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\begin{document}

\title{Temporal variation in translocated Isle Royale wolf diet reflects
optimal foraging.}



\author[1]{Adia Sovie}%
\author[2]{Mark Romanski}%
\author[3]{Elizabeth Orning}%
\author[4]{David Mareweck}%
\author[5]{Rachel Nichols}%
\author[5]{Seth Moore}%
\author[1]{Jerrold Belant}%
\affil[1]{Michigan State University}%
\affil[2]{Isle Royale National Park}%
\affil[3]{SUNY-ESF}%
\affil[4]{Conservation Alpha}%
\affil[5]{Grand Portage Band of Lake Superior Chippewa}%


\vspace{-1em}



  
  \date{May 27, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Wolves (Canis lupus) can exert top-down pressure and shape ecological
communities through selective predation of ungulates and beavers (Castor
Canadensis). Considering their ability to shape communities through
predation, understanding wolf foraging decisions is critical to
predicting their ecosystem level effects. Specifically, if wolves are
optimal foragers, consumers that optimize tradeoffs between cost and
benefits of prey acquisition, changes in these factors may lead to prey
switching or negative-density dependent selection with potential
consequences for community stability. For wolves, factors affecting cost
and benefits include prey vulnerability, risk, reward, and availability
which can vary temporally. We described wolf diet in by frequency of
occurrence and percent biomass and characterized diet in relation to
optimal foraging using prey remains found in wolf scats on Isle Royale
National Park, Michigan, USA during May--October 2019--2020. We used
logistic regression to estimate prey consumption over time. We predicted
prey with temporal variation in cost (vulnerability and/or availability)
such as adult and calf moose (Alces alces) and beaver to vary in wolves'
diet. We analyzed 206 scats and identified 62\% of remains as beaver,
26\% as and moose, and 12\% as other (birds, smaller mammals, and
wolves). Adult moose were more likely to occur in wolf scat in May, when
moose are in poor condition following winter. Similarly, the occurrence
of moose calves peaked June--mid July following parturition but before
their vulnerability declined as they matured. In contrast, beaver
occurrence in wolf scat did not change over time, possibly reflecting
the importance of low handling cost prey items for recently introduced
lone or paired wolves. Our results demonstrate that wolf diet is plastic
and responsive to temporal changes in prey acquisition cost as predicted
by optimal foraging theory. Temporal fluctuation in diet may influence
wolves' ecological role if prey respond to increased predation risk by
altering their foraging or breeding behavior.%
\end{abstract}%



\sloppy


Temporal variation in translocated Isle Royale wolf diet reflects
optimal foraging.

Adia R. Sovie\textsuperscript{1}, Mark C. Romanski\textsuperscript{2},
Elizabeth K. Orning\textsuperscript{3}, David G.
Marneweck\textsuperscript{,3,4}, Rachel Nichols\textsuperscript{3}, Seth
Moore\textsuperscript{5}, Jerrold L. Belant\textsuperscript{1}

\textsuperscript{1} Department of Fisheries and Wildlife, Michigan State
University, 480 Wilson Road, East Lansing, MI 48824, USA

\textsuperscript{2} National Park Service, 800 E Lakeshore Drive, Isle
Royale National Park, Houghton, MI 49931 USA

\textsuperscript{3} State University of New York, College of
Environmental Science and Forestry. 1 Forest Drive, Syracuse, NY 13210

\textsuperscript{4} Conservation Alpha, Johannesburg, South Africa

\textsuperscript{5}Department of Biology and Environment, Grand Portage
Band of Lake Superior Chippewa, 27 Store Rd, Grand Portage, MN 55605 USA

Abstract

Wolves (\emph{Canis lupus} ) can exert top-down pressure and shape
ecological communities through selective predation of ungulates and
beavers (\emph{Castor canadensis} ). Considering their ability to shape
communities through predation, understanding wolf foraging decisions is
critical to predicting their ecosystem level effects. Specifically, if
wolves are optimal foragers, consumers that optimize tradeoffs between
cost and benefits of prey acquisition, changes in these factors may lead
to prey switching or negative-density dependent selection with potential
consequences for community stability. For wolves, factors affecting cost
and benefits include prey vulnerability, risk, reward, and availability
which can vary temporally. We described wolf diet in by frequency of
occurrence and percent biomass and characterized diet in relation to
optimal foraging using prey remains found in wolf scats on Isle Royale
National Park, Michigan, USA during May--October 2019--2020. We used
logistic regression to estimate prey consumption over time. We predicted
prey with temporal variation in cost (vulnerability and/or availability)
such as adult and calf moose (\emph{Alces alces} ) and beaver to vary in
wolves' diet. We analyzed 206 scats and identified 62\% of remains as
beaver, 26\% as and moose, and 12\% as other (birds, smaller mammals,
and wolves). Adult moose were more likely to occur in wolf scat in May,
when moose are in poor condition following winter. Similarly, the
occurrence of moose calves peaked June--mid July following parturition
but before their vulnerability declined as they matured. In contrast,
beaver occurrence in wolf scat did not change over time, possibly
reflecting the importance of low handling cost prey items for recently
introduced lone or paired wolves. Our results demonstrate that wolf diet
is plastic and responsive to temporal changes in prey acquisition cost
as predicted by optimal foraging theory. Temporal fluctuation in diet
may influence wolves' ecological role if prey respond to increased
predation risk by altering their foraging or breeding behavior.

Keywords: Optimal foraging, Isle Royale, beaver, moose, wolf, diet

Introduction

Predation can shape ecosystems through controlling prey population
abundance and influencing where prey forage, resulting in landscape
scale changes to vegetation and nutrient flows (Terborgh \emph{et al.} ,
2001; Halpern \emph{et al.} , 2006; Estes \emph{et al.} , 2011).
Specifically, wolf (\emph{Canis lupus} ) predation can produce strong
top-down structuring in many systems (Bump \emph{et al.,} 2009; Ripple
and Beschta, 2012). While wolf predation can influence prey activity
(Suraci \emph{et al.} , 2016; Kohl et al 2018; Gaynor \emph{et al.} ,
2019), it is less clear if wolves are optimal foragers or if prey
demography and behavior can drive wolf foraging decisions (Abrams, 1992;
Katz \emph{et al.} , 2015). Under optimal foraging theory, consumers
optimize caloric intake by balancing the calories gained from a food
item and the caloric and safety costs of searching for and handling the
item (Pyke \emph{et al.} , 1977). For wolves, the caloric benefits of
prey items are relatively stable while the costs associated with
acquisition {[}search time, subjugation difficulty, and risk of
injury{]} change temporally. If wolves are optimal foragers, foraging
patterns may change over time, resulting in prey switching with the
potential for cascading ecological effects (Garrott \emph{et al.} ,
2007; Latham \emph{et al.} , 2013; Basille \emph{et al.} , 2013).

For wolves, the cost of prey acquisition includes search and handling
time as well as injury risk, factors that can change over weeks or
months (Griffiths, 1980; Mukherjee and Heithaus, 2013). Predators often
target abundant prey to reduce the caloric costs associated with search
time (Griffiths, 1980). The availability of prey can change temporally
as species migrate into or out of a feeding range, emerge from
hibernation or torpor, or following a birth pulse (Metz \emph{et al.} ,
2012; Petroelje \emph{et al.} , 2014). Wolves do not always
preferentially attack the most abundant prey, vulnerability and risk are
also factors driving wolf foraging decisions (Mech \& Peterson 2003;
Tallian \emph{et al.} , 2017; Hoy \emph{et al.} , 2021). Larger animals
require greater effort and risk to subdue (Griffiths 1980;
MacNulty\emph{et al.} , 2009). In contrast, smaller animals or prey in
poor condition exhaust quicker, require less effort to subdue, and are
less likely to inflict injuries (MacNulty \emph{et al.} , 2009; Krumm
\emph{et al.} , 2010). Prey risk and vulnerability change temporally
(Metz et al 2012). For example, ungulates are often nutritionally
deficient after winter (Huggard 1993; Kautz \emph{et al.} , 2020) and
young animals develop the speed and endurance necessary to evade capture
as they mature (Severud \emph{et al.} , 2019). If wolves are optimal
foragers these temporal shifts in prey demography and behavior may alter
wolf diet.

Recently reintroduced wolves on Isle Royale National Park (IRNP),
Michigan, USA provide a unique opportunity to investigate wolf diet
relative to optimal foraging theory. Wolves on IRNP prey on a limited
number of species that predictably vary in caloric value, availability,
and vulnerability, allowing us to evaluate hypotheses of optimal
foraging and prey switching. During the ice-free season (April--October)
wolves in IRNP historically preyed on moose and beavers but also
consumed snowshoe hare (\emph{Lepus americanus)} , small mammals, and
birds (Thurber and Peterson, 1993). Wolves in IRNP consume 4-7kg/wolf of
biomass a day (Thurber and Peterson, 1993), and should select for large
prey to maximize caloric intake (Carbone \emph{et al.} , 1999). Adult
moose (263 kg) are a high calorie prey item but require considerable
effort to locate and subdue (Sand \emph{et al.} , 2005). Adult moose are
most vulnerable in May due to winter malnutrition but regain fat
reserves after green-up in June and July (Parker, 2003; Tischler\emph{et
al.} , 2019). In contrast, moose calves (45kg) are not as calorically
rewarding as adults but require less handling time and risk (Hoy
\emph{et al.} , 2021). Calves are unavailable until parturition in late
May and are guarded by their mothers for the first few weeks of their
lives (Edwards, 1983; Stephens and Peterson, 1984). Moose calves are
most vulnerable to wolf attack in late June and into July, as they spend
less time with their mothers, but are capable of outrunning wolves by
September (Severud \emph{et al.} , 2019). Beavers (12 kg) are a low risk
and abundant food source on IRNP during the ice-free season. In May
following ice-out and in September before ponds freeze, beavers are
vulnerable to wolf predation when they forage on land to repair lodges
or restore food caches (Gable \emph{et al.} , 2017).

We estimated wolf diets from scats in IRNP in relation to optimal
foraging during the ice-free period 2019--2020. We hypothesized that
wolf diets would reflect an optimal foraging strategy that tracks
temporal shifts in prey acquisitions costs. Specifically, we predicted
that due to their high caloric value moose would be the primary
component of wolf diet by both frequency of occurrence and biomass in
scats. We also predicted the presence of moose in the wolf diet will be
highest in May and decline thereafter. Further, we predicted that moose
calf occurrence in scats would follow a nonlinear relationship, peaking
in late-June after parturition and declining in late summer as calves
grow and become less vulnerable to wolf predation. We predicted that
beaver occurrence in wolf diets would vary with peaks in May and
September. Finally, we predicted that other prey (e.g., snowshoe hares,
small mammals, and birds) would increase in occurrence in late summer as
moose become more difficult to kill and wolves increase their use of
alternative prey (Gable \emph{et al.} , 2018).

Methods

\emph{Study Area}

Isle Royale National Park is a 540-km\textsuperscript{2} archipelago in
Lake Superior, 22 km from mainland Ontario, Canada. Over 99\% of the
park is designated wilderness and supports mixed boreal forests
characteristic of the transition between temperate and boreal zones
(Sanders and Grochowski, 2013). Mammals on Isle Royale include moose,
wolves, red foxes (\emph{Vulpes vulpes} ), beavers, river otter
(\emph{Lontra canadensis} ), snowshoe hare, red squirrels
(\emph{Tamiasciurus hudsonicus} ), muskrats (\emph{Ondatra zibethicus}
), deer mice (\emph{Peromyscus maniculatus),} and several species of
bats (Johnsson \emph{et al} ., 1982). Mean daily high temperatures in
summer (June--August) are 21\selectlanguage{ngerman}°C and mean daily high temperatures in
winter (December--February) are 3°C. Isle Royale receives an average of
734 mm in annual precipitation.

Isle Royale National Park personnel and partner organizations released
19 wolves into IRNP in 2018 and 2019 to supplement the population. Eight
of these wolves represented a family group from Michipicoten Island,
Ontario and the remaining wolves were unassociated and unrelated
individuals (Hervey \emph{et al.} , 2021). Before restoration, only two
highly inbreed wolves remained on the island (Hervey \emph{et al.} ,
2021). All translocated wolves received Vectronics or Telonics
radio-collars with on-board Global Positioning System (GPS) scheduled to
take a location every 5 hours (NPS, unpublished data).

\emph{Scat Collection and Processing}

From 5 May--5 October 2019 and 13 June--22 September 2020, we collected
all wolf scats encountered during fieldwork in IRNP. We collected fresh
scats (e.g., strong smell, moist, tracks present) at wolf radio-collar
GPS location clusters (Svoboda \emph{et al.} , 2013) and on established
hiking trails (Fig. 1). We placed each scat in a plastic bag, recorded
the date and location (NAD83, UTM Zone 16N), and froze them for later
processing. We considered the collection date as the date of deposit,
although scats may have been up to 11 days old (Sanchez \emph{et al.} ,
2004).

We processed and identified scat contents following Chenaux--Ibrahim
(2015). We placed frozen scats into nylon stockings, washed them in a
washing machine to remove digestible material and then dried the
contents in an oven. We spread the contents on a 21.5cm x 28cm plate,
overlaid a 25-point grid, and randomly selected one hair from each point
for microscopic identification (Ciucci \emph{et al} ., 2004). We could
determine moose age class (adult or calf) until 15 September, when the
first molt occurs (Muller, 2006). After 15 September we identified all
moose hair as adult. Wolf hair may be ingested due to grooming or
cannibalistic activity, the latter resulting in a higher amount of hair
in scats (James, 1983; Muller, 2006). Therefore, we did not consider
wolf as a prey item unless we detected wolf hair at \textgreater{}10\%
of points in a sample. We pooled all other prey (birds, muskrats, small
mammals, wolves, unidentifiable remains, and vegetation) into an
``other'' category.

\emph{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 \emph{et al.} , 1978; Ciucci \emph{et
al.} , 1996; Klare \emph{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 \emph{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 \emph{+} 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
(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Mooseyear} =-0.15, se=0.28;
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Beaveryear}=-0.09, se=0.34;
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Otheryear}=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
(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Time} = -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 (\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Time} = 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 (\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Time} = 7.11, se = 2.58;
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{Time}\textsuperscript{2} = -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 \textgreater{}60\% of wolf diet by
frequency of occurrence (Carbone \emph{et al.} , 1999; Theuerkauf 2009;
Derbridge \emph{et al.} , 2012; Newsome \emph{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 \textgreater{}1 colony/km\textsuperscript{2} (NPS
unpublished data; Smith and Peterson, 2021) and wolves can ambush and
subdue beavers in less than 5 minutes (Gable \emph{at al.} , 2018). In
contrast, moose density (\textasciitilde{}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 \emph{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 \emph{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/km\textsuperscript{2} (Smith and Peterson, 2021)
compared with a mean of 0.28 colony/km\textsuperscript{2} from
1962--2008 when beaver comprised only 14\% of biomass of the wolf diet
in IRNP (Romanski, 2010; Gable \emph{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 \emph{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 \emph{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
\textgreater{}80\% of the wolf diet and historically (1975--1989)
85--95\% of biomass consumed by IRNP wolves (Thurber and Peterson, 1993;
Newsome \emph{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 \emph{et
al.} , 2012) as moose calves and adults \textgreater{} 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 \emph{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 \emph{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 \emph{et al.} , 2006; Hoy \emph{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 (\emph{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 \emph{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 \emph{et al} ., 2003;
Mattioli \emph{et al.} , 2011) and calves can comprise 60--90\% of
biomass ingested in some moose-wolf systems (Wam and Hjeljord 2003;
Sand\emph{et 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 \emph{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 \emph{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 \emph{et al} ., 2000;
Klare\emph{at al.} , 2011; Massey \emph{et al.} , 2021). Specifically,
in IRNP wolves often scavenge and/or partially consume carcasses (see
Vucetich\emph{et 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
15\textsuperscript{th} 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 \emph{et al.} , 2007; Latham \emph{et al.} ,
2013; Basille \emph{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;
Hoy\emph{et al.} , 2021) and other studies suggesting prey-switching
(Garrott \emph{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 \emph{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

\textbf{Adia Sovie:} ~Writing -- original draft (equal); formal analysis
(lead); writing -- review and editing (equal).~\textbf{Mark Romanski} :
Conceptualization (equal); funding acquisition (lead); writing -- review
and editing (equal).~\textbf{Elizabeth Orning} : Methodology (lead);
investigation (lead); writing -- review and editing
(equal).~.~\textbf{David G. Marneweck} : Investigation (supporting);
writing -- review and editing (equal).~\textbf{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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\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Locations of wolf scats collected (white circles) used to
determine diet composition, Isle Royale National Park, Michigan, USA,
May--October 2019 and June--September 2020.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2. Proportional composition of prey identified in wolf scats,
Isle Royale National Park, Michigan, USA by percent frequency of
occurrence (blue bar), and percent biomass (orange bar). A) Scats
collected with moose adults and calves pooled. B) Scats collected with
adult moose and calves separated.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3. Temporal change in prey occurrence from wolf scats collected
on Isle Royale National Park, Michigan, USA. A) Scats collected with
moose adults and calves pooled. B) Scats collected with adult moose and
calves separated.

Table 1. Occurrence of prey items (hair) identified from 206 wolf
(\emph{Canis lupus} ) scats collected 5 May--22 October 2019 and 13
June--22 September 2020, Isle Royale National Park, Michigan, USA.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Prey Item & Number of points & Number of scats\tabularnewline
Beaver & 3119 & 161\tabularnewline
Moose & &\tabularnewline
Moose Calf\textsuperscript{1} & 446 & 42\tabularnewline
Moose Adult\textsuperscript{1} & 697 & 55\tabularnewline
Moose\textsuperscript{2} & 187 & 15\tabularnewline
Other & &\tabularnewline
Bird & 93 & 8\tabularnewline
Snowshoe Hare & 118 & 9\tabularnewline
Unknown & 444 & 10\tabularnewline
Wolf & 85 & 10\tabularnewline
Total & 5042 & 206\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{1}Age class identified for scats collected between 5
May--15 September

\textsuperscript{2}Hair identified from scats collected after 15
September or when age could not be identified

Table 2. Logistic regression model selection results for moose (adults
and calves pooled), beaver, and other prey occurrence in wolf scats
collected 5 May--22 October 2019 and 13 June--22 September 2020, Isle
Royale National Park, Michigan, USA.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
& Model & K* & AICc* & \selectlanguage{greek}Δ\selectlanguage{english}AICc* & ꞷ\textsubscript{i}* & LL*\tabularnewline
Moose & Linear & 2 & 281.29 & 0 & 0.69 & --138.62\tabularnewline
& Nonlinear & 3 & 283.13 & 1.84 & 0.27 & --138.5\tabularnewline
& Constant & 1 & 287.11 & 5.82 & 0.04 & --142.55\tabularnewline
Beaver & Constant & 1 & 217.30 & 0 & 0.44 & --107.64\tabularnewline
& Linear & 2 & 217.83 & 0.53 & 0.34 & --106.89\tabularnewline
& Nonlinear & 3 & 218.76 & 1.46 & 0.21 & --106.32\tabularnewline
Other & Linear & 2 & 211.74 & 0 & 0.47 & --103.84\tabularnewline
& Nonlinear & 3 & 211.79 & 0.05 & 0.46 & --102.84\tabularnewline
& Constant & 1 & 215.71 & 3.97 & 0.06 & --106.85\tabularnewline
\bottomrule
\end{longtable}

*K= number of parameters in model, AICc = Akaike Information Criterion
corrected for small sample size, \selectlanguage{greek}Δ\selectlanguage{english}AICc= difference between the AICc
value of each model and the lowest AICc model, ꞷ\textsubscript{i=} AICc
weight, LL= negative log likelihood.

Table 3. Logistic regression model selection results for adult and calf
in wolf scats collected 5 May--15 September 2019 and 13 June--15
September 2020, Isle Royale National Park, Michigan, USA.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
& Model & K* & AICc* & \selectlanguage{greek}Δ\selectlanguage{english}AICc* & ꞷ\textsubscript{i}* & LL*\tabularnewline
Adult & Constant & 1 & 192.95 & 0 & 0.66 & --95.46\tabularnewline
& Linear & 2 & 195 & 2.05 & 0.24 & --95.46\tabularnewline
& Nonlinear & 3 & 196.73 & 3.78 & 0.1 & --95.29\tabularnewline
Calf & Nonlinear & 3 & 163.28 & 0 & 0.98 & --78.56\tabularnewline
& Linear & 2 & 171.83 & 8.55 & 0.01 & --83.87\tabularnewline
& Constant & 1 & 175.24 & 11.97 & 0 & --86.61\tabularnewline
\bottomrule
\end{longtable}

*K= number of parameters in model, AICc = Akaike Information Criterion
corrected for small sample size, \selectlanguage{greek}Δ\selectlanguage{english}AICc= difference between the AICc
value of each model and the lowest AICc model, ꞷ\textsubscript{i=} AICc
weight, LL= negative log likelihood.

\selectlanguage{english}
\FloatBarrier
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