Coates, P.S. Milligan, M.C. Brussee, B.E. O'Neil, S.T. Chenaille, M.P. 20240612 raster digital data ScienceBase U.S. Geological Survey data release https://doi.org/10.5066/P1AATW9D We used movement and demographic data to simultaneously evaluate habitat selection by sage-grouse across multiple seasons, and measures of survival during key reproductive life stages (nesting and brood-rearing) to identify priority habitat by linking resource selection to demographic performance. We calculated and mapped composite selection and survival indices across the Bi-State Distinct Population Segment (DPS) to differentiate productive habitat that supported high selection and survival compared to areas of maladaptive selection where selection and survival were misaligned. We then reclassified the indices into categorical rasters representing different classes of selection (high, moderate, low, non-habitat) and survival (high, moderate, low, and very low). As a sagebrush obligate and indicator species for increasingly threatened sagebrush ecosystems, sage-grouse have become central to land management policy throughout the western United States. The integral role of sage-grouse in guiding land management is exemplified by the conservation and management of populations inhabiting the southwestern extent of the species’ range, along the border of California and Nevada. Sage-grouse in this region are recognized as the Bi-State DPS based on both geographic isolation and genetic distinctiveness. This study was completed to provide timely scientific information regarding Greater Sage-grouse population trends, habitat selection, and the efficacy of previous conservation actions implemented to benefit the Bi-State DPS. Specifically, we conducted these analyses to inform the current (2024) status review and pending listing decision for the DPS being undertaken by the U.S. Fish and Wildlife Service. 1995 2044 Seasonal habitat selection tables span 1995 through 2023. Estimated abundnace goes as far back as 1960, and is projected out to 2044. Complete None planned Bi-State Distinct Population Segment of Nevada and California -119.8696 -117.2842 39.4131 37.0554 ISO 19115 Topic Category biota USGS Thesaurus ecology shrubland ecosystems native species animal behavior None demographic response greater sage-grouse habitat selection life stages reproduction survival USGS Metadata Identifier USGS:66481195d34e1955f5a455b5 None Nevada California Bi-State Integrated Taxonomic Information System (ITIS) Centrocercus urophasianus U.S. Geological Survey 2013 Online Database https://doi.org/10.5066/F7KH0KBK www.itis.gov expert identifier 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 None. Please see 'Distribution Info' for details. 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 and physical

3020 State University Drive, Modoc Hall, Suite 4004

Sacramento CA 95819 US 279-782-0607 gs-b-werc_data_management@usgs.gov We coordinated this project in close consultation with the U.S. Fish and Wildlife Service, the Nevada Department of Wildlife, the California Department of Fish and Wildlife, the Bureau of Land Management, the U.S. Forest Service, and the Bi-State Technical Advisory Committee. We would also like to acknowledge the Bi-State Executive Oversight Committee, Bi-State Traditional Natural Resources Committee, and Bi-State Local Area Working Group. We thank M. Ricca (U.S. Geological Survey), J. Small (Nevada Department of Wildlife) for helpful comments in reviewing the report in its entirety. We appreciate the efforts of J. Atkinson (U.S. Geological Survey [USGS]) for assisting with report preparation; and K. Calvert and K. Engelking (USGS) for editing, formatting, and final production of this report. We thank S. Dettenmaier (USGS), K. McGowan, A. Kosic, P. Winters, P. Fuselier, D. Dekelaita, K. Krause (Bureau of Land Management), M. Nelson, K. Schlick, N. Sill (U.S. Forest Service), S. Gardner (California Department of Fish and Wildlife), K. Steele (Nevada State Sagebrush Ecosystem Technical Team), J. Barrett (U.S. Fish and Wildlife Service), and R. Tucker (Los Angeles Department of Water and Power) for their input throughout the study. We thank the 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 Ecosystems Mission Area, U.S. Fish and Wildlife Service, the Nevada Department of Wildlife, and the California Department of Fish and Wildlife. Windows 10 Megan C. Milligan Peter S. Coates Brianne E. Brussee Shawn T. O'Neil Steven R. Mathews Shawn P. Espinosa Dan Skalos Lief A. Wiechman Steve Abele John Boone Kristie Boatner Heather Stone Michael L. Casazza Unknown publication Peter S. Coates Megan C. Milligan Brian G. Prochazka Brianne E. Brussee Shawn T. O'Neil Carl G. Lundblad Sarah C. Webster Cali L. Weise Steven R. Mathews Michael P. Chenaille Cameron L. Aldridge Michael S. O'Donnell Shawn P. Espinosa Amy C. Sturgill Kevin E. Doherty John C. Tull Katherine Miller Lief A. Wiechman Steve Abele John Boone Heather Stone Michael L. Casazza 2024 publication n/a US Geological Survey https://doi.org/10.3133/ofr20241030 R 4.0.3 https://cran.r-project.org/bin/windows/base/old/4.0.3/README.R-4.0.3 R Core Team 20201010 4.0.3 Tools Software https://cran.r-project.org/bin/windows/base/old/4.0.3/ R2jags https://cran.r-project.org/web/packages/R2jags/index.html Yu-Sung Su Masanao Yajima 20200427 0.6-1 Tools Software https://cran.r-project.org/src/contrib/Archive/R2jags/ BayesPostEst 0.3.1 https://cran.r-project.org/web/packages/BayesPostEst/index.html Johannes Karreth Shana Scogin Rob Williams Andreas Beger Myunghee Lee Neil Williams 20210109 0.3.1 Tools Software https://cran.r-project.org/src/contrib/Archive/BayesPostEst/ coda 0.19-4 https://cran.r-project.org/web/packages/coda/index.html Martyn Plummer Nicky Best Kate Cowles Karen Vines Deepayan Sarkar Douglas Bates Russell Almond Arni Magnusson 20200930 0.19-4 Tools Software https://cran.r-project.org/web/packages/coda/index.html dplyr 1.0.5 https://cran.r-project.org/web/packages/dplyr/index.html Hadley Wickham Romain Francois Lionel Henry Kirill Muller RStudio 20210305 1.0.5 Tools Software https://cran.r-project.org/src/contrib/Archive/dplyr/ emdbook 1.3.12 https://cran.r-project.org/web/packages/emdbook/index.html Ben Bolker Sang Woo Park James Vonesh Jacqueline Wilson Russ Schmitt Sally Holbrook James D. Thomson R. Scot Duncan 20200219 1.3.12 Tools Software https://cran.r-project.org/web/packages/emdbook/index.html ggplot2 3.3.5 https://cran.r-project.org/web/packages/ggplot2/index.html Hadley Wickham Winston Chang Lionel Henry Thomas Lin Pedersen Kohske Takahashi Claus Wilke Kara Woo Hiroaki Yutani Dewey Dunnington RStudio 20210625 3.3.2 Tools Software https://cran.r-project.org/src/contrib/Archive/ggplot2/ KernSmooth 2.23-17 https://cran.r-project.org/web/packages/KernSmooth/index.html Matt Wand Cleve Moler Brian Ripley 20200426 2.23-17 Tools Software https://cran.r-project.org/src/contrib/Archive/KernSmooth/ lubridate 1.7.10 https://cran.r-project.org/web/packages/lubridate/index.html Vitalie Spinu Garrett Grolemund Hadley Wickham Davis Vaughan Ian Lyttle Imanuel Costigan Jason Law Dough Mitarotonda Joseph Larmarange Jonathan Boiser Chel Hee Lee Google Inc. 20210226 1.7.10 Tools Software https://cran.r-project.org/src/contrib/Archive/lubridate/ postpack 0.5.2 https://cran.r-project.org/web/packages/postpack/index.html Ben Staton 20200917 0.5.2 Tools Software https://cran.r-project.org/src/contrib/Archive/postpack/ raster 3.4-5 https://cran.r-project.org/web/packages/raster/index.html Rober 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 nstitute 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 20201114 3.4-5 Tools Software https://cran.r-project.org/src/contrib/Archive/raster/ rgdal 1.5-23 https://cran.r-project.org/web/packages/rgdal/index.html Roger Bivand Tim Keitt Barry Rowlingson Edzer Pebesma Michael Sumner Robert Hijmans Daniel Baston Even Rouault Frank Warmerdam Jeroen Ooms Colin Rundel 20210203 1.5-23 Tools Software https://cran.r-project.org/src/contrib/Archive/rgdal/ rgeos 0.5-5 https://cran.r-project.org/web/packages/rgeos/index.html Roger Bivand Colin Rundel Edzer Pebesma Rainer Stuetz Karl Ove Hufthammer Patrick Giraudoux Martin Davis Sandro Santilli 20200907 0.5-5 Tools Software https://cran.r-project.org/src/contrib/Archive/rgeos/ runjags 2.2.0-2 https://cran.r-project.org/web/packages/runjags/index.html Matthew Denwood Martyn Plummer 20210302 2.2.0-2 Tools Software https://cran.r-project.org/src/contrib/Archive/rgeos/ tidyverse 1.3.1 https://cran.r-project.org/web/packages/tidyverse/index.html Hadley Wickham RStudio 20210415 1.3.1 Tools Software https://cran.r-project.org/src/contrib/Archive/tidyverse/ Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007. https://cran.r-project.org/web/packages/arm/arm.pdf Andrew Gelman Yu-Sung Su Masanao Yajima Jennifer Hill Maria Grazia Pittau Jouni Kerman Tian Zheng Vincent Dorie 20200727 1.11-2 Tools Software https://cran.r-project.org/web/packages/arm/index.html Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019, doi:10.1111/rssa.12378). The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'. https://cran.r-project.org/web/packages/bayesplot/bayesplot.pdf Jonah Gabry Tristan Mahr Paul-Christian Bürkner Martin Modrák Malcolm Barrett Frank Weber Teemu Sailynoja Aki Vehtari 20210614 1.8.1 Tools Software https://cran.r-project.org/web/packages/bayesplot/index.html Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package. https://cran.r-project.org/web/packages/bbmle/bbmle.pdf Ben Bolker R Development Core Team Iago Giné-Vázquez 20200203 1.0.23.1 Tools Software https://cran.r-project.org/web/packages/bbmle/index.html Create, store, access, and manipulate massive matrices. Matrices are allocated to shared memory and may use memory-mapped files. Packages 'biganalytics', 'bigtabulate', 'synchronicity', and 'bigalgebra' provide advanced functionality. https://cran.r-project.org/web/packages/bigmemory/bigmemory.pdf Michael J. Kane John W. Emerson Peter Haverty Charles Determan 20191223 4.5.36 Tools Software https://cran.r-project.org/web/packages/bigmemory/index.html Functions and datasets for bootstrapping from the book 'Bootstrap Methods and Their Application' by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S. https://cran.r-project.org/web/packages/boot/boot.pdf Angelo Canty Brian Ripley 20210212 1.3-27 Tools Software https://cran.r-project.org/web/packages/boot/index.html Fast aggregation of large data (e.g. 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 Tyson Barrett Matt Dowle Arun Srinivasan Jan Gorecki Michael Chirico Toby Hocking 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 Leonardo Silvestri Jim Hester Anthony Damico Sebastian Freundt David Simons Elliott Sales de Andrade Cole Miller Jens Peder Meldgaard Vaclav Tlapak Kevin Ushey Dirk Eddelbuettel Benjamin Schwendinger Tony Fischetti Ofek Shilon Vadim Khotilovich Hadley Wickham Bennet Becker Kyle Haynes Boniface Christian Kamgang Olivier Delmarcell Josh O'Brien Dereck de Mezquita Michael Czekanski 20210212 1.14.0 Tools Software https://cran.r-project.org/web/packages/data.table/index.html The 'ggplot2' package is excellent and flexible for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots. https://cran.r-project.org/web/packages/ggpubr/ggpubr.pdf Alboukadel Kassambara 20200607 0.4.0 Tools Software https://cran.r-project.org/web/packages/ggpubr/index.html Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see <doi:10.18637/jss.v033.i01> and <doi:10.18637/jss.v039.i05>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<doi:10.18637/jss.v106.i01>). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited. https://cran.r-project.org/web/packages/glmnet/glmnet.pdf Jerome Friedman Trevor Hastie Rob Tibshirani Balasubramanian Narasimhan Kenneth Tay Noah Simon Junyang Qian James Yang 20210221 4.1-1 Tools Software https://cran.r-project.org/web/packages/glmnet/index.html 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. See ?Lattice for an introduction. https://cran.r-project.org/web/packages/lattice/lattice.pdf Deepayan Sarkar Felix Andrews Kevin Wright Neil Klepeis Johan Larsson Zhijian (Jason) Wen Paul Murrell Stefan Eng Achim Zeileis Alexandre Courtiol 20200219 0.20-41 Tools Software https://cran.r-project.org/web/packages/lattice/index.html Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002). https://cran.r-project.org/web/packages/MASS/MASS.pdf Brian Ripley Bill Venables Douglas M. Bates Kurt Hornik Albrecht Gebhardt David Firth 20200909 7.3-53 Tools Software https://cran.r-project.org/web/packages/MASS/index.html 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/web/packages/mcmcplots/index.html Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with 'Stan', 'NIMBLE', 'JAGS', and other software. https://cran.r-project.org/web/packages/MCMCvis/MCMCvis.pdf Casey Youngflesh Christian Che-Castaldo Tyler Hardy 20220208 0.15.5 Tools Software https://cran.r-project.org/web/packages/MCMCvis/index.html Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family. https://cran.r-project.org/web/packages/mgcv/mgcv.pdf Simon Wood 20210216 1.8-34 Tools Software https://cran.r-project.org/web/packages/mgcv/index.html Interface to the JAGS MCMC library. https://cran.r-project.org/web/packages/rjags/rjags.pdf Martyn Plummer Alexey Stukalov Matt Denwood 20191106 1.8-34 Tools Software https://cran.r-project.org/web/packages/rjags/index.html Provides a set of functions for data manipulation with list objects, including mapping, filtering, grouping, sorting, updating, searching, and other useful functions. Most functions are designed to be pipeline friendly so that data processing with lists can be chained. https://cran.r-project.org/web/packages/rlist/rlist.pdf Kun Ren 20160404 0.4.6.1 Tools Software https://cran.r-project.org/web/packages/rlist/index.html Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc. From this version, 'rgdal', 'maptools', and 'rgeos' are no longer used at all, see <https://r-spatial.org/r/2023/05/15/evolution4.html> for details. 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'Rourk Patrick Hausmann 20210110 1.4-5 Tools Software https://cran.r-project.org/web/packages/sp/index.html Functions for kriging and point pattern analysis. https://cran.r-project.org/web/packages/spatial/spatial.pdf Brian Ripley Roger Bivand William Venables 20210124 7.3-13 Tools Software https://cran.r-project.org/web/packages/spatial/index.html The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode". https://cran.r-project.org/web/packages/splancs/splancs.pdf Roger Bivand Barry Rowlingson Peter Diggle Giovanni Petris Stephen Eglen 20170416 2.01-40 Tools Software https://cran.r-project.org/web/packages/splancs/index.html Methods for computing spatial, temporal, and spatiotemporal statistics as described in Gouhier and Guichard (2014) <doi:10.1111/2041-210X.12188>. These methods include empirical univariate, bivariate and multivariate variograms; fitting variogram models; phase locking and synchrony analysis; generating autocorrelated and cross-correlated matrices. https://cran.r-project.org/web/packages/synchrony/synchrony.pdf Tarik C. Gouhier 20191205 0.3.8 Tools Software https://cran.r-project.org/web/packages/synchrony/index.html We validated our selection models by applying resource selection function (RSF) cross-validation methods to the independent testing data subset. We interpreted each model’s predictive accuracy from linear model fit statistics comparing the proportion of observations in the testing dataset to those expected based on ordinal RSF bins from the fitted model. We validated our survival models by comparing observed encounter histories and fates in the testing data to simulate ‘replicate’ encounter histories generated from final model posterior predictive distributions (Schmidt and others, 2010). We conducted post-hoc 1,000 simulations to calculate a Bayesian predictive P-value, where values approaching 0 or 1 indicate poor model fit, and values closer to 0.5 indicate good fit. References cited: Schmidt, J.H., Schmidt, Walker, J.A., Lindberg, M.S., Johnson, D.S., and Stephens, S.E., 2010, A general Bayesian hierarchical model for estimating survival of nests and young: Auk, v. 127, p. 379-386, https://doi.org/10.1525/auk.2009.09015. The data values fall within expected ranges. All locations correctly fall within the Bi-State Distinct Population Segment boundary. Raster grid extent and projection consistent across all rasters. For each life stage and season, we conducted habitat selection analyses by contrasting used locations with random available locations to infer sage-grouse spatial habitat patterns. Used locations that were included in the model fell within the appropriate time periods. Nesting locations were defined as any time a hen was sitting on the nest, early brood locations were defined as any time a hen had a brood less than 21 days old, and late brood locations represented hens with a brood older than 21 days, but younger than 50 days. Spring season was defined as March 16th through June 30th, summer season was defined as July 1st through October 15th, and winter season was defined as October 16th through March 15th. We applied a mask, erasing waterbodies, towns, and some anthropogenic features. We intentionally did not include anthropogenic features in our models, but we masked out towns, which do not represent habitat for sage-grouse. First, we established a buffer distance of 10 kilometers (km) from the boundaries of the initial mask layer based on known distances of development from town centers across Nevada. We then identified any permanent structures using heads-up digitizing based on the National Land Cover Database’s layer representing imperviousness after excluding roads and Microsoft’s building footprints layer (https://hub.arcgis.com/maps/esri::microsoft-building-footprints-tiles/about). We then reduced any areas within the digitized boundaries by one level (that is, from priority+ to priority, priority to general, general to other, or other to non-habitat). We also masked all buildings from Esri’s building footprints layer with a 100-m buffer to capture the entire land parcel and other infrastructure associated with the building. No completeness report was conducted. No horizontal accuracy report was conducted. Not applicable. Jon Dewitz 2019 Version 2.0 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p96hhbie Digital and/or Hardcopy 2016 observed NLCD Provided estimates of sagebrush height and shrub height. Matthew B Rigge 2021 Version 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 observed RCMAP Provided fractional cover estimates of sagebrush, shrubs, herbaceous vegetation, and bare ground. Stephen P Boyte Bruce K. Wylie 2018 raster digital data https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/P9RIV03D Digital and/or Hardcopy 2018 observed AG2018 Provided fractional cover estimates of annual grasses for 2018. Peter S. Coates K. Ben Gustafson Cali L. Roth Michael P. Chenaille Mark A. Ricca Kimberly Mauch Erika Sanchez-Chopitea Travis J. Kroger William M. Perry Michael L. Casazza 2018 Version 2.0 raster digital data https://doi.org/10.5066/F7348HVC Digital and/or Hardcopy 20100701 20130801 observed PJ Provides fractional cover estimates of Pinyon-Juniper trees. U.S. Forest Service U.S. Geological Survey 20180801 Version 1.0 vector digital data https://doi.org/10.5066/P9IED7RZ https://www.mtbs.gov/ Digital and/or Hardcopy 1984 2019 observed fire Provided fire perimeters, which were used to create annual layers representing cumulative burned area on the landscape. U.S. Geological Survey 2021 vector digital data https://www.usgs.gov/core-science-systems/ngp/national-hydrography https://www.sciencebase.gov/catalog/item/5136012ce4b03b8ec4025bf7 Digital and/or Hardcopy 2021 publication date NHD Provides the locations of water features, specifically for this project we were interested in streams and springs. Euclidean distance to water feature and density of water features were both considered. Area of interest was contained to Nevada and California. U.S. Census Bureau 2019 vector digital data https://www.census.gov/geo/maps-data/data/tiger-line.html Digital and/or Hardcopy 2019 publication date roads Provided the location of roads, so euclidean distance to road could be determined. U.S. Geological Survey 2021 raster digital data The toolbox used to create the additional elevation surfaces can be found here: https://evansmurphy.wixsite.com/evansspatial/arcgis-gradient-metrics- http://ned.usgs.gov/ http://nationalmap.gov/viewer.html Digital and/or Hardcopy 2021 publication date DEM Provided elevation values. It was also used as an input to create the following elevation covariates: curvature, compound topographic index, transformed aspect, and topographic roughness. Stephen P Boyte 2019 raster digital data https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9zek5m1 Digital and/or Hardcopy 2019 observed AG2019 Provided fractional cover estimates of annual grasses for 2019. Bruce Wylie 2017 raster digital data https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/f7445jz9 Digital and/or Hardcopy 2017 observed AG2017 Provided fractional cover estimates of annual grasses for 2017. Boyte, S.P. Wylie, B.K. Major, D.J. 20160623 raster digital data https://www.sciencebase.gov/catalog/item/577fcb6ce4b0ef4d2f45fbf3 Digital and/or Hardcopy 2016 observed AG2016 Provided fractional cover estimates of annual grasses for 2016. Boyte, S.P. Wylie, B.K. 20150702 raster digital data https://www.sciencebase.gov/catalog/item/55ad3a16e4b066a2492409d5 Digital and/or Hardcopy 2015 observed AG2015 Provided fractional cover estimates of annual grasses for 2015. Brady W. Allred Brandon T. Bestelmeyer Chad S. Boyd Christopher Brown Kirk W. Davies Michael C. Duniway Lisa M. Ellsworth Tyler A. Erickson Samuel D. Fuhlendorf Timothy V. Griffiths Vincent Jansen Matthew O. Jones Jason Karl Anna Knight Jeremy D. Maestas Jonathan J. Maynard Sarah E. McCord David E. Naugle Heath D. Starns Dirac Twidwell Daniel R. Uden 20230316 Version 3.0 raster digital data https://rangelands.app/products/#data-download https://rangelands.app/rap/ Digital and/or Hardcopy 1986 2021 observed RAP Provided fractional cover estimates of grasses, shrubs, and tree canopy. U.S. Geological Survey California Department of Fish and Wildlife Nevada Department of Wildlife U.S. Forest Service U.S. Department of Agriculture Bureau of Land Management Unknown vector digital data https://bistatesagegrouse.com/general/page/maps-gis Digital and/or Hardcopy Unknown Unknown DPS Provided a boundary for the Bi-State Distinct Population Segment. U.S. Department of Commerce U.S. Census Bureau 2020 vector digital data https://www2.census.gov/geo/tiger/TIGER2020/UAC/tl_2020_us_uac20.zip https://meta.geo.census.gov/data/existing/decennial/GEO/GPMB/TIGERline/Current_19115/tl_2020_us_uac20.shp.iso.xml Digital and/or Hardcopy 2020 Census is taken every 10 years. The most recent occurred in 2020. Census Provided polygons representing urban areas, which were used to mask the final product. Jon Dewitz 2021 Version 2.0 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9kzcm54 Digital and/or Hardcopy 2019 observed impervious Identified areas with increased imperviousness due to anthropogenic development, and estimated different degrees of imperviousness. Microsoft 20180925 vector digital data https://hub.arcgis.com/maps/esri::microsoft-building-footprints-tiles/about Digital and/or Hardcopy 2018 Originally published in 2018, updated 2020. BuildingFootprint Provides 129,591,852 building footprint polygon geometries divided by 50 US states and the District of Columbia in GeoJSON format. We used it to help mask out- or downgrade- areas in our final product. Selection and Survival We evaluated both habitat selection and survival for two reproductive life stages, nesting and brood-rearing, which was further divided into early (less than or equal to 21 days post-hatch) and late (greater than 21 days post-hatch) brood-rearing periods. We evaluated habitat selection using resource selection functions (RSFs) comparing used and available points. For survival, we used binomial models that accounted for exposure time in a Bayesian framework. Using estimates from the final models and spatial data layers included in the model, we then mapped both selection and survival for each life stage across the Bi-State DPS. For selection models, we transformed estimates using a habitat selection index that indicates relative habitat use proportional to availability on a scale of 0 to 1. We then categorized the continuous selection surface into four categories (non-habitat, low, moderate, and high selection) using the percent isopleth method at used locations with cutoff values at the 50th, 25th, and 5th percentiles. We then used the continuous selection and survival maps for each life stage to calculate composite selection and suitability indices, respectively. To calculate the composite selection index, we first relativized the habitat selection indices described above by dividing by the maximum value in that life stage, which allowed us to weight all three life stages equally when calculating a composite surface. We then multiplied the 3 relativized surfaces together to create a composite selection index, which we categorized into four categories (non-habitat, low, moderate, and high selection) using the percent isopleth method described above. Calculating the suitability index followed a similar procedure, but values were not relativized because the outputs from the survival models represent true survival probabilities. We multiplied the exponentiated survival surfaces that represent overall survival during a given life stage to create a composite suitability index, which we classified following the same methods used for individual survival maps. We only calculated the suitability index for pixels that were considered high, moderate, or low based on the corresponding selection index, thus excluding areas considered non-habitat based on selection models. We created separate habitat layers for each population nadir (1995, 2001, 2008, and 2021) estimated from an integrated population model for each life stage and season using time-varying remotely sensed vegetation cover layers to capture changes in habitat over time. Time-varying covariates included sagebrush cover, shrub cover, herbaceous cover, bare ground, annual grass cover, and cumulative burned area. We also calculated composite selection, survival, and habitat suitability indices to capture overall changes in time. To create the annual composite selection index, we first calculated seasonal composite selection layers, where the spring composite selection index was a combination of seasonal spring, nest, and early brood-rearing selection layers, the summer composite selection index included seasonal summer and late brood-rearing selection layers, and the winter composite selection index only included the seasonal winter selection layer. To calculate the seasonal composite selection indices, we first relativized each layer by dividing by its maximum value and then added the individual selection components together. We then relativized the seasonal composite selection indices, added them together, and scaled the resulting layers between 0 and 1 to calculate the annual composite selection index. To create the annual composite survival index, we followed the same procedure but only created seasonal composite survival indices for spring, which included nest and early brood-rearing survival layers, and summer, which only included the late brood-rearing survival layer. To create the annual composite habitat suitability index, we first added the seasonal composite selection and survival indices together and relativized them to create seasonal composite suitability indices for spring, summer, and winter. We then added the three seasonal composite suitability layers together and scaled the resulting layer between 0 and 1 to calculate the annual composite habitat suitability index. NLCD RCMAP AG2015 AG2016 AG2017 AG2018 AG2019 PJ fire NHD roads DEM 2023 Raster Universal Transverse Mercator 11 0.9996 -117.0 0.0 500000.0 0.0 row and column 30.0 30.000000000000053 meters North_American_Datum_1983 GRS 1980 6378137.0 298.257222101004 GrSG_'lifestage/season'_sel_categories_BS'YYYY'.tif Raster geospatial data file. Producer Defined OID Internal object identifier. Producer Defined Sequential unique whole numbers that are automatically generated. Value Relative likelihood a sage-grouse will select or use an area during different life stages or seasons. Producer Defined 1 Non-habitat; unlikely for a sage-grouse to select this area. Producer defined 2 Low selection probability. Producer defined 3 Moderate selection probability. Producer defined 4 High selection probability. Producer defined Count Number of raster cells with this value. Producer Defined 2840237.0 7771643.0 GrSG_'lifestage'_surv_categories_BS'YYYY'.tif Raster geospatial data file. Producer Defined OID Internal object identifier. Producer Defined Sequential unique whole numbers that are automatically generated. Value Categories identifying relative likelihood of survival for sage-grouse during different life stages and seasons. Producer Defined 1 Very-low likelihood of survival. Producer defined 2 Low likelihood of survival. Producer defined 3 Moderate likelihood of survival. Producer defined 4 High likelihood of survival. Producer defined Count Number of raster cells with this value. Producer Defined 1388919.0 6193768.0 Most methodologies used were applied to 4 or more separate years. For simplicity, we have grouped entity and attribute information for like-rasters. The naming scheme used is as follows: GrSG_'life-stage/season'_'type'_'format'_BS'YYYY'.tif. 'GrSG' stands for Greater Sage-grouse. 'Life stages' are nesting, early brood, and late brood; 'seasons' are spring, summer, and winter. 'Type' refers to either selection, survival, suitability, or source-sink. 'Format' is either 'index,' which is continuous values between 0 and 1, or 'categories,' which are enumerated discrete integer values representing a selection or survival category. 'BS' refers to the Bi-State region. 'YYYY' refers to the calendar year represented by the layer. If the filename makes no mention of life stage or season, then it is a composite layer, created by combining multiple life stages and/or seasons. The details of how this was done are explained in the 'process steps' section of this metadata file. For example, a raster named 'GrSG_Ebrood_sel_categories_BS2001.tif' would be a raster representing Greater Sage-grouse early brood selection categories in the Bi-State region for 2001. Coates, P.S., Milligan, M.C., Brussee, B.E., O'Neil, S.T., and Chenaille, M.P., 2024, Greater sage-grouse habitat selection, survival, abundance, and space-use in the Bi-State Distinct Population Segment of California and Nevada: U.S. Geological Survey data release, https://doi.org/10.5066/P1AATW9D. 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 for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. TIF https://doi.org/10.5066/P1AATW9D None 20240613 Michael P Chenaille U.S. Geological Survey, SOUTHWEST REGION CARTOGRAPHIC TECHNICIAN mailing address

800 Business Park Drive

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