Results
SCAT-BASED PREDATOR RESOURCE SELECTION FUNCTIONS
We surveyed 183 km in 2013, 652 km
in 2014, and 405 km in 2015 for scats recording all predators, and an
additional 82 km in 2016 for cougar scats only, for a total of 1,322 km
over 125 transects. We used detections of 373 bear, 42 cougar, 223
coyote, and 470 wolf scats to determine RSFs. In all species, top RSFs
were better supported than both the respective null and full candidate
models with all 13 variables (Appendix S6, Supporting Information).
Bears selected against conifer
forests (β = -0.71, [95% C.I. -1.23, -0.19]), motorized trails (β =
0.86, [95% C.I. 0.41, 1.31]), and roads (β = 0.00005, [95% C.I.
0.00003, 0.00007]), and for areas with cutblocks (β = 0.84, [95%
C.I. 0.23, 1.45]), high NDVI (β = 0.0002, [95% C.I. 0.00007,
0.00033]), steep slopes (β = 0.02, [95% C.I. 0.019, 0.039]), and
non-motorized trails, particularly when farther from areas with
motorized trails (β = 0.00005, [95% C.I. 0.00001, 0.00009]; Table
1). Wolf scats most likely
occurred near waterways (β = -0.0001, [95% C.I. -0.00015,
-0.00005]), on gentler slopes (β = -0.04, [95% C.I. -0.06,
-0.02]) and non-motorized trails (β = 1.29, [95% C.I0.99, 1.59]),
but farther from motorized trails (β = 0.00005, [95% C.I. 0.00004,
-0.00006]; Table 1).
Equally supported models for coyotes included areas with gentler slopes
(β = -0.05, [95% C.I. -0.08, -0.02]) and vehicle-restricted trails
(β = 1.62, [95% C.I. 1.27, 1.97]), and areas farther from
vehicle-permitted trails (β = 0.00006, [95% C.I. 0.00004, 0.00008];
Table 1); we retained proportion of shrub cover (β = 2.63, [95% C.I.
0.21, 5.05]) because its confidence limits did not overlap zero.
Cougar scats were more likely in areas with less conifer forest cover (β
= -1.92, [95% C.I. -3.38, -0.46]) and high edge density (β = 8.39,
[95% C.I. 1.12, 15.56]) because the confidence limits of only these
two variables did not overlap zero.
Based on model predictions, both wolf and bear scats were more likely
found in the western portion of the study area along river drainages
than in the eastern portion of the study area. Around the Ya Ha Tinda
elk winter range, likelihood of scats was high for wolves but low for
bears (Figs 2a, 2d). Cougar scats increased from the west (low) to east
(high), corresponding with more forest edge in the forest-managed lands.
Probability of coyote scat occurrence was fairly consistent across the
study area (Figs 2b, c).
ELK PRESENCE IN PREDATOR SCATS
We analyzed the contents of 476 scats (130 bear, 33 cougar, 114 coyote,
and 199 wolf); 226 were analyzed via macroscopic analysis and the
remainder via DNA analysis. Elk was equally found in coyote (36% of
scats), wolf (38%), and cougar (46%) scats collected from across the
study area (Wilcoxon Rank Sum test; all pairwise P ≥ 0.27). Bear
scats contained elk less frequently compared to the three other
predators (19%, all pairwise P < 0.001). We did not
use herbaceous forage biomass and elk resource use (RUF) in the same
models for predicting elk in a scat, nor did we use slope, elevation,
and ruggedness in the same models because they were highly correlated
(r > 0.60)
The best-supported, predator-specific models predicting elk presence in
a scat included most consistently the positive effect of herbaceous
biomass, except for cougar; inclusion of other variables depended on the
predator species (Table 2). For bears, the top model included the
positive effect of herbaceous biomass (β = 0.061, [95% C.I. 0.03,
0.10]), and negative effect of open cover type (β = -4.83, [95%
C.I. -8.58, -1.63], Table 2). For wolves, we selected the model
including only herbaceous biomass (β = 0.21, [95% C.I. 0.16,
0.27]), terrain ruggedness (β = 0.84, [95% C.I. 0.53, 1.19]), and
percent of area covered by deciduous forest (β = -36.29, [95% C.I.
-56.85, -18.25]; Table 2) because the confidence limits of the beta
coefficient for burns included zero (Appendix S7, Supporting
Information). We selected the model for elk presence in coyote scat that
included the positive effects of herbaceous biomass (β = 0.048, [95%
C.I. 0.030, 0.070]) and distance to water (β = 0.00032, [95% C.I.
0.000060, 0.00058]) and the negative effect of road density (β =
-0.88, [95% C.I. -1.83, -0.13]; Table 2). The most uncertainty was
found in the models of elk presence in cougar scats largely because of
low sample size (Appendix S7, Supporting Information). Based on
parsimony, we selected the model with only forest edge density (β =
1.25, [95% C.I. 0.31, 2.49]; Table 2).
The western portion of the study area (i.e. , BNP) had low
relative probability of elk being in all predator scats. Predictions
from the above models indicated elk were most likely found in cougar
scats in the eastern part of the study area and wolf and coyote scats at
Ya Ha Tinda (Figs 2-3, Table 3).
SCAT-BASED PREDATION RISK TO ELK
In combining predator distribution and scat contents, the predation risk
to elk was highest from wolves and coyotes at the Ya Ha Tinda (Table 3)
where resident elk summer. Risk from bears was highest for areas west of
Ya Ha Tinda and at the Ya Ha Tinda. In contrast, risk from cougars was
widespread and relatively high only east of the Ya Ha Tinda (Table 3).
Highest total predation risk to elk from all predators occurred for the
YHT resident elk (Table 3). Rank correlation of the total risk (all
predators combined; Fig. 4) corresponded well with predation risk
inferred from kill sites (rs = 0.98, P< 0.0001; Appendix S8, Supporting Information).
Discussion
Because different factors may influence where prey are encountered and
killed (Hebblewhite, Merrill, & McDonald, 2005; Kauffman et al., 2007),
predictions of spatial risk based only on predator distribution may not
be sufficient to assess predation risk in terms of an actual mortality
event but this distinction is rarely acknowledged (Moll et al., 2017).
At the same time, knowing the likelihood of predators being present may
be sufficient to address questions regarding prey responses and
frequency of anti-predator behaviours (Robinson & Merrill,
2013).Indeed, Prugh et al., (2019) argued for even further expansion of
the stages of predation to include engaging, attacking, and surviving
given an encounter to adequately understand and address questions of
predation risk. However, quantifying these stages is challenging and
likely requires time-intensive, observational studies (Wikenros et al.,
2009; Tallian et al., 2017), or detailed fine-scale movements of both
predators and prey (Basille et al., 2015; Greggor et al., 2016). Where
risk of mortality is desired, the most common approach has been to
examine kill-sites. Kill-site data based on annual winter mortality
surveys or marked animals require extended periods and expense to
accumulate sufficient samples, giving an approach based on scats a
distinct advantage in assessing predation risk early in a study. It is
also advantageous in terms of being non-invasive and cost-efficient
(Wasser et al., 2004; Orkin et al., 2016), particularly a multi-predator
community because dogs can simultaneously find scats from multiple
predators. At the same time, reliability of using fecal material to
estimate predator distribution has been questioned because of false
species identification and sampling biases (Morin et al., 2016; DeMatteo
et al., 2018). We found distinguishing between scats of bear species was
more difficult than anticipated and warrants DNA analysis to verify
species where the uncertainty needs to be eliminated. Biases in
detecting scats, can be addressed with refined training and handling of
dogs. For example, in the blind trials we performed, dogs had a high
(> 90%) detection rate of scats (Spilker, 2019). Potential
biases in sampling designs, such as collecting scats only on trails can
be problematic (Steenweg et al., 2015), but we remedied this by locating
transects on and off trails. Nevertheless, features such as steep cliffs
and rugged terrain can constrain sampling, whereas this may not be the
case if large carcasses can be observed during aerial flights.
A second issue for using scats for sampling the distribution of any
species relates to whether deposition reflects where they spend time,
and in the case of predators, where they make an actual kill. We present
evidence for scat location and contents corresponding to these two
components of predation because the factors we found associated with
scat locations and contents (i.e. elk) were similar to those
reported by others for both the distribution of these predators and for
elk kill sites. For example bear scats were associated with high forage
quality (NDVI) and quantity (cutblocks), similar to models for grizzly
bears where bears were associated with greenness and open canopy cover
(Nielsen et al., 2002; Apps et al., 2004). Wolf scats were associated
with flat areas (Hebblewhite & Merrill, 2007) and cougar scats were
associated with areas of high forest edge (Atwood et al., 2009; Elbroch
et al., 2013). Indeed, we found that for wide-ranging species like
wolves and grizzly bears, predictions from the scat-based RSFs
corresponded well with predictions from telemetry-based RSFs in the
study area (Appendix S2, Supporting Information). The ability of our
scat-based approach to make predictions similar to the kill-site
analysis also is not surprising because the approaches include the same
components of predation, i.e. , a risky place is one where there
is a high probability of encountering and being killed by a predator.
For example, elk select for areas with abundant forage biomass in summer
(Hebblewhite & Merrill, 2009; Smolko 2014), which is where we found a
higher probability of elk being present in the scat of all species; we
also found elk being in wolf scats associated with rugged terrain, which
is where Torretta et al., (2018) also reported wolf kill sites,
suggesting ruggedness reduced agility to navigate in steep terrain.
A major consideration in developing and applying estimates of spatial
risk is to appropriately match the approach to the spatial and temporal
scales for the processes and questions addressed (Moll et al., 2017;
Cusack et al., 2019; Prugh et al., 2019). For example, experimental
approaches such as giving-up densities (Altendorf et al., 2001) or
interactions caught on remote cameras (Hernández et al., 2005) may be
most appropriate to make fine-scale, behavioral or site-specific
inferences of predation risk, whereas simultaneously monitoring
sequential movements of predators and prey at short temporal scales may
lead to understanding how certain evasion tactics are successful in only
limited situations. Here, we show that a scat-based approach to
predicting predation risk for elk corresponds well with outputs derived
from modeling kill sites, but submit the approach is most appropriate
when directed at answering questions at broad spatial and temporal
scales (Orkin et al., 2016). For example, because it was not feasible to
collect sufficient scats except over a 12-week summer season, we gained
little insight into the seasonal dynamics of predation risk. At the same
time, the broad-scale spatial patterns in predation risk for elk we
found are consistent with our demographic understanding of predation
rates on both adult and calf elk in this area ( Hebblewhite et al.,
2018; Berg, 2019). Strengthening the link between risk of predation from
the perspective of the prey, as represented in the above metrics, and
kill or predation rates is a key next step for addressing questions of
predator-prey dynamics.
ConclusionWe illustrate a new approach for estimating predation risk to prey based
on distribution and contents of predator scats using scat dogs, and
found it corresponds well with the results of other approaches. It has
the advantage of being able to distinguish key components of predation,
such as where prey may encounter predators and where they are killed. It
can be used to sample broad areas over a relatively short time frame to
get a snapshot of spatial predation risk, which lends itself to repeat
sampling for detecting changes in spatial risk in the same area over
time. As with other methods, appropriate sampling design and reducing
uncertainty with observer training (e.g., dogs and handlers) and
auxiliary data such as DNA to confirm species identification will be key
considerations.