1 Effects of urbanization on taxonomic, functional and phylogenetic avian diversity in 2 Europe 3 4 Running head: Avian diversity in European cities 5 6 Word count: 4951 1 7 Abstract 8 Europe is an urbanized continent characterized by a long history of human-wildlife 9 interactions. This study aimed to assess the effects of specific elements of urbanization 10 and urban pollution on complementary avian diversity metrics, to provide new insights on 11 the conservation of urban birds. 12 Our study recorded 133 bird species in 1624 point counts uniformly distributed in 13 seventeen different European cities. Our results thus covered a large spatial scale, 14 confirming both effects of geographical and local attributes of the cities on avian diversity. 15 However, we found contrasting effects for the different diversity components analyzed. 16 Overall, taxonomic diversity (bird species richness), phylogenetic diversity and relatedness 17 were significantly and negatively associated with latitude, while functional dispersion of 18 communities showed no association whatsoever. At the local level (within the city), we 19 found that urban greenery (grass, bush, and trees) is positively correlated with the number 20 of breeding bird species, while the building cover showed a detrimental effect. Functional 21 dispersion was the less affected diversity metric, while grass and trees and water (rivers or 22 urban streams) positively affected the phylogenetic diversity of avian communities. Finally, 23 the phylogenetic relatedness of species increased with all the main indicators of and 24 25 was only mitigated by the presence of bushes. 26 We argue that maintaining adequate levels of avian diversity within the urban settlements 27 can help to increase the potential resilience of urban ecosystems exposed to the stress 28 provoked by rapid and continuous changes. We listed some characteristics of the cities 29 providing positive and negative effects on each facet of urban avian diversity. 2 30 Keywords: biotic homogenization; bird diversity; conservation; functional diversity; light 31 pollution; noise pollution; urban green 32 3 33 INTRODUCTION 34 The development of human settlements and global urbanization increase habitat loss and 35 fragmentation (Schmiegelow and Mönkkönen, 2002; Sklenicka, 2016; Spellerberg, 1998), 36 negatively affecting the biodiversity at different levels of organization (Crooks et al., 2004; 37 Morelli et al., 2017; Wilson et al., 2016). The urban areas are among the fastest growing 38 land-use types across the globe (McDonald, 2008). It is expected that the number of 39 people living in cities and peri-urban areas will continue increasing to reach approximately 40 68% of the world population in 2050 (United Nations, 2019). Additionally, these types of 41 areas are characterized by very dynamic socio-ecological systems (Folke et al., 2002), 42 constituting an important challenge for ecological communities surrounding or even 43 occupying such areas. 44 The expansion of urban areas alters both biotic and abiotic ecosystem properties, thereby 45 leading to biodiversity loss around the world Federico Morelli. Nonetheless, biodiversity 46 can be partitioned into many facets or components, each one describing a different 47 characteristic of the species assemblages (Meynard et al., 2011; Verde Arregoitia et al., 48 2013). Recently, studies highlighted the importance of considering different facets of 49 communities for better characterizing their conservation status, especially in urban areas 50 (Devictor et al., 2010; Lees and Moura, 2017; Morelli et al., 2017). Taxonomic diversity, 51 simply measured as the number of species in a given assemblage (Magurran, 2004), is 52 often used to describe the species assemblages. On the other hand, functional diversity is 53 an essential aspect linking species assemblage with ecosystem functioning and 54 environmental constraints (Mouchet et al., 2010). For example, functional diversity can 55 indicate the variety of roles that different organisms play in the ecosystem and assembly 56 rules are driven by functional traits (Petchey and Gaston, 2006). Last but not least, 4 57 phylogenetic diversity, which quantifies the evolutionary diversity in communities, 58 describing the evolutionary heritage or relatedness of all species in a given community 59 (Faith and Baker, 2007; Laity et al., 2015; Tucker et al., 2016), is increasingly considered 60 to be a great tool in community ecology and nature conservation (Tucker et al., 2019; 61 Winter et al., 2013). In the specific case of the effects of urbanization on overall 62 biodiversity, is particularly relevant to highlight that strategies based only on taxonomic 63 diversity could be inadequate to consider the ecological role and then the contribution of 64 each species to the community (Safi et al., 2013). 65 Birds are among the group of species most deeply impacted by the urbanization process 66 (Devictor et al., 2008; McKinney and Lockwood, 1999). The effects of urbanization on 67 biodiversity are several, but scientists agree that they are mainly negative (Aronson et al., 68 2014; Grimm et al., 2008; Ibáñez-Álamo et al., 2016; McKinney, 2002). Previous studies 69 have dealt with changes in avian community composition related to functional traits, 70 leading to reductions of functional spaces effectively occupied (Jokimäki et al., 2014; Pauw 71 and Louw, 2012), to changes in urban tolerance (Callaghan et al., 2020) and to declines in 72 the number of specialist species. These effects are commonly attributed to a process 73 known as biotic homogenization (Clergeau et al., 2006; Devictor et al., 2008; Ferenc et 74 al., 2014b). However, the effec 75 continue to be uncertain (Ibáñez-Álamo et al., 2016; Morelli et al., 2016), even though 76 certain clades are known to be more vulnerable than others to anthropogenic pressures 77 (Thuiller et al., 2011). Several structures in urban areas can represent different challenges 78 and opportunities for bird species, depending on how adaptable birds are to coexist with 79 humans (Tryjanowski et al., 2021). The amount and characteristics of urban greenery can 80 determine the capacity of urban areas to support fauna and then be useful for managers 81 and urban planners to mitigate some of the negative effects of urbanization on biodiversity phylogenetic diversity still 5 82 (Escobar-Ibáñez et al., 2020; Villaseñor et al., 2021). Nevertheless, a cautionary principle 83 is needed since the total vegetation abundance could be an inadequate proxy for 84 measuring the urban greenery benefits supporting biodiversity (Berland et al., 2020). 85 Some other anthropogenic structures can also attract bird species to the urban areas, 86 offering suitable sites for perching, nesting, and foraging (Morelli et al., 2014; Palacio, 87 2020; Reynolds et al., 2019). In fact, farmlands, villages and cities provide habitat and food 88 resources for urban exploiters or adapters bird species (Evans et al., 2009b, 2009a; 89 Reynolds et al., 2017; Tryjanowski et al., 2021, 2015). 90 Additionally, the levels of light and/or noise pollution of the cities could be associated with 91 urban birds' distribution because they attract or prevent their presence. There is solid 92 scientific evidence about the negative effect of artificial light at night (ALAN) on many 93 species, including amphibians, birds, mammals, insects and even plants (Bennie et al., 94 2015; Robert et al., 2015). During the last few decades, ALAN increased to such an extent 95 that it pollutes the environment, representing a serious biodiversity threat (Dominoni et al., 96 2016; F. Hölker et al., 2010; Franz Hölker et al., 2010; Kempenaers et al., 2010; Owens et 97 al., 2020). The documented effects of ALAN on bird species are related with alterations of 98 the natural daily, monthly and seasonal light and dark rhythms, capacities of individuals 99 related to navigate using night sky view, and also with changes in natural circadian 100 rhythms, behavioral alterations as well as interferences with migration activities in many 101 species (Adams et al., 2019; Dominoni, 2015). Furthermore, noise pollution also affects 102 the behavior and fitness of bird species, compromising their reproductive success (Díaz et 103 al., 2011; Francis et al., 2012; Ortega, 2012). Noise pollution is a byproduct of the 104 urbanization process, related to the density of human settlements, transport services and 105 industrial activities. The recent rise in noise levels in cities and urban areas is marked in 106 both magnitude and extent, with an estimated 30% of the European population exposed to 6 107 noise levels from road traffic greater than 55dB (decibels) at night 108 (http://www.euro.who.int/en/health-topics/environment-and-health/noise/data-and- 109 statistics), that is significantly above the threshold of 40dB recommended by the World 110 Health Organization. However, despite the potential impact of this novel and widespread 111 environmental force across the globe, only little is known about how this ecologically novel 112 acoustic condition affects natural communities (Francis et al., 2012). 113 A better understanding of the impact of urban characteristics on the mitigation of 114 biodiversity loss can help develop strategies for wildlife management in urban ecosystems 115 (Miller and Hobbs, 2002; Villaseñor et al., 2021). In the last decades several studies 116 focusing on the main effects of urbanization on biodiversity distribution and maintenance 117 (Beninde et al., 2015; Escobar-Ibáñez et al., 2020; Pautasso et al., 2011; Sushinsky et al., 118 2013), as well as in terms of biotic or evolutionary homogenization (Crooks et al., 2004; 119 Morelli et al., 2016; Sol et al., 2017) were published. However, a more accurate 120 assessment of how and which urban characteristics affect different facets of avian diversity 121 is still needed. 122 The main aim of this study is to assess the impact of specific elements of urbanization and 123 urban pollution on complementary avian diversity metrics to provide new insights on the 124 conservation of urban birds in European cities. More specifically, we tested whether 125 geographical patterns as latitude, urban characteristics such as land use composition, 126 building structure and vegetation arrangements, plus light and noise pollution affect 127 taxonomic, functional, and phylogenetic diversity of breeding bird populations in European 128 cities. 129 7 130 METHODS 131 Study area and bird data collection 132 Fieldwork was performed in 17 different cities located along a continent-wide latitudinal 133 gradient in 10 European countries (Fig. 1). The approach involving different urbanized 134 areas is particularly indicated for investigating general patterns at a large spatial scale 135 (Ibáñez-Álamo et al., 2016; Morelli et al., 2016). 136 Data on bird species were collected using the standardized point counts method (Bibby et 137 al. 138 knowledge of whether sites are characterized by rich or poor avian communities, carried 139 out during the 2018 breeding season. The surveys were locally adjusted to the start of the 140 breeding season based on the local 141 or late May in northern Finland) to minimize potential issues related to differences in the 142 detectability of bird species (Kéry et al., 2005). All point counts were positioned in 143 urbanized areas not closer than 500 m from the city border to avoid sampling transitional 144 suburban areas and separated by at least 150 m from the nearest point count. A total of 145 1624 point counts were visited with around 100-point counts in each city, with only a few 146 exceptions (Pesaro: 56, Zielona Góra: 60, Rovaniemi: 83 and Prague: 120). All point 147 counts were visited after the sunrise for 4 hours only during favorable weather conditions 148 for a total of 5 minutes of observations. The location of each point was recorded with a 149 GPS. Data on bird species were collected only by local expert ornithologists to reduce 150 detection issues due to skill differences among observers. All birds seen or heard within 151 50m distance from the observer were recorded, except nocturnal species that were not 152 included in counts because they require a different strategy of surveying. et al., 2010) randomly selected within each city without any prior knowledge (e.g., early April in southern Spain 8 153 Urban characteristics, light pollution, and noise pollution 154 All urban study areas surveyed had multi-storey buildings, single-family houses, roads, 155 and parks. Our classification of environments as urban (proportion of built-up area >50%, 156 building density >10/ha and residential human density >10/ha) followed Marzluff et al. 157 (2001), and it has been used in previous studies of urban avian ecology (Clergeau et al., 158 2006; Loss et al., 2009; Møller et al., 2015; Morelli et al., 2016). In each point count, we 159 collected data on relative vegetation cover and land use composition within a distance of 160 50m from the observer (Díaz et al., 2013). Land use/cover categories were classified into 5 161 types: building (which includes residential building, built with infrastructure and processing 162 areas and roads), grass, water bodies, bushes (which includes plants from gardens), and 163 trees (isolated trees, tree lines and patches). All this information is based on in situ 164 estimations performed by the observers. Additionally, we also calculated the average 165 number of floors of the buildings around the observer, the number of pedestrians walking 166 during the 5-minutes point count, and the number of bird feeders and nest-boxes directly 167 visible around 50m from the observer s position. 168 Information for each point about light pollution was extracted from web 169 https://www.lightpollutionmap.info. We used precalculated values from VIIRS satellite of 170 the year 2018. The values extracted correspond to the Radiance 10-9 W / cm2 * sr, where 171 W = Watts and sr = steradian. 172 We developed noise pollution models with the openoise tool for QGIS 173 (https://plugins.qgis.org/plugins/opeNoise). This plugin allows one to compute the mean 174 noise level in 2D space (e.g., around the point count) generated by point sources or by 175 road sources at fixed receiver points and buildings. We used a noise source based on 176 Urban Atlas land use categories (see Tab. S3 for more details) and Open Street Map 9 177 (OSM) buildings as an advanced input for noise reduction and diffraction. We calculated 178 noise spreading in a 250m range from each source point or line. The results obtained are 179 the model-based noise values in dB units (mean, range and standard deviation) in a range 180 of 50m around the point counts. 181 Community and diversity metrics 182 The bird community at each point count was defined as the total number of bird species 183 recorded during the visit. Species richness was expressed as the total number of bird 184 species recorded in each point count (Magurran, 2004). To describe the functional 185 diversity of the bird species assemblages, we used the functional dispersion (FDis), an 186 index estimated as the mean distance of all species in the assemblage to the weighted 187 centroid of the community in trait space (Cappelatti et al., 2020; Laliberté and Legendre, 188 2010). The functional dispersion was calculated using 18 avian traits provided by Wilman 189 et al. (2014), focusing on diet and foraging stratum of species. The species trait table 190 consists of 10 variables that describe the preferences on diet or food types and eight 191 variables describing the preferences on the substrate from which food is taken. All 192 variables express the composition of diet or foraging substrate using percentages of ten 193 major food items (invertebrates, vertebrates (endotherm), vertebrates (ectotherm), 194 vertebrates (fish), vertebrates (unknown), scavenger, frugivore, nectarivore, granivore, 195 folivore), and percentages of eight major foraging strata (i.e., below surf, around surf, 196 ground, understory, mid-high, canopy, aerial, pelagic) (Wilman et al., 2014). For 197 determining the diet or foraging stratum of each bird species, the proportions were scored 198 from secondary literature based on word order in sentences describing the diet. Thus, the 199 trait data are based on semi-quantitative information assessing the relative importance of 200 each item for the whole life history, characterizing a large port 10 201 of species (see more details about the traits in Wilman et al. (2014)). A similar data type 202 was used in a recent study focusing on bird trophic niche (Pigot et al., 2020). The 203 functional dispersion in each point count was calculated usin 204 (Laliberté et al., 2015). 205 Finally, we calculated phylogenetic diversity (PD) (Faith, 1992) and phylogenetic species 206 variability (PSVs) or phylogenetic relatedness (Helmus et al., 2007) for each species 207 assemblage. The PSVs indicate the degree of average phylogenetic relatedness of 208 species in a given community. To estimate PD and PSVs we built a phylogenetic tree 209 using the relationships among the species in each point count, based on genetic data from 210 all bird species (Jetz et al., 2012) provided in BirdTree (www.birdtree.org), by considering 211 a consensus tree obtained with the function consensus on 100 random trees, with the 212 ape v5.3 package for R (Paradis et al., 2004). Both metrics were estimated using the 213 'Picante' v1.7 package for R (Kembel et al., 2010). 214 Statistical analyses 215 To investigate potential associations between the distribution of each one of the four avian 216 community and diversity metrics (i.e., species richness, functional dispersion, phylogenetic 217 diversity and phylogenetic relatedness) and the latitude of the cities investigated, we used 218 generalized linear models (McCullagh and Nelder, 1989), considering the type of 219 distribution of each response variable (Box and Cox, 1964). When the response variable is 220 assumed or suspected to be correlated with the total number of species in the community 221 (e.g., phylogenetic diversity, Fig. S1), bird species richness (BSR) was added as a 222 predictor into the modeling procedure. The goodness of fit of each model was estimated 223 as the ratio between residual and null deviance of the data indicated in the models 224 outputs. kage for R 11 225 Generalized Linear Mixed Models (GLMMs) were used to study the changes in bird 226 community and diversity metrics concerning land use/cover composition around each point 227 count, the abundance of bushes and trees, number of floors of buildings, number of 228 pedestrians, nest-boxes and bird feeders, level of light and noise pollution, modeled as 229 fixed effects. All predictors were re-scaled and centered with the scale function in R, to 230 avoid convergence warnings during the modeling procedure. No multicollinearity issue was 231 found among the selected predictors, after exploring it through a correlation matrix and 232 visual correlograms in R. City was included as a random effect to account for possible 233 consistent differences among cities. Models were fitted using the package 234 (Bates et al., 2014). The goodness of fit of models was assessed by the mean of the R2. 235 The R2 measure used in this study was an extension of the statistic from Edwards et al. 236 (2008) using penalized quasi-likelihood (PQL) estimation (Jaeger et al., 2017). It was 237 computed by using the package r2glmm for R (Jaeger, 2017). 238 All statistical tests were performed using R software version 3.6.0 (R Development Core 239 Team, 2019). for R 240 12 241 RESULTS 242 Composition, functional and phylogenetic diversity of bird communities in 243 European cities 244 A total of 133 bird species (Table S2) was recorded at 1624 point counts uniformly 245 distributed in seventeen European cities (Fig. 1). The top-ten bird species with the highest 246 frequency of occurrence across the cities were Passer domesticus (60%), Turdus merula 247 (43%), Apus apus (42%), Parus major (37%), Columba palumbus (34%), Columba livia 248 (34%), Streptopelia decaocto (33%), Pica pica (33%), Chloris chloris (26%), and Corvus 249 monedula (21%) (Table S2). 250 The highest mean values of species richness were found in Poitiers (France), Granada 251 (Spain), Athens (Greece) and Zielona Góra (Poland). In contrast, the lowest mean values 252 were found in Jyväskylä (Finland), Budapest (Hungary) and Turku (Finland) (Fig. 1, Table 253 1). The highest mean values of functional dispersion were located in Tartu (Estonia) and 254 Groningen (Netherland), while the lowest values corresponded with Turku (Finland) and 255 Ioannina (Greece) (Fig. 1, Table 1). Regarding the phylogenetic profile of urban bird 256 assemblages, we found the highest phylogenetic diversity in Poitiers and Granada cities, 257 while the lowest values were recorded in Rovaniemi and Jyväskylä (both in Finland) (Fig. 258 1, Table 1). The phylogenetic species variability among urban bird communities was 259 highest in Madrid (Spain) and Groningen (Netherland), and lowest in Rovaniemi and 260 Jyväskylä (both in Finland) (Fig. 1, Table 1). 261 Effects of latitude and local attributes of the cities on avian diversity 262 Overall, the bird species richness was negatively associated with latitude (Fig. 2, Table 263 S4). In contrast, functional dispersion of urban birds communities was not significantly 13 264 correlated with latitude but was positively associated with the number of species in the 265 community (Fig. 2, Table S4). Finally, we found that both phylogenetic diversity and 266 phylogenetic relatedness declined with increasing latitude (Fig. 2, Table S4). 267 The outputs of the modeling procedure showed that taxonomic diversity of urban bird 268 communities significantly decreases as the building cover increases. In contrast, it 269 significantly increases when the vegetation cover (i.e., grass, bushes and trees) increases 270 (Table 2, Fig. 3). On the other hand, the functional dispersion of communities was 271 significantly and negatively correlated with tree cover, and positively correlated with the 272 level of light pollution in the cities (Table 2, Fig. 3). Phylogenetic diversity was significantly 273 and negatively associated with the building coverage, while positively associated with 274 grass, trees and water cover. Finally, the phylogenetic relatedness of European urban bird 275 communities significantly increased when water and building cover increased and the 276 increasing number of building floors, number of pedestrians, and the level of light pollution 277 (Table 2, Fig. 3). Bush cover was the only variable significantly and negatively associated 278 with the overall phylogenetic relatedness of urban bird communities (Table 2, Fig. 3). 279 The level of noise pollution, as well as other characteristics of the city such as the number 280 of bird feeders and nest boxes, were not significantly associated with any of the four avian 281 diversity and community metrics used in our study (Table 2). 14 282 DISCUSSION 283 Urban bird species are expected to be affected by the potential negative effects of the 284 increasing urbanization process (Aronson et al., 2014; Beninde et al., 2015). In this study, 285 we provide an extensive and detailed assessment of avian communities within seventeen 286 different European cities and the relative effects caused by different characteristics of the 287 cities on each component of the avian diversity. This information is important because not 288 all components of avian diversity are affected in the same way by urban development 289 (Ibáñez-Álamo et al., 2019). Our findings offer a framework to focus the main effects of 290 urban greenery, building density and structure, and the potential impact of noise and light 291 pollution on bird species assemblages, which could be used in urban planning to increase 292 the resilience of the urban-nature matrix. 293 We followed a multidimensional approach on the characterization of avian communities, 294 recognizing that the complexity of ecological systems is better described when focusing on 295 different facets of avian diversity. Our results constitute a continental-scale assessment of 296 the phenomena of urbanization impacts on wildlife, and offer valuable information 297 considering the urgent need for a reconciliation between urban development and 298 biodiversity conservation (United Nations, 2016). 299 At a large geographical scale, we found that northern European cities are characterized by 300 avian communities with fewer species. This pattern mirrors a well-recognized ecological 301 pattern also seen on wild communities: A negative association between species richness 302 and the latitudinal gradient (Gaston, 2007; Jarzyna et al., 2021). However, other studies 303 also reported contrasting results for urban birds, showing species richness increasing 304 towards higher latitudes (Ferenc et al., 2014b). The same negative association was found 305 in our study for both phylogenetic diversity and relatedness. This could be partially 15 306 explained by the correlation between species richness and phylogenetic diversity, but not 307 in the case of phylogenetic relatedness (see Fig. S1). Our results indicate that urban bird 308 species are less related to each other at northern than at southern latitudes. Interestingly, 309 the functional dispersion of avian communities within the cities was unrelated to the 310 latitude of the urban settlements. The functional dispersion of a given community could 311 change along environmental stress gradients (Valdivia et al., 2017). Such changes are 312 independent of the overall number of species in the communities (Laliberté and Legendre, 313 2010). 314 When focusing on the main environmental descriptors of the European cities, we found 315 that the coverage of green areas at all levels (e.g., soil level as grass and structure level 316 as bushes and trees) increased the taxonomic diversity of avian communities. In contrast, 317 increasing the building cover decreased the total number of birds in the species 318 assemblages. This result confirms numerous previous studies focused on American 319 (Chapman and Reich, 2007; Melles et al., 2003), Australian (Threlfall et al., 2016), 320 Chinese (Huang et al., 2015) and European cities (Dale, 2017; Ferenc et al., 2014a; 321 Morelli et al., 2018). All the other descriptors were not significantly associated with 322 taxonomic diversity. For example, in our study, the abundance of bird feeders and nest 323 boxes that was supposed to attract a larger number of species (Tryjanowski et al., 2015), 324 was not significantly associated with any change in the overall taxonomic diversity. We 325 expected an effect of bird feeders since urban areas are usually assumed as characterized 326 by lower availability of natural food resources (Tryjanowski et al., 2015). The potential 327 effect of the abundance of nest boxes on avian communities is related to the fact that 328 several birds cavity-nesters may take advantage of the presence of such artificial support 329 in urban areas (Jokimäki, 1999; Luniak, 1992). The absence of the effects of bird feeders 330 and nest boxes in our study may result from our focus on diversity (i.e., the number and 16 331 identity of species), whereas adding these limiting resources most likely have the largest 332 impact on species abundances (i.e., the number of individuals within species). Additionally, 333 we suppose that the role of such elements might be more important during the winter 334 season (Jokimäki and Kaisanlahti-Jokimäki, 2012). 335 The different elements (land use, building characteristics, pedestrian s density or level of 336 pollutants) showed different effects on the functional dispersion of avian communities 337 within the cities. Overall, functional diversity expresses the diversity of functional traits, 338 which are those components of an organism's phenotype that influence its response to 339 environmental stress and its effects on ecosystem properties (Hooper et al., 2005; Petchey 340 and Gaston, 2006). In our study, the effects of urban characteristics on functional 341 dispersion were less evident than for other community and diversity metrics. Only tree 342 cover negatively affected the degree of functional dispersion of 343 speculate that such an association could be due to an overrepresentation of forest birds in 344 areas covered by large patches of trees, which decrease the overall dispersion of 345 functional characteristics of the species in the community. However, forest birds could also 346 be a source of functional heterogeneity, since is well known that such species often 347 partition the forest habitat using different foraging substrates, heights and strategies 348 (Hanzelka and Reif, 2016; Lara et al., 2015). Additionally, we found a positive correlation 349 between this functional diversity measure and the level of light pollution in the city. 350 However, this correlation could be partially explained by the fact that the highest level of 351 light pollution in the Finnish city of Jyväskylä, which was also characterized by the highest 352 values of functional dispersion on urban bird s communities. Another potential explanation 353 could be related to the presence of several insectivorous species (e.g., Apus apus, Apus 354 melba, Delichon urbicum and Hirundo rustica) attracted to foraging activities in areas at 355 high artificial illumination. The presence of such species, in combination with other seed assemblages. We 17 356 eaters, and omnivorous birds, could increase the overall gradient of the functional space 357 used by the species assemblage. 358 Finally, focusing on the phylogenetic print of urban avian communities, we found that some 359 characteristics of cities, such as the grass coverage, the presence of trees and water 360 streams, overall increased the level of phylogenetic diversity of the communities. We can 361 speculate that water bodies could provide habitat for -related birds, typically from different 362 families than more terrestrial birds (e.g., ducks, geese, shorebirds, gulls). Also, tree 363 patches within the urban matrix could provide a similar effect, attracting woodpeckers and 364 even raptors. On the contrary, the density and coverage of buildings significantly 365 decreased the phylogenetic diversity of the communities. This outcome is important, 366 constituting a complementary information to previous studies that highlighted how the 367 urbanization is also filtering negatively the more evolutionary unique bird species (Ibáñez- 368 Álamo et al., 2016; Morelli et al., 2016; Sol et al., 2017). An accurate plan of urban 369 greenery, increasing the density and surface of tree or grass patches and water bodies in 370 some parts of the cities, could help mitigate this negative effect. 371 The last measure evaluated in this study, the phylogenetic species variability or 372 phylogenetic relatedness of species (Helmus et al., 2007), explains how many species in a 373 given community are close in an evolutionary point of view. Closely related species 374 produce a higher phylogenetic relatedness. Interestingly, in all European cities, increasing 375 the building density, the number of building s floors, the density of pedestrians and the 376 level of light pollution (all indicators often used to define the degree of anthropization) we 377 found avian communities composed of birds largely close related from a phylogenetic point 378 of view. This type of phylogenetic homogenization could describe the structure and 379 assembly of urban bird communities. We can expect, also following D 18 380 that in communities with more closely related species, the level of competition should be 381 higher (Godoy et al., 2014). Darwin speculated that niche overlap between more closely 382 related species would hinder their coexistence (Darwin, 1859), and there is evidence that 383 interspecific competition with urbanized species are preventing less urbanized, closely 384 related species from colonizing cities (Møller and Díaz, 2018). However, despite that the 385 evolutionary relatedness could be related to interspecific competition and niche 386 differences, it could also relate to average fitness differences among species (Godoy et al., 387 2014). Anyway, we demonstrated that higher urbanization increases the phylogenetic 388 relatedness of urban avian communities in Europe. Also, the overall surface of water 389 bodies or streams in the cities increased the phylogenetic relatedness of species, clearly 390 because they attract many waterbirds (e.g., Anas platyrhynchos, Anser anser, etc.) that 391 are closely related. 392 The only characteristic of the cities favouring less phylogenetically correlated 393 assemblages, and so a potential tool for urban planning, was the surface of vegetation 394 cover at the level of shrubs and bushes. Such characteristics are mainly associated to 395 urban parks and private gardens. 396 In summary, our findings could be used by local and regional governments as 397 recommendations or guidelines for smart eco-urban planning to incorporate green spaces 398 and urban greening characteristics into urban planning frameworks, maximizing, when 399 possible, the avian diversity by considering taxonomic, functional and phylogenetic 400 dimensions. Specifically, measures such as the creation of small or medium-sized green 401 areas, composed of trees, bushes or also patches with grass in the densest areas of the 402 cities, can increase the number of bird species by providing additional niches to be 403 occupied (Capotorti et al., 2015; Stagoll et al., 2012). Large patches of grass and trees can 19 404 also favor bird communities with higher phylogenetic diversity. At the same time, 405 ornamental plants of urban gardens and bushes in the parks can help to create avian 406 communities less phylogenetically correlated, so characterized by a greater variance in 407 competitive outcomes and niche use. Overall, maintaining adequate levels of avian 408 diversity within the urban settlements can help to increase the potential resilience of urban 409 ecosystems (Elmqvist et al., 2003), a result particularly desired if facing land use and 410 climate change scenarios. 411 20 412 REFERENCES 413 Adams, C.A., Blumenthal, A., Fernández-Juricic, E., Bayne, E., St Clair, C.C., 2019. Effect 414 of anthropogenic light on bird movement, habitat selection, and distribution: A 415 systematic map protocol. Environ. 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Pan-European Common Bird Monitoring Scheme 737 (PECMBS), Bruxelles, Belgium. 34 738 Wilman, H., Belmaker, J., Simpson, J., de la Rosa, C., Rivadeneira, M.M., Jetz, W., 2014. 739 EltonTraits 1.0: Species-level foraging attributes of the world s birds and mammals. 740 Ecology 95, 2027. doi:10.1890/13-1917.1 741 Wilson, M.C., Chen, X.-Y., Corlett, R.T., Didham, R.K., Ding, P., Holt, R.D., Holyoak, M., 742 Hu, G., Hughes, A.C., Jiang, L., Laurance, W.F., Liu, J., Pimm, S.L., Robinson, S.K., 743 Russo, S.E., Si, X., Wilcove, D.S., Wu, J., Yu, M., 2016. Habitat fragmentation and 744 biodiversity conservation: key findings and future challenges. Landsc. Ecol. 31, 219 745 227. doi:10.1007/s10980-015-0312-3 746 Winter, M., Devictor, V., Schweiger, O., 2013. Phylogenetic diversity and nature 747 conservation: where are we? Trends Ecol. Evol. 28, 199 204. 748 doi:10.1016/j.tree.2012.10.015 749 35 750 Tables 751 Table 1. List of the 17 European cities focused on this study, their geographic positions, and values of four investigated avian diversity 752 and community metrics (bird species richness (BSR), functional dispersion (FDis), phylogenetic diversity (PD) and phylogenetic 753 relatedness (PSVs), expressed as mean and standard deviation (sd). Latitude Longitude BSR BSR FDis FDis PD City (mean) (mean) (mean) (sd) (mean) (sd) Granada 37.18263 -3.60464 11.69 4.25 3.5728 0.3399 532.0035 127.7200 0.5480 0.0521 Athens 38.00247 23.78773 9.87 3.77 3.4532 0.5524 489.0876 157.7740 0.5232 0.1068 Ioannina 39.66552 20.85271 5.90 3.18 3.0768 0.6223 342.6522 99.1850 0.5077 0.1066 Toledo 39.86640 -4.03093 7.16 3.14 3.2665 0.7369 385.9180 111.3631 0.5557 0.1023 Madrid 40.44375 -3.70107 5.69 2.70 3.6521 0.6765 372.2879 110.1230 0.6272 0.0819 Pesaro 43.90731 12.90906 6.82 2.72 3.7639 0.4902 409.8594 103.2583 0.5755 0.0869 Poitiers 46.57901 0.344370 12.29 3.12 3.6558 0.2433 565.4686 89.2671 0.5352 0.0456 Budapest 47.50018 19.07067 3.68 1.79 3.1172 0.4751 252.3442 75.8790 0.5452 0.1463 Paris 48.69750 2.185081 6.52 1.95 3.2104 0.5175 392.6384 104.0314 0.5311 0.1126 (mean) PSVs PD (sd) PSVs (mean) (sd) 36 Prague 50.08755 14.44182 7.52 3.35 3.3311 0.5468 389.1877 117.2042 0.5408 0.1120 Zielona Góra 51.92365 15.49755 9.12 3.27 3.4564 0.4433 464.1151 104.3993 0.5677 0.0621 Poznan 52.41576 16.90839 6.51 2.48 3.4412 0.6199 357.4698 88.2566 0.5244 0.1232 Groningen 53.21866 6.559404 6.19 2.61 3.8182 0.6404 392.9685 130.2227 0.5997 0.0880 Tartu 58.37412 26.72117 7.79 2.72 4.0188 0.6640 430.0078 100.4562 0.5371 0.0922 Turku 60.45367 22.28490 3.86 2.00 3.0229 0.7280 258.7926 68.8306 0.4731 0.1516 Jyväskylä 62.24728 25.75270 3.29 1.51 2.9310 0.8055 220.1992 47.7424 0.4186 0.1378 Rovaniemi 66.30277 25.42584 4.18 1.92 2.9640 0.5638 249.8230 58.5599 0.4355 0.1382 754 37 755 Table 2. Results of generalized linear mixed models (GLMM), accounting for variation in 756 bird community and diversity metrics (bird species richness, functional dispersion, 757 phylogenetic diversity, and relatedness) in relation to land use / cover composition around 758 each point count, abundance of bushes and trees, number of floors of buildings, 759 abundance of pedestrians, nest-boxes and bird feeders, and level of light and noise 760 pollution, modeled as fixed effects. City was included as a random effect to account for 761 possible consistent differences among cities. The full model is based on 1211-point counts 762 with complete information. Abbreviations: Std. Error, standard error. Significant variables 763 are highlighted in bold in the table. The R2 of models was 0.341 for BSR, 0.031 for FDis, 764 0.113 for PD and 0.197 for PSVs. Model target: Bird species richness Variables Estimate Std. Error z value p-value (Intercept) 1.91474 0.08848 21.639 < 2e-16 Grass 0.07278 0.01882 3.868 0.00011 Bush 0.04606 0.01412 3.263 0.00110 Tree 0.04936 0.01445 3.417 0.00063 Built -0.08563 0.02086 -4.105 4.0E-05 Water 0.01133 0.00899 1.26 0.20774 Light pollution -0.03839 0.02358 -1.629 0.10341 Noise pollution -0.00320 0.01126 -0.284 0.77648 Pedestrians -0.00821 0.01240 -0.662 0.50789 Building floors -0.00701 0.01560 -0.45 0.65293 Bird feeders 0.00531 0.00974 0.545 0.58547 Nest boxes -0.01757 0.01324 -1.327 0.18466 38 Model target: Functional dispersion Variables (Intercept) Estimate Std. Error t value p-value 3.45436 0.10184 33.921 <0.00001 Grass -0.00825 0.02881 -0.286 0.77472 Bush 0.03818 0.02100 1.819 0.06897 Tree -0.04766 0.02198 -2.169 0.03010 Built -0.01505 0.03096 -0.486 0.62691 Light pollution 0.11208 0.02934 3.82 0.00013 Water 0.01692 0.01450 1.167 0.24335 Noise pollution 0.00407 0.01739 0.234 0.81515 Pedestrians 0.01793 0.01641 1.093 0.27439 Building floors 0.01611 0.02108 0.764 0.44488 Bird feeders 0.00399 0.01482 0.269 0.78805 Nest boxes 0.02224 0.01712 1.299 0.19383 Model target: Phylogenetic diversity Variables (Intercept) Estimate Std. Error t value p-value 390.16640 26.75840 14.581 <0.00001 Grass 18.61930 5.27790 3.528 0.00042 Bush 7.10220 3.69710 1.921 0.05473 Tree 17.85350 3.87450 4.608 4.1E-06 Built -13.14680 5.80860 -2.263 0.02361 Water 9.59370 2.55060 3.761 0.00017 Light pollution 6.37070 5.22080 1.22 0.22237 Noise pollution -5.16680 3.07100 -1.682 0.09248 Pedestrians -0.33440 2.88890 -0.116 0.90786 39 -2.19700 3.71700 -0.591 0.55448 Bird feeders 3.00870 2.60810 1.154 0.24866 Nest boxes -2.47160 3.01000 -0.821 0.41157 Building floors Model target: Phylogenetic relatedness Variables (Intercept) Estimate Std. Error t value p-value 0.52989 0.01864 28.435 <0.00001 Grass -0.00686 0.00486 -1.411 0.15816 Bush -0.00767 0.00341 -2.25 0.02447 Tree 0.00260 0.00357 0.728 0.46687 Built 0.02244 0.00536 4.191 2.7E-05 Water 0.00824 0.00235 3.5 0.00047 Light pollution 0.03046 0.00479 6.362 1.9E-10 Noise pollution -0.00300 0.00283 -1.058 0.29009 Pedestrians 0.00955 0.00267 3.583 0.00034 Building floors 0.00693 0.00343 2.023 0.04310 Bird feeders 0.00101 0.00241 0.42 0.67431 Nest boxes 0.00294 0.00278 1.059 0.28967 765 766 40 767 Figure captions 768 Figure 1. The seventeen European cities focused on this study, with values of species 769 richness (BSR), functional dispersion (FDis), phylogenetic diversity (PD) and phylogenetic 770 relatedness (PSVs) calculated for the avian communities. For graphical purposes, all 771 variables were standardized from 0 (the minimum value) to 1 (the maximum value). The 772 background layer represents the artificial light at night (ALAN) for Europe. The image was 773 produced by mosaicking Defense Meteorological Satellite Program (DMSP) Operational 774 Linescan System (OLS) satellite images (source: ESRI, NASA - Visible Earth). 775 776 Figure 2. Associations between values of (A) species richness (BSR), (B) functional 777 dispersion (FDis), (C) phylogenetic diversity (PD) and (D) phylogenetic relatedness (PSVs) 778 calculated for urban avian communities and latitude. The black line is the linear regression, 779 while the marginal boxplots describe the distribution of data. The figure also shows the 780 estimates and significance of linear regression models. 781 782 Figure 3. Schematic presentation of main associations between values of species richness 783 (BSR), functional dispersion (FDis), phylogenetic diversity (PD) and phylogenetic 784 relatedness (PSVs) calculated for urban avian communities and different characteristics of 785 the cities. Positive associations are indicated in green colour, while negative ones are 786 highlighted in red colour. These results reflect the outputs of the modelling procedure, 787 shown in detail in Table 2. 788 41 789 Fig. 1 790 791 42 792 Fig. 2 793 794 43 795 Fig. 3 796 44