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Home range size of Tengmalm’s owl during breeding in Central Europe is determined by prey abundance

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Animal home ranges typically characterized by their size, shape and a given time interval can be affected by many different biotic and abiotic factors. However, despite the fact that many studies have addressed home ranges, our knowledge of the factors influencing the size of area occupied by different animals is, in many cases, still quite poor, especially among raptors. Using radio-telemetry (VHF; 2.1 g tail-mounted tags) we studied movements of 20 Tengmalm’s owl (Aegolius funereus) males during the breeding season in a mountain area of Central Europe (the Czech Republic, the Ore Mountains: 50° 40’ N, 13° 35’ E) between years 2006–2010, determined their average hunting home range size and explored what factors affected the size of home range utilised. The mean breeding home range size calculated according to 95% fixed kernel density estimator was 190.7 ± 65.7 ha (± SD) with a median value of 187.1 ha. Home range size was affected by prey abundance, presence or absence of polygyny, the number of fledglings, and weather conditions. Home range size increased with decreasing prey abundance. Polygynously mated males had overall larger home range than those mated monogamously, and individuals with more fledged young possessed larger home range compared to those with fewer raised fledglings. Finally, we found that home ranges recorded during harsh weather (nights with strong wind speed and/or heavy rain) were smaller in size than those registered during better weather. Overall, the results provide novel insights into what factors may influence home range size and emphasize the prey abundance as a key factor for breeding dynamics in Tengmalm’s owl.
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RESEARCH ARTICLE
Home range size of Tengmalm’s owl during
breeding in Central Europe is determined by
prey abundance
Marek Kouba
1,2
*, Luděk Bartos
ˇ
1,3
, Va
´clav Toma
´s
ˇek
1,2,4
, Alena Popelkova
´
1,2
,
Karel S
ˇt
ˇastny
´
2‡
, Marke
´ta Za
´rybnicka
´
5‡
1Department of Animal Science and Ethology, Faculty of Agrobiology, Food and Natural Resources, Czech
University of Life Sciences Prague, Prague, Czech Republic, 2Department of Ecology, Faculty of
Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic, 3Department
of Ethology, Institute of Animal Science, Prague, Czech Republic, 4Nature Conservation Agency of the
Czech Republic, Prague, Czech Republic, 5Department of Applied Geoinformatics and Spatial Planning,
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
These authors contributed equally to this work.
These authors also contributed equally to this work.
*marekkouba8@gmail.com
Abstract
Animal home ranges typically characterized by their size, shape and a given time interval
can be affected by many different biotic and abiotic factors. However, despite the fact that
many studies have addressed home ranges, our knowledge of the factors influencing the
size of area occupied by different animals is, in many cases, still quite poor, especially
among raptors. Using radio-telemetry (VHF; 2.1 g tail-mounted tags) we studied move-
ments of 20 Tengmalm’s owl (Aegolius funereus) males during the breeding season in a
mountain area of Central Europe (the Czech Republic, the Ore Mountains: 50˚ 40’ N, 13˚
35’ E) between years 2006–2010, determined their average hunting home range size and
explored what factors affected the size of home range utilised. The mean breeding home
range size calculated according to 95% fixed kernel density estimator was 190.7 ±65.7 ha
(±SD) with a median value of 187.1 ha. Home range size was affected by prey abundance,
presence or absence of polygyny, the number of fledglings, and weather conditions. Home
range size increased with decreasing prey abundance. Polygynously mated males had
overall larger home range than those mated monogamously, and individuals with more
fledged young possessed larger home range compared to those with fewer raised fledg-
lings. Finally, we found that home ranges recorded during harsh weather (nights with
strong wind speed and/or heavy rain) were smaller in size than those registered during
better weather. Overall, the results provide novel insights into what factors may influence
home range size and emphasize the prey abundance as a key factor for breeding dynam-
ics in Tengmalm’s owl.
PLOS ONE | https://doi.org/10.1371/journal.pone.0177314 May 18, 2017 1 / 15
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OPEN ACCESS
Citation: Kouba M, Bartos
ˇL, Toma
´s
ˇek V,
Popelkova
´A, S
ˇt
ˇastny
´K, Za
´rybnicka
´M (2017)
Home range size of Tengmalm’s owl during
breeding in Central Europe is determined by prey
abundance. PLoS ONE 12(5): e0177314. https://
doi.org/10.1371/journal.pone.0177314
Editor: Antoni Margalida, University of Lleida,
SPAIN
Received: December 19, 2016
Accepted: April 25, 2017
Published: May 18, 2017
Copyright: ©2017 Kouba et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
file.
Funding: This work has been supported by grants
from the Czech University of Life Sciences Prague
(CIGA ČZU 20104201 [VT], IGA FZ
ˇP 3116, 3127,
and 3138 [VT]), the Ministry of Education, Youth
and Sports of the Czech Republic (MS
ˇMT 1321/
213205 [LB]), the Ministry of Agriculture of the
Czech Republic (MZERO 0716 [LB]), and the
Ministry of Environment of the Czech Republic (GS
Introduction
As early as Darwin [1] it was noted that the primary characteristic of animal movement is that
most animals use the same areas repeatedly over time. Movements of this type in fairly well-
defined areas within which animals perform their daily activities are often defined using the
home range concept [2]. The first definition of home range (hereafter HR) was provided by Burt
[3] as: “Area traversed by the individual in its normal activities of food gathering, mating, and
caring for young. Occasional sallies outside the area, perhaps exploratory in nature, should not be
considered part of the home range.” Although this basic construct is retained within the concept
of home range to this day, it has usually been refined to include clear definition of the timeframe
involved in a given home range analysis (daily, seasonal, annual, life-time etc.) and in more for-
mal statistical analysis of HR size (e.g., [46]). The HR is characterized typically with descriptors
of its size, shape and structure [7], and must be defined for a specific time interval [2,8,9].
Different biotic and abiotic factors (intrinsic and/or extrinsic) are likely to affect the size,
use, and spatial configuration of individuals’ HR, and all these factors interact along a hierar-
chical pattern according to different spatial and temporal scale [1012]. Using hierarchy
theory [13], McLoughlin and Ferguson [10] offer review of the limiting factors that likely
determine HR size at three spatial levels (among species, populations and individuals) identify-
ing the critical factors as including (inter alia): body size, climate, abundance and distribution
of food, social organisation, population density and risk of predation.
At the species level, positive correlations between size of HR and body mass/size were
found in mammals, birds and lizards (e.g., [1416]), with carnivores as a rule found to have
larger HRs compare to herbivores (reviewed by [17]). At the population level, major determi-
nants of HRs include climate and its effects on general habitat productivity, density, and the
spatial structure of the environment such as primary productivity, seasonality, and food avail-
ability/accessibility (reviewed by [10]). At the individual level, food availability, conspecific
density, and risk of predation are likely the primary determinants of HR size (reviewed by
[10]). In birds and mammals an inverse relationship between HR size and food availability
have been frequently found (e.g., [1821]).
Resource (particularly food) dispersion and abundance affected, largely independently,
the group size and HR area/territory size of socially living carnivores (reviewed by [22]). For
instance in the European badger (Meles meles) the dispersion of pasture patches with high
earthworm availability positively correlated with territory size, however, the group size
depended on the quality of particular foraging patches within the territory [23]. In Pallas’s cat
(Otocolobus manul) males inhabited HRs 4–5 times the size of female, smaller HRs were asso-
ciated with higher coverage of preferred rocky habitats in the HR centre, whereas larger HRs
were associated with higher connectivity of rocky habitats in their periphery, and HR size did
not increase in response to low prey abundance or seasonality [24]. Subadult brown bear
(Ursus arctos) males had larger ranges than females, HRs increased with increasing body size,
decreased with increasing population density, but were not related to a general index of food
availability and individual age [25].
In elk (Cervus canadensis) ranging patterns reflected complex trade-offs that affect foraging,
group dynamics, movement energetics, predation avoidance and thermal regulation [26]. The
HR size in red deer (Cervus elaphus) decreased with increasing conspecifics density, supple-
mental feeding intensity, average annual temperature, and males had a larger HR than females
[27]. The percentage of grassland and the slope of grasslands within the HR were the main
determinants of HR size in male Alpine ibex (Capra ibex ibex), explaining also the differences
between seasons so that HR size in winter and spring was inversely correlated with the amount
of snow depth while in other seasons it was linked to resource exploitation [28]. In Arctic
Home range size of Tengmalm’s owl
PLOS ONE | https://doi.org/10.1371/journal.pone.0177314 May 18, 2017 2 / 15
LČR 5/2006 [KS
ˇ]). The funders had no role in
study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
ground squirrel (Spermophilus parryii) the HR size was 2–7 times smaller when individuals
were food-supplemented regardless of whether large mammalian predators were present or
not [29], and the eastern indigo snake (Drymarchon couperi) used much smaller HRs in frag-
mented landscapes and vice versa [30].
In birds of prey, there are many studies which have addressed home range (e.g., [3133]),
however, those which have examined also factors affecting range size are scarce and their
results are often contradictory [3440].
The Tengmalm’s owl (Aegolius funereus) is a small, nocturnal, cavity-nesting owl (male
body mass ca. 100 g), living in coniferous forests in the boreal zone and in alpine forests fur-
ther south in Eurasia [41]; it feeds mainly on small mammals [4244]. The young stay in the
nest for 27–38 days after hatching [45], and reach independence 5–9 weeks after fledging [46
49]. The great majority of prey brought to the young throughout the late nestling and post-
fledging dependence period (hereafter PFDP), in this particular species, is delivered by the
male [47,48,50], and during good food years polygyny may occur [43,44]. Tengmalm’s owl
searches for prey by the pause-travel mode and locates it by sound [5153].
Studies of the hunting HRs of Tengmalm’s owl males during breeding are essentially agreed
on an average size of ca. 2 km
2
. Older studies which established HRs by the minimum convex
polygon method [54] give the size of the hunting range as 205 ha [55], 181 ±48 ha (mean ±SD;
[56]) and 100–300 ha [57]; the study by Santangeli et al. [34] which determined the size of hunt-
ing range of males by the kernel density estimator [58] suggested a range size of 114 ±20 ha
(mean ±SE). Hakkarainen et al. [59] noticed that in the low phase of vole cycle males hunt up to
4 km from the nest, whereas in good vole years hunting trips are about one-third of that distance.
In this paper, we explore what factors determine the size of hunting HR in Tengmalm’s owl
males during breeding season in different years with contrasting prey abundance.
We predicted that:
1. HRs will be larger during years with low prey abundance since during such years the males
will need to hunt over larger areas to bring enough prey items to the nest [49,59].
2. Polygynous males will have larger HRs compared to males mated monogamously since the
former ones have to care for spatially distant broods and usually also for more young.
3. The HR size will be affected by the number of hatchlings/fledglings raised since more
numerous broods need more prey items to survive and the males will thus need to hunt on
a larger area in order to feed their offspring.
4. Similarly, HR size will increase with the actual age of nestlings/fledglings since older young
will need proportionally more food than younger ones, forcing the males to hunt over
larger areas. Further, one could also expect the HR size will extend after fledging of off-
spring since the males will no longer be tied to their nest-site as the necessary core of their
hunting range [49].
Finally, we also predicted that (v) the HRs will be smaller when recorded during harsh
weather conditions, especially during rainy nights and/or strong wind speed; as reported by
Klaus et al. [60], as little as 5 mm of rain per day caused a decrease in nest feeding visits in
Tengmalm’s owl which presumably implies that hunting itself was restricted.
Materials and methods
Study area
The study was carried out during five breeding seasons 2006–2010 in an area close to the water
reservoir Fla
´je in the Ore Mountains, the Czech Republic (50˚ 40’ N, 13˚ 35’ E). This area was
Home range size of Tengmalm’s owl
PLOS ONE | https://doi.org/10.1371/journal.pone.0177314 May 18, 2017 3 / 15
severely damaged by air-pollution in the 1970s, with most coniferous trees above the altitude
of 500 m a. s. l. dying out as a result; the study area (110 km
2
, 730–960 m a. s. l.) has been artifi-
cially replanted, with the predominant species being blue spruce (Picea pungens, occupying
approximately 28% of the study area), Norway spruce (Picea abies, 26%), birch (Betula sp.,
11%), European mountain ash (Sorbus aucuparia, 5%), European beech (Fagus sylvatica, 4%)
and European larch (Larix decidua, 4%). Outside the forested parts the vegetation is dominated
by wood reeds (Calamagrostis villosa) and solitary European beech [61]. To compensate for
the lack of natural tree cavities, 233 wooden nestboxes lined with wood chips (with the base
25x25 cm, height 40 cm and with an entrance hole 8 cm in diameter) have been installed grad-
ually in the area since 1999, and virtually the whole local population of Tengmalm’s owl breeds
in these nestboxes.
Weather data were obtained from the closest weather stations to the study area. The average
daily temperature (˚C) and wind speed (m/s) were taken from the station in Nova
´Ves v Hor-
a
´ch, located ca. 5 km from the study area. Daily precipitation (mm) was taken from the station
in Česky
´Jiřetı
´n, located ca. 1.5 km from the study area.
Field and laboratory procedures
In all study years, all nestboxes were visited at intervals of 2–3 weeks from early March to July
to find nests, and thereafter, nests were checked 1–2 times per week to know the number of
eggs, hatchlings and fledglings and to determine exact hatching date (±1 day). Twenty males
in total (5, 4, 4, 2 and 5 in 2006–2010, respectively) were captured during nestling phase by
using mist net placed in front of the nestbox or swing-door trap placed at the entrance of the
nestbox. This was done during the night when males were bringing prey items to the nest.
Captured males were ringed, weighed, the length of wing was measured, and age estimated
according to the method of Ho¨rnfeldt et al. [62], before being fitted with tail-mount transmit-
ters of type TW-4 (Biotrack Ltd., UK). Transmitters weighed 2.1 g (lifespan ±10 weeks) which
followed welfare recommendations not to exceed 3% of body weight of tagged individuals
(e.g., [63]); in practice, transmitters averaged 2% of male body weight. At least five days were
left after marked birds had been released before telemetry recordings were made towards
assessment of hunting range so that data recorded should not be influenced by a direct effect
of tagging [4,7,63]. Polygyny in two individuals was detected by trapping each of them at two
different nestboxes, and later also by radio-tracking when they visited both nests.
We radio-tracked each male for an average of 4.7 ±1.7 nights (±SD; range 1–8 nights)
and for the complete night-time period, i.e. from dusk till dawn. Within tracking nights, two
observers (MK and VT) continuously followed each male, recording locations/fixes every 10
minutes (if possible). Observers were connected via walkie-talkies recording exact time of
every single fix, their own positions, direction to the tag/male using a compass and the strength
of the signal received by using MVT-9000 receivers (Yupiteru Industries Co. Ltd., Japan) and
3-element Yagi antennas. Afterwards, each individual location was confirmed by triangulation
in ArcGIS 9.3 software. Experimental calibrations in the field suggested that location accuracy
was approximately 100 m (fixes where we were not sure about their sufficient precision were
discarded from the analysis).
Home range size was estimated by 80%, 95%, and 100% minimum convex polygon method
(MCP; [54,64]) and by kernel density estimator (KDE; [58,65]) with fixed smoothing parame-
ter hestablished by least squares cross-validation method (LSCV; [5,66,67]); HRs were calcu-
lated for both 90% and 95% isopleth [67]. The HR sizes were not dependent neither on the
number of locations used for their calculation, number of radio-tracking nights nor on the
duration of whole radio-tracking period for individual males (number of days between the
Home range size of Tengmalm’s owl
PLOS ONE | https://doi.org/10.1371/journal.pone.0177314 May 18, 2017 4 / 15
first and the last night of radio-tacking) in either of the models subsequently used in analysis
(GLMM I and II; see Result section below). For this reason, we decided not to exclude the four
males/HRs with lowest number of locations (20, 22, 25 and 35) from analyses; HRs for other
males were based on more than 58 locations. Since our data sets ranged from 20 to 167 loca-
tions per male (see Table 1) we followed the approach of Santangeli et al. [34] and calculated
separately a LSCV smoothing parameter for each individual male (range 51.5–149.7), and then
took the median of these values (median = 110.4). The median value obtained was used as the
smoothing parameter to estimate the HRs which were than comparable among individuals
[7,34]. Both types of HR estimates (MCP and KDE) were calculated in Home Range Tools and
Hawth’s Tools [68,69] which are freeware extensions for ArcGIS 9.x software. After De Solla
et al. [70] and others (e.g., [67,71]), we used fixed time interval of recording to maximize the
number of observations included in HR estimations; for our purposes in estimating HRs, loca-
tional fixes did not require serial independence of observations [72].
Prey abundance (small mammals) in the study area was assessed by using snap-traps at the
beginning of June during all study years; snap-traps were set up in three 1 ha squares (with 10
m spacing). The traps were left out for 3 nights and checked daily in the morning. The total
trapping effort was 1089 trap nights (n = 3 locations). The number of mammals captured
per 100 traps-nights was calculated as an index of prey abundance. All trapped individuals
(n = 193 in total; 3, 71, 13, 12 and 94 in 2006–2010, respectively) were identified to the species
level. For details of prey abundance in different study years see Table 1.
Owls were trapped, handled and tagged under permit No. 530/758 R/08-Abt/UL from the
Ministry of the Environment of the Czech Republic, and were ringed under the Ringing Centre
of the National Museum in Prague permit No. 329; all efforts were made to minimize suffering.
Statistical analyses
All data were analysed with the aid of SAS System version 9.4 (SAS Institute Inc.). The analysis
was made in two steps. In order to check for possible multicollinearity we first calculated cor-
relations between the individual variables involved (listed in Table 1). Significant correlation
was found between the date of nesting/hatching and prey abundance (0.69, P = 0.0003),
between the number of eggs (E), hatchlings (H) and fledglings (F)–(EH: 0.91, P<0.0001;
EF: 0.59, P = 0.0036; HF: 0.57, P = 0.0056), mean wind speed and daily precipitation (0.54,
P = 0.0083), and mean wind speed and mean daily temperature (-0.73, P<0.0001). We subse-
quently made a judgment of the extent of intercorrelation and collinearity by checking related
statistics, such as tolerance value or variance inflation factor (VIF), Eigenvalue, and condition
number following the approach of Belsley et al. [73] and using TOL, VIF and COLLIN options
of the MODEL statement in the SAS REG procedure. Low eigenvalues and large condition
indices indicated that date of nesting and hatching, number of eggs and hatchlings, and mean
daily temperature and daily precipitation, were redundant and therefore we omitted these vari-
ables from later analyses.
Associations were subsequently sought between individual male hunting HR size during
breeding season and the remaining variables (fixed and random effects, see below) using a
multivariate General Linear Mixed Model (GLMM, PROC MIXED, SAS, version 9.4). To
account for the use of repeated measures on the same individuals, all analyses were performed
using mixed model analysis with individual male as a random factor. We constructed the
GLMM entering first the factor and/or factors expected to have the most significant effect, sub-
sequently checking the model with addition of other factors which might contribute. The sig-
nificance of each fixed effect in the mixed GLMM was assessed by the F-test. Non-significant
factors (P >0.05) were dropped from the model. Where appropriate we tested interaction
Home range size of Tengmalm’s owl
PLOS ONE | https://doi.org/10.1371/journal.pone.0177314 May 18, 2017 5 / 15
terms. Associations between the dependent variable and fixed effects were estimated by fitting
a random coefficient model using PROC MIXED as described by Tao et al. [74]. We calculated
predicted values of the dependent variable and plotted them against the fixed effects with pre-
dicted regression lines.
Table 1. Home ranges of Tengmalm’s owl males.
range mean ±SD range mean ±SD range mean ±SD range mean ±SD range mean ±SD
Year 2006 2007
1
2008 2009 2010
No. of radio-tracked
males
n = 5 individuals n = 4 individuals n = 4 individuals n = 2 individuals n = 5 individuals
Date of radio-tracking 19.5.–29.6. 3.5.–27.6. 18.5.–28.6. 1.–16.6. 30.5.–24.7.
Duration of tracking
period (days)
7–26 14.0 ±6.5 3–20 10.3 ±6.2 6–16 11.0 ±3.8 5–6 5.5 ±0.5 2–23 9.6 ±7.9
No. of radio-tracking
nights
4–7 5.4 ±1.0 2–8 4.3 ±2.5 5 5.0 ±0.0 4–5 4.5 ±0.5 1–6 4.2 ±2.2
No. of locations/fixes 76–167 126 ±29 20–138 65 ±45 107–149 129 ±15 59–90 75 ±16 22–135 87 ±52
KDE 90% (ha) 107.0–
205.5
150.5 ±35.2 109.7–
207.3
162.9 ±34.9 153.0–
247.6
212.0 ±35.7 129.4–
150.3
139.9 ±10.5 63.7–
216.4
108.9 ±58.2
KDE 95% (ha) 129.9–
263.4
189.0 ±47.8 140.8–
263.7
206.0 ±43.7 181.6–
303.3
256.4 ±45.6 159.3–
181.8
170.6 ±11.2 79.1–
265.1
135.5 ±69.8
MCP 80% (ha) 71.5–
132.2
90.2 ±24.3 30.7–
186.4
95.0 ±56.8 89.7–
214.3
157.1 ±45.5 81.2–
88.6
84.9 ±3.7 20.8–
119.6
51.1 ±37.9
MCP 95% (ha) 83.2–
229.6
152.8 ±58.0 114.9–
271.2
176.4 ±60.4 129.2–
294.9
225.4 ±60.4 117.4–
120.0
118.7 ±1.3 24.7–
242.1
86.6 ±82.2
MCP 100% (ha) 86.8–
304.5
190.7 ±79.0 128.3–
304.6
206.4 ±66.6 147.1–
343.2
250.2 ±69.9 133.1–
139.3
136.2 ±3.1 35.1–
251.4
107.0 ±78.4
Date of nesting (±days) 13.4.–
12.5.
23.4. ±11 12.3.–
16.5.
1.4. ±23 20.3.–
12.4.
29.3. ±9 8.–15.4. 12.4. ±4 30.3.–
31.5.
2.5. ±27
Date of hatching (±
days)
11.5.–
10.6.
21.5. ±11 9.4.–
13.6.
29.4. ±23 20.4.–
12.5.
29.4. ±9 6.–13.5. 10.5. ±4 28.4.–
28.6.
31.5. ±27
No. of eggs 4–6 4.8 ±0.7 3–7 5.2 ±1.3 2–5 3.8 ±1.1 3–4 3.5 ±0.5 5–8 6.4 ±1.2
No. of hatchlings 3–6 4.4 ±1.0 2–7 4.7 ±1.6 2–5 3.5 ±1.1 3–4 3.5 ±0.5 5–8 6.2 ±1.2
No. of fledglings 0–3 1.6 ±1.2 2–7 4.2 ±1.5 2–5 3.5 ±1.1 0–1 0.5 ±0.5 0–7 4.4 ±2.7
Polygamy (no: yes) 5: 0 2: 2
2
4: 0 2: 0 5: 0
Male’s age (years; 1: 2:
3+)
1: 4: 0 0: 0: 4 1: 0: 3 1: 0: 1 0: 1: 2
3
Male’s wing length
(mm)
165–174 169 ±3 164–175 168 ±4 158–168 164 ±4 167–169 168 ±1 161–170 165 ±3
Male’s weight (g) 103–114 108 ±4 110–130 116 ±8 96–106 101 ±4 102–106 104 ±2 97–109 103 ±4
Mean age of offspring
(days)
12–31 20 ±6 11–37 29 ±11 5–43 27 ±14 28–31 29 ±2 17–41 28 ±9
Mean daily precipitation
(mm)
0.5–6.8 3.4 ±2.3 0.0–1.2 0.7 ±0.4 0.2–3.8 1.7 ±1.4 1.0–7.6 4.3 ±3.3 0.1–17.0 4.5 ±6.3
Mean wind speed (m/s) 2.6–7.2 4.0 ±1.8 3.0–3.9 3.4 ±0.4 2.2–4.2 3.2 ±0.8 5.6–5.9 5.7 ±0.1 2.2–5.7 3.4 ±1.4
Mean daily temperature
(˚C)
9.4–18.3 14.3 ±3.5 12.7–
16.6
15.1 ±1.5 11.4–
15.6
13.9 ±1.7 8.4–11.7 10.0 ±1.7 9.3–24.8 17.0 ±5.1
Prey abundance 0.28 6.52 1.19 1.10 10.19
Home range sizes estimated by 90% and 95% fixed kernel density estimator and 80%, 95%, and 100% minimum convex polygon method, and the list of
fixed effects used in the GLMM I and II for the hunting home range size of male Tengmalm’s owls during the breeding season.
1
Data from 2007 regards four individual males but six individual nests because two males were polygynous.
2
Between nestbox distances belonging to the two polygynous males were 410 and 1035 meters.
3
In 2010 only three males were aged.
https://doi.org/10.1371/journal.pone.0177314.t001
Home range size of Tengmalm’s owl
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In the analyses the size of individual male hunting HRs during breeding season established
by (I) KDE and (II) MCP method were taken as the dependent variable. The following factors
were considered as fixed effects in both models: number of fledglings, mean age of nestlings/
fledgling from hatching (days), presence or absence of polygyny, age of male (1, 2 or 3
years), number of radio-tracking nights, number of locations/fixes used for HR calculation,
duration of whole radio-tracking period for individual males (days), male’s weight (g) and
wing length (mm), mean wind speed (m/s) and prey abundance (see Table 1). All relevant data
used in the analyses (GLMM I and II) are presented in S1 Table.
Dependent variables and fixed effects entered into both models were log-transformed in
order to achieve normal distribution of residuals. Interactions tested (prey abundance with
mean wind speed and prey abundance with number of fledged individuals) were not signifi-
cant, and thus excluded from both models (I and II).
Results
The mean size of hunting HRs during breeding season for Tengmalm’s owl males (n = 20) cal-
culated according to 90% kernel density estimator was 153.8 ±53.7 ha (±SD) with a median
value of 152.9 ha, and according to 95% KDE: 190.7 ±65.7 ha with median 187.1 ha;
MCP method offered a range estimate of 94.2 ±53.3 ha (80% MCP) with median 83.1 ha,
152.1 ±79.8 ha (95% MCP) with median 131.8 ha, and according to 100% MCP: 179.4 ±87.4
ha with median 156.7 ha. These ranges were based on 99 ±45 (±SD) locations/fixes on average
with median value 121 locations.
Results of the GLMM I (Table 2) revealed that the size of hunting HRs during breeding sea-
son established by 95% KDE was dependent on prey abundance (Fig 1), presence or absence
of polygyny, number of fledged individuals (Fig 2), and mean wind speed during particular
radio-tracking nights (Fig 3). Results were identical for 90% KDE, and therefore are not
shown.
Home range sizes increased with decreasing prey abundance (Fig 1). Polygynously mated
males had significantly larger HRs in comparison with males mated monogamously. Size of
hunting range also increased significantly with increasing number of successfully fledged off-
spring (Fig 2), and decreased with increasing mean wind speed (Fig 3).
Results of the GLMM II (where size of hunting range was defined from 95% or 100%
MCP) were virtually identical regarding every single fixed effect (Table 2); graphical represen-
tations of relationships were also very similar to figures presented in Figs 13, and are thus not
repeated here.
Discussion
The size of hunting HRs of male Tengmalm’s owls during the breeding season reported in
this study is consistent with results from previous studies by other authors [34,5557] who
Table 2. The results of the GLMM I.
Fixed effect–GLMM I Num DF Den DF F value P =
Log-transformed prey abundance 1 7.81 213.36 0.0001
Presence or absence of polygyny 1 9.74 132.15 0.0001
Log-transformed number of fledglings 1 11.1 9.59 0.0101
Log-transformed mean wind speed 1 10.7 7.82 0.0178
The results of the GLMM I for factors affecting the Tengmalm’s owl males’ hunting home range size during the breeding season established by the 95%
fixed kernel density estimator.
https://doi.org/10.1371/journal.pone.0177314.t002
Home range size of Tengmalm’s owl
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reported range sizes from 100–300 ha. Compared to these studies, our owls occupied a rather
unusual mountain habitat that had in the past been severely damaged by air-pollution and
now consisted of a mosaic of open areas, fragments of young secondary non-native blue spruce
stands, and small patches of tall old-growth Norway spruce. Thus, studies from various parts
of Europe have shown that the sizes of hunting HRs are very similar despite marked variation
in habitat and/or different methods used for HR calculations, and it seems there is no funda-
mental difference in hunting HR size in Tengmalm’s owl during breeding at the population
level (populations of Scandinavia, Central and Western Europe). However, it should be noted
that we followed males for several nights only (4.7 nights on average) as it was done also in
above mentioned studies. It is possible that tracked individuals did not visit every part of their
HR during relatively short tracking period, and thus, our and their results could be a mixture
of HR sizes and HR used patterns.
We found the HR size was dependent on different prey abundance as expected (prediction
i). This is in accordance with other studies on birds of prey which reported larger HRs during
poor food years and vice versa [35,36,75], and this is the first study documenting such effect in
Tengmalm’s owl males during breeding period.
We have reported a similar relationship also for Tengmalm’s owl fledglings during the
PFDP in the same study area that in the season with low prey abundance young owls occupied
larger HRs than in the year with higher prey abundance [49]. However, compared to our pres-
ent results, Santangeli et al. [34] did not find that food supplementation affected hunting HR
size in Tengmalm’s owl males during breeding phase. In their study, HR size was affected by
habitat structure and decreased with cover of spruce forest, which is denser in structure and
Fig 1. Prey abundance. Predicted values of the size (log-transformed) of male Tengmalm’s owls’ hunting
home range during the breeding season established by the 95% fixed kernel density estimator, plotted against
an index of prey abundance.
https://doi.org/10.1371/journal.pone.0177314.g001
Home range size of Tengmalm’s owl
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richer in prey than pine forest and especially clear-cut areas [34]. These contradictory findings
might thus be due to the differences in habitat structure between both study areas. Another
explanation could be that Santangeli et al. [34] radio-tracked males during the beginning of
the PFDP but supplementary food was offered only until fledging. This fact could confuse
the effect of food supplementation on HR size between food supplemented and control nests
because all males already hunted to the full extent at the beginning of the PFDP.
We also found the HR size was dependent on presence or absence of polygyny (prediction
ii). Although data for only two polygynous males were available, these males have significantly
larger HRs compared to those mated monogamously most as a result of having to move
between their two nestboxes (being 410 and 1035 m apart). Large size of HRs of these two
polygynous males could further be exaggerated by the fact that they were supporting a large
number of offspring, since hunting HR size was also positively associated with the number of
fledged individuals (prediction iii).
No such relationship was found in the ferruginous hawk [76], while Pfeiffer and Meyburg
[36] reported a significant negative correlation between HR size and number of young fledged
for the red kite. It would thus appear that both options are possible. We suggest that the con-
tradictory findings could be explained by partly distinct diet habits, the number of young
which these species commonly care for, and the overall size of hunting HR used. The Teng-
malm’s owl feeds primarily on small rodents, lays six eggs on average with a mean hunting HR
during breeding season covering ca. 2 km
2
([34,43,44], this study). The red kite which takes a
wide range of different foods, lays two eggs on average with a mean hunting HR during breed-
ing season covering ca. 64 km
2
[36,77]. One must assume that the negative correlation between
Fig 2. Number of fledglings. Predicted values of the size (log-transformed) of male Tengmalm’s owls’
hunting home range during the breeding season established by the 95% fixed kernel density estimator, plotted
against the log-transformed number of fledglings raised.
https://doi.org/10.1371/journal.pone.0177314.g002
Home range size of Tengmalm’s owl
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HR size and brood size reported is a consequence of the fact that when food is plentiful the
birds do not have to hunt over such large areas and have sufficient resources also to raise a
larger number of young; since they search for food over a relatively large area, we suggest that
the situation is quite distinct from that facing the Tengmalm’s owl.
We did not find support for the fourth prediction (iv) that the HR size should vary posi-
tively with the actual age of nestlings/fledglings, despite the fact that Tengmalm’s owl males
increased feeding rates throughout the nestling period [78]. The reason might be that the dif-
ferences in offspring age between particular nests were simply not large enough (26 ±10 days
from hatching; mean ±SD). The HRs in this study were in most cases recorded during the late
nestling phase and/or at the very beginning of the PFDP. We suggest the HRs might be seen to
differ in size according to this prediction if some of them were recorded during pre-laying
and/or incubation period and compared with ranges registered during the nestling phase and/
or PFDP as we have done here. However, no significant differences in HR size were recorded
among pre-laying, incubation and early nestling period in peregrine falcon [38], but their HRs
tripled in size after the chicks fledged. We speculate that similar enlargement of male hunting
HR size and/or greater difference in HR sizes between good and poor food years might be
detected in Tengmalm’s owl if the ranges would be recorded throughout or at the end of the
PFDP. This would be consistent with different movement patterns of Tengmalm’s owl fledg-
lings who were located more distantly from their nestboxes during poor food year compared
to good one [49].
Finally, our results suggested that the HR size decreased with increasing wind speed and/
or amount of precipitation during the radio-tracking nights when the HRs were recorded
Fig 3. Wind speed. Predicted values of the size (log-transformed) of male Tengmalm’s owls’ hunting home
range during the breeding season established by the 95% fixed kernel density estimator, plotted against the
mean wind speed (log-transformed values) during particular radio-tracking nights.
https://doi.org/10.1371/journal.pone.0177314.g003
Home range size of Tengmalm’s owl
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(prediction v). This reflects the hunting strategy of Tengmalm’s owl. The owl searches for prey
by the pause-travel mode and depends heavily on sound to localize ground-dwelling prey [51
53]. Thus, high wind speed or heavy rain very likely hamper the hunt itself. This agrees with
the observations of Klaus et al. [60] who described that nest feeding visits in Tengmalm’s owl
decreased during rain. We also recorded decreasing frequency of begging calls by fledglings
with increasing precipitation [79]. Therefore, it could be advantageous for the Tengmalm’s
owl to remain on a hunting perch for longer than just for the usual two minutes [52] because
under such conditions hunting success depends more on chance, and waiting for prey on one
place is at least energy expenditure saving. Moreover, Tengmalm’s owl males are able to apply
loose-shift (avoiding of unsuccessful hunting sites between consecutive nights) and win-stay
(returning to successful hunting sites within and/or between nights) strategy while hunting
[55], and we suggest both strategies should also be of value during windy and/or rainy nights,
and increasingly so the longer they stay on every perch.
To conclude: we stress the importance of the time interval during which the HRs are
recorded as shown for instance by studies regarding temporal changes in range use within
years and/or between different parts of breeding season (e.g., [38,80,81]). We detected that
hunting ranges during the breeding season were larger when less prey was available, and fur-
ther that polygynously mated males, and those with more fledglings had overall larger HRs
than males mated monogamously and/or with fewer raised fledglings. We also found that
hunting ranges recorded in harsh weather conditions, and high wind speed and/or heavy rain
in particular, were smaller than those registered during better weather. Finally, our results pro-
vide novel insights into what factors may influence HR size in male Tengmalm’s owls and
emphasize the importance of prey abundance as a key factor.
Supporting information
S1 Table. Supporting information. Relevant data used in the analyses (GLMM I and II).
(XLS)
Acknowledgments
We thank Roman Juras, Kateřina Gdulova
´and Kateřina Mars
ˇa
´lkova
´for their help in the field;
Michael Griesser, Jan Za
´rybnicky
´,
´t Dvořa
´k and Jaroslav Ne
ˇmec for their technical assis-
tance, and Rory Putman for valuable comments on the early draft of the manuscript and
improving English. Finally yet importantly, we would like to thank Bernd Meyburg and one
anonymous reviewer for their comments and suggestions on an earlier draft of the manuscript.
Author Contributions
Conceptualization: MK KS MZ LB.
Data curation: MK.
Formal analysis: LB MK.
Funding acquisition: KS VT MK LB MZ.
Investigation: VT MK AP.
Methodology: MK KS LB MZ VT.
Project administration: VT MK AP KS MZ.
Resources: KS LB VT MK MZ.
Home range size of Tengmalm’s owl
PLOS ONE | https://doi.org/10.1371/journal.pone.0177314 May 18, 2017 11 / 15
Software: LB.
Supervision: KS.
Validation: MK.
Visualization: MK LB.
Writing original draft: MK LB KS MZ.
Writing review & editing: MK LB KS MZ.
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Supplementary resource (1)

... Consequently, HR formation in the majority of vertebrates is attributed to different intrinsic and/or extrinsic factors, the most essential of which are food supply, predation pressure and conspecific density (McLoughlin and Ferguson, 2000). For example, HRs usually decrease with increasing food availability in many animal species (Desy et al., 1990;Broughton and Dickman, 1991;Akbar and Gorman, 1993;Kouba et al., 2017). Burt (1943) argued that HRs do not apply to young adolescents because they are only wandering in search of a home region. ...
... We found fledglings' nocturnal activity HRs throughout the PFDP to be 64 and 37 ha on average, calculated by IID KDE and MCP, respectively, which is up to five times less compared to the mean hunting HR of Tengmalm's owl males during breeding (Sonerud et al., 1986;Jacobsen and Sonerud, 1987;Sorbi, 2003;Santangeli et al., 2012;Kouba et al., 2017). The difference could possibly be much greater because the fledglings' ranges were based on locations collected daily for up to two months, and males' radio-tracking usually only lasted for several nights. ...
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A greater knowledge of the intrinsic and extrinsic factors of animal home range (HR) formation can help us to understand the fundamental biological issues underlying, for instance, movement patterns, habitat selection and survival. However, very little is known about the HRs of birds of prey fledglings, even though the post-fledging phase is recognised as crucial due to the high mortality of juvenile birds. We radio-tracked 138 Tengmalm’s owl (Aegolius funereus) fledglings from 43 broods to determine their HRs during the post-fledging dependence period and to investigate the factors affecting their sizes. The study was conducted during four breeding seasons in Czechia and two seasons in Finland. The mean fledglings’ HR size calculated according to the 95% IID Kernel Density Estimation method was 63.7 ± 43.9 ha (± SD; n = 71) during nocturnal activity and 52.0 ± 46.1 ha (n = 63) during diurnal roosting. The sizes of both nocturnal activity and diurnal roosting HRs increased with the longer individual duration of the post-fledging dependence period and also the higher rank of hatching within a brood. Diurnal roosting HRs were two times smaller in the Czech site, probably because of the very limited number of dense forest patches suitable for roosting as a legacy of the air pollution calamity in the 1970s, during which most coniferous stands died out. There was no difference in the size of nocturnal activity HR between the two study areas, although they differed markedly in terms of night length, altitude, weather, and forest age, structure and composition. This suggests that environmental factors are not decisive in determining the size of nocturnal activity HRs of Tengmalm’s owl fledglings. Since the diurnal HRs always occurred within the area of the nocturnal HRs, we suggest that conservation of the densest and preferably oldest forest stands within the areas of the study species occurrence may offer straightforward conservation tasks for protecting Tengmalm’s owl fledglings and also other species.
... The partly identified extreme PFAS concentrations found in bank voles (>4000 ng/g ww) compared with for example 16 ng/g ww in a PFAS contaminated skiing area in Norway (Grønnestad et al., 2019) are therefore not only a potential health risk for the bank voles, but also for their predators. Since the predators have broader home ranges than their prey (Tengmalm's owl for example ca. 2 km 2 ; Kouba et al., 2017), predators could in addition contribute to secondary distribution of PFAS. ...
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... Placement and size of raptor home ranges has been used to assess the utilization and effectiveness of protected areas (Margalida et al. 2016, Blakey et al. 2020, whereas seasonal variation in range size has clarified the potential threat of wind energy development (Braham et al. 2015). Home range size may also serve as a useful metric of habitat quality (e.g., Forsman et al. 2005, Kouba et al. 2017. ...
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Long-term monitoring data indicate a persistent decline in American Kestrel populations across North America. Loss or alteration of habitat have been listed as potential causal factors, but basic information on kestrel space use, including breeding home range size, is lacking. No study has provided robust estimates of the ranging behavior of breeding kestrels based on tracking data of any resolution. We fitted 19 adult female kestrels with solar-powered GPS transmitters during the incubation period in northern Virginia. High-resolution tracking began during the early nestling stage for 17 birds. We collected an average of 1710 locations per bird through the end of the breeding season (31 August), with 13 birds tracked through the fledging of their young. Autocorrelated kernel density home range estimation showed that female kestrels used breeding home ranges that were smaller (average: 0.32 km2) than most previously published range sizes. Home ranges did not vary significantly in size across breeding stages and demonstrated little overlap with the ranges of neighboring kestrels. Five females shifted their territories in the post-breeding stage (i.e., after disappearance or dispersal of fledglings) between 1.5 and 12.3 km from their nest box; they maintained these new ranges at least to the migration period. We also documented home range excursion forays (n = 128) by all 12 consistently tracked females. Mean (4.0 km) and maximum (127.7 km) foray distances were some of the largest reported among birds and mammals relative to home range size. Weekly foray rates were highest during the nestling stage, and for birds that ultimately shifted from their breeding home range. The existence of long-distance foray behavior and the use of multiple summer home ranges, both shown here for the first time for this species, has a direct impact on interpretation of kestrel nest-site and habitat selection data, and on the assessment of potential threats to this species in the breeding season.
... However, there are variations in the abundance of migratory birds detected in the same months between different years (Figure 2A). This could be explained because migratory patterns are driven by environmental conditions (such as temperature, food availability, and landscape structure) [49,50], which may vary from year to year or be influenced by climate change [51] or by climatic phenomena such as the El Niño-Southern Oscillation (ENSO), the main driver of interannual climate extremes in South America [52]. ...
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The Lluta River is the northernmost coastal wetland in Chile, representing a unique ecosystem and an important source of water in the extremely arid Atacama Desert. During peak season, the wetland is home to more than 150 species of wild birds and is the first stopover point for many migratory species that arrive in the country along the Pacific migratory route, thereby representing a priority site for avian influenza virus (AIV) surveillance in Chile. The aim of this study was to determine the prevalence of influenza A virus (IAV) in the Lluta River wetland, identify subtype diversity, and evaluate ecological and environmental factors that drive the prevalence at the study site. The wetland was studied and sampled from September 2015 to October 2020. In each visit, fresh fecal samples of wild birds were collected for IAV detection by real-time RT-PCR. Furthermore, a count of wild birds present at the site was performed and environmental variables, such as temperature, rainfall, vegetation coverage (Normalized Difference Vegetation Index—NDVI), and water body size were determined. A generalized linear mixed model (GLMM) was built to assess the association between AIV prevalence and explanatory variables. Influenza positive samples were sequenced, and the host species was determined by barcoding. Of the 4349 samples screened during the study period, overall prevalence in the wetland was 2.07% (95% CI: 1.68 to 2.55) and monthly prevalence of AIV ranged widely from 0% to 8.6%. Several hemagglutinin (HA) and neuraminidase (NA) subtypes were identified, and 10 viruses were isolated and sequenced, including low pathogenic H5, H7, and H9 strains. In addition, several reservoir species were recognized (both migratory and resident birds), including the newly identified host Chilean flamingo (Phoenicopterus chilensis). Regarding environmental variables, prevalence of AIV was positively associated with NDVI (OR = 3.65, p < 0.05) and with the abundance of migratory birds (OR = 3.57, p < 0.05). These results emphasize the importance of the Lluta wetland as a gateway to Chile for viruses that come from the Northern Hemisphere and contribute to the understanding of AIV ecological drivers.
... The year-round home range size of resident animals is expected to vary depending on prey abundance (Kouba et al., 2017), distribution of foraging habitat (Legagneux et al., 2009), availability of safe nesting and roosting sites (Popa-Lisseanu et al., 2009), and the state of the individual (sex, age, and reproductive status; Rolando, 2002). ...
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Habitat use of indicator species is used to prioritize management activities. However, habitat use can vary temporally in response to changes in predation risk and foraging rewards. We deployed satellite tags on 20 black oystercatchers (Haematopus bachmani) in four regions of British Columbia, Canada, to examine habitat use and selection decisions across seasonal, diel and tidal cycles. We characterized the shoreline in each region and used GLMMs to investigate how habitat characteristics influenced shoreline use by tracked birds. For individuals, we estimated home range size and the frequency key features of the shoreline were re-visited. Black oystercatchers generally made greater-than-expected use of rocky islets and shoreline with freshwater outflows, less tree cover and greater intertidal area. However, while black oystercatchers preferred islets and shoreline with less tree cover at most/all time periods, they only exhibited preferences for greater intertidal area during low tides, and preferences for shoreline with freshwater outflows during the nonbreeding season, day, and high tides. Individual home ranges, on average, contained 46 km of shoreline (range: 12-156 km) and individuals used 10.4 km (range: 6.7-13.9 km). Individuals made greater use of larger islets with less tree cover that were closer to outflows, and greater use of outflows associated with larger streams, greater intertidal areas and gravel substrates. Black oystercatchers' habitat preferences likely reduce predation risk (rocky islets and shoreline with less tree cover) and increase foraging rewards (shoreline with freshwater outflows, greater intertidal area, and gravel substrates). However, habitat preferences appear sensitive to constraints on movement in the breeding season and changes in foraging rewards across the diel and tidal cycle, highlighting the importance of examining habitat use at multiple temporal scales. Black oystercatchers are considered indicators of rocky intertidal health; therefore, critical habitat is expected to be important for a suite of wildlife dependent on safe and productive coastline.
... In other words, the home range size of an animal is smaller in areas with rich resources and larger in areas with poor resources (Fretwell, 1969;Sutherland, 1996). This has been found in a variety of bird species, such as Tengmalm's Owl Aegolius funereus (Kouba et al., 2017), Eurasian Eagle-owl Bubo bubo (Lourenço et al., 2015), Bluethroat Luscinia svecica (Godet et al., 2015 and Wild Turkey Meleagris gallopavo (Thogmartin, 2001). ...
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Agricultural intensification has modified grassland habitats, causing serious declines in farmland biodiversity including breeding birds. Until now, it has been difficult to objectively evaluate the link between agricultural land‐use intensity and range requirements of wild populations at the landscape scale. In this study of Black‐tailed Godwits Limosa limosa, we examined habitat selection and home range size during the breeding phase in relation to land‐use intensity, at the scale of the entire Netherlands. From 2013 to 2019, 57 breeding godwits were tracked with solar‐Platform Transmitter Terminals (26–216 locations [mean: 80] per bird per breeding phase) and used to estimate their core (50%) and home ranges (90%). Of these, 37 individuals were instrumented in Iberia and therefore unbiased toward eventual breeding locations. The tracks were used to analyse habitat selection by comparing the mean, median and standard deviation of land‐use intensity of core and home ranges with matching iterated random samples of increasing radii, that is, 500 m (local), 5 km (neighbourhood), 50 km (region) and the whole of The Netherlands. Land‐use intensities of the core and home ranges selected by godwits were similar to those at the local and neighbourhood scales but were significantly lower and less variable than those of the region and the entire country. Thus, at the landscape scale, godwits were selected for low‐intensity agricultural land. The core range size of godwits increased with increasing land‐use intensity, indicating high agricultural land‐use intensity necessitating godwits to use larger areas. This is consistent with the idea that habitat quality declines with increasing land‐use intensity. This study is novel as it examines nationwide habitat selection and space use of a farmland bird subspecies tracked independently of breeding locations. Dutch breeding godwits selected areas with lower land‐use intensity than what was generally available. The majority of the Dutch agricultural grassland (94%) is managed at high land‐use intensity, which heavily restricts the viability of breeding possibilities for ground‐nesting birds. The remote sensing methodology described here illustrates the potential to study entire wild populations from the local field level to their whole spatial range.
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A home range determines the resources animals can access, and the size of a home range often reflects resource availability and energetic requirements of individuals. The Flammulated Owl (Psiloscops flammeolus) is a cryptic small forest owl that breeds in mixed conifer forests in western North America. The home ranges of individuals generally comprise open forests with large trees, but we have yet to fully understand temporal variation in this species’ use of space and the habitat structures that drive space use at a fine scale. During the 2017 summer breeding season (May–July), we tracked the movement of six territorial males with GPS tags to estimate temporal variation in space use and examined resource selection for fine-scale habitat characteristics within home ranges. Results suggest that individual movement was more constrained around nests early at night, but the area of space used was consistent between the incubation and nestling stages. The owls’ activity centers near their nests had denser ground cover than available habitat, indicating that the forest understory may be valuable for this species’ breeding ecology. As forested landcover rapidly changes in western North America, understanding the spatial behavior and fine scale habitat associations of this species and others is increasingly important.
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Offspring survival rates in altricial birds during the post-fledging period are an essential factor in determining the fitness of parents and have a significant impact on general population dynamics. However, our current knowledge of post-fledging mortality and its causes remains fragmentary in most bird species, and even less information is available on the mortality of individuals of the same species in different environments. In order to address this gap in our knowledge, we studied fledgling mortality and its causes in Tengmalm’s owls (Aegolius funereus) during six breeding seasons in Central and North Europe using radio-telemetry. A total of 80 nestlings from 18 nests in Czechia (2010–2012, 2015) and 60 nestlings from 24 nests in Finland (2019, 2021) were radio-tracked during the post-fledging dependence period. The overall survival rate was much higher in Czechia (83%) than in Finland (53%), with predation identified as the primary cause of mortality in both areas. Avian predation was far higher in Finland, but mammalian predation was equivalent at both study sites. Pine martens (Martes martes) and goshawks (Accipiter gentilis) were the most common predators in Czechia and Finland, respectively. Starvation and disease, or mostly a combination of both, formed the second most common cause of death in both areas but were much more frequent in Finland than in Czechia. Offspring survival in both study sites was considerably higher in years of food abundance than in those of food scarcity. We suggest that the interactive effects of infections and poor body condition due to scarcity of main prey species induced higher mortality rates in offspring, particularly in the more challenging environment of North Europe. In contrast, fledgling owls were found to be able to fight off infections more successfully during rich food seasons. Finally, we encourage researchers to pay greater attention to the mutual influences of parasites and their definitive hosts and stress the importance of using radio or satellite tracking for mortality studies to identify causes of death more accurately.
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Distribution of Sicista betulina in the Šumava Mts. (Bohemian Forest, SW Bohemia, Czech Republic) based on owl diet analysis (Rodentia: Zapodidae). The Šumava Mountains are one of the three areas of a relict occurrence of the northern birch mouse (Sicista betulina) in the Czech Republic. The present study summarises all records of this species obtained there by the analysis of diet of three species of owls, the Tengmalm’s owl (Aegolius funereus), to a lesser extent of the Ural owl (Strix uralensis) and tawny owl (Strix aluco). A total of 355 sample sets from 228 localities was processed, positive findings concern 57 localities where the northern birch mouse was found (322 individuals). The presence of the species was confirmed in an area of approximately 1,400 km2, which corresponds to 13 mapping fields of the KFME, four of which extend into the territories of Germany and Austria (field numbers 7047, 7148, 7249, 7350). More than a third of the localities (37.0%) and nearly two thirds of the individuals (62.4%) come from four mapping fields (7048, 7049, 7148, 7149) covering mainly a peaty alluvial plain of the upper stream of the Vltava river and its surroundings. Data obtained by observation or capturing of the northern birch mouse give a similar picture. Thus, this territory with the high proportion of relict vegetation can be considered as the core area of the current occurrence of the mouse in the Šumava Mts. The altitude range of the northern birch mouse records from owl pellets is 680–1160 m a. s. l. with a mean of 871.7 m a. s. l. Almost a third of the localities (32.0%) lie in the altitudinal range of 700–800 m a. s. l., and more than four fifths of the records (82.7%) fall within the range of 700–1000 m a. s. l. However, these are only indicative values that can be influenced by the size of the individual hunting territories of owls (especially in a landscape with a steep mountainous relief). Our results confirm the previously described zoogeographical profile of the northern birch mouse in the Šumava Mts., i.e., that its distribution is limited only to the south-eastern half of this mountain range and ends roughly in the Kvildské pláně region in the north-west. Records of the northern birch mouse on the Bavarian side of the mountain range are in full agreement with this. It can be assumed that the species occurrence in the whole Bohemian Forest is primarily shaped by its Holocene history. Furthermore, it has been confirmed that the analysis of the diet composition of the Tengmalm’s owl is an effective method of study of the geographical distribution of the northern birch mouse.
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While resource selection varies according to the scale and context of study, gathering data representative of multiple scales and contexts can be challenging especially when a species is small, elusive and threatened. We explored resource selection in a small, nocturnal, threatened species, the greater bilby, Macrotis lagotis, to test (1) which resources best predict bilby occupancy, and (2) whether responses are sex specific and/or vary over time. We tracked 20 bilbies and examined within home range resource selection over multiple seasons in a large (110 ha) fenced sanctuary in temperate Australia. We tested a set of plausible models for bilby resource selection, and found that food biomass (terrestrial and subterranean invertebrates and subterranean plants) and soil textures (% sand, clay and silt) best predicted bilby resource selection for all sampling periods. Selection was also sex specific: female resource use, relative to males, was more closely linked to the location of high-quality resources (invertebrate biomass). Bilby selection for roads was independent of season but varied over time with males selecting for areas closer to roads when plants increased in density off roads. Our findings demonstrate the importance of considering resource selection over multiple contexts and highlight a method to collect such data on a difficult to study, threatened species. Collecting such data is critical to understanding the habitat required by species.
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Home range size characterizes the interplay between an organism and its environment. Determinants of home range size can be intrinsic or extrinsic to the individual but all factors relate along a hierarchical pattern according to spatial and temporal scale. Determinants of home range size at species and population levels result from relatively slow processes, such as evolutionary changes in body size or global changes in climate. Range determinants at the lower level of individuals, however, can change at a relatively fast rate as they result from more rapid processes, such as the seasonal production of food or annual changes in predation rates. Not only do higher scales constrain those below but also lower level processes combine to affect higher scales. Further, correlates of home range size can differ among scales. To incorporate the possible findings of different patterns at different temporal and spatial scales we recommend using a comparative approach to complement controlled manipulative experiments.
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To date, reliable studies on the spatial area use and home range size of the Red Kite (Milvus milvus) during the breeding season are lacking. Between 2007 and 2014, 43 adult individuals were fitted with GPS transmitters in Germany. The home range sizes of 27 males, which successfully reared 47 broods, ranged between 4.8 and 507.1 km2 based on the 95 % kernel utilization distribution. The median during the nestling and post-fledging dependent periods was 63.6 km2. The home ranges of 12 females, with a total of 21 successful broods, ranged between 1.1 and 307.3 km2. Within a single breeding season, there were considerable differences among home range sizes. There was also considerable variation in the home range size of adults during the course of a season. Across years, the median home range size of all males ranged between 21 and 186 km2, depending on prey availability. For individual males at the same nest site, the home range size varied up to a factor of 28 across years. Kites with very large home ranges had only one fledgling, which indicates that resources were scarce. Individuals with more nestlings had intermediate-sized to small home ranges. The relationship between the number of fledged young and home range size was modelled using a cumulative logit model. Fifty-six, 37, and 26 % of male kite fixes were beyond a 1, 1.5, and 2 km radius around the nest, respectively. Birds with very small or very large home ranges differed considerably from these average figures. Adults sometimes travel very long distances to visit distant grasslands during and shortly after mowing (up to more than 34 km) from the nest, due to the increased likelihood of prey availability at these sites. In conclusion, home rage size serves as a useful indicator of Red Kite habitat quality, which may provide key conservation information at the wider ecosystem level.
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From 1970 to 1974 the activity and food of Tengmalm's Owl was studied by automatic registration of the nest visits at 5 breeding places in Finland and in the GDR. The mean numbers of nest visits per day ("actions") were found to be 2.4; 4.1 and 3.2 during the incubation period and 13.1; 13.0; 4.7; 5.3 and 8.6 during the rest of the breeding season. The number of actions per day was positively correlated with the number of young owls and negatively with the amount of raifall. The circadian rythm was biphasic depending on geographic location, i.e. on the length of the night. In Jena (GDR) the first peak of activity was between 8 and 10 p.m. and and the second peak was between 2 and 5 a.m. In Konnevesi (Finland) the maxima of activity was between 11 and 12 p.m. and from 1 to 2 a.m. In Oulu, Finland, there was only one peak of activity between 11 p.m. and 3 a.m. corresponding to the short night in this region. Species and numbers of prey was estimated from the pellets collected from 5 nesting places and also of other locations in Finland. Using the leg bones in the estimation of the number of prey individuals resulted in higher numbers as estimated by use of skulls.
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We used radio-telemetry to track movements of seven adult male Ferruginous Hawks (Buteo regalis) in southcentral Washington. Home range size was estimated for each male using minimum convex polygon, harmonic mean, adaptive kernel, and fixed kernel-based methods. Minimum convex polygon and harmonic mean home ranges were significantly larger than those previously reported for Ferruginous Hawks. Home ranges varied substantially among males (mean = 90.3 km2, range = 17.7-136.4 km2). There was no relationship between home range size and brood size; however, there was a significant relationship (r2 = 0.964, P = 0.018) between home range size and the distance from the nest site to the nearest irrigated agricultural field where some males hunted. Kernel-based estimates showed two distinct core areas for most males, one around the nest and a second in the agricultural fields where they hunted.
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In order to optimise a preservation policy of cavity-holding trees, it was necessary to know the size of Tengmalm's Owl home range, and to identify the number of natural cavities to be preserved per unit of area. Males were equipped with emitters and monitored through radio tracking. The birds were initially located by triangulation then followed by sight in order to study the use of their home range and to gather data on their behaviour. This monitoring showed that the area covered every night varied between 47 and 75 ha, and that the total home range represented a surface varying between 1 and 3 km2. The home range, which can be shared by different males, is explored methodically, one plot after the other. The species seldom leaves Spruce (Picea abies) woods; firebreaks and small clearings in the process of being re-colonised by spruces are used for hunting. Day-roosting sites of males are always located in very young Spruces woods. It appears that a minimum of 1 to 3 potential nesting cavities should be preserved per 30-ha zone. The material and methods, as well as details on the use of home range and behaviour are given in the article.