Snow depth, temperature, and residual forage experienced by migrating mule deer during autumn (2011–2020), Wyoming, USA

Animal capture and handling From 2014–2020, we captured n = 220 adult female mule deer (>1-yr-old) in the Red Desert via helicopter net-gunning (LaSharr et al., 2022). We outfitted all deer with store-on-board or iridium GPS collars that collected locations every 1–2 hours (Advanced Telemetry Systems, Inc., Isanti, MN, USA; LOTEK Wireless Inc., New Market, Ontario, CAN; Telonics Inc, Mesa, AZ, USA). We used GPS data from a previous study on the Sublette Herd (2011–2013; Sawyer et al., 2016) to analyze movement for an additional n = 27 adult female mule deer (< 1 yr old; n = 66 animal-years) which were outfitted with store-on-board GPS collars that collected locations every 3 hours (Telonics, Mesa, AZ, USA). All animal capture and handling protocols were approved by the Wyoming Game and Fish Department (Chapter 33-937) and an Institutional Animal Care and Use Committee at the University of Wyoming (Protocol 20131111KM00040, 20151204KM00135, 20170215KM00260, 20200302MK00411). Classification of migratory tactics and delineation of seasonal ranges We used net squared displacement (Bunnefeld et al., 2011) to determine start and end dates of migration, delineate migratory routes, and calculate net displacement between each GPS location and the start location of autumn migration. We used 95% kernel utilization distributions (Worton, 1989) to delineate summer ranges (end of spring migration–start of autumn migration). Following methods from Sawyer et al. (2016), we classified migratory tactics based on migration distance and where deer spent summer. Movement rate and stopover use For each animal-year, we divided migration distance (km; Euclidean distance between first and last locations of autumn migration) by duration to determine hourly and daily rates of movement. We used a 10% utilization distribution from a Brownian bridge movement model (Horne et al., 2007) to delineate high-use stopovers (≥ 3 days of use; Rodgers et al., 2021). Because some animals moved back and forth between adjacent stopovers, we aggregated stopovers that were within a 5-km radius to reduce probability of overestimating stopover use. References Bunnefeld, N., L. Börger, B. van Moorter, C. M. Rolandsen, H. Dettki, E. J. Solberg, and G. Ericsson. 2011. “A model-driven approach to quantify migration patterns: individual, regional and yearly differences.” Journal of Animal Ecology 80: 466–476. Horne, J. S., E. O. Garton, S. M. Krone, and J. S. Lewis. 2007. “Analyzing animal movements using Brownian bridges.” Ecology 88: 2354–2363. LaSharr, T. N., S. P. H. Dwinnell, B. L. Wagler, H. Sawyer, R. P. Jakopak, A. C. Ortega, L. Wilde, M. J. Kauffman, K. S. Huggler, P. W. Burke, M. Valdez, P. Lionberger, D. G. Brimeyer, B. Scurlock, J. Randall, R. C. Kaiser, M. Thonhoff, G. L. Fralick, and K. L. Monteith. 2022. “Evaluating risks associated with capture and handling of mule deer for individual-based, long-term research.” Journal of Wildlife Management 87: https://doi.org/10.1002/jwmg.22333. Rodgers, P. A., H. Sawyer, T. W. Mong, S. Stephens, and M. J. Kauffman. 2021. “Sex-specific migratory behaviors in a temperate ungulate.” Ecosphere 12: https://doi.org/10.1002/ecs2.3424. Sawyer, H., A. D. Middleton, M. M. Hayes, M. J. Kauffman, and K. L. Monteith. 2016. “The extra mile: ungulate migration distance alters the use of seasonal range and exposure to anthropogenic risk.” Ecosphere 7: https://doi.org/10.1002/ecs2.1534. Worton, B. J. 1989. “Kernel methods for estimating the utilization distribution in home-range studies.” Ecology 70: 164–168.