Mule Deer Migration Corridors - Bucks Mountain-Mooretown - 2018-2020 [ds2972]

<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span style='font-size:10pt'>The project leads for the collection of this data were Sara Holm and Julie Garcia. Mule deer (4 adult females) from the Bucks Mountain-Mooretown herd were captured and equipped with Lotek Iridium Track MGPS collars, transmitting data from 2018-2020. GPS fixes were between 12-13 hours. The Bucks Mountain-Mooretown herd migrates from winter ranges in the American Valley, Crescent Hill, and various high elevation locations of the Sierra Nevada range east and south to even higher altitude terrain in Plumas National Forest. 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. </span></p><p style='margin:0 0 11 0;'><span style='font-size:10pt'>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 4 migrating deer, including 11 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 13.09 days and 27.77 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 4 individual deer and 6 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.</span></p><p style='margin:0 0 11 0;'><span style='font-size:10pt'>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 m</span><span style='font-size:10pt'>2 </span><span style='font-size:10pt'>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 50</span><span style='font-size:10pt'>th </span><span style='font-size:10pt'>percentile contour of the winter range utilization distribution.</span></p></div></div></div>

Tags: Auth_CDFW, Brownian bridge movement model, CAOpenData, CDFW, California, California Department of Fish And Wildlife, California Natural Resources Agency, DS2972_20220204_wm, GPS, Odocoileus hemionus, Plumas, connectivity, migration, migration mapper, mule deer, stopover, telemetry, winter range
Organization: California Department of Fish and Wildlife