Coates, P.S. Prochazka, B.G. Aldridge, C.L. O'Donnell, M.S. Edmunds, D.R. Monroe, A.P. Hanser, S.E. Wiechman, L.A. Chenaille, M.P. 20221230 ver. 4.0 vector digital data (SHP) Denver, CO U.S. Geological Survey data release https://doi.org/10.5066/P9OQWGIV Brian G. Prochazka Peter S. Coates Cameron L. Aldridge Michael S. O’Donnell David R. Edmunds Adrian P. Monroe Steve E. Hanser Lief A. Wiechman Michael P. Chenaille 2025 publication n/a US Geological Survey http://dx.doi.org/10.3133/dr1217 Greater sage-grouse (Centrocercus urophasianus; hereafter sage-grouse) are at the center of state and national land-use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. This updated population trend analysis provides state and federal land and wildlife managers with the best-available science to help guide management and conservation plans aimed at benefitting sage-grouse populations and the ecosystems they inhabit. This analysis relied on previously published population trend modeling methodology from Coates and others (2021, 2022) and incorporates population lek count data for 1960-2024. Included in this report are methodological updates to lek count data aggregation, state-space model forecasting, and targeted annual warning system signals, which are detailed under individual Modification sections. State-space models estimated 2.9-percent average annual decline in sage-grouse populations between 1966 and 2021 (Period 1, six population oscillations) across their geographical range. Average annual decline among climate clusters for the same number of oscillations ranged between 2.2 and 3.4 percent. Cumulative declines were 41.2, 64.1, and 78.8 percent range-wide during Period 5 (19 years), Period 3 (35 years), and Period 1 (55 years), respectively. Definitions: Watch: Assigned to populations that exhibit evidence of population decline below those of their respective climate cluster (slow signal) over 2 consecutive years. Warning: Assigned to populations that experienced slow signals in 3 out of 4 consecutive years OR a relatively strong magnitude (fast signal) of evidence for 2 out of 3 years. Watches may identify the need for intensive monitoring whereas warnings may identify the need for management intervention aimed at stabilizing populations. References: Coates, P.S., Prochazka, B.G., O’Donnell, M.S., Aldridge, C.L., Edmunds, D.R., Monroe, A.P., Ricca, M.A., Wann, G.T., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2021, Range-wide greater sage-grouse hierarchical monitoring framework-Implications for defining population boundaries, trend estimation, and a targeted annual warning system: U.S. Geological Survey Open-File Report 2020-1154, 243 p., https://doi.org/10.3133/ofr20201154. Coates, P.S., Prochazka, B.G., Aldridge, C.L., O’Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2022, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)-Updated 1960-2021: U.S. Geological Survey Data Report 1165, 16 p., https://doi.org/10.3133/dr1165 The purpose of this data release is to provide updated results on sage-grouse population trends across their geographical range of western United States. This report reflects previous modeling efforts (Coates and others, 2021, 2022) and includes additional years of data to reflect the most current population trends. The U.S. Geological Survey, in cooperation with the Western Association of Fish and Wildlife Agencies and Bureau of Land Management, are providing this scientific information to fulfill a prominent information gap that will help inform status assessments of sage-grouse population trends and conservation management strategies. 1960 2024 Lek attendance was counted annually across this time period Planned Annually western United States -119.5276 -103.4842 49.9086 35.9960 ISO 19115 Topic Category biota USGS Thesaurus ecology long-term ecological monitoring human impacts shrubland ecosystems None adaptive management targeted monitoring triggers warning system USGS Metadata Identifier USGS:637e9b26d34ed907bf76eb1e None western United States California Oregon Washington Idaho Nevada Utah Montana Wyoming Colorado South Dakota North Dakota Integrated Taxonomic Information System (ITIS) Centrocercus urophasianus Artemisia U.S. Geological Survey 2013 Online Database https://doi.org/10.5066/F7KH0KBK www.itis.gov expert advice Domain Eukaryota Kingdom Animalia Subkingdom Bilateria Infrakingdom Deuterostomia Phylum Chordata Subphylum Vertebrata Infraphylum Gnathostomata Superclass Tetrapoda Class Aves Order Galliformes Family Phasianidae Subfamily Tetraoninae Genus Centrocercus Species Centrocercus urophasianus TSN: 175855 Kingdom Plantae Subkingdom Viridiplantae Infrakingdom Streptophyta Superdivision Embryophyta Division Tracheophyta Subdivision Spermatophytina Class Magnoliopsida Superorder Asteranae Order Asterales Family Asteraceae Genus Artemisia TSN: 35431 No access constraints. Please see 'Distribution Info' for details. No use constraints. These data are marked with a Creative Common CC0 1.0 Universal License. These data are in the public domain and do not have any use constraints. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Questions pertaining to appropriate use or assistance with understanding limitations or interpretation of the data are to be directed to the individuals/organization listed in the Point of Contact section. U.S. Geological Survey, Western Ecological Research Center Data Manager mailing address

3020 State University Drive, Modoc Hall, suite 4004

Sacramento CA 95819 US 279-782-0904 gs-b-werc_data_management@usgs.gov We conducted this project in close consultation with the Bureau of Land Management, Columbian Sharp-tailed Grouse and Sage-Grouse Technical Team for the Western Association of Fish and Wildlife Agencies, and the U.S. Fish and Wildlife Service. We thank R. Arkle (U.S. Geological Survey), L. Schreiber (Wyoming Game and Fish Department) for helpful comments in reviewing the report in its entirety. We are also thankful to K. Doherty (U.S. Fish and Wildlife Service) for thoughts and contributions on the pilot efforts to the TAWS and helpful reviews of previous versions. We appreciate the efforts of S. Mathews, B. Brussee, I. Dwight, J. Mintz, S. O'Neil, M. Meyerpeter, and C. Roth (U.S. Geological Survey) for providing thoughtful edits on various sections; E. Tyrrell (U.S. Geological Survey) for helping to compile data and build tables; J. Atkinson (U.S. Geological Survey) for assisting with report preparation; and D. Nahhas and K. Engelking (U.S. Geological Survey) for editing, formatting, and final production of this report. We extend gratitude for the cooperation of personnel from 11 western state wildlife agencies, who provided feedback at various stages on uses of lek data, modeling methods, and constructive reviews at various stages of production. Specifically, we value the contributions from T. Remington (Western Association of Fish and Wildlife Agencies), S. Stiver (Western Association of Fish and Wildlife Agencies), S. Espinosa (Nevada Department of Wildlife), K. Griffin (Colorado Parks and Wildlife), K. Miller (California Department of Fish and Wildlife), A. Moser (Idaho Department of Fish and Game), A. Cook (Utah Division of Wildlife Resources), L. Foster (Oregon Department of Fish and Wildlife), J. Kolar (North Dakota Game and Fish Department), T. Runia (South Dakota Department of Game, Fish and Parks), M. Schroeder (Washington Department of Fish and Wildlife), N. Whitford (Wyoming Game and Fish Department), C. Wightman (Montana Fish, Wildlife & Parks), B. Wakeling (Montana Fish, Wildlife & Parks), S. Vold (Oregon Department of Fish and Wildlife), and M. Cline (Oregon Department of Fish and Wildlife). We thank K. McGowan, K. Andrle, M. Magaletti, A. Kosic, P. Winters (Bureau of Land Management), J. Tull (U.S. Fish and Wildlife Service), K. Borland, M. Nelson (U.S. Forest Service), S. Abele (U.S. Fish and Wildlife Service), S. Gardner, and B. Ehler (California Department of Fish and Wildlife) for their input throughout the initial components of this study. We thank the many researchers and state wildlife agencies for providing or allowing access to data or generating results from evaluating sage-grouse movements among clusters and across cluster levels (hierarchical population monitoring framework). Specifically, we thank Colorado Parks and Wildlife researchers and biologists for evaluating clusters in northwest Colorado, including A. Apa, M. Cowardin, B. Holmes, L. Rossi, and B. Walker; Idaho Department of Fish and Game and Bureau of Land Management researchers and biologists for providing data across the species range in Idaho, including E. Ellsworth (Bureau of Land Management-Idaho), V. Guyer (Bureau of Land Management-Idaho), S. Norman, J. Rabon, B. Schoeberl; Oregon State University researchers for evaluating clusters in southeastern Oregon, including C. Anthony, C. Hagen, and Oregon Department of Fish and Wildlife; Washington State University researchers providing data in Washington, including P.J. Olsoy, D. Thornton, and M. Schroeder of Washington Department of Fish and Wildlife; J. Taylor (currently, U.S. Department of Agriculture), J. Dinkins (currently, Oregon State University), and Wyoming Game and Fish Department for evaluating clusters in the bighorn basin of Wyoming; U.S. Geological Survey and Bureau of Land Management researchers for providing data in South Dakota, including R. Newton, A. Johnston, R. Diel, E. Beever; South Dakota Game, Fish and Parks researchers and biologists for providing data in South Dakota, including C. Sink, J. Gehrt, K. Norton, L. Bichoff, S. Hone; Nevada Department of Wildlife and California Department of Fish and Wildlife for granting permits to the U.S. Geological Survey for marking and tracking sage-grouse. This project could not have been completed without the financial support of the Bureau of Land Management, U.S. Geological Survey, and the funds for the pilot efforts that were provided by the Bureau of Land Management-Nevada and Nevada Department of Wildlife. Windows 11, R 3.4.0 Peter S. Coates Brian G. Prochazka Michael S. O'Donnell Cameron L. Aldridge David R. Edmunds Adrian P. Monroe Mark A. Ricca Gregory T. Wann Steve E. Hanser Lief A. Wiechman Michael Chenaille 2021 publication Open-File Report 1154 n/a U.S. Geological Survey https://doi.org/10.3133/ofr20201154 Brian G. Prochazka Peter S. Coates Michael S. O'Donnell David R. Edmunds Adrian P. Monroe Mark A. Ricca Gregory T. Wann Steve E. Hanser Lief A. Wiechman Kevin E. Doherty Michael P. Chenaille Cameron L. Aldridge 202304 publication Ecological Indicators vol. 148 n/a Elsevier BV ppg. 110097 https://doi.org/10.1016/j.ecolind.2023.110097 Western Association of Fish and Wildlife Agencies 2015 publication https://wafwa.org/wpdm-package/greater-sage-grouse-population-trends-an-analysis-of-lek-count-databases-1965-2015/ Peter S. Coates Brian G. Prochazka Cameron L. Aldridge Michael S. O'Donnell David R. Edmunds Adrian P. Monroe Steve E. Hanser Lief A. Wiechman Michael P. Chenaille 2022 publication n/a U.S. Geological Survey https://doi.org/10.3133/dr1165 Peter S. Coates Brian G. Prochazka Cameron L. Aldridge Michael S. O'Donnell David R. Edmunds Adrian P. Monroe Steve E. Hanser Lief A. Wiechman Michael P. Chenaille 2023 publication Data Report 1175 n/a U.S. Geological Survey https://doi.org/10.3133/dr1175 Brian G. Prochazka Peter S. Coates Cameron L. Aldridge Michael S. O'Donnell David R. Edmunds Adrian P. Monroe Steve E. Hanser Lief A. Wiechman Michael P. Chenaille 2024 publication Data Report 1190 n/a U.S. Geological Survey https://doi.org/10.3133/dr1190 R is ‘GNU S’, a freely available language and environment for statistical computing and graphics that provides a wide variety of statistical and graphical techniques: linear and nonlinear modeling, statistical tests, time series analysis, classification, clustering, etc. https://cran.r-project.org/manuals.html R Core Team 20170421 3.4.0 Tools Software https://www.r-project.org/ Support for simple features, a standardized way to encode spatial vector data. https://cran.r-project.org/web/packages/sf/sf.pdf Edzer Pebesma Roger Bivand Etienne Racine Michael Sumner Ian Cook Tim Keitt Robin Lovelace Hadley Wickham Jeroen Ooms Kirill Muller Thomas Lin Pedersen Dan Baston Dewey Dunnington 20190917 0.8.0 Tools Software https://CRAN.R-project.org/package=sf Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value. 'tidyr' contains tools for changing the shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset, turning deeply nested lists into rectangular data frames ('rectangling'), and extracting values out of string columns. It also includes tools for working with missing values (both implicit and explicit). https://cran.r-project.org/web/packages/tidyr/tidyr.pdf Hadley Wickham Maximilian Girlich RStudio 20190912 1.0.0 Tools Software https://CRAN.R-project.org/package=tidyr Reading, writing, manipulating, analyzing and modeling of spatial data. The package implements basic and high-level functions for raster data and for vector data operations such as intersections. https://cran.r-project.org/web/packages/raster/raster.pdf Robert J. Hijmans Jacob van Etten Michael Sumner Joe Cheng Dan Baston Andrew Bevan Roger Bivand Lorenzo Busetto Mort Canty Ben Fasoli David Forrest Aniruddha Ghosh Duncan Golicher Josh Gray Jonathan A. Greenberg Paul Hiemstra Kassel Hingee Alex Ilich Institute for Mathematics Applied Geosciences Charles Karney Matteo Mattiuzzi Steven Mosher Babak Naimi Jakub Nowosad Edzer Pebesma Oscar Perpinan Lamigueiro Etienne B. Racine Barry Rowlingson Ashton Shortridge Bill Venables Rafael Wueest 20190130 2.8-19 Tools Software https://CRAN.R-project.org/package=terra Provides medium to high level functions for 3D interactive graphics, including functions modelled on base graphics (plot3d(), etc.) as well as functions for constructing representations of geometric objects (cube3d(), etc.). https://cran.r-project.org/web/packages/rgl/rgl.pdf Duncan Murdoch Daniel Adler Oleg Nenadic Simon Urbanek Ming Chen Albrecht Gebhardt Ben Bolker Gabor Csardi Adam Strzelecki Alexander Senger The R Core Team Dirk Eddelbuettel The authors of Shiny The authors of knitr Jeroen Ooms Yohann Demont Joshua Ulrich Xavier Fernandez i Marin George Helffrich Ivan Krylov Michael Sumner Mike Stein 20190312 0.100.19 Tools Software https://CRAN.R-project.org/package=rgl Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, etc. Includes data sets from oceanography. https://cran.r-project.org/web/packages/plot3D/plot3D.pdf Karline Soetaert 20170828 1.1.1 Tools Software https://CRAN.R-project.org/package=plot3D Bridge Regression https://github.com/cran/BayesBridge Nicholas G. Polson James G. Scott Jesse Windle 2014 0.6 Tools Software https://github.com/cran/BayesBridge/tree/master/R Provides a parallel backend for the dopar function using the parallel package. https://cran.r-project.org/web/packages/doParallel/doParallel.pdf Folashade Daniel Microsoft Corporation Steve Weston Dan Tenenbaum 20190802 1.0.15 Tools Software https://CRAN.R-project.org/package=doParallel Provides a 'dopar' adapter such that any type of futures can be used as backends for the 'foreach' framework. https://cran.r-project.org/web/packages/doFuture/doFuture.pdf Henrik Bengtsson 20200111 0.9.0 Tools Software https://CRAN.R-project.org/package=doFuture Support for iterators, which allow a programmer to traverse through all the elements of a vector, list, or other collection of data. https://cran.r-project.org/web/packages/iterators/iterators.pdf Folashade Daniel Revolution Analytics Steve Weston 20190727 1.0.12 Tools Software https://CRAN.R-project.org/package=iterators Support for the foreach looping construct. Foreach is an idiom that allows for iterating over elements in a collection, without the use of an explicit loop counter. This package in particular is intended to be used for its return value, rather than for its side effects. In that sense, it is similar to the standard lapply function, but doesn't require the evaluation of a function. Using foreach without side effects also facilitates executing the loop in parallel. https://cran.r-project.org/web/packages/foreach/foreach.pdf Folashade Daniel Hong Ooi Rich Calaway Microsoft Steve Weston 20200330 1.5.0 Tools Software https://cran.r-project.org/package=foreach The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers, etc. Because of its unified API, there is no need to modify any code in order switch from sequential on the local machine to, say, distributed processing on a remote compute cluster. Another strength of this package is that global variables and functions are automatically identified and exported as needed, making it straightforward to tweak existing code to make use of futures. https://cran.r-project.org/web/packages/future/future.pdf Henrik Bengtsson 20200116 1.16.0 Tools Software https://CRAN.R-project.org/package=future Identifies global (unknown or free) objects in R expressions by code inspection using various strategies (ordered, liberal, or conservative). The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in parallel, distributed compute environments. https://cran.r-project.org/web/packages/globals/globals.pdf Henrik Bengtsson Davis Vaughan 20191207 0.16.3 Tools Software https://cran.r-project.org/package=globals Miscellaneous functions for 'SciViews' or general use: manage a temporary environment attached to the search path for temporary variables you do not want to save() or load(), test if 'Aqua', 'Mac', 'Win', ... Show progress bar, etc. https://cran.r-project.org/web/packages/svMisc/svMisc.pdf Philippe Grosjean Romain Francois Kamil Barton 20180630 1.1.0 Tools Software https://CRAN.R-project.org/package=svMisc Routines for combinatorics https://cran.r-project.org/web/packages/combinat/combinat.pdf Scott Chasalow 20121029 0.0-8 Tools Software https://CRAN.R-project.org/package=combinat Graphical scales map data to aesthetics and provide methods for automatically determining breaks and labels for axes and legends. https://cran.r-project.org/web/packages/scales/scales.pdf Hadley Wickham Dana Seidel RStudio 20180809 1.0.0 Tools Software https://CRAN.R-project.org/package=scales User-friendly interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior. http://cran.nexr.com/web/packages/runjags/runjags.pdf Matthew Denwood Martyn Plummer 20160725 2.0.4-2 Tools Software https://cran.r-project.org/package=runjags Providing wrapper functions to implement Bayesian analysis in JAGS. Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains. https://cran.r-project.org/web/packages/R2jags/R2jags.pdf Yu-Sung Su Masanao Yajima 20150823 0.5-7 Tools Software https://CRAN.R-project.org/package=R2jags A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags'). A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (for example, predictive check plots, traceplots). https://cran.r-project.org/web/packages/jagsUI/jagsUI.pdf Ken Kellner Mike Meredith 20180913 1.5.0 Tools Software https://cran.r-project.org/package=jagsUI A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs and is also flexible enough to handle most nonstandard requirements. https://cran.r-project.org/web/packages/lattice/lattice.pdf Deepayan Sarkar Felix Andrews Kevin Wright Neil Klepeis Johan Larsson Paul Murrell 20170325 0.22-6 Tools Software https://cran.r-project.org/package=lattice An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics. https://cran.r-project.org/web/packages/zoo/zoo.pdf Achim Zeileis Gabor Grothendieck Jeffrey A. Ryan Joshua M. Ulrich Felix Andrews 20190321 1.8-5 Tools Software https://cran.r-project.org/package=zoo Auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0). https://cran.r-project.org/web/packages/emdbook/emdbook.pdf Ben Bolker Sang Woo Park James Vonesh Jacqueline Wilson Russ Schmitt Sally Holbrook James D. Thomson R. Scot Duncan 20190212 1.3.11 Tools Software https://CRAN.R-project.org/package=emdbook Display of maps. https://cran.r-project.org/web/packages/maps/maps.pdf Richard A. Becker Allan R. Wilks Ray Brownrigg Thomas P. Minka Alex Deckmyn 20180403 3.3.0 Tools Software https://cran.r-project.org/package=maps Demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming. https://cran.r-project.org/web/packages/TeachingDemos/TeachingDemos.pdf Greg Snow 20160212 2.10 Tools Software https://cran.r-project.org/package=TeachingDemos Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') and access to projection/transformation operations from the 'PROJ' library. https://cran.r-project.org/web/packages/rgdal/rgdal.pdf Roger Bivand Tim Keitt Barry Rowlingson Edzer Pebesma Michael Sumner Robert Hijmans Daniel Baston Even Rouault Frank Warmerdam Jeroen Ooms Colin Rundel 20190314 1.4-3 Tools Software https://cran.r-project.org/package=rgdal Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, subsetting, printing, summarizing, etc. https://cran.r-project.org/web/packages/sp/sp.pdf Edzer Pebesma Roger Bivand Barry Rowlingson Virgilio Gomez-Rubio Robert Hijmans Michael Sumner Don MacQueen Jim Lemon Finn Lindgren Josh O'Brien Joseph O'Rourke 20180605 1.3-1 Tools Software https://cran.r-project.org/package=sp Functions for convenient plotting and viewing of MCMC output. https://cran.r-project.org/web/packages/mcmcplots/mcmcplots.pdf S. McKay Curtis Ilya Goldin Evangelos Evangelou 'sumtxt' from GitHub 20180622 0.4.3 Tools Software https://cran.r-project.org/package=mcmcplots Fast aggregation of large data (for example, 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development. https://cran.r-project.org/web/packages/data.table/data.table.pdf Matt Dowle Arun Srinivasan Jan Gorecki Michael Chirico Pasha Stetsenko Tom Short Steve Lianoglou Eduard Antonyan Markus Bonsch Hugh Parsonage Scott Ritchie Kun Ren Xianying Tan Rick Saporta Otto Seiskari Xianghui Dong Michel Lang Watal Iwasaki Seth Wenchel Karl Broman Tobias Schmidt David Arenburg Ethan Smith Francois Cocquemas Matthieu Gomez Philippe Chataignon Nello Blaser Dmitry Selivanov Andrey Riabushenko Cheng Lee Declan Groves Daniel Possenriede Felipe Parages Denes Toth Mus Yaramaz-David Ayappan Perumal James Sams Martin Morgan Michael Quinn javrucebo marc-outins Roy Storey Manish Saraswat Morgan Jacob Michael Schubmehl Davis Vaughan Toby Hocking Leonardo Silvestri Tyson Barrett Jim Hester Anthony Damico Sebastian Freundt David Simons Elliott Sales de Andrade Cole Miller Jens Peder Meldgaard Vaclav Tlapak Kevin Ushey Dirk Eddelbuettel Ben Schwen 20190407 1.12.2 Tools Software https://cran.r-project.org/package=data.table Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast'). https://cran.r-project.org/web/packages/reshape2/reshape2.pdf Hadley Wickham 20171211 1.4.3 Tools Software https://cran.r-project.org/package=reshape2 Flexibly restructure and aggregate data using just two functions: melt and cast. https://cran.r-project.org/web/packages/reshape/reshape.pdf Hadley Wickham 20181023 0.8.8 Tools Software https://cran.r-project.org/package=reshape A set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to fit a model to each spatial location or time point in your study, summarize data by panels or collapse high-dimensional arrays to simpler summary statistics. The development of 'plyr' has been generously supported by 'Becton Dickinson'. https://cran.r-project.org/web/packages/plyr/plyr.pdf Hadley Wickham 20160608 1.8.4 Tools Software https://cran.r-project.org/package=plyr Interface to the JAGS MCMC library. https://cran.r-project.org/web/packages/rjags/rjags.pdf Martyn Plummer Alexey Stukalov Matt Denwood 20181019 4-8 Tools Software https://cran.r-project.org/package=rjags Provides functions for summarizing and plotting the output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence to the equilibrium distribution of the Markov chain. https://cran.r-project.org/web/packages/coda/coda.pdf Martyn Plummer Nicky Best Kate Cowles Karen Vines Deepayan Sarkar Douglas Bates Russell Almond Arni Magnusson 20190705 0.19-3 Tools Software https://cran.r-project.org/package=coda Functions for estimating the overlapping area of two or more kernel density estimations from empirical data. https://cran.r-project.org/web/packages/overlapping/overlapping.pdf Massimiliano Pastore Pierfrancesco Alaimo Di Loro Marco Mingione Antonio Calcagni' 20170615 1.5.0 Tools Software https://cran.r-project.org/package=overlapping A system for 'declaratively' creating graphics, based on The Grammar of Graphics. You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. https://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf Hadley Wickham Winston Chang Lionel Henry Thomas Lin Pedersen Kohske Takahashi Claus Wilke Kara Woo Hiroaki Yutani Dewey Dunnington RStudio 20190811 3.2.1 Tools Software https://cran.r-project.org/package=ggplot2 We conducted a validation of the methods which sought to answer three key questions: (1) does the monitoring program successfully detect perturbation-driven declines that can be differentiated from the ebbs and flows of normal population dynamics? (2) do populations assigned to different infraction categories (for example, watch, warning) exhibit unique behaviors immediately prior to and following activation? and (3) assuming the monitoring program is effective at identifying troubled populations, does it alert managers in a timely fashion, such that conservation actions can be applied within an area still occupied by the target species? To evaluate the TAWS in a manner that addressed each of the three questions outlined, we first constructed a linear model (Plummer, 2003; R Core Team, 2019) using SSM estimates of abundance indexed by population and year. For simplicity, we restricted abundance of any specific population and year combination to the median estimates. To relate time to the activation of a watch (or warning), we standardized the index of year, so that it represented the number of years prior to (-28 to -1) or following (1 to 25) population-specific activation events. To avoid confusion, we use the subscript a when referencing the standardized time index. We ran four models in all, which differed according to management scale (for example, lek, neighborhood cluster) and TAWS infraction type (watch or warning). The mean predicted male abundance (U) was defined using parameters B and Y, which represented mean male population size and time-dependent deviation from the mean, respectively. Standard deviation parameters were defined, such that measures of variation in U and Υ could be estimated from the data. Model fit (Gelman and Hill 2006; Kéry and Schaub 2012) and parameter convergence (Brooks and Gelman, 1998) were assessed prior to model inference. References: Brooks, S.P. and Gelman, A. 1998. General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics, 7:4, 434-455, https://doi.org/10.1080/10618600.1998.10474787 Gelman, A. and Hill, J., 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge, Cambridge University Press. Kery, M., and Schaub, M., 2012. Bayesian population analysis using WinBUGS - A hierarchical perspective. San Diego, Calif., Academic Press, 535 p Plummer, M. 2003. JAGS: A Program for Analysis of Bayesian Graphical Models using Gibbs Sampling. 3rd International Workshop on Distributed Statistical Computing (DSC 2003); Vienna, Austria. 124. Data values fall within the expected ranges. Rows with no data are given appropriate no data values for the respective fields. The data are considered complete, but ongoing. Annual updates are planned. The data covers the full extent of Greater Sage-grouse population range within the United States, so there is no plan to geographically expand the study area in the near future. No horizontal accuracy report was conducted nor is it applicable. No vertical accuracy report was conducted nor is it applicable. Matthew B Rigge Brett Bunde Hua Shi Kory Postma 2021 2.0 dataset Denver, CO U.S. Geological Survey https://doi.org/10.5066/P95IQ4BT https://www.mrlc.gov/data Digital and/or Hardcopy 1985 2020 Historic aerial and satellite imagery were used to estimate sagebrush landcover over the last 36 years RCMAP Provided fractional cover estimates of sagebrush, which was used to determine whether or not sagebrush was declining at the lek or neighborhood cluster. Great Basin Landscape Conservation Cooperative Steve Campbell Jeremy Maestas 20160804 raster digital data https://doi.org/10.5066/P98ATRLZ https://chsapps.usgs.gov/apps/land-treatment-exploration-tool/about#datasourceTitle Digital and/or Hardcopy 2016 publication date RR Delineates between soil regimes, which influence the simulated recovery rates in the model. Higher values correspond to soil which is less favorable to sagebrush growth. Michael S. O'Donnell David R. Edmunds Cameron L. Aldridge Julie A. Heinrichs Adrian P. Monroe Peter S. Coates Brian G. Prochazka Steve E. Hanser Lief A. Wiechman Thomas J. Christiansen Avery A. Cook Shawn P. Espinosa Lee J. Foster Kathleen A. Griffin Jesse L. Kolar Katherine S. Miller Ann M. Moser Thomas E. Remington Travis J. Runia Leslie A. Schreiber Michael A. Schroeder San J. Stiver Nyssa I. Whitford Catherine S. Wightman 202107 publication Ecological Informatics vol. 63 n/a Elsevier BV ppg. 101327 https://doi.org/10.1016/j.ecoinf.2021.101327 Digital and/or Hardcopy 1960 2023 observed LekDB Provided lek locations as well as abundance estimates. Locations were used to create the hierarchical nested population structures. Abundances were used to calculate trends and results for the targeted annual warnings system. The Greater sage-grouse lek data either are not 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). Contact the Greater Sage-Grouse Technical Team of the Western Association of Fish and Wildlife Agencies or individual state wildlife agencies (see Acknowledgements) for more information. Michael O'Donnell David R Edmunds Cameron Aldridge Julie A Heinrichs Adrian P Monroe Peter S Coates Brian G Prochazka Steve Hanser Lief A Wiechman 2022 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/P9D1K0LX Digital and/or Hardcopy 1960 2022 observed Clusters Provides polygons representing Greater Sage-grouse population clusters. Jeremy D. Maestas Steven B. Campbell Jeanne C. Chambers Mike Pellant Richard F. Miller 201606 publication Rangelands vol. 38, issue 3 n/a Elsevier BV ppg. 120-128 https://doi.org/10.1016/j.rala.2016.02.002 Digital and/or Hardcopy 2016 publication date Maestas et al. 2016 Sampling was performed using a non-linear relationship between management action and population performance and was based on the underlying index of resilience and resistance. Matthew Rigge Hua Shi Collin Homer Patrick Danielson Brian Granneman 20190603 publication Ecosphere vol. 10, issue 6 n/a Wiley https://doi.org/10.1002/ecs2.2762 Digital and/or Hardcopy 2019 publication date Rigge st al. 2019 We determined recovery pathways by evaluating sagebrush trends (1985 to 2020; 35 years, because 2012 imagery was unavailable) for each lek using back in time estimates. Matthew Rigge Collin Homer Lauren Cleeves Debra K. Meyer Brett Bunde Hua Shi George Xian Spencer Schell Matthew Bobo 20200128 publication Remote Sensing vol. 12, issue 3 n/a MDPI AG ppg. 412 https://doi.org/10.3390/rs12030412 Digital and/or Hardcopy 2020 publication date Rigge et al. 2020 We determined recovery pathways by evaluating sagebrush trends (1985 to 2020; 35 years, because 2012 imagery was unavailable) for each lek using back in time estimates. Hua Shi Matthew Rigge Collin G. Homer George Xian Debbie K. Meyer Brett Bunde 2018 publication Ecosystems vol. 21, issue 5 n/a Springer Science and Business Media LLC ppg. 913-929 https://doi.org/10.1007/s10021-017-0191-3 Digital and/or Hardcopy 2018 publication date Shi et al. 2018 We determined recovery pathways by evaluating sagebrush trends (1985 to 2020; 35 years, because 2012 imagery was unavailable) for each lek using back in time estimates. Michael S. O'Donnell David R. Edmunds Cameron L. Aldridge Julie A. Heinrichs Peter S. Coates Brian G. Prochazka Steve E. Hanser 20190925 publication Ecosphere vol. 10, issue 9 n/a Wiley https://doi.org/10.1002/ecs2.2872 Digital and/or Hardcopy 2019 publication date O'Donnell et al. 2019 Our hierarchical monitoring framework used a comprehensive database of lek locations, within the U.S., to identify biologically relevant spatial scales tied to population structure and function. 1. Defining multiple hierarchical spatial scales of management units Since the 1950s, maximum counts of males attending leks during the breeding season have been the primary method used by state wildlife agencies to monitor sage-grouse population dynamics range-wide. Efforts surveying known leks and searching for new leks increased dramatically between 1990-2000 (Western Association of Fish and Wildlife Agencies, 2015). Our hierarchical monitoring framework used a comprehensive database of lek locations, within the U.S., to identify biologically relevant spatial scales tied to population structure and function (O’Donnell et al., 2022, 2019). Lek locations were grouped across progressively larger and spatially nested units (clusters) based on landscape and climatic characteristics influencing connectivity. 2. Refining population estimates We filtered a range-wide, standardized sage-grouse lek count database (Coates et al., 2021; O’Donnell et al., 2021) to maximize detection probabilities and minimize observation errors. The filtered database was fit to a state-space model (SSM) using Markov chain Monte Carlo techniques (Plummer, 2003) and the statistical programming language and software package, R (R Core Team, 2019). Model output included marginal posterior probability distributions of abundance (N) and intrinsic rate of population change (r-hat) parameters. 3. Developing an index of aberrant decline Annual indices of population performance were formulated to capture moments of aberrant decline, which we defined as a negative rate of population change at the lek or neighborhood cluster that also was below the within-year rate of change of the parent (spatially nested) climate cluster. Identification of aberrant decline is critical to a Targeted Annual Warning System (TAWS) as it accounts for the broad-scale, climatic component of non-stationary trends. 4. Developing thresholds to evaluate an index of aberrant decline The process for selecting optimal thresholds, used in evaluation of the aberrant decline index, was carried out independently for lek and neighborhood cluster scales. The method applied was identical, so we restrict reference to the lek scale when describing each step. The designation of a threshold, as optimal or otherwise, was based on the ability of the threshold to identify a subset of underperforming leks, that when managed via simulation (management assumptions described below), would result in the stability of the parent climate cluster. 5. Simulating management action Simulated management action consisted of improving the r-hat of leks that received a warning, for all years following that event, by imputing the observed r-hat with a value sampled from within-year estimates from across the range. Sampling was performed using a non-linear relationship between management action and population performance and was based on the underlying index of resilience and resistance (RR; Maestas et al. 2016) and whether the decline was probabilistically associated with sagebrush loss or some other unmeasured disturbance. We determined recovery pathways by evaluating sagebrush trends (1985 to 2020; 35 years, because 2012 imagery was unavailable) for each lek using back in time estimates (Shi et al. 2018; Rigge et al. 2019, 2020). We categorized the percent change in sagebrush over the 33 years as either decreasing or stable to increasing. Similarly, we categorized the long-term trend in r-hat for each lek as either decreasing or stable to increasing. 6. Application We applied the TAWS framework to maximum counts of male sage-grouse (70,646) attending leks (4,478) across 11 western states during 1990-2024. Optimal threshold combinations identified through simulation analyses were retrospectively applied to demonstrate real-world utility. 7. Probability of Future Extirpation Expanding on the population trend analysis, we projected N-hat for each lek and NC across three temporal scales that reflected two- (short), four- (medium), and six-periods (long) of oscillation by using the same model and dataset. We used the mean oscillation period (9.2 years) based on estimated N-hat from SSM results. We then calculated the proportion of the posterior probability distribution of N-hat that was less than two males (minimum number to represent a lek) for the last prediction year of each temporal scale. Although this value is not true extirpation (zero or one bird), we refer to it as extirpation to align with state definitions of lek inactivity. Thus, this proportion of the distribution represented the probability of extirpation for each lek and NC at a nadir, approximately at short, medium, and long temporal scales into the future. Extirpation of leks within an NC was thought to reflect a loss of a meta-population resulting from reduced demographic rates. RCMAP RR LekDB Clusters 2024 Vector G-polygon 474 Albers Conical Equal Area 29.5 45.5 -96.0 23.0 0.0 0.0 coordinate pair 0.6096 0.6096 meters WGS_1984 WGS 84 6378137.0 298.257223563 Trends_and_TAWS_GrSG_Version4.shp Table containing attribute information associated with the data set. Producer Defined FID Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. Shape Feature geometry. ESRI Shape type. NC Neighborhood cluster. 13 nested hierarchical sets of polygons were generated for monitoring Greater Sage-grouse populations at various scales. Neighborhood clusters are the second most fine scale of these. Producer Defined The naming scheme for these are to start with the letter code for the climate cluster the neighborhood cluster lies within, then followed by a 3-digit sequential numeric code. The numbering itself is arbitrary. The climate cluster codes are A, B, C, D, E, and F. Each climate has a different number of neighborhood clusters nested within them. An example code for a neighborhood cluster within climate cluster E would be E-113. Prd1 Period 1; median estimate of sage-grouse population trends calculated over six periods of oscillation. Producer Defined -9999 No Data Producer defined 0.919026993 1.015899564 Prd1_low Period 1; lower confidence interval; 2.5 percentile of sage-grouse population trends calculated over six periods of oscillation. Producer Defined -9999 No Data Producer defined 0.892632078 0.98925717 Prd1_up Period 1; upper confidence interval; 97 percentile of sage-grouse population trends calculated over six periods of oscillation. Producer Defined -9999 No Data Producer defined 0.939519535 1.053091916 Prd2 Period 2; median estimate of sage-grouse population trends calculated over five periods of oscillation. Producer Defined -9999 No Data Producer defined 0.898527832 1.025262929 Prd2_low Period 2; lower confidence interval; 2.5 percentile of sage-grouse population trends calculated over five periods of oscillation. Producer Defined -9999 No Data Producer defined 0.861133097 1.003168148 Prd2_up Period 2; upper confidence interval; 97 percentile of sage-grouse population trends calculated over five periods of oscillation. Producer Defined -9999 No Data Producer defined 0.921678802 1.081929231 Prd3 Period 3; median estimate of sage-grouse population trends calculated over four periods of oscillation. Producer Defined -9999 No Data Producer defined 0.894637272 1.037956548 Prd3_low Period 3; lower confidence interval; 2.5 percentile of sage-grouse population trends calculated over four periods of oscillation. Producer Defined -9999 No Data Producer defined 0.858002392 1.000952145 Prd3_up Period 3; upper confidence interval; 97 percentile of sage-grouse population trends calculated over four periods of oscillation. Producer Defined -9999 No Data Producer defined 0.922957871 1.111704481 Prd4 Period 4; median estimate of sage-grouse population trends calculated over three periods of oscillation. Producer Defined -9999 No Data Producer defined 0.886301278 1.084521476 Prd4_low Period 4; lower confidence interval; 2.5 percentile of sage-grouse population trends calculated over three periods of oscillation. Producer Defined -9999 No Data Producer defined 0.837171903 1.026400271 Prd4_up Period 4; upper confidence interval; 97 percentile of sage-grouse population trends calculated over three periods of oscillation. Producer Defined -9999 No Data Producer defined 0.915960747 1.164104678 Prd5 Period 5; median estimate of sage-grouse population trends calculated over two periods of oscillation. Producer Defined -9999 No Data Producer defined 0.836433498 1.066723941 Prd5_low Period 5; lower confidence interval; 2.5 percentile of sage-grouse population trends calculated over two periods of oscillation. Producer Defined -9999 No Data Producer defined 0.792294132 1.010188754 Prd5_up Period 5; upper confidence interval; 97 percentile of sage-grouse population trends calculated over two periods of oscillation. Producer Defined -9999 No Data Producer defined 0.877075464 1.245852418 Prd6 Period 6; median estimate of sage-grouse population trends calculated over one period of oscillation. Producer Defined -9999 No Data Producer defined 0.809569544 1.386847438 Prd6_low Period 6; lower confidence interval; 2.5 percentile of sage-grouse population trends calculated over one period of oscillation. Producer Defined -9999 No Data Producer defined 0.69955411 1.175586005 Prd6_up Period 6; upper confidence interval; 97 percentile of sage-grouse population trends calculated over one period of oscillation. Producer Defined -9999 No Data Producer defined 0.834136476 1.6720347 Ext2 Extirpation probability after 2 population oscillations into the future. Producer Defined -9999 No Data Producer defined 0 0.963 Ext4 Extirpation probability after 4 population oscillations into the future. Producer Defined -9999 No Data Producer defined 0 0.982333333 Ext6 Extirpation probability after 6 population oscillations into the future. Producer Defined -9999 No Data Producer defined 0 0.992 Ext2_mu Mean extirpation probability of all leks that fall within the neighborhood cluster after 2 population oscillations into the future. Producer Defined -9999 No Data Producer defined 0.004333333 0.972 Ext2_sd Standard deviation of extirpation probability of all leks that fall within the neighborhood cluster after 2 population oscillations into the future. Producer Defined -9999 No Data Producer defined 0.004242641 0.573227897 Ext4_mu Mean extirpation probability of all leks that fall within the neighborhood cluster after 4 population oscillations into the future. Producer Defined -9999 No Data Producer defined 0.042666667 0.982333333 Ext4_sd Standard deviation of extirpation probability of all leks that fall within the neighborhood cluster after 4 population oscillations into the future. Producer Defined -9999 No Data Producer defined 0.001178511 0.519959186 Ext6_mu Mean extirpation probability of all leks that fall within the neighborhood cluster after 6 population oscillations into the future. Producer Defined -9999 No Data Producer defined 0.064333333 0.992666667 Ext6_sd Standard deviation of extirpation probability of all leks that fall within the neighborhood cluster after 6 population oscillations into the future. Producer Defined -9999 No Data Producer defined 0.001414214 0.482953932 WtcFst Watch-first; annual average rate of leks activating a watch for the first time. Producer Defined -9999 No Data Producer defined 0 0.033333333 WtcFst_sd Watch-first; annual standard deviation in average rate of leks activating a watch for the first time. Producer Defined -9999 No Data Producer defined 0 0.182574186 Wtc Watch; annual average rate of leks activating a watch, repeat activations included. Producer Defined -9999 No Data Producer defined 0 0.433333333 Wtc_sd Watch; annual standard deviation in average rate of leks activating a watch, repeat activations included. Producer Defined -9999 No Data Producer defined 0 0.490132518 WrnFst Warning-first; annual average rate of leks activating a warning for the first time. Producer Defined -9999 No Data Producer defined 0 0.033333333 WrnFst_sd Warning-first; annual standard deviation in average rate of leks activating a warning for the first time. Producer Defined -9999 No Data Producer defined 0 0.182574186 Wrn Warning; annual average rate of leks activating a warning, repeat activations included. Producer Defined -9999 No Data Producer defined 0 0.533333333 Wrn_sd Warning; annual standard deviation in average rate of leks activating a warning, repeat activations included. Producer Defined -9999 No Data Producer defined 0 0.430183067 Trnd_lks Number of leks used in the trend analysis. Producer Defined 0 76 TAWS_lks Number of leks used in the targeted annual warning system analysis. Producer Defined 0 65 WtcYYYY Watch; a binary field identifying whether or not a watch occurred during the corresponding year. YYYY is replaced by the year for each column. The value resets to 0 after each year. Producer Defined -9999 No Data Producer defined 0 A watch did not occur this year. Producer defined 1 A watch occurred this year. Producer defined WrnYYYY Warning; a binary field identifying whether or not a Warning occurred during the corresponding year. YYYY is replaced by the year for each column. The value resets to 0 after each year. Producer Defined -9999 No Data Producer defined 0 A warning did not occur this year. Producer defined 1 A warning occurred this year. Producer defined WtcFstYear Calendar year the first watch occurred for the neighborhood cluster. Producer Defined -9999 No Data Producer defined 1995 2024 WrnFstYear Calendar year the first warning occurred for the neighborhood cluster. Producer Defined -9999 No Data Producer defined 1995 2024 GS ScienceBase U.S. Geological Survey mailing address

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Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Digital Data (SHP) https://doi.org/10.5066/P9OQWGIV None 20251202 Michael P Chenaille U.S. Geological Survey, Western Ecological Research Center CARTOGRAPHER mailing address

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Dixon CA 95620 US 530-669-5092 mchenaille@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999