Greater Sage-Grouse Population Monitoring Framework: Cheat Sheet
The Greater Sage-grouse Population Monitoring Framework fills a prominent information gap to help inform current assessments of sage-grouse population trends at nested spatial and temporal scales. It is centered on four objectives: (1) create a standardized database of lek counts; (2) develop spatial population structures by clustering leks; (3) estimate spatial trends at different temporal extents; and (4) develop a system to help identify where and when management action is likely to benefit declining populations of sage-grouse at the appropriate spatial scale on an annual basis.
We have prepared a 'cheat sheet' that summarizes the definitions and rules applied to the sage-grouse lek data for each objective mentioned above. This 'cheat sheet' is a valuable resource that will assist users in navigating the framework and interpreting the results. Citations of products describing each objective are listed below to provide all details on methods and results. We recommend reviewing publications to comprehensively understand the products, as many methodological details are not provided here.
Lek conservation definitions (population clusters and lek inclusion)
Conservation status (nCONSVSTS) domain values developed for standardized greater sage-grouse (Centrocercus urophasianus) lek databases. The terminology listed here applies when the definition of a lek is met (for example, more than two displaying males observed for more than two years) (O’Donnell and others, 2021). These definitions were used when developing population clusters and are not used to inform the analysis of trends or the targeted annual warning system. Greater sage-grouse population clusters included leks of conservation status Active, Inactive, and Pending New leks, based on the 2019 lek database. "≥" = more than or equal to.
Lek database column definitions
Greater sage-grouse (Centrocercus urophasianus) standardized observation database field names, field type, and definitions of fields. Source: O’Donnell and others (2021). "≥" = more than or equal to; "<" = less than; ">" = more than; "HD" = high definition; "IR" = infrared.
Field Name | Field Type | Definition |
---|---|---|
nLEKID | String | A standardized LekID name that includes information about the state the lek is located within, species, state lek identifier, and the year the lek was first identified (<STATE >_GRSG_<state LEKID>_<YEAR>). |
nCONSVSTS | String | Defined status of the lek site to assist with habitat management, setting regulations for disturbances, determining the leks to monitor, and for other needs (for example, active, inactive, and historic; Table 1). |
nSURVMETH | String | Defined how the observers surveyed sage-grouse on the lek site (for example, ground [count], Aerial Helicopter HD/IR [Count], and Ground Route [Survey]; see Supplemental, Table S3). |
nRPTCNTS | String | Defined whether an observation count for a given lek included multiple counts at the lek within a single year (and sometimes on a single date). |
nMALE | Integer | A count of males on a lek for a given date or year depending on whether the data represented peak male count, which was the maximum number of males observed for all recorded dates (only year provided), or all observations at a lek within a year (date and/or year provided). |
nLAT_WGS84 | Decimal degrees, float | Latitudinal coordinates in decimal degrees using World geographic coordinate system 1984 for datum. |
nLON_WGS84 | Decimal degrees, float | Longitudinal coordinates in decimal degrees using World geographic coordinate system 1984 for datum. |
nYEAR | String | Year of field observation (YYYY). |
nDATE | String | Date of field observation, if recorded (MM/DD/YYYY). |
nTIME | String | Local hour and minute of field observation, if recorded (HH:MM; 24-hour clock, local time). |
nTSSR_min | String | Derived time since sunrise if local time recorded. |
Lek data standardization
- Lek database standardization: Use the same data formats/types for each column, crosswalk column names used by each state to a common column name, use the same definitions of terms, and systematically aggregate leks and males counted on adjacent leks.
- State-informed information: Individual states provided survey methods for their state and/or subpopulations when the state database did not include this information (for example, when data was peak male count [wide format database] or not recorded). These cases are hardcoded in the software per instructions by each state. States also provided information about how to treat empty records of male counts versus zero males counted (for example, was the lek counted, and did zero denote zero males observed, or did the observer did not visit the lek). States also provide pertinent information on which data to use for lek locations, appropriate coordinate systems when not documented, and states provide peer-reviews of the data each year to help identify potential problems, inconsistencies, or answer questions to help USGS accurately standardize the database.
- When are leks and counts aggregated:
- State lek meets the definition of our standardized lek definition: ≥2 displaying males observed for ≥2 years.
- For all leks that fall within 500 meters (m) of a neighboring lek, we buffer 500 m.
- For all leks within a dissolved buffer, we identify all the leks associated with each group/polygon.
- We then determine from the leks within each group, which has the largest mean during last 20 years (or for duration of observations, if there are fewer than 20 years of data). This lek is assigned the main lek and the rest are assigned as satellites.
- Male counts are summed across leks if observations occur on the same day; Male counts are considered as repeat counts if they occur on different days and within a breeding season.
- If aggregation occurs, the lek identified as having the greatest number of males is used for the lek name. Each state is provided a spreadsheet showing which leks are classified as 'satellite' and linked to the 'main' lek.
Source: O’Donnell and others (2021)
Lek connectivity
Rules used to define how a fully connected, least-cost path minimum spanning tree (LCP-MST) connecting breeding habitat of greater sage-grouse (Centrocercus urophasianus) was decomposed into sub-populations. Rules 1– 4 included distances based on sage-grouse dispersal movements. Rules 5 – 6 captured transportation routes with annual average daily traffic (AADT) potentially attenuating movements. Any LCP-MST edge distance that met a distance rule and any transportation feature that met an AADT threshold and intersected a LCP-MST edge was removed (supplemental S3). "km" = kilometers; ">" = more than; "≥" = more than or equal to; "kV" = kilovolt. Source: O’Donnell and others (2022a).
Rule identifier | Dispersal rule (potential connectivity) | Description and supporting information |
---|---|---|
1 | ≥15 km | Inter-lek movement of 15 km |
2 | ≥30 km | Seasonal movements in Montana (>20 km) and northwest Colorado (30 km) |
3 | ≥50 km | Breeding dispersals in Idaho, Montana, North Dakota, South Dakota and Alberta (50 km) |
4 | ≥80 km | Seasonal movements in Idaho (60–80 km) |
Rule identifier | Resistance rule (functional connectivity) | Description and supporting information |
---|---|---|
5 | Edge intersection | 4,000–7,000 AADT: these transportation routes frequently co-exist with 220 kV transmission lines (≥220 kV), agriculture, and other disturbance types |
6 | Edge intersection | ≥7,000 AADT: most of these routes are ≥10,000 AADT and likely have had significant traffic for a longer period than ≥4,000–7,000 AADT |
Clustering of leks into hierarchical subpopulation tiers
The number of greater sage-grouse (Centrocercus urophasianus) leks (breeding display grounds) recommended to the clustering algorithm (Spatial “K”luster Analysis by Tree Edge Removal) as a constraint-based rule during the identification of hierarchical population units in the western United States. This constraint-based rule will help ensure spatially balanced population units. Source: O’Donnell and others (2022b).
Cluster level | Minimum number of leks | Maximum number of leks | Mean leks per cluster |
---|---|---|---|
1 (smallest areal units) | 10 | 20 | 10 |
2 (Neighborhood Clusters) | 20 | 30 | 10 |
3 | 30 | 45 | 15 |
4 | 45 | 65 | 20 |
5 | 65 | 90 | 25 |
6 | 90 | 120 | 30 |
7 | 120 | 155 | 35 |
8 | 155 | 205 | 50 |
9 | 205 | 245 | 40 |
10 | 245 | 305 | 60 |
11 | 305 | 445 | 140 |
12 | 445 | 705 | 260 |
13 (largest areal units; Climate Clusters) | 705 | 1245 | 540 |
Trends and Targeted annual warning system (TAWS)
Monitoring rules applied to standardized lek database for inclusion in trends and TAWS analysis.
Rule | Definition |
---|---|
1 (breeding season)† | Observations were retained when lek visited from March 1 to May 1. |
2 (sunrise)† | Observations within 30 minutes before and 90 minutes after local sunrise. |
3 (survey method)†‡ | Observations surveyed using one of the following survey methods: ground (count), aerial helicopter camera HD/IR (count), aerial helicopter (count), ground route (count), aerial fixed-wing (count), aerial unknown, ground unknown, and aerial fixed-wing camera HD/IR (count). |
4 (max count) | We aggregated within-year repeat counts by maintaining the maximum count per lek per year. |
†In the absence of a recorded date, time, or survey method, we assumed the observations were done near sunrise, during the breeding season, and were conducted with one of the survey methods mentioned previously.
‡Within the state of Wyoming, we also included observations collected according to the state-defined survey method "ground (survey)," based on the rigorous criteria required to achieve that categorical assignment and the ensuing adequacy of those data for trend estimation.
Trends only
Modeling rules are applied to a standardized lek database for inclusion in trends analysis. Source: Coates and others (2021) and Prochazka and others (2024).
Rule | Definition |
---|---|
5a (post-1960) | Removed counts that were recorded before 1960. |
6a (five active counts) | Removed leks with fewer than 5 years of active counts (greater than or equal to two males) during the 60-year time series. |
Targeted annual warning system only (TAWS)
Modeling rules applied to standardized lek database for inclusion in trends analysis. Source: Coates and others (2021) and Prochazka and others (2024).
Rule | Definition |
---|---|
5b (post-1990) | Removed counts that were recorded before 1990. |
6b (five active counts) | Removed leks with fewer than 5 years of active counts (greater than or equal to two males) during the 60-year time series. |
Trends and years of nadir:
Trends across the six lowest points in the range-wide sage-grouse population oscillations since 1960.
Cluster | Low Point 1 | Low Point 2 | Low Point 3 | Low Point 4 | Low Point 5 | Low Point 6 |
---|---|---|---|---|---|---|
A | 1969 | 1977 | 1983 | 1995 | 2002 | 2008 |
B | 1964 | 1976 | 1987 | 1996 | 2001 | 2008 |
C | 1963 | 1973 | 1984 | 1999 | 2003 | 2011 |
D | 1966 | 1977 | 1986 | 1997 | 2004 | 2014 |
E | 1967 | 1975 | 1985 | 1996 | 2002 | 2013 |
F | 1966 | 1973 | 1987 | 1996 | 2002 | 2013 |
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 TAWS framework to produce results that are delivered in time for annual agency decision making.
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 U.S. 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.
Greater Sage-Grouse Population Monitoring Framework
Data Harmonization for Greater Sage-Grouse Populations
Estimating trends for greater sage-grouse populations within highly stochastic environments
A targeted annual warning system (TAWS) for identifying aberrant declines in greater sage-grouse populations
Hierarchical Units of Greater Sage-Grouse Populations Informing Wildlife Management
Trends and a Targeted Annual Warning System for Greater Sage-Grouse in the Western United States (ver. 3.0, February 2024)
Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2023
Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2022
A targeted annual warning system developed for the conservation of a sagebrush indicator species
Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2021
Defining biologically relevant and hierarchically nested population units to inform wildlife management
Defining fine-scaled population structure among continuously distributed populations
Synthesizing and analyzing long-term monitoring data: A greater sage-grouse case study
Range-wide greater sage-grouse hierarchical monitoring framework—Implications for defining population boundaries, trend estimation, and a targeted annual warning system
grsg_lekdb: Compiling and standardizing greater sage-grouse lek databases (version 1.3.0)
grsg_lekdb: Compiling and standardizing greater sage-grouse lek databases, version 1.2.0
grsg_lekdb: Compiling and standardizing greater sage-grouse lek databases, version 1.1.0
grsg_lekdb: Compiling and standardizing greater sage-grouse lek databases
The Greater Sage-grouse Population Monitoring Framework fills a prominent information gap to help inform current assessments of sage-grouse population trends at nested spatial and temporal scales. It is centered on four objectives: (1) create a standardized database of lek counts; (2) develop spatial population structures by clustering leks; (3) estimate spatial trends at different temporal extents; and (4) develop a system to help identify where and when management action is likely to benefit declining populations of sage-grouse at the appropriate spatial scale on an annual basis.
We have prepared a 'cheat sheet' that summarizes the definitions and rules applied to the sage-grouse lek data for each objective mentioned above. This 'cheat sheet' is a valuable resource that will assist users in navigating the framework and interpreting the results. Citations of products describing each objective are listed below to provide all details on methods and results. We recommend reviewing publications to comprehensively understand the products, as many methodological details are not provided here.
Lek conservation definitions (population clusters and lek inclusion)
Conservation status (nCONSVSTS) domain values developed for standardized greater sage-grouse (Centrocercus urophasianus) lek databases. The terminology listed here applies when the definition of a lek is met (for example, more than two displaying males observed for more than two years) (O’Donnell and others, 2021). These definitions were used when developing population clusters and are not used to inform the analysis of trends or the targeted annual warning system. Greater sage-grouse population clusters included leks of conservation status Active, Inactive, and Pending New leks, based on the 2019 lek database. "≥" = more than or equal to.
Lek database column definitions
Greater sage-grouse (Centrocercus urophasianus) standardized observation database field names, field type, and definitions of fields. Source: O’Donnell and others (2021). "≥" = more than or equal to; "<" = less than; ">" = more than; "HD" = high definition; "IR" = infrared.
Field Name | Field Type | Definition |
---|---|---|
nLEKID | String | A standardized LekID name that includes information about the state the lek is located within, species, state lek identifier, and the year the lek was first identified (<STATE >_GRSG_<state LEKID>_<YEAR>). |
nCONSVSTS | String | Defined status of the lek site to assist with habitat management, setting regulations for disturbances, determining the leks to monitor, and for other needs (for example, active, inactive, and historic; Table 1). |
nSURVMETH | String | Defined how the observers surveyed sage-grouse on the lek site (for example, ground [count], Aerial Helicopter HD/IR [Count], and Ground Route [Survey]; see Supplemental, Table S3). |
nRPTCNTS | String | Defined whether an observation count for a given lek included multiple counts at the lek within a single year (and sometimes on a single date). |
nMALE | Integer | A count of males on a lek for a given date or year depending on whether the data represented peak male count, which was the maximum number of males observed for all recorded dates (only year provided), or all observations at a lek within a year (date and/or year provided). |
nLAT_WGS84 | Decimal degrees, float | Latitudinal coordinates in decimal degrees using World geographic coordinate system 1984 for datum. |
nLON_WGS84 | Decimal degrees, float | Longitudinal coordinates in decimal degrees using World geographic coordinate system 1984 for datum. |
nYEAR | String | Year of field observation (YYYY). |
nDATE | String | Date of field observation, if recorded (MM/DD/YYYY). |
nTIME | String | Local hour and minute of field observation, if recorded (HH:MM; 24-hour clock, local time). |
nTSSR_min | String | Derived time since sunrise if local time recorded. |
Lek data standardization
- Lek database standardization: Use the same data formats/types for each column, crosswalk column names used by each state to a common column name, use the same definitions of terms, and systematically aggregate leks and males counted on adjacent leks.
- State-informed information: Individual states provided survey methods for their state and/or subpopulations when the state database did not include this information (for example, when data was peak male count [wide format database] or not recorded). These cases are hardcoded in the software per instructions by each state. States also provided information about how to treat empty records of male counts versus zero males counted (for example, was the lek counted, and did zero denote zero males observed, or did the observer did not visit the lek). States also provide pertinent information on which data to use for lek locations, appropriate coordinate systems when not documented, and states provide peer-reviews of the data each year to help identify potential problems, inconsistencies, or answer questions to help USGS accurately standardize the database.
- When are leks and counts aggregated:
- State lek meets the definition of our standardized lek definition: ≥2 displaying males observed for ≥2 years.
- For all leks that fall within 500 meters (m) of a neighboring lek, we buffer 500 m.
- For all leks within a dissolved buffer, we identify all the leks associated with each group/polygon.
- We then determine from the leks within each group, which has the largest mean during last 20 years (or for duration of observations, if there are fewer than 20 years of data). This lek is assigned the main lek and the rest are assigned as satellites.
- Male counts are summed across leks if observations occur on the same day; Male counts are considered as repeat counts if they occur on different days and within a breeding season.
- If aggregation occurs, the lek identified as having the greatest number of males is used for the lek name. Each state is provided a spreadsheet showing which leks are classified as 'satellite' and linked to the 'main' lek.
Source: O’Donnell and others (2021)
Lek connectivity
Rules used to define how a fully connected, least-cost path minimum spanning tree (LCP-MST) connecting breeding habitat of greater sage-grouse (Centrocercus urophasianus) was decomposed into sub-populations. Rules 1– 4 included distances based on sage-grouse dispersal movements. Rules 5 – 6 captured transportation routes with annual average daily traffic (AADT) potentially attenuating movements. Any LCP-MST edge distance that met a distance rule and any transportation feature that met an AADT threshold and intersected a LCP-MST edge was removed (supplemental S3). "km" = kilometers; ">" = more than; "≥" = more than or equal to; "kV" = kilovolt. Source: O’Donnell and others (2022a).
Rule identifier | Dispersal rule (potential connectivity) | Description and supporting information |
---|---|---|
1 | ≥15 km | Inter-lek movement of 15 km |
2 | ≥30 km | Seasonal movements in Montana (>20 km) and northwest Colorado (30 km) |
3 | ≥50 km | Breeding dispersals in Idaho, Montana, North Dakota, South Dakota and Alberta (50 km) |
4 | ≥80 km | Seasonal movements in Idaho (60–80 km) |
Rule identifier | Resistance rule (functional connectivity) | Description and supporting information |
---|---|---|
5 | Edge intersection | 4,000–7,000 AADT: these transportation routes frequently co-exist with 220 kV transmission lines (≥220 kV), agriculture, and other disturbance types |
6 | Edge intersection | ≥7,000 AADT: most of these routes are ≥10,000 AADT and likely have had significant traffic for a longer period than ≥4,000–7,000 AADT |
Clustering of leks into hierarchical subpopulation tiers
The number of greater sage-grouse (Centrocercus urophasianus) leks (breeding display grounds) recommended to the clustering algorithm (Spatial “K”luster Analysis by Tree Edge Removal) as a constraint-based rule during the identification of hierarchical population units in the western United States. This constraint-based rule will help ensure spatially balanced population units. Source: O’Donnell and others (2022b).
Cluster level | Minimum number of leks | Maximum number of leks | Mean leks per cluster |
---|---|---|---|
1 (smallest areal units) | 10 | 20 | 10 |
2 (Neighborhood Clusters) | 20 | 30 | 10 |
3 | 30 | 45 | 15 |
4 | 45 | 65 | 20 |
5 | 65 | 90 | 25 |
6 | 90 | 120 | 30 |
7 | 120 | 155 | 35 |
8 | 155 | 205 | 50 |
9 | 205 | 245 | 40 |
10 | 245 | 305 | 60 |
11 | 305 | 445 | 140 |
12 | 445 | 705 | 260 |
13 (largest areal units; Climate Clusters) | 705 | 1245 | 540 |
Trends and Targeted annual warning system (TAWS)
Monitoring rules applied to standardized lek database for inclusion in trends and TAWS analysis.
Rule | Definition |
---|---|
1 (breeding season)† | Observations were retained when lek visited from March 1 to May 1. |
2 (sunrise)† | Observations within 30 minutes before and 90 minutes after local sunrise. |
3 (survey method)†‡ | Observations surveyed using one of the following survey methods: ground (count), aerial helicopter camera HD/IR (count), aerial helicopter (count), ground route (count), aerial fixed-wing (count), aerial unknown, ground unknown, and aerial fixed-wing camera HD/IR (count). |
4 (max count) | We aggregated within-year repeat counts by maintaining the maximum count per lek per year. |
†In the absence of a recorded date, time, or survey method, we assumed the observations were done near sunrise, during the breeding season, and were conducted with one of the survey methods mentioned previously.
‡Within the state of Wyoming, we also included observations collected according to the state-defined survey method "ground (survey)," based on the rigorous criteria required to achieve that categorical assignment and the ensuing adequacy of those data for trend estimation.
Trends only
Modeling rules are applied to a standardized lek database for inclusion in trends analysis. Source: Coates and others (2021) and Prochazka and others (2024).
Rule | Definition |
---|---|
5a (post-1960) | Removed counts that were recorded before 1960. |
6a (five active counts) | Removed leks with fewer than 5 years of active counts (greater than or equal to two males) during the 60-year time series. |
Targeted annual warning system only (TAWS)
Modeling rules applied to standardized lek database for inclusion in trends analysis. Source: Coates and others (2021) and Prochazka and others (2024).
Rule | Definition |
---|---|
5b (post-1990) | Removed counts that were recorded before 1990. |
6b (five active counts) | Removed leks with fewer than 5 years of active counts (greater than or equal to two males) during the 60-year time series. |
Trends and years of nadir:
Trends across the six lowest points in the range-wide sage-grouse population oscillations since 1960.
Cluster | Low Point 1 | Low Point 2 | Low Point 3 | Low Point 4 | Low Point 5 | Low Point 6 |
---|---|---|---|---|---|---|
A | 1969 | 1977 | 1983 | 1995 | 2002 | 2008 |
B | 1964 | 1976 | 1987 | 1996 | 2001 | 2008 |
C | 1963 | 1973 | 1984 | 1999 | 2003 | 2011 |
D | 1966 | 1977 | 1986 | 1997 | 2004 | 2014 |
E | 1967 | 1975 | 1985 | 1996 | 2002 | 2013 |
F | 1966 | 1973 | 1987 | 1996 | 2002 | 2013 |
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 TAWS framework to produce results that are delivered in time for annual agency decision making.
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 U.S. 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.