QC, as described in the results of Objective 1 for the TAWS analysis (Coates and others, 2021).

From 1990 to 2024, we estimated 72.5 and 57.8 percent of 4,914 sage-grouse leks experienced at least one watch or warning (table 3), respectively, across the species’ range. We calculated a mean annual percentage of leks that experienced a first watch or warning (no watches or warnings in any preceding year) to be 2.1 (105 leks) and 1.7 percent (84 leks), respectively. The mean annual percentage of leks that experienced a repeat watch or warning (at least one watch or warning occurred in a preceding year) was 5.9 (291 leks) and 6.1 percent (300 leks), respectively. Climate cluster A had the greatest percentage of watches at leks across the 35 years, where 92.6 percent of leks activated one or more times (table 3). Conversely, CC-E consisted of the greatest number of watches compared to other clusters (number of first watches=1,501; number of repeat watches=4,048; table 3). Climate cluster C had the lowest percentage of watches, at 64.3 percent (number of first and repeat watches=9 and 41, respectively; table 3). Climate cluster F had the greatest percentage (74.9) of warnings, whereas CC-E had the greatest number (first=1,151 and repeat=4,203). The second-highest percentage of watches (89.7) occurred in CC-F, whereas the second-highest percentage of warnings (72.2) occurred in CC-A.

From 1990 to 2024, we estimated 60.6 and 46.7 percent of 426 NCs experienced at least one watch or warning (table 3), respectively, across the species’ range. An average of 1.8 (repeat=0.051) and 1.4 (repeat=0.047) percent of clusters activated per year, which was approximately 7.6 (repeat=21.5) and 5.9 (repeat=20.0) clusters annually. We reported that CC-A and CC-E had the greatest percentage (88.9, 79.1) of watches, whereas CC-E had the greatest number (first=174 and repeat=546) of watches across the 35-year timeframe. For warnings, CC-A had the greatest percentage (77.8; table 3), and CC-E had the greatest number (first=130 and repeat=476).

In 2024, we estimated 0.7 and 0.8 percent of leks experienced a first watch and warning, respectively, range-wide (table 4), which equated to 33 (repeat=123; fig. 7) first watches and 40 (repeat=407; fig. 7) first warnings. In 2024, the greatest percentage of first watches (1.6) and warnings (1.5) were within CC-D, which were 27 (repeat=66) first watches and 26 (repeat=175) first warnings (table 4). In 2024, we estimated 0.5 and 0.2 percent of neighborhoods experienced a first watch and warning, respectively, range-wide (table 4), which equated to 2 (repeat=8; fig. 8) first watches and 1 (repeat=28; fig. 8) first warning. Climate cluster D experienced the greatest percentage of NC watches (1.4) and warnings (0.7) in 2024. No other NC experienced a new watch or warning in 2024. Warnings and watches for each neighborhood cluster are available in Coates and others (2022b).

From 1990 to 2024, 199 of 424 (46.7 percent) NCs experienced at least 1 warning. Of those 199 NCs, 35 (17.6 percent) had their initial chronic warning (warning^N) deactivated (fig. 9). Three of those 35 (8.6 percent) NCs experienced an additional warning after abundance had rebounded, resulting in a second chronic warning. Those three NCs remained in an activated chronic warning state as of 2024. The average length of time for population abundance to rebound was 8.4 years (SD=3.8). In that time, NCs lost an average of approximately 27 percent of their pre-signal population size (unperturbed population size), underperforming parent climate clusters (based on annual rates of population change) by an average of 5.9 percent per year. Once abundance had rebounded, those same populations outperformed their parent climate clusters by an average of 1.8 percent per year.