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Published by Associazione Teriologica Italiana Online ﬁrst – 2019
Hystrix, the Italian Journal of Mammalogy
Available online at:
Winners and losers in an urban bat community: a case study from southeastern Europe
Olga Tzortzakaki1,∗, Eleni Papadatou2, Vassiliki Kati3, Sinos Giokas1
1University of Patras
2Ove Arup & Partners Ltd
3University of Ioannina
Received: 15 July 2019
Accepted: 10 October 2019
We would like to thank Prof. Athanassios Argiriou from the Laboratory
of Atmospheric Physics of the University of Patras for kindly provid-
ing the meteorological data. We are grateful to Robert Nudds for im-
proving and proofreading an earlier version of the manuscript, as well
as to Christina Kassara for statistical support. George Iliopoulos, Di-
mitris Papandropoulos and Philippos Katsigiannis supported the ﬁrst
author during ﬁeld work and provided important information about
the study area. Two anonymous reviewers provided constructive com-
ments, which largely contributed to improving the ﬁnal version of the
Increasing urbanisation is reported to have signiﬁcant eﬀects on bat communities, due to habitat
modiﬁcations, light and noise pollution and reduced prey availability. Recent studies have indicated
that species show varying responses to urbanisation, with a few able to exploit man-made struc-
tures and adjust to the new environmental conditions. This study aimed to identify how landscape
composition inﬂuences bat diversity and community structure along the urbanisation gradient in a
coastal Mediterranean city (Patras, Greece) and whether particular species beneﬁt from the novel
conditions. We conducted acoustic surveys along 45 transects during the post-breeding season for
two years. The eﬀect of land cover, the number of streetlamps (a proxy of artiﬁcial illumination),
the presence of water bodies and weather conditions on bat activity, and community structure were
investigated using Generalized Linear Mixed Models, and multivariate statistics respectively. Eight
bat species and ﬁve species groups were identiﬁed. Bat communities were aﬀected by urbanisation
in general and diversity was low in the entire study area. The community was dominated by the
synurbic species Pipistrellus kuhlii, which comprised more than 70% of the total bat activity recor-
ded. A positive relationship between built-up areas and bat activity was found, probably because P.
kuhlii usually forages around streetlamps in urban areas. In contrast, vegetation cover did not aﬀect
bat activity, even in the less urbanised areas. The remainder of the bat species were not frequently
recorded and were mostly detected close to water bodies, highlighting their value for foraging bats
and the need for their conservation.
Urbanisation is a major cause of land use change, habitat degradation
and fragmentation, as urban areas expand rapidly at the expense of nat-
ural ecosystems. The novel ecosystems that arise are very heterogen-
eous (Pickett et al., 2011), containing a mosaic of artiﬁcial structures
such as buildings and roads, green spaces with ornamental vegetation,
bare ground, agricultural land, natural and artiﬁcial ponds, as well as
patches of remnant natural or semi-natural vegetation (Cadenasso et
al., 2007). In addition, they are characterized by new environmental
conditions, such as increased temperatures (Urban Heat Island), pollu-
tion, anthropogenic noise and artiﬁcial lighting (Grimm et al., 2008)
All these features render urban areas a challenging ecosystem for wild-
To date, numerous studies have shown the detrimental eﬀects of urb-
anisation on biodiversity. At a global scale, urban wildlife communit-
ies have been found to become rather homogeneous along latitudinal
gradients within speciﬁc continents (Clergeau et al., 2006; McKinney,
2006) or even among diﬀerent continents indicating biodiversity loss
(La Sorte et al., 2007). At a local scale, biological communities may
show varying responses to urbanisation intensity, yet, a general pat-
tern has been observed among animal communities: species richness
tends to decrease towards more urbanised areas (McKinney, 2008). A
few generalist species manage to exploit the urban habitat by adjusting
their behaviour and ecology and are usually found in high abundances
(“urban exploiters” or “synurbic”); other species can successfully occur
in both urbanised and natural areas (“urban adapters”), while moderate
Email address: firstname.lastname@example.org (Olga Tzortzakaki)
generalists and specialists tend to become scarcer (“urban avoiders”)
(McKinney, 2006; Devictor et al., 2007; McKinney, 2008; Francis and
Chadwick, 2012; Sullivan et al., 2016).
This pattern has also been observed in bat communities, as only a few
species manage to adjust to the urban environment, while other more
sensitive species avoid it (Avila-Flores and Fenton, 2005; Jung and
Kalko, 2011; Russo and Ancillotto, 2015; Jung and Threlfall, 2018).
Urban life may be harsh for many bat species due to high levels of
light and noise pollution (Schaub et al., 2008; Stone et al., 2015), in-
creased risk of collisions (Medinas et al., 2013) and spread of diseases
(Mühldorfer et al., 2011), increased exposure to predators (Ancillotto
et al., 2013; Threlfall et al., 2013), reduced prey availability and higher
inter- and intraspeciﬁc competition (Russo and Ancillotto, 2015). Fur-
thermore, artiﬁcial nighttime illumination may aﬀect bat commuting,
breeding, roosting, foraging behaviour and hibernation (Stone et al.,
2015; Azam et al., 2016). Anthropogenic noise may also aﬀect bat
feeding success, as noise frequencies often overlap with the frequen-
cies of sounds emitted by bat prey (Schaub et al., 2008). Similarly,
noise might aﬀect communication with conspeciﬁcs by masking bat
social calls (Russo and Jones, 1999).
In general, urbanisation has negative eﬀects on bat activity and
diversity, though these eﬀects are highly species-speciﬁc (Jung and
Threlfall, 2018). Despite the generally adverse conditions in urban en-
vironments, new opportunities may arise for the more tolerant species
(Francis and Chadwick, 2012; Threlfall et al., 2012; Ancillotto et al.,
2015, 2019). Streetlamps attract large numbers of insects, oﬀering an
easily accessible prey for some species (Rydell, 1992; Ancillotto et al.,
2015; Schoeman, 2016 but see Arlettaz et al., 2000; Stone et al., 2015).
In addition, man-made structures such as buildings, roofs and tunnels
Hystrix, the Italian Journal of Mammalogy ISSN 1825-5272 25th November 2019
©cb e2019 Associazione Teriologica Italiana
Hystrix, It. J. Mamm. (2019) — online ﬁrst
may be used as roosts especially due to natural roost loss (Russo and
Ancillotto, 2015). Warmer microclimatic conditions prevailing in arti-
ﬁcial roosts may beneﬁt reproductive females and the growth of their
young (Lausen and Barclay, 2006; Ancillotto et al., 2015). Hence, gen-
eralists may be more widespread and abundant in the urban environ-
ment than specialists by exploiting man-made structures. However,
urban areas may also act as ecological traps for bats (Russo and An-
cillotto, 2015). Gaining an insight into bat responses to urbanisation is
important for the generation of eﬀective management actions and their
contribution to biodiversity conservation.
In recent years, there has been a growing interest in the study of
bat foraging and roosting ecology and behaviour in urban areas, par-
ticularly in Europe (e.g. Lintott et al., 2015; Border et al., 2017;
Suarez-Rubio et al., 2018; Ancillotto et al., 2019), Australia (Threlfall
et al., 2012; Caryl et al., 2016), North America (Dixon, 2012; Krauel
and LeBuhn, 2016; Schimpp and Kalcounis-Rueppell, 2018), Cent-
ral America (Jung and Kalko, 2011; Rodríguez-Aguilar et al., 2017),
South America (Oprea et al., 2009) and South Africa (Schoeman,
2016). However, studies on bat responses to urbanisation in south-
eastern Europe are lacking, despite its high bat diversity (Dietz et al.,
2009). In this study, we aimed to ﬁll this knowledge gap and identify
(a) how bat activity varies along the urbanisation gradient, (b) the en-
vironmental factors that aﬀect bat activity and (c) whether urbanisation
favours particular species in a densely-built Mediterranean coastal city
Materials and methods
Study area and site selection
The study area covered the city of Patras and its surroundings (total
area 110 km2), located in SW Greece (38°140N, 21°440E). Patras is the
third largest city in Greece with approximately 200000 inhabitants (EL-
STAT, 2011). The city has an elongated expansion along the coast of
Patraikos Gulf and is delimited by Mount Panachaikon (1926 m a.s.l.,
Natura 2000 site GR2320007) to the east-southeast (Fig. 1). Due to
various historical reasons and inadequate spatial planning, the town has
a very dense urban core with severely reduced green space (Papadatou-
Giannopoulou, 1991; Tzortzakaki et al., 2019). The surrounding peri-
urban and rural areas consist of agricultural land, orchards (mostly olive
groves), Mediterranean shrublands, and a few scattered small remnant
patches of coniferous or deciduous forests and riparian vegetation. Due
to complex geological processes (i.e. the tectonic uplift caused by the
rifting of the Corinth and Patraikos Gulfs), a number of gorges and
gullies containing streams were formed on Mount Panachaikon, which
ﬂow through the study area into the Patraikos Gulf (Fig. 1). The cli-
mate is typical Mediterranean with a long dry period and mild winter
(average annual temperature 17.9 ◦C), and relatively high precipitation
levels (average annual rainfall: 607 mm; HNMS, 2017).
The study area was stratiﬁed into three zones of decreasing built
cover (b.c.): the "urban zone" (b.c.>50%), the "suburban zone"
(30%<b.c.<50%), and the “peri-urban zone” (b.c.<30%), after overlay-
ing a grid of 500×500 m cells and calculating the proportion of built
cover in each cell using the Urban Atlas (EEA, 2010; Tzortzakaki et
al., 2019). In each zone, 15 grid cells were randomly selected, on the
basis that they were situated at least 500m from their nearest neigh-
bouring cell (Tzortzakaki et al., 2019). Each gird cell encompassed
one bat sampling site (45 sites in total; Fig. 1).
Bat surveys and sound analysis
Bat surveys were conducted during the post-breeding period from the
end of August to the beginning of October in two consecutive years
(2015–2016). In each of the 45 grid cells, a 300 m line transect was
established, which was walked in one direction once in each year (90
transects in total). Surveys started half an hour after sunset (Vaughan
et al., 1997) and were completed within three hours. On average, three
sites were sampled on each survey night, in random order (Dixon, 2012)
and regardless of their location along the urbanisation gradient to re-
duce possible temporal or spatial sampling bias. Sampling was con-
Figure 1 –Map of the study area, showing the distribution of the sampling sites in the
urban (squares), suburban (circles) and peri-urban zone (triangles). Urbanisation zones
were delineated based on land use data from the Urban Atlas (EEA, 2010). Grey diagonal
lines represent the Natura 2000 site of Mount Panachaikon (GR2320007).
ducted by the same individual and under good weather conditions, i.e.,
without rain or strong winds and minimum nightly temperature >19 ◦C.
Bat vocalizations were recorded using a Pettersson M500 USB ultra-
sound microphone (Pettersson Electronik AB, Uppsala, Sweden), set at
approximately 1.8m above the ground and connected to a tablet com-
puter. Recordings were performed with the Pettersson Bat Sound Touch
Lite software at a 500 kHz sampling rate, set in the automatic mode
with a sound power level of -10 dB and a bandwidth ranging from 19
to 120 kHz, which was deﬁned after testing microphone sensitivity in
the ﬁeld to avoid recording low-frequency noise. The bat detector was
automatically triggered by sounds above the lower threshold and was
set to record for 6 seconds each time (5 seconds from the trigger point +
1 second before). Social calls and echolocation calls of Tadarida teni-
otis were recorded while recording other species’ echolocation calls.
Bat ultrasound analysis was performed using SonoBat 3.0 (SonoBat
Bat Call Analysis Software); echolocation call parameters were auto-
matically measured, after applying manual ﬁlters to minimize masking
by noise where necessary. Batsound 4.2 (Pettersson Electronik AB,
Uppsala, Sweden) was additionally used to explore the possible pres-
ence of more than one individual or species or social calls within sound
ﬁles. Spectrograms were visualized in Batsound with a 512 samples
Hanning FFT window.
Bat species identiﬁcation was conducted with the open source auto-
mated software for the identiﬁcation of European bats “iBatsID” (Wal-
ters et al., 2012). iBatsID uses the parameters measured by Sonobat and
assigns each call sequence to one or more species based on a classiﬁc-
ation probability. We considered identiﬁcation at the species level as
trustworthy, when classiﬁcation probability was ≥90%. When prob-
ability was <90%, call sequences were initially assigned to the spe-
cies group with the highest classiﬁcation probability. They were sub-
sequently evaluated manually and identiﬁed to species level where pos-
sible using BatSound. Where frequency of echolocation call and time
parameters fell into the overlap zone of at least two species (Dietz and
Kiefer, 2014), identiﬁcation remained at the species group level, unless
social calls were present (Russo and Jones, 1999; Pfalzer et al., 2003;
Russo and Papadatou, 2014; Nardone et al., 2017). Call sequences that
Bat community of an Eastern Mediterranean city
could not be assigned to species or species groups were recorded as
unknown and were only included in total bat activity estimates.
Calls of Myotis species were grouped, as their identiﬁcation is of-
ten ambiguous (Lintott et al., 2015). Call sequences classiﬁed as P.
nathusii by iBatsID were considered as P. kuhlii in the analysis, be-
cause P. nathusii is rare in southern Greece (Georgiakakis and Papad-
atou, unpubl. data) and none of the social calls recorded belonged to
the latter species.
The number of bat passes of each species or species group within
each recording, i.e., within 6 seconds, was used as an index of bat activ-
ity. A bat pass is deﬁned as a sequence of two or more echolocation
pulses emitted by a bat (Thomas, 1988).
In each sampling site, mean hourly temperature (MHT) and relative
humidity (MHRH) were extracted from the meteorological data that
were provided by the Laboratory of Atmospheric Physics of the Uni-
versity of Patras, given the known inﬂuence of weather conditions on
bat activity (O’Donnell and Sedgeley, 2001). Second, the percent cover
of the predominant land-cover types (buildings, impervious surfaces,
woody vegetation, open green spaces and water bodies) was assessed
within a 200 m radius circular buﬀer zone around each transect centroid
(Tzortzakaki et al., 2019), so that the buﬀer zones ﬁt within the re-
spective grid cells (500×500 m) and do not overlap with nearest neigh-
bouring buﬀers. Proximity of sampling sites to the closest natural (i.e.,
streams and marshes) or artiﬁcial water body (hereafter all referred as
water bodies), and to motorway (Fig. 1) was measured using ArcGIS
10.1 (ESRI). Finally, the number of streetlamps along each transect
was recorded as a proxy of the intensity of illumination in the sampling
First, pairwise comparisons of mean daily temperature (°C), mean
daily relative humidity (%) and total daily rainfall (mm) were per-
formed for each month between the two sampling years with Analysis
of Variance (ANOVA), in order to test for possible diﬀerences in the
meteorological conditions between the two years.
To examine bat activity patterns along the urbanisation gradient, bat
activity was compared among the urbanisation zones for each year sep-
arately, using Kruskal-Wallis tests, because the data were not normally
distributed. A Wilcoxon signed-rank test was used to compare bat
activity at each sampling site between the two years.
To investigate the eﬀect of the environmental parameters on bat
activity, Generalized Linear Mixed Models (GLMMs) were used. Prior
to model building, the relationships among the response and explan-
atory variables were investigated (Zuur et al., 2010). Land-cover
types were highly correlated, therefore Principal Components Analysis
(PCA) was used to reduce their number and only the ﬁrst two axes were
retained: PC1 represented a gradient of decreasing vegetation cover to
increasing built cover, while PC2 characterised a gradient of decreas-
ing open spaces to increasing water cover (Tzortzakaki et al., 2019).
The distance to motorway and the number of streetlamps were excluded
from the analyses, because they were both collinear with PC1 (Pearson
r=0.63 and r=0.78, respectively), as well as MHRH, because it was
correlated with MHT (r=-0.66). No evidence for spatial and temporal
autocorrelation was found after plotting bat activity vs. spatial coordin-
ates and Julian date, respectively (Zuur et al., 2010).
GLMMs were carried out using negative binomial error distribu-
tion with a log link function. Since two visits per site were conduc-
ted, sampling site was considered as a random factor, while PC1, PC2,
MHT, distance to water bodies (DistW) and year of study (Year) were
included as ﬁxed terms. Continuous explanatory variables were stand-
ardised (Schielzeth, 2010).
Model selection was performed with backward stepwise deletion:
the global model was constructed, and then non-signiﬁcant covari-
ates were sequentially removed until the best model with the smallest
Akaike’s Information Criterion (AIC) was achieved. Tests of homogen-
eity of variance, normality of residuals, independence of observations
Figure 2 –Total bat activity (number of bat passes) per urbanisation zone and year of
study. Bat activity was signiﬁcantly higher in 2016, but no statistically signiﬁcant dierences
were found among the urbanisation zones in either year.
and model overdispersion were carried out for model validation (Zuur
et al., 2010). Analyses were carried out with lme4 package (Bates et
al., 2015) in R v.3.3.1 (R Core Team, 2016).
To examine the relationship between community composition and
environmental variables, Redundancy Discriminant Analysis (RDA)
was performed separately for each year using the vegan package (Ok-
sanen et al., 2016) in R. Permutation tests with 999 permutations were
used to assess the signiﬁcance of the relationships between species
composition and the explanatory variables. For this analysis, calls at
the frequency overlap zone that could not be identiﬁed to species level
were excluded, as well as species with less than two bat passes. Prior
to the analysis, bat activity data were Hellinger-transformed (Oksanen
et al., 2016), while environmental data were standardised (Leps and
To assess the relationship between species composition and the urb-
anisation gradient, Permutational Multivariate Analysis of Variance
(PERMANOVA) was carried out separately for each year (Anderson,
2001). PERMANOVA allowed for pairwise comparisons of the com-
munity composition among the urbanisation zones. The analysis was
performed on the community data matrix (same as RDA) using the
Bray-Curtis dissimilarity index with 999 permutations with the “ad-
onis” function of the vegan package (Oksanen et al., 2016), after ap-
plying a Hellinger transformation. Pairwise comparisons among the
urbanisation zones were conducted with the pairwise.Adonis pack-
age (Martinez Arbizu, 2017). Multivariate homogeneity of group dis-
persions (PERMDISP), performed with the “betadisper” function (Ok-
sanen et al., 2016), did not indicate violation of the homogeneity of
variances assumption (p>0.05 for both years).
A total of 653 bat passes were analysed, 67% of which were identiﬁed to
species level. Eight bat species and ﬁve species groups were identiﬁed
(Tab. 1). P. kuhlii was the most common species in the study area,
comprising more than 70% of the bat passes in both years and occurring
at approximately 80% of the sites (Tab. 1). Excepting P. pygmaeus and
T. teniotis, other species were recorded very infrequently (<10% of the
sites in both years). The occurrence of H. savii could not be assessed
precisely, as respective calls were mostly in the overlap zone of H. savii
and P. kuhlii (Tab. 1).
Bat activity did not vary considerably along the urbanisation gradi-
ent in 2015. In contrast, it was remarkably higher in the urban and sub-
urban zones than in the peri-urban zone in 2016 (Fig. 2). However, stat-
istical comparisons of bat activity among the urbanisation zones did not
show any signiﬁcant diﬀerences in either year (2015: Kruskal-Wallis
H=1.281, p=0.527; 2016: H=2.893, p=0.235). Bat activity was signi-
ﬁcantly higher in the second year (Wilcoxon test: V=229, p=0.015),
noting that signiﬁcantly higher temperatures occurred in the study area
during the winter and spring of 2016 (Tab. S1). However, summer and
autumn temperatures were similar across the two years and no diﬀer-
ences were found in most cases for relative humidity (Tab. S1).
Hystrix, It. J. Mamm. (2019) — online ﬁrst
Table 1 –Proportion of bat passes assigned to bat species or species group in each urbanisation zone per year of study (calculated as the proportion of the total annual bat passes).
Occupancy indicates the proportion of sampling sites (out of 45) where a given species or species group was recorded.
Bat passes (%) Occ. (%) Bat passes (%) Occ. (%)
Species / Group Urban Suburban Peri-urban Total Urban Suburban Peri-urban Total
Eptesicus serotinus / Nyctalus
0 0 2.14 2.14 2.22 0 0 0 0 0
Nyctalus noctula 0 0 0 0 0 0 0 0.24 0.24 2.22
Hypsugo savii 0.43 0 0 0.43 2.22 0.48 0.72 0 1.2 4.44
Pipistrellus kuhlii 24.79 22.65 24.36 71.79 80 33.17 23.56 14.9 71.64 82.22
P. kuhlii / H. savii 1.71 3.42 2.99 8.12 31.11 4.09 1.68 1.2 6.97 37.78
Pipistrellus pipistrellus 0.43 0.85 0.85 2.14 8.89 0 3.85 0 3.85 4.44
P. pipistrellus / P. kuhlii 0.43 0 0.43 0.85 4.44 0.48 0.72 0 1.2 8.89
Pipistrellus pygmaeus 0.43 3.85 2.14 6.41 13.33 0 6.97 0.48 7.45 13.33
Miniopterus schreibersii 0 0 0 0 0 0 0.48 0 0.48 2.22
M. schreibersii / P. pygmaeus 0 0 0 0 0 0 1.44 0 1.44 2.22
Myotis spp. 0.43 0.43 2.99 3.85 6.67 0.24 0 0 0.24 2.22
Tadarida teniotis 3.85 0.43 0 4.27 8.89 4.57 0.24 0.48 5.29 13.33
Total 32.48 31.62 35.9 100 43.03 39.66 17.31 100
The GLMMs indicated a positive eﬀect of built cover (PC1), water
cover (PC2) and the year of study on bat activity (Tab. 2). Temperature
and distance to water bodies were non-signiﬁcant and not included in
the ﬁnal statistical model. Bat activity increased with increasing build-
ing and water cover, but it was strongly correlated with P. kuhlii activity
(r = 0.92). Therefore, a positive relationship between these variables
and P. kuhlii activity can be inferred.
RDA performed on the 2015 data did not show any signiﬁcant re-
lationships between the environmental variables and bat community
composition (total variance explained=15.8%; model: F=1.888,
p=0.064; Fig. 3 a). In contrast, in 2016 a signiﬁcant relationship
was found between PC2 (presence of water) and community com-
position (PC2: F=3.539, p=0.034; Fig. 3 ). The environmental vari-
ables accounted for only 14.4% of the total variance (model: F=1.675,
PERMANOVA showed a signiﬁcant relationship between com-
munity composition and the urbanisation gradient, but only for 2016
(2015: F=0.359, p=0.912; 2016: F=2.329, p=0.037). Pairwise com-
parisons indicated a statistically signiﬁcant diﬀerence between the sub-
urban and the urban zone in 2016 (F=3.319, p=0.026), while no diﬀer-
ences were found between the urban and the peri-urban zone (F=0.345,
p=0.721) and between the suburban and the peri-urban zone (F=2.266,
This current study provides the ﬁrst insights into bat activity and
diversity patterns in an urban area in southeastern Europe (Patras,
Greece), demonstrating the sensitivity of bat communities to urban-
isation and the diﬀerential responses among species. Bat diversity was
generally low in the city of Patras and its surroundings, although the
area lies within a biodiversity hotspot containing a high number of spe-
cies (Dietz et al., 2009, Papadatou and Georgiakakis, unpubl. data).
The bat community was dominated by P. kuhlii, which together with
the group P. kuhlii /H. savii comprised approximately 80% of the total
bat activity in the study area in both years. The rest of the species were
Table 2 –Parameter estimates (±standard error) of ﬁxed eects of the best GLMM.
Fixed eﬀects Estimate (±S.E.) Z p
Intercept 1.257 (±0.191) 6.604 <0.001
PC1 0.323 (±0.151) 2.142 0.032
PC2 0.384 (±0.155) 2.479 0.013
Year 0.477 (±0.202) 2.365 0.018
much less abundant, likely implying decreased tolerance to the urban
environment in the study area.
Bat activity and species richness did not diﬀer along the urbanisa-
tion gradient, contradicting the generally observed pattern of bat activ-
ity declining with increasing urbanisation intensity (Walsh and Harris,
1996; Hale et al., 2012; Russo and Ancillotto, 2015; Jung and Threlfall,
2016) or studies showing higher diversity in suburban areas (Hourigan
et al., 2010; Threlfall et al., 2011). Contrary to our expectations, the
peri-urban area did not hold greater bat activity or more species than
the urban and suburban areas, despite the fact that it includes less dis-
turbed landscapes with high vegetation cover (Tzortzakaki et al., 2019).
Bat activity was highest in the urban zone in the second sampling year.
As indicated by several studies, species richness may be generally low
in urban areas, but total abundance and biomass is often increased due
to the occurrence of a few synurbic species (Avila-Flores and Fenton,
2005; Francis and Chadwick, 2012; Krauel and LeBuhn, 2016).
A positive relationship between built cover and bat activity was in-
dicated by the GLMMs. These results may reﬂect the ability of P. kuh-
lii to exploit the urban landscape and forage around sources of artiﬁcial
illumination (Tomassini et al., 2014; Russo and Ancillotto, 2015; An-
cillotto et al., 2019), as the number of streetlamps was highly correlated
with built cover in the study area. Streetlamps seem to play an import-
ant role in P. kuhlii foraging, as they prey upon insects that are attracted
by the light (Rydell, 1992; Tomassini et al., 2014).
Surprisingly, vegetation cover did not have any positive eﬀect on bat
activity, although it is considered a key landscape element for foraging
bats in urban environments, as it is associated with increased insect prey
abundances (Avila-Flores and Fenton, 2005 and references therein).
Furthermore, urban green spaces such as parks (Glendell and Vaughan,
2002; Gehrt and Chelsvig, 2003; Russo and Ancillotto, 2015), gardens
(Hale et al., 2012; Lintott et al., 2016) or even small green areas (Avila-
Flores and Fenton, 2005) can enhance insect abundances, while tree
networks in green spaces can facilitate bat commuting and foraging
(Dixon, 2012; Hale et al., 2012).
The lack of a positive relationship between vegetation cover and
urban green spaces, and bat activity in this study may in part be ex-
plained by the dominance of P. kuhlii. However, green spaces in the
study area may be too small or generally inadequate (e.g. due to in-
adequate vegetation structure) for most bat species, even in the peri-
urban zone. The latter is an agricultural area covered mainly by agri-
cultural mosaics and olive groves, which have been found to support
relatively high bat diversity in other Mediterranean areas (Russo and
Jones, 2003). A negative relationship between rural areas and bat activ-
ity was reported in Illinois, USA, where, unlike Patras, intensive crop
agriculture prevails (Gehrt and Chelsvig, 2003). More natural areas in
Bat community of an Eastern Mediterranean city
Figure 3 –RDA ordination plots showing the relationship between the environmental variables (PC1: built cover, PC2: water cover, Temp: mean hourly temperature, DistW: distance to
water) and bat community composition in (a) 2015 and (b) 2016. No signiﬁcant relationships were found, except for a signiﬁcant relationship between water cover (PC2) and community
composition in 2016.
the surroundings of the study area (e.g. within the adjacent Natura 2000
site) may provide higher prey availability and, thus, be more attractive
to foraging bats.
Similar to other studies (e.g. Li and Wilkins, 2014), the highest bat
activity was in most cases recorded in speciﬁc sites with presence of
water, i.e. along the banks of water bodies. However, these sites varied
between the study years. In general, the highest total bat activity was
recorded at the main wastewater treatment plant of the city, while the
upstream section of Glafkos river (Fig. 1) and a remnant natural mire
also provide important foraging grounds. In the rest of the sites where
water is present such as streams, bat activity was lower than expected.
In contrast, one of the few remaining seaside open green spaces was
also important for foraging bats. Despite the positive eﬀect of water
cover for foraging bats, site proximity to water did not have any ef-
fect on bat activity contrary to the ﬁndings of previous studies (Dixon,
2012; Ancillotto et al., 2015; Krauel and LeBuhn, 2016). These ﬁnd-
ings imply that the main factors inﬂuencing bat activity are possibly
associated with water cover and/or particular characteristics of the wa-
ter bodies (e.g. shape or structure) and not with the site proximity to
water per se.
Water bodies in Patras have generally undergone degradation due
to modiﬁcation of riverbeds, residential and industrial development,
draining and dumping of refuse, and, consequently, degradation of their
riparian character. Lintott et al. (2015) showed that even if waterways
are accessible to bats, their use may largely depend on the riparian ve-
getation cover, while it may also be negatively aﬀected by the propor-
tion of the adjacent sealed surfaces.
Nevertheless, the value of the remaining water bodies for the local
bat community should be emphasised, as the highest bat activity was
recorded at some of these sites. In particular, P. pygmaeus was found
to have a positive (although not statistically signiﬁcant) relationship
with water cover (Fig. 3b), which supports the ﬁndings of Nicholls
and Racey (2006) and Lintott et al. (2016) who found a strong asso-
ciation with freshwater (but see Hale et al., 2012). Water bodies such
as rivers and lakes are highly important for insectivorous bats, because
they provide high insect prey availability and drinking water (Vaughan
et al., 1997; Russo and Jones, 2003; Li and Wilkins, 2014). Linear
water bodies (waterways) may also function as corridors facilitating
bat movements between the urban core and peri-urban natural habitats
(Rouquette et al., 2013; Lintott et al., 2016). Hence, their maintenance
and management are important for bat conservation in large urban set-
tlements (Russo and Ancillotto, 2015).
The year of the study aﬀected bat activity and this was more evid-
ent in sites located along water bodies (Tab. S2). In the second year,
bat activity was signiﬁcantly higher, underlying that changes in spatio-
temporal patterns of bat activity may be due to either stochastic or spe-
ciﬁc factors related to bat biology. Diﬀerences between years may re-
ﬂect changes in environmental conditions or bat behaviour (O’Donnell
and Sedgeley, 2001). In this study, diﬀerences in bat activity observed
between the two years may be attributed to the prevailing weather con-
ditions preceding the sampling, as the winter and spring of the second
year were signiﬁcantly warmer. Higher temperatures may increase in-
sect abundance (Scanlon and Petit, 2008), but may also inﬂuence bat
reproductive success (Ancillotto et al., 2015). They may lead to ad-
vanced parturition, which in turn may result in increased juvenile sur-
vival (Frick et al., 2010) or more females reaching sexual maturity dur-
ing the ﬁrst autumn (Dietz et al., 2009). The scenario of increased prey
availability is considered more plausible, since winters in the study area
are generally mild and do not show strong ﬂuctuations among years,
while the inﬂuence of other stochastic undetermined factors cannot be
Our ﬁndings are in agreement with the conclusion of other studies
that the response of bats to urbanisation is highly species-speciﬁc (e.g.
Avila-Flores and Fenton, 2005; Threlfall et al., 2012; Russo and Ancil-
lotto, 2015). The bat community was mainly represented by P. kuhlii,
particularly in the urban zone in the second year of the study. Pipistrelle
bats such as P. kuhlii,P. pipistrellus and H. savii have been reported to
successfully inhabit urban areas and expand their range across Europe
(Russo and Ancillotto, 2015; Ancillotto et al., 2019). Speciﬁcally in
southern Europe, P. kuhlii has shown notable behavioural and ecolo-
gical ﬂexibility and has adjusted to artiﬁcial structures such as build-
ings and streetlamps for roosting and foraging, respectively (Russo and
Jones, 2003; Ancillotto et al., 2015), while P. pipistrellus occupies a
similar niche in Northern Europe (Vaughan et al., 1997).
Similarly, T. teniotis was mainly recorded in the urban zone, demon-
strating its ability to exploit human-modiﬁed landscapes and man-made
structures (Avila-Flores and Fenton, 2005; Threlfall et al., 2012; Krauel
and LeBuhn, 2016). Tadarida species have been documented ﬂying
over cluttered environments and large areas such as the dense city core
(Russo and Jones, 2003; Avila-Flores and Fenton, 2005; Krauel and Le-
Buhn, 2016), foraging on passing insect swarms (Marques et al., 2004).
It should be noted that in this study, its occurrence may have been un-
derestimated, as it usually ﬂies high above the ground and produces low
frequency sounds (Zbinden and Zingg, 1986).
Hystrix, It. J. Mamm. (2019) — online ﬁrst
Pipistrellus pygmaeus and P. pipistrellus were rarely detected in the
urban zone but were in general not common in the study area (<10%
of the total bat activity). These two pipistrelle species together with
M. schreibersii were principally recorded in the suburban zone and
they showed higher activity in sites near water bodies especially in the
second year (Tab. S2). As mentioned above, P. pygmaeus is associated
with freshwater and avoids densely built areas (Nicholls and Racey,
2006; Lintott et al., 2016), whereas P. pipistrellus is considered a gen-
eralist and more tolerant of intermediate urbanisation levels (“urban
adapter”, Hale et al., 2012), and thus, occurs in a broader spectrum of
habitats (Lintott et al., 2016). The latter species appears to avoid hab-
itats with high freshwater coverage, in order to avoid competition with
P. pygmaeus (Nicholls and Racey, 2006; Lintott et al., 2016). In this
study, P. pipistrellus was principally found in the wastewater treatment
plant foraging together with P. pygmaeus and other species, but it was
rare in any other habitats. Miniopterus schreibersii was only found at
the wastewater treatment plant.
Larger species such as Nyctalus sp./E. serotinus were only detected
in one site in the peri-urban zone. Nyctalus species have been reported
to roost in buildings and forage in parks and city edges, if high veget-
ation and insect densities are provided (Dietz and Kiefer, 2014), and
in urban waterways (Lintott et al., 2015), while E. serotinus often for-
ages in settlements and big cities (Dietz and Kiefer, 2014). Both N.
leisleri and E. serotinus have been recorded to forage at streetlights
(Azam et al., 2015). However, in this study these species were rarely
recorded, thus, no conclusions on their occurrence can be drawn. Sim-
ilarly, Myotis spp. were mostly found in sites out of the urban zone and
close to water bodies, in accordance with other studies (Vaughan et al.,
1997; Russo and Jones, 2003; Avila-Flores and Fenton, 2005; Dixon,
2012; Lintott et al., 2015).
The negative impact of urbanisation on many bat species due to hab-
itat fragmentation and patch isolation (Hale et al., 2012; Threlfall et
al., 2012), reduced vegetation cover (Lintott et al., 2016), and artiﬁcial
lighting (Azam et al., 2016) possibly explains the low number of spe-
cies recorded in the urban and the suburban zones. However, it is un-
clear why bat diversity was low in the peri-urban zone, despite retaining
signiﬁcant natural and semi-natural characteristics (Tzortzakaki et al.,
2019). The eﬀects of some landscape aspects such as land-cover were
investigated at a relatively small scale (200 m), considering that bats are
highly mobile organisms and that species responses may vary depend-
ing on their diﬀerent foraging and roosting requirements (Dixon, 2012;
Hale et al., 2012; Lintott et al., 2016). Previous studies have underlined
the importance of local habitat features such as vegetation structure,
water bodies and streetlamps on urban bat communities, but also of the
surrounding landscape (Walsh and Harris, 1996; Gehrt and Chelsvig,
2003; Threlfall et al., 2012; Lintott et al., 2015). Hence, further in-
vestigation of the local habitat characteristics (e.g. vegetation structure
and the existence of buildings providing bat roosts) and the landscape
structure (e.g. habitat heterogeneity and connectivity) at a larger scale
may provide insight into the factors aﬀecting bat community response
to urbanization, especially in the peri-urban zone. Bat surveys in the
adjacent natural areas would be useful as it would allow the magnitude
of the eﬀect of urbanisation on bat communities to be assessed.
Finally, we need to acknowledge that the low bat diversity and the
rare occurrence of most species may have resulted from the sampling
methodology (i.e., short acoustic transect surveys), which was designed
to be consistent with two companion studies of birds (Tzortzakaki et al.,
2018) and butterﬂies (Tzortzakaki et al., 2019). Additionally, surveys
were not conducted during the breeding season (late spring – mid sum-
mer) and this would likely provide a better assessment of bat activity
patterns in the urban environment.
Despite its limitations, our study provides an insight into the eﬀects
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Associate Editor: L. Ancillotto
Additional Supplemental Information may be found in the online version of this arti-
Table S1 Monthly mean temperature (T), relative humidity (RH) and total monthly
rainfall recorded for each sampling year.
Table S2 Total number of sampling sites with species occurrences, number of sites
near water bodies (<100 m) with species records, and proportion of bat passes
of each species recorded in sites near water bodies for each survey year.