Valerie Hinojoza-Rood Don Whittaker 20260324 vector digital data https://doi.org/10.5066/P1ETBSYE Matthew Kauffman Blake Lowrey Jennifer L. McKee Chloe Beaupre Jeffrey Beck Jon Beckmann Scott Bergen Regan Berkley Nathan Borg Peyton Carl Michelle Cowardin Sarah Dewey Katie M. Dugger Amy Ehrhart Jessica Fort Eric Freeman Ian Freeman Emily R. Gelzer David German Jacob Gray Evan Greenspan Zach Gregory Emily Hagler Makeda Hanson Valerie D. Hinojoza-Rood Pat Hnilicka Nick Jaffe Andrew F. Jakes Aran Johnson Jaron T. Kolek Art Lawson Zach Lockyer Daryl Lutz Cody McKee Jane McKeever Jerod Merkle Matt Mumma Dennis Newman Erika Peckham Jill E. Randall Adele K. Reinking Robert Ritson William J. Rudd Brianna M. Russo Hall Sawyer Cody Schroeder Brandon Scurlock Jeff Short Bret Stansberry Erik Steiner Alethea Steingisser Tom Stephenson Eric VanNatta Cody F. Wallace Brad Weinmeister Don Whittaker Tatjana Woody Sean Yancey 2025 publication Reston, VA U.S. Geological Survey These mapping layers show the location of the stopovers for pronghorn (Antilocapra americana) in the Sheldon-Hart Mountain population in Oregon and Nevada. They were developed from 236 migration sequences collected from a sample size of 75 animals comprising GPS locations collected every 2−17 hours. Migration is widespread across taxonomic groups and increasingly recognized as fundamental to maintaining abundant wildlife populations and communities. Many ungulate herds migrate across the western United States to access food and avoid harsh environmental conditions. With the advent of global positioning system (GPS) collars, researchers can describe and map the year-round movements of ungulates at both large and small spatial scales. Migrations can traverse landscapes that are a mix of different jurisdictional ownership and management. Today, the landscapes that migrating herds traverse are increasingly threatened by fencing, high-traffic roads, energy development, and other types of permanent development. Over the last decade, a model of science-based conservation has emerged in which migration corridors, stopovers, and winter ranges can be mapped in detail, thereby allowing threats and conservation opportunities to be identified and remedied. In 2018, the U.S. Geological Survey (USGS) assembled a Corridor Mapping Team (CMT) to work collaboratively with western States to map migrations of mule deer, elk, and pronghorn. Led by the USGS Wyoming Cooperative Fish and Wildlife Research Unit at the University of Wyoming, the team consists of Federal scientists, university researchers, and biologists and analysts from participating State and Tribal agencies. The first volume of the report series described a total of 42 migrations across five western States and was published in 2020. The second volume described an additional 65 migrations mapped within nine western States and select Tribal lands and was published in April, 2022. The third volume described an additional 45 migrations mapped across seven western States and select Tribal lands and was published in December, 2022. The fourth volume described an additional 31 new migrations throughout nine western States and select Tribal lands and was published in April, 2024. The fifth volume details migrations and seasonal ranges from 36 herds, including 2 herd updates detailed in previous reports. This volume, the sixth in the report series, details migrations and seasonal ranges from 23 herds, including 4 herd updates detailed in previous reports, throughout eight western States and select Tribal lands. As the American West continues to grow, this report series and the associated map files released on USGS’s ScienceBase will allow for migration maps to be used for conservation planning by a wide array of State, Federal, and Tribal stakeholders to reduce barriers to migration caused by fences, roads, and other development. 2011 2023 observed Complete As needed Between Oregon Highway 395 and the Black Rock Desert, Nevada -120.3298 -118.3698 43.5924 40.9334 ISO 19115 Topic Category biota USGS Thesaurus migration (organisms) migratory species animal behavior USGS Metadata Identifier USGS:69babaf6b66b010f61c6ae4f Common geographic areas Sheldon National Wildlife Refuge Nevada United States USGS Biocomplexity Thesaurus Ungulates Integrated Taxonomic Information System (ITIS) 2025 ONLINE_REFERENCE Washington, D.C. Integrated Taxonomic Information System (ITIS) http://itis.gov expert knowledge Kingdom Animalia Subkingdom Bilateria Infrakingdom Deuterostomia Phylum Chordata Subphylum Vertebrata Infraphylum Gnathostomata Superclass Tetrapoda Class Mammalia Subclass Theria Infraclass Eutheria Order Artiodactyla Family Antilocapridae Genus Antilocapra Species Antilocapra americana TSN: 180717 None. Please see 'Distribution Info' for details. Dataset authors will retain ownership of the data provided. The burden for determining fitness for use lies entirely with the user. For purposes of publication or dissemination, citations, or credit should be given to the authors/originators listed herein. Don Whittaker Oregon Department of Fish and Wildlife Ungulate Species Coordinator mailing and physical

4034 Fairview Industrial Dr SE

Salem Oregon 97302 503-947-6325 don.whittaker@odfw.oregon.gov Funding was provided by the Oregon Department of Fish and Wildlife, United States Fish and Wildlife Service, Hart Mountain-Sheldon National Wildlife Refuge Complex, Nevada Department of Wildlife, and Wildlife and Sport Fish Restoration Program. Kauffman et al. 2020 publication https://pubs.usgs.gov/publication/sir20205101 Kauffman et al. 2022 publication https://doi.org/10.3133/sir20225008 Kauffman et al. 2022 publication https://doi.org/10.3133/sir20225088 Kauffman et al. 2024 publication https://doi.org/10.3133/sir20245006 Kauffman et al. 2025 publication https://doi.org/10.3133/sir20245111 No formal attribute accuracy tests were conducted We checked to ensure values were in expected ranges (e.g., locations of corridors were as expected, and dates of GPS observations were consistent with the project time period). Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. No formal positional accuracy tests were conducted No formal positional accuracy tests were conducted Methods varied by data type (i.e., migration routes, migration corridors, stopovers, or winter ranges). Routes: To identify migration routes, we first extracted migration sequences for each individual-year. To identify spring and fall migration start and end dates for a given individual in a given year, we visually inspected the Net Squared Displacement (NSD) curve (Bunnefeld et al. 2011, Bastille-Rousseau et al. 2016) alongside digital maps of the individual’s movement trajectory (Merkle et al. 2017). The NSD represents the square of the straight-line distance between any GPS location of an individual’s movement trajectory and a point within the individual’s winter range. When an individual stays within a defined home range, the NSD varies relatively little over time as the animal travels. However, when an animal migrates away from its winter range, the NSD of each successive location increases until it settles in its summer range. The days with clear breakpoints in the NSD curves represent the start and end dates for migration and were used to define migration sequences for spring and fall migration. Migration routes were mapped by joining successive GPS locations within each given migration sequence. Corridors and stopovers: We applied a three-step process to calculate population-level corridors and to identify stopovers, which generally followed the approach outlined by Sawyer et al. (2009). First, we averaged the UDs (estimated using the Brownian bridge movement model [Horne et al. 2007] or the Fixed Motion Variance method [McKee et al. 2024]) for a given individual’s spring and fall migration sequences across all years to produce a single, individual-level migration UD. We rescaled this averaged UD to sum to one. We then defined a migration footprint for each individual as the 99% isopleth of this UD. We stacked up all the individual footprints for a given population, and defined different levels of corridor use based on the number of individuals using a given pixel. We defined low-use corridors as areas traversed by ≥1 individual during migration, medium-use corridors were used by ≥10% of individuals within the population, and high-use corridors were used by ≥20% individuals within the population. We then converted these corridors from a grid-based format to a polygon format, while removing isolated use polygons of less than 20,000 m^2 (i.e., less than approximately 5 acres). Finally, for the stopover calculation, instead of calculating footprints from each individual-level UD, we averaged all the individual-level UDs to produce a single population-level UD, rescaled to sum to one. We defined stopovers as the top 10% of the area of use from the population-averaged UD values. As with the corridors, we then converted stopovers from a grid-based format to a polygon format, and then removed isolated polygons of less than 20,000 m^2. As an alternative to using UDs estimated from the Brownian bridge movement model or Fixed Motion Variance method, some corridors were delineated using the line buffer method (Merkle et al. 2023). In the line buffer approach, analysts simply buffered the straight line connecting successive GPS locations by a consistent width, often 250 to 300 m. Like with the UD approach described above, we then defined different levels of corridor use based on the number of individuals using a given pixel. Seasonal ranges: We applied a three-step process to calculate population-level seasonal ranges (summer, winter, or annual), which generally followed the approach outlined by Sawyer et al. (2009). First, we isolated seasonal sequences, defined as movements between fall and spring migrations. For each year, we calculated a standard date for start and end of each season and applied one of two options to calculate seasonal range dates based on preference of individual States: (1) for each year, we calculated the start of the seasonal periods using the quantiles or means of the migration start and end dates, or (2) defined a fixed date range based on local expert knowledge for a given herd (e.g., Dec.15 - Mar. 15). We discarded seasonal sequences that spanned less than 30 days. Following the methods for migration corridors, we calculated a population-level UD of winter, summer, or annual use and identified the core range using the 50% isopleth. Bunnefeld, N., Börger, L., van Moorter, B., Rolandsen, C.M., Dettki, H., Solberg, E.J., and Ericsson, G., 2011, A model‐driven approach to quantify migration patterns—Individual, regional and yearly differences: Journal of Animal Ecology, v. 80, no. 2, p. 466–476. [Also available at https://doi.org/10.1111/j.1365-2656.2010.01776.x.] Bastille‐Rousseau, G., Potts, J. R., Yackulic, C. B., Frair, J. L., Ellington, E. H., and Blake, S., 2016, Flexible characterization of animal movement pattern using net squared displacement and a latent state model. Movement Ecology, v. 4, no. 2, p. 2051–3933. [Also available at https://doi.org/10.1186/s40462-016-0080-y.] Horne, J.S., Garton, E.O., Krone, S.M., and Lewis, J.S., 2007, Analyzing animal movements using Brownian bridges: Ecology, v. 88, no. 9, p. 2354–2363. [Also available at https://doi.org/10.1890/06-0957.1. McKee, J.L., Fattebert, J., Aikens, E.O., Berg, J., Bergen, S., Cole, E.K., Copeland, H.E., Courtemanch, A.B., Dewey, S., Hurley, M., Lowrey, B., Merkle, J.A., Middleton, A.D., Nuñez, T.A., Sawyer, H., and Kauffman, M.J., 2024, Estimating ungulate migration corridors from sparse movement data: Ecosphere, v. 15, no. 9, art. e4983, 16 p. [Also available at https://doi.org/10.1002/ecs2.4983.] Merkle, J.A., Gage, J., and Kauffman, M.J., 2017, Migration mapper: University of Wyoming, Department of Zoology and Physiology, Migration Initiative, accessed June 1, 2025, at https://www.migrationinitiative.org/content/migration-mapper. Merkle, J., Lowrey, B., Wallace, C.F., Hall, L.E., Wilde, L., Kauffman, M.J., and Sawyer, H., 2023, Conserving habitat for migratory ungulates—How wide is a migration corridor?: Journal of Applied Ecology, v. 60, no. 9, p. 1763–1770. [Also available at https://doi.org/10.1111/1365-2664.14473.] Sawyer, H., Kauffman, M.J., Nielson, R.M., and Horne, J.S., 2009, Identifying and prioritizing ungulate migration routes for landscape-level conservation: Ecological Applications, v.19, no. 8, p. 2016–2025. [Also available at https://doi.org/10.1890/08-2034.1.] 2025 Vector G-polygon 1 Albers Conical Equal Area 29.5 45.5 -96.0 23.0 0.0 0.0 coordinate pair 0.6096 0.6096 meters NAD83_National_Spatial_Reference_System_2007 GRS 1980 6378137.0 298.257222101 ORNV_Pronghorn_Sheldon-HartMountain_Stopovers.shp Attribute Table 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. State State where tracked herd lives. Producer Defined OR-NV Oregon and Nevada Producer defined Species Species that was tracked. Producer Defined Pronghorn Pronghorn Producer defined Herd Herd name. Producer Defined Sheldon-HartMountain Sheldon-Hart Mountain Producer defined DataType Stopovers. Producer Defined Stopovers Migration stopovers used by Sheldon-Hart Mountain pronghorn. Producer defined Contour Stopovers were defined as the areas representing the highest 10 percent of use from the mean population-level UD. Producer Defined 10 This value represents the highest 10 percent of use from the mean population-level UD. Producer defined U.S. Geological Survey GS ScienceBase mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States of America 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. Digital Data https://doi.org/10.5066/P1ETBSYE None. 20260324 GS-NOROCK Data Steward U.S. Geological Survey Data Steward mailing and physical

2327 University Way Ste 2

Bozeman MT 59715 United States of America 406-994-5034 406-994-6556 norock_data_steward@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999