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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