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ORIGINAL PAPER
Roll with the fear: environment and state dependence
of pill bug (Armadillidium vulgare) personalities
Gergely Horváth
1
&László Zsolt Garamszegi
1,2,3
&Judit Bereczki
4
&Tamás János Urszán
1
&Gergely Balázs
1
&
Gábor Herczeg
1
Received: 8 November 2018 /Revised: 23 January 2019 /Accepted: 24 January 2019 /Published online: 7 February 2019
#The Author(s) 2019
Abstract
Most studies on animal personality evaluate individual mean behaviour to describe individual behavioural strategy, while often
neglecting behavioural variability on the within-individual level. However, within-individual behavioural plasticity (variation
induced by environment) and within-individual residual variation (regulatory behavioural precision) are recognized as biologi-
cally valid components of individual behaviour, but the evolutionary ecology of these components is still less understood. Here,
we tested whether behaviour of common pill bugs (Armadillidium vulgare) differs on the among- and within-individual level and
whether it is affected by various individual specific state-related traits (sex, size and Wolbachia infection). To this aim, we assayed
risk-taking in familiar vs. unfamiliar environments 30 times along 38 days and applied double modelling statistical technique to
handle the complex hierarchical structure for both individual-specific trait means and variances. We found that there are signif-
icant among-individual differences not only in mean risk-taking behaviour but also in environment- and time-induced behavioural
plasticity and residual variation. Wol ba chi a-infected individuals took less risk than healthy conspecifics; in addition, individuals
became more risk-averse withtime. Residual variation decreased with time, and individuals expressed higher residual variation in
the unfamiliar environment. Further, sensitization was stronger in females and in larger individuals in general. Our results suggest
that among-individual variation, behavioural plasticity and residual variation are all (i) biologically relevant components of an
individual’s behavioural strategy and (ii) responsive to changes in environment or labile state variables. We propose pill bugs as
promising models for personality research due to the relative ease of getting repeated behavioural measurements.
Keywords Animal personality .Behavioural plasticity .Residual variation .Individual state .Environmental differences .
Wolb ac hia
Introduction
Behaviour is one of the most flexible traits of animals (West-
Eberhard 2003), yet some level of repeatability in behaviour
across time and ecological situations (i.e. animal personality)
exists (Bell et al. 2009; Garamszegi et al. 2012). Intuitively,
the presence of non-random among-individual behavioural
variation should constrain behavioural plasticity (Niemelä
et al. 2013). This is true to a certain extent, but individuals
still preserve the ability to alter their behaviour in response to
changing environment, while their behaviour relative to each
other remains different (Biro et al. 2010;Dingemanseetal.
2010;Briffaetal.2013; Mathot and Dingemanse 2014).
Further, it seems that in addition to non-random variation in
mean behaviour, individuals can also show variation in their
reaction to environmental change (within-individual
behavioural plasticity) (Dingemanse et al. 2010; Westneat
et al. 2011;DingemanseandWolf2013; Mitchell and Biro
2017). Finally, biological validity and importance of within-
individual behavioural variation not induced by environmen-
tal change, or in other words, the ‘rigidity’of an individual’s
behaviour type in a certain environment (within-individual
residual variation), were recognized recently (Stamps et al.
Communicated by: Rumyana Jeleva
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s00114-019-1602-4) contains supplementary
material, which is available to authorized users.
*Gergely Horváth
gergohorvath@caesar.elte.hu
Extended author information available on the last page of the article
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2012; Biro and Adriaenssens 2013;Briffa2013;Briffaetal.
2013). Hence, within-individual behavioural plasticity (here-
after: behavioural plasticity) and within-individual residual
variation (hereafter: residual variation) should be considered
as potentially independent components of individual behav-
ioural strategy (Dingemanse et al. 2010; Kralj-Fišer and
Schneider 2012; Briffa 2013; Dingemanse and Wolf 2013;
Westneat et al. 2013,2015; Mitchell et al. 2016). However,
background mechanisms affecting emergence of individual
variation in behavioural plasticity and residual variation are
less understood. In addition, it is still not entirely clear whether
these components can evolve independently or individual dif-
ferences in within-individual behavioural variation are related
to personality (Niemelä et al. 2013; Mathot and Dingemanse
2014;Stamps2016).
Recently, a growing body of studies suggests that even
short-term variation in ecological conditions and inherently
labile state-linked traits could create stable differences in be-
havioural strategies (DiRienzo et al. 2016; Lichtenstein et al.
2016; Horváth et al. 2017). Parasites are among the most
important environmental factors known to create stable be-
havioural differences (Barber and Dingemanse 2010; Kortet
et al. 2010; Poulin 2013). Direct or indirect negative effects of
infections (e.g. low body condition) may result in differences
in individual state, which may eventuate the emergence of
individual behavioural strategies in order to cope with these
disadvantages (DiRienzo et al. 2015,2016; Horváth et al.
2016). In arthropods, Wol bac hi a are important parasitic bac-
teria (Hilgenboecker et al. 2008; Werren et al. 2008) with
remarkable effects on hosts’physiology, including partheno-
genesis, reproductive incompatibility, feminization and male
killing (see Werren and Windsor 2000; Werren et al. 2008;Le
Clec’h et al. 2012,2013). Although behavioural impact of
Wolb ac hia is less documented, it seems that infection gener-
ally affects mating behaviour of males (see Ming et al. 2015;
Moreau et al. 2001;Valaetal.2004;Zhaoetal.2013). Also, in
a parasitic wasp (Leptipolinia heterotoma), it was found that
Wolb ac hia reduces locomotor activity of both sexes (Fleury
et al. 2000). Generally speaking, Wolb ac hia is expected either
to decrease behavioural activity by impairing physiological
performance of the hosts or to increase it by host manipulation
or inducing some sort of terminal investment (Sicard et al.
2010; Chevalier et al. 2011).
Conglobation is a special type of tonic immobility and is a
common defensive behaviour for various arthropod taxa (Tuf
et al. 2015). Conglobation can be used as a proxy of risk-
taking behaviour (Carter et al. 2012; Beckmann and Biro
2013). Species of pill bugs (family: Armadillidiidae, order:
Isopoda, subphylum: Crustacea) are capable to roll their body
into an uninterrupted sphere, hiding their vulnerable posterior
appendages (uropods), legs and antennae. Conglobation pro-
tects common pill bug (Armadillidium vulgare) from most
invertebrate and small-sized vertebrate predators, while
larger vertebrate predators easily overlook small, immo-
bile prey (Matsuno and Moriyama 2012;Tufetal.2015).
Considering how easy it is to measure conglobation time,
pill bugs might be excellent models for animal personality
research, where the current statistical approaches are data
hungry and the necessary number of within-individual re-
peated measures is challenging to reach with most species
(Garamszegi and Herczeg 2012).
In the present paper, we studied risk-taking of adult
A. vulgare by performing 30 repeated behavioural assays
in two different environments (familiar vs. unfamiliar).
We studied the effects of environment and various state
variables (Wolbachia infection, body size, sex) on individ-
ual behaviour on different levels. First, we were interested
whether individuals differed in mean risk-taking (i.e.
among-individual variation), in their reaction norms (i.e.
behavioural plasticity) and residual variation. Second, we
tested whether these components are affected by environ-
ment and individual state. We expected lower risk-taking
in the unfamiliar environment. We had no prediction re-
garding the effects of sex or size. Regarding residual var-
iation, theory predicts increased within-individual vari-
ability under predation risk (Hugie 2003)aspreyanimals
may reduce the probability of capture by predators by
displaying unpredictable behaviour (Humphries and
Driver 1970; Jones et al. 2011). However, empirical data
are somewhat contradictory (see Briffa 2013; Velasque
and Briffa 2016; Urszán et al. 2018). Displaying the
highest residual variation may not be the best antipredator
strategy; also, level of behavioural rigidity may depend on
prevailing environmental conditions (Richardson et al.
2018), but more importantly, on development (see
Bierbach et al. 2017). Thus, although we did not form a
directional hypotheses regarding how different environ-
ments would affect residual variation of risk-taking in pill
bugs, we expect that the level of residual variation indeed
differs between familiar vs. unfamiliar environments.
Further, we expected both environmentally induced plas-
ticity in the form of lower risk-taking in the unfamiliar,
potentially dangerous environment and plasticity along
time in the form of habituation to the laboratory condi-
tions. The latter was expected to be stronger in the unfa-
miliar environment.
Methods
Study animals
We collected 60 A. vulgare individuals (26 males, 33 females,
1 N/A [damaged specimen]) on 9 May 2014 in the
Kamaraerdő,Budapest(47°26′19.90″N, 18° 58′52.57″E).
During May, this oakwood forest is characterized by a
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relatively dense vegetation and high cover of leaf litter. We
searched for animals under leaves and decaying wood at dif-
ferent sites. Only one individual was collected at a certain
spot, and at least 50 m was left between spots in order to
reduce the chance of sampling individuals from the same fam-
ily. Individuals were transported to the facilities of Eötvös
Loránd University, where they were housed individually in
white opaque plastic boxes (15.5 cm × 11 cm × 12 cm, length,
width, height, respectively) with a 1-cm-deep substrate
consisting of mixture of coconut fibre and soil. Humidity
was maintained by spraying the substrate with distilled water
twice a day, and fresh carrot was provided as food weekly. The
experimental animals have always eaten from the food, but
never fully consumed it; hence, the experimental animals were
fed ad libitum. We provided 12-h light period per day during
the experiment. Dim light was provided by Repti Glo 2.0 Full
Spectrum Terrarium Lamps (Exo Terra, Rolf V. Hagen Inc.,
Holm, Germany), which do not emit considerable heat but
mimics the full spectrum natural light. At the end of the ex-
periment, we sexed the individuals and measured the body
size (diameter of conglobated individuals; to the nearest
0.01 mm) and specimens were conserved in 96% ethanol for
Wolbachia screening (see below). All individuals were
screened for Wolbac hi a (see Supplementary Material for
details).
Behavioural assays
Individual behaviour was evaluated 30 times during 38 days
between 12 May and 19 June. Days without measurements
were distributed randomly. Each individual’s behaviour was
evaluated in two different environments (familiar vs. unfamil-
iar) daily, resulting in 60 repeats per individual. Behavioural
assays were carried out between 9.00 and 12.00 a.m. (UTC +
02.00), and 1-h break was provided between the two assays.
The order of individuals within and between environments
was randomized daily.
Risk-taking was estimated by latency to restart activity
(time spent immobile in conglobation) after a simulated pred-
ator attack. Animals were removed from their home boxes,
after which the experimenter gently squeezed the animal to
trigger conglobation, then dropped it to the surface depending
on treatment from a standard (10 cm) height. This treatment is
similar to manipulation by larger vertebrate predators (e.g.
birds and lizards) (see Tuf et al. 2015). In the familiar envi-
ronment treatment, individuals were elevated from and
dropped back to their home boxes, while in the unfamiliar
environment treatment, individuals were dropped to a white
plastic sheet illuminated directly (with 40 × G4 Halogen Light
Bulb, 10 W, 12 V). Animals were considered to restart activity
when they fully stretched their body and started to move their
legs in an attempt to escape. If an individual did not restart
activity in 15 min, the assay was stopped and the individual
was assigned maximum score (900 s). This happened only in
seven cases, including four individuals; hence, we used the
maximum score in the subsequent analyses. For unknown
reasons, more than half of the collected individuals died soon
after being transported to the laboratory and one individual
had to be removed from the analyses because of its extremely
outlying size. Thus, we used data (60 risk-taking measure-
ments) from 25 individuals, 11 males and 14 females.
Statistical analysis
Latency data were log-transformed to achieve normal distri-
bution of model residuals. To be able to fit reaction norms,
environment was treated as a continuous measure by
assigning −1tothe‘familiar’situation and 1 to the ‘unfamil-
iar.’Continuous variables were centred for the analyses by
bringing them to scale with a zero mean and unit variance.
Dummy variables were created for the categorical variables to
use them in the Bayesian modelling (see below). To describe
the hierarchically structured behavioural data, we relied on a
framework based on linear mixed modelling (LMMs) accord-
ing to the following equation:
log Yijk
¼β0þind0iþday0j
þβ1þind1i
ðÞx1ijk
þβ2þind2i
ðÞx2ijk þβ3x3iþβ4x4iþβ5x5i
þβ6x6kþεijk ð1Þ
where Y
ijk
is the latency for individual imeasured in day jand
at the kth observation within a day, β
0
is the population mean
latency, β
1
–β
6
are the mean level parameters that describe the
effect of covariates x
1
–x
6
(environment, day, sex, size, infec-
tion status and order within a day, respectively), ind
0i
is the
random effect term capturing the deviation of individual i
from the population mean, while day
0j
is the random ef-
fect term depicting the deviation caused by day-specific
effects. The model also considers random slopes ind
1i
and
ind
2i
to deal with among-individual differences in plastic-
ity with respect to novel environment and with respect to
day (habituation), respectively. The model requires the
following assumptions:
ind0i∼N0;σ2
ind0
ð2Þ
day0j∼N0;σ2
day0
ð3Þ
ind1i∼N0;σ2
ind1
ð4Þ
ind2i∼N0;σ2
ind2
ð5Þ
εijk ∼N0;σ2
res
ð6Þ
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Accordingly, the random terms are assumed to be nor-
mally distributed with a mean of zero and a respective
variance, i.e. σ2
ind0(among-individual variance in mean
latency), σ2
day0(among-day variance in mean latency),
σ2
ind1(among-individual variance in plasticity) and σ2
ind2
(among-individual variance in habituation). The error
term is assumed to rely on a common residual variance
σ2
res (within-individual variance).
We defined the above starting model based on a list of
considerations. First, all of the fixed predictors are biolog-
ically relevant and can be hypothesized to affect the focal
behavioural trait either at the among-individual or at the
within-individual level. Second, the random effects were
chosen to describe the hierarchical structure of the data
arising from the design of the study, as well as to accom-
modate our main predictions concerning individual differ-
ences in plasticity and habituation. Accordingly, with this
model, we could test whether (i) risk-taking behaviour
was linked to individual characteristics like sex, size or
health status and whether (ii) environment- or time-
induced (i.e. habituation) behavioural plasticity was pres-
ent. For simplicity and to avoid too many parameters to
be estimated, we did not define interaction terms among
the fixed predictors. Similarly, we have not considered
covariance between random slopes and intercepts. To ver-
ify that the random part of the above model is appropriate
for further evaluation, we defined alternative models in
the lme4 R package (Bates et al. 2015) and examined their
goodness of fit relative to the model described in Eq. (1)
by using likelihood ratio test. These investigations re-
vealed that both random intercept terms are significant
(P<0.001 for both ind
0i
and day
0j
), that random intercept
and slope models offer better fit to the data than the ran-
dom intercept only models (P= 0.018 for ind
1i
and P
<0.001 for ind
2i
) and that allowing correlation between
random intercepts and slopes does not imply further im-
provement statistically (P= 0.052 for the correlation be-
tween ind
0i
and ind
1i
,P= 0.373 for the correlation be-
tween ind
0i
and ind
2i
).
Given Eq. (6), the above model assumes that residuals have
a homogeneous variance, which is a strict assumption and
does not accommodate a possibility for testing among-
individual differences in predictability, which corresponds to
one of the main hypotheses of this study. To account for het-
erogeneous residual structure, we adopted an approach based
on double hierarchical general linear models, which allows
fitting the main and the dispersion parts of an LMM within
the same statistical framework (Lee and Nelder 1996,2006),
with the latter capturing the essence of residual variation
(Westneat et al. 2013; Cleasby et al. 2015; Mitchell and Biro
2017). Accordingly, we kept the above model and added an-
other model for the standard deviation (SD) of risk taking, as
follows. First, we assumed heterogeneous residual variance by
replacing Eq. (6) with
εijk ∼N0;σ2
yijk
ð7Þ
which permits distinct residuals for each observation that can
be further described as
log σyijk
¼γ0þindσ0iþdayσ0j
þγ1x1ijk þγ2x2ijk
þγ3x3iþγ4x4iþγ5x5iþγ6x6kð8Þ
In this equation, γ
0
is the mean log residual SD, γ
1
–γ
6
are
parameters describing the effect of covariates (environment,
day, sex, size, infection status and order within a day, respec-
tively) on predictability. The random terms ind
σ0i
and day
σ0j
reflect individual- and day-specific deviations, respectively,
from the population-specific mean log SD. For these random
terms, we assumed that
indσ0i∼N0;σ2
σind
ð9Þ
dayσ0j∼N0;σ2
σday
ð10Þ
yielding that different individuals and days can be character-
ized by different residual variation, and the among-individual
and among-day variance ofresidual variation can be estimated
as σ2
σind and σ2
σday.
We were also interested in whether individual-specific be-
havioural plasticity and habituation can be linked to individual
characteristics. To this end, we further extended the modelling
framework by adding linear regressions that describe
individual-specific plasticity and habituation. Therefore, we
replaced Eqs. (4)and(5)with
ind1i∼Nplast
i;σ2
ind1
ð11Þ
ind2i∼Nhab
i;σ2
ind2
ð12Þ
in which each individual can be described by a specific mean
plasticity and habituation value depending on their individual
characteristics as specified by
plasti¼0þδ1x3iþδ2x4iþδ3x5ið13Þ
habi¼0þφ1x3iþφ2x4iþφ3x5ið14Þ
Here, δ
1
–δ
3
and φ
1
–φ
3
stand for parameters that link the
main individual-specific attributes (x
3
–x
5
, sex, size and infec-
tion status) to plasticity and habituation, respectively. Note
that the regressions are forced through the origin (intercept is
zero); thus, the individual-specific mean is fixed to be 0.
The parameters of the above models are estimated iterative-
ly and depend on one another allowing the test of our main
predictions in a single statistical framework (see graphical
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representation of the whole model structure in electronic
supplementary material). Such inferences typically require
Bayesian approaches based on Markov chain Monte Carlo
(MCMC) processes (Gelman et al. 2004). For this purpose,
we applied procedures available in program JAGS (Plummer
2003) which we controlled from within the R statistical envi-
ronment (R Developmental Core Team 2018)usingthepack-
age rjags (Plummer 2014). For each model, we defined three
MCMC chains from different starting values and with 10,000
iterations of burn in period before sampling the posterior dis-
tribution. The posterior sample relied on the subsequent
100,000 iterations that were combined across chains.
Before interpreting the results, we applied the convention-
al model diagnostic procedures for each chain to verify
convergence and the lack of autocorrelation (Gelman
et al. 2004). Bayesian methods require priors to be de-
fined for each parameter estimated. We set these values
to have a minimal influence on the posterior distribution,
as we had no previous knowledge about them (uninfor-
mative, flat priors). The means of the posterior distribu-
tions were used for further interpretation by also consid-
ering the associated 95% credible intervals (95% CrI). If
the generated 95% CrI of the posterior distribution for a
parameter did not include zero, the parameter was consid-
ered to have a significant effect. Model codes are avail-
able in the electronic supplementary material.
Results
Our models suggest the directional effect of time across the
experiment (β
2
=0.165, 95% CrI = [0.034, 0.297]): individ-
uals became slightly risk-averse as time progresses (Fig. 1). In
addition, Wolbachia-infected individuals were more fearful
than parasite-free conspecifics (β
5
=0.647 [0.144, 1.156]).
Risk-taking had an individual- (random intercept: σ2
ind0=
0.454 [0.328, 0.636]) and day-specific (random intercept:
σ2
day0= 0.121 [0.09, 0.216]) expression. Pill bugs substantial-
ly differed in environmentally induced behavioural plasticity
(random slope: σ2
ind1= 0.176 [0.065, 0.191]; Fig. 2a) and
time-induced habituation (random slope: σ2
ind2= 0.146
[0.114, 0.262]; Fig. 2b). For remaining non-significant effects,
see Table 1.
We found that individuals displayed decreased residual var-
iation across time (γ
2
=−0.089 [−0.165, −0.012]; Fig. 3).
Also, the level of residual variation was affected by the envi-
ronment: pill bugs’behaviour was less predictable (i.e. high
residual variation) in the unfamiliar, than in the familiar envi-
ronment (γ
1
= 0.074 [0.034, 0.115]). There was a substantial
among-individual (random intercept: σ2
σind = 0.267 [0.185,
0.383]) and across day (random intercept: σ2
σday =0.176
[0.116, 0.254]) variation in residual within-individual varia-
tion. For remaining non-significant effects, see Table 1.
None of the fixed effects affected individual-specific
behavioural plasticity (Table 1); on the other hand, habit-
uation was stronger in females (φ
1
=−0.230 [−0.428, −
0.030]) and in larger individuals (φ
2
= 0.110 [0.015,
0.206]; Fig. 4;Table1).
Discussion
Here, we demonstrated substantial differences in risk-taking
of A. vulgare at several hierarchical level of behavioural var-
iation. Pill bugs showed significant between-individual differ-
ences not just in mean risk-taking (i.e. among-individual var-
iation), but in the degree to which they adjust their behaviour
to previous environmental conditions (i.e. behavioural plastic-
ity), and how consistently express their behaviour in any given
environment (i.e. residual variation). These patterns add to
prior studies indicating that both behavioural plasticity and
residual variation can be seen as potentially independent com-
ponents of animal behavioural variation (Dingemanse et al.
2010;Stampsetal.2012; Biro and Adriaenssens 2013;
Briffa 2013;Briffaetal.2013;DingemanseandWolf2013;
Stamps 2016; Chang et al. 2017; Guayasamin et al. 2017;
Lichtenstein et al. 2017). Further, in line with several prior
studies, we found that individual variation in residual variation
was influenced by individual state or environmental differ-
ences (Briffa 2013; Bridger et al. 2015;Westneatetal.
2015). Our results provide empirical support to the notion that
inter-individual differences in within-individual behavioural
variation may be the outcome of adaptive processes rather
than reflecting non-functional variation (Biro and
Adriaenssens 2013). Here, we discuss how these effects on
Fig. 1 Differences in risk-taking over time (habituation) in common pill
bug (Armadillidium vulgare). Note that risk-taking is a latency variable,
i.e. lower values represent higher risk-taking
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among-individual variation, residual variation and behaviour-
al plasticity fit to the existing theories.
Among-individual variation
A. vulgare individuals infected with Wolba chia took less risk
than their uninfected conspecifics. It has been suggested that
infection with mild effects on the hosts’fitness is coupled with
higher rates of behavioural activity, as infection often in-
creases energetic needs (Lafferty and Morris 1996; García-
Longoria et al. 2014;Gyurisetal.2016). On the other hand,
parasites with severe negative effects on their hosts’fitness are
more likely to reduce behavioural activity, due to heavily re-
duced state (e.g. low body condition; Ferguson et al. 2011;
Hammond-Tooke et al. 2012;Poulin2013), which may even-
tuate the emergence of individual behavioural strategies in
order to cope with these disadvantages (Barber and
Dingemanse 2010;Kortetetal.2010; Horváth et al. 2016).
Pathological effects of Wolba ch ia infection are rarely stud-
ied; however, it is known that the parasite is able to avoid or
even manipulate the host’s immune system and affect senes-
cence processes directly (Braquart-Varnier et al. 2008;Le
Clec’hetal.2012). In A. vulgare, different strains have differ-
ently severe effects on the host. For instance, wVulC is a wide-
spread and invasive feminizing strain, inducing low
haemocyte level and intense septicemia, reducing the host
lifespan considerably (Braquart-Varnier et al. 2008; Sicard
et al. 2010; Chevalier et al. 2011;LeClec’hetal.2012,
2013). On the other hand, wDil has no proven direct effects
on A. vulgare fitness, but does induce cytoplasmic incompat-
ibility which may generate indirect costs (Sicard et al. 2010;
Valette et al. 2013). Here, lowered risk-taking in infected pill
bugs could be a result of compensation for lowered body
condition and physiological performance. However, consider-
ing the low prevalence of Wolba chia infection in or sample,
this finding should be interpreted with caution (Bell et al.
2010). Thus, our conclusions regarding strength and true
mechanisms behind this pattern here are rather tentative.
Nevertheless, we believe that this finding at least warrants
more targeted research on the potential effect of Wolbac hi a
infection on hosts’behaviour.
Residual variation
Residual variation in behaviour was found to decrease across
days, in correspondence with human psychology literature as
well as observations on various vertebrate and invertebrate
taxa showing residual variation decreasing with increasing
experience (Stamps et al. 2012; Stamps and Krishnan 2014).
On the contrary, in a recent study performed on guppies
(Poecilia reticulata), Mitchell et al. (2016)reportnochange
in residual variation across a timespan similar to ours. It is
known that adult individuals may acclimate by their novel
environment quicker and express lower residual variability
within shorter periods (Biro 2012); nevertheless, ontogenetic
effects are rather implausible in our case. A more likely pos-
sibility isthat residual variation in risk-taking decreased due to
continued acclimation to our experimental procedure (Biro
and Adriaenssens 2013;Mitchelletal.2016).
Pill bugs express significantly higher residual variation in
the unfamiliar than in the familiar environment. This finding is
consistent with recent reports from both vertebrate and inver-
tebrate taxa (Dingemanse et al. 2010; Stamps et al. 2012;
Briffa 2013;Nakayamaetal.2016), suggesting that potential-
ly risky environments decrease predictability of behaviour
(but see Urszán et al. 2018). Thus, high residual variation
Fig. 2 Individual behavioural reaction norms across atime (sensitization) and benvironments in common pill bug (Armadillidium vulgare). Note that
risk-taking is a latency variable, i.e. lower values represent higher risk-taking
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could be seen as an antipredator response (Biro and
Adriaenssens 2013; Briffa et al. 2013). Predation pressure
substantially affects behavioural actions of individuals in
order to avoid potentially risky encounters (Bell and Sih
2007; Kortet et al. 2010; Luttbeg and Sih 2010; Engqvist
et al. 2015; Sih et al. 2015). However, taking that substantial
differences in residual variation were present irrespective of
environmental factors, other most likely internal factors
should also affect residual variation (Sih et al. 2004,2015;
Briffa 2013;Bierbachetal.2017). Again, we cannot be sure
regarding exact background mechanisms of this pattern. It is
known that in A. vulgare, mating is linked to moulting cycle,
during which individuals are more vulnerable to predators
(Beauché and Richard 2013). It is likely that high residual
variation in emergence from conglobation during the repro-
ductive season helps secure survival and thus future reproduc-
tive success of the individuals.
Table 1 Sources of variation in risk-taking of common pill bug
(Armadillidium vulgare). Estimates were derived from a double
hierarchical general linear model
Model Posterior mean (95% CrI)
(a)
Mean β
Intercept −0.001 (−0.272, 0.271)
Environment 0.075 (−0.015, 0.165)
Day 0.165 (0.034, 0.297)
Sex −0.319 (−0.770, 0.125)
Size 0.105 (−0.110, 0.321)
Wolbachia 0.647 (0.144, 1.156)
Order −0.019 (−0.056, 0.018)
σ
2
Individual (random intercept) 0.454 (0.328, 0.636)
Day (random intercept) 0.121 (0.09, 0.216)
Individual × environment (random slope) 0.176 (0.065, 0.191)
Individual × day (random slope) 0.146 (0.114, 0.262)
(b)
Residual variation γ
Intercept −0.319 (−0.494, −0.145)
Environment 0.074 (0.034, 0.115)
Day −0.089 (−0.165, −0.012)
Sex −0.039 (−0.320, 0.242)
Size 0.065 (−0.069, 0.197)
Wol ba ch ia 0 .2 70 ( −0.040, 0.581)
Order −0.024 (−0.065, 0.017)
σ
2
Individual (random intercept) 0.267 (0.185, 0.383)
Day (random intercept) 0.176 (0.116, 0.254)
(c)
Behavioural plasticity δ
Sex −0.023 (−0.173, 0.128)
Size −0.013 (−0.085, 0.06)
Wol ba ch ia −0.008 (−0.188, 0.171)
(d)
Sensitization φ
Sex −0.230 (−0.428, −0.030)
Size 0.110 (0.015, 0.206)
Wol ba ch ia 0 .1 20 ( −0.112, 0.350)
Day (day of behavioural trial), order (order of familiar vs. unfamiliar
environments during behavioural testing), sex (factor with two levels:
male vs. female), body size and Wolbachia infection (factor with two
levels: infected vs. uninfected) were fitted as fixed effects without inter-
actions. Posterior means and 95% credible intervals (CrI) are shown.
Effects strongly supported by the model (95% CI not overlapping) are
in italic font. Effects on (a) means, (b)the residual variation (c) variance in
individual plasticity and (d) variance in individual sensitization
Fig. 4 Association between behavioural plasticity of risk-taking over
time (sensitization) and size in common pill bug (Armadillidium
vulgare). Individual sensitization is represented by the slope of the
individual behavioural reaction norm in response to time
Fig. 3 Differences in risk-taking residual variation over time in common
pill bug (Armadillidium vulgare). Estimates were obtained from the
statistical model (see Table 1)
Sci Nat (2019) 106: 7 Page 7 of 11 7
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Behavioural plasticity
We found significant decrease of risk-taking with time. As
habituation is assumed to reduce unnecessary antipredator re-
sponses (i.e. length of conglobation) (Rodríguez-Prieto et al.
2010,2011; Vincze et al. 2016), our pattern rather suggests a
reverse response that is known as sensitization; an internal
mechanism intensifies behavioural response to constant stim-
ulation (Bee 2001; Martin and Réale 2008; Stamps et al. 2012;
Osborn and Briffa 2017). Theory predicts that sensitization
eventually will fade and habituation becomes the main pattern
of behavioural change over time, but empirical studies provide
limited support for this (Bee 2001;OsbornandBriffa2017).
Our data indicate no sign of habituation (i.e. lowered risk-
taking) during 30 days of experiment in either the familiar or
in the unfamiliar environment. High level of sensitization is
assumed to be linked to high stimulus rate and intensity (Bee
2001), and since our assays were conducted mostly on a daily
basis, the result is somewhat in line with this prediction.
Individual-specific behavioural plasticity was not affected
by individual state. On the other hand, among-individual var-
iance in slope was influenced by sex and size, i.e. females and
larger specimens (irrespective of sex) became shier with time.
Empirical data suggest that individual-specific differences in
behavioural plasticity are the outcome of variability of inher-
ently stable or labile state variables (Luttbeg and Sih 2010;
Wolf and Weissing 2010; Mathot and Dingemanse 2014;
Araya-Ajoy and Dingemanse 2017; Mitchell and Biro 2017).
However, based on current correlative data, we cannot recon-
struct the exact biological mechanism in the background of this
pattern, especially if we take into account that differences in
state are affected by both genetic and environmental variation
(Mathot and Dingemanse 2014). The most plausible explana-
tion is that high behavioural plasticity likely secures future
reproductive success of females and large pill bugs. In general,
the patterns reported in this subsection have also added to the
growing number of data indicating that despite behaving in a
consistent way, individuals still maintain the capability to ad-
just their behaviour to changing environmental conditions (i.e.
being behaviourally plastic) (DeWitt et al. 1998;Dingemanse
and Wolf 2013; Mathot and Dingemanse 2014).
Conclusions
Taken together, we found components of behavioural varia-
tion (intercept, slope and residuals) to exhibit among-
individual variation and to be sensitive to different variables
related to individual state. These results suggest that all three
components are integral parts of an individual’sbehavioural
strategy and that individuals are indeed plastic upon environ-
mental challenge and within-population behavioural variation
can be at least partly explained by variation in fixed and labile
state variables. We recommend studying behaviouralvariation
in an integrative approach and along longer observational pe-
riods, as animal personality sensu lato, or in other words,
individual behavioural strategy seems to be indeed more than
just variation in mean behaviour.
Acknowledgements Open access funding provided by Eötvös Loránd
University (ELTE). Our sincere thanks go to David Westneat for his
valuable comments and advices on statistical methods which helped us
highly improve our manuscript. We are also grateful for two anonymous
reviewers for their helpful comments. We thank Joel Almeida, Natalia
Peixoto Henriques, Antonio Scaruda and Tamara Vieira for their help
during the collection and the experiments. Help provided by Erzsébet
Hornung in species identification and sexing is much respected. In addi-
tion, we thank technical assistance of Valéria Mester in the molecular
work. This work was supported by the Hungarian State PhD
Scholarship (for GeH, TJU and GB), the Hungarian Scientific Research
Fund (OTKA-K 105517 for GH and OTKA-K 109223 for JB) and the
János Bolyai Scholarship of the Hungarian Academy of Sciences (for GH
and JB). GeH, GH and BG were also supported by the National Research,
Development and Innovation Fund for international cooperation (SNN
125627). LZG was supported by funds from the Ministry of Economy
and Competitiveness in Spain (CGL2015-70639-P) and the National
Research, Development and Innovation Office in Hungary (K 115970;
K 129215).
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Ethical approval Experiments were performed according to the guide-
lines of the Hungarian Act of Animal Care and Experimentation (1998,
XXVIII, section 243/1998), which conforms to the regulation of animal
experiments by the European Union.
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.
Publisher’snote Springer Nature remains neutral with regard to jurisdic-
tional claims in published maps and institutional affiliations.
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Affiliations
Gergely Horváth
1
&László Zsolt Garamszegi
1,2,3
&Judit Bereczki
4
&Tamás János Urszán
1
&Gergely Balázs
1
&
Gábor Herczeg
1
1
Behavioural Ecology Group, Department of Systematic Zoology and
Ecology, Eötvös Loránd University, Pázmány Péter Sétány 1/c,
Budapest H-1117, Hungary
2
Department of Evolutionary Ecology, Estación Biológica de
Donaña-CSIC, c/ Americo Vespucio, 26, 41092 Seville, Spain
3
MTA-ELTE, Theoretical Biology and Evolutionary Ecology
Research Group, Department of Plant Systematics, Ecology and
Theoretical Biology, Eötvös Loránd University, Pázmány Péter
Sétány 1/c, Budapest H-1117, Hungary
4
Department of Evolutionary Zoology and Human Biology, Institute
of Biology and Ecology, University of Debrecen, Egyetem tér 1,
Debrecen H-4032, Hungary
Sci Nat (2019) 106: 7 Page 11 of 11 7
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