Understanding environmental patterns of canid predation on white-tailed deer (Odocoileus virginianus)

Abstract

The outcome of encounters between predators and prey affects predation rates and ultimately population dynamics. Determining how environmental features influence predation rates helps guide conservation and management efforts. We studied where gray wolves (Canis lupus Linnaeus, 1758) and coyotes (Canis latrans Say, 1823) killed white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) in northern Wisconsin, USA. We monitored 499 white-tailed deer for cause-specific mortality between 2011 and 2014 using VHF radio collars. We investigated the locations of 125 deer mortalities and determined that 63 were canid (wolf or coyote) kill sites. We analyzed spatial patterns of kill sites using resource selection functions in a model selection framework, incorporating environmental variables including vegetative cover, human development, snow depth, and water. We found no evidence that vegetative cover or human development affected predation risk; however, we did find that increasing snow depth resulted in increased relative predation risk. This finding is consistent with existing research on the influence of snow cover on white-tailed deer survival. Our results suggest that understanding the spatial and temporal patterns of white-tailed deer predation requires a better understanding of snow depth variation in space and time. As climate change scenarios predict changes in snowfall throughout the northern hemisphere, understanding the effect on predator–prey spatial dynamics will be important for management and conservation efforts.

Interactive effects of wildfires, season and predator activity shape mule deer movements

“…We used a general linear model to test if deer were more likely to die from predation in burns relative to unburned areas. We coded each deer mortality location as ones and paired these with 20 locations randomly selected from their used telemetry points, which were coded as zeros following Olson et al (2021). Locations were excluded for the first 3 weeks post capture so any deer dying in this window were likewise excluded.…”

“…Studies from Isle Royale, USA (Post et al, 1999) and Banff National Park, Canada (Hebblewhite, 2005) have shown that snowpack strongly influences an ungulate's ability to evade predators, favouring predators over prey in deep, low‐density snow that may accumulate in recent burns. For example, deeper snow increased rates of predation from wolves and coyotes ( Canis latrans ) on white‐tailed deer ( Odocoileus virginianus ) because the higher ungulate foot load caused deer to sink deeper into the snow than carnivores, impeding escape from predators (Nelson & Mech, 1986; Olson et al, 2021). Because adult ungulates suffer the highest rates of predation mortality in winter relative to other seasons, snowpack characteristics could strongly influence their populations (Brodie et al, 2013; Cosgrove et al, 2021; Forrester & Wittmer, 2013).…”

A phenology of fear: Investigating scale and seasonality in predator–prey games between wolves and white‐tailed deer

“…Thus, by allowing wolves to move “faster and farther” (Dickie et al, 2017), linear features may be useful for increasing access to un‐wary prey and allowing wolves to dilute risk‐response over broader extents. Importantly, previous in‐state research suggests the location and timing of deer mortality has little connection to forest cover or linear features, but instead closely tracks snow dynamics (Norton et al, 2021; Olson et al, 2021). That is, deer were not most likely to be killed in the contexts used most frequently by wolves or the contexts where they exhibited the strongest risk‐response behaviors.…”

“…Our study encompassed much of Wisconsin and a small portion of Michigan, USA. Canids are the primary predators of adult deer here (Norton et al, 2021; Olson et al, 2021), although coyote predation is believed to be incidental. While more common than wolves, coyotes may pose less risk to deer, and we employed them as a contrast, expecting deer to respond less strongly to coyotes (Lima & Bednekoff, 1999; Schuttler et al, 2017).…”

Virtual snow stakes: a new method for snow depth measurement at remote camera stations

“…Because multiple objects can be overlaid using the edger package developed for the method, snow can be measured at one or multiple points in an image depending on the scale of data needed for a particular research question. The fine‐scale data will be useful for multiple natural resources disciplines and allow for new inferences into wildlife habitat selection and survival and forest‐snow interactions (Olson et al 2021).…”