Brianne E. Brussee Peter S. Coates Shawn T. O'Neil Michael L. Casazza Shawn P. Espinosa John D. Boone Elisabeth M. Ammon Scott C. Gardner David J. Delehanty 20220824 raster digital data Denver, CO U.S. Geological Survey data release https://doi.org/10.5066/P9B593DZ Brianne E. Brussee Peter S. Coates Shawn T. O'Neil Michael L. Casazza Shawn P. Espinosa John D. Boone Elisabeth M. Ammon Scott C. Gardner David J. Delehanty 202209 publication Global Ecology and Conservation vol. 37 n/a Elsevier BV https://doi.org/10.1016/j.gecco.2022.e02147 These data represent habitat selection of greater sage-grouse during the late portion of the brood rearing process. Numerous wildlife species within semi-arid shrubland ecosystems across western North America are experiencing substantial habitat loss and fragmentation. These changes in habitat are often attributed to a diverse suite of factors including prolonged and increasingly severe droughts, conifer expansion, anthropogenic development, domestic and feral livestock grazing, and invasion of exotic annual grasses which promotes increased wildfire frequency and severity. Greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) are considered an indicator of sagebrush ecosystem health and have experienced widespread population decline associated with habitat loss and degradation, as well as changes in predator communities. Our objectives were to model and map sage-grouse habitat selection and survival during the important brood-rearing life stage in relation to landscape-scale environmental predictors. Furthermore, we sought to understand impacts of wildfire and annual grass invasion on brood habitat, as these accelerated disturbance regimes are a primary cause of habitat loss within the Great Basin region of the USA. R packages used: rjags v.4-10, jagsUI v.1.5.2, raster v.3.4-13, rgdal v.1.5-23, foreign v.0.8-81, boot v.1.3-28, lme4 v1.1-27.1, mgcv v.1.8-36, spatial v.7.3-14, sp v.1.4-5, lubridate v.1.7.10, reshape2 v.1.4.4 2020 observed Complete None planned Nevada and California -121.1340 -113.7436 42.1292 37.0277 USGS Thesaurus native species shrubland ecosystems human impacts ecosystems fires None adaptive habitat selection chick survival ecological trap habitat functional response maladaptive habitat selection resource selection function USGS Metadata Identifier USGS:62fd72ffd34e3a444286cd4d None western United States Great Basin 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 279-782-0904 279-782-0607 gs-b-werc_data_management@usgs.gov U.S. Geological Survey, Nevada Department of Wildlife, Great Basin Bird Observatory, California Department of Fish and Wildlife, Department of Biological Sciences at Idaho State University Windows 11, R 3.5.0, hsi_lb.img, 17 MB Interface to the JAGS MCMC library. https://CRAN.R-project.org/package=rjags To fully understand how JAGS works, you need to read the JAGS User Manual. The manual explains the basics of modelling with JAGS and shows the functions and distributions available in the dialect of the BUGS language used by JAGS. It also describes the command line interface. The rjags package does not use the command line interface but provides equivalent functionality using R functions. Plummer, Martyn Stukalov, Alexey Denwood, Matt 20191106 4-10 Tools Software 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 (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers. https://CRAN.R-project.org/package=jagsUI User Manual: https://cran.r-project.org/web/packages/jagsUI/jagsUI.pdf Kellner, Ken Meredith, Mike 20210618 1.5.2 Tools Software 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/package=raster See the manual and tutorials on https://rspatial.org/ to get started. Hijmans, Robert J. van Etten, Jacob Sumner, Michael Cheng, Joe Baston, Don Bevan, Andrew Bivand, Roger Busetto, Lorenzo Canty, Mort Fasoli, Ben Forrest, David Ghosh, Aniruddha Golicher, Duncan Gray, Josh Greenberg, Jonathan A. Hiemstra, Paul Hingee, Kassel Ilich, Alex Karney, Charles Mattiuzzi, Matteo Mosher, Steven Naimi, Babak Nowosad, Jakub Pebesma, Edzer Perpinan Lamigueiro, Oscar Racine, Etienne B. Rowlingson, Barry Shortridge, Ashton Venables, Bill Wueest, Rafael 20210618 3.4-13 Tools Software Provides bindings to the Geospatial Data Abstraction Library (GDAL) (version 1.11.4 and later) and access to projection/transformation operations from the PROJ library. Please note that rgdal will be retired by the end of 2023, plan transition to sf/stars/'terra' functions using GDAL and PROJ at your earliest convenience. Use is made of classes defined in the sp package. Raster and vector map data can be imported into R, and raster and vector sp objects exported. https://CRAN.R-project.org/package=rgdal The GDAL and PROJ libraries are external to the package, and, when installing the package from source, must be correctly installed first. Bivand, Roger Keitt, Tim Rowlingson, Barry Pebesma, Edzer Sumner, Michael Hijmans, Robert J. Baston, Daniel Rouault, Evan Warmerdam, Frank Ooms, Jeroen Rundel, Colin 20210203 1.5-23 Tools Software Reading and writing data stored by some versions of Epi Info, Minitab, S, SAS, SPSS, Stata, Systat, Weka, and for reading and writing some dBase files. https://CRAN.R-project.org/package=foreign Reference manual: https://cran.r-project.org/web/packages/foreign/foreign.pdf R Core Team Bivand, Roger Carey, Vincent J. DebRoy, Saikat Eglen, Stephen Guha, Rajarshi Herbrandt, Swetlana Lewin-Koh, Nicholas Myatt, Mark Nelson, Michael Pfaff, Ben Quistorff, Brian Warmerdam, Frank Weigand, Stephen Free Software Foundation, Inc. 20201224 0.8-81 Tools Software 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/package=boot Reference manual: https://cran.r-project.org/web/packages/boot/boot.pdf Canty, Angelo Ripley, Brian 20210503 1.3-28 Tools Software Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the Eigen C++ library for numerical linear algebra and RcppEigen glue. https://CRAN.R-project.org/package=lme4 Reference manual: https://cran.r-project.org/web/packages/lme4/lme4.pdf Bates, Douglas Maechler, Martin Bolker, Brian Walker, Steven Christensen, Rune H.B. Singmann, Henrik Dai, Bin Scheipl, Fabian Grothendieck, Gabor Green, Peter Fox, John Bauer, Alexander Krivitsky, Paval N. 20210622 1.1-27.1 Tools Software 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. https://CRAN.R-project.org/package=mgcv 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. Wood, Simon 20210601 1.8-36 Tools Software Functions for kriging and point pattern analysis. https://CRAN.R-project.org/package=spatial Reference manual: https://cran.r-project.org/web/packages/spatial/spatial.pdf Ripley, Brian Bivand, Roger Venables, William 20210503 7.3-14 Tools Software 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. https://CRAN.R-project.org/package=sp Reference manual: https://cran.r-project.org/web/packages/sp/sp.pdf Pebesma, Edzer Bivand, Roger Rowlingson, Barry Gomez-Rubio, Virgilio Hijmans, Robert Sumner, Michael MacQueen, Don Lemon, Jim Lindgren, Finn O'Brien, Josh O'Rourke, Joseph 20210110 1.4-5 Tools Software Functions to work with date-times and time-spans: fast and user friendly parsing of date-time data, extraction and updating of components of a date-time (years, months, days, hours, minutes, and seconds), algebraic manipulation on date-time and time-span objects. The lubridate package has a consistent and memorable syntax that makes working with dates easy and fun. Parts of the CCTZ source code, released under the Apache 2.0 License, are included in this package. See https://github.com/google/cctz for more details. https://CRAN.R-project.org/package=lubridate Reference manual: https://cran.r-project.org/web/packages/lubridate/lubridate.pdf Spinu, Vitalie Grolemund, Garrett Wickham, Hadley Vaughan, Davis Lyttle, Ian Costigan, Imanuel Law, Jason Mitarotonda, Doug Larmarange, Joseph Boiser, Jonathan Lee, Chel Hee Google Inc. 20210226 1.7.10 Tools Software Flexibly restructure and aggregate data using just two functions: melt and dcast (or acast). https://CRAN.R-project.org/package=reshape2 Reference manual: https://cran.r-project.org/web/packages/reshape2/reshape2.pdf Wickham, Hadley 20200409 1.4.4 Tools Software Using both the training dataset and independent testing data collected during 2018 - 2019 (377 early brood locations; 338 late brood locations), we validated our final early and late brood habitat models for fit and predictive accuracy using resource selection function cross-validation methods. Additionally, we used the independent testing data to generate used-habitat calibration (UHC) plots. Using the training dataset, these methods allowed us to evaluate model fit, whereas using the independent testing dataset allowed us to evaluate the ability of the models to accurately predict brood habitat. Model validation methods are described fully in the Supplementary Materials, Appendix S4 of the accompanying publication in the 'larger work' citation. We present our results based on relative importance of covariates within our final model calculated as the difference of the median of the standardized distribution of predicted used habitat (i.e. from UHC plots) from the median of the standardized distribution of available habitat. Standardization allows differences to be compared across covariates on the same scale. No logical consistency report conducted. No completeness report conducted. No horizontal accuracy report conducted. No applicable. Matthew Rigge Collin Homer Lauren Cleeves Debra K. Meyer Brett Bunde Hua Shi George Xian Spencer Schell Matthew Bobo 20210604 raster digital data https://s3-us-west-2.amazonaws.com/mrlc/nlcd_2016_land_cover_l48_20210604.zip 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 2016 observed MRLC Shrubland Provides fractional cover estimates for covariates used in the model. The cover types used from this data set are big sagebrush, perennial herbaceous, annual herbaceous, bare ground, other sagebrush, non-sagebrush shrub, sagebrush height, and litter . 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. 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. 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 raster digital data https://doi.org/10.5066/F7348HVC Digital and/or Hardcopy 20100701 20130801 observed PJ Represents pinyon-juniper percent cover, used to create a raster representing conifer cover class 1, which is less than 10 percent pinyon-juniper cover. 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. Platts 2009 vector digital data There are fees associated with the use of this transmission line product. https://www.spglobal.com/platts/en/products-services/electric-power http://www.gisdata.platts.com/ Digital and/or Hardcopy 2009 observed Electric Represents the location of high powered transmission lines, euclidean distance and density of these transmission lines were both used in the modeling process. Didan, K. 2015 raster digital data https://lpdaac.usgs.gov/products/mod13q1v006/ https://doi.org/10.5067/MODIS/MOD13Q1.006 Digital and/or Hardcopy 2009 2018 observed NDVI Provides NDVI at 16-day intervals and 30 meter resolution. U.S. Fish and Wildlife Service 20211201 vector digital data https://www.fws.gov/wetlands https://www.fws.gov/wetlands/Data/Mapper.html https://www.fws.gov/wetlands/data/Data-Download.html https://www.fws.gov/wetlands/Data/KML/Wetlands-Data.kml https://www.fws.gov/wetlandsmapservice/services/Wetlands/MapServer/WMSServer https://www.fws.gov/wetlandsmapservice/rest/services https://www.fws.gov/wetlands/Data/Wetlands-Product-Summary.html Digital and/or Hardcopy 20211201 observed wetland Provides the location of wetlands in the United States. These locations were used to derive rasters representing distance to- and density of- wetlands. U.S. Department of Agriculture National Agricultural Statistics Service 20211201 vector digital data Publication date refers to date the dataset was accessed https://cropcros.azurewebsites.net/ Digital and/or Hardcopy 2021 observed NASS Provides the location of croplands in the United States. These locations were used to derive rasters representing distance to- and density of- croplands. U.S. Forest Service U.S. Geological Survey 20180801 vector digital data https://www.mtbs.gov/ Digital and/or Hardcopy 1984 2016 observed fire Provided locations of fires, allowing analysts to construct a [cumulative burned area] raster, which then was used to create rasters representing distance to- and density of- burned areas. We used remotely sensed GIS products encompassing sage-grouse population management units within the Great Basin to quantify spatially explicit environmental covariates potentially associated with sage-grouse brood habitat selection and/or survival. We specified these covariates to broadly encompass landscape attributes representing dominant vegetation type, hydrologic features, topography, potential anthropogenic disturbance, and other potential disturbance such as drought and wildfire. Habitat use is scale-dependent, thus we considered landscape variables at three different spatial scales relevant to sage-grouse movement patterns. We used a circular moving window to estimate land cover within radii representing the minimum, mean, and maximum, daily distances traveled by sage-grouse. We included an additional, finer scale for analysis of brood site selection to accommodate landscape attributes more directly associated with cover characteristics at the brood location. We implemented a two-step variable selection technique to reduce the number of predictors included in our final models of brood habitat selection and brood survival. We used a resource selection study design to separately infer patterns of early and late sage-grouse brood site selection. Given the relatively coarse temporal nature of our data, and limited number of locations per individual (1 to 2 locations every 10 days), our analysis represented brood habitat selection patterns at the sub-population (as opposed to individual) level. Thus, our used locations represent a sample from multiple individual home ranges during the brood-rearing period, and because sage-grouse brood movements are partially constrained based on previous breeding stages (lek attendance and nest location), we designated potential brood-rearing areas (i.e., available habitat) based on buffers around nests. For our data, we calculated the maximum distance of broods from their nests for early (about 18 km) and late broods (about 39 km). We used these distances to calculate buffers around nests within which we conditioned the available distribution of background locations. These distances were necessary to incorporate all used locations in our analysis, thereby facilitating use to availability comparisons. Buffer sizes are also likely representative of the overall potential habitat available to breeding females relative to lek locations, accommodating potential for females to select nest sites (and subsequently brood habitat) based on potential brood-rearing resources. To capture the landscape heterogeneity within the buffers, we generated 20 random locations within each nest buffer for each early and late brood. The random locations were used to characterize the availability of landscape characteristics within the nest buffers and 20 locations represented approximately 5 to 10 times the representation of used locations. We extracted values of landscape vegetation characteristics, distance metrics, topographic indices, hydrologic features, anthropogenic, and disturbance metrics at both used and random locations. We used a generalized linear mixed model (GLMM) in a Bayesian framework to evaluate the effects of environmental predictors on early and late brood-rearing habitat selection. We used JAGS 4.3.0 within rjags in R (version 3.5.0) to estimate the parameters of our resource selection function (RSF) using Markov Chain Monte Carlo methods. The BLISS model was the first of two steps for modeling early and late brood habitat selection. For each early and late brood model, we ran 3 chains of 20,000 iterations, following a burn-in of 5,000 iterations, and retained every 4th sample. We then subset to only the covariates selected using BLISS to fit the final model. To prevent overfitting the model, for all fixed effects in the models, we implemented L-1 regularization by specifying Lasso prior distributions with an uninformative hyperprior for the tuning parameter lambda. By including only those landscape habitat predictors identified from the BLISS process, we prevented the inclusion of correlated covariates (r greater than 0.6) in models. For our final models, we ran 3 longer chains of 50,000 iterations, following a burn-in of 25,000 iterations, and retained every 5th sample. We verified chain convergence visually and using the Gelman-Rubin statistic (r-hat less than 1.05). For parameter estimates, we report median values and 95 percent credible intervals of the posterior distribution. Because one of our primary objectives was to understand the impacts of wildfire on sage-grouse brood-rearing habitat, we sought to understand how wildfire influenced selection patterns of vegetation characteristics. Thus, within our final model we included linear interactions of the best CBA covariate with the best covariates from groups representing annual grasses, perennial grasses, shrubs, and litter. MRLC Shrubland DEM NHD PJ Roads Electric NDVI wetland NASS fire 2022 Raster Grid Cell 18642 20382 1 Universal Transverse Mercator 11 0.9996 -117.0 0.0 500000.0 0.0 row and column 30.0 30.0 meters North_American_Datum_1983 GRS 1980 6378137.0 298.257222101 hsi_lb.img Raster geospatial data file. Producer Defined OID Table row number, automatically generated by ArcMap. Producer defined 0 4 Value Pixel values represent habitat suitability for Greater sage-grouse in the late brood rearing season. Producer defined NoData No Data Producer defined 1 non-habitat Producer defined 2 low selection Producer defined 3 moderate selection Producer defined 4 high selection Producer defined 5 highest selection Producer defined Count The number of pixels for each value class. Producer defined 4667883 104276108 U.S. Geological Survey GS ScienceBase mailing address

Denver Federal Center, Building 810, Mail Stop 302

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. IMG https://doi.org/10.5066/P9B593DZ None 20220824 Michael P Chenaille U.S. Geological Survey, Western Ecological Research Center 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