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Wolf activity varies considerably among differentstudies that explained activity patterns by factors suchas human activity, breeding status or prey availability(Fancy and Ballard 1995; Vila` et al. 1995; Ciucci et al.1997; Theuerkauf et al. 2003; Kusak et al. 2005; Chavezand Gese 2006; Theuerkauf et al. 2007). However, it islikely that the high variability of wolf activity patterns isa result of their ability to react to various environmentalconditions (Packard 2003). Activity of individual wolvesmay also vary among days, switching from being diurnalto nocturnal. The purpose of our study was to examinethe variability of wolf activity between days and seasonsand to determine the most important factors thatinfluence activity in the Bieszczady Mountains, Poland.The study was conducted in the Bieszczady Moun-tains (Polish Carpathians) over an area of about1000km
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Daily and seasonal variation in wolf activity in the Bieszczady Mountains,
SE Poland
Julia Eggermann
, Roman Gula
, Bartosz Pirga
¨rn Theuerkauf
Hiroshi Tsunoda
, Barbara Brzezowska
, Sophie Rouys
, Stephan Radler
Faculty of Biology and Biotechnology, Ruhr University Bochum, Universita
¨tsstr. 150, 44801 Bochum, Germany
Museum and Institute of Zoology, Polish Academy of Sciences, Wilcza 64, 00-679 Warsaw, Poland
Wildlife Conservation Laboratory, Graduate School of Agriculture, Tokyo University of Agriculture and Technology,
3-5-8 Saiwai-cho, Fuchu-shi, Tokyo 183-8509, Japan
Department of Animal Ecology, Institute of Environmental Sciences, Jagiellonian University, Gronostajowa 7,
30-387 Krako
´w, Poland
Conservation Research New Caledonia, BP 25 49, 98846 Noumea, New Caledonia
University of Applied Forest Sciences Rottenburg, Schadenweilerhof, 72108 Rottenburg, Germany
Received 9 January 2008; accepted 30 May 2008
Keywords: Activity pattern; Canis lupus; Distance travelled; Movements; Poland
Wolf activity varies considerably among different
studies that explained activity patterns by factors such
as human activity, breeding status or prey availability
(Fancy and Ballard 1995;Vila
`et al. 1995;Ciucci et al.
1997;Theuerkauf et al. 2003;Kusak et al. 2005;Chavez
and Gese 2006;Theuerkauf et al. 2007). However, it is
likely that the high variability of wolf activity patterns is
a result of their ability to react to various environmental
conditions (Packard 2003). Activity of individual wolves
may also vary among days, switching from being diurnal
to nocturnal. The purpose of our study was to examine
the variability of wolf activity between days and seasons
and to determine the most important factors that
influence activity in the Bieszczady Mountains, Poland.
The study was conducted in the Bieszczady Moun-
tains (Polish Carpathians) over an area of about
1000 km
(491190–491500N, 221150–221450E). Roughly,
60% of the area is covered with forest. The remaining
parts are fields, meadows or villages. Human population
density in the region is 44 inhabitants per km
administration data of 2004), and road density is
0.64 km/km
. Red deer (Cervus elaphus), roe deer
(Capreolus capreolus) and wild boar (Sus scrofa) are
the major preys of wolves inhabiting the area (Gula
2004, 2008). The mean temperature is 14 1C in summer
and 31C in winter, with annual precipitations of
800–1200 mm. Snow cover lasts for 90–140 days, with an
average depth of 10–40 cm.
We caught three wolves with Belisle foot snares.
Capture and handling of wolves was done according to a
procedure approved by the Ethical Commission of the
Polish Ministry of Science and Higher Education.
During 78 continuous 24-h sessions, we radio-tracked
a breeding female (4224 locations during 44 radio-
tracking days from March 2002 to October 2004),
1616-5047/$ - see front matter r2008 Deutsche Gesellschaft fu
¨ugetierkunde. Published by Elsevier GmbH. All rights reserved.
doi:10.1016/j.mambio.2008.05.010 Mamm. biol. 74 (2009) 159–163
Corresponding author
E-mail address: (J. Theuerkauf).
Present address: Laboratory of Water Resource Planning, United
Graduate School of Agriculture, Tokyo University of Agriculture and
Technology, Saiwai-cho 3-5-8, Fuchu, Tokyo 183-8509, Japan.
a non-breeding female (480 locations during 5 days from
September 2005 to May 2006) and a breeding male wolf
(2688 locations during 28 days from June 2003 to
July 2006) of three different established packs. We
assessed the breeding and pack status of wolves based
on snow-tracking, howling surveys, radio-tracking and
den searching as described in Tsunoda et al. (2008)
and Gula (2008). We located wolves by VHF ground
telemetry and triangulation, as described in Theuerkauf
and J˛edzejewski (2002), with a location accuracy of
about 250 m (Theuerkauf et al. 2007). We estimated
distances travelled as straight line distances between
consecutive radio-tracking locations taken at 15-min
intervals (always 96 locations per day). To estimate the
proportion of time active, we assigned a value of 1 when
wolves were active and locations changed; a value of 0
when wolves were not active and locations did not
change; and a value of 0.5 when wolves were either
active or locations changed (as described in Theuerkauf
and J˛edzejewski 2002). We eliminated auto-correlation
among consecutive activity estimates or radio-locations
by calculating one value of the time spent active or
distance travelled for each hour (as described in
Theuerkauf et al. 2007). We then calculated the
coefficient of variance (CV) of each hour as variation
among the radio-tracking sessions. A single radio-
tracking day is therefore the sample unit. We subdivided
the year into four periods: winter from November to
February, spring as March and April, summer from
May to August and autumn as September and October.
Periods of the day were dawn (sunrise 71 h), day (1 h
after sunrise to 1 h before sunset), dusk (sunset 71h)
and night (1 h after sunset to 1 h before sunrise). Activity
and inactivity bouts were periods of uninterrupted
activity or inactivity. To estimate nocturnality (propor-
tion of activity at night compared with the whole day)
during different seasons, we used Ivlev’s electivity index
(Jacobs 1974)
nocturnality index ¼ðpnpdÞðpnþpd2pnpdÞ1,
with p
being the percentage of time a wolf was active at
night (from sunset to sunrise) and p
the percentage of
time active in the day (from sunrise to sunset). The
nocturnality index can range from +1 (totally noctur-
nal) to 1 (totally diurnal). To measure the daily range
and its variation between four seasons, we used 100%
minimum convex polygons (MCP) for each of the 24-h
monitoring sessions (96 consecutive radio-locations for
each day) and subsequently calculated mean values for
each season.
Daily patterns of the mean time spent active and the
CV of activity of the breeding female and the breeding
male were similar. The activity and CV patterns of the
non-breeding female were highly variable, likely caused
by the small sample size (n¼5 days). Wolves were
active on average at 3272% [95% confidence interval
0 6 12 18 24
0 6 12 18 24
0 6 12 18 24
Fig. 1. Daily pattern of mean time spent active per hour
(shaded area) and coefficients of variance (bold continuous
line) with CI of a breeding female (A, n¼44 days), a breeding
male (B, n¼28) and a non-breeding female (C, n¼5) wolf,
radio-tracked in the Bieszczady Mountains (SE Poland)
between 2002 and 2006. The grey line indicates the daily mean
of time active. The horizontal bar indicates length and
variation of night (black), dawn and dusk (grey) and day
J. Eggermann et al. / Mamm. biol. 74 (2009) 159–163160
(CI), n¼78 days] of all radio-locations, with a main
peak of activity at dawn (Fig. 1). The CV was lowest in
the early morning hours and subsequently increased.
Wolves were more active, moved more often and
travelled greater distances at dawn than at other times
of the day (U-test, n¼78, all po0.001; Table 1).
Activity bouts were longer at night and dawn (U-test,
n¼78, p¼0.022), whereas bouts of inactivity lasted
longer in the day and at sunset; these differences were,
however, not significant (Table 1). The daily patterns
of mean distances travelled did not vary among the
four seasons (ANOVA two-way interactions, p¼0.240;
Fig. 2). Although the patterns followed the timing
of sunrise and sunset, the activity peak was highest
in winter and lowest in summer. Wolves travelled the
furthest in the early morning (03:00–06:00 h) and the
least around noon (10:00–16:00 h) in all seasons (Fig. 2).
The average distance travelled per hour was the shortest
in summer (Table 1) and differed from other seasons
(spring/autumn: U-test, n¼67, p¼0.022; winter:
n¼40, p¼0.003), while there were no significant
differences among the other seasons (spring, autumn
and winter). The lengths of active or inactive bouts did
not vary much among seasons (Table 1). In summer, the
wolves tended towards diurnal activity, but were more
nocturnal in other seasons. The CIs were, however, large
and all of them included the value zero. The mean daily
MCP was smallest in summer and largest in autumn and
winter (Table 1).
Wolves in our study area had a crepuscular activity
pattern with a distinct peak around dawn, but their
activity was highly variable. The only part of the day
when variance was low and thus activity more pre-
dictable was the early morning, which might be related
to hunting. Theuerkauf et al. (2003) showed that wolves
hunt mainly around dawn and dusk in Eastern Poland,
which coincided with peaks in activity and movement.
In Minnesota, wolves were the most active at night and
dawn, when they hunted for deer and moose (Chavez
and Gese 2006). Our results are in keeping with those
of Fancy and Ballard (1995) who found that wolves
generally have distinct patterns of activity, but that their
activity is highly flexible and may be adapted to various
environmental conditions. The reason for a lower
mobility during summer is denning, when wolf activity
concentrates on the den site and the breeding female
does not move far from the den (Harrington and Mech
1982;Ballard et al. 1991;Schmidt et al. 2008;Tsunoda
et al. 2008). Our results about daily ranges calculated by
daily MCPs support this idea. Although the sample size
in our study was small, we conclude that apart from the
times when they are hunting, wolf activity and distance
travelled are highly variable, which reflects the plasticity
of their behaviour.
This study was a part of the Bieszczady Wolf Project
funded by the Polish National Committee for Scientific
Research (KBN 6P04F 006), budget of the Museum and
Institute of Zoology, PAS, and financially supported by
Table 1. Means (with 95% confidence intervals) of the time spent active, time moving, distance travelled, length of activity bouts,
nocturnality index and daily range (MCP) at different periods of the year and different times of the day of three wolves (n¼78 days)
radio-tracked in the Bieszczady Mountains (SE Poland) between 2002 and 2006
Time active
Time moving
Distance travelled
Active bout
length (h)
Inactive bout
length (h)
Daily range
34.474.7 25.074.5 0.6870.15 1.670.5 1.570.4 0.1270.25 13.977.0
32.876.0 23.076.1 0.5770.15 1.370.3 1.570.3 0.1370.24 9.174.4
29.074.3 19.174.7 0.4070.15 1.270.3 1.770.3 0.1170.18 4.872.0
32.574.6 23.974.5 0.6170.27 1.270.3 1.870.3 0.1370.24 14.877.9
46.175.4 35.075.7 0.8970.18 1.770.4 1.570.4
27.173.3 17.773.0 0.3770.06 0.870.1 1.670.3
25.974.5 16.074.0 0.3870.11 1.370.4 2.070.5
30.275.5 22.275.1 0.5870.16 1.870.5 2.270.5
J. Eggermann et al. / Mamm. biol. 74 (2009) 159–163 161
scholarships of the German Academic Exchange Service
(to J.E.), the German Donors’ Association for the
Promotion of Sciences and Humanities (to J.T.) and
the Japan Student Services Organization (to H.T.).
We thank L. Aubry, M. Barreteau, M. Carruthers,
M. Diemert, S. Drevet, M. Januszczak, S. Kiener,
K. Lahongre, M. Le Peutrec, K. Mayer, N. Schmidt,
M.-C. Schultz, M. Skuban and W. Schwimmer for their
help during the fieldwork.
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0 6 12 18 24
0 6 12 18 24
0 6 12 18 24
0 6 12 18 24
Fig. 2. Seasonal variation of mean time spent active (hatched
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... Pack members accumulate these marks on the edge of their territories or surrounding rendezvous sites (Zub et al. 2003, Barja et al. 2005, Mech & Boitani 2010, Stenglein et al. 2011. Wolf activity is highly flexible and may be adapted to various environmental conditions with a peak of activity during summer, which corresponds to the nursing period (Fancy & Ballard 1995, Eggermann et al. 2008; however, breeding females decrease their mobility during summer (Eggermann et al. 2008). The reasons for lower mobility during summer are denning and nursing, when wolf activity concentrates on the den site and the breeding female does not move far from the den (Eggermann et al. 2008, Tsunoda et al. 2009). ...
... Wolf activity is highly flexible and may be adapted to various environmental conditions with a peak of activity during summer, which corresponds to the nursing period (Fancy & Ballard 1995, Eggermann et al. 2008; however, breeding females decrease their mobility during summer (Eggermann et al. 2008). The reasons for lower mobility during summer are denning and nursing, when wolf activity concentrates on the den site and the breeding female does not move far from the den (Eggermann et al. 2008, Tsunoda et al. 2009). The intensity of territorial marking predicts wolf reproduction (Llaneza et al. 2014). ...
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This publication is a review of the application of remote sensing methods for wildlife population monitoring in zoology. Remote sensing methods are divided into four categories. The first category of methods examines the application of camera traps, the second is about application of Unmanned Aerial Vehicles (UAV), the third category examines the application of satellite images, and the fourth category is summarizing studies focused on GPS tracking. The use of camera traps for monitoring wildlife populations proved to be a very applicable and reliable field method for collecting appropriate data on large mammals. Monitoring animal populations through satellites is a method that allows many biogeographic hypotheses related to migration and species distribution. Data obtained from satellite platforms is used as an indirect method in zoology to assess the habitats and food base of animals, as well as to create models of species distribution. Images obtained from UAVs are a new method for conducting studies of wildlife populations, showing greater accuracy and efficiency than conventional methods. GPS tracking is an extremely suitable method used to identify breeding and wintering sites, migration routes, and resting places of many species of migratory birds and mammals. The literature review shows that monitoring animal populations through remote sensing methods are cost and time effective methods that can be successfully performed without exposing researchers to risk. It allows monitoring of difficult-to-access places and large territories as well as to create models of species distribution
Wolves use howls to maintain large territories, intra-pack communication and social bonding. Besides their physical presence, howls are also instrumental in creating fear and impacting foraging behaviour among the lower cascade. Anthropocene-led behavioural alteration in vocalization has been observed in a wide range of species, but the effect on wolf howl is unknown. In this context, we have studied the howling behaviour of the Indian wolf through playback surveys (n = 264) across the anthropogenic gradient. We found a disparity in their howl response – based on the distance to villages. In the low disturbed East-Maharashtra (EM), wolves mostly avoided responding to howling surveys (HS) if done within 1200 m of villages [response rate (RR) = 0.03 ± 0.021], but they did respond once it was done far from villages (>1200 m) (RR = 0.226 ± 0.075). In high human-density West-Maharashtra (WM), wolves showed high RR within 1200 m from the villages (RR = 0.148 ± 0.031). But the RR within 500 m from villages was less as howling near villages might lead to easy detection. The collared wolf data showed significantly high RR (0.635 ± 0.067) in their home-range core, but low RR if the core area was close to a village. Therefore, howling too close to a village is disadvantageous, although their tolerance for responding to HS has increased in the human-dominated landscape. The extent of the village may increase further with development, which will leave fewer areas for the wolf to defend territory with a long-range howl. The wolves might behaviourally adapt to a human-modified landscape by reducing their howling intensity. Adaptation to a fragmented habitat may save the wolves from extinction, but the repercussions of the fundamental behavioural alteration might adversely impact wolf behaviour and the ecological cascade. Whereas ecologists are mainly concerned with the extinction of species, this study highlights the vulnerability of fundamental behaviour of a keystone species attributed to human-induced contemporary evolution.
Illumination at night is determined by moon phase, naturally affecting the reproductive activities of nocturnal animals. The European badger (Meles meles Linnaeus, 1758) is a large-sized mustelid that lives throughout Bulgaria. Since information on its reproductive activity is scarce for the country, a two-year camera trap survey was conducted at five main setts. It aimed to establish the mating period of the badger, as well as some behavioral activities of the observed individuals in a semi-mountainous area in Central Bulgaria (Sarnena Sredna gora Mts). In the present study, badger mating behavior was only observed in winter. Winter is the main breeding period for badgers from Central Bulgaria, which corresponds to greater activity in front of the setts. Darker lunar phases (i.e., new moon) significantly increased the badger's reproductive activities at their setts. Further research is needed to elucidate the underlying mechanism responsible for the nocturnality and preferences to darkness of reproductive badgers.
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Expansion of the wolf in the Czech Republic results in an increasing conflict between nature conservationists and other landscape users. In March 2020, the Ministry of the Environment of the Czech Republic issued the "Wolf Management Programme". The document provoked negative reactions from organisations of farmers, breeders, and hunters. The article deals with the key issues triggered by the conflict and the attitudes of actors involved. We want to clarify to what extent the solutions designed by individual parties help to mitigate the conflict and how the financial demands related to this issue have been evolving. The problem may seem marginal in the Czech Republic, but the wolf population density in some regions may already be close to its bearable maximum. Key problems are the identification of specific target numbers of wolves, the absence of zoning as a future option, and clear rules for dealing with direct encounters of wolves with humans. The benefit of wolf management is primarily the sum of preventive measures it aims to address, but the relationship with other main actors and the area of education and promotion is debatable as it represents a further increase in the absorption of public funds without guarantees of effectiveness.
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We analyzed radiotracking data from wolves (Canis lupus) in the Bia l / owie z . a Forest, Poland, to determine (1) differences between methods of estimating mean wolf activity and daily activity patterns, (2) whether activity estimated by changes in signal strength is dependent on the distance between the radiotracked wolf and the track -er, (3) radiotracker influence on wolf activity estimates, and (4) accuracy of radio locations. Daily patterns of wolf activity estimated by changes in signal strength, movements, and activity sensors were similar. However, the mean time spent active estimated by changes in signal strength (55%of the time) was higher than those estimated by movements (34%) or sensors (32%). We obtained the most accurate estimates of activity by a combination of move -ment, signal strength, and sensor data (43%) or by combining movement and signal strength data (44%). Activity estimated by changes in signal strength did not vary with the distance between radiotracked animals and radio -trackers. The trackers had no detectable influence on activity and movements of wolves when the tracker-to-wolf distance was >200 m. There was a small but not significant influence if trackers were <200 m away during the day. The mean radiotracking error was 194 m (95%CI: 157 – 231 m). We recommend that data on movements always be included in estimates of mean time spent active because activity sensors lead to underestimates and changes in sig -nal strength to overestimates. Distances traveled obtained by radiotracking should not be regarded as minimal dis -tances traveled, since the likelihood that they are overestimated or underestimated depends on the relation between the accuracy of radio locations and the mean distance that an animal travels per radiotracking interval. JOURNAL OF WILDLIFE MANAGEMENT 66(3):859–864
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until recently. The radio tracked wolves of three packs moved throughout the day with one major peak around dawn. Wolves avoided the area around main public roads more at night (up to a distance of 1.5 km) than in the day (up to 0.5 km). Wolves avoided a 0.5-km area around secondary public roads and paved forest roads both at night and in the day but did not avoid the surroundings of settlements. As compared with other studies, wolves in this study were the least nocturnal although human density was the highest. We conclude that human activity is unlikely to be the reason for nocturnal activity in wolves.
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were active 45.2% 6 0.9 SE of the time and traveled 0.92 6 0.05 km/h. The mean length of activity bouts was 0.76 6 0.05 h, whereas inactivity bouts averaged 1.02 6 0.07 h. Wolves were active throughout the day, but their activity peaked at dawn and dusk, which coincided with periods when they killed most prey. Periods of reproduction and high tem- peratures had less pronounced effects on activity patterns. Human activity and other factors did not significantly affect the wolves' daily activity patterns. The influence of humans may be indirect if hunting of ungulates by humans modifies activity patterns of the wolves' prey. We conclude that the daily activity patterns of wolves in our study area were mainly shaped by their pattern of hunting prey.
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Sex, age, bone marrow fat (BMF) content, degree of carcass utilisation and terrain features were analysed for 118 ungulates killed by wolves Canis lupus in the Bieszczady Mountains, Poland, during the winters of 1992-1995 to assess the influence of snow depth on the wolves' predation patterns. In Bieszczady, the snow conditions during the study period were milder than average, with an average total annual snow depth of 1,372 cm and an average snow cover lasting for 94 days. Red deer Cervus elaphus were the primary wolf prey (81%), whereas wild boar Sus scrofa and roe deer Capreolus capreolus were killed less often (9% and 10%, respectively). The majority of prey (74%) was killed in creeks and ravines. The carcass exploitation by wolves was high; of the recovered prey, 55% was more than 60% consumed. The average condition of red deer, as based on BMF, was high (83.4%). BMF varied most among red deer stags and calves, and varied with annual snow depth (N = 29, P < 0.01; N = 28, P = 0.09) and monthly mean snow depth (τ = -0.37, P < 0.005; τ = -0.25, P = 0.06). Wolves killed adult red deer in creeks and ravines with the same frequency regardless of snow depth, whereas calves were killed less often in these places than should be expected from their overall proportion in the sample (N = 95, χ2 = 24.34, P < 0.001). During periods with thinner snow cover, consumption of red deer carcasses was slightly higher than during periods in which the snow cover was deep (τ = -0.42, P < 0.045).
Wolves (Canis lupus) have expanded their distribution into areas of the midwest United States that have not had wolves for several decades. With recolonization of wolves into agricultural areas, there is increasing concern of wolf–livestock conflicts. To assess the risk wolves may pose to livestock, we initiated a 3-year study investigating the activity patterns, movements, habitat use, visitation to livestock pastures by wolves, and the occurrence of depredation events in an agricultural–wildland matrix in northwestern Minnesota, USA. From June 1997 to November 1999, we captured 23 wolves, including pups, from 3 packs; we radiocollared 16 of these wolves. We tracked radioed wolves intensively on a 24-hour basis during the spring, summer, and autumn of 1998 and 1999. We found wolves passed directly through a pasture containing cattle on 28% of the nights of tracking; 58% and 95% of the wolf locations were ≤1 km and ≤5 km from a pasture, respectively. Space use of wolves showed that while they visited livestock pastures during the 24-hour tracking sessions, they apparently were passing through these pastures with cattle and not preying on livestock. When compared to random simulations of movements, wolves appeared to encounter livestock pastures randomly. Thirty percent of random movements passed directly through a pasture; 65% and 95% of random movements were within ≤1 km and ≤5 km of a pasture, respectively. Wolves were more active at night than during the day. Wolves avoided pastures during the day and visited pastures at night when depredations were most likely (i.e., human presence was low). Visitation of livestock pastures was not related to any discernible characteristics of the pastures (i.e., pasture size, cattle density, distance to human habitation, percent forest cover, index of deer abundance). However, pastures in which livestock were killed by wolves contained more cattle than pastures without depredations, but in 1998 only. While the risk of wolf predation on livestock was potentially high (wolves were within ≤1 km of a pasture on 58% of nights), few livestock were actually killed. During the 3-year study, only 8 animals (all young or vulnerable livestock) were depredated by wolves. Maintaining healthy wild prey populations, removing offending wolves that kill livestock, and encouraging effective and proper husbandry practices (e.g., disposal of carcasses) among livestock producers, should allow for the persistence of wolves in northwestern Minnesota, USA, while minimizing their impact to farmers in this agriculture–wildland matrix.