DOI: 10.5061/dryad.c866t1gdc
title: Snow depth, temperature, and residual forage experienced by migrating mule deer during autumn (2011–2020), Wyoming, USA

description: Growing evidence supports the hypothesis that temperate herbivores surf
 the green wave of emerging plants during spring migration. Despite the
 importance of autumn migration, few studies have conceptualized resource
 tracking of temperate herbivores during this critical season. We adapted
 the Frost Wave Hypothesis (FWH), which posits that animals pace their
 autumn migration to reduce exposure to snow but increase acquisition of
 forage. We tested the FWH in a population of mule deer in Wyoming, USA by
 tracking the autumn migrations of n = 163 mule deer that moved 15–288 km
 from summer to winter range. Migrating deer experienced similar amounts of
 snow but 1.4–2.1 times more residual forage than if they had naïve
 knowledge of when or how fast to migrate. Importantly, deer balanced
 exposure to snow and forage in a spatial manner. At the fine scale, deer
 avoided snow near their mountainous summer ranges and became more
 risk-prone to snow near winter range. Aligning with their higher tolerance
 of snow and lingering behavior to acquire residual forage, deer increased
 stopover use by 1 ± 1 day (95% CI) day for every 10% of their migration
 completed. Our findings support the prediction that mule deer pace their
 autumn migration with the onset of snow and residual forage but refine the
 FWH to include movement behavior en route that is spatially dynamic.
description: Animal capture and handling From 2014–2020,
 we captured <em>n =</em> 220 adult female mule deer
 (&gt;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 (&lt; 1 yr
 old; <em>n</em> = 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.
description: Title of Dataset: Snow depth, temperature, and residual forage experienced
 by migrating mule deer during autumn (2011–2020), Wyoming, USA --- This
 dataset contains the following variables for n = 163 unique adult female
 mule deer (n = 334 animal-years) that migrated 15–288 km between their
 summer and winter ranges in western Wyoming, USA: 1. Mean snow depth (m)
 and residual forage (lbs/acre) experienced by each migratory tactic and
 their null counterpart at each kilometer along the migratory route between
 summer and winter range 2. Mean snow depth (m) and residual forage
 (lbs/acre) experienced by empirical deer and their null counterpart
 throughout the entire autumn migration 3. Mean daily minimum temperature
 (degrees C) and mean daily snow depth (m) on the summer ranges of long-,
 medium-, and short-distance migrants between July 1 and December 31 4.
 Mean daily NDVI (scaled between 0 and 1) on the summer ranges of long-,
 medium-, and short-distance migrants between January 1 and December 31 5.
 Mean snow depth (m) and residual forage (lbs/acre), along with the
 selection ratio of snow depth and residual forage, experienced by
 long-distance migrants on high-use stopovers and over their entire
 migration 6. Mean snow depth (m) experienced by long-distance migrants and
 the maximum snow depth (m) at each kilometer along the migratory route
 between summer and winter range ## Description of the Data and file
 structure Each sheet in the excel file represents the data used to create
 the figures in the manuscript. Depending on the sheet, each row in the
 excel file may represent the environmental variables experienced by an
 animal-year or by the migratory tactic at a particular kilometer along the
 migratory route from 2011–2020. ## Sharing/access Information Links to
 other publicly accessible locations of the data: N/A Was data derived from
 another source? Yes. If yes, list source(s): Sawyer, H., Middleton, A. D.,
 Hayes, M. M., Kauffman, M. J. & Monteith, K. L. The extra mile:
 ungulate migration distance alters the use of seasonal range and exposure
 to anthropogenic risk. Ecosphere 7 (2016) doi:10.1002/ecs2.1534.