Fear, foraging and olfaction: how mesopredators avoid costly interactions with apex predators

Abstract

Where direct killing is rare and niche overlap low, sympatric carnivores may appear to coexist without conflict. Interference interactions, harassment and injury from larger carnivores may still pose a risk to smaller mesopredators. Foraging theory suggests that animals should adjust their behaviour accordingly to optimise foraging efficiency and overall fitness, trading off harvest rate with costs to fitness. The foraging behaviour of red foxes, Vulpes vulpes, was studied with automated cameras and a repeated measures giving-up density (GUD) experiment where olfactory risk cues were manipulated. In Plitvice Lakes National Park, Croatia, red foxes increased GUDs by 34% and quitting harvest rates by 29% in response to wolf urine. In addition to leaving more food behind, foxes also responded to wolf urine by spending less time visiting food patches each day and altering their behaviour in order to compensate for the increased risk when foraging from patches. Thus, red foxes utilised olfaction to assess risk and experienced foraging costs due to the presence of a cue from gray wolves, Canis lupus. This study identifies behavioural mechanisms which may enable competing predators to coexist, and highlights the potential for additional ecosystem service pathways arising from the behaviour of large carnivores. Given the vulnerability of large carnivores to anthropogenic disturbance, a growing human population and intensifying resource consumption, it becomes increasingly important to understand ecological processes so that land can be managed appropriately.

Electronic supplementary material
The online version of this article (10.1007/s00442-018-4133-3) contains supplementary material, which is available to authorized users.

Full text

Vol.:(0123456789)
1 3
Oecologia (2018) 187:573–583
https://doi.org/10.1007/s00442-018-4133-3
HIGHLIGHTED STUDENT RESEARCH
Fear, foraging andolfaction: howmesopredators avoid costly
interactions withapex predators
PeterM.Haswell1,2 · KatherineA.Jones1· JosipKusak3· MattW.Hayward1,4,5,6
Received: 13 June 2017 / Accepted: 27 March 2018 / Published online: 13 April 2018
© The Author(s) 2018
Abstract
Where direct killing is rare and niche overlap low, sympatric carnivores may appear to coexist without conflict. Interference
interactions, harassment and injury from larger carnivores may still pose a risk to smaller mesopredators. Foraging theory
suggests that animals should adjust their behaviour accordingly to optimise foraging efficiency and overall fitness, trading off
harvest rate with costs to fitness. The foraging behaviour of red foxes, Vulpes vulpes, was studied with automated cameras
and a repeated measures giving-up density (GUD) experiment where olfactory risk cues were manipulated. In Plitvice Lakes
National Park, Croatia, red foxes increased GUDs by 34% and quitting harvest rates by 29% in response to wolf urine. In
addition to leaving more food behind, foxes also responded to wolf urine by spending less time visiting food patches each
day and altering their behaviour in order to compensate for the increased risk when foraging from patches. Thus, red foxes
utilised olfaction to assess risk and experienced foraging costs due to the presence of a cue from gray wolves, Canis lupus.
This study identifies behavioural mechanisms which may enable competing predators to coexist, and highlights the potential
for additional ecosystem service pathways arising from the behaviour of large carnivores. Given the vulnerability of large
carnivores to anthropogenic disturbance, a growing human population and intensifying resource consumption, it becomes
increasingly important to understand ecological processes so that land can be managed appropriately.
Keywords Mesopredator release· Risk· Giving-up density· Gray wolf· Red fox
Introduction
Direct interactions between predators and other species can
lead to indirect consequences further down the food web
via trophic cascades (Ripple etal. 2016). Direct preda-
tion as well as behavioural/trait-mediated mechanisms can
be important drivers of such processes (Beckerman etal.
1997; Schmitz etal. 2004; Trussell etal. 2006). Evidence for
trophic cascades stemming from large carnivores is growing
(Ripple etal. 2014); however influence strength and study
validity are hotly debated (Allen etal. 2017; Kauffman etal.
2010; Newsome etal. 2015). Understanding the importance
of trophic interactions is a fundamental ecological question
(Sutherland etal. 2013). Understanding mechanisms, con-
sequences and behavioural responses to predation pressure
are crucial first steps in understanding the importance of
trophic interactions.
Mesopredator release describes the increase of meso-
predator populations after a decline in larger, apex preda-
tors (Crooks and Soulé 1999; Soulé etal. 1988). Intraguild
predation, competitive killing and interference competition
are common where niches overlap (Lourenco etal. 2014;
Palomares and Caro 1999; Ritchie and Johnson 2009). Inter-
ference interactions from larger carnivores pose a risk to
smaller mesopredators and may ultimately affect population
demography (Linnell and Strand 2000). Apex predators do
not always suppress spatial occupancy and mesopredator
abundance (Lesmeister etal. 2015; Lyly etal. 2015). How-
ever, continent-wide patterns of mesopredator release have
been identified (Letnic etal. 2011; Newsome and Ripple
Communicated by Christopher Whelan.
Having noteworthy implications for wildlife conservation and
management; this paper provides significant insight in the study of
giving-up densities, foraging ecology and intraguild interactions.
Electronic supplementary material The online version of this
article (https ://doi.org/10.1007/s0044 2-018-4133-3) contains
supplementary material, which is available to authorized users.
* Peter M. Haswell
p.m.haswell@bangor.ac.uk
Extended author information available on the last page of the article
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

574 Oecologia (2018) 187:573–583
1 3
2014; Pasanen-Mortensen and Elmhagen 2015). Suppressive
interactions between carnivores combined with bottom-up
effects of environmental productivity can ultimately drive
predator and prey species abundance (Elmhagen etal. 2010;
Elmhagen and Rushton 2007).
Gray wolves, Canis lupus have been observed to kill and
chase foxes (Mech and Boitani 2005, p. 269). Some evidence
also suggests wolves may contribute to the control of red fox,
Vulpes vulpes populations (Elmhagen and Rushton 2007).
In much of eastern and southern Europe, red foxes co-occur
with wolves (Hoffmann and Sillero-Zubiri 2016; Mech and
Boitani 2010). A negligible presence of fox hair in wolf diet
suggests foxes are not regularly eaten by wolves in Europe
(Krofel and Kos 2010; Stahlberg etal. 2017; Štrbenac etal.
2005). Low mortality could reflect effective avoidance of
larger predators (Durant 2000). However, interspecific kill-
ing may of course occur without consumption (Murdoch
etal. 2010). Even in the absence of direct killing, it is plau-
sible that wolves may still behaviourally suppress red foxes
through harassment, injury and fear of encounters. Literature
suggests minimal dietary overlap between the two carni-
vores (Bassi etal. 2012; Patalano and Lovari 1993). Com-
petition for landscape features such as den sites, scavenging
opportunities and kleptoparasitism however, could still yield
negative interactions. Conversely, foxes scavenge from wolf
kills in Europe (Selva etal. 2005; Wikenros etal. 2014),
suggesting they may exhibit positive behavioural responses
toward the species presence even where kleptoparasitism
might be risky.
Foxes alter their behaviour in response to the presence
of larger carnivores, habitat features and hazardous objects
(Berger-Tal etal. 2009; Hall etal. 2013; Vanak etal. 2009).
This suggests they are capable of assessing and responding
to environmental risk cues. Red foxes have well-developed
sensory systems and are known for their flexible behav-
iour, diet and ability to thrive in anthropogenic landscapes
(Bateman and Fleming 2012; Lesmeister etal. 2015; Randa
etal. 2009). Olfaction plays an important role in detecting
scavengeable food sources (Ruzicka and Conover 2012)
and logic suggests it would also play an important role
in risk evaluation. A wealth of research exists supporting
the recognition and behavioural response of prey species
towards odours of their predators (Apfelbach etal. 2005).
However we know of only two studies examining the influ-
ence of olfactory predation risk cue’s on food harvest by red
foxes under the giving-up density (GUD) framework (Leo
etal. 2015; Mukherjee etal. 2009). We expanded upon this
knowledge by investigating the role of urine in risk analy-
sis and studying behavioural responses in order to explain
changes in food harvest.
When responding to predation risk, foragers must trade-
off the fitness benefits of avoiding predators with the costs
of avoidance in any given context (Brown and Kotler 2007;
Brown etal. 1999; Haswell etal. 2017). The better an indi-
vidual animal is at assessing risk, the more effectively it can
forage, balance its energetic cost-benefits and the greater its
overall fitness. Methodologies developed by Brown (1988;
1992) and Mukherjee etal. (2009) were adapted to inves-
tigate fox giving-up densities (GUDs) and foraging behav-
iour (methodological considerations, online resource 1). A
GUD is the amount of food left behind in a given food patch
after the forager quits the patch (Brown 1988). As a for-
ager devotes time to harvesting a food patch (assuming it is
depletable), the available resources decline as does the har-
vest rate (Brown 1988). Foragers should leave a given patch
once the harvest rate (H) is equal to the sum of the metabolic
costs (C), predation costs (P) and missed opportunity costs
(MOC) i.e. H = C + P + MOC (Brown 1988; Shrader etal.
2012). By holding other parameters constant between food
patches, it is possible to investigate species or habitat spe-
cific differences in predation cost (Brown 1988). Increases
in predation risk should increase the GUD with animals for-
aging less in risky patches (Brown 1988). GUDs can help
measure the response of organisms to olfactory cues and
their perception of the predation costs (P) associated with
foraging, thus illuminating ecological processes.
Understanding the contribution of different biodiversity
components to ecosystem functioning is vital (Sutherland
etal. 2013). Suitable scientific information becomes espe-
cially essential if wildlife is to be properly managed in pub-
lic trust (Treves etal. 2017). The existence of mesopreda-
tor release has become more widely supported (Newsome
etal. 2017; Ritchie and Johnson 2009), yet understanding
of the mechanisms and processes are still needed if the
consequences of anthropogenic intervention are to be fully
understood. Furthermore, cross-context assumptions should
be avoided and there is still great need to understand the
impacts of large carnivores for any given system (Haswell
etal. 2017; Kuijper etal. 2016). This paper examined red
fox foraging behaviour in response to an olfactory risk cue
(wolf urine) in order to test the importance of olfaction in
risk analysis, identify any resultant suppression and the for-
aging strategies employed where apex predators pose risk.
Methods
Study site
Plitvice Lakes National Park (PLNP) is in the Dinaric Alps,
Croatia between 44°44′34″ and 44°57′48″N and 15°27′32″
and 15°42′23″E (Šikić 2007). The park (297km2) is a
mosaic of mountains and valleys with altitude ranging from
367 to 1279m above sea level (Romanic etal. 2016). The
karst (limestone and dolomite) landscape of the park is
characterised by underground drainage systems, sink holes
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

575Oecologia (2018) 187:573–583
1 3
and caves, and contains ~ 1% surface water with a series of
streams, rivers, lakes and waterfalls (Šikić 2007). Topogra-
phy can influence microclimates within the park but in gen-
eral, summers tend to be mild and sunny and winters long
with heavy snowfall; temperatures range between winter
lows of − 3°C and summer maximums of 36°C and annual
precipitation is 1550mm (Šikić 2007).
Romanic etal. (2016) estimate approximately 1770 peo-
ple occupy 19 settlements within the park’s boundaries.
Being a national park, the only economic uses permitted
within the boundaries are tourism and recreation (Firšt etal.
2005).
Between July and September 2015, foraging experiments
were conducted within the mixed beech (Fagus sylvatica)
and fir (Abies alba) forests of PLNP. Forest roads were sur-
veyed for carnivore signs with the assistance of a detection
dog ≥ 1week prior to the experiments—maximising data
yield by selecting sites with fox presence. During surveys
the dog did not leave the road. Population density of red fox
in Croatia is estimated at 0.7 animals per km2, with a ter-
ritory size of 1.43km2 per fox (Galov etal. 2014; Slavica
etal. 2010). Home ranges between fox group members can
often overlap (30–100%) (Poulle etal. 1994). Fox individu-
als could not be identified by pelage markings but distance
between sites (≥ 1.5km) ensured site independence and was
greater than distances previously used (e.g., Leo etal. 2015;
Mukherjee etal. 2009). Twelve sites were attempted. In early
July, foxes foraged from three of those sites in the north-west
of the park; a less accessible area, partly open to hiking and
local traffic but receiving far fewer tourists than the lakes.
These sites were then repeated in late August to give a better
temporal representation of response consistency.
GUD methodology
Feeding stations were positioned similarly to those used by
Altendorf etal. (2001) with each site consisting of a 2 × 3
grid with six food patches spaced 60m apart. Patches were
placed in woodlands, with three patches on either side of an
unpaved forest road to maximise detection likelihood and
keep road related risk consistent. Each food patch contained
twenty 4g dog food pieces (80g per patch, Bakers Complete
Meaty Meals Chicken), systematically mixed in 8 L of local
substrate put through a 5mm sieve and placed inside a 14 L
bucket half submerged in the ground. To increase detection
of the food patches by foragers, 5ml of liquid leached from
raw meat was applied to the surface of the soil within the
bucket each day. We measured GUDs and replenished food
pieces daily. Sites were visited in the hottest parts of the day
(afternoon) to ensure foragers were not disturbed.
To standardise harvest rate (H), the structure of artifi-
cial patches was kept consistent (substrate and food). The
substrate to food ratio was chosen after trials with less
soil were harvested completely and trials with more soil
were harvested minimally (PMH unpubl. data). A decline
in harvest rate over time was thus ensured through the
use of a depletable food source in a suitable volume of
inedible soil matrix (Bedoya-Perez etal. 2013; Brown
1988). Six food patches were available to the same for-
ager to ensure consistent missed opportunity costs (MOC).
Patch consistency kept energetic costs (C) consistent and
data collection occurred during typical summer weather
conditions. Habitat-associated risks were kept somewhat
consistent by using just mixed beech and fir woodlands.
Although not explicitly mentioned in earlier studies (Leo
etal. 2015; Mukherjee etal. 2009), the influence of human
scent contamination was minimised during data collection
by wearing thick gloves, a mouth mask and long sleeved
clothes kept in the presence of the liquid leached from
meat rather than smelling of detergent. Predation costs (P)
were manipulated using scent treatments.
Foxes foraged from feeding stations within a day dur-
ing pilot studies (PMH unpubl. data). The first day of the
11-day experimental cycle was untreated to provide an
opportunity for detection and acclimatisation. A control
scent consisting of 25g of sand scented with 3ml of
mint extract (Asda extra special peppermint extract) was
spread across a piece of locally sourced moss (15 × 15cm)
placed on the ground 15cm to the north of the bucket
on day2 and left during the remaining control-treatment
days. On day7, the control treatment was removed from
all patches and 25g of granules scented with wolf urine
(PredatorPee®, Wolf Urine Yard Cover Granules) were
placed on fresh moss in the same location as the pro-
cedural control. Throughout the 5-day treatment peri-
ods, both odours and volumes used were detectable by
researchers.
Daily replenishment of GUDs should result in higher
predictability and exploitation of patches by foragers
in what has been termed the “magic pudding” effect
(Bedoya-Perez etal. 2013). An 11-day window was used
for each experiment to reduce the likelihood of foragers
becoming over-reliant upon predictable food patches. We
deemed that there was less expectation of a response to
wolf urine given its application later in the test procedure
when foxes would be more familiar and reliant upon food
patches. Thus, the experimental approach was considered
conservative.
During the experiment, automated cameras were set
to record 30-s videos with 30-s intervals. Cameras were
positioned 0.4m high on trees 2m from feed stations and
angled to ensure buckets were in central view. Camera-
traps permitted accurate species identification of those
responsible for the GUDs as well as the collection of addi-
tional behavioural data.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

576 Oecologia (2018) 187:573–583
1 3
Additional variables
Soil penetration could affect GUDs if some substrates were
harder to dig through than others. This was measured by
dropping a wooden 1m ruler into the bucket from shoulder
height and measuring the depth that the ruler penetrated the
soil.
A photograph was taken from each GUD patch towards
the road, 30m away. Photos were taken consistently with a
3 megapixel camera always fully zoomed out. A systematic
grid sample of 100 pixels (10 × 10) was analysed from each
photograph (0.003% of pixels). Pixels were assigned to cat-
egories of open (no material blocking view to the road) or
other (biotic or abiotic material) to calculate the percentage
visibility to the road (number of open pixels) at each loca-
tion. Pictures were analysed using SamplePoint V1.58—a
method that provides accuracy comparable with field meth-
ods for ground cover measurements (Booth etal. 2006).
Data for the fraction of moonlight illuminated at mid-
night were obtained from the US naval observatory (http://
aa.usno.navy.mil/data/index .php). Due to each experiment
day beginning one afternoon and running overnight until
the next afternoon, an experimental day beginning on the
afternoon of June 26th and finishing on the afternoon of
June 27th for example, was ascribed “moonlight data” from
midnight on June 27th.
GUD analysis
Camera-trap videos were used to identify the last known
forager and assign GUD data for each experiment day. On
rare occasions where cameras failed to trigger but the patch
had been visited (N = 8 from 195 total GUDs), field signs
were used to confirm fox visits. GUD scores were assigned
to foxes when they were the last species identified foraging
at the patch (every occasion foxes visited) with the exception
that once a patch was discovered by foxes, all following days
where a visit was not recorded were assigned the maximum
GUD of 20 to ensure data reflecting patch avoidance was
also included. Foxes were captured on video during both
scent treatments for all sites, so death of subjects could be
ruled out.
Following Leo etal. (2015), we treated GUDs as count
data. The counts were commonly occurring (food pieces
were often left behind resulting in higher GUDs) and, as
such, a negative binomial regression (negative binomial dis-
tribution with a log link) generalized linear mixed model
(GLMM) was used to examine the influence of independent
variables upon GUDs (Heck etal. 2012). All analysis was
conducted in IBM SPSS Statistics 22. The fixed effect was
scent treatment. Covariates were percentage visibility to the
road, soil penetration (cm) and fraction of the moon illu-
minated. The repeated measures aspect of data points from
the same patch and a random effect for patch location were
also included. Robust standard error estimation was used to
handle any violations of model assumptions and the Satter-
thwaite approximation was applied to denominator degrees
of freedom (few level 2 units, unbalanced data and more
complex covariance matrices).
Behavioural analysis
The number of visits and total visit duration per experiment
day was extracted from the videos. New visits were consid-
ered to begin if the period between two videos was greater
than 15min. Visit duration was recorded as the amount of
time in seconds from the beginning of the first video and the
exact time the fox (any body part) was no longer visible on
the last video for that visit. The influence of scent treatment,
percentage visibility to the road, soil penetration and frac-
tion of the moon illuminated upon total visit duration was
analysed with a negative binomial regression GLMM. Visit
frequency per experiment day was analysed with a loglinear
(Poisson distribution and log link) GLMM. All other model
parameters were the same as for the GUD analysis.
Where foxes visited patches, behavioural data were
extracted from videos taken by automated cameras using
Solomon Coder Beta 15.11.19. Strict definitions of behav-
iours were described in an ethogram (online resource 2).
Given that identification of most behaviour required the ori-
entation of the head or neck to be identifiable, the length of
videos was recorded as only the duration during which the
animals head orientation was identifiable i.e. once the head
and neck had left the visible field, video timing stopped. Vid-
eos where animals were not present throughout the entirety
of the 30-s video did not then skew the data. Duration of
time spent engaging in major and minor vigilance, foraging
from the bucket and sniffing the ground were extracted from
each video. Percentage of time spent enacting behaviours
[(total behaviour duration/total video length) × 100] was
calculated for each patch and experiment day. Percentage of
time spent enacting behaviours were analysed with negative
binomial regression GLMMs. All other model parameters
were the same as for the GUD analysis.
Quitting harvest rate curves
Following the protocol of Kotler etal. (2010) quitting harvest
rates (QHR) were calculated for each treatment. Overall han-
dling time (h) was estimated with Kotler and Brown’s (1990)
multiple regression equation derived from Holling’s (1959)
disc equation: t = (1/a) [ln (N0/Nf)] + h (N0− Nf). t = the total
time spent at patches (visit durations obtained from camera
trap footage), a = attack rate, N0 = Initial amount of dog food
pieces in the patch (20) and Nf = the GUD. Two variables,
ln (N0/Nf) and (N0− Nf) were created, these variables were
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

577Oecologia (2018) 187:573–583
1 3
then regressed against values for t, the coefficients of which
yielded estimates for 1/a and h, respectively.
We then used h, in this case 16.79s/food piece to create
a new variable tnew [tnew = t − h (N0− Nf)]. Using the regres-
sion tnew = (1/a) [ln (N0/Nf)], subsets of values for tnew and
[ln (N0/Nf)] were then used to obtain coefficients giving esti-
mates for 1/a and thus a (1/coefficient value = a) for each
scent treatment. Estimates of h and treatment specific a were
then used in Hollings disc equation to calculate QHR for
each resource density (1–20 food pieces): QHR = (a*GUD)/
(1 + a*h*GUD). Mean GUDs were also used to obtain a
characteristic QHR for each treatment. In order to fully
characterize risk management strategy, the treatment spe-
cific harvest rate curves and QHR for mean GUD’s were
then plotted.
Results
GUDs
A total of 195 fox GUD measures were obtained. Even with
a conservative experimental approach (less expectation of a
response to wolf urine given its application later in the test
procedure when foxes would be more familiar and reliant
upon food patches), there was a significant effect of scent
treatment upon GUDs (F1,93 = 17.243, P < 0.001). GUDs
were significantly higher (less food harvested from patches)
during wolf urine treatment (14.98 ± 6.94 SD, N = 127) than
under the control treatment (mint, 11.16 ± 7.10 SD, N = 68).
Soil penetration (F1,45 = 0.376, P = 0.54), percentage visibil-
ity to road (F1, 5 = 2.629, P = 0.17) and fraction of the moon
illuminated (F1,38 = 0.747, P = 0.39) did not have a signifi-
cant effect on GUDs.
Behavioural analysis
Visit duration andfrequency
In total, 790 videos of fox visits were used to calculate total
visit duration (s) for 187 experiment days (camera malfunc-
tions excluded N = 8). Scent treatment had a significant
effect on total daily visit duration to the feeding patches
(F1,9 = 10.570, P = 0.01). Visits were longer under the con-
trol scent (mint, 269.14 ± 307.22 SD, N = 63) than with
wolf urine (132.59 ± 212.47 SD, N = 124). Soil penetration
(F1, 10 = 0.279, P = 0.61) and percentage visibility to road
(F1,6 = 1.396, P = 0.28) did not have a significant effect on
total daily visit duration. Even though moonlight levels did
not affect GUDs, total daily visit duration had a positive rela-
tionship with fraction of the moon illuminated (F1,11 = 7.388,
P = 0.021, Fig.1). No independent variables significantly
influenced visit frequency per experiment day.
Percentage oftime spent enacting behaviours
Behaviour was identifiable from 782 of the 790 videos
of fox visits, providing behavioural data for 114 experi-
ment days (72 patch avoidance days with no videos, 8days
with camera malfunctions, and 1day with fox on video
but behaviour identification not possible due to head
being out of view). At patches, foxes spent significantly
more of their time enacting major vigilance during wolf
urine treatment than when the control scent was pre-
sent (F1,26 = 31.996, P < 0.001, Fig.2). Soil penetration
(F1,9 = 3.679, P = 0.087), percentage visibility to road
(F1,8 = 0.037, P = 0.85) and fraction of the moon illumi-
nated (F1,104 = 2.493, P = 0.12) did not have a significant
effect. No independent variables had a significant effect
upon time spent enacting minor vigilance.
Foxes spent significantly less of their time foraging at
patches with wolf urine than with the control (F1,52 = 6.132,
P = 0.017, Fig.2). Soil penetration (F1,24 = 2.128, P = 0.16),
percentage visibility to road (F1,6 = 0.847, P = 0.39) and frac-
tion of the moon illuminated (F1,29 = 0.121, P = 0.73) did not
have a significant effect.
When at patches, foxes spent significantly more of their
time sniffing the ground during wolf urine treatment than the
control (F1,44 = 5.381, P = 0.025, Fig.2). Percentage of time
spent sniffing the ground had a negative relationship with
increasing soil penetration (F1,4 = 20.530, P = 0.009, Fig.3).
Percentage visibility to road (F1,5 = 0.489, P = 0.52) and
fraction of the moon illuminated (F1,109 = 2.892, P = 0.092)
did not have a significant effect.
Fig. 1 Total visit duration by red foxes, Vulpes vulpes, to food
patches each day had a positive relationship with fraction of the moon
illuminated
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

578 Oecologia (2018) 187:573–583
1 3
Quitting harvest rate curves
Lower mean GUD and characteristic quitting harvest rate
(QHR) during mint treatment (0.034 food pieces/s) corre-
sponds with greater time allocation (Fig.4), as also shown
by our analysis of time spent at patches. Higher character-
istic QHR under wolf urine (0.044 food pieces/s) suggest
foxes required higher remuneration when predation costs
were higher. The QHR slope was however steeper and the
attack rate higher under wolf urine (10.86 × 10−3/s) than
under mint treatment (6.97 × 10−3/s), indicating quicker food
harvest under wolf urine treatment.
Discussion
We show that wolf urine signifies risk for foxes and olfac-
tion is a mechanism by which foxes assess risk. The behav-
ioural responses of foxes to wolf urine presumably reduced
predation risk but also reduced their ability to utilise food
resources. These behavioural strategies help explain how
foxes are able to persist in sympatry with wolves, but also
help explain some of the suppressive impacts wolves have
on foxes.
When living in sympatry with larger carnivores, meso-
predators often employ strategies such as vigilance, spatial
or temporal avoidance, response to risk cues and adjustments
in feeding behaviour (Durant 2000; Hayward and Slotow
2009; Wikenros etal. 2014). In the presence of large carni-
vores, anti-predator strategies permit avoidance of danger
but can carry costs such as decreased activity, restricted
Fig. 2 Mean percentage of time spent by red foxes enacting major
vigilance (mint, 18.83 ± 13.37 SD, N = 48, wolf ur ine, 30.30 ± 16.56
SD, N = 66), minor vigilance (mint, 5.88 ± 5.44 SD, N = 48, wolf
urine, 7.48 ± 14.33 SD, N = 66), foraging (mint, 55.48 ± 21.38 SD,
N = 48, wolf urine, 44.09 ± 24.64 SD, N = 66) and sniffing the ground
(mint, 6.85 ± 13.80 SD, N = 48, wolf urine, 12.48 ± 23.46 SD, N = 66)
at artificial feeding stations during two scent treatments, a control
(mint) and wolf urine. Error bars represent ± 1 SEM
Fig. 3 Percentage of time spent by red foxes sniffing the ground had a
negative relationship with soil penetration Fig. 4 Harvest rate curves for red foxes foraging under two scent
treatments, a control (mint, solid line) and wolf urine (dashed line).
Quitting harvest rates (QHR) were plotted as a function of the num-
ber of food pieces in the patch. Points represent characteristic QHR
for mean GUD’s under each scent treatment
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

579Oecologia (2018) 187:573–583
1 3
habitat use and reduced nutrient intake (Hernandez and
Laundre 2005; Lesmeister etal. 2015).
At least at a localised scale, wolves negatively affected
red fox foraging efficiency with foxes exploiting patches less
thoroughly in the presence of wolf urine. Reduction in time
spent at patches came at a cost of lower food harvest from
patches, with the amount of food left behind (mean GUD)
being 34% higher under wolf urine and quitting harvest rates
for mean GUDs being 29% larger under wolf urine than
under mint treatment. This indicates that foxes required a
higher payoff when olfactory cues suggested wolf presence.
Such fitness costs of antipredator responses could affect
survival and reproduction, ultimately impacting population
dynamics (Creel and Christianson 2008). Such processes
could contribute to the effect apex predators have on the
distribution of mesopredators (Newsome etal. 2017).
Contrary to expectation, additional strategies employed
by foxes in response to wolf urine did not come at a cost to
harvest rates. Kotler etal. (2010) proposed that a steeper
QHR curve (quicker harvest) suggests less time investment
in apprehensive behaviours. Our video analysis however
shows that foxes spent a significantly greater percentage of
time engaging in some forms of apprehension (major vigi-
lance and sniffing the ground) and a lower percentage of time
foraging under the wolf urine treatment, yet still achieved
higher harvest rates. For some species harvest rates may be
a product of more than just time allocation to apprehension
and foraging. They may also be affected by how these activi-
ties are performed as well as time allocation to different
types of apprehensive behaviour and other activities.
Having the head up in major vigilance, permits visual,
auditory and scent based detection of danger and likely rep-
resents an effective, albeit costly, investment of time spent
in risky food patches. Higher levels of predator detection
behaviour do not always come at a cost to foraging perfor-
mance and harvest rates can increase alongside proportion
of time spent vigilant (Cresswell etal. 2003). It is feasible
that foxes increased their digging speed and encounter rates
when foraging under wolf urine in order to compensate for
the reduction in time spent foraging.
Foxes were less casual and more focused about how time
was spent under wolf urine, investing highly in major vigi-
lance and spending less time engaging in “other” behaviours
that were not productive to obtaining food or ensuring safety
e.g. masticating without being vigilant (PMH unpubl. data).
Mastication could not be measured in a comparable way to
the behaviours recorded in this study as the jaws could not
always be seen, however we note that, where observable,
mastication without vigilance appeared to be the dominant
“other” behaviour. Herbivores have been observed to tem-
porally and spatially partition their ruminating behaviour
from their foraging behaviour (Lynch etal. 2013; Nelle-
mann 1998). Mesopredators like foxes may also adjust their
digestive behaviour in response to predation risk. Foxes may
have chewed more quickly, chewed less or even swallowed
pieces whole under wolf urine treatment, digesting away
from risky patches instead of investing time aiding the diges-
tive process by masticating while at patches. Mastication
may also be reduced in risky locations because it can inhibit
auditory vigilance (Lynch etal. 2013, 2015).
Mesopredators likely have a more complex olfactory
landscape than organisms on the periphery of food webs
and behavioural response to scent could be affected by scent
strength, integrity and context (Jones etal. 2016). Previ-
ous works investigating the response of foxes to alternative
risk cues have yielded varying results. Observations of red
(Scheinin etal. 2006) and Indian foxes, Vulpes bengalensis
(Vanak etal. 2009) only showed significant reductions in
food bait take in response to direct predator presence (golden
jackal, Canis aureus and domestic dog Canis lupus famil-
iaris, respectively), but not to olfactory risk cues (urine, or
scat and urine, respectively).Observations were short and
scents fresh so it could be concluded that foxes did not
respond to these particular risk cues and only responded to
immediate threats, or that foxes in these studies were bigger
risk takers than in our study. However, these studies did not
follow a GUD framework so responses to scent may have
reflected experimental setup more than fox behaviour. For-
aging may have been too easy or profitable and food to sub-
strate ratios in these experiments may have only permitted
observation of strong responses. Nonetheless, food take and
behavioural responses towards live animals in both studies
still suggest fearful responses of foxes towards larger preda-
tors. The studies also suggest that fearful responses to the
actual presence of predators are likely to be stronger than to
risk cues alone.
Under a GUD framework, Mukherjee etal. (2009)
observed that foxes foraged more from patches with wolf
scat present. They suggested that scat may provide infor-
mation of a predator’s whereabouts and could indicate that
a predator has moved on and that the patch in fact carries
less risk. The responses observed in this study suggest urine
presents a more immediate predator presence cue. Scat can
act as a territorial marker and conveyer of information about
the depositor (Barja 2009). Peters and Mech (1975) however
concluded that raised leg urination was probably the most
effective method of territory maintenance. Competitors may
associate higher risk with urine than with scat. Canids also
preferentially faecal mark on visually conspicuous features,
suggesting scat placement is an important aspect of com-
munication (Barja 2009; de Miguel etal. 2009; Hayward and
Hayward 2010). Dependent on the context and placement,
scat may communicate risk but could also be positively asso-
ciated with scavengeable food sources.
Mukherjee etal. (2009) also suggested that the lower
presence of wolves in the study area and higher presence
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

580 Oecologia (2018) 187:573–583
1 3
of the larger striped hyena, Hyaena hyaena, could have
been responsible for their observations. Aversion to foreign
odours likely requires a social unit to have experience of
antagonistic events (Peters and Mech 1975). At 1.4–1.6
wolves per 100km2 (JK unpubl. data, estimates based on
100% MCP polygons and snow tracking of two packs utilis-
ing PLNP during 2015), wolf density was higher in PLNP
than the Croatian average of 1.3 (Štrbenac etal. 2005).
Given fox responses to wolf urine and wolf density, encoun-
ter rates might also have been higher in PLNP.
Leo etal. (2015) examined fox GUDs in response to a
combination of canid body odour (an indicator of close
proximity and hence immediate threat) and scat (territorial
demarcation and a less proximate threat). GUDs were higher
under dingo odour than control treatments. This is unsur-
prising given the threat dingoes (Canis lupus dingo) pose to
foxes through direct killing (Marsack and Campbell 1990;
Moseby etal. 2009). The dingo has a different ecology to
the wolf and exists in unique ecosystems (Mech and Boitani
2005; Purcell 2010). While interactions may vary depending
on context, the findings of Leo etal. (2015) suggest that the
combination of body odour and scat at locations such as den
sites are likely to affect foxes as well.
Context can be an important driver of interspecific rela-
tionships between predators (Haswell etal. 2017). The stud-
ies discussed suggest that cue type, species composition,
experience and demography might be important factors in
driving response to risk cues. A forager’s response to risk
may also vary dependent on factors such as social structure,
food patch quality and energetic state (Fortin etal. 2009;
Harvey and Fortin 2013; Hayward etal. 2015). Nonethe-
less, cues informing of more immediate risk (direct preda-
tor presence, urine or body odour) should in general yield
stronger behavioural responses. Inferences and responses to
olfactory cues will depend upon selection pressures (Jones
etal. 2016). Apex predator impacts may be weaker farther
away from core areas such as den sites (Miller etal. 2012).
The recently proposed “enemy constraint hypothesis” also
predicts weaker mesopredator suppression at peripheries
of large carnivore range (Newsome etal. 2017). At range
edges, reduction in apex predator presence and risk cues
would be expected. A reduction in behavioural suppression
through mesopredator response to olfactory risk cues would
thus also be expected. Factors affecting scent demarcation
and landscape use by apex predators should in-turn affect
risk perception and behavioural responses of mesopredators.
Suppression by larger predators can affect the abundance
and behaviour of mesopredators, often but not always hav-
ing consequent impacts upon mesopredator prey species
(Ritchie and Johnson 2009). Mesopredator response to risk
landscapes can have behavioural knock-on effects, influenc-
ing landscape and resource use by prey species (Palacios
etal. 2016). Predator odours including those of foxes have
a range of behavioural and physiological effects upon prey
species (Apfelbach etal. 2005). Foxes can also have stabi-
lising effects upon their prey populations (O’Mahony etal.
1999) or interact competitively with smaller carnivores (Bis-
chof etal. 2014; Petrov etal. 2016). Behavioural interac-
tions clearly play a part in maintaining functioning stable
ecosystems. Anthropogenic disturbance or direct loss of
processes through trophic simplification can however inter-
fere with these complicated systems, leading to problems
(Estes etal. 2011; Frid and Dill 2002; Prugh etal. 2009).
Removal or disturbance of large carnivores may interfere
with behavioural processes which also require consideration
when managing human landscape use.
Data availability Datasets analysed during the study can be
made available from the corresponding author on reason-
able request.
Acknowledgements We would like to thank Bangor University, The
UK Wolf Conservation Trust, The Coalbourn Charitable Trust, Ann
Vernon Memorial Travel Fund and Sir Ian McKellen for funding the
work. Thanks to Nacionalni park Plitvička Jezera who provided accom-
modation and logistical support throughout fieldwork. PMH would
like to acknowledge M. Van Berkel for assistance during his internship
and Alfred for assistance with carnivore sign surveys. We thank Dr.
V. Leo (Australian Wildlife Conservancy) and Dr. J. Gibbons (Bangor
University) for their advice on statistical analysis. We are grateful to
the handling editor, Dr. C.J. Whelan, Prof. B.P. Kotler and another
anonymous reviewer for their useful comments that helped strengthen
the manuscript.
Author contribution statement The study was conceived, designed
and executed by PMH who also wrote the manuscript. MWH contrib-
uted to the design, analysis and writing of the manuscript. KAJ contrib-
uted to the design and analysis. JK assisted with permits, logistics and
execution of the study. MWH, KAJ and JK provided editorial advice.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest in the authorship of this article.Use of product or corporation
names is for descriptive purposes only and implies no endorsement by
any author or affiliation.
Ethical approval All applicable institutional and/or national guidelines
for the care and use of animals were followed.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution 4.0 International License (http://creat iveco
mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribu-
tion, and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

581Oecologia (2018) 187:573–583
1 3
References
Allen BL, Allen LR, Andrén H, Ballard G, Boitani L, Engeman RM,
Fleming PJS, Ford AT, Haswell PM, Kowalczyk R, Linnell JDC,
Mech LD, Parker DM (2017) Can we save large carnivores with-
out losing large carnivore science? Food Webs 12:64–75.https ://
doi.org/10.1016/j.foowe b.2017.02.008
Altendorf KB, Laundré JW, López González CA, Brown JS (2001)
Assessing effects of predation risk on foraging behavior of mule
deer. J Mammal 82:430–439. https ://doi.org/10.1644/1545-
1542(2001)082<0430:AEOPR O>2.0.CO;2
Apfelbach R, Blanchard CD, Blanchard RJ, Hayes RA, McGregor IS
(2005) The effects of predator odors in mammalian prey species:
a review of field and laboratory studies. Neurosci Biobehav R
29:1123–1144. https ://doi.org/10.1016/j.neubi orev.2005.05.005
Barja I (2009) Decision making in plant selection during the faecal-
marking behaviour of wild wolves. Anim Behav 77:489–493.
https ://doi.org/10.1016/j.anbeh av.2008.11.004
Bassi E, Donaggio E, Marcon A, Scandura M, Apollonio M (2012)
Trophic niche overlap and wild ungulate consumption by red fox
and wolf in a mountain area in Italy. Mamm Biol 77:369–376.
https ://doi.org/10.1016/j.mambi o.2011.12.002
Bateman PW, Fleming PA (2012) Big city life: carnivores in
urban environments. J Zool 287:1–23. https ://doi.org/10.111
1/j.1469-7998.2011.00887 .x
Beckerman AP, Uriarte M, Schmitz OJ (1997) Experimental evi-
dence for a behavior-mediated trophic cascade in a terrestrial
food chain. Proc Natl Acad Sci USA 94:10735–10738. https ://
doi.org/10.1073/pnas.94.20.10735
Bedoya-Perez MA, Carthey AJR, Mella VSA, McArthur C, Banks
PB (2013) A practical guide to avoid giving up on giving-up
densities. Behav Ecol Sociobiol 67:1541–1553. https ://doi.
org/10.1007/s0026 5-013-1609-3
Berger-Tal O, Mukherjee S, Kotler BP, Brown JS (2009) Look before
you leap: is risk of injury a foraging cost? Behav Ecol Sociobiol
63:1821–1827. https ://doi.org/10.1007/s0026 5-009-0809-3
Bischof R, Ali H, Kabir M, Hameed S, Nawaz MA (2014) Being
the underdog: an elusive small carnivore uses space with
prey and time without enemies. J Zool 293:40–48. https ://doi.
org/10.1111/jzo.12100
Booth DT, Cox SE, Berryman RD (2006) Point sampling digital
imagery with “SamplePoint’. Environ Monit Assess 123:97–
108. https ://doi.org/10.1007/s1066 1-005-9164-7
Brown JS (1988) Patch use as an indicator of habitat preference, pre-
dation risk, and competition. Behav Ecol Sociobiol 22:37–47.
https ://doi.org/10.1007/bf003 95696
Brown JS (1992) Patch use under predation risk: I. Models and pre-
dictions. Ann Zool Fenn 29:301–309
Brown JS, Kotler BP (2007) Foraging and the ecology of fear. In:
Stephens DW, Brown JS, Ydenberg RC (eds) Foraging behav-
iour and ecology. University of Chicago Press, Chicago USA,
pp 438–480
Brown JS, Laundré JW, Gurung M (1999) The ecology of fear: opti-
mal foraging, game theory, and trophic interactions. J Mammal
80:385–399. https ://doi.org/10.2307/13832 87
Creel S, Christianson D (2008) Relationships between direct preda-
tion and risk effects. Trends Ecol Evol 23:194–201. https ://doi.
org/10.1016/j.tree.2007.12.004
Cresswell W, Quinn JL, Whittingham MJ, Butler S (2003) Good
foragers can also be good at detecting predators. Proc R Soc B
270:1069–1076. https ://doi.org/10.1098/rspb.2003.2353
Crooks KR, Soulé ME (1999) Mesopredator release and avifaunal
extinctions in a fragmented system. Nature 400:563–566. https
://doi.org/10.1038/23028
de Miguel FJ, Valencia A, Arroyo M, Monclús R (2009) Spatial dis-
tribution of scent marks in the red fox (Vulpes vulpes L.): do red
foxes select certain plants as signal posts? Pol J Ecol 57:605–609
Durant SM (2000) Living with the enemy: avoidance of hyenas and
lions by cheetahs in the Serengeti. Behav Ecol 11:624–632. https
://doi.org/10.1093/behec o/11.6.624
Elmhagen B, Rushton SP (2007) Trophic control of mesopredators
in terrestrial ecosystems: top–down or bottom–up? Ecol Lett
10:197–206. https ://doi.org/10.1111/j.1461-0248.2006.01010 .x
Elmhagen B, Ludwig G, Rushton SP, Helle P, Lindén H (2010) Top
predators, mesopredators and their prey: interference ecosystems
along bioclimatic productivity gradients. J Anim Ecol 79:785–
794. https ://doi.org/10.1111/j.1365-2656.2010.01678 .x
Estes J, Terborgh J, Brashares J, Power M, Berger J, Bond W, Car-
penter S, Essington T, Holt R, Jackson J, Marquis R, Oksanen
L, Oksanen T, Paine R, Pikitch E, Ripple W, Sandin S, Schef-
fer M, Schoener T, Shurin J, Sinclair A, Soulé M, Virtanen R,
Wardle D (2011) Trophic downgrading of planet earth. Science
333:301–306. https ://doi.org/10.1126/scien ce.12051 06
Firšt B, Frković A, Gomerčić T, Huber Đ, Kos I, Kovačić D, Kusak J,
Majić-Skrbinšek A, Spudić D, Starčević M, Štahan Ž, Štrbenac
A (2005) Lynx management plan for Croatia. State Institute for
Nature Protection, Zagreb
Fortin D, Fortin ME, Beyer HL, Duchesne T, Courant S, Dancose K
(2009) Group-size-mediated habitat selection and group fusion-
fission dynamics of bison under predation risk. Ecology 90:2480–
2490. https ://doi.org/10.1890/08-0345.1
Frid A, Dill L (2002) Human-caused disturbance stimuli as a form of
predation risk. Conserv Ecol 6(1):11. http://www.conse col.org/
vol6/iss1/art11
Galov A, Sindičić M, Andreanszky T, Čurković S, Dežđek D, Slavica
A, Hartl GB, Krueger B (2014) High genetic diversity and low
population structure in red foxes (Vulpes vulpes) from Croa-
tia. Mamm Biol 79:77–80. https ://doi.org/10.1016/j.mambi
o.2013.10.003
Hall LK, Day CC, Westover MD, Edgel RJ, Larsen RT, Knight RN,
McMillan BR (2013) Vigilance of kit foxes at water sources: a
test of competing hypotheses for a solitary carnivore subject to
predation. Behav Process 94:76–82. https ://doi.org/10.1016/j.
bepro c.2012.12.007
Harvey L, 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 8:8. https ://doi.org/10.1371/
journ al.pone.00733 24
Haswell PM, Kusak J, Hayward MW (2017) Large carnivore
impacts are context-dependent. Food Webs 12:3–13. https ://doi.
org/10.1016/j.foowe b.2016.02.005
Hayward MW, Hayward GJ (2010) Potential amplification of territorial
advertisement markings by black-backed jackals (Canis mesome-
las). Behaviour 147:979–992. https ://doi.org/10.1163/00057
9510x 49943 4
Hayward M, Slotow R (2009) Temporal Partitioning of activity in large
African carnivores: tests of multiple hypotheses. S Afr J Wildl Res
39:109–125. https ://doi.org/10.3957/056.039.0207
Hayward MW, Ortmann S, Kowalczyk R (2015) Risk perception by
endangered European bison Bison bonasus is context (condi-
tion) dependent. Landscape Ecol 30:2079–2093. https ://doi.
org/10.1007/s1098 0-015-0232-2
Heck RH, Thomas S, Tabata L (2012) Two-level models with count
data. Multilevel modeling of categorical outcomes using IBM
SPSS. Routledge Academic, New York, pp 329–398
Hernandez L, Laundre JW (2005) Foraging in the ‘landscape of fear’
and its implications for habitat use and diet quality of elk Cervus
elaphus and bison Bison bison. Wildl Biol 11:215–220. https ://
doi.org/10.2981/0909-6396(2005)11[215:fitlo f]2.0.co;2
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

582 Oecologia (2018) 187:573–583
1 3
Hoffmann M, Sillero-Zubiri C (2016) Vulpes vulpes. The IUCN red list
of threatened species 2016: e.T23062A46190249, http://dx.doi.
org/10.2305/IUCN.UK.2016-1.RLTS.T2306 2A461 90249 .en.
Accessed 25 May 2017
Holling CS (1959) Some characteristics of simple types of predation
and parasitism. Can Entomol 91:385–398. https ://doi.org/10.4039/
Ent91 385-7
Jones ME, Apfelbach R, Banks PB, Cameron EZ, Dickman CR, Frank
A, McLean S, McGregor IS, Müller-Schwarze D, Parsons MH,
Sparrow E, Blumstein DT (2016) A nose for death: integrat-
ing trophic and informational networks for conservation and
management. Front Ecol Evol 4:124. https ://doi.org/10.3389/
fevo.2016.00124
Kauffman MJ, Brodie JF, Jules ES (2010) Are wolves saving Yel-
lowstone’s aspen? A landscape-level test of a behaviorally
mediated trophic cascade. Ecology 91:2742–2755. https ://doi.
org/10.1890/09-1949.1
Kotler BP, Brown JS (1990) Rates of seed harvest by two spe-
cies of gerbilline rodents. J Mammal 71:591–596. https ://doi.
org/10.2307/13817 98
Kotler BP, Brown J, Mukherjee S, Berger-Tal O, Bouskila A (2010)
Moonlight avoidance in gerbils reveals a sophisticated inter-
play among time allocation, vigilance and state-dependent for-
aging. Proc R Soc B 277:1469–1474. https ://doi.org/10.1098/
rspb.2009.2036
Krofel M, Kos I (2010) Scat analysis of gray wolves (Canis lupus) in
Slovenia. ZbGL 91:85–88
Kuijper DPJ, Sahlen E, Elmhagen B, Chamaille-Jammes S, Sand H,
Lone K, Cromsigt JPGM (2016) Paws without claws? Ecologi-
cal effects of large carnivores in anthropogenic landscapes. Proc
R Soc B 283:20161625. https ://doi.org/10.1098/rspb.2016.1625
Leo V, Reading RP, Letnic M (2015) Interference competition:
odours of an apex predator and conspecifics influence resource
acquisition by red foxes. Oecologia 179:1033–1040. https ://doi.
org/10.1007/s0044 2-015-3423-2
Lesmeister DB, Nielsen CK, Schauber EM, Hellgren EC (2015) Spatial
and temporal structure of a mesocarnivore guild in midwestern
North America. Wildl Monogr 191:1–61. https ://doi.org/10.1002/
wmon.1015
Letnic M, Greenville A, Denny E, Dickman CR, Tischler M, Gordon C,
Koch F (2011) Does a top predator suppress the abundance of an
invasive mesopredator at a continental scale? Glob Ecol Biogeogr
20:343–353. https ://doi.org/10.1111/j.1466-8238.2010.00600 .x
Linnell JD, Strand O (2000) Interference interactions, co-existence and
conservation of mammalian carnivores. Divers Distrib 6:169–176.
https ://doi.org/10.1046/j.1472-4642.2000.00069 .x
Lourenco R, Penteriani V, Rabaca JE, Korpimaki E (2014) Lethal
interactions among vertebrate top predators: a review of concepts,
assumptions and terminology. Biol Rev 89:270–283. https ://doi.
org/10.1111/brv.12054
Lyly MS, Villers A, Koivisto E, Helle P, Ollila T, Korpimaki E (2015)
Avian top predator and the landscape of fear: responses of mam-
malian mesopredators to risk imposed by the golden eagle. Ecol
Evol 5:503–514. https ://doi.org/10.1002/ece3.1370
Lynch E, Angeloni L, Fristrup K, Joyce D, Wittemyer G (2013) The
use of on-animal acoustical recording devices for studying ani-
mal behavior. Ecol Evol 3:2030–2037. https ://doi.org/10.1002/
ece3.608
Lynch E, Northrup JM, McKenna MF, Anderson CR, Angeloni L, Wit-
temyer G (2015) Landscape and anthropogenic features influence
the use of auditory vigilance by mule deer. Behav Ecol 26:75–82.
https ://doi.org/10.1093/behec o/aru15 8
Marsack P, Campbell G (1990) Feeding-behavior and diet of Din-
goesin the Nullarbor region, Western-Australia. Aust Wildl Res
17:349–357
Mech LD, Boitani L (2005) Wolves: Behaviour, ecology and conserva-
tion. University of Chicago Press, United States of America
Mech LD, Boitani L (2010) Canis lupus. The IUCN redlist of threat-
ened species 2010: e.T3746A10049204, http://dx.doi.org/10.2305/
IUCN.UK.2010-4.RLTS.T3746 A1004 9204.en. Accessed 25 May
2017
Miller BJ, Harlow HJ, Harlow TS, Biggins D, Ripple WJ (2012)
Trophic cascades linking wolves (Canis lupus), coyotes (Canis
latrans), and small mammals. Can J Zool 90:70–78. https ://doi.
org/10.1139/z11-115
Moseby KE, Stott J, Crisp H (2009) Movement patterns of feral pred-
ators in an arid environment—implications for control through
poison baiting. Wildl Res 36:422–435. https ://doi.org/10.1071/
wr080 98
Mukherjee S, Zelcer M, Kotler BP (2009) Patch use in time and
space for a meso-predator in a risky world. Oecologia 159:661–
668. https ://doi.org/10.1007/s0044 2-008-1243-3
Murdoch JD, Munkhzul T, Buyandelger S, Sillero-Zubiri C
(2010) Survival and cause-specific mortality of corsac and
red foxes in Mongolia. J Wildl Manag 74:59–64. https ://doi.
org/10.2193/2009-059
Nellemann C (1998) Habitat use by muskoxen (Ovibos moschatus) in
winter in an alpine environment. Can J Zool 76:110–116. https ://
doi.org/10.1139/cjz-76-1-110
Newsome TM, Ripple WJ (2014) A continental scale trophic cascade
from wolves through coyotes to foxes. J Anim Ecol 84:49–59.
https ://doi.org/10.1111/1365-2656.12258
Newsome TM, Ballard GA, Crowther MS, Dellinger JA, Fleming PJS,
Glen AS, Greenville AC, Johnson CN, Letnic M, Moseby KE,
Nimmo DG, Nelson MP, Read JL, Ripple WJ, Ritchie EG, Shores
CR, Wallach AD, Wirsing AJ, Dickman CR (2015) Resolving the
value of the dingo in ecological restoration. Restor Ecol 23:201–
208. https ://doi.org/10.1111/rec.12186
Newsome TM, Greenville AC, Ćirović D, Dickman CR, Johnson CN,
Krofel M, Letnic M, Ripple WJ, Ritchie EG, Stoyanov S, Wirsing
AJ (2017) Top predators constrain mesopredator distributions. Nat
Commun 8:15469. https ://doi.org/10.1038/ncomm s1546 9
O’Mahony D, Lambin X, MacKinnon JL, Coles CF (1999) Fox pre-
dation on cyclic field vole populations in Britain. Ecography
22:575–581. https ://doi.org/10.1111/j.1600-0587.1999.tb005 46.x
Palacios MD, Warren DT, McCormick MI (2016) Sensory cues of a
top-predator indirectly control a reef fish mesopredator. Oikos
125:201–209. https ://doi.org/10.1111/oik.02116
Palomares F, Caro T (1999) Interspecific killing among mammalian
carnivores. Am Nat 153:492–508. https ://doi.org/10.1086/30318 9
Pasanen-Mortensen M, Elmhagen B (2015) Land cover effects on
mesopredator abundance in the presence and absence of apex
predators. Acta Oecol 67:40–48. https ://doi.org/10.1016/j.actao
.2015.04.002
Patalano M, Lovari S (1993) Food habits and trophic niche overlap
of the wolf Canis lupus, L. 1758 and the red fox Vulpes vulpes
(L. 1758) in a mediterranean mountain area. Rev Ecol Terre Vie
48:279–294
Peters RP, Mech LD (1975) Scent-marking in wolves. Am Sci
63:628–637
Petrov PR, Popova ED, Zlatanova DP (2016) Niche partitioning among
the red fox Vulpes vulpes (L.), Stone Marten Martes foina (Erx-
leben) and Pine Marten Martes martes (L.). in two mountains in
Bulgaria. Acta Zool Bulg 68:375–390
Poulle ML, Artois M, Roeder JJ (1994) Dynamics of spatial rela-
tionships among members of a fox group (Vulpes-vulpes,
Mammalia, Carnivora). J Zool 233:93–106. https ://doi.
org/10.1111/j.1469-7998.1994.tb052 64.x
Prugh LR, Stoner CJ, Epps CW, Bean WT, Ripple WJ, Laliberte AS,
Brashares JS (2009) The rise of the mesopredator. Bioscience
59:779–791. https ://doi.org/10.1525/bio.2009.59.9.9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

583Oecologia (2018) 187:573–583
1 3
Purcell B (2010) Dingo. CSIRO Publishing, Collingwood
Randa LA, Cooper DM, Meserve PL, Yunger JA (2009) Prey switch-
ing of sympatric canids in response to variable prey abundance. J
Mammal 90:594–603. https ://doi.org/10.1644/08-mamm-a-092r1
.1
Ripple WJ, Estes JA, Beschta RL, Wilmers CC, Ritchie EG, Heb-
blewhite M, Berger J, Elmhagen B, Letnic M, Nelson MP,
Schmitz OJ, Smith DW, Wallach AD, Wirsing AJ (2014) Status
and ecological effects of the world’s largest carnivores. Science
343:1241484. https ://doi.org/10.1126/scien ce.12414 84
Ripple WJ, Estes JA, Schmitz OJ, Constant V, Kaylor MJ, Lenz A,
Motley JL, Self KE, Taylor DS, Wolf C (2016) What is a trophic
cascade? Trends Ecol Evol 31:842–849. https ://doi.or g/10.1016/j.
tree.2016.08.010
Ritchie EG, Johnson CN (2009) Predator interactions, mesopredator
release and biodiversity conservation. Ecol Lett 12:982–998. https
://doi.org/10.1111/j.1461-0248.2009.01347 .x
Romanić SH, Kljaković-Gašpić Z, Bituh T, Žužul S, Dvoršćak M,
Fingler S, Jurasović J, Klinčić D, Marović G, Orct T, Rinkovec
J, Stipičević S (2016) The impact of multiple anthropogenic con-
taminants on the terrestrial environment of the Plitvice Lakes
National Park. Croatia. Environ Monit Assess 188:16. https ://
doi.org/10.1007/s1066 1-015-5030-4
Ruzicka RE, Conover MR (2012) Does weather or site characteris-
tics influence the ability of scavengers to locate food? Ethology
118:187–196. https ://doi.org/10.1111/j.1439-0310.2011.01997 .x
Scheinin S, Yom-Tov Y, Motro U, Geffen E (2006) Behavioural
responses of red foxes to an increase in the presence of golden
jackals: a field experiment. Anim Behav 71:577–584. https ://doi.
org/10.1016/j.anbeh av.2005.05.022
Schmitz OJ, Krivan V, Ovadia O (2004) Trophic cascades: the primacy
of trait-mediated indirect interactions. Ecol Lett 7:153–163. https
://doi.org/10.1111/j.1461-0248.2003.00560 .x
Selva N, Jedrzejewska B, Jedrzejewski W, Wajrak A (2005) Factors
affecting carcass use by a guild of scavengers in European temper-
ate woodland. Can J Zool 83:1590–1601. https ://doi.org/10.1139/
z05-158
Shrader AM, Kerley GI, Brown JS, Kotler BP (2012) Patch Use in
Free-ranging goats: does a large mammalian herbivore forage like
other central place foragers? Ethology 118:967–974
Šikić Z (2007) Plitvice Lakes National Park managementplan. Minis-
try of Culture of the Republic of Croatia, Zagreb, p 169
Slavica A, Severin K, Čač Ž, Cvetnić Ž, Lojkić M, Dež–dek Konjević
D, Pavlak M, Budinšćak Z (2010) Model širenjasilvatične
bjesnoće na teritoriju Republike Hrvatske tijekom perioda
odtrideset godina. Vet Stanica 41:199–210
Soulé ME, Bolger DT, Alberts AC, Wrights J, Sorice M, Hill S (1988)
Reconstructed dynamics of rapid extinctions of chaparral-requir-
ing birds in urban habitat islands. Conserv Biol 2:75–92. https ://
doi.org/10.1111/j.1523-1739.1988.tb003 37.x
Stahlberg S, Bassi E, Viviani V, Apollonio M (2017) Quantifying
prey selection of Northern and Southern European wolves (Canis
lupus). Mamm Biol 83:34–43. https ://doi.org/10.1016/j.mambi
o.2016.11.001
Štrbenac A, Huber D, Kusak J, Majić-Skrbinšek A, Frković A, Štahan
Ž, Jeremić-Martinko J, Desnica S, Štrbenac P (2005) Wolf man-
agement plan for Croatia. State Institute for Nature Protection,
Zagreb
Sutherland WJ, Freckleton RP, Godfray HCJ, Beissinger SR, Benton
T, Cameron DD, Carmel Y, Coomes DA, Coulson T, Emmerson
MC, Hails RS, Hays GC, Hodgson DJ, Hutchings MJ, Johnson
D, Jones JPG, Keeling MJ, Kokko H, Kunin WE, Lambin X,
Lewis OT, Malhi Y, Mieszkowska N, Milner-Gulland EJ, Norris
K, Phillimore AB, Purves DW, Reid JM, Reuman DC, Thomp-
son K, Travis JMJ, Turnbull LA, Wardle DA, Wiegand T (2013)
Identification of 100 fundamental ecological questions. J Ecol
101:58–67. https ://doi.org/10.1111/1365-2745.12025
Treves A, Chapron G, Lopez-Bao JV, Shoemaker C, Goeckner AR,
Bruskotter JT (2017) Predators and the public trust. Biol Rev
92:248–270. https ://doi.org/10.1111/brv.12227
Trussell GC, Ewanchuk PJ, Matassa CM (2006) Habitat effects on
the relative importance of trait- and density-mediated indirect
interactions. Ecol Lett 9:1245–1252. https ://doi.org/10.111
1/j.1461-0248.2006.00981 .x
Vanak AT, Thaker M, Gompper ME (2009) Experimental examina-
tion of behavioural interactions between free-ranging wild and
domestic canids. Behav Ecol Sociobiol 64:279–287. https ://doi.
org/10.1007/s0026 5-009-0845-z
Wikenros C, Stahlberg S, Sand H (2014) Feeding under high risk
of intraguild predation: vigilance patterns of two medium-
sized generalist predators. J Mammal 95:862–870. https ://doi.
org/10.1644/13-mamm-a-125
Aliations
PeterM.Haswell1,2 · KatherineA.Jones1· JosipKusak3· MattW.Hayward1,4,5,6
1 School ofBiological Sciences, Bangor University, Bangor,
GwyneddLL572UW, UK
2 UK Wolf Conservation Trust, Butlers Farm, Beenham,
BerkshireRG75NT, UK
3 Department ofBiology, Veterinary Faculty, University
ofZagreb, Heinzelova 55, 10000Zagreb, Croatia
4 School ofEnvironment Natural Resources andGeography,
Bangor University, Bangor, GwyneddLL572UW, UK
5 Centre forAfrican Conservation Ecology, Nelson Mandela
Metropolitan University, PortElizabeth, SouthAfrica
6 Centre forWildlife Management, University ofPretoria,
Pretoria, SouthAfrica
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com