Mule Deer Migration Corridors - Mendocino - 2004-2013, 2017-2021 [ds3014]

<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span><span>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).</span></span></p><p style='margin:0 0 11 0;'><span><span>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. </span></span></p><p style='margin:0 0 11 0;'><span><span>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.</span></span></p><p style='margin:0 0 11 0;'><span><span>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 m</span></span><span><span>2</span></span><span><span> 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><span><span>th</span></span><span><span> percentile contour of the winter range utilization distribution.</span></span></p></div></div></div>

Tags: Auth_CDFW, Brownian bridge movement model, CAOpenData, CDFW, California Department of Fish And Wildlife, California Natural Resources Agency, DS3014_20221207_wm, GPS, Odocoileus hemionus, connectivity, migration mapper, mule deer, stopover, telemetry, winter range
Organization: California Department of Fish and Wildlife