Cemeteries support avian diversity likewise urban parks in European cities: Assessing taxonomic, evolutionary and functional diversity

Accepted Manuscript Title: Cemeteries support avian diversity likewise urban parks in European cities: Assessing taxonomic, evolutionary and functional diversity Authors: Federico Morelli, Peter Mikula, Yanina Benedetti, Rapha¨el Bussi`ere, Piotr Tryjanowski PII: DOI: Reference:

S1618-8667(18)30314-5 https://doi.org/10.1016/j.ufug.2018.10.011 UFUG 26234

To appear in: Received date: Revised date: Accepted date:

16-5-2018 14-9-2018 26-10-2018

Please cite this article as: Morelli F, Mikula P, Benedetti Y, Bussi`ere R, Tryjanowski P, Cemeteries support avian diversity likewise urban parks in European cities: Assessing taxonomic, evolutionary and functional diversity, Urban Forestry and amp; Urban Greening (2018), https://doi.org/10.1016/j.ufug.2018.10.011 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Cemeteries support avian diversity likewise urban parks in European cities: Assessing taxonomic, evolutionary and functional diversity

Federico Morelli1,2, Peter Mikula3, Yanina Benedetti1, Raphaël Bussière4, Piotr Tryjanowski5

Czech University of Life Sciences Prague, Faculty of Environmental Sciences, Department

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of Applied Geoinformatics and Spatial Planning, Kamýcká 129, CZ-165 00 Praha 6, Czech Republic

Faculty of Biological Sciences, University of Zielona Góra, Prof. Z. Szafrana 1, PL-65-516

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Zielona Góra, Poland 3

Department of Zoology, Faculty of Science, Charles University, Viničná 7, CZ-128 43 Praha

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2, Czech Republic 4 route de la Loge, 86800 Liniers, France

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Institute of Zoology, Poznań University of Life Sciences, Wojska Polskiego 71C, PL-60-625

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Poznań, Poland

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Highlights

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Word count: 8067

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Running title: Bird diversity in parks and cemeteries

We visited 79 parks and 90 cemeteries in the Czech Republic, France, Italy and Poland

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We assessed bird taxonomic and functional diversity and evolutionary distinctiveness (ED)

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We found that both parks and cemeteries supports in similar way the urban avian diversity

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Species richness and ED were positively correlated with tree coverage and site size

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Evolutionary distinctiveness was lower in urban parks and cemeteries than in rural ones

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Abstract The aim of this study was to explore different components of avian diversity in two types of urban green areas, parks and cemeteries, in four European countries in relation to environmental characteristics. We studied bird species richness, functional diversity and evolutionary distinctiveness in 79 parks and 90 cemeteries located in four European

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countries: the Czech Republic, France, Italy and Poland. First, we found no significant differences between cemeteries and parks in bird diversity. However, in both parks and cemeteries, only: two community metrics were affected by

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different environmental characteristics, including local vegetation structure and presence of

human-related structures. Species richness was positively correlated with tree coverage and site size, functional diversity was unrelated to any of the measured variables, while the mean

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evolutionary distinctiveness score was positively correlated with tree coverage and

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negatively associated with the coverage of flowerbeds and number of street lamps. Our findings can be useful for urban planning: by increasing tree coverage and site size it is

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possible to increase both taxonomic richness and evolutionary uniqueness of bird

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communities. In both parks and cemeteries, the potential association between light pollution and bird species richness was negligible. We also identified some thresholds where bird diversity was higher. Bird species richness was maximized in parks/cemeteries larger than

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1.4 ha, with grass coverage lower than 65%. The evolutionary uniqueness of bird communities was higher in areas with tree coverage higher than 45%. In conclusion, the

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findings of this study provide evidence that cemeteries work similarly than urban parks

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supporting avian diversity.

Keywords: bird diversity; cemeteries; evolutionary distinctiveness; functional diversity; urban

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green areas

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INTRODUCTION Understanding the main ecological characteristics of anthropogenic or urbanized areas that can support biodiversity is a keystone for urban conservation and ecological planning, in order to protect the ecosystem functioning in these areas (Kang et al., 2015; Pereira et al., 2012). Conservation planning as well as management strategies are essentially based mainly on data about spatial distribution of biodiversity (Margules and Pressey, 2000; Rodrigues et al., 2007; Wiens et al., 2008), but also on the availability of urban green areas

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in the most human-dominated landscapes (Kabisch et al., 2016). The spatial distribution of

the animal species that are able to exploit or adapt to urban environments could be affected by natural characteristics of the habitat, but also by the presence of human-related

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structures.

Negative effects of urbanization on biodiversity have already been explored in many studies (Cardinale et al., 2012; McKinney, 2002; Newbold et al., 2016; Shochat et al., 2010). One of

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the most important issues is related to the biotic homogenization of animal or plant

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communities (Devictor et al., 2007; McKinney, 2006). The biotic homogenization is characterized by the replacement of specialist species by generalists, increasing similarities

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across communities in space and time (McKinney & Lockwood, 1999; McKinney, 2006). The

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replacement promotes similar communities, with few dominant species, among different urban locations (Møller et al., 2012). However, even if the effects of urbanization on biodiversity were assessed many times, few studies have focused on exploring these effects

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considering a multi-level approach or focusing on different components of biodiversity (Guerrero et al. 2011; Morelli et al. 2017). Considering, for example, bird communities, many

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studies focused mainly on taxonomic diversity (Ciach and Fröhlich, 2016; Lee et al., 2004; Plieninger et al., 2013), while other studies focused on changes in functional diversity or degree of specialization of species in the assemblages (Aue et al., 2014; Doxa et al., 2010;

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Morelli et al., 2014b). Recently, some studies have also focused on the phylogenetic or evolutionary diversity of communities (Frishkoff et al., 2014; Ibáñez-Álamo et al., 2016; Morelli et al., 2016; Sol et al., 2017).

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Parks and cemeteries have often been recognized as biodiversity hotspots in urban environments (Clergeau et al., 2001; Fernández-Juricic and Jokimäki, 2001; Jokimäki, 1999; Stagoll et al., 2012). Nowadays, urban parks and greenery constitute important refuges for wildlife in more and more urbanized global environments (Alvey, 2006). Urban parks can provide resources for increasing or maintaining urban biodiversity, especially for bird species (Chiesura, 2004; Schütz and Schulze, 2015; Strohbach et al., 2009). Similarly, cemeteries are green areas easily exploited by some wildlife species and potentially providing 3

“biodiversity islands” within the urban matrix (Banaszak-Cibicka et al., 2016; Bonnet et al., 2016; Łopucki and Kitowski, 2016). Furthermore, cemetery and park infrastructures or human-related structures (e.g. buildings, monuments, street lamps, flowerbeds, etc.) can play a role in supporting breeding species, providing birds with nesting and perching sites, and food provision. Also artificial lights can affect many bird species, both by changing the light-dark pattern, as well as by attracting insects and thus modifying the local food supplies (Ciach and Fröhlich, 2016; Dominoni, 2017; Klem, 2007; Kociolek et al., 2011). In a recent

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study, Tryjanowski et al. (2017) explored species richness of birds between urban parks and cemeteries in Europe, based on data from published papers and unpublished sources. Even if the results showed some differences in terms of species richness, mainly associated with

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the coverage and age of trees, both functional and evolutionary diversity were not taken into account. Additionally, the majority of studies dealing with ecological patterns in urban areas were conducted in single cities, so further studies comparing results among cities or using data from different countries to focus on general ecological patterns are needed (Hedblom

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and Murgui, 2017).

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The aim of this study was to explore differences in bird diversity and community metrics

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between two types of urban, peri-urban and rural green areas – parks and cemeteries – in four European countries in relation to environmental characteristics (site size, altitude, local

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vegetation composition and human-related structures). We assessed relative associations of environmental characteristics with three components of diversity of bird assemblages:

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species richness, functional diversity and mean evolutionary distinctiveness score of

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communities.

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METHODS Study sites and bird data collection Data on bird communities of parks and cemeteries were collected in urban, peri-ruban and rural areas of the Czech Republic, France, Italy and Poland (Fig. 1). We collected information in a congruent number of parks and cemeteries in each country. To minimize the potential bias due to spatial and temporal changes in species composition of bird

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assemblages at the studied sites and to avoid possible detection biases, the majority of study sites were selected and studied in the following way: (1) we tried to find parks and cemeteries of similar size in close areas for each city, (2) birds on both site types were

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approached during the same day and the censuses were carried out during the same time window and under uniform weather conditions (days without marked rainfall or wind). We used this approach because closely spatially and temporally related sites are expected to

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share most of potentially confounding environmental characteristics.

Each study site was visited twice per season with an interval of 3–4 weeks, i.e. in the first

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half of April to late April and in the second half of May to early June 2014, respectively. At

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each sampling point we conducted a 10-minute point count (all surveys for a particular country were done by the same person) in the early morning (from 6:00 to 10:00, when birds

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reach their peak vocal activity). All bird individuals seen or heard then were identified to the species level and registered. Birds were recorded within a radius of 100 m from the point

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count, so each census plot covered an area of about 3 hectares. The radius of the buffer was estimated in the field, considering known distances to visible objects of the landscape (buildings, walls, etc.), and then distances were confirmed by counting the steps (1 step

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correspond coarsely to 1 meter). In larger parks/cemeteries (> 9 hectares) we counted birds at more than one sampling point per study site), spaced at least 200 m apart. This census

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method is designed to provide information on the distribution and abundance of diurnal songbirds (Bibby et al., 1992). Raptors, nocturnal species and aerial feeders (mainly swallows and swifts) were not included in the analysis since the survey method applied in this study is not suitable for estimating their abundance.

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Environmental characteristics and human-related structures Firstly, for each study site we determined its GPS coordinates (i.e. longitude and latitude) and altitude (obtained from the online tool https://www.daftlogic.com/sandbox-google-mapsfind-altitude.htm). Further, sites were classified into three main categories according to their location on the rural–urban gradient as rural, peri-urban and urban on the basis of criteria used in previous studies (Ibáñez-Álamo et al., 2016; Morelli et al., 2016): Urban study sites 5

were located in areas with multi-storey buildings, single-family houses, roads and parks, while nearby rural areas had open farmlands and woodlands and did not contain continuous patches of urban elements like those mentioned above (Morelli et al., 2016). Areas were considered urban when the proportion of built-up surface was > 0.5 while areas where the proportion of built-up surface was ≤ 0.2 were considered as rural areas (Marzluff et al., 2001). Areas characterized by built-up surface higher than rural but lower than urban, were classified as peri-urban. For more details, see Marzluff (2001) and Morelli et al. (2016).

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Information on environmental characteristics of parks and cemeteries was collected directly in the field, within a 100 m-radius around each count point (or smaller if the 100-m radius

exceeded the borders of the park/cemetery), as suggested by previous studies (Morelli et al.,

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2013, 2014). We used the following descriptors: size of the site (park/cemetery), local vegetation characteristics (grass, shrub and tree coverage), and local human-related structures (number of street lamps; chapel, flowerbed and tombstone coverage). The

vegetation structure attributes around each sampling point were estimated for the ground

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level (referred to as grass coverage, although we took into account also other herbaceous

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plants), medium level (shrub coverage) and tree level (tree coverage). Each layer of

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vegetation was assessed independently, by visual estimations of percentage cover (from 0 to 100%) within the 100-m radius. The human-related structures were also assessed visually

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within the 100-m radius from the sampling point. For each park/cemetery, we determined its size (in hectares), using estimations derived from Google Earth. Additionally, we estimated

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the minimum distance (m) from the border of each urban park/cemetery to the nearest green area. This estimation was irrelevant in rural or peri-urban areas, where parks/cemeteries

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were surrounded by natural or managed green areas. Diversity and community metrics

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We used three different measures of diversity or community metrics, calculated for each bird community (sampling point): (1) Bird species richness (BSR): a measure of taxonomic diversity of the bird community (Magurran, 2004), expressed as the total number of recorded bird species at each

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sampling point.

(2) Functional diversity (FD): a biodiversity metric based on species-trait approaches, focusing on functional aspects of biodiversity, and constituting an additional tool to the traditional taxonomic approach (de Bello et al., 2010). The FD index was calculated using the avian niche traits provided in Pearman et al. (2014), based on feeding and breeding ecology of species. The trait table consists of 72 variables (ESM, Table S1) that describe 6

the niche of each bird species, including variables across (a) body mass; (b) food types (13 variables); (c) behaviours used for acquiring food (9 variables); (d) substrate from which food is taken (9 variables); (d) period of day during which a species forages actively (3 variables); (e) foraging habitat (20 variables) and (e) nesting habitats (17 variables) (Pearman et al., 2014). All the variables except body mass are binomial (scored as either 0 or 1). The FD index was then calculated as Rao’s Quadratic Entropy (RaoQ). This index considers relative abundance of bird species and pairwise functional

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differences between species (Pena et al., 2017; Ricotta and Moretti, 2011), being an indirect measure of functional evenness, since the higher values correspond to greater

species dissimilarity and thus high functional evenness in bird communities. RaoQ was

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calculated using ‘FD’ package for R (Laliberté et al., 2015).

(3) Mean evolutionary distinctiveness (ED): also named evolutionary uniqueness, was used to estimate phylogenetic diversity and species uniqueness of bird communities (Isaac et al., 2007). The ED score is calculated by summing the product of each branch length in

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the phylogenetic tree divided by the number of descendant taxa (leaves) below that

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branch (Isaac et al., 2007). We downloaded the ED score for each bird species from

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www.edgeofexistence.org (Zoological Society of London, 2008). Using the ED score, we calculated the mean ED for each bird assemblage (Morelli et al., 2016; Tucker et al.,

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2016).

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Statistical analysis

In order to avoid redundant variables, we explored the data by means of a principal component analysis (PCA) on environmental variables measured during field work in parks

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and cemeteries (Janželkovič and Novak, 2012) (ESM, Fig. S1). We also used Pearson’s product-moment correlations to explore the relationships among all predictors during the

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preliminary examination of the dataset. Predictors were included in models only if their correlations were below 0.6, in order to avoid multicollinearity issues (Graham, 2003). Spatial autocorrelation in the dataset was checked using Mantel tests (Legendre and Fortin, 2010; Mantel, 1967), based on a matrix of geographic distances (derived from the latitude

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and longitude) and a second matrix of differences in BSR, FD and mean ED among sampling points, respectively, applying Monte Carlo permutations with 9999 randomizations (Oksanen et al., 2016). Sampling points were treated as statistically independent observations because spatial autocorrelation was not detected (rM < 0.11, 9999 randomizations, p > 0.05 for BSR, FD and mean ED).

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We applied a generalized linear mixed model (GLMM) accounting for variation in BSR, FD and mean ED score of bird communities in relation to the interaction between the type of site (park or cemetery) and category of urbanization (rural, peri-urban and urban), site size, altitude, local vegetation characteristics (grass, shrub and tree coverage), and local humanrelated structures (number of street lamps; chapel, flowerbed and tombstone coverage), in each sampling point in the four European countries. Full models also included the value of minimum distance from the border of each park/cemetery to the nearest green area and

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altitude as predictors. City was added as a random factor in the statistical models. Latitude and longitude were not included as predictors because of the redundancy with cities used as a random factor. Models were fitted assuming a Poisson distribution for BSR, log normal

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distribution for FD and normal distribution for mean ED score, after exploring the distribution of variables (Box and Cox, 1964) using the package ‘MASS’ (Venables and Ripley, 2002),

and ‘glmmADMB’ in R (Fournier et al., 2012; Skaug et al., 2013). Potential over-dispersion of data was checked using the package ‘AER’ in R (Kleiber and Zeileis, 2008), but data were

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no over dispersed. Full models included all the variables described above. The model selection was based on lower Akaike information criterion (AIC) and was performed using

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the package ‘AICcmodavg’ in R (Mazerolle, 2016).

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Furthermore, we performed a regression tree analysis (De’ath, 2002) in order to quantify

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thresholds of vegetation characteristics (grass, shrub and tree coverage), human-related structures (number of street lamps; chapel, flowerbed and tombstone coverage) and size of

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park/cemetery, changing the values of BSR, FD and mean ED. Regression tree analysis is a machine-learning method, which builds prediction models where data are split into multiple blocks recursively and the prediction model is fitted to each such partition (Breiman et al.,

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1984; De’ath, 2002; Prasad et al., 2006). Then, the result of the analysis is a tree in which terminal groups of sites (or nodes) are composed of subsets of sites selected to minimize

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the within-group sums of squares, but where each successive partition is defined by a threshold value of the explanatory variables (Borcard et al., 2011). The analysis was performed with the R package ‘party’ (Hothorn et al., 2006). All statistical tests were

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performed with R 3.4.3 software (R Development Core Team, 2017).

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RESULTS In total, 169 sites in four European countries were visited during this study: 79 parks and 90 cemeteries were explored by 404 point counts of birds (ESM, Table S2). Table S3 in ESM shows mean values for environmental descriptors of all sites. During the field work, a total of 86 breeding bird species were recorded (ESM, Table S4). The five most frequent species were: Parus major, Turdus merula, Fringilla coelebs, Columba palumbus and Cyanistes

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caeruleus (ESM, Table S4). The highest value of BSR was recorded in one of Polish parks (28 bird species). The

maximum value of BSR for a cemetery was also found in Poland, followed by cemeteries

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from France and Italy. The maximum value of FD of a bird community was found in a park in the Czech Republic, but the highest mean values were found, respectively, for a park in France and a cemetery in Italy (Fig. 2). The mean ED scores of bird communities were rather similar between parks and cemeteries in Poland and the Czech Republic, while

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slightly higher in parks than in cemeteries for Italy and France (Fig. 2). The type of site

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(park/cemetery) was not statistically significant in explaining the variation in BSR, FD and ED score of bird communities (Table 1). In parks/cemeteries in urban areas, we found that the

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with any of the community metrics (Table 1).

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minimum distance from the border to the nearest green area was not statistically associated

The GLMM analysis revealed that variations in bird community metrics were associated with

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site size, local vegetation characteristics and human-related structures, although with different responses in each particular metric. In both parks and cemeteries, BSR was slightly positively correlated with site size and tree coverage around the sampling point, while

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negatively associated with altitude (Table 1, Fig. 3). In contrast, FD of bird communities was statistically unrelated to all environmental characteristics estimated at the sampling point

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(Table 1). Finally, the mean ED score of bird communities was positively correlated with tree coverage, but negatively associated with flowerbed coverage and the “urban” category of urbanization (Table 1, Fig. 4). The list and description of full and best models selected for BSR and mean ED is provided in ESM (Table S5). Both best models were optimized when

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selecting only three predictors (ESM, Table S5). Regression tree models showed different responses for each metric per sampling point. While FD was unrelated to any variable measured, the BSR was lower when grass coverage exceeded 65% around the sampling point, and was higher when grass coverage was < 65% and park/cemetery area was > 1.4 ha (Fig. 5). The mean ED score of bird communities was mainly affected by tree coverage, and was higher in areas with tree coverage > 45% (Fig. 6). 9

DISCUSSION Several studies have suggested that factors such as type of management (typically, topdown) and level of human disturbance support lower biodiversity in cemeteries and parks than in the case of other types of urban green areas, where a bottom-up management practice is followed and human activity is less tangible (Adams et al., 2005; Lussenhop, 1977). It has been shown that urban conditions may act as a filter of species based on their biological traits, thus influencing bird community patterns, such as species composition,

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community richness and population sizes of birds (Coetzee and Chown, 2016; Croci et al., 2008a; Maklakov et al., 2011; Tscharntke et al., 2012).

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We have found no differences in avian diversity (assessed considering taxonomic, functional and evolutionary diversity) between parks and cemeteries in the four European countries. The findings of this study highlight how cemeteries work similarly than urban parks supporting avian diversity. However, considering separately each metric, we found some

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clear patterns associated to each facet of avian diversity.

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Focusing on BSR, the diagrams in Fig. 3 indicate a positive association with tree and shrub

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coverage, and a negative correlation with grass coverage (see Fig. 3). Though, considering the differences related to the cities or towns, the results of mixed models showed that the

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only significant associations were the positive ones with tree coverage and site size (Table 1), and the negative one with altitude. The number of species was higher in the largest sites,

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but this association was the strongest in cemeteries, while in parks BSR was almost constant. This association was strongest in cemeteries probably because the largest sites monitored in this study were some cemeteries. Other studies already suggested a positive

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association between area of a green patches in the urban tissue and bird diversity (Callaghan et al., 2018; Fontana et al., 2011; Schütz and Schulze, 2015). The negative

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association between BSR and altitude found in this study supports the well-established statement that links a decrease in species richness with increasing elevation gradient, mainly due to a gradual reduction of temperatures and decrease in habitat productivity (Hortal et al., 2013; Rohde, 1992).

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The regression tree analysis also supported the main results obtained for mixed models, providing additional information: a threshold value, where the number of species starts to decline. Combining the results of regression tree analysis, we can expect that in both parks and cemeteries, BSR should be higher in areas where grass coverage is lower than 65% and area size is larger than 1.4 ha.

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The negative association between BSR in parks or cemeteries with grass coverage higher than 65% is intriguing. First of all, grassy areas produced open space and edge effect, which is important for some species as singing places, for example: Emberiza citrinella, Miliaria calandra (Jokimäki, 1999). Secondly, what is probably much more important, open grasslands offer foraging places for Sturnus vulgaris, thrushes Turdus sp., as well as corvids and birds of prey (Hogg and Nilon, 2015; Mikula et al., 2014). However, probably parks and cemeteries with grass coverage higher than 65% are also less heterogeneous green areas.

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We can hypothesize that by increasing the grass coverage and decreasing the habitat heterogeneity, the availability of new niches for many bird species should be also reduced. Focusing on FD, we found that both local vegetation characteristics and human-related

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structures measured in the field did not affect significantly this diversity metric of bird

assemblages. We can hypothesize that this lack of association detected is due mainly to the fact that focusing on bird species from urban areas (distinguished from rural and peri-urban ones) we are recording an important number of species with very similar characteristics in

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terms of traits, and also subject to the pressure of biotic homogenization. For this reason, the

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variations in FD are not so strong among the sites monitored; implying also those effects of

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external factors on FD should be not so strong or easy to be detected. A setback of this fact is that we cannot provide any guidelines or practical indication for ecological planning about

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how to increase the FD of bird communities inhabiting parks and cemeteries, if considering only our findings.

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Focusing on evolutionary diversity of bird assemblages, we found that in both types of green areas (parks and cemeteries), mean ED was positively correlated with tree coverage, but

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negatively associated with flowerbed coverage. Additionally, we found that the mean ED of bird communities was lower in parks and cemeteries located in urban areas than in rural or peri-urban areas. Positive associations between bird communities with high evolutionary

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uniqueness and coverage of trees or large trees were already tested in another study, focused on urban parks (Morelli et al., 2017), and our results confirm the same pattern. Our findings also highlight the importance of tree coverage in maintaining both species richness and evolutionary uniqueness of bird communities across urban green areas. Croci et al.

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(2008) reported that urban adapters prefer tree-rich habitats and habitat availability in cities seems to be a key determinant of the biological filter that affects the success of species invasion from natural areas to the urban environment (Croci et al., 2008a). A potential explanation for the association between tree coverage and mean ED score of bird communities is that higher tree coverage can guarantee the habitat suitability to bird species characterized by evolutionary uniqueness, for example the Eurasian Hoopoe, Upupa epops, 11

with an ED score higher than 35 (see ESM, Table C). The contemporary occurrence of a few species characterized by high ED scores could increase the mean values of the urban bird assemblage, often characterized by not very rich communities. It is less easy to interpret the negative association between the mean ED score of bird communities and flowerbed coverage. One possibility is an indirect association: we can expect a negative – even if slight – association between tree and flowerbed coverage, expecting more flowerbeds in the open areas of urban parks. For this reason, some species

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– like the above-mentioned Eurasian Hoopoe and the Firecrest, Regulus ignicapilla, or the

Goldcrest, Regulus regulus, which are more related to dense tree coverage – should be less likely to be recorded in areas characterized by the presence of flowerbeds, causing the

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decline in the mean ED score of the assemblages. The fact that also our study described

bird communities characterized by lower evolutionary uniqueness (mean ED score) seems to confirm that urbanization can affect the bird communities, promoting not only biotic homogenization (Devictor et al., 2008; Reif et al., 2013) but also evolutionary

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homogenization of species assemblages (Ibáñez-Álamo et al., 2016; Morelli et al., 2016). Finally, the fact that the minimum distance to the nearest green area was not selected as a

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predictor for any diversity or community metric calculated in bird assemblages in parks or

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cemeteries from urban areas, could simply suggest that the urban areas visited were characterized by similar ecological connectivity.

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While the human-made structures, such as buildings, roads, railway and pylons, are well recognized as having a negative impact on biodiversity, recently their positive effect on certain bird species and communities has been recognised (Morelli et al., 2014a;

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Tryjanowski et al., 2014). Such structures may provide novel microhabitat types, perches, nesting substrates, decrease predation pressure or prolong diurnal activity, as in the case of

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street lamps (Tryjanowski et al., 2014). In the present study, however, we have found no effect of the number of street lamps on the number of species, functional diversity or evolutionary uniqueness of bird communities. This may suggest that: (1) some human-made structures may play a positive role for the avian community in, for instance, farmlands

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(Tryjanowski et al. 2014), but it does not seem to apply to less homogenous and more fragmented urban areas; (2) in urban areas, where the majority of space is covered by builtup areas, the parks and cemeteries are refugees of less urban-tolerant species, such as forest birds requiring larger natural-like patches of habitats; and (3) in a majority of the parks and cemeteries studied, any potential negative effect of light pollution on bird communities (due to the presence of street lamps) was not evident in terms of change in bird species composition. This is important also for the diurnal use of the habitats, because the effect of 12

light pollution, even if is more evident during the nocturnal hours, could affect the diurnal activities of birds by attracting more rich insect communities, because increased illumination can modify or extend diurnal behaviours of many species, for example, foraging under artificial lights (Longcore and Rich, 2004) and because light pollution can affect the timing of reproductive behaviour in some songbird species (Kempenaers et al., 2010). In our study, we focused the analyses mainly in the land use composition and presence of human related structures typical from urban parks and cemeteries. A potential limitation of

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this approach could be the lack of information about the age of parks and cemeteries,

information that is not easy to be collected. On this regard, we suggest to use the size of

trees (data that was not collected in this study) as a potential good surrogate about the age

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of urban green areas in future studies.

In conclusion, our results suggest that both parks and cemeteries could represent important biodiversity hotspots in urbanized areas. We found no differences between parks and

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cemeteries in terms of BSR, FD and mean ED of bird communities in four European

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countries. Larger parks and cemeteries may provide an increased heterogeneity of habitats and offer suitable habitats for larger species, which have larger home ranges (Andrén, 1994;

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Bowman, 2003; Van Dorp and Opdam, 1987). Urban adapters also preferentially inhabit

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forest environments (Croci et al., 2008b), and this may be linked to the positive effect of tree coverage but also to the negative effect of flowerbed or chapel coverage on bird diversity in both types of sites and of tombstones in cemeteries. Finally, our results have thus

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consequences for the planning of urban green space. Some findings can be easily translated into practice for eco-friendly urban planning. The importance of size of urban green areas as

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support for biodiversity is most significant for taxonomic and evolutionary diversity of bird communities. Also by increasing the tree coverage, it is possible to enhance both taxonomic richness and evolutionary uniqueness of bird communities in parks and cemeteries of rural,

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peri-urban and urban areas. Similar urban planning strategies could be applied for parks and cemeteries, because both constitute a similar support for avian diversity.

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Acknowledgements We are very grateful to Martin Hromada for discussions at the initial stage of the study. We thank also the anonymous reviewers for their useful suggestions, which helped us to improve the final version of the manuscript. We are grateful also to Sylwia Ufnalska, EASE Council member, her valuable comments during the English proof reading of the manuscript. 13

F.M. and Y.B. were financially supported by the Czech Science Foundation GAČR (project

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number 18-16738S).

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Figures Figure 1. Study sites (parks and cemeteries) where bird data were collected in the Czech

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Republic, France, Italy and Poland.

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Figure 2. Comparison of bird species richness, functional diversity and community evolutionary distinctiveness (mean ED) score between parks and cemeteries in the Czech Republic, France, Italy and Poland. The y-axis represents the bird diversity metrics. Box plots show the median (bar in the middle of rectangles), mean (yellow circle), upper and lower quartiles, maximum and minimum values (vertical lines), jittered points (small green

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dots) and outliers (black dots).

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Figure 3. Associations between bird species richness, functional diversity (RaoQ) and mean evolutionary distinctiveness (ED) score of bird communities in relation to local vegetation characteristics (grass, shrub and tree coverage expressed in %) in parks and cemeteries in the Czech Republic, France, Italy and Poland. Envelopes around lines are 95% confidence

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intervals.

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Figure 4. Associations between bird species richness, functional diversity (RaoQ) and mean evolutionary distinctiveness (ED) score of bird communities in relation to size of park/cemetery and local human-related structures (number of street lamps; chapel, flowerbed and tombstone coverage expressed in %), in parks and cemeteries in the Czech

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Republic, France, Italy and Poland. Envelopes around lines are 95% confidence intervals.

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Figure 5. Conditional inference tree based on local vegetation characteristics (grass, shrub and tree coverage), human-related structures (number of street lamps; chapel, flowerbed and tombstone coverage) and size of park/cemetery as predictors of bird species richness, in the Czech Republic, France, Italy and Poland. Under each node ‘n’ is the number of sampling points in the leaf or group. Values shown in the boxplots denote the species richness of the group. Leaves of the tree indicate mean catch weight per two of each group, given the conditions and thresholds stipulated by the splits. The first split is for grass

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coverage (65%), and the second split is for size of park/cemetery (1.4 ha), in areas with

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grass coverage higher than 65%.

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Figure 6. Conditional inference tree based on local vegetation characteristics (grass, shrub and tree coverage), human-related structures (number of street lamps; chapel, flowerbed and tombstone coverage) and size of park/cemetery as predictors of mean evolutionary distinctiveness of bird assemblages in the Czech Republic, France, Italy and Poland. Under each node, ‘n’ is the number of sampling points in the leaf or group. Values shown in the boxplots denote the species richness of the group. Leaves of the tree indicate mean catch weight per two of each group, given the conditions and thresholds stipulated by the splits.

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The only split is for tree coverage (45.3%).

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Tables Table 1. Results of generalized linear mixed models (GLMMs), accounting for variation in bird species richness and mean evolutionary distinctiveness (ED) score of bird communities in relation to the interaction between type of site (park or cemetery) and category of urbanization (rural, peri-urban and urban), local vegetation characteristics (grass, shrub and tree coverage), human-related structures (number of street lamps; chapel, flowerbed and tombstone coverage), altitude and size of park/cemetery in four European countries: the

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Czech Republic, France, Italy and Poland. The cities or towns where data were collected were added as a random factor in the models (groups: 49). Only significant variables are

shown. Abbreviations: ES – estimate; CI – confidence interval (min, max); SE – standard

Model/variable

ES

SC R

error. CI

SE

Bird species richness (2.345 / 2.874)

Trees

0.005

(0.002 / 0.008)

Site size

0.003

Altitude

-0.001

19.367

< 2e-16

0.001

3.713

2.0e-05

(5e-04 / 0.006)

0.001

2.301

0.021

(-0.002 / -4e-04)

4e-04

-2.850

0.004

(6.766 / 8.064)

0.313

22.383

< 2e-16

(0.007 / 0.019)

0.003

4.327

3.6e-05

Trees

0.013

Flowerbeds

A

-0.018

(-0.036 / -7e-04)

0.009

-2.044

0.046

-0.568

(-0.955 / -0.181)

0.197

-2.953

0.044

A

CC E

PT

Category (urban)

ED

7.415

M

Mean ED Intercept

P

0.134

U

2.609

N

Intercept

z/t

30