Prey Occurrence and Biomass
For each scat we calculated frequency of occurrence (FO) and percent biomass of each prey item (Mech, 1966; Thurber and Peterson, 1993). We calculated FO as the total number of occurrences on the point grid divided by the total number of points in the sample. As FO can overrepresent small prey and younger animals, we also calculated the percent biomass ingested (Floyd et al. , 1978; Ciucci et al. , 1996; Klare et al. , 2011). To calculate biomass for each species we multiplied the number of occurrences on the point frame by a correction factor y = 0.439 + 0.008 * x, where x is the live mass of the prey item (Floyd et al. , 1978). We used the live mass of adult moose, calf moose, and beavers on Isle Royale (Thurber and Peterson, 1993). Due to identification uncertainty, we did not calculate the biomass contribution of species categorized as “other”.
We used logistic regression to estimate temporal variation in wolf diet. For each scat we recorded presence/absence for each prey species and built models representing our hypothesis of how wolf diet may change over time. For each species we built three models with species/presence absence as the response and constant, linear, or non–linear time as predictors. We considered coefficients from models informative when the 95% confidence interval around the beta estimate did not overlap 0. We used Akaike Information Criterion corrected for small samples (AICc) and AICc weight to select the most parsimonious model (Burnham and Anderson, 2002). We considered models within two AICc units of the best model as competing models unless they we more complex than the top model and the coefficients of the additional variables were not informative (Burnham and Anderson, 2002).
Due to difficulty identifying moose to age class after September 15 we ran two sets of models for each statistical approach. The first included all scats with all moose pooled and the second set included scats collected 5 May–15 September with moose separated by age class.
Results
We collected 206 scats, 126 in 2019 and 90 in 2020, and detected seven prey items (Table 1). Scats on average contained 1.6 + 0.8 standard deviation [SD] items per scat with 56% containing 1 item. We did not detect a difference in prey occurrence in scats between years (βMooseyear =-0.15, se=0.28; βBeaveryear=-0.09, se=0.34; βOtheryear=0.26, se=0.34) so we pooled all samples for further analysis. The most frequent prey overall in wolf scats was beaver (62%), followed by moose (26%), and other prey (11%; Fig. 2). When we pooled moose age classes, moose comprised 61% of biomass ingested and beaver 39% (Fig. 2A). In scats collected 5 May–15 September, beaver remained the most common prey item (66%), followed by adult moose (15%), calf moose (10%), and other prey (8%) (Fig. 2B). From this subset of scats, beaver and adult moose were the most important prey items by biomass (47% and 42% respectively), while moose calves represented 10% of the wolf diet. Wolf was rare in samples, with only one scat comprised entirely of wolf hair (100% of points in the frame).
Using all scats combined, the linear time model was the most parsimonious for moose and other prey (Table 2). While non-linear time models were supported by the data, the additional parameters were not informative (Sup 1). The probability of moose hair occurrence declined (βTime = -0.31, se = 0.03) from 0.66 in May to 0.36 in October (Fig. 3A). The probability of other prey items occurring in scat increased (βTime = 0.41, se = 0.19) from 0.09 in May to 0.33 in October (Fig. 3A). The most parsimonious model for beaver was constant over time, with the overall probability of occurrence in wolf scat 0.78 (95% CI = 0.72–0.84) (Fig. 3A). A linear time models was supported by the data but the additional parameter did not improve parsimony and was not informative (Sup 1).
When considering moose by age class, 5 May–15 September, the most parsimonious model for adult moose was constant over time. The probability of adult moose hair in scat was 0.30 (95% CI = 0.23–0.37) (Table 3, Fig. 3B). In contrast, the best model for the presence of calf hair in scats was nonlinear, with the probability of calf hair greatest in the first week of July (0.44, 95% CI = 0.30–0.58) and declining through mid-September (βTime = 7.11, se = 2.58; βTime2 = -7.75, se = 2.65).
Discussion
We found that wolf foraging decisions in IRNP are likely influenced by the population dynamics and vulnerability of their prey. As predicted under optimal foraging theory, wolves on Isle Royale shifted their diet in response to prey vulnerability and availability. Viewing wolf diet in relation to optimal foraging can explain why the diet of wolves in IRNP diverged from previous studies. In North America and Europe large and medium-size ungulates comprise >60% of wolf diet by frequency of occurrence (Carbone et al. , 1999; Theuerkauf 2009; Derbridge et al. , 2012; Newsome et al ., 2016). In contrast, in IRNP ungulates comprised only 26% of the wolf diet by FO. Our findings likely reflect the relative availability and vulnerability of moose and beaver in IRNP. Beavers in IRNP are at historically high densities of >1 colony/km2 (NPS unpublished data; Smith and Peterson, 2021) and wolves can ambush and subdue beavers in less than 5 minutes (Gable at al. , 2018). In contrast, moose density (~3.7/km2) in IRNP is within its historic [1960–2020] range (Smith and Peterson, 2021). Also, wolves often exert considerable energy to subdue moose, chasing them up to 1 km (Mech, 1966; Paquet, 1989). While moose are an order of magnitude more calorically profitable than beavers, the abundance and ease of capture of beavers (Mech et al. , 2015) makes them an important prey of wolves in IRNP. Thus, aligning with optimal foraging theory, wolves on IRNP appear to select prey (i.e. beavers) that are highly available (benefit) and easier to catch (lower costs) than larger ungulates (i.e. moose).
Contrary to our prediction that beaver would be an important secondary and temporally variable food item, wolves on Isle Royale consumed beaver at high rates throughout the ice-free season. The high amount of beaver consumption we observed is atypical for IRNP (Thurber and Peterson, 1993) and wolves in general (Newsome et al. , 2016) and likely reflects high beaver availability and vulnerability in conjunction with weak wolf pack formation. Beaver densities appear to be at historic high levels with 1 colony/km2 (Smith and Peterson, 2021) compared with a mean of 0.28 colony/km2 from 1962–2008 when beaver comprised only 14% of biomass of the wolf diet in IRNP (Romanski, 2010; Gable et al. , 2017). High beaver densities likely reduce wolf search time and may increase beaver vulnerability as they forage farther from water as palatable trees near ponds become limited (Gable at al. , 2018). In addition, approximately half of the wolves in IRNP were not associated with a pack during our study (NPS unpublished data), and smaller prey may be less risky for solo or small packs of wolves to attack (Escobedo et al ., 2015).
While beavers were the most frequently consumed prey item, moose contributed 50% more biomass to the wolf diet. Moose comprised most of the biomass ingested by wolves, which supported our predictions, however, moose comprised less of the wolf diet than we expected. In the Great Lakes Region of North America, ungulates comprise >80% of the wolf diet and historically (1975–1989) 85–95% of biomass consumed by IRNP wolves (Thurber and Peterson, 1993; Newsome et al. , 2016). This shift in wolf diet may be a result of high beaver density, moose age class structure, or lack of pack formation. Prey age structure can impact wolf kill rates (Sand et al. , 2012) as moose calves and adults > 6 years old are most vulnerable to wolf predation. Our temporal analyses highlight the importance of available vulnerable moose (i.e. nutritionally deficient adults in early spring and calves after parturition) and a lack of vulnerable moose may skew the wolf diet on IRNP. Further, the likely limited pack cohesion in recently introduced wolves (NPS unpublished data) may account for the low occurrence of moose in the diet. Cooperative hunting increases the efficiency of capturing larger prey, and there is a positive correlation between group size and prey size among social carnivores (Macdonald, 1983; MacNulty et al ., 2014). While paired and single wolves can kill moose (Thurber and Peterson, 1993), increased pack size can improve wolf success rate for difficult to capture prey (MacNulty et al. , 2014). For paired or single wolves, hunting beaver in IRNP is less risky and potentially as energetically efficient as hunting moose.
In accordance with our predictions, adult moose were more likely to occur in wolf scat early in the ice-free season, consistent with previous studies demonstrating the importance of individuals in poor condition in the wolf diet (Stahler et al. , 2006; Hoy et al. , 2021). In addition, the prevalence of moose in the wolf diet early in the ice-free season may indicate the importance of scavenging starving or winter tick (Dermacentor albipictus ) infested individuals (Forbes and Theberge, 1992). Our results suggest that scavenging may be a common early summer strategy in wolf-moose systems (Messier and Crete, 1985; Forbes and Theberge, 1992; Huggard, 1993; Orning et al. , 2021).
The occurrence of moose calf hair in wolf scats peaked in late-June, as predicted under optimal foraging theory. However, moose calves made up less of the wolf diet than we expected with calves comprising 10% of the biomass ingested, similar to rates reported by Thurber and Peterson (1993). This amount of consumption of calves is low for wolves, which tend to select for juvenile ungulates (Husseman et al ., 2003; Mattioli et al. , 2011) and calves can comprise 60–90% of biomass ingested in some moose-wolf systems (Wam and Hjeljord 2003; Sandet al. , 2008). Possibly, increased cow vigilance on IRNP (Edwards, 1983; Stephens and Peterson, 1984), may result in a shorter period of calf vulnerability than in other systems, reducing the importance of calves in the wolf ice-free diet. Interestingly, our results differ from Hoy et al. (2021) who reported strong, negative-frequency dependent selection for moose calves by IRNP wolves in winter. This divergence in summer/winter feeding may be a result of the larger prey base available to wolves in the ice-free season. Possibly, the availability and vulnerability of beaver may alleviate predation pressure on moose calves at low densities.
As moose became more difficult to capture the occurrence of less calorically valuable items (snowshoe hare, small mammals and birds, categorized as “other”) increased in wolf scats. Our results highlight the importance of alternative prey in supporting wolves through resource limited periods. Prey switching can allow higher densities of wolves to persist on a landscape as well as alter the population and behavior of secondary prey (Garrot et al. , 2007; Latham, 2013). Snowshoe hare act as an important herbivore on Isle Royale (Belovsky, 1984) and increased wolf predation of hares in late summer may help limit the effects of hare browsing on vegetation.
Our results may be limited by the inherent difficulty of estimating predator diets in forested landscapes. Scat analysis provides strong advantages over tracking or GPS cluster investigation to document small or rare items in the wolf diet (Klare et al., 2011). However, scat analysis is prone to observer error, can over-represent small prey, assumes constant scat deposition rates, and biomass calculations rely on strong assumptions of carcass use (Spaulding et al ., 2000; Klareat al. , 2011; Massey et al. , 2021). Specifically, in IRNP wolves often scavenge and/or partially consume carcasses (see Vucetichet al. , 2011), which could bias our biomass calculations. Also, due to the difficulty of identifying moose calves after the first molt, we assumed all moose hair in scats after September 15th were from adults. This assumption may inflate the biomass of moose consumed after 15 September as these could include young of the year post molt. Finally, because of our opportunistic scat collection, some individuals may be overrepresented (i.e. radio collared wolves and their pack members). However, our study was unique in that all but two wolves in IRNP were radio collared at the time of our study and contributed to GPS clusters, increasing the probability that we sampled the entire population.
Wolves appear to optimize tradeoffs between the costs and benefits of prey acquisition temporally, dynamically responding to changes over time. Just as prey respond to seasonal variability in predation risk, we found wolves responded to seasonal changes in prey availability and vulnerability (Garrott et al. , 2007; Latham et al. , 2013; Basille et al. , 2013). The tendency of wolves to prey switch in response to changes in prey availability is unclear, with some studies indicating negative-frequency dependence (Tallian et al., 2017; Hoyet al. , 2021) and other studies suggesting prey-switching (Garrott et al. , 2007; Latham et al., 2013). We found that wolves appeared to shift their diet in response to prey availability and vulnerability, supporting the prey switching hypothesis. The dynamic summer foraging behavior of wolves may have important cascading and landscape consequences. Specifically, the high rate of beaver predation may restore ecological function and influence landscape-level change in IRNP. Beavers are at historically high densities in IRNP, and the wetlands they create can dramatically alter landscape–level water and nutrient flow (Rosell 2005). Wolf predation could reduce the number and duration of these impoundments (Gable et al. , 2020) and restore interrupted ecological functions in these areas. Further, the relatively low occurrence of moose in the wolf diet may indicate that, when vulnerable alternative prey are available, wolves may not exert top-down regulation on moose populations. Our results support that wolves are dynamic optimal foragers and their ability to shape ecosystems is likely dependent on the behavior and demography of their prey.
Acknowledgements
This project was supported by the US National Park Service. M. Cooper, H. Boone, L. McTigue, M. Petersohn, J. Olson, C. Ratterman, and T. Schwelling collected field samples.
Author Contributions
Adia Sovie:  Writing – original draft (equal); formal analysis (lead); writing – review and editing (equal). Mark Romanski : Conceptualization (equal); funding acquisition (lead); writing – review and editing (equal). Elizabeth Orning : Methodology (lead); investigation (lead); writing – review and editing (equal). . David G. Marneweck : Investigation (supporting); writing – review and editing (equal). Rachel Nichols : Investigation (supporting); writing – review and editing (supporting). Seth Moore: Resources (lead). Jerrold Belant: Conceptualization (equal); supervision (lead); writing – original draft (equal) ; writing – review and editing (equal).
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