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Mammalian Biology 77 (2012) 434–440
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The inﬂuence of daylight regime on diurnal locomotor activity patterns of the
European hare (Lepus europaeus) during summer
Stéphanie C. Schai-Brauna,∗, Heiko G. Rödelb, Klaus Hackländera
aInstitute of Wildlife Biology and Game Management, University of Natural Resources and Life Sciences, Vienna, Gregor Mendel Strasse 33, A-1180 Vienna, Austria
bUniversité Paris 13, Sorbonne Paris Cité, Laboratoire d’Ethologie Expérimentale et Comparée (LEEC), F-93430 Villetaneuse, France
Received 1 February 2012
Accepted 13 July 2012
Knowledge on diurnal locomotor activity pattern in wild nocturnal medium-sized mammals, such as
the European hare (Lepus europaeus) is scarce. In this study, we tracked nine European hares during
the vegetation period using GPS-transmitters. In particular, we focused on the question how the timing
of sunset and sunrise inﬂuences the activity peaks in this species. The horal distances between two
consecutive hare positions were used as a measure of locomotor activity. European hares showed two
distinct peaks in their daily activity. If sunset or sunrise were earlier, the maximum activity peaks of
individual European hares occurred after sunset or sunrise, whereas activity peaks were shifted before
sunset or sunrise when sunset or sunrise were later. During summer, when the nights are probably
too short to allow the hares to cover their energetic requirements, the study animals regularly showed
activity peaks in full daylight. In conclusion, our results imply that, although daylight regime normally
regulates the diurnal locomotor activity pattern in mammals, other additional factors may play a role in
modifying this regulation in European hares during summer.
© 2012 Deutsche Gesellschaft für Säugetierkunde. Published by Elsevier GmbH. All rights reserved.
In mammals, circadian rhythms are predominantly regulated by
light (Goldman 1999; Cermakian and Sassone-Corsi 2002; van der
Merwe et al. 2011). Numerous studies have shown that the impact
of light as zeitgeber might be affected by various other intrinsic and
extrinsic factors, such as food availability, weather, temperature,
sex, season, reproductive status, and age (Getz 1961; Garshelis and
Pelton 1980; Zielinski et al. 1983; Ferguson et al. 1988; Larivière
et al. 1994; Kolbe and Squires 2007; Wronski et al. 2006; Rödel
et al. 2012). However, sunrise and sunset have been suggested to
trigger the onset and cessation of activity in a wide range of species
(Daan and Aschoff 1975; Benstaali et al. 2001).
Hares (genus Lepus) have been described as mostly nocturnal
mammals (Chapman and Flux 2008), although this seems to be true
only during winter (Homolka 1986; Pépin and Cargnelutti 1994;
Holley 2001). In summer, activity of hares appears to be less con-
sistent and partly diurnal (Mech et al. 1966; Cederlund and Lemnell
1980; Figala et al. 1984). Irrespective of this, also in hares sunset
and sunrise appear to play a major role concerning the onset and
cessation of activity, respectively (Mech et al. 1966; Figala et al.
1984; Pépin and Cargnelutti 1994; Holley 2001).
E-mail address: firstname.lastname@example.org (S.C. Schai-Braun).
In winter, hares start their daily activity shortly after sunset and
end it shortly before sunrise (Cederlund and Lemnell 1980; Pépin
and Cargnelutti 1994). However, studies on different hare species
report contradictory results regarding the inﬂuence of sunrise and
sunset as zeitgebers during late spring or summer. Snowshoe hare’s
(Lepus americanus) cessation of activity has been reported to be on
average 1 h before sunrise (Mech et al. 1966; Figala et al. 1984),
however, with notable variation. For some individuals the onset of
activity was 1 h after sunset (Mech et al. 1966), whereas another
one was observed to start its activity more than 2 h before sun-
set (Figala et al. 1984). European hares began to leave their forms
before sunset and to enter them after sunrise as the nights short-
ened in the early part of the year (Holley 2001). That means, in all
studies during late spring or summer sunrise and sunset somehow
trigger onset and cessation of hares’ activity, but the impact of these
zeitgebers is various.
It has been argued that the impact of sunrise and sunset in sum-
mer was altered by the number of daily night hours (Holley 2001).
In this European hare study the duration of the activity period did
not remain constant but decreased from 15 h in January to 12 h at
the end of March, mirroring the number of daily night hours. At this
point, the activity period was not further contracted but the hares
suddenly started to increase their activity period by including day-
light activity. This sudden transition from a totally nocturnal to a
partially diurnal regime was explained by an aversion to daylight
activity. Consequently, we suppose that the number of daily night
1616-5047/$ – see front matter © 2012 Deutsche Gesellschaft für Säugetierkunde. Published by Elsevier GmbH. All rights reserved.
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S.C. Schai-Braun et al. / Mammalian Biology 77 (2012) 434–440 435
hours alter the impact of the predominant zeitgebers sunrise and
sunset. Hence, the hares’ activity pattern should display a sudden
start of daylight activity at the beginning and an abrupt withdrawal
from daylight activity at the end of summer.
In addition, a high ambient temperature might inﬂuence the
activity pattern of mammals. For example, in black bears (Ursus
americanus) it was shown that temperatures above 25 ◦C substan-
tially reduced the level of activity (Garshelis and Pelton 1980).
Furthermore, the meadow vole (Microtus pennsylvanicus) was
found to abandon diurnal activity in favour of nocturnal and/or
crepuscular activity when the temperature rose above 20 ◦C(Getz
1961). We hence assumed that the impact of the zeitgebers sun-
rise and sunset might be altered by a high ambient temperature
in which case the hares’ activity would be restricted to the dark
However, detailed quantitative and individual-based data on
daily activity pattern in this genus are still scarce. In this study we
investigated the European hare’s (Lepus europaeus) diurnal loco-
motor activity patterns during summer. In particular, we did not
only focus on the timing of onset and cessation of activity but also
studied subtle changes of activity during 24 h. Our hypothesis was
that sunrise and sunset, the predominant zeitgebers for the Euro-
pean hare’s diurnal locomotor activity pattern, are slightly altered
in their impact by the number of daily night hours (season) and the
temperature. We tested this hypothesis by equipping nine individ-
uals with GPS collars, allowing us to assess their diurnal locomotor
Material and methods
The study was conducted in Lower Austria near Zwerndorf
(48◦20N, 16◦50E) and the study area consisted of 270 ha arable
land with cereals as the main crop and an average ﬁeld size of 3.1
(±0.3 SE) ha. Hare density in the study site was estimated in autumn
2009 by spotlight counts (Langbein et al. 1999) and accounted 35
European hares per 100 ha (SSB & KH, unpubl.).
Nine adult European hares (4 males, 5 females) were caught
in un-baited box traps from May until September 2009. All ani-
mals were sexed according to secondary sexual characteristics and
equipped with a 70 g GPS collar (Telemetry Solutions, Quantum
4000 Enhanced). The collars were programmed to start working
right after the animal’s release and take 1 ﬁx per hour. For additional
information on the individual hares’ GPS-data see the electronic
appendix. The accuracy of the GPS collars was tested beforehand
(see Harris et al. 1990) and yielded a mean precision of 3.5 m (±1.0
SE). Weather data and the time of sunrise and sunset were provided
by the Austrian Central Institute for Meteorology and Geodynamics.
Temperatures were recorded daily at 7 am and 7pm CET.
Calculation of positional data
The positional data were digitised using the software ArcGIS
9.2 (ESRI). We only included locations with a solution in three-
dimensional mode (based on ≥4 satellites) (Frair et al. 2010).
The distances (in metres) between two consecutive hare positions
(horal distance) were calculated. Although the horal distance does
not reveal the effective distance the hare covered between these
two ﬁxes, it exposes a minimal distance the hare must have moved
during this hour. In the following, the “horal distance” is used as a
measure of hares’ activity, and the term “activity” is always used in
the sense of the hares’ locomotor activity.
Statistical data analysis
We analysed the data using multivariate (generalized) linear
mixed-effects models, allowing for the use of repeated measure-
ments. Statistical analyses were done with the software R 2.12.0 (R
Development Core Team 2011). Generalized linear mixed-effects
models were ﬁtted using the package lme4 (Bates 2005). P-values
were extracted by likelihood ratio tests (Faraway 2006). When
using linear models, we visually checked normality of the model
residuals by normal probability plots. For all models, the homo-
geneity of variances and goodness of ﬁt were examined by plotting
residuals versus ﬁtted values (Faraway 2006).
We initially included sex in all models tested. However, since
there were never any signiﬁcant effects of sex in our multivariate
analyses (p> 0.10), this factor was omitted from the models before
Diurnal activity pattern
We tested the effect of time of the day (covariate, in hours)
on the response variable horal distance by a linear mixed effects
model. In addition, we tested similar models only including data
subsets: one subset included all positional data with a shorter time
interval to sunset than to sunrise, and the other one comprised the
remainder. For all models, the response variable (horal distance)
was log-transformed in order to obtain a normal distribution of the
Since we expected a non-linear time course with at least one
maximum peak, we used polynomials to model the data. For this,
we gradually increased the complexity of the polynomials until the
9th order. All of these models were tested for signiﬁcance, and,
in addition, we directly compared the support of these models by
using AICc (Burnham and Anderson 1998). The model with the low-
est AICc score can be considered as the best approximating model
of the model set. Note that different models can be considered to
ﬁnd equally good support by the data when the AICc is smaller
All (mixed effects) models included hare identity as a random
factor, in order to allow for the repeated measurements collected
from the different hares, and also an individual-speciﬁc code for
the day (“date”) as a second random factor in order to account for
the time series measured for each of the study animals during the
different days of the study. As it could be expected, there was sig-
niﬁcant individual variation among the hares’ locomotor activity
(signiﬁcant random factor “individual hare”: 2= 199.44, p< 0.001)
with a variance of 0.21 m/h (±0.55 SD).
Inﬂuence of different parameters on the activity peaks
A peak can be described mathematically by a parabola, i.e. by
a 2nd-order polynomial. The ﬁrst derivative is used to ﬁnd the
vertex of a parabola as the ﬁrst derivative equals zero at the ver-
tex. At the maximum point of the parabola the hare’s activity is
highest. Therefore, if the horal distance is a function of the time
interval to and since sunrise or sunset, the independent variable
of this function indicates the time interval of maximum locomotor
Firstly, the time interval with the highest activity was deter-
mined. As the time of sunset and sunrise changes at about 1 min
per day in summer, and as each of the nine hares provided a dif-
ferent number of horal distances, several maximum points for each
animal were calculated. The horal distances of one day did not yield
enough data to calculate a morning and an evening maximum point.
For this reason, we pooled the horal distances of ﬁve days for every
hare to calculate one evening and one morning maximum point. If
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436 S.C. Schai-Braun et al. / Mammalian Biology 77 (2012) 434–440
Fig. 1. Locomotor activity (measured as the horal distances moved by the hares)
described by a 6th-order polynomial regression model. Data (squares) are shown as
medians with 25th/75th percentiles. Time of sunrise/sunset during the study period
is indicated by grey shading. See text for details on statistics.
the ﬁrst derivative at zero described a minimum point or an impos-
sible maximum point, the point was excluded from further analysis.
Out of 171 individual hare days (comprising 3506 hare locations) 36
extrema were calculated for the morning as well as for the evening.
47% of the morning extrema and 75% of the evening extrema were
sensible maximum points.
Secondly, the mean time of sunset and sunrise was calculated for
every ﬁve day interval. Each evening maximum point was paired
with its corresponding time of sunset and each morning maximum
point with its corresponding time of sunrise. Subsequently, the cor-
relations were tested by two linear mixed-effects models with hare
identity as random factor (see Fig. 3a and b). The same models were
used to test for correlations between activity peaks and the num-
ber of daily night hours (see Fig. 3c and d) and the temperature,
Light conditions and the occurrence of a morning activity peak
Here, we analysed two subsets of the data. One subset included
all ﬁxes taken when the evening activity peak was during the dark
phase, the other one comprised the remainder (see Fig. 4a). To test
the effect of the light conditions on the occurrence of a morning
activity peak, we used a generalized linear mixed effects model for
binomial data with a logit-link function (“logistic regression”). Also
here, hare identity was included as a random factor (see Fig. 4b).
The mean number of satellites used for the location of the ﬁxes
was7(±0.03 SE). The overlapping individual study periods were
on average 11 days (±9.2 SE) long with a minimum of 2 and a max-
imum of 91 days. That is, the number of GPS-ﬁxes taken per animal
ranged from 25 to 2127 with an average of 230 (±219.4 SE). This
resulted in a total of 3528 ﬁxes and thus a total of 3519 horal dis-
tances available for analysis (709 for males, 2810 for females). For
additional information on the individual hares’ GPS-data see the
Diurnal activity pattern
The daily time course of the hares’ activity, measured as
the horal distance covered between two ﬁxes, was signiﬁcantly
explained by a 6th order polynomial (2
6=1078.6, p< 0.001). This
model predicted for the European hares’ diurnal locomotor activity
Results of linear mixed-effects models for the effects of different predictor variables
on the timing of locomotor activity peaks, including hare identity as a random fac-
tor. Analyses are based on nevening = 27 and nmorning = 17 ﬁve-day activity intervals of
repeated measurements of nine hares.
Response variables Predictor variables Slope 2
Sunrise −5.16 25.06 <0.001
Number of daily night hours −2.31 22.88 <0.001
Morning temperature 0.39 1.57 0.21
Sunset −1.82 10.68 <0.001
Number of daily night hours 1.02 11.06 <0.001
Evening temperature −0.18 1.09 0.30
two distinct activity peaks during night time (see model graph
in Fig. 1). We also veriﬁed that the 6th order polynomial was
the best approximating model describing this relationship by a
comparison with higher and lower parameterised models using
AICc values. This model selection procedure revealed that this
model had the lowest AICc score, with AICc > 87 in comparison
to all lower parameterised models, and AICc >1 in comparison to
higher parameterised models.
In relation to sunset and sunrise, the model including a 4th
order polynomial predicted that the hares’ activities were highest
1 h after sunset and again 5h later (2
4=489.75, p< 0.001; Fig. 2a).
Furthermore, a model including a 3rd order polynomial predicted
that hares were most active 5–3 h before sunrise (2
p< 0.001; Fig. 2b). The animals did not show any notable activities
5–8 h before sunset and 1 h before sunrise until 8 h after sunrise,
when the average distance moved by the hares was less than 10 m
within 1 h. Also here, we veriﬁed by model selection with AICc,
that the chosen polynomials were the best approximating mod-
els compared to models including lower-order (AICc > 76 and
AICc > 70, respectively) or higher-order polynomials (AICc > 2
and AICc > 2, respectively).
Inﬂuence of different parameters on the activity peaks
There were signiﬁcant and negative correlations between both
sunset and the timing of the evening activity peak and between
sunrise and the timing of the morning activity peak (Table 1). If
sunset or sunrise were earlier, the maximum activity peaks of indi-
vidual hares occurred later, whereas activity peaks were shifted
before sunset or sunrise when sunset or sunrise were later (Fig. 3a
and b). Note that the regression slope of the correlation between
sunset and the timing of the evening activity peak was steeper than
between sunrise and the timing of the morning activity peak.
Number of daily night hours
As expected, we found similar effects when testing the variable
number of daily night hours on hare activity peaks (Fig. 3c and d,
Table 1), simply because the number of daily night hours was highly
collinear with sunrise and sunset (correlation of sunrise vs. number
of daily night hours: R2= 0.99, F1,100 = 1.5 ×1031 ,p< 0.001, sunset
vs. number of daily night hours: R2= 0.97, F1,100 = 3628, p< 0.001).
There were no statistically signiﬁcant correlations between the
morning temperature and the timing of the morning activity peaks
and between evening temperature and the timing of the activity
peaks in the evening (Table 1).
Light conditions and the occurrence of a morning activity peak
There was a signiﬁcant effect of light conditions at the time of
the evening activity peak on the number of observed activity peaks.
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S.C. Schai-Braun et al. / Mammalian Biology 77 (2012) 434–440 437
Fig. 2. Locomotor activity (measured as the horal distances moved by the hares) in relation to the time of (a) sunset described by a 4th-order polynomial regression model
and (b) sunrise described by a 3th-order polynomial regression model. Data (squares) are shown as medians with 25th/75th percentiles. Dark phase is indicated by grey
The signiﬁcant models predicted two distinct maxima when the
evening activity peak was during daylight (2
6=230.09, p< 0.001,
Fig. 4a; dashed line), whereas there was only one activity maximum
when the evening activity peak was during the dark phase (2
731.61, p< 0.001, Fig. 4a; solid line). In other words, there was a
higher probability of occurrence of a second activity peak (in 79%
of cases) when the evening activity peaks of individual hares were
in full daylight (2
1=15.50, p< 0.001; Fig. 4b).
Locomotor activity pattern in the course of the day
The hares of our study showed two peaks in their daily activity,
having a distinct one in the evening and another less pronounced
one in the morning. Thus, it can be concluded that European hares
have a time of reduced activity in-between an enhanced activity at
the beginning and end of the night. When looking at the activity
of the European hare in relation to sunset, the data indicated an
activity peak around sunset followed by a decrease and a second
increase in activity during the dark phase. During the hour of high-
est activity, hares moved on average 70 m. As the average ﬁeld size
in the study area was 3.1 ha, we suggest that hares used only a few
different ﬁeld types per night while being active. Furthermore, the
model predicted a peak before sunrise followed by a long period of
almost no activity. That is, the predicted periods of inactivity did not
last for the whole light phase. As a consequence, there was notable
locomotor activity well before sunset and after sunrise.
Factors inﬂuencing the diurnal locomotor activity rhythm
Inﬂuence of sunrise and sunset on the diurnal locomotor activity
There was a signiﬁcant negative correlation between both the
timing of the morning activity peak and sunrise as well as between
the timing of the evening activity peak and sunset. As we assumed
that the activity peaks are collinear with onset and cessation of
activity, our results imply that the activity and inactivity of Euro-
pean hares in summer are closely tied to sunset and sunrise. This is
in accordance with other hare studies describing the major role of
sunrise and sunset concerning the onset and cessation of activity
(Mech et al. 1966; Figala et al. 1984; Pépin and Cargnelutti 1994;
Holley 2001). Nevertheless, the negative correlations in summer
imply that the impact of sunrise and sunset differ from winter.
While in winter European hares consistently started their daily
activity shortly after sunset and ended it shortly before sunrise
(Pépin and Cargnelutti 1994; Holley 2001), in summer the hare’s
activity peaks occurred after sunset or sunrise when sunset or sun-
rise were earlier, whereas activity peaks shifted before sunset or
sunrise when sunset or sunrise were later. Our results during sum-
mer conﬁrm the ones of Holley (2001) who observed European
hares’ starting to leave their forms before sunset and enter them
after sunrise in the early part of the year. Thus, we conclude that
the power of the zeitgebers sunrise and sunset is altered in summer
by a seasonal factor.
The steep regression slope between sunrise and the timing of
the morning activity peak resulted in an 8–11 h shift in the timing
of the morning activity peak. In comparison, the shift of the timing
of the evening activity peak was much lower i.e. 5–6 h. This result,
indicating that sunset might be a stronger zeitgeber for the Euro-
pean hare’s diurnal locomotor activity pattern than sunrise, is in
line with another European hare study conducted in winter (Holley
2001). The latter study reported a stronger tie between onset of
activity and sunset than between cessation of activity and sunrise.
Inﬂuence of season on the diurnal locomotor activity rhythm
The negative correlation between the timing of the activity
peaks and sunrise or sunset might be explained by the European
hare’s need to fulﬁl its daily energetic demands, because the length
of the dark phase might limit the hare’s feeding time. Hackländer
et al. (2002) showed that European hares fed with a diet compara-
ble to the composition of stomach contents in free-ranging hares
had a higher food intake yet assimilated less energy than hares fed
with a high fat diet. This might be due to a trade-off between the
energy beneﬁt of increasing food-intake and the additional weight
load in a ﬂight animal (Hackländer et al. 2002). Such a trade-off
might also account for the small resting periods dispersed during
activity (Averianov et al. 2003) which may be used by the European
hares to digest before new food intake can take place. The results
of another study indicated that the duration of European hares’
activity period cannot be contracted further a certain point (Holley
2001). A possible explanation may be the hare’s need to fulﬁl its
daily energetic demands in combination with required digestive
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438 S.C. Schai-Braun et al. / Mammalian Biology 77 (2012) 434–440
Fig. 3. Correlations between (a) evening activity peaks and sunset, (b) morning activity peaks and sunrise, (c) evening activity peaks and the number of daily night hours
and (d) morning activity peaks and the number of daily night hours with the regression lines (statistically signiﬁcant). Dark phase is indicated by grey shading. See text for
details on statistics.
breaks. Sunset was late and sunrise early until middle of July and the
hare’s evening and morning activity peaks proceeded in full day-
light. During this time the dark phase seems not to be long enough
for the European hare to accomplish its energetic requirements. The
number of daily night hours was longer in August and September,
and hare’s evening and morning activity peaks took place during
the dark phase. We propose that during the vegetation period the
usual trigger of activity and inactivity, namely sunrise and sunset,
is slightly altered in its impact by the hares’ instinct to accomplish
their daily energetic requirements. Hence, the season in the form
of the number of daily night hours had an inﬂuence on the activity
pattern of the European hare and altered the impact of the usual
zeitgebers sunrise and sunset. There was no difference noticeable
in the inﬂuence of sunrise and sunset and the numbers of daily
night hours as these parameters were collinear.
Moreover, the season seems to have an impact on the hare’s
inactivity period during daytime. Our results are in line with pre-
viously reported activity data in European hares (Homolka 1986;
Holley 2001) showing that activity during the day is displayed
predominantly in summer. Outside the summer period, on the
contrary, European hare studies reported almost no activity during
daytime (Homolka 1986; Pépin and Cargnelutti 1994; Holley
2001). However, our study animal’s period of inactivity exceeded
the previously reported length of inactivity in European hares
during summer. Homolka (1986) showed that in Moravia (Czech
Republic) during a one all-day visual observation in July 30–50% of
the hares were active with foraging throughout the day except for
about 4 h in the middle of the day. The hares’ distinct length of inac-
tivity during the day might be explained by the different climate
in Southern Moravia and the eastern part of Lower Austria. The
Pannonian climate is responsible for hot and dry summers in our
study area, whereas the continental climate of Southern Moravia
is more moderate. Our study animals may react to the Pannonian
climate in summer with an extended period of inactivity during
the day. We therefore conclude that the length of inactivity during
daytime depends both on season and climatic conditions.
Inﬂuence of temperature on the diurnal locomotor activity rhythm
The ambient temperature has been proposed to inﬂuence the
activity pattern of some mammal species (Getz 1961; Garshelis
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S.C. Schai-Braun et al. / Mammalian Biology 77 (2012) 434–440 439
Fig. 4. (a) Polynomial regression models describing locomotor activity (measured as the horal distances moved by the hares) with the evening activity peak being in the
light (dashed line) or dark phase (solid line). (b) Probability of the existence of a morning activity peak when the preceding evening activity peak was during the dark or light
phase. See text for details on statistics.
and Pelton 1980). However, this does not seem to be the case for
the European hare, at least not during the vegetation period, as we
could not ﬁnd signiﬁcant correlations between the activity peaks
and the temperature. An explanation might be that the European
hare, originally coming from the savannas of Eurasia (Averianov
et al. 2003), is adapted to high ambient temperatures, and, as a
result, the impact of the zeitgebers sunrise and sunset is not altered
by the daily ambient temperature.
Light conditions and the number of activity peaks
Our results revealed a signiﬁcant inﬂuence of light conditions
at the time of the evening activity peak on the number of activ-
ity peaks. If the evening activity peaks of individual hares were in
full daylight, there was a higher probability of occurrence of a sec-
ond activity peak. It seems as if some unknown factor provokes an
increased activity by antedating the evening activity peak into full
daylight and hereinafter by evoking a second activity peak in the
early morning hours. No such ﬁndings were ever reported in the lit-
erature regarding hare’s activity patterns. We suggest that future
studies on hares’ activity patterns during the vegetation period
might carefully examine the number of activity peaks during the
night and decipher the factor(s) provoking an increased activity.
In conclusion, our study clearly shows that also in summer,
sunrise and sunset were the zeitgebers for the European hare’s
diurnal locomotor activity pattern. However, the results indicate
that this relationship was altered by the number of daily night
hours (season). We speculate that the hares’ feeding activity during
the short summer nights is not sufﬁcient to cover their daily ener-
getic requirements during the vegetation period. The temperature,
however, had no inﬂuence on the locomotor activity pattern of the
We thank the hunting society of Zwerndorf for cooperation,
especially Walter Metz for his help with hare trapping. The
study was funded by the following foundations or organisations:
Parrotia-Stiftung, Stiftung Dr. Joachim de Giacomi, Basler Stiftung
für biologische Forschung, Messerli Stiftung, Carl Burger Stiftung,
CIC Schweiz, CIC Deutschland, Paul Schiller Stiftung and Karl Mayer
Stiftung. The study complies with the current laws of Austria.
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