François Chiron
email: françois.chiron@universite-paris-saclay.fr

Romain Lorrillière

Carmen Bessa-Gomes
email: carmen.bessa-gomes@universite-paris-saclay.fr

Piotr Tryjanowski
email: piotr.tryjanowski@gmail.com

Joan Casanelles-Abella

Lauri Laanisto
email: lauri.laanisto@ut.ee

Ana Leal

Anskje Van Mensel
email: anskje.vanmensel@uantwerpen.be

Marco Moretti
email: marco.moretti@wsl.ch

Babette Muyshondt
email: babette.muyshondt@uantwerpen.be

Romain Lorrilliere
email: romain.lorrilliere@mnhn.fr

Ülo Niinemets
email: ylo.niinemets@emu.ee

Marta Alós Ortí
email: martaalosorti@gmail.com

Pedro Pinho

Roeland Samson
email: roeland.samson@uantwerpen.be

Nicolas Deguines
email: nicolas.deguines@univ-poitiers.fr

Affiliations

How do urban green space designs shape avian communities? Testing the area-heterogeneity trade-off

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In cities, nature develops in all places but faces multiple pressures due to human activities that are more intense than in non-urban areas [START_REF] Muratet | Manual of urban ecology[END_REF]. Consequently, urbanisation has significant impacts on species distribution, ecological interactions and diversity of communities [START_REF] Marzluff | An international perspective on the interaction between humans and nature[END_REF]. At the city level, improving the suitability and permeability of urban habitats is essential in order to facilitate the movements of living organisms and maintain viable population sizes and complex ecological networks. In this regard, the meta-analysis of [START_REF] Beninde | Biodiversity in cities needs space: a meta analysis of factors determining intra urban biodiversity variation[END_REF] stressed the importance of green space design strategies and identified land cover area and its heterogeneity as the main predictors of species richness of several taxa. For instance, species richness responded positively to area, with the strongest effects on birds followed by insects and plants. Land cover heterogeneity promotes species richness through resource use differences (i.e. niche partitioning) by increasing the opportunities for different living conditions and the coexistence of consumer species [START_REF] Finke | Niche partitioning increases resource exploitation by diverse communities[END_REF]. Habitat amount and heterogeneity (e.g. habitat diversity) have attracted much attention in ecology, as researchers seek to determine the ecological rules that shape populations and communities and objectively define the threshold or minimum viable area below which diversity decreases [START_REF] Drinnan | The search for fragmentation thresholds in a southern Sydney suburb[END_REF].

While the positive relationship between the heterogeneity or area of land cover and species richness is widely acknowledged, the effects of heterogeneity and area have rarely been tested together. However, these two factors are dependent. As heterogeneity increases, in the sense of diversity of different land-cover types, the total amount of a given land cover decreases within a landscape (Figs. 1a-b). In addition to the effect of compositional heterogeneity on the total amount of each land cover type, dividing a land cover patch into several units increases configurational heterogeneity and reduces the average patch size of land cover (Figs. 1c-d;[START_REF] Fahrig | Functional landscape heterogeneity and animal biodiversity in agricultural landscapes[END_REF].

Ignoring these negative correlations, heterogeneity trade- [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF], may lead to mischaracterising the effects of land cover heterogeneity on species richness.

In this respect, [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF] suggested that environmental heterogeneity has a general unimodal rather than a positive effect on species richness. Using empirical data on birds, [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF] validated this hypothesis and provided a comprehensive evaluation of the mechanisms underlying this pattern. For relatively low levels of heterogeneity, when area is not a limiting factor, species richness is expected to increase with heterogeneity due to species colonisation with new species adapted to the newly available niches (Figs. 1e-f). As heterogeneity continues to increase, richness is expected to decrease when the amount of effective area available for the individual species decreases, which possibly reduces population sizes and increases the likelihood of stochastic extinctions [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF]. We expect that this unimodal relationship is particularly fitted to environments where the trade-off between habitat area (or mean patch area) and heterogeneity is accentuated by the limited space left for nature as in cities. In Europe, only 18% of urban areas are covered by vegetation, thus reducing the possibility for large habitat patches [START_REF] Fuller | The scaling of green space coverage in European cities[END_REF]. Small elements like gardens are a major component of the green area proportion in many cities [START_REF] Goddard | Scaling up from gardens: biodiversity conservation in urban environments[END_REF]. In addition to the small size of green spaces, stakeholders such as public managers and private landowners also promote a plurality of habitat conditions by dividing them into smaller lots covered by lawns, grasslands, dense or sparse tree cover and shrub layers in addition to aquatic habitats. The diversification of land cover types can thus split green spaces into very small fragments.

Despite the large diversity of green spaces in European cities, the purposes and practices behind the creation of land cover mosaics are common standards used by most city managers across the continent [START_REF] Kohout | Green space ideas and practices in European cities[END_REF]. These standards aim to provide people with cultural (e.g., aesthetics), social (e.g., recreational), economic and health benefits [START_REF] Muratet | Manual of urban ecology[END_REF] while conserving urban biodiversity [START_REF] Muratet | Perception and knowledge of plant diversity among urban park users[END_REF]. Managers of urban green spaces often assume that environmental heterogeneity increases species diversity [START_REF] Aronson | Biodiversity in the city: key challenges for urban green space management[END_REF]. It was therefore proposed that management actions should endeavour to increase the heterogeneity of habitat conditions to promote local species diversity [START_REF] Kati | Towards the use of ecological heterogeneity to design reserve networks: A case study from Dadia National Park, Greece[END_REF]. Based on this overview, decisions that promote greater heterogeneity in cities might thus lead to unintended results, especially if the overall size of the urban green spaces is small and species abundance is low.

The predicted effects of land cover heterogeneity on birds are unlikely to be uniform across species. As shown by [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF], the position of the inflection point, i.e. the level of heterogeneity that maximizes richness in the unimodal relationship, depends on niche width of the species. Niche width is also [START_REF] Devictor | Defining and measuring ecological specialization[END_REF]. If the patch area is not a limiting factor, land cover heterogeneity may increase richness of specialist species, and of species that specifically benefit from the juxtaposition of complementary habitats, such as woody structures for nesting or as shelter and open areas for foraging. On the contrary, generalists (i.e., species with broader niches) can persist in a wide variety of environmental conditions across space or time, are less sensitive to reduced habitat patch areas and less prone to local extinctions [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF]. Richness of specialists are thus expected to increase with land cover heterogeneity (at constant patch size area), and with habitat patch size (at constant land cover heterogeneity) at a higher rate than generalists. In birds, habitat specialist species have lower dispersal abilities and cannot thrive in human-degraded ecosystems [START_REF] Clavel | Worldwide decline of specialist species: toward a global functional homogenization?[END_REF]. They are less abundant and decline at a greater rate than generalists along the urbanisation gradient [START_REF] Guetté | Measuring the synanthropy of species and communities to monitor the effects of urbanization on biodiversity[END_REF] such that avian specialisation is negatively associated with tolerance to urban stressors (e.g., pollution, fragmentation, low habitat quality) (Callaghan et al., 2020a). Testing the area heterogeneity trade-off in urban green spaces in terms of avian diversity and according to the degree of species urban avoidance is thus particularly relevant. To date, this has not been assessed in cities. Untangling the relative effects of area and heterogeneity on avian fauna is critical if we are to design urban green areas that can conserve the most urban-sensitive species.

In this study, we first investigated whether there is a trade-off between land cover heterogeneity and mean patch area in urban green spaces. We predicted a negative correlation between these two variables. We explored this relationship in six European cities from boreal to Mediterranean climates. Although landscape design, planting and green space management practices are often the same across cities, we expected some variations due to the geographical context and size of the cities in question [START_REF] Alós Ortí | Negative relationship between woody species density and urban green spaces in seven European cities[END_REF]. Second, we tested whether this trade-off shapes the avian communities and results in the expected unimodal relationship between avian species richness and land cover heterogeneity in urban green spaces. We predicted a higher bird richness at intermediate than at low and high heterogeneity levels. Finally, we evaluated the proportion of urban avoider species in cities, and if and how their traits explain the response of bird richness to mean patch area and land cover heterogeneity. We expected that urban avoider species (i.e., species that usually breed in natural or semi-natural habitats but not in human settlements or other artificial environments) would show a stronger positive relationship with mean patch area, and a stronger positive relationship with land cover heterogeneity than urban-adapted species, all other variables being equal.

Methods

Study areas

We sampled avian communities in six European cities: Almada and Lisbon (Portugal), Paris (France), Zurich (Switzerland), Poznan (Poland) and Tartu (Estonia). The selected cities represent a gradient of latitude and longitude to cover most of the climatic conditions in mainland Europe.

To ensure the consistency of the spatial extent between cities, in some cases we used their full extent (e.g., Tartu) and in others only a section (e.g., Paris). In all cases, we verified that a density gradient of urban areas was represented by asking coordinators of the study for each city to select a spatial extent with low and high proportion of impervious areas [START_REF] Pinho | Research agenda on biodiversity and ecosystem functions and services in European cities[END_REF].

Sampling site selection

To avoid heterogeneity between national databases, we used a pan-European land cover classification based on the European Urban Atlas ( 2018), which provides maps for urban areas across Europe (i.e., minimum mapping unit = 0.25 ha, minimum mapping width = 10 m). For consistency in land use intensity and typology across Europe

Code 14100 in the Mapping Guide v6.2 for the Urban Atlas) to select the sampled sites. UGAs are public green areas for predominantly recreational use such as gardens, parks or castle parks, or forests and other green areas extending from the surroundings into urban areas.

Within the study areas, we selected UGAs according to two independent gradients: 1) UGA size and 2) structural connectivity with green elements (i.e., UGAs and other areas considered as favourable land covers for birds such as forest and low urban density with <30% impervious surface). We calculated the degree of connectivity between green areas using the Proximity Index (PI) in Fragstats within a 5 km radius from every selected UGA. The PI is the area of all nearby green elements (m²) within the selected radius weighted by edge to edge distance squared to a specific UGA [START_REF] Mcgarigal | FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps[END_REF].

All possible UGAs six size classes and six PI classes equally splitted along the two gradients and similarly across cities to allow comparisons, leading to 36 possible combinations. Within these combinations and keeping a minimum distance of 500 m between UGAs (except for two sites in Zurich, and two sites in Tartu separated by 260 m and 170 m respectively), UGAs were selected randomly. Due to UGAs limitations, we used between 15 (Almada) and 35 (Paris) combinations (total of 176 UGAs, see Table 1 for more details).

Description of land cover at UGA and landscape levels

We computed land cover composition and configuration metrics at the local (within UGAs) and landscape levels, which were potentially drivers of the avian community.

At the local level, we produced a land cover map of each UGA using the photo-interpretation of high-resolution aerial images (i.e., 0.5 m) from 2015 with ESRI ArcMap 10.4. The photointerpretation was performed at a scale of 1:600, allowing us to distinguish between the main land cover types within green areas [START_REF] Alós Ortí | Negative relationship between woody species density and urban green spaces in seven European cities[END_REF]. We classified land cover into 10 categories known to influence bird diversity: allotments gardens (i.e. parcels containing cultivated areas with fruits or vegetables, as well as a shed for tools and shelter), artificial surfaces (i.e. houses, roads and playgrounds), bare land, coniferous trees, broadleaved trees, lawns and grasslands, lakes and ponds, riparian vegetation, rivers and streams, and shrubs [START_REF] Bino | Accurate prediction of bird species richness patterns in an urban environment using Landsatderived NDVI and spectral unmixing[END_REF]. In each UGA, we mapped all patches of land cover. Aerial images did not allow us to precisely classify the extent of all land cover types due to the overlapping vertical layers. Therefore, our land cover maps provided accurate information about tree canopy (i.e. the upper layer) and the other layers not covered by trees. Based on this mapping, we measured UGA total area and the heterogeneity of land cover categories [START_REF] Fahrig | Functional landscape heterogeneity and animal biodiversity in agricultural landscapes[END_REF]. The Shannon index is the most applied metric to capture the variety and the richness of land use/land cover and its characteristics in a given extent or spatial unit (Zhang et al., 2023). For UGA land cover configuration, we calculated mean patch area and contagion index, which measures the degree of aggregation of the same land cover within each UGA (Riitters et al., 1996). This index is computed based on the frequencies by which different pairs of land covers occur as adjacent pixels on a map.

To describe land cover composition at the landscape level, we used all land cover classes of the Urban Atlas m buffer distance from the edge of the UGAs. This distance is a good compromise to detect the maximum landscape effect on diversity and composition of urban bird communities [START_REF] Melles | Urban bird diversity and landscape complexity: species environment associations along a multiscale habitat gradient[END_REF][START_REF] Chang | Multiscale heterogeneity within and beyond Taipei city greenspaces and their relationship with avian biodiversity[END_REF]Villaseñor et al., 2021), while minimizing overlap between buffers and ensuring independence in landscape analysis (pairwise distances among UGAs were larger than 750 m in 99% of cases, Zuckerberg et al., 2020). Because buffer area depends on the UGA size and its shape, we controlled for the effect of the buffer area around each UGA on landscape heterogeneity. We used the residuals of the relationship between the land cover heterogeneity and buffer area in the statistical analyses. For landscape connectivity, we used the PI as calculated above (i.e., within a 5 km radius from every selected UGA). Except for the PI, all local and [START_REF] Hesselbarth | landscapemetrics: an open-source R tool to calculate landscape metrics[END_REF].

Bird sampling

In 2019, we counted birds using one point count per UGA, located as close as possible to its centroid on a location where the main land cover types (trees, bush, grassland, water) are present in a 50 m radius. In this manner, we collected information on potential bird species living in different types of land covers.

Within each country, we hired an experienced birdwatcher to count birds within 4 hours from daybreak to standardise the time of sampling along latitudes. Each plot was sampled twice to detect both more residential and more migratory bird species during the breeding season. First counts started early April (for Almada and Lisbon) and second counts ended late May (for Tartu and

Poznan) with at least 3 weeks between the two counts for each city. We adapted the sampling day to the local weather conditions to avoid rain, fog and wind. All observations were conducted on weekdays from Monday to Friday. UGAs surveyed on the same day had the order of sampling randomised for the second survey to ensure that the time of the day did not influence species detection.

During each visit, the birdwatcher counted and identified all individuals heard or seen for 10 min.

This duration is a minimum but represents a non-negligible investment of time that we considered as reasonable to detect a large proportion of bird species at sampling point and estimate bird responses to main local and landscape drivers . Birds were counted inside a 100 m radius area . Birds flying over the sampling point (transients) were recorded separately, as they neither arrived or left the sampled area during the point count nor clearly indicated their use of the area as a breeding territory. However, flying birds feeding or hunting in the air (i.e., swifts) were noted as observed within the 100 m radius.

Species richness was the total number of species counted within a 100 m radius during the two visits for each UGA.

We used a constant-area sampling (i.e. inside a 100 m radius area) rather than an area-balanced approach with more sampling points in the largest UGAs for two reasons. First, we aimed at understanding influences of variables shaping the area -heterogeneity trade-off without the influence of sampling effort. Second, replicating count points in large green areas is cumbersome, demanding a lot of field work if we wanted to cover the full green area extent (e.g. one point count location for every 250 m by 250 m area). To avoid potential bias due to our sampling strategy, we verified that counting area (3.14 ha) covered the full extent of most UGAs (63% of them were smaller than this area) or was representative of the UGA land cover heterogeneity. For UGAs bigger than 3.14 ha, there was a good correlation in land cover heterogeneities between the UGA and the counting area levels, and a good correlation in mean patch areas between these two levels too ( = 0.77 and 0.97 respectively, p < 0.01). Bird sampling was thus conducted in an area representative of the whole UGAs studied.

As ambient noise during bird surveys could influence bird detection, we accounted for in statistical models (see below). During the first 4 minutes of counting birds, the birdwatcher collected noise data and measured the maximum sound volume in decibels (dB) in the environment for 1 minute in each of the four cardinal directions using a digital decibel metre (device Preciva®, measuring range: 30-130 dB; accuracy: ±1.5 dB) set on a tripod at the height of 1 metre. We then calculated s we surveyed each point twice (temporal replicate). Any potential noise peak bias during the 1-minute sampling period would be smoothed by the second survey. Finally, we recorded temperature (in °C) and wind (Beaufort scale) to exclude potential observations collected during bad weather conditions.

Defining avian avoidance in urban areas

We built a continuous gradient of urban avian avoidance, which is consistent with the breeding ecology of species, and integrated a variety of habitat requirements for nesting (Keller et al., 2020).

For that purpose, we used the life-history trait database of Storchová & (2018), which compiles information about bird occurrences in 15 nesting habitats: deciduous forest, coniferous forest, woodland, shrub, savanna, tundra, grassland, mountain meadows, reed, swamps, desert, freshwater, marine, rocks and human settlements. This information was derived from The Birds of the Western Palearctic handbook [START_REF] Cramp | The birds of the western Palearctic interactive[END_REF] (2018). We removed them from the habitat preference analysis and regression models using this information. We then carried out principal component analysis (PCA function, R package FactoMineR; [START_REF] Husson | FactoMineR: Factor Analysis and Data Mining with R. R package version 1.04[END_REF] using information about bird occurrences in each nesting habitat. We evaluated the multivariate relationships between species coordinates on principal components and habitat variables to define the avian avoidance of urban areas (see Results).

Our approach differs from using expert knowledge to define species preferences to cities. Expertbased knowledge is valuable, but species ranking along a gradient of urban avoidance may vary vs Even though Callaghan & colleagues (2020b) previously estimated urban avoidance for European birds, we could not use their database because 25 species included in our study were missing from it (21% of the total number found here).

Statistical analyses

Trade-offs between mean patch areas and land cover heterogeneity of UGAs

To evaluate the area-heterogeneity trade-offs in our study area, we fitted a linear mixed model with -transformed) as (log-transformed) (R package lme4; [START_REF] Pinheiro | nlme: linear and nonlinear mixed effects models[END_REF].

In this model, we used the whole dataset, i.e. all cities, and account for the nested design of the study [START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF]. To assess the robustness of these relationships at the city level, we separately repeated the analysis for each city using a linear model and the same predictors.

Local and landscape effects on bird richness

We built a generalised linear mixed model using a Poisson family (glmer, R package lme4; [START_REF] Pinheiro | nlme: linear and nonlinear mixed effects models[END_REF] avian richness to test for non-linear effect of the area heterogeneity trade-off. As fixed effects, we added other variables that might influenc -transformed) and . We included as a potentially confounding variable

We of predictors as it was significantly correlated with correlation coefficient = -0.72 with p-value < 0.01; Appendix H). We used the whole dataset and added . Additionally, we repeated this analysis for each city separately using generalised linear models (glm) with the same predictors.

Influence of urban avoidance on the response of species richness to local and landscape variables

Based on the PCA, we assigned each bird species to a coordinate on the second component (PC2)

representing their level of urban avoidance (Appendix A). We then categorised species into three terciles corresponding to minimum, intermediate and high values of urban avoidance along PC2.

To define terciles, w would have been very unbalanced making statistical analysis problematic. Our splitting minimises species number differences among terciles which resulted in 37, 45 and 33 species for minimum, intermediate and high values of urban avoidance respectively.

For all UGAs, we calculated the avian richness of terciles as the number of species belonging to each urban avoidance tercile, and used it as the response variable in a glmer using a Poisson family. its non--(log-transformed),

to assess the effect of urban avoidance on the response of species richness to UGA variables. We

Finally, we computed a chi-squared ( 2) test on the response variable (i.e., avian richness of terciles) to assess variability in the distribution of tercile avian richness between cities.

Prior to fitting the models described above, we standardised the explanatory variables by subtracting the mean and dividing by the standard deviation. We assessed statistical significance 2 and Z statistics for the Poisson and Gaussian families, respectively) and performed Tukey HSD post-to compare avian richness between cities and terciles.

To check for multicollinearity between explanatory variables, we computed variance inflation factors (VIF) using check_collinearity function (R package performance). To alleviate moderate level of multicollinearity (VIF < 4) between some explanatory variables [START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF], we glmer) to obtain independent contribution of each explanatory variables based on semi-partial R² and quantified its uncertainty using parametric bootstrapping (Walsh & McNally, 2008;Stoffel et al., 2021). For all models, we statistically verified that the residuals met the assumptions of homoscedasticity and normality using DHARMa package [START_REF] Hartig | DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models[END_REF]. For Poisson models, overdispersion was checked . We also graphically confirmed the spatial independence of the residuals by computing a spline correlogram using the ; [START_REF] Bjørnstad | ncf: Spatial Covariance Functions version 1.2 8[END_REF].

package in R (Wickham, 2019;version 3.3.3). All analyses were performed in R version 4.1.2 (R Core Team, 2021).

Results

In total, we recorded 119 nesting bird species (7523 individuals) in the 176 green areas studied across the six European cities (see the list of species in Appendix B). Mean bird richness per UGA was 14.7 species (between 4 and 28, Table 1).

Relationships between mean patch area, UGA land cover heterogeneity and avian richness

Results from our linear models (Appendix C) confirmed a significant area-heterogeneity trade-off in urban green areas, as mean patch area in UGAs was negatively associated with land cover heterogeneity (Appendix I). Additionally, mean patch area was positively associated total area, as expected. These relationships were consistent across cities.

Species richness was positively correlated with UGA total area and land cover heterogeneity at the landscape level (Figs. 2b-c). Contrary to our prediction and after controlling for effects of these two latter variables we found no effect of UGA land cover heterogeneity on avian species richness (neither linear nor unimodal) in a model combining all cities (Appendix D, Figs. 2a). At city level, we found a unimodal relationship between land cover heterogeneity and avian richness for Zurich only (Appendix E).

Continuum of urban avoidance trait in birds

Based on the trait database of Storchová & (2018), we found that the birds encountered in UGAs have nesting habitats encompassing all categories found in Europe from marine and coastal habitats to swamps and forests, with the exception of deserts. The most frequent habitats occupied by the observed bird species were deciduous forests (56 sp.), coniferous forests (41 sp.) and human settlements (35 sp.). The vast majority of species occupied different habitat types (2.6 on average), although 17 species (14%) were attributed to a single habitat. Among them, two species nest only on human settlements (Streptopelia decaocto and Alopochen aegyptiaca), making them strict urban dwellers [START_REF] Fischer | Categorizing wildlife responses to urbanization and conservation implications of terminology[END_REF].

The PCA for the nesting habitat trait revealed three principal components, which explained 20.0%, 14.2% and 11.1% of the total variance, respectively (Figs. 3a-b, Appendix F). The first axis (PC1) separated forest and woodland species from marine and freshwater species ( -grassland and avoidance of urban ar be discussed further.

To assess the robustness of PC2 as an index of urban avoidance, we correlated it with a continuous ated based on the eBird observations of bird occurrences along a gradient of urbanisation in Europe [START_REF] Callaghan | Avian trait specialization is negatively associated with urban tolerance[END_REF]. This score was calculated as the distributional response of species to VIIRS night-time lights, a proxy of human population density and urbanization. Although some species were lacking from the urban score (21% of our species list), the two indices were negatively correlated coefficient = -0.45 with p-value < 0.001), thus validating the use of PC2 as an urban avoidance index. Our measure extended the calculation of urban avoidance to 25 other species in European cities.

Avoider species and their variation among cities

Based on PC2 coordinates (Fig. 3b), we grouped species into three terciles. [START_REF] Fischer | Categorizing wildlife responses to urbanization and conservation implications of terminology[END_REF].

The number of species in the three terciles varied among cities ( ²= 39.18, df = 10, p-value<0.001).

The proportion of species belonging to the high avoidance group (Tercile 3) was higher in Poznan and Tartu (28% of species) than in Zurich, Almada, Paris and Lisbon (between 10% and 18%, Fig. 4, Appendix G).

Influence of urban avoidance on the response of species richness to local and landscape variables

We found significantly contrasting responses of species richness to mean patch area of UGA depending on the level of urban avoidance, shifting from negative for species associated with human settlements (Tercile 1, low avoidance) to positive for urban avoider species (Terciles 2 and 3 for medium and high avoidance groups respectively, Fig. 5a, Table 2). We found similar relationships regarding the species richness response to land cover heterogeneity of UGA depending on the level of urban avoidance of birds. Relationships gradually shifted from negative to positive between species associated with human settlements (Tercile 1) and urban avoider species (Terciles 2 and 3) (Fig. 5b, Table 2). Variance contribution of UGA land cover heterogeneity (including its interactive effect with urban avoidance level) to species richness variation (R²=0.031, Table 2) was however lower than contribution of UGA mean patch area (R²=0.056, Table 2).

Discussion

Area heterogeneity trade-off and their effect on avian richness

Our results highlight that the mean patch area and land cover heterogeneity of UGAs are not independent factors: as heterogeneity increases, land cover patch size decreases. This pattern is the consequence of dividing UGA area into different land cover types (i.e., heterogeneity of composition) and the same land cover into small fragments (i.e., heterogeneity of configuration or fragmentation). This relationship was consistent across cities and confirms that despite the dominated environments [START_REF] Parris | Existing ecological theory applies to urban environments[END_REF]. The area heterogeneity trade-offs thus apply to urban landscapes, which underscores the need to study patch area and land cover heterogeneity in the same framework of analysis if we want to disentangle their roles in avian diversity.

Contrary to our expectations, we found neither positive nor unimodal relationships between land cover heterogeneity and avian species richness within UGAs, as proposed by [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF]. Despite the different approaches used for measuring the heterogeneity of local conditions in UGAs (e.g., through land cover richness, vegetation structure or its complexity, presence of microhabitats), empirical studies generally observed a positive correlation between heterogeneity and species richness [START_REF] Husté | Bird communities in suburban patches near Paris: determinants of local richness in a highly fragmented landscape[END_REF][START_REF] Nielsen | Species richness in urban parks and its drivers: a review of empirical evidence[END_REF][START_REF] González-Oreja | Does habitat heterogeneity affect bird community structure in urban parks. Urban bird ecology and conservation[END_REF]. This relationship was illustrated for various taxonomic groups such as plants, birds, mammals and reptiles [START_REF] Cornelis | Biodiversity relationships in urban and suburban parks in Flanders[END_REF]Garden et al., 200 ;[START_REF] Matthies | Determinants of species richness within and across taxonomic groups in urban green spaces[END_REF]. In these studies, part of the species-heterogeneity relationships can be attributed to larger parks that tend to harbour a greater number of habitat units (e.g., lawn, forest, pond, etc.) and hence greater species richness than smaller ones [START_REF] Cornelis | Biodiversity relationships in urban and suburban parks in Flanders[END_REF], but this relationship was also found in small parks (between 0.1 and 2 ha in Pachuca city, Mexico, Carbó-Ramírez & Zuria 2011). Controlling the effect of land cover heterogeneity by UGA area (total or mean patch size) in our study should thus not entirely explain the gap between our results and previous studies.

The species-heterogeneity relationship is an essential element of niche-based theories of species diversity and a key concept of the unimodal relationship presented by [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF]:

environmental heterogeneity should provide suitable conditions for a larger number of species with different ecological requirements, thereby increasing the local species pool [START_REF] Chase | Ecological Niches: Linking Classical and Contemporary Approaches[END_REF]. No relationship with heterogeneity may result from the reduced land cover diversity in the studied UGAs, which hampered our ability to detect changes in avian communities. This is unlikely because our sampling clearly evidenced variability in the land cover types (Table 1). The absence of a relationship might also arise from the relatively limited occurrence of key habitats such as aquatic environments. The occurrence of waterbodies such as lakes, ponds, river streams and riparian habitats has plummeted in urban ecosystems due to significant degradation from drainage and changes in the hydrological regimes of cities [START_REF] Moggridge | Urban Aquatic Ecosystems: the good, the bad and the ugly[END_REF]. In our sampling, these environments were underrepresented compared with terrestrial ones. However, 34% of sampled species (n=40) were associated with one or several aquatic habitats, thus indicating that these species were not absent from our sampling.

Finally, birds may only occupy a few patches among those accessible for nesting in UGAs, thus making the relationship between bird richness and land cover heterogeneity inaccurate at the UGA level. For instance, ground nesting birds rarely reproduce in open urban habitats like lawns and grasslands because of multiple threats, which could affect the behaviour, reproduction and survival of chicks and adults (e.g., higher disturbance caused by pedestrians, domestic animals, or management actions like grass cutting, [START_REF] Fernandez-Juricic | A habitat island approach to conserving birds in urban landscapes: case studies from southern and northern Europe[END_REF]). In urban woodlands, cleared understory vegetation (no shrub or herbaceous vegetation layers, and dead wood being removed) may increase disturbances and predation risks and limit food availability [START_REF] Croci | Small urban woodlands as biodiversity conservation hot-spot: a multi-taxon approach[END_REF].

Avian richness was positively influenced by the total area of UGAs. Larger UGAs increase the effective area available for individual species, thereby increasing population sizes and decreasing the likelihood of stochastic extinctions [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF]. Our results confirmed the speciesarea relationship: a well-known pattern already observed in birds, mammals and plants within

UGAs and in more natural ecosystems [START_REF] Nielsen | Species richness in urban parks and its drivers: a review of empirical evidence[END_REF][START_REF] Matthies | Determinants of species richness within and across taxonomic groups in urban green spaces[END_REF]. Interestingly, avian richness also increased with land cover heterogeneity in the neighbouring landscape. The land cover heterogeneity of the adjacent matrix is related to the diversity of terrestrial and aquatic natural habitats. Birds nesting in these habitats may spill over into UGAs and use them as foraging, nesting or refuge sites [START_REF] Mayorga | Local and landscape drivers of bird abundance, species richness, and trait composition in urban agroecosystems[END_REF].

Urban avoider species richness is greater in large and diversified green patches of land covers

The share of urban avoider species (terciles 2 and 3) in terms of species richness was greater in urban green areas with large land cover patches than in small ones. Richness of urban avoiders can simply be explained by habitat preferences and distribution of selected habitats among small and large green patches.. Also, urban avoiders are less able to persist in small habitat patches (high edge-to-area ratio), because they prefer the interior of large patches, similar to many specialists of forest and aquatic ecosystems [START_REF] Fernandez-Juricic | Avian spatial segregation at edges and interiors of urban parks in Madrid, Spain[END_REF]. Within-patch habitat quality is unlikely to be constant as UGA size and heterogeneity change. On the contrary, the number of urban adapted species (tercile 1, urban dwellers and utilisers) decreased with patch size within UGAs.

Urban dwellers can persist in urban environments independently of the natural areas, while urban utilisers can exploit resources supplied by human activities [START_REF] Fischer | Categorizing wildlife responses to urbanization and conservation implications of terminology[END_REF]. The latter can be widely distributed and locally abundant in cities [START_REF] Ducatez | Are urban vertebrates city specialists, artificial habitat exploiters, or environmental generalists?[END_REF]. Being mostly generalist, they can thrive in fragmented environments, because they are able to nest and feed in various environments.

Competitive interactions between urban adapted and avoider species may also explain these relationships. Urban adapted species may gradually disappear in large natural patches to avoid increasing competition with urban avoiders, which are specialist birds (Staude et al., 2021). This process could partly explain the segregation of habitat specialist and generalist birds between large and small patches. In small and fragmented patches, urban adapted birds can be widespread and behaviourally dominant species [START_REF] Ducatez | Are urban vertebrates city specialists, artificial habitat exploiters, or environmental generalists?[END_REF]. They can also exclude rarer and more subordinate species such as urban avoiders through competitive interference [START_REF] Martin | Species interactions limit the occurrence of urban-adapted birds in cities[END_REF]).

Although we did not find any effect of land cover heterogeneity of UGAs on total avian richness, we evidenced its positive effect on the richness of urban avoider species. Because these latter are often habitat specialists, land cover heterogeneity can provide the suitable conditions for more species with different ecological requirements, thereby increasing their local species pool [START_REF] Chase | Ecological Niches: Linking Classical and Contemporary Approaches[END_REF]. Since land cover heterogeneity is negatively correlated with mean patch area of UGAs, effect of land cover heterogeneity on urban avoider species cannot be analysed apart from the effect of mean patch size. We thus revealed a trade-off on species richness depending on species urban tolerance.

Relevance of green space design for bird conservation

We reported a relatively rich bird diversity (21% of European breeding bird species, Keller et al., 2020), thus making UGAs relevant areas for bird conservation. Most of these areas correspond to parks, renowned as urban biodiversity hotspots [START_REF] Nielsen | Species richness in urban parks and its drivers: a review of empirical evidence[END_REF]. It was therefore crucial to understand how designing UGAs for nurturing a diversity of bird species. Achieving this target is the result of a compromise between land cover surface area and its heterogeneity.

Although a recent analysis on the importance of natural areas recognised the value of small spaces for biodiversity conservation [START_REF] Riva | The disproportionately high value of small patches for biodiversity conservation[END_REF], we highlight the necessity to put effort into protecting large UGAs composed of big enough land cover patches to conserve bird richness.

Compared to small patches, large areas provide better protection for urban avoiders, which are most at risk globally [START_REF] Clavel | Worldwide decline of specialist species: toward a global functional homogenization?[END_REF]. Even though small green areas such as woodlots and public or private gardens are widespread and sometimes the only natural areas in cities, large areas are scarce and regularly exposed to urban development pressure [START_REF] Fuller | The scaling of green space coverage in European cities[END_REF].

Land cover diversification of UGAs (e.g., mixing woodlots, grasslands, waterbodies, and so on.) may increase the potential number of ecological niches and hence species richness of local assemblages (Callaghan et al., 2019). Restoring or protecting different land cover types is achievable in large UGAs where space is not a limiting resource compared to smaller ones. In small UGAs however, land cover diversification should not be regarded as a panacea by urban planners [START_REF] Kati | Towards the use of ecological heterogeneity to design reserve networks: A case study from Dadia National Park, Greece[END_REF] because it could lead to a drastic reduction in the average amount of a given habitat and its effective area available for specialist birds or birds that exploit complementary habitats. This may unintendedly reduce the occurrence of the most endangered species in cities.

For birds, a minimum area of 4.4 ha and of 53.3 ha were described to prevent the loss of urban adapters and of area-sensitive species like urban avoiders [START_REF] Beninde | Biodiversity in cities needs space: a meta analysis of factors determining intra urban biodiversity variation[END_REF]. We thus recommend limiting land cover heterogeneity on small UGAs to maintain green patches as large as possible.

To optimize biodiversity at city level, activities of design and management should be coordinated all across a city. Actions should diversify ecological conditions across green areas and not just within. In small areas, maximizing the coverage of a specific habitat type but different across UGAs would maintain urban avoiders populations in local species assemblages and enhance betadiversity. Actions should target ecological context from which UGAs are originate to preserve their uniqueness as city tend to homogenize local conditions [START_REF] Groffman | Ecological homogenization of urban USA[END_REF]. Although this could work for biodiversity, designing UGAs dominated by a few land cover types in small

UGAs could be negatively perceived by users if it does not take into consideration preference of people for some green area characteristics (e.g. presence of open areas, playgrounds, flowerbeds, waterbodies, tree cover, see [START_REF] Palliwoda | What do people value in urban green? Linking characteristics of[END_REF][START_REF] Palliwoda | What do people value in urban green? Linking characteristics of[END_REF][START_REF] Madureira | Preferences for urban green space characteristics: A comparative study in three Portuguese cities[END_REF]. A key challenge for UGA design is thus combining human needs with ecological requirements for preserving and enhancing avian communities especially green area sensitive species.

Finally, our study pointed out the importance of maintaining a variety of land cover types in the urban matrix surrounding green areas, which appears to be as important as within UGA heterogeneity for conserving local bird assemblages. The ongoing urban densification process observed in some European cities [START_REF] Li | Global trends and local variations in land take per person[END_REF] can thus pose a threat by reducing land cover diversity (e.g. through the loss of private gardens or rare habitats) and richness of bird species in UGAs. Conservation strategies should thus promote actions at the city level to conserve land cover heterogeneity and high quality habitats closed to public green areas.

Conclusions

We evidenced the marked land cover area-heterogeneity trade-off in urban green areas. We showed the predominant role of green area size on avian richness. Urban avoiders thrive in urban green areas with large and heterogeneous patches but are at risk in smaller ones unless they exhibit large habitat patches. We suggest the importance of coordinating design and management actions at the city levels across green areas to optimise bird conservation depending on UGA size and their heterogeneity level. We also highlight the need to confront ecological theories with empirical data across different bioclimatic zones to better understand urban ecosystems and advise stakeholders with robust and ecologically informed knowledge.

Our study opens the door to future research questions. We intentionally used a trait to quantify species preference to urban habitats and indirectly to the notion of habitat specialisation, which lies at the heart of niche theory. To go further, future research should investigate the species colonisation/extinction processes in land cover patches. For this purpose, long-term studies are required on persistence across small patches versus large ones (or across homogeneous versus heterogeneous). Also, the role of species interaction in the distribution of urban generalists and specialists (avoiders) along a urban gradient remains to be explored. Whether the increase in generalists is due to the relaxed interspecific competition from specialist species is unknown. Table 2: Results of regression model analysing the influence of urban avoidance (tercile groups) on the response of species richness to local variables of UGAs and landscape (500 m buffer around each urban green area, UGA) across the six cities. Our model controlled for maximum noise as random effect to account for possible consistent differences among cities. (UGA land cover heterogeneity)² is the squared term of UGA land cover heterogeneity to test for a non-linear relationship. We reported the slope estimate, its standard error (Std. Error), degree of freedom (df), the p-value (model without the interaction effect), and semi-partial R² (part R²) values that provide an estimate of the variance explained by a fixed effect independent of other predictors. Total variance explained by the model was 0.692 (Marginal R² value). Significant variables are highlighted in bold in the table (*p<0.05, **p<0.01, ***p<0.001). Statistical tendencies (0.050<p<0.010) are followed by a full stop (.). When the urban green area (UGA) is limited (urban parks are perfect examples of such nonextensible zones), the area heterogeneity trade-off (a) describes the decrease in the area of various land covers expected with an increase in land cover heterogeneity (i.e., compositional heterogeneity) [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF] equally distributed land covers (black and dark grey squares; A/2 each), increasing land cover heterogeneity to four land cover types decreases the area of each one (A/4 in this example). (b) In our sample of 176 UGAs from six European cities (see their distribution on the inserted map; Lisbon and Almada overlap at this scale), we can empirically confirm the expected relationship displayed in (a). Note that the y-axis in (b) is the area of land covers of a given UGA standardised by its total area, which accounts for variability in the sampled UGAs (i.e., the relationship in (a) is expected at a constant area). (c) Theoretically, while compositional heterogeneity is independent from configurational heterogeneity [6], both co-occur in European UGAs. Thus, the reduction in patch area (i.e., measure of configurational heterogeneity) for all the types of land cover present here occurs when land cover heterogeneity (i.e., compositional heterogeneity) increases. (d) This is verified by the relationship displayed in (c) in the studied urban green areas. Note that the y-axis in (d) is the mean patch size of a given UGA divided by its total area to account for variability in our UGAs (i.e., the relationship in (c) is expected at constant area). (e) Land cover heterogeneity allows for the colonisation of new species adapted to the newly available niches. Concurrently, the decreasing area of each land cover type (see (a-b)) can push species to local extinction, and species requiring the largest effective area of a given land cover are at the highest risk [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF][START_REF] Ben-Hur | Heterogeneity diversity relationships in sessile organisms: a unified framework[END_REF]. (f) The area heterogeneity trade-off thus leads to the hypothesis that species richness has a unimodal response to land cover heterogeneity: when reaching the level of heterogeneity at which extinction and colonisation are equal, further land cover diversification decreases species richness. The exact types of relationships depicted here (a, c, d, e) can differ depending on the studied ecosystem and species [START_REF] Ben-Hur | Heterogeneity diversity relationships in sessile organisms: a unified framework[END_REF], as illustrated by the differences between panels (a) vs (b) and (c) vs (d). While the area heterogeneity trade-off and its implications are still under debate [START_REF] Carnicer | Improved empirical tests of areaheterogeneity tradeoffs[END_REF], here we investigate whether the area heterogeneity trade-off holds true in UGAs, leading to a unimodal response of species richness in avian communities to land cover heterogeneity.

Figure 2: Effects of urban green area (UGA) and landscape (500 m buffer around each UGA) variables on species richness of avian communities for all cities (a). We computed the estimates and confidence interval (CI) based on the regression models presented in Appendix D. (UGA land cover heterogeneity)² is the squared term of UGA land cover heterogeneity. dBmax is the maximum sound volume during bird counting. We provide estimated mean relationships between species richness and total area of UGAs (b ; P=0.004) and landscape land cover heterogeneity of UGAs (c ; P=0.020). We used different dot shapes for each city: Pa, Ta, Lx, Po, Al and Zu stand for the cities of Paris, Tartu, Lisbon, Poznan, Almada and Zurich, respectively.

Figure 3: Principal component analysis with (a) variables and (b) individuals (i.e., species coded as the first three letters of the genus and species; see Appendix A for a summary of the species names) with regard to nesting bird habitat preferences. This analysis positioned the species along a gradient of urban avoidance on PC2 from low avoidance (low value) to high avoidance (high value). The blue colour gradient indicates the contribution of variables to PC1 and PC2 (a), and the different colours in (b) indicates the species according to terciles 1 (low urban avoidance; green), 2 (medium urban avoidance; orange) and 3 (high urban avoidance; purple).

Figure 4: Avian species richness for the different levels of avian urban avoidance (terciles) in the six cities. For each city, we pooled the bird species belonging to terciles 1 (low avoidance; green), 2 (medium avoidance; orange) and 3 (high avoidance; purple). We ordered the cities according to the total number of species from the least to the most. We provided the proportion of species per tercile in white.

Figure 5: Responses of species richness to the mean patch area (a) and land cover heterogeneity of UGAs (b) according to the level of avian urban avoidance: low urban avoidance (green, tercile 1), medium avoidance (orange, tercile 2) and purple (high avoidance, tercile 3). Grey shaded bands indicate are 95% confidence intervals estimated from the generalized linear mixed model (Table 2). When the urban green area (UGA) is limited (urban parks are perfect examples of such nonextensible zones), the area heterogeneity trade-off (a) describes the decrease in the area of various land covers expected with an increase in land cover heterogeneity (i.e., compositional heterogeneity) [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF] equally distributed land covers (black and dark grey squares; A/2 each), increasing land cover heterogeneity to four land cover types decreases the area of each one (A/4 in this example). (b) In our sample of 176 UGAs from six European cities (see their distribution on the inserted map; Lisbon and Almada overlap at this scale), we can empirically confirm the expected relationship displayed in (a). Note that the y-axis in (b) is the area of land covers of a given UGA standardised by its total area, which accounts for variability in the sampled UGAs (i.e., the relationship in (a) is expected at a constant area). (c) Theoretically, while compositional heterogeneity is independent from configurational heterogeneity [6], both co-occur in European UGAs. Thus, the reduction in patch area (i.e., measure of configurational heterogeneity) for all the types of land cover present here occurs when land cover heterogeneity (i.e., compositional heterogeneity) increases. (d) This is verified by the relationship displayed in (c) in the studied urban green areas. Note that the y-axis in (d) is the mean patch size of a given UGA divided by its total area to account for variability in our UGAs (i.e., the relationship in (c) is expected at constant area). (e) Land cover heterogeneity allows for the colonisation of new species adapted to the newly available niches. Concurrently, the decreasing area of each land cover type (see (a-b)) can push species to local extinction, and species requiring the largest effective area of a given land cover are at the highest risk [START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF][START_REF] Ben-Hur | Heterogeneity diversity relationships in sessile organisms: a unified framework[END_REF]. (f) The area heterogeneity trade-off thus leads to the hypothesis that species richness has a unimodal response to land cover heterogeneity: when reaching the level of heterogeneity at which extinction and colonisation are equal, further land cover diversification decreases species richness. The exact types of relationships depicted here (a, c, d, e) can differ depending on the studied ecosystem and species [START_REF] Ben-Hur | Heterogeneity diversity relationships in sessile organisms: a unified framework[END_REF], as illustrated by the differences between panels (a) vs (b) and (c) vs (d). While the area heterogeneity trade-off and its implications are still under debate [START_REF] Carnicer | Improved empirical tests of areaheterogeneity tradeoffs[END_REF], here we investigate whether the area heterogeneity trade-off holds true in UGAs, leading to a unimodal response of species richness in avian communities to land cover heterogeneity. Figure 2: Effects of urban green area (UGA) and landscape (500 m buffer around each UGA) variables on species richness of avian communities for all cities (a). We computed the estimates and confidence interval (CI) based on the regression models presented in Appendix D. (UGA land cover heterogeneity)² is the squared term of UGA land cover heterogeneity. dBmax is the maximum sound volume during bird counting. We provide estimated mean relationships between species richness and total area of UGAs (b ; P=0.004) and landscape land cover heterogeneity of UGAs (c ; P=0.020). We used different dot shapes for each city: Pa, Ta, Lx, Po, Al and Zu stand for the cities of Paris, Tartu, Lisbon, Poznan, Almada and Zurich, respectively. For each city, we pooled the bird species belonging to terciles 1 (low avoidance; green), 2 (medium avoidance; orange) and 3 (high avoidance; purple). We ordered the cities according to the total number of species from the least to the most. We provided the proportion of species per tercile in white.

Figure 1 :

 1 Figure 1: Area heterogeneity trade-offs in urban ecosystems.When the urban green area (UGA) is limited (urban parks are perfect examples of such nonextensible zones), the area heterogeneity trade-off (a) describes the decrease in the area of various land covers expected with an increase in land cover heterogeneity (i.e., compositional heterogeneity)[START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF] equally distributed land covers (black and dark grey squares; A/2 each), increasing land cover heterogeneity to four land cover types decreases the area of each one (A/4 in this example). (b) In our sample of 176 UGAs from six European cities (see their distribution on the inserted map; Lisbon and Almada overlap at this scale), we can empirically confirm the expected relationship displayed in (a). Note that the y-axis in (b) is the area of land covers of a given UGA standardised by its total area, which accounts for variability in the sampled UGAs (i.e., the relationship in (a) is expected at a constant area). (c) Theoretically, while compositional heterogeneity is independent from configurational heterogeneity [6], both co-occur in European UGAs. Thus, the reduction in patch area (i.e., measure of configurational heterogeneity) for all the types of land cover present here occurs when land cover heterogeneity (i.e., compositional heterogeneity) increases. (d) This is verified by the relationship displayed in (c) in the studied urban green areas. Note that the y-axis in (d) is the mean patch size of a given UGA divided by its total area to account for variability in our UGAs (i.e., the relationship in (c) is expected at constant area). (e) Land cover heterogeneity allows for the colonisation of new species adapted to the newly available niches. Concurrently, the decreasing area of each land cover type (see (a-b)) can push species to local extinction, and species requiring the largest effective area of a given land cover are at the highest risk[START_REF] Allouche | Area heterogeneity tradeoff and the diversity of ecological communities[END_REF][START_REF] Ben-Hur | Heterogeneity diversity relationships in sessile organisms: a unified framework[END_REF]. (f) The area heterogeneity trade-off thus leads to the hypothesis that species richness has a unimodal response to land cover heterogeneity: when reaching the level of heterogeneity at which extinction and colonisation are equal, further land cover diversification decreases species richness. The exact types of relationships depicted here (a, c, d, e) can differ depending on the studied ecosystem and species (Ben-Hur & Kadmon, 2020), as illustrated by the differences between panels (a) vs (b) and (c) vs (d). While the area heterogeneity trade-off and its implications are still under debate[START_REF] Carnicer | Improved empirical tests of areaheterogeneity tradeoffs[END_REF], here we investigate whether the area heterogeneity trade-off holds true in UGAs, leading to a unimodal response of species richness in avian communities to land cover heterogeneity.

Figure 3 :

 3 Figure 3: Principal component analysis with (a) variables and (b) individuals. This analysis positioned the species along a gradient of urban avoidance on PC2 from low avoidance (low value) to high avoidance (high value). The blue-red colour gradient indicates the contribution of variables to PC1 and PC2 (a), and the different colours in (b) indicates the species according to terciles 1 (low urban avoidance; green), 2 (medium urban avoidance; orange) and 3 (high urban avoidance; purple). Each symbol in (b) represents one or several species (n = 115 species ; habitat occurrences for four species were and were not plotted). To illustrate gradients, we positioned three species per tercile (English and Latin names). See Appendix B for a summary of the species names with regard to urban avoidance values and tercile group. Photo credits: R. Knight (Common cuckoo), W. van der Ham (Willow warbler), L. Lukasik (Common pheasant), N. Wulf (Rock dove), R. Bartz (Mallard), C.J. Sharp (Rose-ringed parakeet), K. Billington (Sedge Warbler), J. Bleak (Short-toed treecreeper), D. Delso (Black-billed magpie).

Figure 4 :

 4 Figure4: Avian species richness for the different levels of avian urban avoidance (terciles) in the six cities. For each city, we pooled the bird species belonging to terciles 1 (low avoidance; green), 2 (medium avoidance; orange) and 3 (high avoidance; purple). We ordered the cities according to the total number of species from the least to the most. We provided the proportion of species per tercile in white.

Figure 5 :

 5 Figure5: Responses of species richness to the mean patch area (a) and land cover heterogeneity of UGAs (b) according to the level of avian urban avoidance: low urban avoidance (green, tercile 1), medium avoidance (orange, tercile 2) and purple (high avoidance, tercile 3). Grey shaded bands indicate are 95% confidence intervals estimated from the generalized linear mixed model (Table2).

  Buteo buteo that usually nest outside developed urban areas in forest patches but can self-sustain populations in large contiguous UGAs. This tercile also included a few urban utilisers like Turdus merula and Anas platyrhynchos. Tercile 3 (PC2: 0.11 to 5.65, 33 species, high avoidance) was composed of aquatic (e.g., Remiz pendulinus, Cettia cetti, Acrocephalus schoenobaenus) as well as shrub and grassland species (e.g., Lululla arborea, Hippolais icterina), which very rarely occur in urban areas. Although such species are generally absent from developed areas, they may persist in networks of natural areas embedded in an urban matrix

	Tercile 1 (PC2: -3.2 to
	-0.69, 37 species, low urban avoidance) consisted of urban dweller species such as Columbia livia,

Streptopelia decaocto and Apus apus that nest on man-made structures, and urban utiliser species such as Corvus corone and Columba palumbus that are so-called generalist species. Tercile 2 (PC2:

-0.69 to 0.11, 45 species, medium urban avoidance) grouped avoider species such as Dryocopus martius and

Table 1 :

 1 Staude, I. R., Overbeck, G. E., Fontana, C. S., Bencke, G. A., Silva, T. W. D.,Mimet, A., & Pereira, H. M. (2021). Specialist birds replace generalists in grassland remnants as land use change intensifies. Frontiers in Ecology and Evolution, 483. https://doi.org/10.3389/fevo.2020.597542 Summary table of the urban green areas (UGAs) and landscape areas in the six cities investigated in this study as well as the avian species richness expressed as mean, minimum and maximum values. Land cover heterogeneity of UGAs and landscapes corresponds to the Shannon diversity index of habitat types respectively within UGAs (i.e., ten land cover categories) and within a 500-metre buffer around the UGAs (i.e., land use categories of the Urban Atlas). The contagion index measures the degree of aggregation of the same land cover within each UGA. dBmax is the maximum number of decibels in the environment (dBmax) during bird counts in each UGA. The proximity index measures the degree of green area isolation and is the sum of the area of all nearby green spaces divided by the square of the distance to the focal green area. Climate data were collected for the variables of temperature (Mean temp.) and precipitation (Annual prec.) between 1991 and 2021 and are issued from the European Centre for Medium-Range Weather Forecasts (ECMWF, source www.ecmwf.int, access 23/06/2022). All coordinate data for the cities are taken from the OpenStreetMap project (source https://www.openstreetmap.fr, access 23/06/2022).

	Stoffel, M. A., Nakagawa, S., & Schielzeth, H. (2021). partR2: partitioning R2 in generalized
	linear mixed models. PeerJ, 9, e11414.
				history characteristics of European birds. Global Ecology
	and Biogeography, 27(4), 400-406. https://doi.org/10.1111/geb.12709
	Ornitologicka Cso. ISBN : 978-80-903554-3-9
	Walsh, C.J., & McNally, R.M., 2008. Hierarchical Partitioning R package
	version 1.0-3.
	Wickham, H. (2019). ggplot2: Elegant Graphics for Data Analysis, 1st ed, ggplot2. Springer New
	York, NY. https://doi.org/10.1007/978-0-387-98141-3
	Zhang, P., Ghosh, D., & Park, S. (2023). Spatial measures and methods in sustainable urban
	morphology: A systematic review. Landscape and Urban Planning, 237, 104776.
	https://doi.org/10.1016/j.landurbplan.2023.104776
	Zuckerberg, B., Cohen, J. M., Nunes, L. A., Bernath-Plaisted, J., Clare, J. D., Gilbert, N. A., ... &
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Although our study assessed the trade-off in different biogeographical contexts, whether the areaheterogeneity relationship hold true in more specific environments (e.g. aquatic or terrestrial) has not yet been explored. Villaseñor, N. R., Truffello, R., & Reyes-Paecke, S. (2021). Greening at multiple scales promote biodiverse cities: a multi-scale assessment of drivers of Neotropical birds. Urban Forestry & Urban Greening, 66, 127394. https://doi.org/10.1016/j.ufug.2021.127394 A best practice guide for wild bird monitoring schemes. Ceska Spolecnost

Table 2 :

 2 Results of regression model analysing the influence of urban avoidance (tercile groups) on the response of species richness to local variables of UGAs and landscape (500 m buffer around each urban green area, UGA) across the six cities. Our model controlled for maximum noise

cities. (UGA land cover heterogeneity)² is the squared term of UGA land cover heterogeneity to test for a non-linear relationship. We reported the slope estimate, its standard error (Std. Error), degree of freedom (df), the p-value (model without the interaction effect), and semi-partial R² (part R²) values that provide an estimate of the variance explained by a fixed effect independent of other predictors. Total variance explained by the model was 0.692 (Marginal R² value). Significant variables are highlighted in bold in the table (*p<0.05, **p<0.01, ***p<0.001). Statistical tendencies (0.050<p<0.010) are followed by a full stop (.).

Table 1 :

 1 Summary table of the urban green areas (UGAs) and landscape areas in the six cities investigated in this study as well as the avian species richness expressed as mean, minimum and maximum values. Land cover heterogeneity of UGAs and landscapes corresponds to the Shannon diversity index of habitat types respectively within UGAs (i.e., ten land cover categories) and within a 500metre buffer around the UGAs (i.e., land use categories of the Urban Atlas). The contagion index measures the degree of aggregation of the same land cover within each UGA. dBmax is the maximum number of decibels in the environment (dBmax) during bird counts in each UGA. The proximity index measures the degree of green area isolation and is the sum of the area of all nearby green spaces divided by the square of the distance to the focal green area. Climate data were collected for the variables of temperature (Mean temp.)and precipitation (Annual prec.) between 1991 and 2021 and are issued from the European Centre for Medium-Range Weather Forecasts (ECMWF, source www.ecmwf.int, access 23/06/2022). All coordinate data for the cities are taken from the OpenStreetMap project (source https://www.openstreetmap.fr, access 23/06/2022).

	Species	richness	mean	(min;max)							14.5 (9;22)	14.6 (4;22)	12.6 (6;19)	18.3 (10;28)	14.6 (8;23)	13.8 (6;18)
		City coordinates				Longitude Latitude	(deg.) (deg.)						E	E
		City climate			Mean Annual	temp. prec.	(°C) (mm)	16.5 591	16.7 591	11.7 720	9.7 649	6.4 727	9.7 1460
	Landscape (500 m)	mean (min;max)			Land cover Proximity	heterogeneity index		1.6 (1.2;2.0) 3.1 (0.2;10.0)	1.7 (1.1;2.0) 2.8 (0.4;9.1)	1.4 (0.7;2.0) 3.4 (0.5;9.5)	1.6 (0.8;2.1) 3.8 (0.7;9.4)	1.7 (1.2;2.0) 2.9 (0.6;6.4)	1.8 (1.5;2.3) 4.1 (1.5;10.2)
							dBmax	(decibels)		64.9	(60.1;71.1)	66.9	(61.6;81.9)	67.9	(52.9;119.0)	68.9	(59.7;72.8)	69.9	(49.5;64.2)	70.9	(54.4;70.2)
							Contagion	index		59.8	(48.5;68.7)	61.1	(49.8;88.3)	63.4	(43.4;83.7)	63.0	(44.9;88.4)	68.7	(50.1;100)	60.5	(48.9;86.0)
	Urban green area	mean (min;max)	Number	Land cover of	heterogeneity habitat	types	1.2 (0.4;1.8) 5.3 (2;10)	1.1 (0.1;1.7) 5.0 (2;8)	0.9 (0.4;1.7) 4.6 (2;7)	0.9 (0.3;1.3) 4.3 (2;8)	0.8 (0.0;1.4) 4.6 (1;7)	1.1 (0.2;1.4) 4.8 (3;7)
						Mean	patch area	(ha)	0.1	(0.02;0.36)	0.2	(0.03;0.70)	0.1	(0.02;0.42)	0.2	(0.03;2.41)	0.4	(0.03;3.48)	0.1	(0.02;0.14)
								Area (ha)			6.7	(0.4;43.2)	5.3	(0.4;31.4)	3.2	(0.2;18.5)	7.8	(0.4;108.7)	5.3	(0.3;32.0)	3.8	(0.2;28.2)
			Number	of	UGAs					15	31	35	32	32	31
					City							Almada	Lisbon	Paris	Poznan	Tartu	Zurich

  Area heterogeneity trade-offs in urban ecosystems.

	Figure 1:
	UGA land cover
	heterogeneity	0.166	0.067
	(tercile 3)
	Mean patch area (log): Urban avoidance			19.925 2 <0.001*** 0.056
	Mean patch area (log) (tercile 1)	-0.045	0.049
	Mean patch area (log) (tercile 2)	0.182	0.049
	Predictor of species richness Mean patch area (log) (tercile 3)	Estimate 0.242	Std. Error 0.070	value Chisq-	df p-value	part R²
	(Intercept)	1.980	0.123	40.240 1 <0.001***
	Mean patch area (log)	0.064	0.045	2.070 1	0.150	<0.001
	UGA total area (log)	0.103	0.036	8.385 1 0.004**	0.004
	UGA land cover heterogeneity	0.021	0.031	0.043 1	0.510	<0.001
	(UGA land cover heterogeneity)²	-0.012	0.015	0.732 1	0.392	<0.001
	Proximity index	0.003	0.021	0.018 1	0.893	<0.001
	Landscape land cover heterogeneity	0.056	0.024	5.519 1	0.019*	0.005
	dBmax	-0.050	0.028	3.346 1	0.067.	0.005
	Urban avoidance			527.052 2 <0.001*** <0.001
	Tercile 1	1.980	0.123
	Tercile 2	-0.338	0.042
	Tercile 3	-1.424	0.062
	UGA land cover heterogeneity: Urban avoidance			6.390 2	0.041*	0.031
	UGA land cover
	heterogeneity	-0.020	0.040
	(tercile 1)
	UGA land cover
	heterogeneity	0.060	0.046
	(tercile 2)

Table 2 )

 2 .Appendix B: List of the 119 bird species recorded in six European cities (within a total of 176 urban green areas) along with their urban avoidance value (PC2, this study), tercile group (1: low urban avoidance, 2: medium urban avoidance, 3: high urban avoidance), species occurrence (% of sites occupied) and number of individuals counted per city. NA: information not available.

							Number of individuals
			Urban					counted
	Species (latin name)	Species code	avoidance index (PC2)	Tercile group	Occurrence (%)	Almada	Lisbon	Paris	Poznan	Tartu	Zurich
	Acanthis cannabina	acacan	0.15	3	6.7	0	0	0 18 0 0
	Accipiter nisus	accnis	0.09	2	1	0	0	0	1 0 0
	Acrocephalus arundinaceus	acraru	3.64	3	1.5	0	0	0	3 0 0
	Acridotheres cristatellus	acrcri	-0.25	2	1.5	5	0	0	0 0 0
	Acrocephalus dumetorum	acrdum	0.73	3	1.5	0	0	0	0 2 0
	Acrocephalus palustris	acrpal	1.64	3	2.1	0	0	0	3 1 0
	Acrocephalus schoenobaenus acrsch	4.36	3	2.1	0	0	0	1 2 0
	Acrocephalus scirpaceus	acrsci	2.01	3	3.1	0	0	1	4 0 2
	Aegithalos caudatus	aegcau	0.73	3	5.1	0	4	3	1 0 7
	Alopochen aegyptiaca	aloaeg	-0.78	1	2.1	0	6	0	0 0 0
	Anas platyrhynchos	anapla	0.01	2	16.4	0	15 11 26 12 15
	Anas streptera	anastr	-0.04	2	1	0	0	0	2 0 0
	Anser anser	ansans	NA	NA	1	0	12 0	0 0 0
	Apus apus	apuapu	-2.27	1	36.9	14 15 57 83 15 53
	Apus melba	apumel	-2.27	1	2.1	0	0	0	0 0 11
	Apus pallidus	apupal	-3.15	1	8.2	15 110 0	0 0 0
	Ardea cinerea	ardcin	0.01	2	2.1	0	0	1	0 0 2
	Aythya fuligula	aytful	0.01	2	1	0	0	0	1 0 0
	Branta canadensis	bracan	-0.78	1	1.5	0	0	2	1 0 0
	Bubulcus ibis	bubibi	NA	NA	1.5	0	5	0	0 0 0
	Buteo buteo	butbut	0.09	2	3.1	2	2	0	0 0 1
	Carduelis carduelis	carcar	-0.78	1	39.5	28 46 4 18 23 40
	Carduelis chloris	carchl	0.03	2	52.3	42 45 8	0 35 14
	Carduelis spinus	carspi	-0.02	2	3.6	0	0	0	0 0 1
	Cecropis daurica	cecdau	-1.48	1	1	3	0	0	0 0 0
	Certhia brachydactyla	cerbra	0.09	2	36.4	10 29 27 14 0 17
	Certhia familiaris	cerfam	-0.02	2	2.6	0	0	0	0 4 0
	Cettia cetti	cetcet	4.15	3	1	1	0	0	0 0 0
	Chroicocephalus ridibundus chrrid	0.76	3	3.1	0	13 0	0 0 46

a Number of UGAs and marginal R² value

Acknowledgements

We thank the municipalities of Greater Paris, Lisbon, Almada, Poznan, Tartu and Zurich for their participation in the BioVEINS project. We are grateful for the help provided by the birdwatchers in each city. We thank Victoria Grace for editing the English and improving quality of the manuscript.

Funding

This work was supported by the ERA--vERsA32015104). J.C.A. was supported by the Swiss National Science Foundation (project 31BD30_172467), A.V.M. by the University of Antwerp, P.T. by Polish funding and B.M. by the Research Foundation Flanders (FWO) (1S84819N). The funding sources had no involvement in the study design or execution.

Data accessibility

Data are available on request

List of appendices

Appendix A: Correlation coefficients between bird nesting habitats and the first three principal components (PCs).

Appendix B: List of the 119 bird species recorded in six European cities.

Appendix C: Results of the regression models between mean patch area, land cover heterogeneity and area of UGAs.

Appendix D: Results of the regression model analysing the effects of landscape (500 m buffer around each urban green area, UGA) and UGA variables on species richness of avian communities using dataset from all cities.

Appendix E: Results of full regression models analysing the effects of landscape (500 m buffer around each urban green area, UGA) and UGA variables on species richness of avian communities in the six studied cities.

Appendix F: Correlation coefficients between bird nesting habitats and the first three principal components (PCs).

Appendix G: Tukey HSD (Honestly Significant Difference) post-hoc tests.

Appendix H: Correlogram plot of the predictors for all cities pooled together.

Appendix I: Relationship between mean patch area and index of land cover types) of the studied urban green areas.

Appendix A: Correlation coefficients between bird nesting habitats and the first three principal components (PCs). Data on bird occurrences in 15 nesting habitats in Europe were compiled by Storchová &

The Birds of the Western Palearctic handbook [START_REF] Cramp | The birds of the western Palearctic interactive[END_REF] Appendix D: Results of the regression model analysing the effects of landscape (500 m buffer around each urban green area, UGA) and UGA variables on species richness of avian communities using dataset from all cities (176 UGAs with complete land cover information). Relationships were to account for possible consistent differences among cities (generalised linear mixed model). erm of UGA land cover heterogeneity to test for a non-linear relationship. We reported the slope estimate, standard error (Std. Error) and degree of freedom (df) for each explanatory variable. We added semi-partial R² (part R²) values that provide an estimate of the variance explained by a fixed effect independent of other predictors. Significant variables are highlighted in bold in the table (*p<0.05, **p<0.01, ***p<0.001). Marginally significant statistical tendencies (0.050<p<0.010) are followed by a full stop (.). Nonsignificant two-way interactions or quadratic terms (i.e. UGA land cover heterogeneity²) were removed from the models in order to better estimate single effects of predictors. Appendix H: Correlogram plot of the predictors for all cities pooled together. The colour and intensity of the circles are proportional to the correlation coefficients in grey. Except for latitude, only the contagion index, which measures the degree of aggregation of the same land cover within each UGA, was above the high correlation threshold ( > 0.7 with p-value < 0.01) [START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF]. We thus removed this latter variable as it was highly correlated with UGA land cover heterogeneity.

Appendix I index of land cover types) of the studied urban green areas (n=176 UGAs). The different slopes represent the relationship for each city. Al, Pa, Ta, Lx, Po and Zu stand for the cities of Almada, Paris, Tartu, Lisbon, Poznan and Zurich, respectively. Model results are presented in Table S3.

Conflict of interest declaration

The authors declare that they have no known competing financial interests or personal relationships that have or could be perceived to have influenced the work reported in this article.


La fiscalité des résidences secondaires : un outil de régulation des marchés immobiliers locaux ? -Fonciers en débat https://fonciers-en-debat.com/la-fiscalite-des-residences-secondaires-un-outil-de-regulation-des-marches-immobiliers-locaux/ 1/

Dans certains territoires au marché immobilier tendu, la forte attractivité résidentielle et touristique et les difficultés d'accès au logement poussent de nombreux acteurs locaux à se mobiliser en faveur du maintien des résidences principales. Celles-ci font en effet face à la concurrence accrue des résidences secondaires et des meublés de tourisme, qui mobilisent une partie du parc de logements, pouvant faire défaut aux « résidents permanents [START_REF]Voir le dossier de presse « Lutter contre l'attrition des logements permanents en zone touristique[END_REF] ». Face à ce constat, de premières mesures visant à réguler l'usage des logements émergent, dans la lignée des réglementations sur les locations de courte durée déjà engagées dans certains territoires. Ces dispositifs, notamment fiscaux, font l'objet de débats et leur portée demeure encore mal définie.

Cet article analyse le rôle et les limites de la fiscalité immobilière comme outil d'action publique visant à réguler le développement des résidences secondaires et des meublés de tourisme. Il vise à démontrer que les dispositifs actuels ne parviennent pas à orienter l'usage (touristique ou résidentiel) du parc de logements, et qu'une régulation nécessite un pouvoir accru des exécutifs locaux ainsi qu'une meilleure territorialisation des outils fiscaux à leur disposition.

La majoration de la taxe d'habitation sur les résidences secondaires (THRS) : entre incitation et recettes communales

Depuis 2017, l'article 1407 ter du Code général des impôts donne la possibilité aux conseils municipaux situés en zone tendue [START_REF]Dont la liste est fixée par décret n° 2013-392 du 10 mai 2013 relatif au champ d'application de la taxe annuelle sur les logements vacants instituée par l'article 232 du code général des impôts[END_REF] de majorer de 5 % à 60 % la cotisation de taxe d'habitation sur les résidences secondaires leur revenant. Cette majoration, concomitante à la disparition de la taxe d'habitation sur les résidences principales, permet aux élus locaux de conserver un pouvoir sur le niveau de prélèvement (dit « pouvoir de taux ») et une certaine autonomie fiscale en complément de celle dont ils disposent avec la taxe foncière sur les propriétés bâties. Mais elle est aussi présentée comme un levier permettant aux communes de maintenir un parc de logements accessibles pour leurs populations « permanentes » qui peinent à se loger : selon un rapport interministériel de 2022, la majoration "doit permettre de réorienter les logements du parc des communes concernées vers des résidences principales [START_REF] Dubertret | Lutte contre l'attrition des résidences principales dans les zones touristiques en Corse et sur le territoire continental, rapport de l[END_REF]".

Bien que l'application de cette majoration soit récente et ne fasse pas encore l'objet d'évaluations, de nombreux acteurs doutent de sa portée incitative, à l'instar du maire de Sceaux (Hauts-de-Seine): "C'est une mesure qui peut avoir un intérêt, mais il ne faut pas en surestimer l'impact ou trop en attendre. La majoration maximale, soit 60%, représente quelques centaines d'euros de plus par an. Je ne suis pas certain que ce soit vraiment dissuasif pour les gens qui possèdent une résidence secondaire [START_REF] Castagnet | Propos recueillis par[END_REF]". A titre d'exemple, parmi les 255 communes ayant appliqué la majoration de la THRS en 2022, le montant annuel moyen de la majoration était de 351 euros/résidence secondaire (quand la taxe dans son ensemble représente en moyenne 1481 euros/résidence secondaire) [5].

Par Watine Julien -novembre 2023 https://fonciers-en-debat.com/la-fiscalite-des-residences-secondaires-un-outil-de-regulation-des-marches-immobiliers-locaux/ 2/8

En effet, alors que la majorité des résidences secondaires des communes au marché immobilier tendu est détenue par des ménages aisés (34% en moyenne nationale selon l'INSEE, 2021 [START_REF]2021, dans cette étude, les ménages « aisés » sont ceux dont le niveau de vie, issu du dispositif Filosofi, est au-dessus du 9ᵉ décile de niveau de vie national en[END_REF]), il est probable que l'effet dissuasif de la taxe soit limité. Au contraire, elle pourrait même accroître les dynamiques d'accumulation et de dépossession du patrimoine immobilier [START_REF]quart des ménages français détient près de 70% du parc de logements possédés par des particuliers[END_REF], en excluant par exemple de "petits" propriétaires ayant hérité d'une résidence familiale qu'ils pourraient se voir contraints de revendre.

Si la THRS contribuerait peu à faire évoluer l'usage des logements vers de la résidence principale, elle pourrait en revanche y Mettre fin aux avantages fiscaux des meublés de tourisme pour favoriser les locations de longue durée ?

Si la majoration de la THRS, plutôt que de réorienter le parc de logements vers des résidences principales peut inciter à sa mise en location sur les plateformes de location saisonnière[12], sa simple application sans mesure de régulation des locations de courte durée peut s'avérer contre-productive.

Ainsi, conjointement à la question de la régulation des résidences secondaires, de nombreuses voix s'élèvent contre les avantages fiscaux dont bénéficient les locations saisonnières, incarnées par le succès de la plateforme Airbnb. Outre le fait qu'elles ne sont pas soumises à certains dispositifs contraignants (encadrement des loyers, interdiction à la location des "passoires énergétiques"), elles bénéficient d'un traitement fiscal très favorable : les loueurs de meublés touristiques classés disposent en effet d'un abattement forfaitaire sur leurs revenus locatifs de 71 % (jusqu'à 176 200 euros de recettes, régime "Micro BIC[13]") et de 50% pour les locations meublées non classées, contre seulement 30% pour les locations classiques vides (jusqu'à 15 000 euros de loyers, Régime "Micro Foncier").

Cet avantage fiscal se combine à d'autres facteurs, comme une forte rentabilité [START_REF]Des études ont établi qu'en quatre mois de location de courte durée, un propriétaire pouvait rentabiliser son logement davantage qu'en le louant tout au long de l'année[END_REF] et une plus grande flexibilité locative, qui rendent la location de courte durée nettement plus attractive que la location "à l'année" pour de nombreux propriétaires. Cela contribue à accroître l'érosion du parc locatif de longue durée, remplacé notamment par des locations saisonnières : la France en comptait plus de 800.000 en 2021 (dont 500.000 sur Airbnb), contre 300.000 en 2016 [START_REF] Selon | proposition de loi n°1176 visant à remédier aux déséquilibres du marché locatif en zone tendue[END_REF].

Face à l'ampleur du phénomène, les pouvoirs publics se saisissent de la question : en juin 2023 le ministre de l'économie Bruno Le Maire déclarait sur RMC-BFMTV ne pas "comprendre la fiscalité très favorable qui s'applique aujourd'hui à Airbnb", avant que le gouvernement n'affirme un mois plus tard via la parution d'un dossier de presse "engager des réflexions sur la fiscalité des revenus locatifs afin de favoriser les locations de longue durée [START_REF] Dans | [END_REF]". Pourtant, en avril 2023, une proposition de loi transpartisane visant à mettre fin aux avantages fiscaux et réglementaires des locations de courte durée et portée par des députés Renaissance, Horizons et le socialiste Iñaki Echaniz, s'est vue retirée de l'agenda parlementaire par la majorité présidentielle [START_REF] Meur | [END_REF]. Dernièrement, le projet de loi de finances pour 2024, soumis à l'Assemblée nationale le 18 octobre 2023 ne prévoit pas de mesures allant dans le sens d'un rééquilibrage de la fiscalité locative et pérennise au contraire certains avantages fiscaux des locations de courte durée comme l'exonération de la TVA [START_REF] Szeftel | [END_REF]. Les débats demeurent encore vifs sur le sujet, et de https://fonciers-en-debat.com/la-fiscalite-des-residences-secondaires-un-outil-de-regulation-des-marches-immobiliers-locaux/ 4/8

nombreux parlementaires déplorent les « cadeaux fiscaux[19] » consentis à Airbnb, quand des élus locaux réclament une plus grande "autonomie de régulation pour les collectivités[20]", tant d'un point de vue fiscal que réglementaire.

En effet, à ce jour, seule une minorité de communes peut appliquer des règlements contraignants permettant de réguler les meublés de tourisme, comme la procédure de changement d'usage ayant vocation à « protéger l'habitat à destination de la population permanente » [START_REF]Guide pratique de la réglementation des meublés e tourisme à destination des communes[END_REF]. Cette procédure a pour effet de soumettre à autorisation la transformation d'un logement en meublé de tourisme, et peut le cas échéant être conditionnée à des règles (l'obligation de compenser la « perte » du logement en créant une nouvelle surface habitable comme à Paris ou Bordeaux ; l'obligation de respecter des quotas de meublés touristiques par quartiers comme à Saint Malo…).

Malgré les récentes annonces du ministre délégué au logement, qui prévoit « une mission parlementaire sur la réforme de la fiscalité locative[22] » ainsi qu'un « projet de loi sur la décentralisation de la politique du logement [START_REF][END_REF] » pour le printemps 2024, les élus locaux restent majoritairement démunis d'outils leur permettant de garantir le maintien d'un parc locatif de longue durée à destination des habitants locaux.

Tableau 2 : Les principaux outils de régulation du parc de logements applicables par les communes

De nouveaux outils fiscaux aux mains des collectivités locales ?

https://fonciers-en-debat.com/la-fiscalite-des-residences-secondaires-un-outil-de-regulation-des-marches-immobiliers-locaux/ 5/8 [START_REF]Propos recueillis par Etienne MVE[END_REF], alors même qu'elle fait partie des villes de Bretagne où les prix immobiliers sont les plus élevés et les plus dynamiques [START_REF]Les prix immobiliers de Vannes (4 204 €/m² dans l'ancien) ont augmenté de 20% entre 2021 et 2022. Ils sont plus élevés qu'à Rennes (3 989 €/m²), et près de de deux fois plus qu'à Lorient (2 397 €/m²) et Brest (2 288 €/m²)[END_REF]. Les élus des villes non retenues dans le dispositif craignent un report des acquisitions de résidences secondaires sur leur commune, et regrettent que le périmètre ne soit pas pensé au niveau de l'intercommunalité, généralement considéré comme l'échelle pertinente en matière de politiques du logement [START_REF]Parallèlement, un autre zonage des tensions immobilières (le zonage ABC) fait lui aussi l'objet de débats et d'évolutions. Il a été révisé le 3 octobre 2023, en intégrant 150 communes en zone A (voir reclassement de plus de 200 communes au titre du zonage ABC[END_REF].

Si les réactions se multiplient face au manque de cohérence et à l'illisibilité du zonage, c'est avant tout la portée des outils qui pose question. Alors que les mesures fiscales actuelles ne semblent pas de nature à bouleverser les marchés du logement des zones tendues (cf supra), des élus plaident pour des dispositifs fiscaux plus contraignants et mieux adaptés aux réalités des territoires. Nil Caouissin, conseiller régional de Bretagne et notamment connu pour défendre l'idée d'un statut de résident en Bretagne [START_REF] Caouissin | Manifeste pour un statut de résident en Bretagne[END_REF], propose dans son dernier manifeste l'idée d'une taxe régionale progressive, modulée sur le patrimoine des ménages et sur le degré de tension des marchés immobiliers locaux [START_REF] Caouissin | Taxer les résidences secondaires ? Presses populaires de Bretagne[END_REF]. D'autres élus souhaitent agir sur la fiscalité des plusvalues, comme la maire de Guéthary qui propose une majoration de la taxe sur les plus-values en cas de mutation d'une résidence principale en secondaire ; et, à l'inverse, la réduction ou suppression de ces taxes lorsqu'un bien est vendu à la commune ou à un bailleur social [START_REF] Cruz | [END_REF]. Ces propositions sont appuyées par le député des Pyrénées Atlantiques Jean-Paul Mattei, qui a conçu un amendement dans le projet de loi de finances pour 2023 suggérant de taxer à hauteur de 5 % toute plus-value immobilière, et de flécher ces sommes vers des établissements publics fonciers aptes à réaliser des préemptions pour construire des logements. De même, alors qu'une majorité des résidences secondaires sont la propriété de ménages de plus de 60 ans[33],

des élus proposent une exonération des droits de succession dans le cas où l'héritier s'engage à faire du logement une résidence principale [START_REF] Roseren | Territoires touristiques : le droit de vivre au pays est-il menacé ? Se loger ne peut pas être que secondaire[END_REF].

Si de nombreuses propositions fiscales émergent pour répondre aux problématiques de tensions immobilières, les initiatives locales restent néanmoins tributaires des décisions législatives à l'échelle nationale. Pour compenser la faible marge de manoeuvre réglementaire dont ils disposent, certains territoires sollicitent le droit à l'expérimentation des collectivités locales, qui https://fonciers-en-debat.com/la-fiscalite-des-residences-secondaires-un-outil-de-regulation-des-marches-immobiliers-locaux/ 6/8

« autorise les collectivités territoriales et leurs groupements à déroger à titre expérimental et pour un objet et une durée limitée, aux dispositions législatives ou réglementaires qui régissent l'exercice de leurs compétences [START_REF]Dans Loi organique n° 2021-467 du 19 avril 2021 relative à la simplification des expérimentations mises en oeuvre sur le[END_REF] ». C'est dans ce cadre qu'un projet de loi visant à expérimenter un droit de préemption et une taxe sur les résidences secondaires [START_REF]Proposition de loi n° 3928 relative à l'évolution statutaire de la collectivité de Corse afin de lutter contre les spéculations foncière et immobilière dans l'île déposée[END_REF] spécifiques à la Corse pour « lutter contre le phénomène de spéculation foncière et immobilière dans l'île » a été adopté en première lecture à l'Assemblée nationale [37]. Le projet de loi, qui doit encore être examiné par le Sénat, constituerait en cas d'adoption un pas important en vue d'une meilleure adaptation territoriale des politiques du logement. Dans la lignée de cette proposition, le gouvernement a annoncé la mise en place de dix "territoires touristiques pilotes" soumis à des tensions immobilières, qui pourront bénéficier d'un "accompagnement « sur-mesure » afin de définir une stratégie territoriale et de mobiliser les outils disponibles ou à structurer" [START_REF] Lesay | Logements en zones touristiques : des territoires pilotes pour des stratégies locales » dans Banque des territoires/ Localtis du 23 mars 2023 Julien Watine est diplômé de l'Ecole d'urbanisme de Paris et réalise une thèse sur[END_REF]. La portée du dispositif demeure floue, et son périmètre comme les outils confiés aux territoires n'ont pas encore été communiqués.

Malgré ces initiatives encore embryonnaires, la latitude d'action des collectivités territoriales demeure limitée et de nombreux élus locaux peinent à réguler l'usage de leur parc de logement.

Conclusion

Face aux tensions immobilières croissantes dans certains territoires tendus, la régulation des résidences secondaires et des meublés de tourisme cristallise les débats sur le droit au logement. Pour endiguer les phénomènes d'érosion des résidences permanentes, de premiers leviers, notamment fiscaux, sont appliqués ou font l'objet de réflexions. Cependant, leur efficacité semble à ce stade limitée, et les exécutifs locaux peinent à agir pleinement sur l'usage de leur parc de logement et à arbitrer entre résidence principale, secondaire, meublé de tourisme ou logement vacant. Si les collectivités locales sont porteuses de propositions adaptées à leur territoire, la faible marge de manoeuvre dont elles disposent soulève des enjeux en termes de décentralisation des politiques du logement.

  concourir indirectement par les recettes qu'elle génère. Ainsi, selon le maire des Sables d'Olonne et président de l'Association Nationale des Elus du Littoral (ANEL), c'est le réinvestissement des sommes collectées par les communes qui pourrait servir à mener des actions en faveur d'un meilleur accès au logement : "Majorer la taxe d'habitation sur les résidences secondaires est le seul levier de fiscalité locale qui reste aux communes pour financer une politique volontariste en faveur des résidents permanents[8]." Bien que le produit de la taxe ne soit pas fléché et que son usage dépende du bon vouloir des communes, les sommes perçues pourraient notamment permettre d'acheter des terrains en vue de produire du logement social. A titre d'exemple, la commune de Saint Nazaire, l'une des premières à avoir porté la majoration à son taux maximum de 60%, récoltait en 2021 environ 800.000€ de recettes fiscales supplémentaires au titre de cette majoration, soit 2,5% des dépenses réelles d'équipement et presque l'équivalent de ses acquisitions foncières annuelles[9]. Selon l'un des adjoints au maire interrogé par la presse locale, la somme est bien orientée vers des politiques locales en faveur du logement[10]. Alors que la THRS peut générer d'importantes recettes communales (cf tableau 1) mais influence peu l'usage du parc existant, sa faible portée incitative s'explique aussi par les stratégies de contournement et les aléas déclaratifs dont elle peut faire l'objet.En effet, des études ont montré que la taxe sur les logements vacants, souvent moins élevée que la taxe d'habitation, peut https://fonciers-en-debat.com/la-fiscalite-des-residences-secondaires-un-outil-de-regulation-des-marches-immobiliers-locaux/ 3/8inciter les propriétaires à déclarer leur logement comme inoccupé plutôt qu'en résidence secondaire[11]. Par ailleurs, la majoration peut même générer des effets contraires aux buts initiaux, en incitant les propriétaires à louer leur bien sur les plateformes de location de courte durée afin d'en couvrir les surcoûts.Le seul levier de la taxe d'habitation sur les résidences secondaires paraît donc insuffisant pour garantir le maintien d'un parc de résidences principales, et doit s'associer à d'autres outils, comme une taxation plus contraignante sur la vacance résidentielle et les meublés de tourisme.

  Pour la majorité des communes françaises, il n'est pas possible d'augmenter la taxation des résidences secondaires indépendamment des autres impositions locales, ni de réguler les meublés de tourisme sans accord préfectoral[24] (cf tableau 2). Ainsi, les principaux outils de régulation des résidences secondaires et des meublés de tourisme ne peuvent être mobilisés que par les 1434 communes dites « tendues » au sens du zonage de la taxe sur les logements vacants, à savoir « les villes appartenant à une zone d'urbanisation continue de plus de 50.000 habitants où un déséquilibre marqué entre l'offre et la demande de logements entraîne des difficultés sérieuses d'accès au logement[25] ».Ce zonage, a été actualisé par décret en août 2023, en y intégrant 2 259 communes "touristiques" supplémentaires, portant leur nombre total à 3 693. Alors que cet élargissement doit concerner des municipalités qui comptent des "difficultés sérieuses d'accès au logement" se caractérisant par "un niveau des loyers et des prix d'acquisition des logements anciens élevés" et une "forte proportion de logements non affectés à la résidence principale[26]", de nombreux élus regrettent que leur commune n'y soit pas intégrée et se retrouvent privés d'outils permettant de réguler leur parc de logement. En effet, les critères de ce zonage posent question dans la mesure où des villes présentant de fortes tensions immobilières en sont exclues. C'est notamment le cas de Vannes (Morbihan), non incluse dans le périmètre de « zone tendue » contrairement à douze communes de son agglomération