Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for California's wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.

The Owinza mule deer herd summers in higher elevation habitats in the Pioneer and Soldier Mountains, as well as Craters of the Moon National Preserve (Figure 1). Starting in spring, this herd will start to migrate from ‘terminal’ winter range through portions of the big desert near Owinza and Sid Butte towards higher elevation habitats within the Sawtooth National Forest surrounding the Big Wood River Valley. In autumn, the Owinza mule deer herd seasonally migrates toward lower elevation habitats. Within this herd, some mule deer will migrate towards terminal winter range south of Perine, whereas others will hold on winter range between the Picabo Hills and northeast of Dietrich. When winter snows start to accumulate, these holdover individuals will move further to the south towards terminal winter range. Often these winter movements will not occur until January or February and are over 50 miles in straight-line distance. About one-fifth of the Owinza mule deer winter herd use this type of winter movement strategy to complete their annual life history.

Vector data showing areas of dense oil and gas development that mule deer are expected to avoid, for twelve study sites in the Book Cliffs region in Utah.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

This data layer depicts North Dakota Game and Fish Department Mule Deer Range Map.

The purpose of the data is to provide a comprehensive list and spatial location of North Dakota Mule Deer Range Map. This dataset is primarily used as a framework data layer for use in GIS and other mapping applications and does not represent a land survey of the range.

Constraints:
Not to be used for navigation, for informational purposes only. See Game and Fish disclaimer for more information.

This analysis uses location data collected on mule deer that were fitted with GPS collars in Idaho for 2003 – 2019. Individuals using a winter range (as defined as a winter herd), were used for the analysis if their location data was available at the time of the analysis. Each individual’s location dataset is used to estimate winter and summer ranges, and seasonal spring and fall migration using net-squared displacement techniques (Bunnefeld et al. 2011). Fall and spring migration locations are used for the migration route analysis. After individual mule deer spring and fall migration locations are determined, a Brownian Bridge Movement Model (BBMM, Horne et al. 2007) is used to estimate the individuals Utilized Distribution (UD) during the seasonal migrations. Depending of the frequency of the location data, either a BBMM or a Forced Motion Variance model (FMV) are used as an estimate of that season’s migration UD. If locations collected at a < 7hr schedule, the migration used BBMM modeling techniques. If the schedule was greater than 7 hrs a FMV modeling technique was used (Fatteberge et al, in review). Further, FMV techniques that allowed for a 14 hour gap in location schedule were preferred over FMV models that used a maximum of 27 hr gap. When an individual had several seasonal migrations, the resulting UDs distributions are combined and averaged to create a single UD of all the seasonal migrations conducted by that individual. Individual UDS are then combined for all individuals in the winter herd with available UD information. For migration routes, the following classes were delineated based on the area’s use across the winter herd, used by 1 individual, used by 2individuals to 10% of the winter herd, 10 to 20% use of the winter herd, and greater than 20% use by the winter herd. The combined individual UDS are aggregated to estimate winter herd stopover locations. From the combined winter herd UD, the top 10% of recorded values are selected to represent population level stopovers.Palisades Mule Deer Migration StatisticsAnalyzed/Prepared by: Jodi Berg and Scott BergenDecember 2020Spatial MetricsAverage length of Migration: 28.0 milesMaximum Migration Length: 53.7 milesMinimum Migration Length: 6.2 milesTotal Migrations Analyzed 28Total Number of Individuals: 21Total Number Spring Migrations: 17Total Number Fall Migrations: 11Of 28 individual seasonal migrations, 2 used Brownian bridge movement models with an 8-hour time-lag, 5 used forced motion variance (1000 m) models with a 14-hour time-lag, and 21 used force motion variance (1000 m) models with a 27-hour time-lag.Temporal Data Extent of Study: April 23, 2013 – December 20, 2018Spring MigrationFall MigrationStart Date AverageApril 30November 9 Minimum April 4September 20 MaximumJune 5December 14End Date AverageMay 30December 6 MinimumMay 5November 16 MaximumAugustDecember 20Duration Average1614 Minimum22 Maximum6552Migration Use Class StatisticsUse ClassAcres 1 individual176,084 Low (>2 individuals)88,411 Medium (10-20%)47,624 High (>20%)21,869 Stopover17,900

The project leads for the collection of this data were Julie Garcia and Evan King. Mule deer (47 adult females) from the Manache herd were captured and equipped with Lotek LiteTrack Iridium collars, transmitting data from 2020-2021. GPS fixes were set for 2-hour intervals. The Manache herd migrates from winter ranges just west of Route 395 on the steep slopes and valleys of the Sierra Nevada range near Dunmovin and Haiwee eastward to some of the higher altitude terrain in the continental USA in Inyo and Sequoia National Forests. Due to a high percentage of poor fixes, likely due to highly variable topographic terrain, between 2-18 percent of GPS locations per deer were fixed in 2-dimensional space and removed to ensure locational accuracy. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors and stopovers. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 39 migrating deer, including 97 migration sequences, _location, date, time, and average _location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 7.95 days and 14.23 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. Separate models using Brownian bridge movement models (BMMM) and fixed motion variances of 1000 were produced per migration sequence and compared for the entire dataset, with best models being combined prior to population-level analyses (31 percent of sequences selected with BMMM). Corridors were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Winter range analyses were based on data from 42 individual deer and 96 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Additional migration routes and winter range areas likely exist beyond what was modeled in our output.Corridors are visualized based on deer use per cell in the BBMMs, with greater than or equal to 1 deer, greater than or equal to 4 deer (10 percent of the sample), and greater than or equal to 8 deer (20 percent of the sample) representing migration corridors, moderate use, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.

This layer shows critical and non-critical winter and summer ranges, fall holding areas, fawning grounds and migration corridors for deer (Odocoileus hemionus) in CDFW Region 2, North Central Region, Ca. In 1990, CDFW Wildlife Biologists compiled these data from the CDFW Migratory Deer Herd Management Plans, biotelemetry studies, personal knowledge, and predicted use of habitats. These data were subsequently digitized onto USGS 15' quadrangle maps to produce this dataset.

The Silver City mule deer winter east of Silver City, Idaho, along Sinker Creek and west of Idaho State Highway 78 (Figure 1). This herd generally spends the summer at higher elevations in Game Management Unit (GMU) 40 south and west of the historic mining towns of Silver City, Idaho, and De Lamar, Idaho. Migration distances averaged 17 mi (27 km), but some migrations were considerably longer (>40 mi) and spilled into GMU 41, as far south as the basin of Little Jacks Creek. The Silver City mule deer herd shares summer ranges in the Owyhee Mountains with several distinct mule deer herds, which winter in eastern Oregon. Summer habitats comprise mixed-conifer forest, aspen, and some mountain mahogany-woodland communities, whereas their migration and winter range habitats comprise a mix of Wyoming big sagebrush steppe, invasive annual grasses, and juniper woodland. Seasonal migrations near Silver City, Idaho, span Federal, State, and private land ownership. The area includes important mineral reserves and potential renewable energy sources, and provides abundant recreational opportunities, so mitigating adverse effects from these land uses may be an important consideration for environmental planning.

CDFW BIOS GIS Dataset, Contact: CWHR California Wildlife Habitat Relationships, Description: Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for California's wildlife.

This analysis uses location data collected on mule deer that were fitted with GPS collars in Idaho for 2003 – 2019. Individuals using a winter range (as defined as a winter herd), were used for the analysis if their location data was available at the time of the analysis. Each individual’s location dataset is used to estimate winter and summer ranges, and seasonal spring and fall migration using net-squared displacement techniques (Bunnefeld et al. 2011). Fall and spring migration locations are used for the migration route analysis. After individual mule deer spring and fall migration locations are determined, a Brownian Bridge Movement Model (BBMM, Horne et al. 2007) is used to estimate the individuals Utilized Distribution (UD) during the seasonal migrations. Depending of the frequency of the location data, either a BBMM or a Forced Motion Variance model (FMV) are used as an estimate of that season’s migration UD. If locations collected at a < 7hr schedule, the migration used BBMM modeling techniques. If the schedule was greater than 7 hrs a FMV modeling technique was used (Fatteberge et al, in review). Further, FMV techniques that allowed for a 14 hour gap in location schedule were preferred over FMV models that used a maximum of 27 hr gap. When an individual had several seasonal migrations, the resulting UDs distributions are combined and averaged to create a single UD of all the seasonal migrations conducted by that individual. Individual UDS are then combined for all individuals in the winter herd with available UD information. For migration routes, the following classes were delineated based on the area’s use across the winter herd, used by 1 individual, used by 2individuals to 10% of the winter herd, 10 to 20% use of the winter herd, and greater than 20% use by the winter herd. The combined individual UDS are aggregated to estimate winter herd stopover locations. From the combined winter herd UD, the top 10% of recorded values are selected to represent population level stopovers. Morgan Creek Migration Statistics:Analyzed/Prepared by: Jodi Berg and Scott BergenDecember 2019Spatial Metrics:Average length of Migration: 25.7milesMaximum Migration Length: 87 milesMinimum Migration Length: 5.5 milesTotal Migrations Analyzed: 114Total Number of Individuals: 39Total Number Spring Migrations: 66Total Number Fall Migrations: 48Of 114 individual seasonal migrations 1 used Brownian bridge movement models, 66 used forced motion variance (1000m) with a 14 hour time-lag, and 48 used force motion variance (1000M) with a 27 hour time-lag.Temporal Data: Extent of Study: May 13 2012 to December 31 2018Spring MigrationFall MigrationStart Date AverageApril 30October 29 Minimum March 8September 9 MaximumJune 26March 4End Date AverageMay 16December 23 MinimumMarch 14October 12 MaximumJune 29March 14Duration Average16.2 days54.6 days Minimum1.6 day2.7 days Maximum48.2 days163.5 daysMigration Use Class Statistics:Migration Use Class:Acres 1 individual548,599 >2 indv – 10%212,486 Medium (10-20%)61,529 High (>20%)16,902 Stopover36,399

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Vector data showing areas of dense oil and gas development that mule deer are expected to avoid, for twelve study sites in the Book Cliffs region in Utah.

The project lead for the collection of this data in California was Terri Weist. She, along with Danielle Walsh, Shelly Blair, and other personnel, captured 30 adult female mule deer from July 2012 to November 2014, equipping the deer with Iridium satellite collars manufactured by Lotek. The data was collected from the interstate Carson River herd, where a portion of the population spends the summer months in the Sierra range of California and the winter months in western Nevada. An additional 57 deer were collared in Nevada and provided by Cody Schroeder of the Nevada Department of Wildlife. Summer range is mostly within Alpine County, California, but also extends into El Dorado County and Mono County. Winter range is confined to the California-Nevada border area in Alpine County, CA. and Douglas County, NV. GPS location data was collected between February 2012 to July 2019. Between 2 and 12 location fixes were recorded per day, with a maximum of a fix taken every 2 hours during migration sequences. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors in a single deer population. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 45 deer, including location, date, time, and average location error as inputs in Migration Mapper. Due to the large study area and a concentration of deer movement east of Lake Tahoe in the Carson Range, the population was split into two distinct sub-herds. Twenty deer contributing 52 migration sequences were used in the modeling analysis for the Carson Range. Twenty-five deer contributing 58 migration sequences were used from the rest of the population surrounding the Carson Valley. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Winter range analyses were based on data from 48 individual deer and 92 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd would likely expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell, with greater than or equal to 1 deer, greater than or equal to 2 deer (10% of the sample), and greater than or equal to 4 deer (20% of the sample) from the Carson Range dataset and greater than or equal to 1 deer, greater than or equal to 3 deer (10% of the sample), and greater than or equal to 5 deer (20% of the sample) from the Carson Valley dataset representing migration corridors, moderate use, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Winter range is visualized as the 50thpercentile contour of the winter range utilization distribution.

The project leads for the collection of this data were Sara Holm and Julie Garcia. Mule deer (11 adult females) from the Downieville-Nevada City herd were captured and equipped with Lotek Iridium Track MGPS collars, transmitting data from 2018-2021. GPS fixes were between 11-14 hours. The Downieville-Nevada City herd migrates from winter ranges in the western foothills of the Sierra Nevada range north and east of Nevada City to high altitude terrain near Rattlesnake Mountain north of Interstate 80 and Jackson Meadows Reservoir. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 8 migrating deer, including 19 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 14.11 days and 32.18 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to the majority of BBMMs producing variance rates greater than 8000, a fixed motion variance of 1000 was set per migration sequence. Winter range analyses were based on data from 8 individual deer and 10 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. This collar project was not specifically designed to pinpoint precise migration routes or winter range designations, hence the low sample size. Additional migration routes and winter range areas likely exist beyond what was modeled in our output.Corridor tiers (low, medium, high) could not be computed with such a small dataset. Therefore, all corridors were given the same weight and designation in this analysis. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.

This is an interactive story map with information regarding the Wyoming Mule Deer Migration Corridor Risk Reduction as published by the Game and Fish Department.

South of Interstate 40 mule deer reside in Game Management Units (GMU) 8 and 6B in Arizona. The herd summers in high-elevation open meadows and ponderosa pine habitat southwest of Flagstaff, Arizona. In late October, the herd migrates west to lower elevation pinyon-juniper and shrub habitats near the junction of Interstate 40 and U.S. Highway 89. With funding support by the U.S. Department of the Interior (USDI) through Secretarial Order 3362, research on this herd’s migration began in February 2020. Additional GPS collars were deployed in January 2022 with support from the U.S. Forest Service, Mule Deer Foundation, and other partners. Primary threats to the herd’s migration involve high volume roads including Interstate 40, and U.S. Highways 89 and 89A. These mapping layers show the location of the migration routes for mule deer (Odocoileus hemionus) in the South of I-40 population in Arizona. They were developed from 20 migration sequences collected from a sample size of 7 adult ...

The project leads for the collection of these data were David Casady (CDFW) and Heiko Wittmer (Victoria University of Wellington). Black-tailed deer (65 adult females) from the Mendocino/ Clear Lake/ Alder Springs herd complex (herafter: Mendocino herd) were captured and equipped with store-onboard GPS collars (Lotek Wireless models 3300 and 4400 M, Telonics model TGW-3500), transmitting data from 2004-2013. An additional 24 female black-tailed deer were captured from the Mendocino herd and fit with Lotek Iridiumtrack M GPS collars, transmitting data from 2017-2021. The project lead for this overlapping dataset was Josh Bush (CDFW). Mendocino mule deer exhibit variable movement patterns and strategies. This population includes traditional seasonal migrants, full-time residents, and multi-range migrants (i.e., deer with long-term spring and/or fall stopovers). Full-time residents were excluded from the analysis, but individual deer exhibiting any type of directed movement between high-use ranges were considered a migrant and included. Based on this analysis, the portion of the population that migrates between seasonal ranges does so from a multitude of lower elevation areas within the mountainous Mendocino National Forest in winter to higher elevation summer ranges. Migrants vary in their movements from shorter (2 km) to longer (25 km) distances. While this analysis clearly demonstrates certain movement corridor areas with higher concentrations of migrating deer, with a larger dataset, local biologists predict high-use winter ranges throughout valley bottoms in Mendocino National Forest, and possible high fidelity to summer range sites for individual deer in the area. Numerous black-tailed deer papers have been published as a result of this data collection effort (Casady and Allen 2013; Forrester et al. 2015; Lounsberry et al. 2015; Marescot et al. 2015; Bose et al. 2017; Bose et al. 2018; Forrester and Wittmer 2019).GPS locations were fixed between 1-13 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 50 migrating deer, including 125 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The dataset was divided into four overlapping subgroups (i.e., north, central, south, east) and analyzed separately, but visualized together as a final product. The average migration time and average migration distance for deer was 7.43 days and 11.22 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. Corridors were best visualized using a 200 m buffer around the lines due to large Brownian motion variance parameters per sequence. Winter ranges and stopovers were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 400. Winter range analyses were based on data from 45 individual deer and 65 wintering sequences. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell, with greater than or equal to 1 deer, greater than or equal to 3 deer (10% of the subgroup sample), and greater than or equal to 5 deer (20% of the subgroup sample) representing migration corridors, moderate use corridors, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.

The project leads for the collection of this data were Julie Garcia and Richard Shinn. Female mule deer were captured in February 2017 and equipped with satellite collars manufactured by Lotek. Location fixes were collected from these collars between 2017 and 2020. Additional GPS data was collected between 1999-2001 from deer captured in 1999. The earlier dataset was included in the analysis to supplement the small sample size of the 2017-2020 dataset. The data was collected from deer throughout Modoc County with a priority to ascertain general distributions, survival, and home range, and not to model migration routes, hence the low sample sizes. Deer with overlapping winter ranges were defined as from the same herd. The Modoc Interstate deer herd migrates from a winter range near Clear Lake Reservoir in Modoc County, California north into Oregon in Klamath and Lake counties for the summer. GPS locations were fixed at 12-hour intervals in the 2017-2020 dataset and 8-hour intervals in the 1999-2001 dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 21 migrating deer, including 52 migration sequences. Resident deer with winter ranges overlapping those of migrant deer were removed from the analysis; only migrants were used in the mapping of corridors, stopovers, and winter ranges. GPS locations, date, time, and average location error were used as inputs in Migration Mapper. Sixteen migration sequences from 12 deer, with an average migration time of 23.89 days and an average migration distance of 69.71 km, were used from the 1999-2001 dataset. Thirty-six migration sequences from 9 deer, with an average migration time of 19.53 days and an average migration distance of 87.57 km, were used from the 2017-2020 dataset. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 1000. Winter range analyses were based on data from 20 individual deer and 32 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd would likely expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell, with greater than or equal to 1 deer, greater than or equal to 3 deer (10% of the sample), and greater than or equal to 5 deer (20% of the sample) representing migration corridors, moderate use, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50thpercentile contour of the winter range utilization distribution.