Influence of body mass and environmental conditions on winter mortality risk of a northern ungulate: Evidence for a late‐winter survival bottleneck

Abstract

A relationship between winter weather and survival of northern ungulates has long been established, yet the possible roles of biological (e.g., nutritional status) and environmental (e.g., weather) conditions make it important to determine which potential limiting factors are most influential. Our objective was to examine the potential effects of individual (body mass and age) and extrinsic (winter severity and snowmelt conditions) factors on the magnitude and timing of mortality for adult (>2.5 years old) female white‐tailed deer (Odocoileus virginianus [Zimmerman, 1780]) during February–May in the Upper Peninsula of Michigan, USA. One hundred and fifty deer were captured and monitored during 2009–2015 in two areas with varying snowfall. February–May survival ranged from 0.24 to 0.89 (mean = 0.69) across years. Mortality risk increased 1.9% with each unit increase in cumulative winter severity index, decreased 8.2% with each cumulative snow‐free day, and decreased 4.3% with each kg increase in body mass. Age and weekly snow depth did not influence weekly deer survival. Predation, primarily from coyote (Canis latrans [Say, 1823]) and wolves (Canis lupus [L., 1758]), accounted for 78% of known‐cause mortalities. Our results suggest that cumulative winter severity, and possibly to a lesser degree deer condition entering winter, impacted deer winter survival. However, the timing of spring snowmelt appeared to be the most influential factor determining late‐winter mortality of deer in our study. This supports the hypothesis that nutrition and energetic demands from weather conditions are both important to northern ungulate winter ecology. Under this model, a delay of several weeks in the timing of spring snowmelt could exert a large influence on deer survival, resulting in a survival bottleneck.

White‐Tailed Deer, Weather and Predation: a New Understanding of Winter Severity for Predicting Deer Mortality

“…However, elevated mortality rates during early spring from Walsh et al (2018) occurred when we documented little to no snow, relative to periods of deep snow in early winter. This finding is similar to Kautz et al (2020) and indicative of mortality risk being more related to poor nutritional condition of individuals than dangerous but short‐term snow conditions that hinder escape from predators.…”

“…Among these studies, however, those quantifying cold temperature tended to have a relatively minor or no effect on mortality, and no studies evaluated the effect of warming days when temperature was above freezing. Kautz et al (2020) reported that winter length, as quantified by the number of cumulative snow-free days from February 1 through May 31, was positively related to deer survival, a similar finding to our relationship with increasing number of days >0°C accumulated from January through May. Days above freezing, which typically accumulate during the spring thaw, are likely a better indicator of survival than days of extreme cold because they represent variation in winter length.…”

“…During the 2012–2013 winter, Walsh et al (2018) found juvenile mortality was elevated during intervals March–May, as winter conditions persisted into May. Similarly, Kautz et al (2020) tended to observe more mortality during late winter‐early spring than earlier in winter, and Fuller (1990) observed higher mortality rates during the months of February, March, and April than during early winter and summer months, suggesting lagged effects of winter. We concluded chronic severe weather throughout the winter resulted in the delayed mortality of deer rather than an acute effect when the weather occurred.…”

Quantifying behavior and life‐history events of an Arctic ungulate from year‐long continuous accelerometer data

“…This was supported by direct observations in one of the collared individuals, whose body weight declined from 217 to 97 kg from capture to death, corresponding to a 55% loss of body mass in 230 d (Schmidt et al 2020). It thus seem that our approach was able to provide insight into how fine-scale behavioral changes over time are linked to different fate outcomes, opening future applications of such datasets for understanding factors affecting animals' life-history events, such as adult mortality (Gunn et al 1989, Kautz et al 2020.…”

Interference competition between wolves and coyotes during variable prey abundance

“…We estimated probability of occurrence by adult female and fawn deer across the landscape using a resource utilization function (RUF; Marzluff et al., 2004) to regress the occurrence distribution (OD) of individual deer on landscape covariates thought to influence their use. To estimate ODs, we used VHF relocation data from radio‐collared adult female white‐tailed deer ( n = 113) captured using Clover traps (Clover, 1956) and neonate fawn deer ( n = 100) captured using vaginal implant transmitter guided searches or opportunistically during 2013–2015 (Kautz et al., 2019, 2020). We used Brownian bridge movement models (BBMM) in package “BBMM” (Nielson et al., 2013) for program R (version 3.01, R Development Core Team, 2018) to produce a 99% OD for each deer/time period (i.e., PPP, LMP, SMP) combination (Figure 1).…”