The raw dataset consisted of GPS way points collected from Advanced Telemetry Solutions (ATS) Iridium LITE/GPS model G2110L collars with SureDrop collar break off mechanisms, or Tellus small iridium collars equipped with Tellus RL-Drop off on mule deer in the upper San Joaquin River watershed. Migratory deer within the San Joaquin Watershed occupy most of the watershed above Kerckhoff Reservoir, Fresno and Madera Counties, California. The data was collected from 2013-2016 by Tim Kroeker. Fix rates varied between 2 and 12 hours. Human infrastructure in the watershed is widespread and includes residential, water control, hydroelectric power, and recreational use developments. Steep topography between winter and summer range limit crossing points along the San Joaquin River. Habitat conditions favoring deer declined from a peak around 1950, resulting in a reduction in the deer population. The current deer population is believed to be about 4,000. A massive wildfire burned through most of the watershed in 2020, dramatically changing habitat conditions in some areas. To improve the quality of the data set as per Bjørneraas et al. (2010), theGPS 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 30 deer, including location, date, time, and average location error as inputs in Migration Mapper. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 30 m with a fixed motion variance parameter of 1000 using a sequential fix interval of less than 27 hours. Winter range analyses were based on data from 32 individual deer. A separate BBMM was created for all deer locations designated as winter range using a fixed motion variance parameter 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 in the BBMMs, with greater than 1 deer, greater than or equal to 3 deer (10% of the sample), and greater than or equal to 6 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.