For many species, behavioral modification is an effective strategy to mitigate negative effects of harsh and unpredictable environmental conditions. When behavioral modifications are not sufficient to mitigate extreme environmental conditions, intrinsic factors may be the primary determinant of overwinter survival. We investigated how movement behavior, and internal (i.e., nutrition and age) and external (i.e., food availability and snow depth) states affect survival over winter of a long-lived and highly faithful species (mule deer; Odocoileus hemionus). We first tested if animals changed their behavior during winter based on internal and external states; we subsequently investigated how behavior and state interacted to influence survival risks in the face of extraordinary winter conditions. Movement behavior changed minimally as a function of age and nutrition; and yet, movement behavior affected survival—animals that exhibited more restricted movements were more likely to succumb to mortality overwinter compared with animals with less restricted movements. Additionally, nutrition and cumulative snow depth had a strong effect on survival; animals that were exposed to deep snow and began winter with low fat were much less likely to survive. Behavior was an effective tool in securing survival during mild or moderate winters, but nutrition ultimately underpinned survival during harsh winters.

We studied a population of mule deer that winters on two distinct winter ranges in the Wyoming and Salt River mountain ranges in western Wyoming, USA (42°25°N, 110°42°W) from December 2015 to March 2021. Adult, female mule deer were fit with GPS collars that recorded fixes every 2 hours.

We used data on movement, nutritional condition, age, and survival of adult (≥2 yrs old), female mule deer from December 2015 to March 2021. Beginning in December 2015, we captured 70 adult, female mule deer using helicopter net gunning. Each March and December following that initial capture, we recaptured all surviving individuals and captured new individuals to maintain a sample size of 70 animals. At the initial capture event, we extracted one insiciform canine to assess age of individuals using cementum annuli and assigned a unique animal identifier (e.g., “001”). At every capture event, we measured body mass, determined nutritional condition, and fit all animals with a GPS radio-collar programmed to take satellite fixes every two hours from December 2015 to March 2018 and every one hour from December 2018 to March 2021 (LaSharr et al. 2022). Nutritional condition of individuals was determined using developed protocols for mule deer which include measuring subcutaneous rump fat via ultrasonography and body palpation to estimate a body condition score (Stephenson et al. 2002; Cook et al. 2007). We calculated percent ingesta-free body fat (hereafter, body fat) of all animals (Cook et al. 2007). For each winter, animals were assigned a unique animal-year identifier. GPS collars recorded locations every 1 to 2 hours throughout winter. First, we subset all collars that had hourly relocation data to 2 hours. Next, we subset movement data for each winter for each individual from 1 December to 30 April. Because we were interested in movement on winter range, and not during migration, if animals were still migrating into the beginning of December, we began the subset on the day following their last migration date in autumn. If animals began migrating in April, we ended the subset the day before their first migration date in spring. If animals spent more than 50% of December or April migrating, we removed those animal-years from the analyses.