New Mexico Cooperative Fish and Wildlife Research Unit James Cain 20220407 Version 1.0 vector digital data https://doi.org/10.5066/P9TKA3L8 Matthew Kauffman Blake Lowrey Jeffrey Beck Jodi Berg Scott Bergen Joel Berger James Cain Sarah Dewey Jennifer Diamond Orrin Duvuvuei Julien Fattebert Jeff Gagnon Julie Garcia Evan Greenspan Embere Hall Glenn Harper Stan Harter Kent Hersey Pat Hnilicka Mark Hurley Lee Knox Art Lawson Eric Maichak James Meacham Jerod Merkle Arthur Middleton Daniel Olson Lucas Olson Craig Reddell Benjamin Robb Gabe Rozman Hall Sawyer Cody Schroeder Brandon Scurlock Jeff Short Scott Sprague Alethea Steingisser Nicole Tatman 2022 publication The mule deer (Odocoileus hemionus) of the Jemez Springs herd winter in the southwestern Jemez Mountains, south and east of the town of Jemez Springs. The area has experienced two severe wildfires, the Las Conchas and Thompson Ridge fires, within the last decade, burning a total of 180,555 acres. The data used in this report was collected to examine the responses of mule deer to these wildfires and forest restoration treatments. The winter range is located among the foothills of the Jemez Mountains, consisting primarily of pinyon-juniper woodlands. Individuals migrated an average of 26.1 miles, either to the western edge of the Jemez Mountains, near Blue Bird Mesa, or to the Valles Caldera. The central migration route follows the top of a 1,300 foot escarpment that parallels and eventually crosses NM State Route 4 twice. The summer range is dominated by ponderosa pine and mixed-conifer forests, along with open grasslands in the Valles Caldera. The herd is only partially migratory, as resident individuals are found on the winter range and on Lake Fork Mesa, west of the Caldera. State Route 4 may act as a slight obstacle to migration, as one of the larger stopover sites was located between the two road crossings. These data provide the location of migration routes for Mule Deer from the Jemez Springs Herd in New Mexico. They were developed from fixed motion variance movement models using 35 migration sequences collected from a sample size of 11 adult mule deer comprising GPS locations collected every 5 or 6 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. The 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, oil and gas 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, the team consists of federal scientists, university researchers, and biologists and analysts from participating state and tribal agencies. The first set of maps described a total of 42 migrations across five western states and was published in 2020 as the first volume of this report series. This second volume describes an additional 65 migrations mapped within nine 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 and federal stakeholders to reduce barriers to migration caused by fences, roads, and other development. 20130101 20170118 observed Complete None planned Jemez Mountains -106.8949 -106.4892 36.0046 35.6517 ISO 19115 Topic Category health imageryBaseMapsEarthCover USGS Thesaurus migration migratory species animal behavior migration (organisms) USGS Metadata Identifier USGS:620e4b35d34e6c7e83baa3b0 Common geographic areas New Mexico United States Jemez Mountains None Odocoileus hemionus Kingdom Animalia animals Subkingdom Bilateria Infrakingdom Deuterostomia Phylum Chordata chordates Subphylum Vertebrata vertebrates Infraphylum Gnathostomata Superclass Tetrapoda Class Mammalia mammals Subclass Theria Infraclass Eutheria Order Artiodactyla artiodactyls cloven-hoofed ungulates even-toed ungulates Family Cervidae cervids caribou deer moose wapiti Subfamily Capreolinae Genus Odocoileus mule deer white-tailed deer Species Odocoileus hemionus mule deer Mule Deer TSN: 180698 None. Please see 'Distribution Info' for details. The USGS, New Mexico Cooperative Fish and Wildlife Research Unit will retain ownership of the data provided and must approve of any additional use before other analyses, research, or publications are initiated. 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 USGS, New Mexico Cooperative Fish and Wildlife Research Unit. James W Cain U.S. Geological Survey, ECOSYSTEMS Assistant Unit Leader, Research Wildlife Biologist mailing address

2980 Espina St, NM State Univ Knox Hall

Las Cruces NM 88003 US 575-646-3382 575-646-1281 jwcain@usgs.gov Tanya M. Roerick James W. Cain J.V. Gedir 201909 publication Forest Ecology and Management vol. 447 n/a Elsevier BV ppg. 169-179 https://doi.org/10.1016/j.foreco.2019.05.067 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 animal’s movement trajectory (Merkle and others, 2017). The NSD represents the square of the straight-line distance between any GPS location of an animal’s movement trajectory and a point within the animal’s winter range. When an animal 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 (Fig. 1). 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 multi-step process to calculate population-level corridors and to identify stopovers, which generally followed the approach outlined by Sawyer et al. (2009). First, we used Brownian bridge movement models (BBMM) (Horne and others, 2007) to estimate an occurrence distribution (in other words, the probability of where the animal could have traveled during its migration, hereafter, utilization distribution [UD]) for each individual spring and fall migration sequence using a 50-meter (164-foot) resolution. The UDs were then averaged across years for each individual 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 m2 (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 five acres. Winter ranges: We applied a three-step process to calculate population-level winter ranges, which generally followed the approach outlined by Sawyer et al. (2009). First, we isolated winter sequences, defined as movements between fall and spring migrations. For each year, we calculated a standard date for start and end of winter and applied one of two options to calculate winter range dates based on preference of individual States: (1) for each year, we calculated the start of winter as the 95% quantile of the end dates of all fall migrations, and the end of winter as the 5% quantile of the start dates of all spring migrations, or (2) we defined a fixed date range based on local expert knowledge for a given herd (e.g., Dec.15 - Mar. 15). We discarded winter sequences that spanned less than 30 days. Following the methods for migration corridors, we calculated a population-level UD of winter use and identified the core winter range using the 50% isopleth. Citations -Bunnefeld, N., Borger, 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. 15, 12 p. [Also available at https://doi.org/10.1186/s40462-016-0080-y.] -Merkle, J.A., Gage, J., and Kauffman, M.J., 2017, Migration mapper: Laramie, Wyo., University of Wyoming, Department of Zoology and Physiology, Migration Initiative, accessed June 1, 2020, at https://migrationinitiative.org/content/migration-mapper. -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.] 2021 Vector String 35 Albers Conical Equal Area 29.5 45.5 -96.0 23.0 0.0 0.0 coordinate pair 0.6096 0.6096 meters North_American_Datum_1983 GRS_1980 6378137.0 298.257222101 MD_NM_JemezSprings_Routes_Ver1_2020.shp Attribute Table These data provide the location of migration routes for Mule Deer (Odocoileus hemionus) from the Jemez Springs Herd in New Mexico. They were developed from fixed motion variance movement models using 35 migration sequences collected from a sample size of 11 adult mule deer comprising GPS locations collected every 5 or 6 hours. Producer Defined FID Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. Shape Feature geometry. ESRI Coordinates defining the features. mig Unique route ID Producer Defined Unique identifier for route merging animal ID, season, and year firstdate Beginning date of migration Producer Defined Beginning date of migration lastdate Ending date of migration Producer Defined Ending date of migration id Animal ID Producer Defined Unique Animal ID season Season of migration route Producer Defined fa fall migration Producer defined sp spring migration Producer defined year Year of migration route Producer Defined 2013.0 2018.0 Shape_Leng Length of the feature Producer Defined 8042.62888214 72400.25499 Meters 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. Digital Data https://doi.org/10.5066/P9TKA3L8 None 20220407 James W Cain U.S. Geological Survey, ECOSYSTEMS Assistant Unit Leader, Research Wildlife Biologist mailing address

2980 Espina St, NM State Univ Knox Hall

Las Cruces NM 88003 US 575-646-3382 575-646-1281 jwcain@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999