Noise, an obvious effect of urbanization, has a negative impact on animal vocalizations and the hunting efﬁciency of acoustic predators. However, the inﬂuence of noise pollution on the spatial distribution of populations remains understudied. The aim was to assess the factors shaping the distribution pattern of an acoustic predator (long-eared owl Asio otus) in an urban–farmland matrix. We hypothesized that the probability of an acoustic predator occurring decreases with growing nocturnal noise emission. This owl survey was conducted in Krako´ w (S Poland) on 79 ran- domly selected sample plots (1 km 1 km). Six habitat variables (area of parks, woodlands, grass- land, arable land, habitat diversity index, and noise pollution) were identiﬁed and correlated with the probability of the species’ occurrence. Proximity to pedestrian routes and roads, habitat frag- mentation, and noise intensity was also deﬁned at nest sites and random sites. Long-eared owls occurred on 37% of the sample plots. Occupied plots had a greater area of grassland and arable land as well as a lower level of noise pollution than the unoccupied ones. A multivariate model revealed that area of grassland and nocturnal noise emission was signiﬁcantly correlated with the probability of long-eared owls occurring and that the high probability of occurrence recorded on plots with large areas of grassland was reduced by noise pollution. The noise intensity recorded at nest sites was also signiﬁcantly lower than at random sites. This study suggests that apart from habitat factors, the distribution of acoustic predators in an urban matrix is driven by noise pollu- tion. This highlights the importance of proper landscape management, that is, maintaining large grassland areas and preventing noise from increasing within them. Key words: noise pollution, nocturnal predator, road effect, species distribution, urban ecology, urban effect. In the last 100 years the human population has risen very rapidly range of herbivores (Tang et al. 2014; Broyer et al. 2017); these sub- and is putting unprecedented pressure on wildlife (Czech et al. 2000; stances also poison animals directly, an effect that is potentiated at Wittemyer et al. 2008). This ever larger number of people requires every successive trophic level (Mineau et al. 1999; Gervais et al. more and more food (Daily et al. 1998), which entails expanding 2000). As a consequence, changes in farming practices are causing the area of farmed land or intensifying crop cultivation. This, in sensitive farmland species to disappear and a general decline in bio- turn, leads to changes in the farmland landscape structure (Tilman diversity (Leptich 1994; McLaughlin and Mineau 1995; Melman et al. 2011; Su et al. 2014), depriving it of microhabitats and key ele- et al. 2008; Simons et al. 2017). ments of the landscape that are indispensable for many species This diminishing biodiversity of the agricultural landscape is (McLaughlin and Mineau 1995; Aschwanden et al. 2005; Downs affecting a great many systematic groups (McLaughlin and Mineau et al. 2016; Yahya et al. 2016; Simons et al. 2017). The use of artifi- 1995; Simons et al. 2017): plants (Tang et al. 2014), insects (Duelli cial fertilizers and pesticides not only impoverishes the living condi- et al. 1999), amphibians (Kolozsvary and Swihart 1999), birds tions of plants consumed by or functioning as hosts for a whole (Gibbs 2000; Parris and Schneider 2009), and mammals (Butet and V C The Author (2017). Published by Oxford University Press. 1 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact email@example.com Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox061/4582952 by guest on 13 July 2018 2 Current Zoology, 2017, Vol. 0, No. 0 Leroux 2001; Hodara and Poggio 2016). Predators are exception- agricultural machinery (Kropsch and Lechner 2016) and increasing ally sensitive to changes in the farming environment; reacting traffic in rural areas in the proximity of cities (Ciach 2012). strongly to variations in the species composition and numbers of Long-eared owl Asio otus is widely distributed in the northern prey (Korpimaki and Norrdahl 1991; Leptich 1994; Aschwanden hemisphere and has a large population size (BirdLife International et al. 2005); the disappearance of foraging habitats, perching, and 2016). The original habitats of this species are forest steppes nesting sites (Gibbs 2000; Aschwanden et al. 2005; Yahya et al. (Mikkola 1983; Barashkova et al. 2013), from which population 2016); as well as the toxic action of pesticides and other contami- expanded into farming landscapes, sparse woodlands, and human- dominated habitats (Henrioux 2000, 2002; Martı ´nez and nants (Mineau et al. 1999; Geduhn et al. 2016). Zuberogoitia 2004; Aschwanden et al. 2005), including towns, cities, A serious threat to the wildlife of farmland landscapes is urban and their suburbs (Zhang et al. 2009; Go ¨c ¸er 2016; Milchev and sprawl onto former farmland (Czech et al. 2000; Mcdonald et al. Ivanov 2016). In Poland, first records of long-eared owl nesting in 2008; Jokima ¨ ki and Suhonen 2016), reducing and fragmenting habi- towns and cities come from the 19th century and presently it is a tat area (Trombulak and Frissell 2000; Su et al. 2014). In addition, widespread breeding and wintering bird in urban environments of socio-economic changes are turning villages close to towns and cities into residential areas, with a concomitant decline in their originally the country (Tomiałojc and Stawarczyk 2003; Dziemian et al. 2012; rural character and the species associated with them (Ciach 2012; Turzanska and Turowicz 2014). Long-eared owl is considered as a Sushinsky et al. 2013). Urban areas are also sources of air pollution food-specialist, feeding mainly on voles (Mikkola 1983; Korpimaki and rodenticides, which accumulate in the bodies of animals and Norrdahl 1991). However, it shows dietary plasticity and (Esselink et al. 1995; Berglund et al. 2011; Geduhn et al. 2016). The remarkable spatial and temporal variation in food composition pressure of human beings and their pets disturbs wild animals depending on food availability and abundance (Bertolino et al. 2001; (Hathcock 2010; Cavalli et al. 2016), and artificial lighting disrupts Romanowski and Zmihorski 2008; Birrer 2009; Mori et al. 2014; biological rhythms (Da Silva et al. 2015) and causes disorientation, Mori and Bertolino 2015). The diet of urban long-eared owl may particularly important during migration periods (Longcore and Rich include rats (Laiu and Murariu 1998; Pirovano et al. 2000), birds (Kiat et al. 2008; Go ¨c ¸er 2016), bats (Zhang et al. 2009; Tian et al. 2004). The urban road network is another factor with a deleterious 2015), and insects (Ciach 2006; Birrer 2009). Occasionally, carrion effect on nature (Trombulak and Frissell 2000), which leads to habi- consumption may enlarge the trophic spectrum (Mori et al. 2014). tat fragmentation, limits the movements of terrestrial animals In most of its range the long-eared owl is a typical farmland species (Kolozsvary and Swihart 1999; Ascens~ ao et al. 2017), and forces (Glue 1977; Mikkola 1983), the distribution of which depends closely birds to occupy larger territories (Redpath 1995; Trombulak and on the intensification of agriculture (Martı ´nez and Zuberogoitia 2004; Frissell 2000). Moreover, roads are lit up at night (De Molenaar and Aschwanden et al. 2005; Moreno-Mateos et al. 2011). However, the Sanders 2006), and the traffic on them is a source of air pollution distribution of this species in urbanized habitats and the factors affect- (Esselink et al. 1995; WIOS 2014) and causes roadkill (Trombulak ing this are poorly understood. The aim of this study was to assess the and Frissell 2000; Hager 2009). environmental parameters shaping the distribution pattern of long- Among the dangers to the farming landscape that have not eared owls in an urban–farmland matrix. We hypothesized that the received so much attention are the noise and vibrations emitted by availability of primary foraging and nesting habitats, which in the case the road infrastructure passing through farmland (Parris and of long-eared owls are farmland and wooded areas, respectively, would Schneider 2009; Ciach and Fro ¨ hlich 2017) and also by farming increase the probability of this species occurring. However, we also activities (Kropsch and Lechner 2016). Noise is one of the more assumed that noise—intense and constantly present in urban environ- important factors leading to the homogenization of wildlife (Proppe ments—would be the factor responsible for reducing the probability of et al. 2013; Ciach and Fro ¨ hlich 2017), as it attenuates acoustic sig- their occurrence. Moreover, we predicted that noise levels would influ- nals and raises the energy expenditure that animals incur when they ence nest-site preferences and that the owls would select sites with low communicate with each other (Lengagne and Slater 2002; Parris and noise intensity. Schneider 2009). Songbirds are seriously endangered by noise, as they use acoustic signals to establish boundaries of and maintain ter- ritories, and to find mates (Parris and Schneider 2009; Nemeth et al. Materials and Methods 2013; Proppe et al. 2013). Predators like most species of bats (Schaub et al. 2008; Siemers and Schaub 2011) and owls (Delaney Study site et al. 1999; Mason et al. 2016), which detect their prey acoustically, This study was carried out in the city of Krako ´ w (southern Poland, 0 0 are another group of animals endangered by noise. Because owls use 50 05 N, 19 55 E), which covers an area of 327 km and has a their hearing to locate and grasp their prey (Mikkola 1983), they are population density of 2,331 persons/km (GUS 2016). Krako ´w is less efficient hunters where ambient noise levels are high (Delaney characterized by a broad urbanization gradient—from the densely et al. 1999; Mason et al. 2016). This may compel them to abandon built-up city center, through extensive suburbs with a moderate their breeding territories (Hindmarch et al. 2012) or to avoid seem- number of buildings to the scattered buildings typical of a farmland ingly suitable habitats (Silva et al. 2012). landscape (Figure 1). The human settlements cover 6% of the study The influence of road infrastructure on owls in the farmland area, which range from the compact, continuous structures that landscape remains poorly investigated (Hindmarch et al. 2012; Silva cover the ground completely through taller and shorter blocks of et al. 2012; Scobie et al. 2014), and there are but a handful of papers flats to detached and semi-detached houses, with varying amounts examining the negative impact of noise on birds in open terrain of greenery in between (MIIP 2016). (Parris and Schneider 2009). Nonetheless, noise may be a more Open areas make up 37% of the study area, that is, arable land pressing problem in the farming landscape than in woodland areas (14%), spontaneous ruderal communities (13%), meadows and pas- because the former has thinner distribution of trees, which effec- tures (8%), and wetland vegetation (2%). Open habitats are situated tively absorb noise (Fang and Ling 2005; Martı ´nez-Sala et al. 2006). primarily on the city’s outskirts, although there are also some nearer Moreover, farmland habitats may suffer from noise produced by the city center, surrounded by densely built-up areas. Urban Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox061/4582952 by guest on 13 July 2018 Fro¨ hlich and Ciach Long-eared owls and noise pollution 3 Figure 1. Study area, main habitat types, and distribution of sample plots in Krako´ w (S Poland) used for assessing habitat variables that inﬂuence the distribution pattern of long-eared owls Asio otus in an urban environment. greenery (47%) consisting of native and non-indigenous species in after long-eared owl recordings (Mikkola 1976; Nilsson 1984; various spatial arrangements, forms of management, and stages of Romanowski 1988). This causes the birds to fly over a loudspeaker succession, includes gardens (14%), squares, road verges and play- or provokes loud alarm calls/calls of young birds [in the present grounds (10%), allotments and orchards (4%), parks and cemeteries work 72.4% (N ¼ 29) of all observations were visual confirmations (3%), and other green areas (15%). Forests and natural woodlands of birds turning up in the vicinity of the observer; 44.8% (N ¼ 29) constitute 11% of the city area: natural and semi-natural scrub of this visual records took place during playback of tawny owl (5%), deciduous and mixed forests (4%), and damp, riparian for- calls]. ests, and transformed tree stands (2%) (Dubiel and Szwagrzyk The standard version of the method adapted for owl surveys in natural habitats advises locating the playback (voice stimulation) 2008). Surface waters make up 1% of the study area, and the princi- pal waterway is the River Wisła (Vistula); 6 medium-sized tributa- point at a distance of 250–500 m from one another (Zuberogoitia ries and numerous smaller watercourses flow into the Wisła within and Campos 1998; Rodriguez et al. 2006). However, for urban envi- the city limits (MIIP 2016). The city’s roads and railway lines make ronments these recommendations have to be modified because of up 4% of its overall area (Dubiel and Szwagrzyk 2008). The quality the noise level, which may limit detectability; therefore, the distribu- of air in Krako ´ w is among the worst in Europe, containing high tion of playback points has to be sufficiently dense to ensure a high levels of suspended particulate matter, nitrogen dioxide and ben- level of bird detectability. Based on field experiments into the audi- bility of voice playbacks in urban conditions (Fro ¨ hlich and Ciach, zo(alpha)pyrene (WIOS 2014; AQIE 2015). 2017), a distance of 300 m between playback points was applied to ensure complete coverage of survey area (this meant that the maxi- Sample plot selection and fieldwork mum distance between the observer and a potential calling owl Seventy-nine sample plots were surveyed during the long-eared locality was 150 m). A systematic grid of 13 playback points was owl’s breeding periods in 2015 and 2016 (Figure 1). Initially, the assigned on each sample plot (grid of playback points was situated city was divided into 389 1 km 1 km squares from which the sam- obliquely in relation to plot border). The actual conditions on the ple plots were selected at random using Quantum GIS software ground (existing buildings, walls, fences, etc.) meant that the real (QGIS 2013). The grid of squares was based on a point with coordi- playback points had to be displaced to the nearest convenient site. nates 50 N and 20 E. According to recommendation provided by The playback work was done between the hours of midnight and Hardey et al. (2006), 2 surveys of territorial adults were carried out 04:00 CET (when road traffic intensity is the lowest) exclusively in during the breeding season: early (01–31.03) and late (01–30.04). A rain-free and windless weather. The plots were walked at an average period of at least 2 weeks had to elapse between consecutive surveys. speed of 2 km/h. A single survey of a study plot took around 4 h and Counting using the standard mapping technique was combined with all bird records and their activity were entered on the maps. In order playback dedicated to owl surveys (Bibby et al. 1992; Zuberogoitia to locate nesting sites, additional surveys in all territories recorded and Campos 1998). Two-minute recordings of courtship and con- on sample plots were conducted in May–June. Observers searched tact calls were played back through a 3-W loudspeaker at playback the area for potential nesting sites, looking for nests or young. The points. On completion of playback, 3 min was allowed to elapse to precise locations of nests were established using a GPS device. enable the birds to react (territorial calls). Because playback meth- ods in long-eared owl surveys are not very effective (Zuberogoitia and Campos 1998; Martı ´nez et al. 2002), recordings of calls of Environmental variables at the landscape-scale potentially co-occurring or competing owl species (little owl Athene The habitat parameters were defined within the boundaries of the noctua and tawny owl Strix aluco, respectively) were played back sample plots based on existing spatial database resources using Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox061/4582952 by guest on 13 July 2018 4 Current Zoology, 2017, Vol. 0, No. 0 Geographic Information System tools (Table 1). The total surface Local-scale approach (nest-site selection) To evaluate the impact of noise on nesting site selection by long- areas of the 4 habitat types of primary importance for long-eared owl were calculated using the polygon vector layer of the atlas of eared owls we compared noise intensities between nests and refer- the real vegetation of Krako ´w (UMK 2012), which is the effect of ence locations (NOISE_NEST). For these measurements we used detailed location data of nests found in 18 territories. Reference fieldwork done in 2006 (Dubiel and Szwagrzyk 2008). The atlas cat- locations were randomly selected within each of these territories and egorizes the city area into 58 habitat types, which have been allo- represented potentially suitable long-eared owl nesting sites. First, cated to one of the 4 primary habitat types. A separate polygon all patches of primary foraging habitat (grassland) up to 500 m from vector layer was created for each of these. Parks (PARK) included a given nest were identified in each territory (the maximum distance parks and cemeteries; woodlands (WOODLAND) included natural between nests and the nearest grassland patch was 485 m). Then, forests and woodlands, consisting of deciduous and coniferous tree around these potential foraging areas were delineated 500 m buffers species, mixed stands, and naturally growing shrubs; grassland in which the reference location was selected at random. Since long- (GRASSLAND) included meadows, pastures, uncultivated and fal- eared owls depend heavily on large nests constructed by other birds low land, rock vegetation, swards, heaths, and the communities of (mainly corvids), these random locations were shifted to the nearest trampled areas; finally, arable lands (ARABLE_LAND) included existing potentially suitable nest (habitats were surveyed in order to fields used for agricultural production. Each of these layers was find the nest nearest to the reference location). We considered large reduced by the layer containing the outlines of buildings, roads, and open nests of corvids to be suitable nesting sites for long-eared owls the layer of surface waters (WODGiK 2015), which yielded the (Mikkola 1983; Henrioux 2002; Lo ¨ vy and Riegert 2013). actual surface area of a given habitat type. Nocturnal noise intensity at occupied nest sites and randomly The Shannon–Wiener habitat diversity index (HABITAT_ selected nest sites (NOISE_NEST) was measured using a portable DIVERSITY) was calculated on the basis of the proportions of the sonometer accurate to 0.1 dB. To exclude the effect of variation in particular habitats within the boundaries of the sample plots. Ten traffic volume, measurements were made solely within a strictly types of habitat were used for this purpose: parks (parks and ceme- defined period of the day, that is, when road traffic was moderate teries), squares (squares, road verges, and playgrounds), gardens (21:00–00:00 h), excluding moments with extreme noise picks (e.g., (gardens, allotments, and orchards), managed greenery of commer- plane flight, ambulance siren). Noise measurements were made pair- cial properties, natural forest (deciduous and coniferous forest, wise, with a 15 min interval at most between the measurements at mixed woodland, and naturally growing shrubs), grassland (mead- the nest and the corresponding reference location. This variable was ows, pastures, uncultivated and fallow land, rock vegetation, expressed as the mean value of a single point in time measurements swards, heaths, and the communities of trampled areas), arable taken in 4 directions at right angles to each other. lands (UMK 2012), surface waters (rivers and bodies of water) Since noise level could be correlated with the presence of urban (WODGiK 2015), built-up areas (total area of buildings), and roads infrastructure such as roads and pedestrian routes, this effect needed (total area roads and railways) (WODGiK 2015). to be separated from the presence of such structures. Therefore, for The nocturnal noise emission parameter (NOISE) was deter- each of the occupied nests and randomly selected nest sites we meas- mined from the map of road noise emission (MIIP 2016). The mean ured the distance from the nearest (1) route used by pedestrians, that noise class weighted by its range area was calculated for every sam- is, pavements, pedestrian routes (PAVEMENT) and (2) road used by ple plot. Noise values during the hours of darkness were used for motor vehicles (ROAD). The measurements were made on the basis these calculations. The map shows the noise level expressed in 9 of orthophotographs from 2015 (GUGiK 2016) accurate to 1 m. classes of sound intensity (dB) (1 to <45, 2 to 45–50, 3 to 50.1–55, To control the proper selection of reference locations, we calcu- 4 to 55.1–60, 5 to 60.1–65, 6 to 65.1–70, 7 to 70.1–75, 8 to 75.1– lated the total area of grassland around the occupied nests and ran- 80, 9 to> 80). The map was compiled jointly by the Provincial domly selected nest sites (GRASSLAND_NEST), which is a major Environmental Conservation Inspectorate in Krako ´ w and the landscape-scale driver of the species’ occurrence, and the perimeter- Krako ´ w City Council based on the data collected in 2012 (MIIP to-area ratio as a measure of the shape and fragmentation of primary 2016). The map shows the data in the form of a vector layer. foraging habitat (GRASSLAND_GEOMETRY). These parameters Table 1. Habitat and environmental parameters measured at the landscape-scale (in sample plots situated in an urban environment) and the local-scale (at nest sites of long-eared owls Asio otus and randomly selected nests) Variable Code Unit Data source LANDSCAPE-SCALE Area of parks PARK ha UMK (2012) Area of woodland WOODLAND ha UMK (2012) Area of grassland GRASSLAND ha UMK (2012) Area of arable land ARABLE_LAND ha UMK (2012) Habitat diversity index HABITAT_DIVERSITY index UMK (2012); WODGiK (2015) Nocturnal noise emission NOISE class MIIP (2016) LOCAL-SCALE Total grassland area within 900 m buffer GRASSLAND_NEST ha UMK (2012) Grassland perimeter-to-area ratio within 900 m buffer GRASSLAND_GEOMETRY ratio UMK (2012) Distance between the nest and the nearest pedestrian route PAVEMENT m GUGiK (2016) Distance between the nest and the nearest road ROAD m GUGiK (2016) Nocturnal noise intensity NOISE_NEST dB Fieldwork Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox061/4582952 by guest on 13 July 2018 Fro¨ hlich and Ciach Long-eared owls and noise pollution 5 were calculated for a buffer of 900 m radius around the nests and The multivariate model revealed that area of grassland and noctur- random sites based on the average home-range radius of long-eared nal noise emission were significantly correlated with the probability of long-eared owls occurring (Table 3). A high such probability recorded owls breeding in urban areas (Lo ¨ vy and Riegert 2013). on a plot with a large area of grassland was reduced by nocturnal noise emissions (Figure 2). This model indicates that an increase in Data analysis noise intensity to 50–60 dB (3–4 noise class) lowered the probability Mean (6SD) and median (with quartiles) values of each environ- of long-eared owls occurring to 0.4–0.6 where large areas of grass- mental variable for plots (1) occupied and (2) unoccupied by long- land (80–100 ha) were available. Where the area of grassland was eared owls were calculated, the differences between the 2 groups small (<20 ha), even a small increase in noise intensity strongly being analyzed with Mann–Whitney’s U-test. To control for multi- reduced the likelihood of long-eared owls occurring (Figure 2). collinearity between environmental variables, a Spearman rank cor- Noise levels at the nest sites were significantly lower than at the relation matrix of all the variables was plotted (the correlation was random sites (Table 4). The distance to the nearest pedestrian route <0.5 for all variable pairs). Spatial autocorrelation of plots occupied or road did not differ significantly between nests and random sites. by long-eared owls was assessed with Moran’s tests (Rangel et al. The total area of grassland and the grassland perimeter-to-area ratio 2010). We detected no evidence of spatial autocorrelation (Moran’s did not differ significantly between nest sites and random sites, I was close to zero for all separation distances and semi-variance did although the P value of the latter variable was approaching the sig- not increase with lag distance). nificance level (Table 4). Factors determining the probability of long-eared owls occurring in an urban environment were investigated using a generalized linear model with binomial error distribution (Bolker et al. 2009). For this Discussion purpose we used environmental variables potentially explaining the We have shown that the probability of long-eared owls occurring in presence of long-eared owls and took the area of 4 primary habitat urban environments is positively correlated with the availability of types (PARK, WOODLAND, GRASSLAND, ARABLE_LAND), the their primary foraging habitat (grassland) but is negatively correlated habitat diversity index (HABITAT_DIVERSITY), and nocturnal with ambient noise intensity. Earlier papers hinted at the adverse noise emission (NOISE) to be explanatory variables. Then, a logistic effects of the road network and its associated traffic on the occurrence regression model (with species absence/presence as a dichotomous of owls (Hindmarch et al. 2012; Silva et al. 2012), but they did not dependent variable) was run for the variables, indicated in the linear analyze ambient noise levels in the context of owl occurrence. Since model as being of major importance for the probability of long- these birds use their hearing to locate prey (Mikkola 1983), they will eared owls occurring, in order to detect threshold values determin- need more time to do so when noise levels are high, and hunting effi- ing the species’ presence. ciency will be impaired (Delaney et al. 1999; Mason et al. 2016). Differences at the nest-site scale were analyzed using Student’s Where noise is short-lived, owls can break off hunting until it dies paired t-test. We regarded total area of grassland (GRASSLAND_ down (Delaney et al. 1999; Scobie et al. 2014). But where noise is NEST) and its perimeter-to-area ratio (GRASSLAND_GEOMETRY), distance from the nearest pedestrian route (PAVEMENT), distance Table 3. Generalized linear model predicting the probability of long- from the nearest road (ROAD) and noise intensity (NOISE_NEST) as eared owls Asio otus occurring in an urban environment (Krako´w, S explanatory variables. The statistical procedures were performed Poland; for parameters, see Table 1); signiﬁcant values in bold using Statistica 12 software (StatSoft Inc. 2014). Estimate SE Wald’s 95% CI P stat. Results INTERCEPT 2.394 2.254 1.13 6.812 to 2.024 0.288 Long-eared owls were recorded on 36.7% of the sample plots PARK 0.003 0.040 0.01 0.075 to 0.082 0.933 WOODLAND 0.005 0.024 0.04 0.042 to 0.051 0.848 (N ¼ 79). Occupied plots contained significantly more grassland and GRASSLAND 0.038 0.017 4.88 0.004 to 0.071 0.027 marginally more arable land (Table 2). The level of noise on the ARABLE_LAND 0.007 0.021 0.12 0.048 to 0.034 0.725 occupied plots was significantly lower than on the unoccupied plots HABITAT 1.820 1.131 2.59 0.398 to 4.037 0.108 (Table 2). Neither the area of parks and woodlands nor habitat _DIVERSITY diversity differed significantly between occupied and unoccupied NOISE 1.043 0.475 4.82 1.974 to 0.112 0.028 plots (Table 2). Table 2. Descriptive statistics and Mann–Whitney’s U-test results for landscape scale variables analyzed in the study plots occupied and unoccupied by long-eared owls Asio otus in an urban environment (Krako´ w, S Poland; for parameters, see Table 1); signiﬁcant values in bold Occupied (N ¼ 29) Unoccupied (N ¼ 50) Z P Variable Mean SD Median Quartiles range Mean SD Median Quartiles range PARK 3.9 6.1 1.4 0.0–5.9 5.1 8.3 2.4 0.1–6.4 0.96 0.337 WOODLAND 10.2 12.1 7.8 1.7–14.6 8.5 12.0 3.6 0.1–10.2 1.33 0.183 GRASSLAND 30.1 18.8 30.2 17.4–41.7 18.1 17.9 10.1 4.3–25.9 2.71 0.007 ARABLE_LAND 8.9 11.3 2.6 0.0–15.4 8.0 17.1 0.0 0.0–4.4 1.98 0.048 HABITAT_DIVERSITY 1.6 0.3 1.6 1.4–1.7 1.5 0.3 1.6 1.4–1.7 0.70 0.486 NOISE 1.6 0.6 1.3 1.1–2.2 2.1 0.7 2.0 1.6–2.6 2.84 0.004 Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox061/4582952 by guest on 13 July 2018 6 Current Zoology, 2017, Vol. 0, No. 0 Figure 2. Logistic regression model of the probability of long-eared owls Asio otus occurring, the area of grassland and nocturnal noise emission (see “Materials and Methods” section, Table 1) in an urban environment (Krako´ w, S Poland). Table 4. Descriptive statistics and paired Student’s t-test results for local-scale variables analyzed at the nest sites of long-eared owls Asio otus and randomly selected nest sites in an urban environment (Krako´ w, S Poland; for parameters, see Table 1); signiﬁcant values in bold Nest site (N ¼ 18) Random site (N ¼ 18) tP Variable Mean SD Mean SD GRASSLAND_NEST 101.9 65.2 100.2 96.5 0.09 0.930 GRASSLAND_GEOMETRY 0.04 0.03 0.05 0.04 1.98 0.064 PAVEMENT 12.50 15.34 12.72 15.35 0.04 0.965 ROAD 77.17 89.76 50.11 50.37 1.08 0.297 NOISE_NEST 42.6 3.2 46.7 4.8 5.19 0.000 continuous, such as that generated by road traffic, owls may avoid efficiency (Delaney et al. 1999; Mason et al. 2016) and/or communi- areas close to roads or compensate for a habitat’s poorer quality by cation (Lengagne and Slater 2002) in noisy areas. increasing its area, and that may imply a lower population density Our research shows that long-eared owl occurrence is strongly (Silva et al. 2012). positively correlated with the area of primary foraging habitat— This is the first paper to analyze simultaneously the effect of grassland. The original habitat of long-eared owls was forest steppes noise in conjunction with the very presence of urban infrastructure (Mikkola 1983; Barashkova et al. 2013), which in Europe have been (roads and pedestrian routes) on the distribution of owl nests. replaced by farming landscapes, which provide suitable hunting Earlier papers examining the influence of road networks on owls did habitats (Getz 1961; Mikkola 1983; Holt 1997; Henrioux 2000) not explain which road effects were key to limiting owl populations and nesting sites (Mikkola 1983; Henrioux 2002; Lo ¨ vy and Riegert (Hindmarch et al. 2012; Silva et al. 2012; Scobie et al. 2014). A 2013). The present work also indicates that grassland fragmentation dense road network in owl habitats has other negative impacts, such has little negative impact on nest site selection in long-eared owls. as increased roadkill (Erritzøe 1999; Trombulak and Frissell 2000; The species’ original habitat (forest steppes) is a mosaic of open Hager 2009), air pollution reducing individual condition (Esselink areas and woodland; consequently, the scattered/fragmented mead- et al. 1995; Trombulak and Frissell 2000; Berglund et al. 2011), ows may be a suitable hunting habitat (Mikkola 1983; Henrioux greater human pressure disturbing birds (Hathcock 2010; Cavalli 2000; Lo ¨ vy and Riegert 2013). However, the loss of habitat integrity et al. 2016), and habitat fragmentation, which may require owls to could be a serious threat to some predators. Several studies indicate occupy larger territories or to avoid highly fragmented habitats that woodland owl species are sensitive to habitat fragmentation (Redpath 1995; Trombulak and Frissell 2000; Grossman et al. (Galeotti 1994; Redpath 1995; Grossman et al. 2008). 2008). Our results strongly suggest that noise is a road effect shap- Although long-eared owls often nest in wooded areas (Holt ing the spatial distribution of owls, as this may reduce hunting 1997; Henrioux 2000, 2002), our results indicate that such habitats Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zox061/4582952 by guest on 13 July 2018 Fro¨ hlich and Ciach Long-eared owls and noise pollution 7 (woodlands and parks) are of minimal significance for the occur- and from their nests at heights well above the traffic. However, rence of this species. This may well be due to this owl’s flexibility long-eared owls do become potential roadkill victims mainly during when it comes to choosing a nest site (Mikkola 1983; Holt 1997; their zig-zag foraging flights low over the ground, when they are Rodriguez et al. 2006). Corvids—the prime suppliers of nests for hunting for rodents (Mikkola 1983) or during occasional feeding on long-eared owls (Mikkola 1983; Henrioux 2002; Lo ¨ vy and Riegert roadkills (Erritzoe et al. 2003; Mori et al. 2014). 2013)—nest fairly frequently in urban areas: they are present in Roads may potentially influence prey resources available to owls woodlands and parks, as well as in all types of urban greenery as they have either positive effects or no effect on small mammals (Jokima ¨ ki and Suhonen 2016). Wooded areas, however, are prob- abundance and distribution (Fahrig and Rytwinski 2009). ably an unsatisfactory hunting habitat for this species (Getz 1961; Moreover, traffic noise is not considered as a factor leading to Mikkola 1983; Henrioux 2000). Moreover, telemetric studies have avoidance of the roads by small mammals (McGregor and Bender shown that foraging birds avoid urban greenery like parks, which in 2008; Fahrig and Rytwinski 2009). Therefore, differences in food their structure (Lo ¨ vy and Riegert 2013)—thinly distributed trees resources in the vicinity of roads should not be considered as a driver and plenty of grassland—to some extent resemble forest steppes, the of owl occurrence. Moreover, considering relatively high dietary natural biotopes of long-eared owls (Mikkola 1983; Barashkova plasticity of long-eared owl, which may switch to alternative prey et al. 2013). It may be difficult for owls to hunt in parks because of (e.g., Birrer 2009; Mori and Bertolino 2015), the reduced possibility disturbance by humans and their dogs (Hathcock 2010; Cavalli of successful hunting rather than food availability should be respon- et al. 2016). sible for avoidance of areas with high noise intensity. The regression model we have used in our work indicates that In summary, this study has demonstrated that apart from habitat the negative impact of noise on the probability of occurrence of factors, long-eared owl distribution is associated with noise pollu- long-eared owls is mitigated if a large area of grassland is available. tion. Our results suggest that the probability of long-eared owls The owls can refrain from hunting during the noisiest times of the occurring at a site is determined mainly by the area of grassland, this night or else search for foraging areas more distant from sources of owl’s preferred foraging habitat, but also by nocturnal noise emis- noise (Delaney et al. 1999; Scobie et al. 2014; Mason et al. 2016). sions, which may reduce hunting efficiency. This study adds to the Since number and area of noise-free sites is relatively low and these growing body of evidence that noise has a negative impact on owl are scattered within the city owls are forced to locate territories only assemblages and highlights the importance of appropriate farmland in suitable habitat patches (Galeotti 1994). This may be an impor- management, that is, the maintenance of large grassland patches tant reason why owls have smaller territories in urban areas (Lo ¨vy and the suppression of noise within them. and Riegert 2013). On the other hand, if only small areas of hunting habitat are available, even a small increase in noise levels will drasti- Acknowledgements cally reduce the probability of these owls being present there. At sites affected by high noise levels, long-eared owls are presumably We wish to express our gratitude to Julia Barczyk, Mateusz Bernat, Mirosław unable to compensate for an impoverished basic habitat by turning Brzozowski, Katarzyna Bul, Anna Chomczynsk a, Mateusz Dutko, Mateusz to other habitats where prey is not so easy to come by. This result Go ´ rniak, Joanna Jaszczyk, Mirosław Kata, Gabriela Kuglin, Magdalena Kukla, Krzysztof Kus, Przemysław Lelito, Jacek Maslanka, Cezary Mozgawa, underscores the strongly adverse reaction of habitat specialist spe- Patrycja Pisarska, Karolina Ptak, Monika Ptak, Fabian Przepio ´ ra, Maciej cies to noise. Pyzik, Zuzanna Sidorowicz, Urszula Sienkow, Anna Sitarz, Katarzyna Our findings suggest that noise intensity may also reduce the Staszynska, Daria Stra˛czynska, Teresa Stra ˛czynsk a, Agata Uliszak, Paweł number of potential nesting sites of long-eared owls. Begging calls, Wieczorek, Jakub Wyka, Błazej _ Zamojski, and Anna Zie ˛ cik for their help frequent in young long-eared owls, stimulate the parent birds to with the ﬁeldwork. We want to thank anonymous reviewers for their con- hunt and enable them to locate their scattered fledglings (Mikkola structive comments on the manuscript. Financial support for this study was 1983). High noise intensities may reduce the effectiveness of com- provided by the Polish Ministry of Science and Higher Education by statutory munication between family members and in theory, therefore, may grant (DS 3404). tend to weaken family bonds and lower reproductive output. Continuous noise around the nest might hinder young birds from Conflict of interest acquiring the ability to use their hearing when hunting (Mikkola 1983; Mason et al. 2016). In addition, noise can interfere with the The authors declare that they have no conﬂict of interest. vocalizations of adult birds when they are establishing territories; in long-eared owls this may be particularly serious given that their ter- References ritorial calls are relatively quiet (Mikkola 1983; Zuberogoitia and Campos 1998). AQIE (Air Quality in Europe), 2015. Air Quality Now—Comparing Although disturbance by people can elicit adverse reactions in Cities—Current Situation. 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