Olfactory cues and the value of information: voles interpret cues based on recent predator encounters

Abstract

Prey strategically respond to the risk of predation by varying their behavior while balancing the tradeoffs of food and safety. We present here an experiment that tests the way the same indirect cues of predation risk are interpreted by bank voles, Myodes glareolus, as the game changes through exposure to a caged weasel. Using optimal patch use, we asked wild-caught voles to rank the risk they perceived. We measured their response to olfactory cues in the form of weasel bedding, a sham control in the form of rabbit bedding, and an odor-free control. We repeated the interviews in a chronological order to test the change in response, i.e., the changes in the value of the information. We found that the voles did not differentiate strongly between treatments pre-exposure to the weasel. During the exposure, vole foraging activity was reduced in all treatments, but proportionally increased in the vicinity to the rabbit odor. Post-exposure, the voles focused their foraging in the control, while the value of exposure to the predator explained the majority of variation in response. Our data also suggested a sex bias in interpretation of the cues. Given how the foragers changed their interpretation of the same cues based on external information, we suggest that applying predator olfactory cues as a simulation of predation risk needs further testing. For instance, what are the possible effective compounds and how they change “fear” response over time. The major conclusion is that however effective olfactory cues may be, the presence of live predators overwhelmingly affects the information voles gained from these cues.

Significance statement
In ecology, “fear” is the strategic response to cues of risk an animal senses in its environment. The cues suggesting the existence of a predator in the vicinity are weighed by an individual against the probability of encounter with the predator and the perceived lethality of an encounter with the predator. The best documented such response is variation in foraging tenacity as measured by a giving-up density. In this paper, we show that an olfactory predator cue and the smell of an interspecific competitor result in different responses based on experience with a live-caged predator. This work provides a cautionary example of the risk in making assumptions regarding olfactory cues devoid of environmental context.

Full text

ORIGINAL ARTICLE
Olfactory cues and the value of information: voles interpret cues
based on recent predator encounters
Sonny S. Bleicher
1,2
&Hannu Ylönen
2
&Teemu Käpylä
2
&Marko Haapakoski
2
Received: 26 July 2018 / R evised: 4 November 2018 / Accepte d: 9 November 2018 / Published online: 26 November 2018
#The Author(s) 2018
Abstract
Prey strategically respond to the risk of predation by varying their behavior while balancing the tradeoffs of food and safety. We
present here an experiment that tests the way the same indirect cues of predation risk are interpreted by bank voles, Myodes
glareolus, as the game changes through exposure to a caged weasel. Using optimal patch use, we asked wild-caught voles to rank
the risk theyperceived. We measured their response to olfactory cues in the form of weasel bedding, a sham control in the formof
rabbit bedding, and an odor-free control. We repeated the interviews in a chronological order to test the change in response, i.e.,
the changes in the value of the information. We found that the voles did not differentiate strongly between treatments pre-
exposure to the weasel. During the exposure, vole foraging activity was reduced in all treatments, but proportionally increased in
the vicinity to the rabbit odor. Post-exposure, the voles focused their foraging in the control, while the value of exposure to the
predator explained the majority of variation in response. Our data also suggested a sex bias in interpretation of the cues. Given
how the foragers changed their interpretation of the same cues based on external information, we suggest that applying predator
olfactory cues as a simulation of predation risk needs further testing. For instance, what are the possible effective compounds and
how they change Bfear^response over time. The major conclusion is that however effective olfactory cues may be, the presence
of live predators overwhelmingly affects the information voles gained from these cues.
Significance statement
In ecology, Bfear^is the strategic response to cues of risk an animal senses in its environment. The cues suggesting the existence
of a predator in the vicinity are weighed by an individual against the probability of encounter with the predator and the perceived
lethality of an encounter with the predator. The best documented such response is variation in foraging tenacity as measured by a
giving-up density. In this paper, we show that an olfactory predator cue and the smell of an interspecific competitor result in
different responses based on experience with a live-caged predator. This work provides a cautionary example of the risk in
making assumptions regarding olfactory cues devoid of environmental context.
Keywords Predator-prey interactions .Giving-up density .Perceived risk .Evolutionary game theory .Y-ma ze
Introduction
Perhaps one of the greatest questions occupying behavioral
ecologists, as well as some neurobiologists today, is how an-
imals interpret information they gather in their environment
(e.g., Dielenberg and McGregor 2001;Zimmeretal.2006;
Pakanen et al. 2014;Drakeleyetal.2015). In many instances,
animals born in the lab, even first generation, exhibit weak-
ened responses to predators which they would encounter on a
day-to-day basis in nature (e.g., Burns et al. 2009;Feenders
et al. 2011; Troxell-Smith et al. 2015). Similarly, but on a
larger scale, ecologists have been puzzling over the inability
Communicated by C. Soulsbury
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s00265-018-2600-9) contains supplementary
material, which is available to authorized users.
*Sonny S. Bleicher
bleicherss@gmail.com
1
Environmental Science and Policy Department, George Mason
Univesity, Fairfax, VA 22030, USA
2
Konnevesi Research Station, University of Jyväskylä, P.O. Box 35,
40014 Jyväskylä, Finland
Behavioral Ecology and Sociobiology (2018) 72: 187
https://doi.org/10.1007/s00265-018-2600-9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

of prey to recognize risk from unfamiliar, invasive, predators
(Carthey and Banks 2014). From the opposite side, some wild
species, such as Australian bush rats (Rattus fuscipes), have
been shown to exhibit stress when exposed to urine and fur of
red foxes (Vulpes vulpes) despite sharing no evolutionary his-
tory with any foxes (Banks 1998; Spencer et al. 2014). These
examples have led to an increased debate on the value of
information gained from cues of predation risk, especially in
the absence of an actual predator.
To bring an example of this debate, we once more return to
the prey naiveté literature and to the Australian researchers
who pioneer this research. Banks and Dickman (2007)offer
a framework to think about how prey interpret risk cues of
novel predators as three levels of naiveté. They argue that
multiple behavioral mechanisms are acting to prevent proper
interpretation and responses to those novel risks. The first is
the lack of a neurological pathway to recognize the cue as a
predator cue altogether (e.g., Blumstein et al. 2000;Carthey
et al. 2017). The second level is a mismatch between the
interpretation of the cue and the behavioral response. The best
example for this type of naiveté is New Zealand’s kakapo
parrot (Strigops habroptilus)thatrecognizescats(Felis catus)
as predators but stares them down as opposed to fleeing (Karl
and Best 1982). Last, they argue that simply being bad at
using your tools against the novel predator is a form of
naiveté, and in our opinion, that is still up for debate.
We began this introduction on the broad and applied impli-
cations of responding to predator cues because Bfear^re-
sponses are some of the strongest traits inherited from ances-
tors. Anti-predator traits currently used by living beings were
refined by millions of years of necessity to avoid predation
(Vincent and Brown 2005). Otherwise, the current species
would have experienced extinction along their line of decent
(Darwin 1859). Based on this simple Darwinistic logic, we
expect and assume that animals would recognize the odor of
their evolutionarily known predators as a sign of risk. This
logical assumption is still controversial.
Some studies, both in the laboratory and in the field veri-
fied the expected responses of prey to scent-mediated increase
in predation risk from known predators (Ylönen and
Ronkainen 1994; Koskela et al. 1997; Fuelling and Halle
2004;Ylönenetal.2006). However, an equally large number
of studies have found weak or no correlations between olfac-
tory cues and anti-predator behaviors (Mappes et al. 1998;
Trebatická et al. 2012). The ecological literature is filled with
examples of predator odor impacting the behavior of prey in
varying ways. For example, small mammal traps sprayed by
odor, regardless if predator or conspecific, yield better trap-
ping success than odorless traps (Apfelbach et al. 2005).
Passerines will nest in boxes sprayed with weasel scent when
competition for odor-free boxes gets high. However, in this
case, the stress in the weasel scent–sprayed boxes resulted in
increased fledging rate (Monkkonen et al. 2009). But perhaps
one of the strongest examples of a mismatch is derived from
native Australian marsupials which fail to appropriately re-
spond to dingo scents, for example, the western gray kanga-
roos that instead of avoiding the predator odors are drawn to
them (Parsons et al. 2005;Mella et al. 2014).
So why do we find variations in response to predator odor?
A recent review by Parsons et al. (2018) formulated the path-
ways by which predator odors function and are recognized.
They state that three complementary catalysts can generate a
response to such cues: neurobiological, chemical, and contex-
tual. They suggest that these interact to determine whether a
scent is perceived as risky or attractive. Thus, we assume that
the information gleaned from the olfactory cue can be
interpreted in a different manner in different environmental
or temporal context, in different habitats, and under different
social regimes. We speculate that human researchers underes-
timate the cognitive and neural abilities of prey individuals to
interpret the information in a cue. In colloquial and anthropo-
morphic terms, we can observe a number of examples: (1) Not
BDanger: a predator is here!^, but BA predator was here a
while ago and I can do whatever until it comes back^;(2)
Not BI smell predator X therefore I must run or hide^,but
BPredator X poses Y amount of risk to me. Therefore, I can
take some risk to get food my competitors may avoid.^
The reason we can speculate that the responses are more
complex lies in the basic difference between the neurological
approach to predation risk (and post-traumatic stress) and that
of behavioral ecology. Neurology approaches Bfear^as the
activation of a pathway between the hippocampus and the
amygdala following an unforeseen exposure to a risk cue
(Gross and Canteras 2012), i.e., as the response to an BAct
of God^. However, as ecologists, we must argue here that an
anti-predator behavior is more than an instinctive freezing or
fleeing response. Instead, it is a strategic response based on the
innate and acquired information an animal processes in the
cerebral cortex, which in turn influences and regulates the
production of stress hormones in the amygdala (Brown 2010).
In this paper, we set out to test how the interpretation of an
olfactory cue may change based on the available information a
prey individual has on the acute danger from predators in
close vicinity. Using an interview chamber approach (cf.
Bleicher 2014; Bleicher and Dickman 2016), we aim to Bask^
the animals how they perceived the difference between olfac-
tory cues of a predator, a cue of a competitor (herbivore) as a
sham control, and a cue-less control. We request the forgive-
ness of some sensitive readers for the use of colloquiality, but
we perceive this anthropomorphism (an interview) to be ap-
propriate in this context. We apply this method (a type of
bioassay) to determine how an individual animal perceives a
Bquestion^we pose. We then follow-up with repeated mea-
sures to address how the individual’s perception changes over
time or based on the experience we subject it to. In this case,
we interview individuals to assess how they interpret the
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information posed by an olfactory cue of a predator. We are
interested in understanding the changes in an animal’sper-
ceived risk on three scenarios: (1) after a few weeks in the
lab and before exposure to a live predator, (2) while being
exposed to a caged predator in an adjacent room, and (3) after
that exposure. We decided to approach this example using a
model system studied for decades, the bank vole (Myodes
glareolus) and the least weasel (Mustela nivalis nivalis)as
its predator (cf. Korpimäki et al. 1996; Sundell et al. 2008).
In this experiment, we expect that the voles would use
resources optimally, balancing the tradeoffs of food and safety
(cf. Brown 1988). We hypothesize that the odor of a predator
would cause the voles to increase their vigilance and thus
reduce their foraging in patches (cf. Brown 1999). We also
expect the interpretation of the predator’s cue to change based
on the exposure to the predator. From other empirical studies
with small mammals, we can expect that exposure to a pred-
ator can have a lingering effect in the apprehension sensed by
the prey (Dall et al. 2001). Last, we hypothesize that the en-
ergy needs, and the corresponding behavioral response, of the
two sexes would not be the same. In the bank vole, thefemales
are territorial and take the brunt of the energetic costs of re-
production while males move through the landscape in search
of copulations (Horne and Ylönen 1996; Trebatická et al.
2012). In early spring, at the start of the breeding season, this
sex bias in energetic tradeoffs should be at its peak. Therefore,
we expect that the males would show greater apprehension
around the predator cues as their transient nature exposes them
to more predation risk than the females who are more sessile
in their territories and resource focused at this time of year.
Given these expectations, we aim to answer four questions
in this three-stage experiment:
1. Do voles differentiate between the odor of a predator and
that of a competitor? If so, do they forage less in the
presence of the odor of a predator than that of a
competitor?
2. Does recent exposure to the predator invigorate a response
to an olfactory cue of that predator?
3. Does information about an active predator nearby affect
the activity patterns of the prey species in relation to both
the predator and the competitor cues?
4. Does the information about a live predator linger post-
exposure? If so, for how long?
Methods
Study species
The bank vole is one of the most common small rodents in
northern temperate and boreal forests (Stenseth 1985). It is
granivorous-omnivorous (Hansson 1979) and can live in a
wide range of forest habitats. In central Finland, bank voles
are known to breed between three and five times within the
breeding season, May through September. Their average litter
size is 5–6 pups. Bank voles are prey for a diverse predator
assemblage which includes the least weasel and the stoat
(Mustela erminea)(Ylönen1989).
The least weasel is a specialist predator on rodents and the
major cause of mortality in boreal voles, especially during a
population’s decline (Korpimäki et al. 1991; Norrdahl and
Korpimäki 1995,2000). Bank voles are able to detect the odor
of mustelids and change their behavior accordingly (Ylönen
1989;Jędrzejewska and Jędrzejewski 1990;Jędrzejewski and
Jędrzejewska 1990; Mappes and Ylönen 1997;Mappesetal.
1998; Bolbroe et al. 2000; Pusenius and Ostfeld 2000;Ylönen
et al. 2003). The least weasel, like other small mustelids, has a
very potent anal gland secretion (Apfelbach et al. 2005)which
has been shown to be interpreted by voles as a cue of predation
risk (e.g., Ylönen et al. 2006;Haapakoskietal.2012).
The study was conducted at the Konnevesi Research Station
of the University of Jyväskylä, 70 km north of Jyväskylä. The
study was conducted in the laboratory and the bank voles were
trapped from the forests surrounding the research station (N 62°
41′18″,E26°17′12″) as well as in the forests near Oulainen (N
64° 17′56″,E24°49′35″). The voles were housed in solitary
standard laboratory rodent boxes (43 × 26 × 15 cm
3
). Wood
chips were used to keep the cages dry, hay was provided as
bedding material, and rodent food pellets and fresh water were
available ad libitum. Light: dark time ratio in the animal rooms
was set to 18:6 h, which corresponds roughly to the natural
light-dark regime during the experimental period. Four days
prior to the experiment, the animals were removed from ad
libitum food and put on a diet of poor-quality food, 3 g of millet
per day, while meeting the basic energy needs of these animals
(cf. Eccard and Ylönen 2006). The change to a poor diet was
givenasanincentivefortheanimalstokeepforaginginthe
novel environment of our study systems and counteract
neophobia expected in satiated animals (Amézquita et al.
2013; Näslund and Johnsson 2016). All applicable internation-
al, national, and/or institutional guidelines for the use of animals
were followed and were approved by the animal experimenta-
tion committee of the University of Jyväskylä, permit number:
ESAVI/6370/04.10.07/2014.
Study system design
Six interview chamber systems were constructed in concor-
dance with Bleicher (2014) and Bleicher and Dickman (2016)
and Bleicher et al. 2018. Each system was constructed from a
30 cm diameter bucket (as a nest box) attached by 5 cm di-
ameter, 30 cm long PVC tubing to three gray plastic storage
bins (hereafter rooms) 40 × 30 × 23 cm high (Appendix S1).
Each room was equipped with a square (19 × 19 × 10 cm high)
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box (henceforth patch) with two 5-cm-diameter holes drilled
in the side to allow the vole access (Appendix S2). Each patch
was filled with 1 l of sand and was set with 1.5 ± 0.02 g of
millet. The total amount of food in the system equaled one and
a half times the daily needed energy for a foraging vole (cf.
Eccard and Ylönen 2006).
The rooms were equipped with a cue-box 11 × 11 × 6 cm
attached to the roof of the room to create different treatments:
weasel bedding (wood shavings, urine, hair, and fecal matter),
rabbit (Oryctolagus cuniculus) bedding, and a control (cf.
Sundell et al. 2008). Each cue-box was replenished with fresh
bedding daily. The cardinal directions in which each treatment
was placed was randomized between systems.
On direct predator exposure nights, two of the three-room
interview systems (Nos. 5 and 6), were converted to four-
room systems adding a room with hardware cloth screen di-
rectly adjacent to a cage (30 × 52 × 26 cm) in which a live
weasel was caged. The weasel was caged only during exper-
iments and was released into a larger 2-m-long holding pen
when not in use (Appendix S3). To adjust for the larger sys-
tems, we decreased the amount of food per patch to 1.1 ±
0.02 g and to maintain the same encounter rate with a food
item, decreased the amount of sand in the patch to 0.75 l. To
avoid the sound of the caged weasel traveling throughout the
system, the rooms adjacent to the weasel cage were located in
a different lab-room with the PVC tubes drilled through the
wooden wall between the lab-rooms.
We define an experimental round as the time an animal spent
in a system on a given night. At the start of every round, a single
volewasplacedinthenestboxandhadaccesstoeachofthe
different patches via the PVC tubing. Each vole was allowed
2 h to forage, following the protocol of Bleicher (2012), and the
expectation that this allowed sufficient time for voles to move
between, and forage in, the different treatments while not giving
enough time for habituation to the treatments. For the short
period of time, diverging from the Bnormal^patch-use protocol
(Bedoya-Perez et al. 2013) was aimed at getting the initial re-
sponse of the animal, i.e., its Bgut feeling^and not measure its
ability to understand how we are manipulating it on the long
run. We did not control for conspecific odors but avoided cross-
contamination by consistently keeping each olfactory treatment
in the same rooms consistently.
Brown (1988) stated that an animal foraging in a patch will
quit harvesting when the costs associated with resource harvesting
coupled with the costs associated with predation risk equal the
energetic value of the patch as perceived by the forager. As the
vole depletes a patch, the diminishing returns render other patches
more valuable (a missed opportunity cost). The difference in
missed opportunity costs drives animals across the landscape (be-
tween rooms) examining and comparing patches (Smith and
Brown 1991;Berger-TalandKotler2014). At the end of each
round, the amount of resources the forager did not use in the
patch, due to the aforementioned costs, is the giving-up density
(GUD). We ran up to five rounds (of 2 h each) per Bnight^starting
at 17:00 in accordance with activity patterns recorded by Ylönen
(1988). The GUD as the measurement in our systems provides for
data collection that is independent of the observer, a blinded de-
sign, thus allowing for avoidance of sampler bias.
Forty field-caught bank voles were interviewed (20 male
and 20 females) for nine nights between the nights of May
6th–May 26th, 2018. Each individual vole was Binterviewed^
for nine nights in a row, randomizing systems and hours of the
interview as to minimize the effects of time and location. Of
the nine nights, the first two nights an animal was interviewed
were without the live predator (hereafter pre-exposure). On
the third and fourth nights, an animal was exposed to both a
live weasel in addition to the olfactory cues (hereafter expo-
sure). On the remaining five nights, the animal was exposed
again to olfactory cues only (hereafter post-exposure). At the
end of each round, the animal was removed and returned to its
holding container and fed with extra 3 g of millet. Each of the
patches was sieved and the weight of remaining resources
recorded to obtain the GUD. The systems were reset after each
2-h round with fresh new patches and the next round run with
a new vole.
Data analyses
For the analysis of the data in this paper, we did not use the
GUD as in a traditional approach. To be able to compare the
three-room and four-room system data we preferred to use the
proportion of resources harvested (initial density-GUD/initial
density). Because the runs are limited in time (2 h), the GUD
does not reflect a quitting density but more of an initial re-
sponse (if prolonged, we expected habituation and thus loss of
relevance). Therefore, the traditional approach to the analysis
of GUD data (e.g., St Juliana et al. 2011; Shrader et al. 2012),
a general linear model (GLM), was not meaningful in this
instance and would result in low explanatory power (we
provide this weak analysis in Appendix S4). It is important
to note that we excluded the live weasel treatment from all
analyses (except for random forests analysis) to allow for a
fully crossed experimental design. We felt confident in our
ability to do this as only two voles had foraged in the live
weasel patches, and those under 0.02 g of millet within the
margin of error. For the same reason, we also only used the
first two nights of the post-exposure rounds (marginal varia-
tion in 5 days post-exposure Appendix S4).
As an alternative, we used a combination of three statistical
approaches. First, we ran log-linear tabulations for foraging ac-
tivity (foraged vs. unforaged patches) (cf. Bleicher et al. 2016).
We tabulated the data as a factor of chronology (pre-exposure,
exposure, post-exposure) and treatment. We ran the analysis as a
three-way contingency table using Vassar Stats calculator. We
used this analysis to test the effects of each variable, the interac-
tion of the two, and each nested within the other.
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Second, we used Statistica© to run a series of tests of con-
cordance as a repeated measure testing the response of indi-
viduals using the proportion of resources harvested as the
dependent variable. Using Friedman’s tests of concordance,
we first tested whether the vole’s foraging tenacity repeated
the same pattern based on the chronology of the experiment.
In the second test, we compared their response to olfactory
cues. We also compared the foraging tenacity of individuals as
factors of the interaction of treatment and chronological order,
as well as treatment nested within chronological order.
Last, we chose to run our data through a random-forest re-
gression analysis. This Bayesian machine-learning test uses the
data and repeated sampling to determine the importance of
factors in the decision-making process of voles. In this analysis,
we used a normalized proportion of resources harvested (within
patch) using an arcsine × √transformation as the dependent
variable. We used chronology, treatment (including live wea-
sel), sex, and round (night within chronology). This analysis
generates a decision tree ranked by importance from the top
node to the lowest importance in final nodes. The final nodes
on the tree provide likely hypotheses for significant differences
but do not constitute statistical pairwise comparisons.
Data availability
All data generated or analyzed during this study are included
in this published article and its supplementary information
files.
Results
When we measured the individual’s response to risk using
foraging tenacity and tested whether they were in agreement
about the value of patches in the different treatments, we
found that there was no concordance between the voles in
response to the cues as a main effect (Table 1). However, we
found concordance in the change in foraging tenacity between
the three segments of the experiment (pre-, post-, during-ex-
posure). Here, we found that the interviews during-exposure
resulted in a mean rank, the lower the rank the greater the risk,
of 1.31. This compared to 2.46 and 2.23 for pre- and post-
exposure interviews, respectively.The interaction of treatment
and chronology, ranking the nine subcategories, found con-
cordance between the individuals as well.
When we measured concordance between treatments nested
under each of the chronological interviews, we found concor-
dance in risk assessment only during- and post-exposure.
During the exposure, the voles ranked the weasel-cue treatment
as the greatest risk with the rabbit and controls having similar
ranks. Post-exposure, the voles ranked the control as the least
dangerous and avoided both rabbit and weasel cues similarly.
Both the voles’foraging activity (number of patches visit-
ed) and tenacity (proportion of resources harvested) were im-
pacted similarly by the absence of the predator pre- and post-
exposure (Fig.1and Fig. 2A, respectively). The vole’sactivity
was lower in all types of patches when the weasel was nearby
(Table 2). In addition, they foraged less in patches than on
nights when the predator was absent (Appendix S4).
In the three-way contingency table, the voles were more
active in control patches as expected pre- and post-exposure.
In the sham control (rabbit bedding), they were most active
during nights of direct exposure to the weasel (Fig. 1).
Proportionally, there was no difference in activity for
weasel-cue treatment patches pre- and post-exposure. The ma-
jor difference between the pre-exposure interviews to the post-
exposure ones was the activity rate in the sham treatments
decreasing from 64 to 54%.
Addressing the foraging tenacity, the proportion of food for-
aged in the patches was better addressed using a random-forest
analysis (Table 3) than the traditional method of GLM
(Appendix S4). The decision-tree risk estimates were low,
0.0217 ± 0.001 standard error and 0.025 ± 0.002 for the training
and test of the model, respectively. This analysis ranked the
variables by importance, i.e., the proportion of the decisions it
influenced in the model. Chronology was the most important
influencing 100% of decisions. Treatment influenced 85% of
decisions while the sex of the vole influenced 17% and the
repetition (round 1 or 2) influenced 16% of decisions.
Reading the decision tree is from left to right (Fig. 3). The
higher (further left) the split in the tree, the more important that
split variable is in influencing the decision-making process of
the foraging animal. The major split was chronology based,
separating the exposure from the interviews where there were
cues alone (Fig. 2B). We will present the results first for the
direct exposure interviews and then in a separate paragraph
address the pre- and post-exposure interviews.
Under direct exposure, only twice were the patches in di-
rect view of the weasel foraged. Thus, the fact that this cate-
gory resulted in a terminal node this early is suggestive of a
high importance to that treatment. The analysis then revealed
that there is no difference in response to all the other treat-
ments. Females were as weary in the first and second night of
an interview, foraging 14.4% and 15.3% of patches respec-
tively. Meanwhile, males decreased foraging from 12% on the
first night to 8% on the second.
Pre- and post-exposure, voles gave greater attention to the
cue treatments. The model analyzes the dataset remaining after
each split in the tree and chooses the next split based on the
categories and variable that have the greatest variance in means.
Therefore, we present both the important splits revealed in the
decision-making of a population of voles (Table 3,Fig.3), but
also present the mean proportion of resources harvested within
each split category. Pre-exposure, the control patches were for-
aged to a mean of 19.4%, but after exposure, the foraging
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increased to 30.1% (Figs. 2,and3). The sexes then diverged on
the response between the weasel and rabbit (sham) cues. Males
foraged less, a mean of 16.7%, while females foraged resources
to a mean 21.9%. For the females, the no significant difference
appears between weasel (lower with 23.7%) and rabbit (higher
with 24.1%) treatments on nights when no live predator is pres-
ent. Males were more attuned to the differences between the
rabbit and the weasel cues, foraging 17.8% and 15.8% of re-
sources respectively. Post-exposure, the males took more risk
with the weasel cues foraging 19.4% of resources compared
with 11.1% pre-exposure.
Discussion
The voles clearly made decisions regarding the use of space
when interacting with olfactory cues. The information a forager
gains from olfactory cues varies based on a number of environ-
mental and temporal variables (Sih 1992; Lima and Bednekoff
1999; Gonzalo et al. 2009). The interpretation of the cues clear-
ly impact the strategic behaviors of the foragers and the way the
animals will balance the tradeoffs of food and safety (Brown
et al. 1999;Bythewayetal.2013; Bleicher 2017). This exper-
iment provides a clear example of how the same cues vary in
the information they provide over a short period of time.
We believe the strongest evidence supporting the fact that a
cue changes its meaning is the lack of a main effect to the cue
treatments in all three analyses. This suggests immediately that
each cue is not interpreted in the same way at each of the chro-
nological intervals. This is a reflection of the changes in the state
the animal is in. The animals in this experiment were wild
caught; however, they came into the experiment after a few
weeks in a lab setting with ad libitum resources available to them.
At the first encounter with our systems, the animal arrives
with a certain naiveté towards the cues. This could likely sug-
gest that the animals may not have perceived a risk cue as
relevant to their existence as lab animals. Previous laboratory
studies show anti-predatory behavioral responses to olfactory
cues manifested on larger scales in both movement and forag-
ing decisions (e.g., Ylönen et al. 2006; Haapakoski et al. 2015).
However, recent observations suggest that individuals approach
and inspect any odor cue scented trap or box, inspecting its
riskiness more carefully and adjusting responses. That study
shows evidence for variation in personality traits within a
Table 1 Compilation of
Freidman’s tests of concordance Variable Nested factors Ndf X
f
PW
Chronology 39 2 29.077 < 0.001 0.373
Treatment 39 2 3.273 0.195 0.042
Chronology (treatment) Pre-exposure 39 2 1.66 0.436 0.021
During-exposure 39 2 6.764 0.034 0.087
Post-exposure 39 2 10.839 0.004 0.08
Chronology × treatment 39 8 54.645 < 0.001 0.175
Nsample size, df degrees of freedom, X
f
Friedman’s chi-squared, Pprobability value, WKendall’s coefficient of
concordance
Fig. 1 Cumulative proportion of patches harvested by voles in the
systems. Each bar represents the foraging activity of 39 voles foraging
for two consecutive nights (one female removed showing signs of
pregnancy). On the x-axis, we state the olfactory treatments as collected
at the different chronological states of the experiment, pre-, post-, and
during-exposure to the live weasel. The value for the live weasel was
excluded from the statistical analysis of activity patterns but is presented
here for the comparative power it provides
187 Page 6 of 11 Behav Ecol Sociobiol (2018) 72: 187
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

population, and sex biases in risk taking (Korpela et al. 2011).
Accordingly, the trend of decrease in foraging between the
control and the weasel (with sham in between) suggests the
weasel cue may be interpreted as suspicious, and the animals
cautiously investigated those patches. The fact that the changes
between treatments intensified in the repeated measures sug-
gests that with the realization that the predator is a threat, all
the other cues also change in value (Parsons et al. 2018).
This experiment provides other examples of the shifting value
of information. The relative partiality of voles to forage in the
rabbit-cue (sham) treatment during-exposure provides another
strong example. The simplest explanation of this result lies in
the fact that a prey leaves cues for its predators to interpret
(Ylönen et al. 2003). On exposure nights, the voles are aware
of the imminent danger lurking in the system. Therefore, the
optimal strategy they can apply is to forage in the environment
that masks their presence mixed with competitor cues. Similar to
the idea of safety in numbers (e.g., Rosenzweig et al. 1997), a
forager can hide behind the smell of a prey of higher caloric
value. The rationale behind this strategy is that the predator
would approach that environment in search of a different type
of prey providing enough time for the less valuable forager to
escape. A number of model papers by Lima and Dill (1990)and
Brown (1992,1999) suggest that evolution would drive prey to
sacrifice resources in the form of competition due to risk of
predation. This is even strengthened by empirical evidence of
some species even cloaking their own odor as in the example
of ground squirrel masking their odor towards snakes with the
musk of other snakes (Clucas et al. 2008).
Fig. 2 Mean proportion of
resources harvested ± SE based
on (A) the chronological order of
interviews (x-axis) and (B) olfac-
tory cue treatments nested under
each chronological order. The
value for the live weasel was ex-
cluded from the statistical analy-
sis of activity patterns but is pre-
sented here for the comparative
power it provides. Note that these
values are the pre-normalized
values which were transformed
using a arcsine × sqrt
transformation
Table 2 Log-linear analysis using a three-way contingency table com-
paring the cumulative ratio of foraged to unforaged patches
Va r i ab l e G
2
df P
Treatment 10 2 0.0067
Chronology 58.86 2 < 0.0001
Treatment × chronology 74.12 12 < 0.0001
Treatment (chronology) 15.28 6 0.018
Chronology (treatment) 64.14 6 < 0.0001
df degrees of freedom, treatment olfactory cue type, chronology order of
interviews (pre-, during-, post-exposure)
Behav Ecol Sociobiol (2018) 72: 187 Page 7 of 11 187
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Post-exposure, the voles’foraging and visitation in both
weasel and rabbit-cue treatments were low while increasing
in the control. Two possible explanations, separate or com-
bined, would result in this observed decision. The first pos-
sibility is that the reduced foraging over the exposure
nights resulted in animals with higher energetic needs,
i.e., starved individuals. Hungry individuals can be forced
to move large distances to find resources (e.g.,
Haythornthwaite and Dickman 2006); however, they are
also likely to exploit resources to a greater extent when they
encounter a valuable patch (Raveh et al. 2011). The oppo-
site explanation would suggest that after experiencing the
existential fear during the exposure nights, the voles are
now more sensitive to the cue of the predator and fear being
flushed out by the Bcompetition^(the non-present rabbit)
into a risky situation. While this second possible hypothesis
stands in contradiction to the explanation we gave for the
observation we made on exposure nights, the amount for-
aged on those nights was significantly lower. We will not be
able to tease the strategic reasoning the voles are taking in
this instance, and this could be used for future experimen-
tation managing the animal’s energetic state.
In addition to the overwhelming effect of the live pred-
ator, the caged weasel, the random-forest analysis sug-
gested sex-dependent differences. Similarly, to energy
state-dependent decision-making, sexual selection drives
individuals of the opposite sexes to make decisions related
to mate finding and territoriality. In the bank vole, females
are territorial (Koskela et al. 1997) while males move large
distances in search of mates (Kozakiewicz et al. 2007). As a
virtue of living a sessile life, as females do, the risk asso-
ciated with cues are more relevant. Therefore, we do not see
habituation from one night to the next in the initial inter-
view. However, when the cues persist—and the level of risk
Table 3 Tree structure for
random-forest decision tree No. Child
node 1
Child
node 2
NμNode
var
Split variable Split
cons.
Split cat.1 Split
cat.2
1 2 3 359 0.157 0.025 CHRONO DURING
2 4 5 183 0.102 0.018 TREATMENT LIVE
WEAS-
EL
3 6 7 37 0.005 0.001 ROUND 1.5
41600
5 21 0.008 0.001
6 8 9 146 0.127 0.020 SEX MALES
7 10 11 66 0.101 0.015 ROUND 1.5
8 26 0.127 0.019
9 40 0.084 0.011
10 12 13 80 0.149 0.023 ROUND 1.5
11 40 0.144 0.026
12 40 0.153 0.019
13 14 15 176 0.213 0.026 TREATMENT WEASEL RABBIT
14 16 17 128 0.194 0.025 SEX MALES
15 18 19 62 0.167 0.018 TREATMENT WEASEL
16 20 21 35 0.158 0.019 CHRONO PRE
17 15 0.111 0.019
18 20 0.194 0.016
19 27 0.178 0.016
20 22 23 66 0.219 0.030 ROUND 1.5
21 24 25 41 0.239 0.031 TREATMENT RABBIT
22 20 0.237 0.023
23 21 0.241 0.039
24 25 0.187 0.027
25 26 27 48 0.263 0.027 CHRONO POST
26 17 0.194 0.029
27 31 0.301 0.022
Nnode sample size, μmean normalized proportion harvested, var node variance, cons. constant, cat.category,
CHRONO order of interviews (pre-, during-, post-exposure), TREATMENT cue type (weasel, rabbit olfactory
cues, control, or live weasel)
187 Page 8 of 11 Behav Ecol Sociobiol (2018) 72: 187
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is tuned up by exposure the predator—the opposite is cor-
rect. Post-exposure, we observe that females decreased for-
aging on the second night of the interview.
The transient nature of males means they are encounter-
ing the cues, and are assessing them, as novel cues each
time. We therefore find no habituation to the treatments in
the systems, but instead an increase in perceived risk from
the first to the second night of each interview. In direct
contact with the weasel, the entire harvest rate of males
drops by 25% from the first to the second night of the inter-
view. To the males, moving on larger scales, the repeated
encounter with the weasel may be a measure of greater wea-
sel encounter chances. Previous studies show that males
respond to mammalian predators by decreasing their explor-
atory behavior (Norrdahl and Korpimäki 1998).
Our setup allowed us to examine how the interpretationof a
cue changed based on the change of individual voles’envi-
ronment and state from partial naiveté to predators through a
period of heightened risk to gradually fading memory of dan-
ger in a post-predator environment. We found that by exam-
ining the relationship between cues simultaneously, we can
begin to extract some of the building blocks of the strategic
behaviors exhibited by a forager. While not surprising that the
responses to cues varied, the novelty here is that it calls the
interpretation of predation cues into scrutiny. The findings
here particularly question the way we administer predation
stress using olfactory cues. We are approaching the time
where we will be able to observe the responses an individual
makes as it stimulates neurobiological circuits. We must reck-
on with the fact that an individual’s cognition and neural pro-
cesses which takes places in animals’brain affect the
interpretation of cues to a much greater extent than we ac-
knowledge in our simplified models. We, as a scientific com-
munity, need to consider Bfear^responses in future
experimentation.
Further, this framework allows us to ponder the value of
the information prey gather from encountering novel pred-
ators in the invasive species scenarios we started with.
While a major emphasis is put in studies to why prey are
naïve to the cues left by a predator, we suggest that some
effort be directed towards the investigation of what the an-
imals are actually interpreting these cues to mean. Finally,
due to the inherent dependency of the olfactory cues on the
predators that leave them behind—the movement of the
predator generates a temporal andspatialpatterninwhich
the prey navigate, forage, and mate. We show here the result
of the way prey respond in an evolutionary game, where the
prey evolved the optimal ability to interpret the innuendos
of the odor left behind by their predator in a complex
environment.
Acknowledgments Open access funding provided by University of
Jyväskylä (JYU). We thank the technical staff of the Konnevesi
Research Station for building study systems. We also thank the reviewers
whose suggestions made this paper more meaningful and clear.
Funding The experiment was financially supported by the Finnish
Academy research grant 2015–2019 for HY, No. 288990, 11.5.2015.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Fig. 3 Decision-tree visualization
based on a random-forest analysis
using proportion of patches har-
vested (normalized using an arc-
sine × sqrt transformation). The
further up the tree a split occurs
the more important that variable is
to the decision-making process of
the voles. The asterisk denotes a
terminal node (bold frame) which
in the original tree produced fur-
ther nodes; however, the differ-
ence in foraging was lower than
1% and was thus removed from
the figure for clarity purposes
Behav Ecol Sociobiol (2018) 72: 187 Page 9 of 11 187
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Ethical approval The study was conducted under permission for animal
experimentation of the University of Jyväskylä, permit number: ESAVI/
6370/04.10.07/2014.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, 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.
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