Introduction
Large herbivores, like most prey species, make substantial investments
in avoiding predation risk (Tolon et al., 2009). Risk of predation
influences large herbivore habitat selection, grouping dynamics, and
anti-predator behaviours (Hebblewhite et al., 2006; Christianson &
Creel, 2010). As a result, large herbivore prey are often faced with
making trade-offs in pursuing foraging opportunities while avoiding
areas of high predation risk (Creel et al., 2005; Hebblewhite &
Merrill, 2009).
Lima & Dill (1990) established a conceptual model of predation risk by
identifying two fundamental components of Holling’s (1959) disk equation
for the risk of a prey being killed per unit time:
P (death) = 1 - exp(-αd T) eqn 1
where α is the probability of encounter and d is the probability
of death given an encounter during time (T). Their approach considers
the two main stages of predation and highlights the conditional nature
of mortality on attacks. However, it does not explicitly account for how
predation risk may vary spatially.
Because spatial data for predator and prey are increasingly available,
predation risk to prey has been related to a predator’s abundance,
occurrence, intensity of use or resource selection (Theuerkauf & Rouys,
2008; Thaker et al., 2011; Moll et al., 2017). For example, White et al.
(2009) related wolf (Canis lupus ) density as a metric of
predation risk to changes in elk (Cervus canadensis ) nutrition in
Yellowstone National Park during and after wolf recolonization, whereas
predation risk from wolves and bears was estimated for a range of prey
using RSFs (Gustine et al., 2006). Hebblewhite & Merrill (2007)
combined these approaches by weighting RSFs of wolves by their spatial
abundance to reflect the importance of the numeric response in predation
risk. Although commonly used, these metrics ignore a key component of
predation — the probability of death given an encounter (Lima & Dill,
1990). Encounters, however, are extremely difficult to observe directly,
even indirectly (e.g ., Eriksen et al., 2009; Whittington et al.,
2011). As a result, studies have estimated risk of mortality solely
using prey kill sites (Smith et al., 2005). For example, Kauffman et al.
(2007) compared wolf kill sites to random locations in Yellowstone
National Park to identify landscape features associated with where elk
might be killed if visited. Disadvantages in using kill sites is that
they often are biased towards large prey that are more readily detected
(Webb et al., 2008; Bacon et al., 2011), and adequate samples take
considerable time to accumulate.
An alternative to kill sites is to combine spatial distributions of
predators and contents of their scats. Scat contents reflective a
predation event and the scat location indicates the broad-scale spatial
overlap of predators and prey. A scat-based approach may be advantageous
over telemetry-based kill sites because scats can be less invasive and
more cost efficient when using scat-detection dogs (Wasser et al., 2004;
Mumma et al., 2017), particularly in multi-predator communities. In this
paper, we compare a scat-based approach to quantifying spatial predation
risk from bears (Ursus arctos/U. americanus ), cougars (Puma
concolor ), coyotes (C. latrans ), and wolves for a partially
migratory elk population in the eastern slopes of the Rocky Mountains,
Canada. We compared predictions of predator-scat occurrence combined
with the relative probability of elk being in the scat to a model of
predation risk from elk kill sites. We used this approach to assess
expected differences in predation risk between migratory and resident
elk on their summer ranges.