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

<p>The Akaike information criterion score (AICc), the -2log, difference between the given model and the most parsimonious model (Δ) and the Akaike weight (<i>w</i>) are listed. Explanation of variable codes: Feeders—number of bird feeders, CitySize—human population size in the city, Month—month of survey (December vs. January), Environment—type of the environment (urban vs. rural), Longitude—geographical longitude, PCA1—the first principal component of environmental variables describing the gradient of increasing proportion of open agricultural habitats, PCA2—the second principal component of environmental variables describing gradient from semi-natural grasslands to intensively managed amenity grasses.</p><p>Best generalized linear mixed models (GLMM) describing the abundance of the 10 most numerous bird species during the winter.</p

Location of the study areas.

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

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

<p>The DCA with supplementary environmental variables carried out on the bird count data from Polish urban areas and paired rural areas. A. Species codes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130299#pone.0130299.t005" target="_blank">Table 5</a>) 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.</p

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.

<p>Standard errors (SE) and 95% confidence limits (CL) are also presented. Tests of significance of variables are given in the final two columns. Explanation of variable codes: <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130299#pone.0130299.t005" target="_blank">Table 5</a>.</p><p>* A reference variable</p><p>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.</p

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).

<p>Benjamini-Hochberg corrected significance level (BH) is given in brackets under a header of the columns for each hypothesis. Codes are used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130299#pone.0130299.g002" target="_blank">Fig 2A</a>. Where rural/urban comparisons were significantly different the higher mean is in bold. Species in alphabetical order of Latin names.</p><p>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).</p

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

<p>After varimax raw rotation, highly significant loading factors of the variables on the PCA axes are emboldened. Comm. (%) is the percentage of the total communality of each variable extracted by the first two PCA axes.</p><p>Results of the Principal Components Analysis (PCA) performed on the correlation matrix of the environmental variables describing cover of different habitat types.</p

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

<p>The Akaike information criterion score (AICc), the -2log, the difference between the given model and the most parsimonious model (Δ) and the Akaike weight (<i>w</i>) are listed. Explanation of variable codes: Month—month of survey (December vs. January), Environment—environment type (rural vs. urban), PCA1—the first principal component of environmental variables describing the gradient of increasing proportion of open agricultural habitats.</p><p>Best generalized linear mixed models (GLMM) describing species richness, abundance and species diversity of birds in rural and urban areas during winter.</p

The effect of environment type on wintering birds.

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

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.

<p>Standard errors (SE) and 95% confidence limits (CL) are also presented. Tests of significance of variables are given in the final two columns.</p><p>* A reference variable</p><p>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.</p