Introduction

Urban development is increasing across the globe, with major impacts on animal life-histories [1,2,3]. Ecological effects of urbanization have long been recognized, e.g. disturbance regimes, changes in light conditions, habitat distribution, predation pressure, and species composition [4,5,6,7]. In addition, urban environments support more anthropogenic food resources, and the climate of urban areas differs from that of nearby rural environments [6,8,9] due to the so-called urban heat island phenomenon [9].

Urban environments provide more stable and predictable food supplies, higher temperatures and reduced temperature variability [2,10,11]. Food may be more readily available in the proximity of humans during winter, thereby facilitating urbanization of wildlife, at least in sedentary and partially migratory species [12]. In consequence, survival in cities may be easier than in other habitats [13,14,15,16]. On the other hand, non-natural habitats, non-natural food resources, traffic related mortality and disease risk, may negatively impact birds living in urban environments [9,11]. The structure of habitats may be complex in some urban areas, which can be especially important in winter when birds may need to forage in different locations to meet energetic demands and find roosting sites [4,6,13]. However, to date, the majority of studies on the effect of urbanization, and comparisons of rural and urban avifauna, have only been carried out in the breeding season, and have focussed on local scales [3,4,17,18]. Therefore, knowledge about the large scale distribution and diversity of birds in winter appears crucial for understanding the effects of faster urbanization rates in recent decades [3,4].

The objectives of this study were to identify differences in bird communities between rural and urban areas in winter. Differences in species richness and population density between urban and nearby rural environments provide an estimate of the extent to which different species have adapted to the urban environment [17]. Obviously, some factors other than urbanization level (e.g. human disturbance, microclimate, difference in dispersal, predation pressure) may influence bird density, and may affect different ecological groups and particular species in different ways [19]. To reduce potential local effects we decided to carry out our study at a national scale, with study sites located throughout Poland. We paid special attention to the location and characteristics of study squares (e.g. the cover of different microhabitats) which might influence bird species richness and density during winter. Our focus on a large geographical area covers a wide range of winter environments, because in Poland there is a marked increase in winter severity from the north to the south (from the Baltic Sea to the Carpathian Mountains), and even more so from the west to the east (from Atlantic influence to a more continental climate; [20]). The severity of winter has been suggested as the main factor affecting winter bird communities in temperate zones [13,16,21].

Therefore, mainly due to the availability of additional food and thus improved survival, we predict higher species richness and population densities of birds in urban areas during winter than in the surrounding rural areas. Although this idea is simple it is surprising that, to the best of our knowledge, this has not been investigated in winter at so large a geographical scale.

Material and Methods

Study areas

Using the same methods, we recorded wintering birds in 26 towns and cities (hereafter called urban areas), each paired with a nearby rural area, across Poland (Fig 1; for more details see S1 Table). The study areas were chosen to cover all of Poland and span the entire Polish winter climate. Within each urban and rural area there were three square plots (25 ha) where birds were surveyed. Thus, the total number of squares was 156. The distance between paired rural and urban squares was 1–12 km. The benefit of this approach is that paired rural and urban study squares were characterised, as far as is practical, by similar climatic conditions. Squares were classified as urban or rural based on two criteria which both had to be met: (1) local authority designated as urban or rural (land management and policy in cities differs from that in rural districts); (2) squares in both environments had to include built up areas. For example, squares consisting only of arable land in urban local authorities were not considered. On average, each observer surveyed 1.81 ± 0.17 SE paired areas (range: 1–3), and all paired squares (urban-rural) were always visited by the same observer.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 1. Location of the study areas.

Location of the 26 paired areas used to study winter differences in birds between rural and urban environments in Poland.

https://doi.org/10.1371/journal.pone.0130299.g001

Field methods.

Birds were counted twice in the winter of 2012/2013: firstly in December 2012 and repeated in January 2013. At this time of year, only truly wintering birds occur in Poland. As stated above, counts were carried out within three 0.25 km2 squares (500 × 500 m) in each of the urban areas, and in three 0.25 km2 squares within the neighbouring rural area, with surveys paired in time as closely as possible. The order of recording rural and urban squares was chosen randomly by observers. Birds were surveyed during favourable weather conditions (no snowfall or rain, wind below 4 m s-1) between 8:00 and 13:00. Single observers, with at least 10 years’ experience in counting wintering birds, walked in a zig-zag pattern in order to cover the entire square visually and to note bird vocalizations [13]. The duration of the survey was ca 2 hours for each square. Only birds exhibiting resting or foraging behaviour were included in the analysis. Hence, for example, high flying gulls, geese and corvids were ignored. The survey time ensured that birds were mainly foraging, and flights were rare. In some species, flights occurred between foraging and roosting places, however these occur mainly in the early morning and late afternoon and are thus outside the period devoted to fieldwork.

We measured the following environmental variables potentially affecting bird species richness (number of species) and abundance (number of individuals recorded in study squares):

1. type of environment: urban or rural;
2. cover (%) within the square of: trees, amenity grass, arable, fallow, meadows, buildings and roads, water;
3. number of bird feeders;
4. human population size in the urban area;
5. geographical latitude and longitude.

Data handling and analysis

Cover variables were calculated with ImageJ software from detailed maps and aerial photos of the studied squares or directly in the field using a GPS. We used high resolution images, freely available from the National Data Base Geoportal (http://maps.geoportal.gov.pl/webclient/). Basic characteristics of the investigated urban areas are summarised in S1 Table, and their location within Poland is presented in Fig 1. For each square the total number (richness) of species, total number of birds (abundance) and Gini-Simpson Index of Diversity [22]) were calculated. The Gini-Simpson index gives the probability that two randomly chosen birds (individuals) from a community are not the same species. Thus, the higher values of this index the more diverse the community is. The index was moderately correlated with species richness (Spearman rs = 0.455, P<0.001, n = 312) but not with number of birds (Spearman rs = -0.072, P = 0.206, n = 312) which justified its use in the analyses.

Although detectability of species and of individuals in winter is relatively high [23,24,25], we also calculated the number of species and number of birds corrected for imperfect detection. The problem of detectability of species in our analyses was solved by using probabilistic methods for correcting species richness known as the Chao1 bias-corrected estimator of species richness [26,27]. Chao’s estimator is commonly used in ecological science and it was derived from the observation that rare species are undetectable because they are represented mostly by single individuals (singletons) or two individuals (doubletons). The formal equation for this index is: E(S) = Sobs+ n1(n1-1)/ 2(n2+1), where: E(S) is the estimated number of species, Sobs is the observed number of species, n1 is the number of singleton species, and n2 is the number of doubleton species [26]. A simulation study showed the superior performance (lower bias, higher precision and accuracy) of this index over many parametric methods (e.g. rarefaction curves) [28].

The corrected number of species in a square was calculated for each survey month separately in Spade software [29]. Then, to estimate the detectability of species we subtracted the recorded number of species from the estimated number of species for each square. This estimate indicates how many species remained undetected in a square and thus may be used to compare differences in species detectability between environments, months, and observers. The difference between observed and estimated numbers of species was analysed with generalized linear mixed models (see below).

We used two methods, proposed by Royle [30] and by Kendall et al. [31] and implemented in the Presence 6.1. software [32], to correct for imperfect detection of individuals. The Royle estimator [30] (contrary to many other methods based on presence-absence data and devoted to calculation of detectability) directly takes the number of individuals into account. However, the method assumes the population is closed, which might not be entirely true in winter. Violation of this assumption causes the estimated detection probability to be always lower than in reality and leads to excessive estimation of abundance, although this should still correctly represent differences between environments. In order to validate the Royle estimator, the unbiased estimator of detectability proposed by Kendall [31] was used. This method relaxes the closure assumption within a season by permitting staggered entry and exit times for the species of interest at each site (square). However, this method requires at least three surveys to estimate confidence intervals but we were only able to perform two surveys (December and January). Thus, we calculated the correlation coefficient between Royle and Kendall estimators which was statistically significant (S1 Fig). Moreover, we validated the Royle estimator by plotting species-estimates of detectability against their body sizes (= body length). Detectability is usually positively related to body size [33] and we found that this was also the case in our study (S2 Fig). Thus, we used the Royle estimator to correct abundances for imperfect detection in our data.

The detection probabilities of species and individuals were calculated for each environment and survey (December and January) separately. We conducted and present two sets of analyses—with and without corrections for detectability. Although corrected and uncorrected data were significantly correlated (S3 and S4 Figs) the results of statistical analyses were different. Therefore, we present only results based on corrected data.

Before formal testing of the effects of environmental variables on species richness, abundance and diversity index we had to perform data reduction. Since there were seven habitat composition variables which were correlated with each other (S2 Table), we used Principal Components Analysis (PCA) to calculate a reduced number of independent variables. We included longitude and latitude as supplementary variables in PCA to obtain ordination scores of environmental variables which were not correlated with geographical coordinates. The first two principal components explained 54% of the variability in habitat cover variables (Table 1, S5 Fig). The first principal component (PCA1, eigenvalue = 1.952) was associated with a gradient from built up areas to open agricultural habitats. The second principal component (PCA2, eigenvalue = 1.190) described a gradient from semi-natural grasslands to intensively managed amenity grasses (Table 1, S5 Fig).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 1. Results of the Principal Components Analysis (PCA) performed on the correlation matrix of the environmental variables describing cover of different habitat types.

https://doi.org/10.1371/journal.pone.0130299.t001

A generalized linear mixed model (GLMM) with Gaussian error and identity link function was carried out on the summary variables (bird species richness, total number of birds, Gini-Simpson Index of Diversity) from all 156 surveyed squares and both months. Two variables: environment type (urban/rural) and month (December/January) and their interaction were fixed categorical factors. Number of bird feeders, human population size, longitude, latitude, PCA1 and PCA2 scores (described above) were covariates. Interaction terms between environment type and covariates were also included in GLMMs to test for a different response of dependent variables to covariates in the two environments.

Observer identity, urban area pairing and square identity were random blocking factors in the GLMMs. Square identity was nested in urban-rural area pair. We used Akaike Information Criterion (AICc) to select the best reduced model and we present results for models which had values of ΔAICc (the difference between the models with lowest AICc and the given model) below 2 [34]. We used model averaging to get estimates of the function slopes (using a 99% confidence set).

Similar GLMMs were built for 10 of the most abundant species to test if environment type, geographical location, PCA1 and PCA2 scores, number of bird feeders and human population size affected their abundance. Interaction terms between the environment type and covariates were also included. Random factors were the same as described above. When analysing these species we encountered right-skewed distributions which are typical for count data with zeros. Thus, for species with excess zero counts we fitted a GLMM with a negative binomial error and logarithmic link function. The choice of Gaussian or negative binomial error variance was determined by examining AICc scores and the model with the lowest AICc was chosen [34].

Moreover, we also built GLMMs for all individual species with reasonable sample size, testing differences in abundance between the two environments and between the two months. The effect of covariates was omitted from this analysis. Bird species with low counts (< 10 individuals) were not individually analysed. The choice of Gaussian or negative binomial error variance was determined by examining AICc scores as described above.

Unless otherwise stated all GLMMs and correlation analyses were carried out using the SPSS 21 package [35]. An ordination of the mean counts for the 26 urban and 26 rural areas (i.e. averaged across three squares and two months) was undertaken in the CANOCO package [36]. We used a Detrended Correspondence Analysis (DCA), a multivariate statistical technique widely used by ecologists, to elucidate the relationships between biological assemblages of species and their environment [36, 37]. We used DCA because most ordination methods suffer from two major problems: the arch effect (caused by unimodal species response curves) and compression of the ends of the environmental gradient. Because of the first problem, the second ordination axis is an artefact and cannot be interpreted. The second problem is that the spacing of species (or samples) along the first axis is not necessarily related to the amount of change along the primary gradient. DCA overcomes these problems by dividing the first axis into segments, and rescales each segment to have mean value of zero on the second axis—this effectively compresses the curve to become flat. It also rescales the axis so that the ends are no longer compressed relative to the middle [37]. Species data were log x+1 transformed and downweighted for rare species in DCA. Downweighting was applied because ordination analyses are sensitive to rare species which influence analytical results to a much greater extent than would be predicted by their abundance [36]. The downweighting procedure replaces the abundance values of rare species in the data set, aij, with new values, aij’. A species is defined as being rare if its frequency in the data set, f1, is lower than fi,max/5, where fi,max is the maximum frequency of any species. For the rare species, the formula [38] for downweighted abundance is: aij’ = aij × [fi/(fi,max/5)]. DCA was carried out with the above mentioned twelve environmental variables (seven cover variables, environment type, bird feeders, human population size, latitude and longitude) used as supplementary variables, i.e. not influencing the original ordination [36].

In our analyses we performed multiple tests. However, we did not apply corrections for multiple testing. There are two basic reasons for this decision. First, we tested hypotheses on different species and obviously each species has a unique life-history and different biology. Therefore, there is no reason to assume that species responses would behave as random statistical processes. Secondly, the number of species tested was high, thus if the correction for multiple tests had been applied then one would not have been able to effectively test any hypothesis (for example with 50 species tested the Bonferroni corrected critical p value is 0.001 which means that tests are unfeasible and interpretation impossible). This problem has been discussed in many papers and the pitfalls of using such corrections are discussed in a paper by Garcia [39]. However, we provide information in the text and tables about corrected critical p-values for each set of tested hypotheses after using the Benjamini-Hochberg method for false discovery rates in multiple statistical tests [40].

Means are given with standard errors (SE).

Results

A total of 72 bird species and 89,710 individual birds were recorded in this study. Across all sites, nine species were only recorded as singletons, at the other extreme there were 18,864 records of House Sparrow Passer domesticus. The best model explaining species richness contained the effect of month (Tables 2 and 3). Mean species richness was lower in December than in January (Table 3). The number of species did not differ between the two environments (Fig 2). Mean species richness in the urban environment was 14.18±0.86 in December and 15.86±0.86 in January. In the rural environment species richness was 14.25±0.86 in December and 16.50±0.86 in January. The best model explaining abundance of birds included two variables: environment type and month. Abundance in urban areas was higher than in rural areas (Tables 2 and 3, Fig 2) and was higher in January than December (Tables 2 and 3, Fig 3). Mean abundance in the urban environment was 625.9±1.2 in December and 774.3±1.2 in January. In the rural environment mean abundance was 307.1±1.2 in December and 466.4±1.2 in January. There were two best models explaining species diversity (Table 2). Species diversity in urban areas was higher than in rural areas (Tables 2 and 3, Fig 2). Mean species diversity index in the urban environment was 0.76±0.02 both in December and January. In rural environment the index was 0.73±0.02 in December and 0.74±0.02 in January. Species diversity also increased with PCA1 (increasing proportion of open agricultural areas) but this effect was stronger in rural areas (Tables 2 and 3, significant interaction between environment type and PCA1). We found that the observer effect was non-significant in all analyses (S3 Table). City identity did not contribute in a significant way to variation in bird abundance, richness or diversity index (S3 Table). Among random effects only square identity was always significant which is trivial since squares differed in habitat composition from each other. Full sets of tested GLMMs for species richness, abundance and species diversity index are presented in S4, S5 and S6 Tables, respectively.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 2. Best generalized linear mixed models (GLMM) describing species richness, abundance and species diversity of birds in rural and urban areas during winter.

https://doi.org/10.1371/journal.pone.0130299.t002

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 3. Averaged estimates of the function slopes of variables present in the most parsimonious GLMMs describing the corrected species richness, abundance and species diversity of birds in rural and urban areas during winter.

https://doi.org/10.1371/journal.pone.0130299.t003

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 2. The effect of environment type on wintering birds.

The effect of environment type on mean (a) species richness, (b) abundance and (c) species diversity of wintering birds. Whiskers are 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0130299.g002

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 3. The DCA with environmental variables carried out on bird count data.

The DCA with supplementary environmental variables carried out on the bird count data from Polish urban areas and paired rural areas. A. Species codes (Table 5) are shown for the 48 most common species; the remaining codes omitted and some jittering of codes has been done for clarity, B. The ordination of locations (grey symbol = rural, solid black symbol = urban), C. The ordination of supplementary environmental variables.

https://doi.org/10.1371/journal.pone.0130299.g003

The mean difference between the uncorrected and corrected number of species was higher in rural areas (mean difference 2.213±0.253) than in urban areas (mean difference 1.395±0.253, GLMM F1,286 = 6.79, P = 0.010), indicating that species detectability was probably slightly lower in rural environments after accounting for habitat, month and all random effects. The estimates of detectability for individual species are presented in S7 Table.

The first two axes of the DCA explained 25.5% and 11.0% respectively (sum 36.5%) of the variance in the bird count data (Fig 3). The supplementary environmental variables explained 51.2% of the variance in the species-environment relationship. Attributes associated with rural areas were grouped to the right of axis 1 and those of urban areas to the left of this axis. Axis 2 appears to be a geographical (mainly longitudinal) gradient. There was almost a complete separation of rural and urban bird communities on axis 1 (Fig 3).

GLMMs were built for 49 individual species with abundances greater than 10 individuals (Table 4). These models revealed 27 species had a statistically significant preference; 17 to rural areas and 10 to urban areas (Table 4). For example 100% of Common gulls Larus canus were recorded in urban areas whilst 95% of 593 Yellowhammers Emberiza citrinella were recorded in rural areas (Table 4). The most widespread species was Great Tit Parus major, absent from just two of the 312 square/month combinations.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 4. The percentage of the 156 square/month combinations for both rural (R) and urban (U) areas in which each species (at least one individual) was recorded, the total number of individuals (n) recorded, the mean number for rural and urban areas, the percentage of records recorded from urban areas (%U), whether the model was based on negative binomial (N) or Gaussian (G) distribution, and the significance level of rural/urban, month and interaction terms from GLMM (month means not shown to save space).

https://doi.org/10.1371/journal.pone.0130299.t004

GLMMs testing the effect of environmental variables on the abundance of the ten most numerous species are shown in Tables 5 and 6. Results were mixed, but negative effects of longitude and land use intensity (PCA1) were apparent for several species. Interestingly, in the best models there were statistically significant interactions between environment type and geographical variables (Tables 5 and 6). The best model explaining abundance of House Sparrow contained the effects of month, human population size and latitude (Table 5). The abundance of this species decreased with latitude but increased with human population size (Table 6). Abundance of this species was also lower in December than in January (Table 6).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 5. Best generalized linear mixed models (GLMM) describing the abundance of the 10 most numerous bird species during the winter.

https://doi.org/10.1371/journal.pone.0130299.t005

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 6. Averaged estimates of the function slopes of variables present in the most parsimonious GLMMs describing the corrected abundance of the 10 most numerous recorded bird species.

https://doi.org/10.1371/journal.pone.0130299.t006

The best model explaining the abundance of Feral Pigeon Columba livia contained environment type, human population size and the interaction between these variables (Table 5). The abundance of this species increased with human population size and was higher in the urban environment (Table 6). The interaction term also indicated that the abundance of Feral Pigeons was more strongly correlated with human population size in the rural environment (Table 6).

The best model explaining the abundance of Rook Corvus frugilegus contained environment type, human population size, geographical longitude and PCA1 score (Table 5). Moreover, these models contained interaction terms between environment type and both longitude and PCA1 score (Table 5). The abundance of this species was higher in the urban environment, and also increased with human population size and decreased with longitude (Table 6). The negative impact of longitude on abundance was greater in the rural environment (Table 6). The effect of PCA1 (the increasing cover of open agricultural areas) on Rook abundance was negative in the rural, but positive in the urban, environment (Table 6).

The best models describing the abundance of Great Tit contained environment type and PCA1 (Table 5). The abundance of this species was higher in urban environments and it increased with PCA1 scores (the increasing cover of agricultural habitats) (Table 6).

The best model explaining the abundance of Jackdaw Corvus monedula contained environment type, human population size and the interaction between environment type and longitude (Table 5). The abundance of this species increased with human population size and was higher in the urban environment (Table 6). The abundance of Jackdaws decreased with longitude in rural, but not in urban, environments (Table 6).

The best models explaining the abundance of Greenfinch Chloris chloris contained month, feeder numbers, and the interaction between environment type and both number of bird feeders and human population size (Table 5). The abundance of Greenfinch was lower in December than in January and increased with the number of bird feeders (Table 6). The effect of bird feeder number on abundance was modified by the environment type; bird feeders had a greater positive effect on the number of Greenfinches in urban than in rural environments (Table 6). Similarly, human population size positively affected the abundance of this species but the relationship was stronger in rural environments (Table 6).

The best model describing the abundance of Eurasian Tree Sparrow only contained the effect of environment (Table 5). This species was more abundant in rural environments (Table 6).

The best models explaining the abundance of Eurasian Collared Dove Streptopelia decaocto contained environment type, number of bird feeders, PCA1, PCA2, human population size and the interaction between environment type and PCA1 (Table 5). The abundance of this species was higher in rural environments (Table 6). Abundance was positively correlated with the number of bird feeders and PCA2 scores (increasing cover of amenity grasses) but negatively with human population size and PCA1 scores (increasing cover of open agricultural habitats, Table 6). However, the effect of PCA1 was modified by the environment type; PCA1 had a positive effect on abundance in the urban, but not in the rural, environment (Table 6).

The best models explaining the abundance of Bohemian Waxwing Bombycilla garrulus contained environment type, longitude, PCA1 scores, PCA2 scores and the interaction between environment type and longitude (Table 5). The abundance of this species was higher in the urban environment and it increased with longitude but decreased with PCA1 (increasing cover of open agricultural habitats) and PCA2 (increasing cover of amenity grasses) (Table 6). However, the effect of longitude was modified by the environment type; the abundance increased with longitude in the rural, but not in the urban, environment (Table 6).

The best models describing the abundance of Magpie Pica pica contained environment type, human population size, PCA1 scores and two interaction terms: between environment type and both PCA1 and human population size (Table 5). The number of Magpies was higher in urban environments and increased with human population size but decreased with PCA1 scores (increasing cover of agricultural habitats) (Table 6). The effects of human population size and PCA1 were different in the two types of the environment. The positive effect of human population size was greater in urban environments and PCA1 had a stronger negative impact in urban environments (Table 6).

Discussion

Our study shows differences between rural and urban areas in the number of individuals, the whole assemblage, as well as in the densities of particular species in winter. However, our results do not seem as well supported as those described in many studies during the breeding season [16,17,41]. Indeed, among our summary variables, we only detected statistical significance for the number of individuals, which, on average, was more than twice as high in urban than in rural areas. Species diversity was also higher in urban areas. However, for individual species there were strong preferences between rural and urban environments in winter, which is probably not related to urbanization per se, but to food availability, microhabitat preferences, and direct and indirect human activity [2,13,14].

Recently, many studies have indicated that rural and urban populations of birds differ from one another [8]). The main finding of our study, i.e. differences in the density of particular species, also supports this view. However, the factors affecting wintering bird communities were related not only to the main environment difference (urban vs. rural), but also to other variables. For example, our study clearly revealed that longitude, human population size and bird feeders have an important impact on wintering birds. The importance of these variables for birds, mostly during the breeding season, has been already identified (e.g. [2,8,9]). Areas located in western Poland had a significantly higher abundance of some species than those in the eastern part of the country. This is not surprising, because in western Poland the winter climate is characterized by higher temperature and lower snow cover [20]. Both these factors generally positively affect wintering bird species [12,16,42]. However, our results indicated that longitude had a stronger effect on some bird species in rural areas. For example, for Rooks and Jackdaws longitude negatively affected abundance, however the effect was greater in rural areas indicating that urban areas buffer against harsh winter climate mediated by geographical location. Thus, it is possible that urban environments located within colder areas are an especially good wintering habitat for birds and, consequently, urbanization processes may be especially rapid in towns and cities located in cold climates. The effect of human population size also positively influenced some birds, such as Eurasian Greenfinch and Magpie. A statistically significant interaction between this variable and environment type indicated that the positive effect of human population size was stronger in urban areas, suggesting dependence of bird populations on human-related resources in urban environment. The dependence of some species on human resources was also detected as a positive relationship between the number of bird feeders and bird abundance, e.g. in Eurasian Greenfinch or Eurasian Collared Dove.

The significant difference between early (December) and late (January) winter may be important in understanding changes in wintering bird communities. These changes are probably related to large geographical bird movements due to winter severity [23,43], because differences between environments are similar, as indicated by the non-significance of the interaction term in analyses. Results also indicate that birds in midwinter move closer to humans, both in cities and villages, because access to food is easier there, especially during snowy days [23,41,42].

Our results indicate that habitat variables are also important for the diversity of wintering species. To the best of our knowledge there are only a few large scale studies of birds wintering in rural and urban environments [44,45,46,47]. Studies in Finland [13,14] showed that residential areas had higher densities of birds during winter than areas occupied by other types of development, roads and open grassland, but generally those authors underlined the importance of cities to wintering birds under the harsh winter conditions in Finland. On the other hand, a negative effect of urban areas on the density and number of bird species in adjacent rural areas has been shown [48]. One potential explanation is that birds used urban areas for wintering and therefore avoided rural habitats in winter. For particular species, other traits of the study squares were also important, such as amenity grass (with a positive effect for some species), mainly used as a foraging place for birds, especially in bigger agglomerations [23]. Interestingly, our study suggests that urban areas may be important for many bird groups including seed-eating passerines and insectivores. Considering the strong decline of many common farmland birds in Europe, including sedentary species [49], it is of interest to note that not only rural habitats, including villages and small farms, but also urban areas may be one of the key habitats providing refuge and food resources, and, eventually may improve the winter survival of some farmland species [18,50].

As in every large-scale study our methodology has some issues that must be taken into account when interpreting results. Time spent on bird counting was long and pseudoreplication might have played a role. However, birds were noted on maps and carefully watched to avoid counting the same individuals more than once. Moreover, the generally low number of species allowed individual birds to be followed. On the other hand, if the duration of observation had been shorter, then problems in species detectability would have been more serious.

We found that detectability corrections played a role in analyses and interpretation of findings. The analysis of differences between uncorrected and corrected numbers of species revealed that observers usually detected, on average, one or two more species in the urban environment than in the rural one. It must also be stressed that for some species we were not able to calculate detectability due to their low numbers. This, however, should not affect inter-environment and inter-survey (December-January) analyses since these rarer species did not contribute much to total abundance.

In conclusion, we have shown that winter density and species diversity of birds differs between urban and rural areas, and that preferences for the two types of environment exist. Obviously those preferences appear to be highly species-specific, but in both environments birds are responding to environmental variables, such as habitat cover and geographic location (longitude) and human related food resources.

Supporting Information

Acknowledgments

Z. Kwieciński, M. Wezyk, M. Kazmierczak, R. Sandecki & Z. Koludzki helped with logistics during fieldwork.