DOI: 10.5061/dryad.8kprr4xsj
title: Migration statistics and animal biometrics for mule deer that migrated long-distances (2011–2020), Wyoming, USA

description: Billions of animals migrate to track seasonal pulses in resources.
 Optimally timing migration is a key strategy, yet the ability of animals
 to compensate for phenological mismatches en route is largely unknown. We
 studied a population of mule deer (Odocoileus hemionus) in Wyoming that
 lack reliable cues on their desert winter range, causing them to start
 migration 70 days ahead to 52 days behind the wave of spring green-up. By
 adjusting movement speed and stopover use, however, individual deer arrive
 at the summer range within an average 6-day window. Late migrants move 2.5
 times faster and spend 72% less time on stopovers than early migrants,
 which allows them to catch the green wave. Ungulates, and potentially
 other migratory species, possess cognitive abilities to recognize where
 they are in space and time relative to key resources. Such behavioral
 capacity may allow migratory taxa to maintain foraging benefits amid
 rapidly changing phenology.
description: <em><strong>Animal capture and
 handling</strong></em> From 2014–2020, we
 captured n = 220 adult female mule deer (&gt;1-yr-old) in the Red
 Desert near Rock Springs, Wyoming, USA (41° 35′N, 109° 12′W) as part of a
 long-term study. We recaptured deer each March and December for a total of
 n = 528 animal-years of data. All deer were captured via helicopter
 net-gunning<sup>1,2</sup>. Mule deer in this portion of the
 Sublette Herd migrate a variety of distances to their summer ranges in
 northwestern Wyoming<sup>3,4</sup>. Herein, we focused on n =
 72 long-distance migrants (n = 152 animal-years) that migrated 134–293 km
 and spent the summer north of Pinedale, WY (42° 51′N, 109° 51′W). During
 captures, we used an electronic platform scale (± 0.1 kg) to measure body
 mass (kg) and a portable ultrasound (Ibex, E.I. Medical Imaging, Loveland,
 CO) to measure maximum rump fat (mm). Following previously applied
 methods<sup>5</sup>, we used body mass, maximum rump fat, and
 a body-condition score to estimate percent-scaled ingesta-free body fat
 (IFBFat)<sup>5,6</sup>. For captures in March, we used an
 ultrasound to determine pregnancy, including fetal rate (number of fetuses
 per deer) and fetal development via measures of the fetal eye diameter
 (mm). To estimate the age of each deer, we extracted the lower right
 incisiform canine and used cementum annuli aging
 technique<sup>7–9</sup>, which was conducted by the Matson’s
 Laboratory in Manhattan, Montana, USA. From 2014–2020, we outfitted all
 deer with store-on-board or iridium GPS collars that collected locations
 every 1–2 hrs (Advanced Telemetry Systems, Isanti, MN, USA; Lotek
 Wireless, Newmarket, ON, CAN; Telonics, Mesa, AZ, USA). We also included
 GPS collar data from a previous study on the Sublette Mule Deer Herd
 (2011–2013)<sup>4</sup> to analyze movement for n = 27
 additional deer (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
 (20131111KM00040, 20151204KM00135, 20170215KM00260,
 20200302MK00411). <em><strong>Delineation
 of migratory routes and seasonal
 ranges</strong></em> We used Net Square
 Displacements (NSDs<sup>10</sup>) to determine the timing of
 spring and autumn migration, delineate migratory routes, and determine the
 net displacement (km) between the start of spring migration and each GPS
 location along the migratory route. We determined winter range use for
 each deer by extracting GPS locations between the end of autumn migration
 and the start of spring migration (or between the time of capture and
 start of spring migration if the end of autumn migration was unknown). We
 determined summer range use for each deer by extracting GPS locations
 between the end of spring migration and the start of autumn migration (or
 between the end of spring migration and time of collar failure or
 mortality on summer range). We used a 95% Kernel Utilization Distribution
 (KUD<sup>11</sup>) to delineate the winter range (41.63 ± 7.26
 km<sup>2</sup> [x̄ ± 95% CI]) and summer range (7.26 ± 1.61
 km<sup>2</sup>) of each animal-year. We removed 0.10%, 0.09%,
 and 0.24% of all GPS locations during migration, on winter range, and on
 summer range, respectively, because the movement rate between consecutive
 locations was greater than 10.8 km/hr and indicated an inaccurate GPS
 fix. <em><strong>Green wave
 surfing</strong></em> We evaluated the
 ability of mule deer to track green-up of plants during spring migration
 by analyzing the synchronicity between movement and peak IRG. We
 determined IRG by extracting the first derivative of double-logistic
 curves that were fitted to the annual time series of
 NDVI<sup>12</sup>. Days from peak IRG (hereafter referred to
 as Days-From-Peak) were calculated as the difference in days between the
 date of every GPS location for a deer and the date of peak IRG at the same
 GPS location<sup>12,13</sup>. A theoretically perfect surfer
 occupies a location on the same day that peak IRG occurs (Days-From-Peak =
 0)<sup>12</sup>. We calculated mean Days-From-Peak and IRG for
 each day and kilometer of an individual’s migration to reduce
 pseudoreplication and account for irregular GPS
 fixes<sup>12,13</sup>. We quantified an individual’s location
 on the green wave at the start and end of spring migration by calculating
 the difference in days between the mean date of peak IRG on each seasonal
 range and the date an individual departed their winter range or arrived at
 their summer range.
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