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.

The project leads for the collection of most of this data were Heiko Wittmer, Christopher Wilmers, Bogdan Cristescu, Pete Figura, David Casady, and Julie Garcia. Mule deer (82 adult females) from the Siskiyou herd were captured and equipped with GPS collars (Survey Globalstar, Vectronic Aerospace, Germany; Vertex Plus Iridium, Vectronic Aerospace, Germany), transmitting data from 2015-2020. The Siskiyou herd migrates from winter ranges primarily north and east of Mount Shasta (i.e., Shasta Valley, Red Rock Valley, Sheep Camp Butte, Sardine Flat, Long Prairie, and Little Hot Spring Valley) to sprawling summer ranges scattered between Mount Shasta in the west and the Burnt Lava Flow Geological Area to the east. A small percentage of the herd were residents. GPS locations were fixed between 1-2 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 67 migrating deer, including 167 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 12.09 days and 41.33 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 often produced BBMM variance rates greater than 8000, separate models using BBMMs and fixed motion variances of 1000 were produced per migration sequence and visually compared for the entire dataset, with best models being combined prior to population-level analyses (62 percent of sequences selected with BMMM). Winter range analyses were based on data from 66 individual deer and 111 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. 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 4 deer (10 percent of the sample), and greater than or equal to 7 deer (20 percent of the sample) representing migration corridors, medium 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 lead for the collection of this data was Richard Shinn. Pronghorn (30 adult females and 1 adult male) were captured and equipped with GPS collars (Sirtrack, Havelock North, NZ) transmitting data from 2014-2020. The Likely Tables herd contains migrants, but this herd does not migrate between traditional summer and winter seasonal ranges. Instead, much of the herd displays a somewhat nomadic migratory tendency, slowly migrating north for the summer using various high use areas as they move. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. A high use area being used during winter by many of the collared animals is west of the Warner Mountains, east of Highway 395, and north of the Modoc County line. Additionally, a few individuals persist east of Highway 395, seemingly separated from the rest of the herd. Summer ranges are spread out, with some individuals moving into the Modoc National Forest and as far north as Goose Lake. A few outliers in the herd moved long distances south or east. GPS locations were fixed between 1-4 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 pronghorn 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 the herd’s home range and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 17 migrating pronghorn, including 29 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for pronghorn was 15.42 days and 38.02 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 varying fix rates in the data, separate models using Brownian bridge movement models (BMMM), with an adaptable variance rate, and fixed motion variances of 1000 were produced per migration sequence and visually compared for the entire dataset, with best models being combined prior to population-level analyses (72% of sequences selected with BBMM). In general, fixed motion variances were used when BBMM variances exceeded 8000. Home range analyses were based on data from 20 pronghorn and 25 year-round sequences using a combination of BBMMs and fixed motion variances of 1000 (84% of sequences selected with BBMM). Home range designations for this herd may expand with a larger sample, filling in some of the gaps between home range polygons in the map. Large water bodies were clipped from the final outputs.

Corridors are visualized based on pronghorn use per cell, with greater than or equal to 1 pronghorn and greater than or equal to 3 pronghorn (20% of the sample) representing migration 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. Home range is visualized as the 50th percentile contour of the home range utilization distribution.