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19 August 2020 Seasonal and inter-annual variation in diet for gray wolves Canis lupus in Prince Albert National Park, Saskatchewan
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Plains bison Bison bison bison were extirpated from most of their historical range in the late nineteenth century, and few studies have examined the interactions of bison with gray wolves Canis lupus. The Sturgeon River plains bison (SRPB) population in Prince Albert National Park, Saskatchewan is one of only a few populations of plains bison in their historical range in Canada and have declined around 50% since 2005. This study examined the inter- and intra-annual variation in wolf diet using stable isotope analysis (SIA), to assess the importance of bison and other ungulates to wolf diet relative to the decline of the SRPB. We used wolf hair (n = 35) and blood (n = 29) collected from 30 individuals from 2011 to 2017 to estimate the diet of two packs for summer and winter, and visited potential wolf kill sites (n = 270) during the winter from 2013 to 2017 to collect prey samples. We used wolf scats (n = 465) collected in the winter and summer of 2012–2013 as priors for our Bayesian stable isotope mixing models. We found the percentage of bison (median range: 26–39%), deer/elk (21–24%) and moose (16–33%) consumed in the summer was consistently high, compared to winter when white-tailed deer Odocoileus virginianus comprised the highest percentage of wolf diet (40–49%). We observed small inter-annual variation in wolf diet. We examined differences between packs and found that wolves that had greater overlap with the SRPB had more bison in their diet, particularly in winter (26–40%). Results from SIA were consistent with percentages of prey found at wolf kill sites. Overall, bison constitute a lower proportion of wolf diet compared to other wild ungulates, and our findings support the assumption that wolf predation is not the main contributing factor to SRPB population decline.

Studying predator–prey dynamics requires an understanding of both predator diet and prey availability (Fryxell and Lundberg 1994, Křivan and Sikder 1999). Predators can affect other species directly through predation events or indirectly through competition or trophic facilitation with other predators, trophic cascades and apparent competition (Lima and Dill 1990, Schmitz et al. 2000, Wilmers et al. 2003, Kortello et al. 2007, Wittmer et al. 2013). Insights into predator–prey interactions are important for understanding the factors behind prey decline, particularly for small populations at risk of extinction (Sinclair et al. 1998, Wittmer et al. 2005, Mech and Fieberg 2014).

Gray wolves Canis lupus have been studied to examine both the direct and indirect effects of predation on other species, as well as conflicts with humans through predation on livestock (Wilmers et al. 2003, Kortello et al. 2007, Wittmer et al. 2013, Nelson et al. 2016, Santiago-Avila et al. 2018). Wolf predation has been studied on wild ungulates such as deer (Odocoileus spp.), elk Cervus elaphus, and moose Alces alces, and may influence population dynamics of prey (Huggard 1993a, Hebblewhite et al. 2002, Smith et al. 2004). Less-commonly studied are interactions between wolves and plains bison Bison bison bison, as there are fewer areas where they co-exist (Carbyn and Trottier 1988, Smith et al. 2000, Jung 2011, MacNulty et al. 2014, Tallian et al. 2017). Historically, bison numbered in the tens of millions but were nearly driven to extinction in the late nineteenth century due to overhunting and habitat loss (Samson and Knopf 1994, Isenberg 2001). Bison have since increased in abundance, but most herds consist of farmed or captive animals with cattle gene introgression (Halbert and Derr 2006, Freese et al. 2007). Therefore, studies investigating wolf predation on wild, genetically-pure bison are limited to only a few areas in North America (Samson and Knopf 1994, Smith et al. 2000, Freese et al. 2007, Harvey and Fortin 2013).

The diet of wolves can be estimated using direct observation of kills (Boyd et al. 1994, Sand et al. 2005), scat and stomach content analysis (Floyd et al. 1978, Ciucci et al. 1996, Merkle et al. 2009), and stable isotope analysis (SIA; Szepanski et al. 1999, Derbridge et al. 2012, Stanek et al. 2017, O'Donovan et al. 2018). SIA quantifies the change in isotopic ratios as nutrients are consumed, metabolized and reorganized at each trophic level, which allows for the relative proportion of each prey item to be determined for a consumer (DeNiro and Epstein 1978, 1981, Peterson and Fry 1987). SIA can examine diet at the individual, group or population level (Urton and Hobson 2005, Derbridge et al. 2012, O'Donovan et al. 2018). As well, tissues used in SIA can provide diet history spanning weeks (e.g. blood) to months (e.g. hair) to lifetimes (e.g. bone collagen), allowing researchers to explore temporal differences in diet (Chisholm et al. 1982, Tieszen et al. 1983, Hilderbrand et al. 1996, Darimont and Reimchen 2002, Hall-Aspland et al. 2005, Gómez et al. 2018).

We used SIA to examine seasonal and inter-annual variation in the diet of wolves for two packs that overlap with plains bison range in and around Prince Albert National Park (PANP), Saskatchewan. The Sturgeon River plains bison (SRPB) are one of only a few wild populations of plains bison in their historical range in Canada, and have declined by greater than 50% since 2005, when the population was estimated to be around 500 individuals (Merkle et al. 2015, Cherry et al. 2019). Current population estimates are lower than the target management threshold required to prevent losses to genetic diversity (Cherry et al. 2019). Disease and annual harvests have contributed to past bison mortality (Shury et al. 2009, Merkle et al. 2015), but the role of wolf predation in the SRPB decline is unclear. Population simulations for the SRPB indicate unsustainable harvest is likely the main factor limiting population recovery; however, these models assume predation rates similar to other bison populations (Cherry et al. 2019, Simon and Fortin 2019). No studies have directly investigated the role of predation by gray wolves on the SRPB population. As free-ranging populations of plains bison are reintroduced into their historical range, it is important to understand the effects of predation on bison population dynamics (Steenweg et al. 2016).

We hypothesized that wolf diet would vary seasonally, between summer and winter, due to variation in prey availability and vulnerability. We predicted that wolves would primarily consume wild ungulates in winter, as ungulate movements are inhibited by deep snow and body condition is generally poorer, making prey more susceptible to predation (Sweeney and Sweeney 1984, Telfer and Kelsall 1984). Pack cohesiveness is also higher during winter (Benson and Patterson 2014), potentially allowing wolves to consume larger prey such as bison (Zimen 1976, Metz et al. 2011). During summer, we expected beaver Castor canadensis to contribute more to wolf diet, as they are more accessible outside of their lodges. In summer, there is also overlap between pack territory and summer pasture of cattle, which may increase the occurrence of predation on livestock. We also hypothesized that diet would differ between wolf packs. Since wolves are territorial, and buffer zones that separate adjacent territories can be as wide as 1–2 km, overlap between wolves from different packs is uncommon and often results in inter-pack conflict (Mech 1977, White et al. 1996). We predicted bison would constitute a higher proportion of diet for wolves that show greater overlap with the SRPB range (Bergeson 1993).

Material and methods

Study area

Our study area was located in the southwest corner of PANP, where wolves overlap with plains bison range (centered at 53°72′46″N, 106°67′54″W; Fig. 1). The area is characteristic of an aspen parkland ecotone, with remnant fescue grassland in the southeast section of our study area. The climate is distinguished by long, cold winters and short, warm summers. There are two wolf packs in this area (Amyot and Nesslin), that have been monitored since 2006 using GPS-collars.

Wolf kill site visits and sample collection

Wolves were located and captured using a helicopter, and either physically restrained with a net-gun or chemically immobilized with Telazol (tiletamine and zolazepam) using a dart gun in November to April from 2011 to 2017 (Proulx et al. 2012). All captures and handling followed Parks Canada protocol and were approved by the Parks Canada Agency Animal Care Task Force (Permit numbers: 2011196-1, 2014009-1, 2014009-2, 2014009-3). We used wolf GPS-collar data (Argos- and Iridium-linked, Telonics Inc, Mesa, AZ, USA) from two wolf packs (Amyot and Nesslin) to track and identify kill sites (n = 270) where prey samples were collected (Irvine 2019). We identified kill sites using a rule-based algorithm programmed in Python TM language (Python Software, Hampton, NH, USA) that identified clusters of GPS points that were within 300 m and four days of each other (Sand et al. 2005, Knopff et al. 2009). This method can identify large-bodied prey consumed by wolves, and up to 83% of deer kills in other study systems (Webb et al. 2008). We were most interested in detecting bison (large-bodied) kills and so we preferentially visited larger clusters (three or more GPS points in a cluster). We waited a minimum of seven days between the initial cluster formation and our field visit, to ensure enough time had elapsed for the wolves to consume and scavenge their kill (Merrill et al. 2010). Kill sites were visited in November to March from 2013 to 2017.

Hair samples for deer, moose, bison and elk were collected by plucking from the carcass or from clumps of hair on the ground at the kill site. Hair from beaver was obtained from trappers west of PANP. Hair from cattle was collected from private landowners within the vicinity of PANP. Hair and blood from wolves were collected during GPS-collaring events from 2011 to 2017. Guard hairs were plucked from the back or shoulder and blood was drawn from the cephalic, saphenous or jugular vein of immobilized wolves. All samples were stored frozen (–20°C) until analysis.

Stable isotope analysis

SIA of blood and hair samples were performed at the Chemical Tracers Lab (University of Windsor, Windsor, ON, Canada). Blood samples were freeze-dried, homogenized and lipids were removed, since variation in lipid concentrations can influence measurements of carbon isotope ratios (Rau et al. 1992, Post et al. 2007, Logan et al. 2008). Two milliliters of a 2:1 chloroform methanol mixture was added to dried blood samples, which were then vortexed for two seconds and placed in a water bath for 24 h at 30°C. Samples were centrifuged, and the chloroform methanol solution was drained. Another two milliliters of the chloroform methanol solution was then added, and samples were centrifuged and drained again. We removed underfur from hair samples and rinsed samples under a ventilation hood in a 2:1 chloroform/methanol solution to remove fine debris and oils (Darimont et al. 2007). All samples were air dried before weighing.

Figure 1.

The approximate range and territory size of plains bison and gray wolves, respectively, in Prince Albert National Park. The summer and winter range of bison was estimated using 95% minimum convex polygons. Location data for bison were collected from GPS-collars deployed from 2011 to 2017 over both summer/autumn (June–October) and winter (November–March). Location data for wolves were collected from GPS-collars deployed from 2013 to 2017 in winter (November–March).


Samples were weighed (∼1 mg) and placed into tin capsules for continuous-flow mass spectrometry analysis using a Delta V Advantage mass spectrometer coupled to a Costech 4010 elemental combustion system and a ConFlo IV gas interface. During continuous-flow mass spectrometry analysis, samples were combusted, resulting in the separation of CO2 and N2, which we used to quantify isotopic ratios (Fry 2006). Isotope values are expressed in delta notation as:


where X is 13C or 15N, and R is 13C/12C or 15N/14N. The standards used in SIA are Pee Dee Belemnite limestone for carbon, and atmospheric N2 for nitrogen (DeNiro and Epstein 1978, DeNiro and Epstein 1981).

Statistical analysis and stable isotope mixing models

We used guard hairs and red blood cells (RBCs) for SIA of wolves. Isotopes in hair do not turnover, and thus reveal the diet of an individual when it is growing (Roth and Hobson 2000). Wolves undergo an annual moult in the spring, and guard hair is grown from summer to autumn (Young and Goldman 1964, Darimont and Reimchen 2002, Darimont et al. 2009). Therefore, guard hair reflects the summer and autumn diet of wolves. Cellular components of blood differ in their isotopic half-lives, with plasma reflecting diet integrated approximately one to two weeks before collection, and RBCs representing the diet of the previous several months (Hilderbrand et al. 1996, Thomas and Crowther 2015, Rode et al. 2016). Since we used RBC samples collected from wolves in November to April, we expected δ13C and δ15N values to be more representative of wolf diet in late autumn and winter.

Aerial wildlife surveys, scat analysis and wolf kill site visits were used to select prey to include in our mixing models (Szepanski et al. 1999, Darimont and Reimchen 2002, Derbridge et al. 2012, O'Donovan et al. 2018). We used nonparametric Mann–Whitney U tests to examine if species were isotopically distinct, and accounted for error associated with multiple comparisons using a Bonferroni correction (Bland and Altman 1995, Cabin and Mitchell 2000). We used diet-hair and diet-blood discrimination actors (hair: δ13C = 2.6%, δ15N = 3.2%; blood: δ13C = 0.7%, δ15N = 2.6%) from a captive feeding study on red foxes Vulpes vulpes (Roth and Hobson 2000) to account for isotopic change through trophic levels (Del Rio and Anderson-Sprecher 2008, Parnell et al. 2013, McLaren et al. 2015). To augment our study, we used data analyzed from wolf scat as priors for our mixing models (Merkle et al. 2014). Wolf scat was collected opportunistically during summer and winter of 2012–2013 and analyzed for the percent frequency of occurrence of prey. Mammalian hairs in each scat were identified by microscopic examination of the cuticular pattern, the medulla and the cross section. Prey biomass estimates were calculated using a regression equation that accounted for the ratio of indigestible to digestible remains (Floyd et al. 1978, Weaver 1993; Table 1).

We ran Bayesian stable isotope mixing models to estimate wolf diet during summer and winter from 2011 to 2017 (Moore and Semmens 2008, Semmens et al. 2009). We included a process*residual error term in our models (Stock and Semmens 2016a). For each model we ran three chains that were 100 000 iterations long, with a burn-in of 50 000 and thinned every 50th iteration. We used Gelman–Rubin diagnostic tests to assess convergence for each model, not allowing more than one value above 1.1 for all variables in the model, as well as trace plots produced by MixSIAR (Stock and Semmens 2016b). We also used a pairs plot to examine tradeoffs between any strongly correlated species before interpreting diet results (Stock and Semmens 2016b). All models were run using JAGS and R software (Plummer 2003, <>) and the R package MixSIAR (Stock and Semmens 2016b). We presented mixing model results as median ranges in the text of the results section and the 95% credible intervals (CI) for the median ranges in Table 3, 4.

Table 1.

Percent of total biomass (%) of prey items from wolf scats. Wolf scats (n = 465) were collected opportunistically during summer and winter of 2012–2013 in the southwest corner of Prince Albert National Park, Saskatchewan. Mammalian hairs in each scat were identified by microscopic examination of the cuticular pattern, the medulla and the cross section. Biomass estimates were calculated using a regression equation, which accounted for the ratio of indigestible to digestible remains (Floyd et al. 1978, Weaver 1993).



Parameters validation

Hair samples were obtained from 14 deer, 16 moose, 12 bison, 4 elk, 10 beaver and 7 cattle. We collected hair (n = 35) and blood (n = 29) from 30 wolves (some wolves were resampled in multiple years). The means and standard errors of δ13C and δ15N varied between wolf packs as well as diet sources (Table 2). Elk could not be isotopically separated from deer, violating an assumption of stable isotope mixing models. Therefore, we combined deer and elk before running our models, as they shared the same region of the mixing space and are taxonomically closely-related. All other prey were isotopically distinct. Isotopic values for hair and blood samples from wolves were centered on ungulates in the mixing space, and no outliers were observed.

SIA of summer and winter wolf diet

For combined pack data, the percentage of bison, deer/elk and moose consumed in summer was consistently high for all years (Table 3; bison: median range = 26–39%; deer and elk: 21–24%; moose: 16–33%). Wide 95% CIs for the posterior distribution of models resulted in considerable overlap between prey. Cattle (3–7%) and beaver (1–9%) comprised a lower percentage of wolf diet in summer for all years. Both wolf packs had similar prey contributions in summer. The contribution of bison to the diet of the Amyot pack was highest in 2011, 2013 and 2014 (Table 3).

Table 2.

Means () and standard errors (SE) of δ13C and δ15N values estimated from hair and blood tissue of wolves, and hair tissues from diet sources. Hair and blood samples were collected from 2011 to 2017 in Prince Albert National Park, Saskatchewan.


In winter, we found tendencies towards higher percentages of deer/elk (40–49%) consumed by wolves for all years, compared to summer (Table 4). We found a similar percentage of bison consumed in winter (25–45%) compared to summer, and almost no contribution from cattle or beaver in winter. Bison comprised a higher percentage of diet for the Amyot pack (26–40%) compared to the Nesslin pack (20–23%), while the median percentage of moose in wolf diet (19–35%) was higher for the Nesslin pack in 2013, 2014 and 2016.


We found wolf diet differed between summer and winter, with a more even contribution of wild ungulates to wolf diet in summer. Deer/elk comprised the highest percentage of wolf diet in winter, supplemented by bison and moose. While we found evidence that wolves consumed beaver and cattle in summer, diet contributions were comparatively small. Relative proportions of prey contributions were similar among years. Bison comprised a higher percentage of the diet for the Amyot pack in both summer and winter, while the contribution of moose in winter was higher in some years for the Nesslin pack.

Wolves were centered on ungulates in the isotope mixing space, which is supported by other studies on wolf diet in the region Of the wolf kill sites visited (n = 270) in PANP from 2013 to 2017, 215 had prey remains: 57.2% (n = 123) were white-tailed deer, 18.2% (n = 39) were moose, 8.4% (n = 18) were deer for which genetic testing was inconclusive, 7.9% (n = 17) were mule deer, 5.1% were bison (n = 11), 1.9% were black bears (n = 4) and 1.4% (n = 3) were elk (Irvine 2019). Urton and Hobson (2005) performed SIA on wolves in central Saskatchewan, which overlapped with our study area in PANP, and found elk and deer dominated wolf diet, although they did not include bison in their mixing model.

The main prey of wolves shows considerable variation across regions, and includes moose (Ballard et al. 1987, Mech and Fieberg 2014), deer (Ballard et al. 1987, Huggard 1993a), elk (Huggard 1993a, Smith et al. 2000, 2004, Hebblewhite et al. 2002, Kortello et al. 2007, Atwood et al. 2007), and caribou Rangifer tarandus (Ballard et al. 1987, Merkle et al. 2017). Wolves may also supplement their diet with smaller prey that show a minor contribution to wolf diet when included in mixing models (Szepanski et al. 1999, Milakovic and Parker 2011, Derbridge et al. 2012, O'Donovan et al. 2018). The primary prey of wolves can be dependent on habitat overlap between predator and prey, prey density, and thus the encounter rate between wolves and prey (Huggard 1993a).

Wolves are opportunistic and prey switching can occur as a result of a number of factors (that are not mutually exclusive), including in response to seasonal and environmental changes (Nelson and Mech 1986, Szepanski et al. 1999), the occurrence of other predators (Kortello et al. 2007, Merkle et al. 2017), prey vulnerability (Bergman et al. 2006, Garrott et al. 2007, Metz et al. 2012), or changes in the abundance of prey (Garrott et al. 2007, Fortin et al. 2015, Sand et al. 2016). We found that winter diet for wolves centered on wild ungulates, particularly deer, which is what we had predicted. Deep snow (>70 cm) can inhibit ungulate movement, restricting access to forage and increasing energetic output, making prey more susceptible to predation by wolves (Telfer and Kelsall 1984, Huggard 1993b, Mech et al. 2001, Smith et al. 2004). We expected bison contributions to be higher in winter relative to bison contributions in summer, as wolf pack cohesiveness is higher (Benson and Patterson 2014), and deeper snow may make bison more susceptible to predation by wolves. However, bison harvests most often occur in the late summer and autumn, and it is likely that wolves scavenge gut piles and/or consume bison that are wounded or stressed during unsuccessful hunting attempts (Tallian et al. 2017), which may have increased diet contributions of bison during summer. Beaver and cattle are less available to wolves in winter, as beavers reside in lodges and forage under the ice (Benson and Patterson 2014), and cattle are on their winter pasture. In summer when there was greater overlap between wolves and cattle pasture, we found little evidence of cattle in wolf diet. Other studies have shown wolf predation on livestock occurs infrequently when wild ungulates are readily available (Meriggi and Lovari 1996, Oakleaf et al. 2003, Treves et al. 2004).

Variation in diet may also be observed between wolf packs due to differences in prey availability. Prey abundance can differ between packs as a result of habitat and landscape heterogeneity between regions, which can influence foraging and migration patterns of prey (Ballard et al. 1987, Gustine and Parker 2008, Fortin et al. 2015, Muposhi et al. 2016). Variation in habitat type/quality not only influences prey availability and density, but it can also affect prey vulnerability by modifying prey detection, access, and/or the success of an attack (McPhee et al. 2012a, b, Torretta et al. 2017). We found bison comprised a higher percentage of diet for the Amyot pack compared to the Nesslin pack. There is greater overlap between the SRPB population and the Amyot wolf pack (Bergeson 1993), which would have increased the encounter rate between wolves and bison. In addition, landscape characteristics within the SRPB range, including flat, open meadows, may have increased vulnerability of bison to wolf predation relative to forested areas (Bergeson 1993, McPhee et al. 2012b, Torretta et al. 2017). Finally, the Amyot pack was larger than the Nesslin Pack, which may increase the acquisition and consumption of larger prey, such as bison, for wolves in the Amyot pack, due to benefits of cooperative hunting (MacNulty et al. 2014, Tallian et al. 2017).

Table 3.

Posterior median estimates and 95% credible intervals (CI) of summer diet proportions for Amyot, Nesslin and combined wolf packs, from Bayesian stable isotope mixing models. Wolf diet was estimated from 2011 to 2017 in Prince Albert National Park, Saskatchewan. Years that data were unavailable are indicated by N/A.


Overall, SIA, kill site investigation and scat analysis reveal that wolves are consuming bison in PANP. There was considerable variation in the posterior distribution of our stable isotope mixing models, which confounds our interpretation of prey contributions. In our mixing models, we incorporated uncertainty in both our isotope and red fox discrimination factors. While controlled feeding studies have been performed on wolves (Derbridge et al. 2015, L'Hérault et al. 2018), red fox discrimination factors have been commonly-used for wolf diet studies (Ballard et al. 1987, Derbridge et al. 2012, Rode et al. 2016). Derbridge et al. (2015) found that specifying wolf fractionation rates made little practical difference when estimating wolf diet. The red fox discrimination factors placed wolves within the prey hypervolume in iso-space, suggesting that it is accurately representing wolf diet-tissue discrimination. We also incorporated priors into our models using results from wolf scat analysis, which often improves mixing model outputs by guiding parameter estimates and reducing the variance of prey contributions (Moore and Semmens 2008). However, there was considerable overlap among the posterior distributions of our mixing models, which was most likely a result of the similarity of our sources in the isotope mixing space and is common when using SIA to reconstruct carnivore diet (Derbridge et al. 2012).

Table 4.

Posterior median estimates and 95% credible intervals (CI) of winter diet proportions for Amyot, Nesslin and combined wolf packs, from Bayesian stable isotope mixing models. Wolf diet was estimated from 2011 to 2016 in Prince Albert National Park, Saskatchewan.


Deer and elk could not be isotopically separated, as they display high dietary overlap of browse and grass species, particularly during summer (Hansen and Reid 1975, Krysl and Bryant 2016). While moose and beaver spend a large portion of time foraging in aquatic habitats, beaver may consume greater quantities of browse, leading to higher δ13C values (Drucker et al. 2003). Bison had higher δ13C, indicating they are foraging primarily on C3 plants, which is expected in an aspen parkland ecotone (Chisholm et al. 1986). The observed overlap between δ13C and δ15N signatures is normal when prey occupy a similar ecological niche, particularly when compared with δ13C and δ15N values from other ecosystems (Chisholm et al. 1982, Szepanski et al. 1999, Webb et al. 2017). Therefore, caution should be used when reconstructing the diet of a terrestrial carnivore that shows a considerable overlap in prey isotopic signatures, as this may make conclusions difficult to discern. Using other methods of diet analysis (e.g. direct observations of kills and/or scat or stomach content analysis) to support or refute conclusions drawn from SIA can be helpful in this regard.

In conclusion, bison constitute a smaller proportion of wolf diet when compared to other ungulates, which is consistent with results from other wolf–bison predator–prey systems (Smith et al. 2000, 2004, Jędrzejewski et al. 2002). However, predation in combination with other mortality factors, such as disease and harvest, may lead to population decline and increase extinction risk of the SRPB population (Joly and Messier 2004, Sigaud et al. 2017, Simon and Fortin 2019). Extinction risk for the SRPB was highest in population simulations when the effects of harvest and disease were combined (Cherry et al. 2019). But, small populations can be more vulnerable to extinction caused by predation (Festa-Bianchet et al. 2006). Therefore, management of the SRPB population should focus on reducing harvests, rather than wolf predation, to assist with the population recovery of the SRPB.


We thank Parks Canada and Prince Albert National Park staff for their support of this project, including N. Stolle, T. Stene, D. Guedo, D. Thompson and J. Watson. We also thank C. Irvine, M. Sigaud, J. Curran, E. Knight, B. Godden, M. Kennedy, K. Young, M. Ottway, E. Kanak, J. Cormack, R. Simon, T. Connolly, M. Peterson and M. Jacklin for assistance during field work. Aspects of this study were supported by the Natural Sciences and Engineering Council of Canada.

Permits – All captures and handling followed Parks Canada protocol and were approved by the Parks Canada Agency Animal Care Task Force (permit numbers: 2011196-1, 2014009-1, 2014009-2, 2014009-3).



Atwood, T. C. et al. 2007. Comparative patterns of predation by cougars and recolonizing wolves in Montana's Madison Range. – J. Wildl. Manage. 71: 1098–1106. Google Scholar


Ballard, W. B. et al. 1987. Ecology of an exploited wolf population in south-central Alaska. – Wildl. Monogr. 98: 3–54. Google Scholar


Benson, J. F. and Patterson, B. R. 2014. Spatial overlap, proximity and habitat use of individual wolves within the same packs. – Wildl. Soc. Bull. 39: 31–40. Google Scholar


Bergeson, D. 1993. A comparative assessment of management problems associated with the free-roaming bison in Prince Albert National Park. – MSc thesis, Univ. of Manitoba. Google Scholar


Bergman, E. J. et al. 2006. Assessment of prey vulnerability through analysis of wolf movements and kill sites. – Ecol. Appl. 16: 273–284. Google Scholar


Bland, J. M. and Altman, D. G. 1995. Multiple significance tests: the Bonferroni method. – Br. Med. J. 310: 170. Google Scholar


Boyd, D. K. et al. 1994. Prey taken by colonizing wolves and hunters in the Glacier National Park area. – J. Wildl. Manage. 58: 289–295. Google Scholar


Cabin, R. J. and Mitchell, R. J. 2000. To Bonferroni or not to Bonferroni: when and how are the questions. – Bull. Ecol. Soc. Am. 81: 246–248. Google Scholar


Carbyn, L. N. and Trottier, T. 1988. Descriptions of wolf attacks on bison calves in Wood Buffalo National Park. – Arctic 41: 297–302. Google Scholar


Cherry, S. G. et al. 2019. Managing genetic diversity and extinction risk for a rare plains bison (Bison bison bison) population. – Environ. Manage. 64: 553–563. Google Scholar


Chisholm, B. et al. 1986. Assessment of prehistoric bison foraging and movement patterns via stable-carbon isotopic analysis. – Plains Anthropol. 31: 193–205. Google Scholar


Chisholm, B. S. et al. 1982. Stable-carbon isotope ratios as a measure of marine versus terrestrial protein in ancient diets. – Science 216: 1131–1132. Google Scholar


Ciucci, P. et al. 1996. A comparison of scat-analysis methods to assess the diet of the wolf Canis lupus. – Wildl. Biol. 2: 37–48. Google Scholar


Darimont, C. et al. 2007. Stable isotopic niche predicts fitness of prey in a wolf–deer system. – Biol. J. Linn. Soc. 90: 125–137. Google Scholar


Darimont, C. T. et al. 2009. Landscape heterogeneity and marine subsidy generate extensive intrapopulation niche diversity in a large terrestrial vertebrate. – J. Anim. Ecol. 78: 126–133. Google Scholar


Darimont, C. T. and Reimchen, T. E. 2002. Intra-hair stable isotope analysis implies seasonal shift to salmon in gray wolf diet. – Can. J. Zool. 80: 1638–1642. Google Scholar


Del Rio, C. M. and Anderson-Sprecher, R. 2008. Beyond the reaction progress variable: the meaning and significance of isotopic incorporation data. – Oecologia 156: 765–772. Google Scholar


DeNiro, M. J. and Epstein, S. 1978. Influence of diet on the distribution of carbon isotopes in animals. – Geochim. Cosmochim. Acta 42: 495–506. Google Scholar


DeNiro, M. J. and Epstein, S. 1981. Influence of diet on the distribution of nitrogen isotopes in animals. – Geochim. Cosmochim. Acta 45: 341–351. Google Scholar


Derbridge, J. J. et al. 2012. Using Bayesian stable isotope mixing models to estimate wolf diet in a multi-prey ecosystem. – J. Wildl. Manage. 76: 1277–1289. Google Scholar


Derbridge, J. J. et al. 2015. Experimentally derived delta C-13 and delta N-15 discrimination factors for gray wolves and the impact of prior information in Bayesian mixing models. – PLoS One 10: e0119940. Google Scholar


Drucker, D. G. et al. 2003. Evidence for shifting environmental conditions in southwestern France from 33 000 to 15 000 years ago derived from carbon-13 and nitrogen-15 natural abundances in collagen of large herbivores. – Earth Planet. Sci. Lett. 216: 163–173. Google Scholar


Festa-Bianchet, M. et al. 2006. Stochastic predation events and population persistence in bighorn sheep. – Proc. R. Soc. B 273: 1537–1543. Google Scholar


Floyd, T. J. et al. 1978. Relating wolf scat content to prey consumed. – J. Wildl. Manage. 42: 528–532. Google Scholar


Fortin, D. et al. 2015. A spatial theory for characterizing predator-multiprey interactions in heterogeneous landscapes. – Proc. R. Soc. B 282: 20150973. Google Scholar


Freese, C. H. et al. 2007. Second chance for the plains bison. – Biol. Conserv. 136: 175–184. Google Scholar


Fry, B. 2006. Stable isotope ecology. – Springer. Google Scholar


Fryxell, J. M. and Lundberg, P. 1994. Diet choice and predator–prey dynamics. – Evol. Ecol. 8: 407–421. Google Scholar


Garrott, R. A. et al. 2007. Evaluating prey switching in wolf–ungulate systems. – Ecol. Appl. 17: 1588–1597. Google Scholar


Gómez, C. et al. 2018. Assessing seasonal changes in animal diets with stable-isotope analysis of amino acids: a migratory boreal songbird switches diet over its annual cycle. – Oecologia 187: 1–13. Google Scholar


Gustine, D. and Parker, K. 2008. Variation in the seasonal selection of resources by woodland caribou in northern British Columbia. – Can. J. Zool. 86: 812–825. Google Scholar


Halbert, N. D. and Derr, J. N. 2006. A comprehensive evaluation of cattle introgression into US federal bison herds. – J. Hered. 98: 1–12. Google Scholar


Hall-Aspland, S. A. et. al. 2005. Stable carbon and nitrogen isotope analysis reveals seasonal variation in the diet of leopard seals. – Mar. Ecol. Prog. Ser. 305: 249–259. Google Scholar


Hansen, R. M. and Reid, L. 1975. Diet overlap of deer, elk and cattle in southern Colorado. – J. Range Manage. 28: 43–47. Google Scholar


Harvey, L. and Fortin, D. 2013. Spatial heterogeneity in the strength of plant–herbivore interactions under predation risk: the tale of bison foraging in wolf country. – PLoS One 8: 1–8. Google Scholar


Hebblewhite, M. et al. 2002. Elk population dynamics in areas with and without predation by recolonizing wolves in Banff National Park, Alberta. – Can. J. Zool. 80: 789–799. Google Scholar


Hilderbrand, G. V. et al. 1996. Use of stable isotopes to determine diets of living and extinct bears. – Can. J. Zool. 74: 2080–2088. Google Scholar


Huggard, D. J. 1993a. Prey selectivity of wolves in Banff National Park. I. Prey species. – Can. J. Zool. 71: 130–139. Google Scholar


Huggard, D. J. 1993b. Effect of snow depth on predation and scavenging by gray wolves. – J. Wildl. Manage. 57: 382–388. Google Scholar


Irvine, C. C. 2019. Discriminating grey wolf (Canis lupus) predation events in a multi-prey system in central Saskatchewan. – MSc thesis, Trent Univ. Google Scholar


Isenberg, A. C. 2001. The destruction of the bison: an environmental history, 1750–1920. – Cambridge Univ. Press. Google Scholar


Jędrzejewski, W. et al. 2002. Kill rates and predation by wolves on ungulate populations in Białowieża Primeval Forest (Poland). – Ecology 83: 1341–1356. Google Scholar


Joly, D. O. and Messier, F. 2004. Testing hypotheses of bison population decline (1970–1999) in Wood Buffalo National Park: synergism between exotic disease and predation. – Can. J. Zool. 82: 1165–1176. Google Scholar


Jung, T. S. 2011. Gray wolf (Canis lupus) predation and scavenging of reintroduced American bison (Bison bison) in southwestern Yukon. – Northwest Nat. 92: 126–130. Google Scholar


Knopff, K. H. et al. 2009. Evaluating global positioning system telemetry techniques for estimating cougar predation parameters. – J. Wildl. Manage. 73: 586–597. Google Scholar


Kortello, A. D. et al. 2007. Interactions between cougars (Puma concolor) and gray wolves (Canis lupus) in Banff National Park, Alberta. – Ecoscience 14: 214–222. Google Scholar


Křivan, V. and Sikder, A. 1999. Optimal foraging and predator–prey dynamics, II. – Theor. Popul. Biol. 55: 111–126. Google Scholar


Krysl, L. and Bryant, F. 2016. Food habits and dietary overlap of elk and mule deer in Guadalupe Mountains National Park, Texas. – Tex. J. Agric. Nat. Res. 14: 84–90. Google Scholar


L'Hérault, V. et al. 2018. Discrimination factors of carbon and nitrogen stable isotopes from diet to hair in captive large Arctic carnivores of conservation concern. – Rapid Commun. Mass Spectrom. 32: 1773–1780. Google Scholar


Lima, S. L. and Dill, L. M. 1990. Behavioral decisions made under the risk of predation: a review and prospectus. – Can. J. Zool. 68: 619–640. Google Scholar


Logan, J. M. et al. 2008. Lipid corrections in carbon and nitrogen stable isotope analyses: comparison of chemical extraction and modelling methods. – J. Anim. Ecol. 77: 838–846. Google Scholar


MacNulty, D. R. et al. 2014. Influence of group size on the success of wolves hunting bison. – PLoS One 9: e112884. Google Scholar


McLaren, A. A. D. et al. 2015. Carbon and nitrogen discrimination factors of wolves and accuracy of diet inferences using stable isotope analysis. – Wildl. Soc. Bull. 39: 788–796. Google Scholar


McPhee, H. M. et al. 2012a. Hierarchical predation: wolf (Canis lupus) selection along hunt paths and at kill sites. – Can. J. Zool. 90: 555–563. Google Scholar


McPhee, H. M. et al. 2012b. Time-to-kill: measuring attack rates in a heterogenous landscape with multiple prey types. – Oikos 121: 711–720. Google Scholar


Mech, L. D. and Fieberg, J. 2014. Re-evaluating the northeastern Minnesota moose decline and the role of wolves. – J. Wildl. Manage. 78: 1143–1150. Google Scholar


Mech, L. D. et al. 2001. Winter severity and wolf predation on a formerly wolf-free elk herd. – J. Wildl. Manage. 65: 998–1003. Google Scholar


Mech, L. D. 1977. Wolf-pack buffer zones as prey reservoirs. – Science 198: 320–321. Google Scholar


Meriggi, A. and Lovari, S. 1996. A review of wolf predation in southern Europe: does the wolf prefer wild prey to livestock? – J. Appl. Ecol. 33: 1561–1572. Google Scholar


Merkle, J. A. et al. 2009. Summer diet of the Mexican gray wolf (Canis lupus baileyi). – Southwest Nat. 54: 480–485. Google Scholar


Merkle, J. A. et al. 2014. Habitat use and spatial distribution of plains bison in relation to management practices and wolf predation in Prince Albert National Park. – Parks Canada Agency. Google Scholar


Merkle, J. A. et al. 2015. Bison distribution under conflicting foraging strategies: site fidelity vs energy maximization. – Ecology 96: 1793–1801. Google Scholar


Merkle, J. A. et al. 2017. Dietary niche partitioning among black bears, grizzly bears and wolves in a multiprey ecosystem. – Can. J. Zool. 95: 663–671. Google Scholar


Merrill, E. et al. 2010. Building a mechanistic understanding of predation with GPS-based movement data. – Phil. Trans. R. Soc. B 365: 2279–2288. Google Scholar


Metz, M. C. et al. 2011. Effect of sociality and season on gray wolf (Canis lupus) foraging behavior: implications for estimating summer kill rate. – PLoS One 6: e17332. Google Scholar


Metz, M. C. et al. 2012. Seasonal patterns of predation for gray wolves in the multi-prey system of Yellowstone National Park. – J. Anim. Ecol. 81: 553–563. Google Scholar


Milakovic, B. and Parker, K. L. 2011. Using stable isotopes to define diets of wolves in northern British Columbia, Canada. – J. Mammal. 92: 295–304. Google Scholar


Moore, J. W. and Semmens, B. X. 2008. Incorporating uncertainty and prior information into stable isotope mixing models. – Ecol. Lett. 11: 470–480. Google Scholar


Muposhi, V. K. et al. 2016. Habitat heterogeneity variably influences habitat selection by wild herbivores in a semi-arid tropical savanna ecosystem. – PLoS One. 11: e0163084. Google Scholar


Nelson, A. A. et al. 2016. Native prey distribution and migration mediates wolf (Canis lupus) predation on domestic livestock in the Greater Yellowstone Ecosystem. – Can. J. Zool. 94: 291–299. Google Scholar


Nelson, M. E. and Mech, L. D. 1986. Relationship between snow depth and gray wolf predation on white-tailed deer. – J. Wildl. Manage. 50: 471–474. Google Scholar


Oakleaf, J. K. et al. 2003. Effect of wolves on livestock calf survival and movements in central Idaho. – J. Wildl. Manage. 62: 299–306. Google Scholar


O'Donovan, S. A. et al. 2018. Intrapopulation variability in wolf diet revealed using a combined stable isotope and fatty acid approach. – Ecosphere 9: e02420. Google Scholar


Parnell, A. C. et al. 2013. Bayesian stable isotope mixing models. – Environmetrics 24: 387–399. Google Scholar


Peterson, B. J. and Fry, B. 1987. Stable isotopes in ecosystem studies. – Annu. Rev. Ecol. Syst. 18: 293–320. Google Scholar


Plummer, M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. – Proc. 3rd Int. Workshop on Distributed Statistical Computing. Google Scholar


Post, D. M. et al. 2007. Getting to the fat of the matter: models, methods and assumptions for dealing with lipids in stable isotope analyses. – Oecologia 152: 179–189. Google Scholar


Proulx, G. et al. 2012. Humane and efficient capture and handling methods for carnivores. – In: Boitani, L. and Powell, R. A. (eds), Carnivore ecology and conservation: a handbook of techniques. Oxford Univ. Press, pp. 70–129. Google Scholar


Rau, G. H. et al. 1992. 15N/14N and 13C/12C in Weddell Sea birds, seals, and fish: implications for diet and trophic structure. – Mar. Ecol. Prog. Ser. 84: 1–8. Google Scholar


Rode, K. D. et al. 2016. Isotopic incorporation and the effects of fasting and dietary lipid content on isotopic discrimination in large carnivorous mammals. – Physiol. Biochem. Zool. 89: 182–197. Google Scholar


Roth, J. D. and Hobson, K. A. 2000. Stable carbon and nitrogen isotopic fractionation between diet and tissue of captive red fox: implications for dietary reconstruction. – Can. J. Zool. 78: 848–852. Google Scholar


Samson, F. and Knopf, F. 1994. Prairie conservation in North America. – BioScience 44: 418–421. Google Scholar


Sand, H. et al. 2016. Prey selection of Scandinavian wolves: single large or several small? – PLoS One 11: e0168062. Google Scholar


Sand, H. et al. 2005. Using GPS technology and GIS cluster analyses to estimate kill rates in wolf–ungulate ecosystems. – Wildl. Soc. Bull. 33: 914–925. Google Scholar


Santiago-Avila, F. J. et al. 2018. Killing wolves to prevent predation on livestock may protect one farm but harm neighbors. – PLoS One 13: e0189729. Google Scholar


Schmitz, O. J. et al. 2000. Trophic cascades in terrestrial systems: a review of the effects of carnivore removals on plants. – Am. Nat. 155: 141–153. Google Scholar


Semmens, B. X. et al. 2009. Quantifying inter-and intra-population niche variability using hierarchical Bayesian stable isotope mixing models. – PLoS One 4: e6187. Google Scholar


Shury, T. K., et al. 2009. Anthrax in free-ranging bison in the Prince Albert National Park area of Saskatchewan in 2008. – Can. Vet. J. 50: 152–154. Google Scholar


Sigaud, M. et al. 2017. Collective decision-making promotes fitness loss in a fusion–fission society. – Ecol. Lett. 20: 33–40. Google Scholar


Simon, R. N. and Fortin, D. 2019. Linking habitat use to mortality and population viability to disarm an ecological trap. – Biol. Conserv. 236: 366–374. Google Scholar


Sinclair, A. R. E. et al. 1998. Predicting effects of predation on conservation of endangered prey. – Conserv. Biol. 12: 564–575. Google Scholar


Smith, D. W. et al. 2000. Wolf–bison interactions in Yellowstone National Park. – J. Mammal. 81: 1128–1135. Google Scholar


Smith, D. W. et al. 2004. Winter prey selection and estimation of wolf kill rates in Yellowstone National Park, 1995–2000. – J. Wildl. Manage. 68: 153–166. Google Scholar


Stanek, A. E. et al. 2017. Seasonal foraging strategies of Alaskan gray wolves (Canis lupus) in an ecosystem subsidized by Pacific salmon (Oncorhynchus spp.). – Can. J. Zool. 95: 555–563. Google Scholar


Steenweg, R. et al. 2016. Assessing potential habitat and carrying capacity for reintroduction of plains bison (Bison bison bison) in Banff National Park. – PLoS One 11: e0150065. Google Scholar


Stock, B. C. and Semmens, B. X. 2016a. Unifying error structures in commonly used biotracer mixing models. – Ecology 97: 2562–2569. Google Scholar


Stock, B. C. and Semmens, B. X. 2016b. MixSIAR GUI user manual [Internet]. Ver. 3.1. – <>. Google Scholar


Sweeney, J. M. and Sweeney, J. R. 1984. Snow depths influencing winter movements of elk. – J. Mammal. 65: 524–526. Google Scholar


Szepanski, M. M. et al. 1999. Assessment of anadromous salmon resources in the diet of the Alexander Archipelago wolf using stable isotope analysis. – Oecologia 120: 327–335. Google Scholar


Tallian, A. et al. 2017. Predator foraging response to a resurgent dangerous prey. – Funct. Ecol. 31: 1418–1429. Google Scholar


Telfer, E. S. and Kelsall, J. P. 1984. Adaptation of some large North American mammals for survival in snow. – Ecology 65: 1828–1834. Google Scholar


Thomas, S. M. and Crowther, T. W. 2015. Predicting rates of isotopic turnover across the animal kingdom: a synthesis of existing data. – J. Anim. Ecol. 84: 861–870. Google Scholar


Tieszen, L. L. et al. 1983. Fractionation and turnover of stable carbon isotopes in animal tissues: implications for δ13C analysis of diet. – Oecologia 57: 32–37. Google Scholar


Torretta, E. et al. 2017. Wolves and wild ungulates in the Ligurian Alps (western Italy): prey selection and spatial–temporal interactions. – Mammalia 81: 537–551. Google Scholar


Treves, A. et al. 2004. Predicting human–carnivore conflict: a spatial model derived from 25 years of data on wolf predation on livestock. – Conserv. Biol. 18: 114–125. Google Scholar


Urton, E. J. and Hobson, K. A. 2005. Intrapopulation variation in gray wolf isotope (δ15N and δ13C) profiles: implications for the ecology of individuals. – Oecologia 145: 316–325. Google Scholar


Weaver, J. L. 1993. Refining the equation for interpreting prey occurrence in gray wolf scats. – J. Wildl. Manage. 57: 534–538. Google Scholar


Webb, E. C. et al. 2017. The influence of varying proportions of terrestrial and marine dietary protein on the stable carbon–isotope compositions of pig tissues from a controlled feeding experiment. – STAR 3: 28–44. Google Scholar


Webb, N. F. et al. 2008. Statistical methods for identifying wolf kill sites using global positioning system locations. – J. Wildl. Manage. 72: 798–807. Google Scholar


White, K. et al. 1996. A model for wolf-pack territory formation and maintenance. – J. Theor. Biol. 178: 29–43. Google Scholar


Wilmers, C. C. et al. 2003. Trophic facilitation by introduced top predators: grey wolf subsidies to scavengers in Yellowstone National Park. – J. Anim. Ecol. 72: 909–916. Google Scholar


Wittmer, H. U. et al. 2005. The role of predation in the decline and extirpation of woodland caribou. – Oecologia 144: 257–267. Google Scholar


Wittmer, H. U. et al. 2013. Conservation strategies for species affected by apparent competition. – Conserv. Biol. 27: 254–260. Google Scholar


Young, S. P. and Goldman, E. A. 1964. The wolves of North America. – Dover Publications. Google Scholar


Zimen, E. 1976. On the regulation of pack size in wolves. – Z. Tierpsychol. 40: 300–341. Google Scholar
© 2020 The Authors. This is an Open Access article. This work is licensed under the terms of a Creative Commons Attribution 4.0 International License (CC-BY). The license permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Justin R. Shave, Seth G. Cherry, Andrew E. Derocher, and Daniel Fortin "Seasonal and inter-annual variation in diet for gray wolves Canis lupus in Prince Albert National Park, Saskatchewan," Wildlife Biology 2020(3), (19 August 2020).
Accepted: 23 June 2020; Published: 19 August 2020

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