Defining the danger zone: Critical snow support thresholds for
predator-prey interactions
Abstract
Snowpack dynamics have a major influence on wildlife movement ecology
and predator-prey interactions. Specific snow properties such as
density, hardness, and depth determine how much an animal sinks into the
snowpack, which in turn drives both the energetic cost of locomotion and
predation risk. Here, we quantified the relationships between 15
field-measured snow variables and snow track sink depths for widely
distributed predators (bobcats [Lynx rufus], coyotes [Canis
latrans], wolves [C. lupus]) and sympatric ungulate prey (caribou
[Rangifer tarandus], white-tailed deer [Odocoileus virginianus],
mule deer [O. hemionus], and moose [Alces alces]) in interior
Alaska and northern Washington, USA. We first used generalized additive
models to identify which snow metrics best predicted sink depths for
each species and across all species. For species occurring in both
sites, we then tested whether the snow metric-sink depth relationship
differed across regions. Finally, we used breakpoint regression to
identify thresholds for the best-performing predictor of sink depth for
each species (i.e., values wherein tracks do not appreciably sink into
the snow). Near-surface (0-10cm) snow density was the strongest
predictor of sink depth across species. This relationship varied
slightly by region for wolves and moose but did not differ for coyotes.
Thresholds of support occurred at snow densities of 230 kg/m3 for
coyotes, 280 kg/m3 for bobcats, 290 kg/m3 for wolves, 340 kg/m3 for
deer, 440 kg/m3 for caribou, and 550 kg/m3 for moose. Together, these
critical thresholds define the bounds of “danger zones,” the range of
snow density in which carnivores have a comparative movement advantage
over ungulates. These results can be used to link predator-prey
relationships with spatially explicit snow modeling outputs and
projected future changes in snow density. As climate change rapidly
reshapes snowpack dynamics, these danger zones provide a useful
framework to anticipate likely winners and losers of future winter
conditions.