Behavioral
Ecology
The official journal of the
ISBE
International Society for Behavioral Ecology
Behavioral Ecology (2016), 27(4), 1097–1103. doi:10.1093/beheco/arw016
Original Article
Drivers of synchronized vigilance in wild
boar groups
Tomasz Podgórski,a Sanne de Jong,b Jakub W. Bubnicki,a Dries P.J. Kuijper,a Marcin Churski,a
and Bogumiła Jędrzejewskaa
aMammal Research Institute, Polish Academy of Sciences, Waszkiewicza 1, 17-230 Białowieża, Poland
and bVan Hall Larenstein University of Applied Sciences, Postbus 1528, 8901 BV Leeuwarden, The
Netherlands
Received 1 April 2015; revised 21 January 2016; accepted 28 January 2016; Advance Access publication 18 February 2016.
There is a growing evidence that members of animal groups synchronize their vigilance behavior to minimize predation risk. Because
synchronized vigilance deviates from the classical vigilance models, which assume independent scanning, it is important to understand when and why it occurs. We explored vigilance behavior of wild boar (Sus scrofa) in a population subject to spatial variation in
human hunting risk and seasonal variation in food availability. Group members synchronized their vigilance behavior. We hypothesized
that vigilance synchronization would be context dependent and the trade-off between energy gain and safety would shape the relationship between the degree of vigilance synchronization and group size. We predicted weaker synchronization in large groups under
heavy predation risk, due to benefits of numerical dilution, and stronger synchronization in large groups when food is limiting, due to
intense food competition. The degree of synchronization decreased with increasing group size in the area where human hunting added
another risk factor to the natural predation, pointing at the safety benefits of vigilance synchrony for members of small groups and
the role of human-induced risk in shaping vigilance synchrony. We found no relation between vigilance synchrony and group size in
a food scarce, winter season. However, low levels of vigilance and its synchronization observed in winter indicated that energy gain
was prioritized over safety. Thus, members of wild boar groups can adjust levels of vigilance and its synchronization depending on the
forage-risk trade-off set by the ecological context.
Key words: competition, group size, human disturbance, predation risk, Sus scrofa, synchronization.
INTRODUCTION
Individuals can take account of the antipredatory behaviors of
other group members, such as vocalizations and alert postures,
when responding to predation risk (Sirot 2006; Roth et al. 2008;
Beauchamp 2009; Pays, Goulard, et al. 2009). Group members which detect a predator and flee immediately have higher
chance of escaping than those with a delayed response (Hilton
et al. 1999; Quinn and Cresswell 2005). This difference makes
late escapees more vulnerable to the attacks (Bednekoff and Lima
1998a). Therefore, individuals are expected to monitor and copy
vigilant behavior of neighbors to minimize individual predation
risk (Sirot and Touzalin 2009). This copying process is a proximate mechanism responsible for vigilance synchronization among
group members, a phenomenon frequently documented in recent
years (e.g., Pays, Jarman, et al. 2007; Pays, Renaud, et al. 2007;
Beauchamp 2009; Pays, Dubot, et al. 2009; Favreau et al. 2010;
Ge et al. 2011; Öst and Tierala 2011; Pays et al. 2012). Because
Address correspondence to T. Podgórski. E-mail: [email protected].
© The Author 2016. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved. For
permissions, please e-mail: [email protected]
synchronized vigilance deviates from the classical vigilance models,
which assumed that group members scan for predators independently of one another (Pulliam 1973; Bednekoff and Lima 1998b;
Scannell et al. 2001), it is important to understand when and why
it occurs. Previous studies on vigilance in wild boar supported the
independent scanning hypothesis (Quenette and Gerard 1992) but
also found a nonrandom, periodical pattern of scanning bouts at
the individual level (Quenette and Desportes 1992), hinting at the
possibility of synchronization.
Synchronization of activities among group members is important
for social and spatial cohesion and allows individuals to benefit from
being in a group (Conradt and Roper 2000). In the context of antipredatory vigilance, copying vigilant behavior of others and, consequently, its synchronization could provide adaptive advantages for a
group member: better assessment of the threats and faster response to
the attack (Quinn and Cresswell 2005; Sirot 2006; Roth et al. 2008).
Thus, vigilance synchronization reduces individual predation risk
and we can expect it to be stronger when predation risk is high (Pays,
Dubot, et al. 2009). Members of large groups benefit from numerical dilution and confusion effects which can reduce predation risk on
Behavioral Ecology
1098
late escapees, that is, attack-prone individuals lagging behind alerted
group mates (Bednekoff and Lima 1998a). Lack of these protective
effects in small groups means that their members should follow and
readily copy vigilant behaviors of the neighbors, because late escapees
have high chances of being attacked. Therefore, stronger synchronization of vigilance can be expected in small groups. When group size
increases, individual predation risk dilutes and incentives for vigilance
synchronization diminish on account of other activities, such as foraging. Additionally, in large groups, vigilance synchronization may
be restricted only to a part of the group due to group geometry or
size (Beauchamp et al. 2012). When food availability becomes critical for survival (e.g., in winter), energy gain can be prioritized over
safety leading to low vigilance as a result of intense food competition
(Beauchamp and Ruxton 2003). In such conditions, group members
will try to maximize food intake and avoid being alert when everyone
else is foraging, thus synchronizing their activities, including vigilance.
Because food competition is more intense in large groups, synchronization should increase with group size. In this case, vigilance synchronization would be a by-product of individual efforts to gain foraging
opportunities rather than genuine antipredatory benefits. Thus, the
trade-off between energy gain and safety can drive the relationship
between the degree of vigilance synchronization and group size in the
opposite directions, that is, induce stronger synchronization in larger
groups if resources are the limiting factor and, conversely, stronger
synchronization in small groups if predation prevails. In some species,
vigilance synchrony increases with group size (red-necked pademelon
Thylogale thetis: Pays, Dubot, et al. 2009; common eider Somateria mollissima: Öst and Tierala 2011), while in the other, the effect is not significant (gray eastern kangaroo Macropus giganteus: Pays, Jarman, et al.
2007; Favreau et al. 2010). This variation may reflect relative contribution of competition intensity and predation pressure in shaping the
relationship between vigilance synchronization and group size.
Here, we explore vigilance behavior of wild boar (Sus scrofa) in
the presence of wolves (Canis lupus) predation in the entire area and
human hunting in part of the area, adding another risk factor to
the natural predation. Food shortage in winter plays a major role
in limiting this wild boar population (Jędrzejewska et al. 1997) and
hence we expect food competition to be most severe in winter. We
determined the effect of group size, food availability, and human
predation risk on individual and collective vigilance. We expected
to observe 1) a decrease of individual vigilance in larger groups and
lower vigilance of group members compared to collective vigilance
time (Pulliam 1973; Lima 1995), 2) increased vigilance in highrisk area due to human hunting (Lima and Bednekoff 1999), and
3) reduced vigilance during the food scarce season (Beauchamp and
Ruxton 2003). We then tested for vigilance synchronization among
group members and hypothesized that the strength of synchronization would vary with group size as a function of risk and food
resources. We predicted that the degree of vigilance synchronization would 1) decrease with group size in high-risk area with hunting, due to greater safety benefits of synchrony for the members
of small groups and 2) increase with group size in a food scarce
season, due to stronger competition in larger groups and increased
synchronization to avoid losing foraging opportunities.
METHODS
Study area and species
The study was conducted in Białowieża Primeval Forest (BPF), a
continuous forest complex of 1450 km2 (52°30′–53°00′N, 23°30′–
24°15′E) that straddles the Polish–Belarusian border. The BPF is
the last remnant of the European temperate lowland forest and
is unique among other European woodlands due to high share
of natural stands and outstanding diversity of flora and fauna
(Jędrzejewska et al. 1997). Most of the Polish part of the BPF
(83%) is managed by the State Forestry, while the rest comprises the
Białowieża National Park (BNP). Within the BNP, hunting and logging is prohibited, and tourists access is restricted. Within the commercial part of the BPF, forest is managed, opened to the public,
and individual hunting is permitted.
Native wild boar population is largely shaped by natural factors,
such as mean annual temperature, acorn crop, and winter severity (Jędrzejewska et al. 1997). Wild boar live in cohesive, spatially
distinct, and temporarily stable social groups (Podgórski et al. 2014)
and is predated by wolf C. lupus (19% of wild boar natural mortality) and occasionally by lynx Lynx lynx (1%), which occur throughout the BPF at the density of 2–3 and 1–3 individuals per 100 km2,
respectively (B. Jędrzejewska and W. Jędrzejewski 1998; Schmidt
et al. 2009). Wolves are active throughout the day with activity
peaks at dawn and dusk (Theuerkauf et al. 2003). Wild boar hunting season is opened from 1st of April till the end of February for
juveniles and yearlings of both sexes and male adults and from 15
August to 15 January for female adults. Hunters remove 20–30%
of the population annually (Regional Directorate of State Forests,
Białystok and unpublished data of the Mammal Research Institute,
Polish Academy of Sciences).
Data collection
We used automated camera traps (DVREye™ with Sony CCD
video camera) to record behavioral data. The camera trap is
triggered by animal movement or body heat and keeps on video
recording as long as animals are in the view of the camera. Build-in
infrared illumination allowed for recording in low light conditions
and night. Our field observations confirmed that infrared illumination or the camera itself did not increase alertness of the animals.
The camera traps had a detection range of up to 27 m and the
horizontal field of view of 75°.
We collected video recordings opportunistically throughout 2009
and 2010 at the sampling locations both within and outside of the
BNP (Figure 1). Outside the BNP, the camera traps were placed at
the sites used for hunting. For each recording, the parts in which
some of the group members moved outside of the camera’s field of
view were removed from the analyses and only those sequences with
all individuals present were retained for further analysis. In this way,
we obtained 89 video sequences lasting on average 98 s (min–max:
20–444 s) in which 210 individuals were sampled. Fifty-four of these
sequences contained single individuals and the remaining 35 contained groups of 2–14 animals. All video sequences were treated as
observations of separate individuals or groups and random effects
were used to account for potential repeated sampling (see Data analysis). During sampling, animal was considered vigilant when it stood
still and scanned its surroundings with head lifted, interrupting the
ongoing behavior. Recognition of vigilant act was unambiguous.
For each individual within each group, we determined animal’s vigilant activity (vigilant: 1; nonvigilant: 0) at each second of the video
sequence. Hence, behavior of all group members was recorded
simultaneously at exactly the same time steps. In total, we determined wild boar vigilant activity in 11 651 one-second snapshots.
Five variables were assigned to the recording of each individual
in each group: group size, season (winter: November–April or summer: May–October), area (outside or inside of the BNP), daylight
(day or night), and video length to control for the observation time.
Podgórski et al. • Vigilance in wild boar
1099
Legend
Sampling locations with
a number of recordings
Biatowieza National Park
Poland-Belarus border
Build-up areas
Forest
0
1
2
3
4
5 km
Figure 1
Sampling locations and number of wild boar video recordings collected within the BPF, eastern Poland.
Season was used as proxy of food availability (low food availability
in winter and high in summer) and area as a risk factor (higher risk
outside of the national park where hunting and human disturbance
occur and lower risk within the national park where hunting is prohibited and human activity is low).
Data analysis
For each animal, individual vigilance was characterized as proportion
of time spent vigilant (Vind). Prior to model fitting, this proportion
was logit-transformed: logit (Vind) = ln ((Vind + 0.025)/(1 − (Vind −
0.025))), to improve normality and reduce skewness. We investigated
the relationship between Vind and group size, season, daylight, and
human disturbance using linear mixed-models (Pinheiro and Bates
2000) with group identity nested in sampling location as a random
effect to account for the unexplained variation between groups.
Here, we used the entire data set (210 individuals). Next, for each of
35 groups, we calculated the proportion of collective vigilance (Vcoll),
that is, the proportion of time when at least one member of the
group showed vigilance, and investigated the relationship between
Vcoll (logit-transformed) and the 5 explanatory variables as described
above. Random effect of sampling location accounted for potential
repeated observations of the same groups. Here, we used only data
from groups (156 individuals from 35 groups). Due to limited and
unbalanced sample sizes, resulting in convergence problems, we
could not consider interactions in our models fitted to the original
dataset. Therefore, we pooled the data from the 5 largest groups in 1
category (>7 individuals) and tested the effects of interactions using
such modified group size variable (hereafter “group size 2”). We performed backward stepwise model selection using “dropterm” function from the R-package MASS (Venables and Ripley 2002). The
least significant terms were sequentially removed from the full model
until the Akaike Information Criterion was minimized and thus the
most parsimonious model was obtained.
To test whether animals tended to scan their environment independent of one another, we compared, for each group, the observed
collective vigilance to expected proportion of collective vigilance
time under the assumption of independent scanning. This expected
proportion was calculated as Vexp = 1 − ∏ (1 − pk ) , where pk was
the proportion of time the individual k spent in vigilance and n was
the group size (Pays, Renaud, et al. 2007). Observed and expected
proportions were compared with a Student’s t-test for paired samples. The difference between observed and expected proportions
would not be different from 0 if individuals within a group scanned
the environment independently, greater than 0 if individuals coordinated their vigilance in nonoverlapping bouts, and below 0 if
individual vigilance bouts were synchronized. We then tested the
relationship between the difference between observed and expected
collective vigilance and our explanatory variables using linear
mixed-model with sampling location as a random factor.
To obtain direct measure of vigilance synchronization at the interindividual level, we calculated the proportion of time when 2 individuals from the group were simultaneously vigilant to the total time
of their vigilance, that is, dyadic overlap of vigilance bouts (Sobs).
This proportion was calculated for all possible pairs of individuals
from each observed group and it ranged from 0 (scanning always at
different times) to 1 (scanning always together). Then, we compared
Sobs of each pair with proportions expected under the assumption
that individuals scan independently of one another (Sexp). These
expected proportions were obtained with the following simulation
procedure: vigilant bouts of each individual in the observed groups
were randomly permuted 1000 times, at each permutation step proportion of vigilance overlap in each pair was calculated and averaged over all permutations to obtain Sexp. Observed and simulated
proportions were then compared with Wilcoxon Signed-Rank Test
for paired samples. Nonsignificant difference would indicate that
individuals tend to scan independently of one another, while higher
or lower values of observed proportions compared to simulated ones
would indicate that individuals synchronize or coordinate their vigilance, respectively. Finally, we investigated the effects of group size,
season, daylight, and human disturbance on Sobs (logit-transformed)
using linear mixed-model approach with group identity nested in
sampling location as a random factor. All statistical analyses were
performed in R software (R Core Team 2014).
n
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RESULTS
Individual and collective vigilance
The mean (±SE) proportion of the time individual wild boar
spent vigilant was 0.1 ± 0.01 (n = 210). The proportion of time an
individual spent in vigilance decreased with group size (Table 1,
Figure 2) and was 36% lower in winter than in summer (mean ±
SE: 0.09 ± 0.01 and 0.14 ± 0.03, respectively; Table 1). There was
no significant effect of interaction between group size and season (F1,58 = 0.009, P = 0.92), group size and area (F1,58 = 2.353,
P = 0.13), or season and area (F1,23 = 0.281, P = 0.60) on the
individual vigilance time. Although time of the day and area
with human disturbance and hunting had no significant effects on
individual vigilance (Table 1), we found that proportion of night
recordings outside of the BNP (91%) was higher than inside the
BNP (39%) (Fisher’s exact test: P < 0.001). This suggests that wild
boar shifted to nocturnal activity where exposed to human disturbance. Collective vigilance, measured as the mean (±SE) proportion of time when at least one group member was vigilant,
was 0.29 ± 0.04 (n = 35). Time of collective vigilance was consistently higher compared to the time that single members of the
group were vigilant (0.08 ± 0.01; Wilcoxon test for paired samples:
V = 629, n = 35, P < 0.0001; Figure 3). It was also higher compared to the vigilance time of the solitary individuals (0.14 ± 0.03;
Mann–Whitney test: W = 1461, P < 0.0001). None of our explanatory variables had significant effect on the collective vigilance levels
(group size: F1,30 = 0.004, P = 0.95; season: F1,30 = 0.119, P = 0.73;
daylight: F1,30 = 0.489, P = 0.49; hunting: F1,30 = 0.144, P = 0.71).
Consequently, the null model was the most parsimonious one
(Table 1).
Synchronization of vigilance
The difference between the observed (Vcoll) and expected collective
vigilance assuming independent scanning (Vexp) was significantly
different from 0 (Student’s t-test for paired samples: t34 = −3.731,
P = 0.0006). Thus, individuals within groups were vigilant not
independently of one another. The difference between observed
and expected proportions was usually below 0 (Figure 4), indicating that individuals synchronized their vigilant behavior. Vigilance
synchronization (i.e., the difference between Vcoll and Vexp) was
stronger in larger groups (coefficient ± SE: −0.001 ± 0.0003,
P = 0.0016; Figure 4). Group size was the only variable with
significant effect on the difference between Vcoll and Vexp. The
mean (±SE) observed dyadic overlap of vigilance bouts Sobs
(0.178 ± 0.015), that is, degree of vigilance synchronization, was
higher than the simulated one Sexp (0.021 ± 0.002), expected under
the assumption of independent scanning (Wilcoxon test for paired
samples: V = 12 896, n = 339, P < 0.0001; Figure 5). Thus, individuals within groups tended to synchronize their vigilant behavior
in overlapping bouts.
Degree of synchrony between any 2 group members (Sobs) was
shaped by group size, season, and the interaction between these 2
variables (Table 1). The relationship between group size and the
degree of vigilance synchronization was positive only in summer
(coefficient ± SE: 0.604 ± 0.139, P < 0.001; Table 1) and insignificant in winter (coefficient ± SE: −0.268 ± 0.364, P = 0.469;
Table 1). Observed degree of synchrony was lower in winter
than in summer (mean ± SE: 0.05 ± 0.01 and 0.41 ± 0.02, respectively; Table 1). Synchronization degree decreased with group size
outside of the national park (coefficient ± SE: −0.847 ± 0.378,
P = 0.031; Table 1), while within the national park, the relationship was not significant (coefficient ± SE: 0.024 ± 0.088,
P = 0.785; Table 1). Seasonal differences in synchronization
degree did not vary across the 2 areas as indicated by nonsignificant interaction term (Table 1). Three-way interaction between
season, area, and group size was not significant in the full model
(F1,25 = 0.0001, P = 0.992) and was dropped early in the model
selection process.
Table 1
Variables included in the most parsimonious models explaining variation in individual vigilance, collective vigilance, and degree of
vigilance synchronization among group members in wild boar
Response variable
Individual vigilance
Collective vigilance
Synchronization degree
Synchronization degree
Parameter
Intercept
Group size
Season (summer)
Area (inside BNP)
Intercept
Intercept
Group size
Season (summer)
Intercept
Group size 2
Season (summer)
Area (inside BNP)
Group size 2 × Season
Group size 2 × Area
Season × Area
n
210
35
339
339
Coefficient
SE
df
t value
P value
−2.037
−0.093
−0.512
−0.442
0.254
0.027
0.217
0.238
121
61
61
24
−8.029
−3.457
−2.356
1.854
<0.001
0.001
0.022
0.076
−1.073
0.244
22
−4.391
<0.001
−2.734
0.152
−1.458
0.526
0.054
0.404
304
32
32
−5.192
2.827
−3.601
<0.001
0.008
0.001
−4.312
0.604
0.939
2.971
−0.579
−0.871
1.122
0.956
0.139
1.125
2.075
0.163
0.371
0.801
304
28
28
28
28
28
28
−4.511
4.339
0.834
1.431
−3.549
−2.349
1.401
<0.001
<0.001
0.411
0.163
0.001
0.026
0.172
Response variables (all logit-transformed) were fitted with linear mixed-effect models. Candidate explanatory variables included group size, length of video
recording (continuous variables), and 3 factors: season (winter or summer), area (inside or outside of the BNP), and daylight (day or night). Second model
explaining variation in synchronization degree was fitted to modified dataset, which allowed to test interactive effects of explanatory variables (see Methods
section for details). Reference level for factors are given in the parentheses.
Podgórski et al. • Vigilance in wild boar
1101
0.05
1
0.00
-0.05
0.6
Vcoll - Vexp
Individual vigilance
0.8
0.4
-0.10
-0.15
-0.20
0.2
-0.25
0
2
4
6
8
Group size
10
12
2
14
Figure 2
Group-size effect on individual vigilance, that is, proportion of time
(Vind) that an individual (n = 210) spent in vigilance. Each dot represents
individual values and the solid line represents the relationship between the
group size and the individual vigilance based on the most parsimonious
linear mixed-effects model.
6
8
Group size
10
12
14
Figure 4
Difference between the observed (Vcoll) and expected (Vexp) collective
vigilance time for each observed group. Values of Vexp were obtained under
an assumption of independent scanning. Differences below 0 indicate that
group members synchronized their vigilant behavior. Solid line represents
the relationship between the group size and the difference between Vcoll −
Vexp obtained with linear mixed-effects model.
1
1
Individual vigilance
Collective vigilance
0.6
0.4
0.2
Observed
Simulated
0.8
Synchronization degree
0.8
Vigilance
4
0.6
0.4
0.2
0
0
2
4
6
8
Group size
10
12
14
Figure 3
Mean values of individual (proportion of time during which an individual
in the group was vigilant, Vind) and collective (proportion of time during
which at least one individual in the group was vigilant, Vcoll) vigilance
observed in 35 wild boar groups. Curves are based on the estimates from
the linear mixed-effects models.
DISCUSSION
Effects of predation risk by humans and wolves
The proportion of time individual wild boar spent vigilant was relatively low. Low levels of vigilance in wild boar has been reported
before (Quenette and Gerard 1992; Kuijper et al. 2014) and can be
attributed to relatively low predation pressure on wild boar across
2
4
6
8
Group size
10
12
14
Figure 5
The relationship between the group size and the observed (Sobs) and
expected (Sexp) degree of vigilance synchronization, that is, dyadic overlap
of vigilance bouts calculated for all possible pairs of individuals from
each observed group (n = 339). Expected values were simulated under the
assumption that group members scan the environment independently of
one another. Curves represent the effect of group size on Sexp (coefficient
± SE: −0.045 ± 0.027, P = 0.107) and Sobs (coefficient ± SE: 0.152 ± 0.054,
P = 0.008) obtained with linear mixed-effects model.
most of the species’ geographic range (Jędrzejewski et al. 2011). In
our study area, wolf predation contributes 19% to wild boar natural
mortality (B. Jędrzejewska and W. Jędrzejewski 1998) and wild boar
do not respond to olfactory cues of wolf presence with increased vigilance (Kuijper et al. 2014), indicating low perceived predation risk.
Behavioral Ecology
1102
As predicted, we observed a decrease of individual vigilance time with
increasing group size. This supports classical vigilance models (Pulliam
1973; Roberts 1996; Bednekoff and Lima 1998b) and corresponds
with empirical evidence (e.g., Fernández et al. 2003; Pays, Jarman, et al.
2007; Pays, Renaud, et al. 2007), including wild boar (Quenette and
Gerard 1992). Variance in individual vigilance level declined in larger
groups (Figure 2), which may indicate that some unmeasured, groupsize-dependent variables (such as group composition or geometry) could
have influenced this relationship, potentially leading to underestimation
of the group-size effect on the individual vigilance (Beauchamp 2013).
Collective vigilance time was significantly higher than individual levels
of groups members and solitary individuals. Thus, group members benefited from collective detection (Lima 1995). Collective vigilance time did
not increase with group size, as expected when vigilance bouts of groups
members overlap, that is, vigilance is synchronized. Contrary to our prediction, we found no effect of human-induced risk on the levels of individual vigilance. Our results suggest that instead of increased vigilance
wild boar shifted to nocturnal activity outside of the national park. Wild
boar hunting is mainly performed around dawn and dusk and therefore nocturnal activity could minimize interference with humans. Shifts
in activity and habitat use appear to be a common behavioral response
to human disturbance and hunting adopted by wild boar (Keuling et al.
2008; Podgórski et al. 2013).
We found that individual wild boar synchronized their vigilance behavior with other group members, in contrast to classical
vigilance models assuming independent scanning (Pulliam 1973;
Bednekoff and Lima 1998b). The simplest explanation for synchronized vigilance is simultaneous response of all group members to
external disturbance, that is, an approaching human or predator. It
seems unlikely that external cues alone regulated synchronization
in our case because the degree of synchrony was clearly group size
dependent and thus also modified by intragroup mechanism. The
process of allelomimetic copying and amplification effect seem to
operate for vigilance (Pays, Goulard, et al. 2009; Sirot and Touzalin
2009; Beauchamp et al. 2012). Individuals are more likely to be
vigilant if the proportion of other vigilant group members is high
(Pays, Goulard, et al. 2009; Michelena and Deneubourg 2011),
which suggests that individuals consider behavior of group mates
as indicative of potential risk. This could be an adaptive strategy to
minimize predation risk by not lagging behind the alerted individuals when attack occurs (Sirot and Touzalin 2009; Sirot 2012).
Environmental pressures exerted on individuals may vary on a
group size scale and we predicted that high predation risk would
result in a negative relationship between the degree of vigilance
synchronization and group size. We found that synchronization
was stronger in small groups in the area with natural and anthropogenic predation risks, while it did not vary with group size in the
lower risk area, where only natural predation occurred. Thus, our
results point at the safety benefits of vigilance synchronization for
members of small groups and highlight the role of human-induced
risk in shaping the relation of vigilance synchrony and group size.
Members of large groups benefit from numerical dilution and confusion effects which can reduce predation risk on late escapees, that
is, attack-prone individuals which do not copy vigilant behavior of
alerted group mates (Bednekoff and Lima 1998a). Lack of these
protective effects in small groups can stimulate vigilance synchrony
when predation pressure is high. Our study covered only one area
where human hunting added another risk factor to the natural
predation. Although lack of replication is a limitation, our results
already demonstrate how human-related risks modify antipredatory
behavior of prey.
Effects of food resources
Individual vigilance time varied seasonally, with lower levels of individual vigilance in winter than in summer. It is unlikely that hunting
pressure, which occurs throughout the year except for March, and
predation by wolves, which shows little seasonal variation (Jędrzejewski
et al. 2000), had a strong seasonal impact on the observed vigilance
patterns. Overwinter mortality due to shortage and inaccessibility of
food prevails over all other natural mortality factors of wild boar in
BPF, including predation (B. Jędrzejewska and W. Jędrzejewski 1998).
Hence, the food scarce, winter season provides the conditions in which
food competition is excepted to be most severe. If time allocated to
vigilance represents a trade-off between energy gain and safety (Lima
1987; Brown and Kotler 2004), we would expect lower levels of vigilance when starvation risk outweighs predation risk. We therefore
interpret low levels of vigilance during winter as being the result of
limiting food resources. Additionally, increased intragroup competition for food could have contributed to the reduction of vigilance time
in winter (Beauchamp and Ruxton 2003; Randler 2005).
We found no relation between vigilance synchrony and group
size in the food scarce season. Hence, our prediction that competition would induce stronger vigilance synchronization in larger groups
when resources are limited was not supported. Although competition
for food could still be more intense in large groups, it was not manifested in the vigilance behavior. Low levels of individual vigilance
were most likely responsible for weak synchronization in winter, since
synchronization degree can be shown to be dependent on the individual vigilance time. In winter, when starvation risk outweighs predation risk (B. Jędrzejewska and W. Jędrzejewski 1998), fitness benefits
obtained from extra resources could outweigh fitness costs caused
by reduced safety. Low vigilance is expected when scramble competition is high and safety compromised (Beauchamp and Ruxton
2003). Similarly, vigilance synchronization, with its primary function
of reducing predation risk (Quinn and Cresswell 2005; Sirot 2006;
Roth et al. 2008), may be reduced when food gains are prioritized.
Our hypothesis predicting a context-dependent relationship between
vigilance synchronization and group size was only supported with
regard to predation risk but not to intragroup competition for food.
We used season as a proxy of food availability and assumed stronger
food competition during winter when food is scarce. The studied wild
boar population is constrained by inaccessibility and shortage of food
resources in winter, which may affect behavioral decisions including
vigilance. We thus believe that our analyses reflect influence of food
availability and increased competition but an experimental approach,
with food density controlled at the foraging patch level, would be
needed to evaluate our assumptions. Summarizing, members of wild
boar groups can adjust levels of vigilance and its synchronization
depending on the forage-risk trade-off set by the ecological context.
FUNDING
This work was supported by the Polish Ministry of Science
and Higher Education (grant numbers N304 253 935 and
N309 137 335). In addition, the work of D.P.J.K. was supported by the National Science Center, Poland (grant number
2012/05/B/NZ8/01010).
We thank R. Kozak, A. Waszkiewicz, and many students who assisted with
the fieldwork. We are grateful to L. Barrett and 3 anonymous reviewers for
valuable comments on the earlier version of the manuscript.
Handling editor: Louise Barrett
Podgórski et al. • Vigilance in wild boar
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