1. There is growing evidence that prey perceive the risk of predation and alter their behaviour in response, resulting in changes in spatial distribution and potential fitness consequences. Previous approaches to mapping predation risk across a landscape quantify predator space use to estimate potential predator-prey encounters, yet this approach does not account for successful predator attack resulting in prey mortality. An exception is a prey kill site that reflects an encounter resulting in mortality, but obtaining information on kill sites is expensive and requires time to accumulate adequate sample sizes.

2. We illustrate an alternative approach using predator scat locations and their contents to quantify spatial predation risk for elk (Cervus canadensis) from multiple predators in the Rocky Mountains of Alberta, Canada. We surveyed over 1300km to detect scats of bears (Ursus arctos/U. americanus), cougars (Puma concolor), coyotes (Canis latrans), and wolves (C. lupus). To derive spatial predation risk, we combined predictions of scat-based resource selection functions (RSFs) weighted by predator abundance with predictions that a predator-specific scat in a location contained elk. We evaluated the scat-based predictions of predation risk by correlating them to predictions based on elk kill sites. We also compared scat-based predation risk on summer ranges of elk following three migratory tactics for consistency with telemetry-based metrics of predation risk and cause-specific mortality of elk.

3. We found a strong correlation between the scat-based approach presented here and predation risk predicted by kill sites and (r = 0.98, P < 0.001). Elk migrating east of the Ya Ha Tinda winter range were exposed to the highest predation risk from cougars, resident elk summering on the Ya Ha Tinda winter range were exposed to the highest predation risk from wolves and coyotes, and elk migrating west to summer in Banff National Park were exposed to highest risk of encountering bears, but it was less likely to find elk in bear scats than in other areas. These patterns were consistent with previous estimates of spatial risk based on telemetry of collared predators and recent cause-specific mortality patterns in elk.

4. A scat-based approach can provide a cost-efficient alternative to kill sites of quantifying broad-scale, spatial patterns in risk of predation for prey particularly in multiple predator species systems.

Scat locations and scat contents [YHT_scat_locs_contents.csv]

We collected scats using scat-detection dogs along transects randomly located within a systematic grid of 57 5 x 5-km cells during 1 July – 30 September, 2013– 2016. Upon scat detection, we recorded age of scat, scat diameter and physical description to identify scats to species (Weaver & Fritts, 1979; Rezendes, 1992; Elbroch, 2003), and collected DNA on a subsample of scats to assess our species identification accuracy. Age of scats was adapted from Wasser et al. (2004) and included fresh to very old (Spilker 2019). We omitted old scats judged to be deposited prior to 1 May from all analyses. We combined grizzly and black bears into one ursid category because we found low accuracy in our ability to discriminate the two based on DNA (< 65% correctly classified, n = 24; Spilker, 2019).

We developed RSFs for predators (Manly et al., 2002), where ‘used’ samples were the locations of predator-specific scats along transect lines and ‘available’ samples were random locations within a 50-m x 1.3-km linear distance centered on the scat. We used this linear distance because it was the average distance moved by black bears in a 24-hour period, which was the shortest 24-hr movement distance among the carnivore species being analyzed, and it standardized availability among predators (Spilker 2019). The 50-m width reflected the estimated detection distance of dog of scats. We sampled 10 random (available) points within the buffer around each scat location for a density of ~ 0.8 random points per km², which is just under the 1 random point/km not uncommonly used in telemetry-based selection studies at the home-range scale (Hebblewhite and Merrill 2008; Mumma et al. 2017). We used an exponential RSF model deriving parameters using logistic regression (Johnson et al., 2006).

We analyzed the contents of a subset of scats detected randomly selected from those detected in 2013 - 2016 for the presence of elk hair using either macroscopic analysis (n = 226) or DNA analysis (n = 250). For macroscopic analysis, we randomly selected 20 hairs from each scat, prepared hairs using standard methods (Ciucci et al., 1996), and identified the species based on characteristics of the hairs’ medulla, cuticle scale patterns, and scale margin distance using dichotomous keys (Moore et al., 1974; Kennedy & Carbyn, 1981). Three trained observers who analyzed the scats were subject to blind trials on known hairs, obtaining a minimum of 80% correct classification rate prior to analysis.

DNA was extracted from hair shafts using QIAGEN’s DNeasy Tissue kits (QIAGEN Inc., Valencia, USA). Polymerase chain reaction (PCR) was used to amplify DNA and prey species identification was confirmed via a partial sequence analysis of a hypervariable region of the mitochondrial 16S rRNA gene. This approach identified the most dominant prey species in the scat (i.e., based on the proportion of DNA); mixed samples where there was no dominant species (or equal amounts of DNA from each species) were re-run with ungulate-specific primers to determine if elk DNA was present. 

Elk kill sites [elk_kills_2002to2016_summer.csv]

We captured and radio-collared adult female elk from 2001-2016 in February-March). During the years 2001-2012, captures were conducted using baited corral traps and aerial net-gunning. After 2013, all elk were captured using ground-darting from horseback. Elk were fitted with a Global Positioning System (GPS) and/or a Very High Frequency (VHF) collar to provide location and mortality data. Trained personnel visited mortality sites and determined cause of death as soon as mortality was detected (on average within 5.2 days (SE= 8.0), from Hebblewhite and Merrill (2011). Only predator kills that occured in summer months (May to September) are included in the dataset.