Lucía Villarroya-Villalba

Lucía Villarroya
email: lucia.villarroya.v@gmail.com

Joan Casanelles-Abella

Marco Moretti

Pedro Pinho

Roeland Samson

Anskje Van Mensel

François Chiron

Florian Zellweger

Martin K Obrist

Caragh Threlfall
email: caragh.threlfall@unimelb.edu.au

Jérémy Froidevaux
email: jeremy.froidevaux@gmail.com

Response of bats and nocturnal insects to urban green areas in Europe

Keywords: urbanisation, urban biodiversity, nocturnal fauna, flying arthropods, Chiroptera, Antwerp, Paris, Zurich urbanisation, urban biodiversity, nocturnal fauna, flying arthropods, Chiroptera, Antwerp, Paris, Zurich

Animal biodiversity in cities is generally expected to be uniformly reduced, but recent studies show that this is modulated by the composition and configuration of Urban Green Areas (UGA). UGA represent a heterogeneous network of vegetated spaces in urban settings that have repeatedly shown to support a significant part of native diurnal animal biodiversity. However, nocturnal taxa have so far been understudied, constraining our understanding of the role of UGA on maintaining ecological connectivity and enhancing overall biodiversity. We present a well-replicated multi-city study on the factors driving bat and nocturnal insect biodiversity in three European cities. To achieve this, we sampled bats with ultrasound recorders and flying insects with light traps during the summer of 2018. Results showed a greater abundance and diversity in the city of Zurich, followed by Antwerp and Paris. We identified artificial lighting in the UGA to lower bat diversity by probably filtering out light-sensitive species. We also found a negative correlation between both bat activity and diversity and insect abundance, suggesting a top-down control. An in-depth analysis of the Zurich data revealed divergent responses of the nocturnal fauna to landscape variables, while still showing top-down control of bats on insect abundance. Thus, to effectively preserve biodiversity in urban environments, UGA management decisions should take into account the combined ecological needs of bats and nocturnal insects and consider the specific spatial topology of UGA in each city.

INTRODUCTION

Urbanisation is a heterogeneous, worldwide phenomenon imposing important challenges to wildlife conservation. Rapid urbanisation in the last decades negatively impacts biodiversity in complex ways (e.g. [START_REF] Parris | Ecology of urban environments[END_REF][START_REF] Rivkin | A roadmap for urban evolutionary ecology[END_REF]. However, the specific effects of urbanisation are neither linear nor constant, but vary among and within cities [START_REF] Beninde | Biodiversity in cities needs space: A meta-analysis of factors determining intra-urban biodiversity variation[END_REF]. In Europe, most cities have developed in former agricultural lands sharing similar environmental histories.

Nonetheless, they are still heterogeneous in their cityscape. Although this heterogeneity has been noticed previously [START_REF] Ramalho | Time for a change: Dynamic urban ecology[END_REF], most urban ecology studies still follow a single-city approach hampering the extrapolation of the results to other cities. Finally, despite urbanisation reducing the amount of available habitat, cities also contain a network of urban green areas (UGA) that have been shown to be a key factor enhancing biodiversity [START_REF] Mcintyre | Ground arthropod community structure in a heterogeneous urban environment[END_REF][START_REF] Sattler | Response of arthropod species richness and functional groups to urban habitat structure and management[END_REF].

In most European cities, densely built-up districts with little or no green represent a small fraction of the whole urban areas. The majority of the urban districts have intermediate levels of urbanisation and contain a variable and sometimes dominant proportion of UGA. UGA represent a network of highly heterogeneous patches usually distributed forming mosaics, such as parks, green roofs or tree pits [START_REF] Lepczyk | Biodiversity in the City: Fundamental Questions for Understanding the Ecology of Urban Green Spaces for Biodiversity Conservation[END_REF]. Locally, UGA strongly vary in many features such as size, structure, vegetation composition, water availability, ownership or management. For instance, UGA may range from large sites with a complex, multi-layered vegetation structure to linear, highly managed lawn stripes. At the landscape scale, the contribution of UGA to the habitat amount is mediated by their composition and configuration as well as by the permeability of the surrounding matrix [START_REF] Lepczyk | Biodiversity in the City: Fundamental Questions for Understanding the Ecology of Urban Green Spaces for Biodiversity Conservation[END_REF]. Altogether, this hamper disentangling the factors shaping biodiversity and ultimately making conservation assessments. Finding tools to successfully measure and synthesise these complex relationships is a key step towards a more holistic urban biodiversity management.

A long-lasting problem in urban ecology is how to reliably infer the effects of urban intensification (e.g. modifications on habitat amount, heterogeneity, disturbances, stressors or isolation) on biodiversity. Remote sensing tools such as LiDAR (i.e. Light Detection and Ranging) or artificial light at night (ALAN) maps are becoming more accessible, and have been proven to be good proxies of habitat amount and heterogeneity for the first and disturbance for the latter [START_REF] De | Colour remote sensing of the impact of artificial light at night (I): the potential of the International Space Station and other DSLR-based platforms[END_REF]. Still, the potential of remote sensing tools remains underutilised as few studies to date have included this type of metrics (but see [START_REF] Hale | The ecological impact of city lighting scenarios: Exploring gap crossing thresholds for urban bats[END_REF][START_REF] Zellweger | Environmental predictors of species richness in forest landscapes: Abiotic factors versus vegetation structure[END_REF], probably because their potential is not yet fully understood. Habitat, land-cover and landuse maps are generally available for most cities in Europe and used as proxies of habitat amount, heterogeneity and connectivity (e.g. [START_REF] Munzi | Lichens as ecological indicators in urban areas: Beyond the effects of pollutants[END_REF]. However, they significantly vary in coarseness and most importantly, on the ecological information used to define the different mapping categories. Finally, ALAN maps have been used to show the responses of a wide variety of organisms to light pollution [START_REF] Hale | The ecological impact of city lighting scenarios: Exploring gap crossing thresholds for urban bats[END_REF][START_REF] Knop | Artificial light at night as a new threat to pollination[END_REF]. Nevertheless, there is limited understanding of the effects of artificial lightning on biotic communities (Sanders & Gaston, 2018).

Bats and nocturnal insects represent an understudied assemblage showing ecological features that make them a striking group to monitor the effects of urbanisation. First, they represent a prey-predator system as European bats feed on nocturnal insects. Prey-predator systems might exhibit two types of responses towards modifications of the habitat amount or disturbances: bottom-up, such as greater feeding bat activity with increasing insect biomass (Threlfall, Law, & Banks, 2012a) or top-down, for instance insect pest control by bats [START_REF] Heim | The Importance of Landscape Elements for Bat Activity and Species Richness in Agricultural Areas[END_REF]. Second, nocturnal animals, such as bats and nocturnal insects, might have a different susceptibility towards anthropogenic disturbances (e.g. light pollution) than diurnal ones. Finally, they strongly differ from diurnal organisms in terms of the orientation system they use to move and forage. These two traits regulate the scale at which the organisms perceive the environment and thus the modifications and disturbances that occur [START_REF] Concepción | Impacts of urbanisation on biodiversity: The role of species mobility, degree of specialisation and spatial scale[END_REF]. Additionally, bats can be classified in three general guilds (i.e. long-, mid-and short-range echolocators) with differences in their foraging strategies (see Frey-Ehrenbold, Bontadina, Arlettaz, & Obrist, 2013;[START_REF] Froidevaux | Optimizing passive acoustic sampling of bats in forests[END_REF]. Therefore, bats can be considered a potential umbrella system to monitor effects of land-use changes, particularly urbanisation, but studies on these groups are still scarce.

Prior research testing the effects of urbanisation on bats and nocturnal insects has yielded mixed results. Generally, urbanisation has been reported to simplify bat communities by filtering out sensitive species and keeping those ones with generalistic traits leading to a certain degree of biotic homogenisation [START_REF] Russo | Sensitivity of bats to urbanization: A review[END_REF], a process documented also for other taxa [START_REF] Chong | Not all green is as good: Different effects of the natural and cultivated components of urban vegetation on bird and butterfly diversity[END_REF]but see Fournier, Frey, & Moretti, 2020). For instance, earlier research has revealed a decrease in bat diversity following reductions of the amount of available habitat due to urban intensification (e.g. [START_REF] Kurta | Bat Community Structure in an Urban Park[END_REF]. Nonetheless, other urban intensification drivers have unclear effects.

Light pollution has been shown to negatively impact many groups, particularly nocturnal animals.

Still, some studies propose that this disturbance might not have a significant effect on bats, as they could move to more suitable patches [START_REF] Krauel | Patterns of bat distribution and foraging activity in a highly urbanized temperate environment[END_REF]; or even be advantageous for them.

For instance, streetlights on linear paths attract large amounts of nocturnal insects facilitating bat foraging [START_REF] Russo | Sensitivity of bats to urbanization: A review[END_REF]. On the other hand, the effects of urbanisation on nocturnal insects have been little investigated but some patterns have been noticed. Similarly to bats, a negative relationship appears between building density and nocturnal insects diversity [START_REF] Krauel | Patterns of bat distribution and foraging activity in a highly urbanized temperate environment[END_REF][START_REF] Russo | Sensitivity of bats to urbanization: A review[END_REF]. [START_REF] Threlfall | Influence of Landscape Structure and Human Modifications on Insect Biomass and Bat Foraging Activity in an Urban Landscape[END_REF] suggest that the negative effects on nocturnal insects' biomass could be explained from the increase in impervious surfaces, leading to loss and fragmentation of available habitat and, thus, to low primary productivity. On the contrary, urban native vegetation has been proposed to sustain nocturnal insect populations [START_REF] Russo | Sensitivity of bats to urbanization: A review[END_REF]. Thus, it is expected that insect abundance and its drivers greatly influence bats distribution in urban areas [START_REF] Krauel | Patterns of bat distribution and foraging activity in a highly urbanized temperate environment[END_REF].

In this paper, we studied the influence of biotic and abiotic factors in shaping the diversity, activity and abundance of bats and nocturnal insects in urban green areas using two contrasting designs in terms of the number of cities included and the type of the urban intensification proxies used. First, we investigated the responses across three European cities (Antwerp, Paris and Zurich) to test whether the effects of urbanisation on the response variables were consistent. We used a standardised set of urbanisation proxies across all cities including land use and light pollution maps. Second, we additionally studied the same set of responses of bats and nocturnal insects but adding high resolution predictors available for the city of Zurich to further infer the amount of available habitat and the role of vegetation structure.

MATERIAL AND METHODS

Study region and selection of sampling sites

The study region is Western and Central Europe, in particular Paris, France (48°51′23′′N, 2°20′58′′E), Zurich, Switzerland (47°22′40′′N, 8°32′23′′E) and Antwerp, Belgium (51°12′48′′N, 4°24′55′′E). These cities vary in terms of population density, size and percentage of green areas. Paris has a density of 21,000 inhabitants/km 2 and around 10% of UGA [START_REF] Mimet | Contribution of private gardens to habitat availability, connectivity and conservation of the common pipistrelle in Paris[END_REF]; Zurich municipality, 4,400 inhabitants/km 2 and 45% of UGA (Stadt Zürich, 2013); and Antwerp, 2,500 inhabitants/km 2 and 30% of UGA (Stad Antwerpen, 2013). Paris is considered one of the four megacities in Europe, while Zurich and Antwerp belong to the most frequent size class, i.e. medium-sized cities (United Nations, 2019).

We focused on the urban green areas mapped and defined in the European Urban Atlas (see EEA, 2012) to select patches (the polygons provided by the European Urban Atlas). We used an orthogonal gradient of patch size (area in m 2 ) and connectivity. Connectivity was calculated using the Proximity Index (PI) which considers the area and the distance to all nearby patches with a favourable habitat, within a given search radius. The value for each patch (the focal patch) is the sum of the area of all nearby patches with favourable habitat divided by the square of the distance to the focal patch. Thus, the PI measures the degree of patch isolation, with highest values given to less isolated patches [START_REF] Mcgarigal | FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps[END_REF]. We considered as favourable habitat all patches with high probability of having trees (besides urban green areas, also forest and low density urban, with less than 30% impervious surface, see EEA, 2012). The search radius was set to 5 km from each focal patch (the maximum possible with the available cartography), in order to accommodate all possible animal mobility ranges. Lower buffer values (from 500 m onwards) did not greatly change the PI values, because the distance is squared, thus greatly limiting the impact of patches beyond a certain distance. To select patches using the orthogonal design, all possible patches were classified in 6 size classes and 6 classes of the PI (36 possible combinations). Within these combinations patches were selected randomly (random stratified sampling design). Due to resource limitations we only used ⅓ of the possible combinations in Paris and Antwerp (maximizing the gradient) and the full range of combinations in Zurich (32 combinations, the other combinations were not available in the city). This resulted in the final selection of 56 sites: 12 in Paris, 12 in Antwerp and 32 in Zurich.

Field data collection

Cities were visited twice from mid-May to mid-July 2018. Bat recordings and nocturnal insect collections were conducted simultaneously at the 12 selected UGA of each city, except the 32 sites in Zurich, which were split in two sampling periods due to limited equipment. Samplings were scheduled for 5 consecutive nights per site for each of the two sampling periods, in order to avoid insufficient data sizes and reliably sample the species community. Samplings were carried out under favourable weather conditions, that is, without rain and temperatures above 12ºC [START_REF] Hutson | Microchiropteran Bats: Global Status Survey and Conservation Action Plan[END_REF].

Acoustic bat survey

Echolocation sampling was conducted with acoustic data loggers installed on the trunk of a suitable tree with open canopy, between 3-4 m height above ground. Bat foraging activity was measured with the autonomous ultrasound recorders Batlogger M [START_REF] Elekon | Detect and observe bats successfully[END_REF]. The recording systems were sensitive from 10 to 150 kHz (± 5 dB) and were set up to record from 15 minutes before sunset to 15 minutes after sunriseadapted to each city and dateduring 5 consecutive nights. Bat echolocation calls were identified using Batscope 3 (http://www.batscope.ch; [START_REF] Obrist | BatScope manages acoustic recordings, analyses calls, and classifies bat species automatically[END_REF]. This software automatically processes the sequences and assigns each single call to a suitable species with 95.7% of average correct classification rate [START_REF] Obrist | BatScope manages acoustic recordings, analyses calls, and classifies bat species automatically[END_REF]. Not all calls could be identified to species level for some cryptic calls. In such cases, classification was done manually to the best possible taxonomic level (Supplementary Table A.1), thereby avoiding errors that can occur in automated species identification [START_REF] Russo | The use of automated identification of bat echolocation calls in acoustic monitoring: A cautionary note for a sound analysis[END_REF][START_REF] Rydell | Testing the performances of automated identification of bat echolocation calls: A request for prudence[END_REF]. Subsequently, taxa were classified into three ecologically meaningful guilds, according to their clutter resistance and echolocation range: short-range echolocators (SRE), mid-range echolocators (MRE) and long-range echolocators (LRE) (Frey-Ehrenbold et al., 2013;[START_REF] Froidevaux | Optimizing passive acoustic sampling of bats in forests[END_REF].

Bat foraging activity was calculated on a daily basis, computing the activity of the three considered guilds in windows of 5 minutes, to balance for possible extended foraging bouts of a single bat around a site. Counts were then weighted by the number of possible observational 5 minute periods per night, resulting in a relative activity. Bat diversity was estimated per night with the Shannon Index in two ways: using the best possible taxonomic level (i.e. bat group diversity) and only reliable identified species (i.e. bat species diversity).

Nocturnal insect collection

We collected nocturnal flying insects (hereafter referred to as insects) in parallel to bats. Based on the assumption that nocturnal insects possess positive phototaxis (Price & Baker, 2016;van Grunsven et al., 2014), we designed an intercept LED light-trap to sample insects (for details see Supplementary after each sampling night. In the laboratory, insects were classified to the order level using an Olympus SZ40 stereo microscope, entomological guidebooks (e.g. [START_REF] Chinery | Insectes de France et d'Europe occidentale[END_REF]) and expert's advice. Insect diversity was estimated per night with the Shannon Index and insect abundance as the counts of each sample. As organism type or size may influence its quality as bat food [START_REF] Hutson | Microchiropteran Bats: Global Status Survey and Conservation Action Plan[END_REF], each order was assigned a relative factor according to their length and a biomass index calculated per sample. Some orders were additionally divided into size classes (e.g. Lepidoptera: <5 mm, 5-20 mm, >20 mm; Supplementary Table A.2). Note that insect variables could not be estimated for all the sites, as 12 of the 20 light-traps were vandalised in the course of the sampling. From the initial 56 sampling patches, we obtained unbalanced data for 12 sites in Paris, 11 in Antwerp and 26 in Zurich.

Measures of biotic interactions

We attempted to study the bottom-up and top-down controls, as our groups represent a prey-predator system. To infer the bottom-up, we used insect diversity and abundance, while top-down control was measured with bat group and species diversity and both total bat and guild relative activity (Table 1).

Measures of environmental and landscape variables

We used environmental and landscape variables of ecological relevance for the studied organisms [START_REF] Cusimano | Importance of meteorological variables for aeroplankton dispersal in an urban environment[END_REF][START_REF] Schnitzler | Echolocation by Insect-Eating Bats[END_REF][START_REF] Threlfall | Sensitivity of insectivorous bats to urbanization: Implications for suburban conservation planning[END_REF].

We obtained climatic predictors including night temperature, precipitation levels and wind speed for Zurich (Meteo Schweiz database), Antwerp and Paris (Weather Underground, 2018). Landscape variables were calculated with different buffer radii from the focal sampling point: 50, 100, 350 and 500 m, in order to suit the dispersal abilities of insects [START_REF] Ropars | Wild pollinator activity negatively related to honey bee colony densities in urban context[END_REF] and bats [START_REF] Hutson | Microchiropteran Bats: Global Status Survey and Conservation Action Plan[END_REF]. For each sampling site, we used the European Urban Atlas (EEA, 2012) for calculating the size (area of each patch), the Proximity Index, the edge-to-edge distance to the nearest UGA and the proportion of impervious surfaces (e.g. urban fabric, roads). We estimated the distance to the nearest water body following [START_REF] Krauel | Patterns of bat distribution and foraging activity in a highly urbanized temperate environment[END_REF] and Price and Baker (2016). The land-use heterogeneity [START_REF] Matthies | Determinants of species richness within and across taxonomic groups in urban green spaces[END_REF][START_REF] Mcintyre | Ground arthropod community structure in a heterogeneous urban environment[END_REF] was calculated as the Shannon Index of habitats per site, using the European Urban Atlas for the three cities and in Zurich also using a high resolution land-cover map (Gruen Stadt Zuerich, 2010) (see Table 1). All the connectivity and landscape measurements were calculated in ESRI ArcMap 10.4.1.

Moreover, for the city of Zurich we used Airborne Laser Scanning (ALS) metrics of woody vegetation (>1 m). These types of remote sensing data are surrogates of habitat amount and vertical heterogeneity of vegetation structure and have been shown to be good predictors of biodiversity in urban ecosystems [START_REF] Frey | Predation risk shaped by habitat and landscape complexity in urban environments[END_REF][START_REF] Zellweger | Environmental predictors of species richness in forest landscapes: Abiotic factors versus vegetation structure[END_REF]. The habitat amount was estimated as the woody vegetation cover and the heterogeneity of structures as the standard deviation of woody vegetation heights at 50, 100, 350 and 500 m radii (Table 1; for details see [START_REF] Frey | Predation risk shaped by habitat and landscape complexity in urban environments[END_REF].

Increasing Artificial Light At Night (ALAN) have been shown to disturb bat and insect biodiversity [START_REF] Knop | Artificial light at night as a new threat to pollination[END_REF][START_REF] Lewanzik | Transition from conventional to light-emitting diode street lighting changes activity of urban bats[END_REF][START_REF] Sanders | Low Levels of Artificial Light at Night Strengthen Top-Down Control in Insect Food Web[END_REF]. We estimated ALAN by measuring illuminance levels in a set of randomly selected points in each city (Paris = 50, Zurich = 40, Antwerp = 43) using a lux meter Testo 540 (https://www.testo.com). We intended to cover the radiance range of every city to calibrate a night image from the International Space Station (Earth Science and Remote Sensing Unit NASA, 2018). Once the raster was calibrated, light emission in lux at each sampling site was extrapolated. However, the correlation of the lux meter readings with the calibration data was low in Paris (R² = 0.1189), thus the raw RGB color values (of the night image) of the raster were calculated at each focal sampling point (pixel size of 40x40 m) and used as surrogate of ALAN (Table 1).

Statistical analysis

All the analyses and statistical figures were done with the statistical computing software R v.3.5.1 (R Core Team, 2018) and packages lme4 v.1.1-20, glmmLasso v.1.5.1 and car 3.0-3.

Variable selection

Due to the high number of predictors, we first selected the relevant ones using a L1-Penalised Estimation via the glmmLasso function [START_REF] Groll | Variable selection for generalized linear mixed models by L1-penalized estimation[END_REF]. In addition, we also included possible interactions (identified by exploratory analysis) and ecological meaningful variables. Finally, we checked the correlation among predictors and discarded those highly correlated (ρ>0.7) to avoid collinearity [START_REF] Zuur | A protocol for data exploration to avoid common statistical problems[END_REF].

Effects of predictors on bat and insect response variables in the three cities and in Zurich

We performed Generalised Linear Mixed-Effects Models (GLMMs) on insect abundance and relative bat activity (i.e. total, LRE, MRE and SRE), with a Poisson and binomial error structure, respectively [START_REF] Crawley | The R Book[END_REF]. We ran the function glmer. In each model, the sampling site was included as a random factor to allow for repeated measures of the same UGA on different nights. Meteorological values were also included as random factors to avoid them masking the effects of other predictors.

Once the models were fitted, the Variance Inflation Factor (VIF) was used to identify multicollinearity via the function vif. When VIF >3, we sequentially dropped the predictor with the highest VIF and recalculated the VIFs [START_REF] Zuur | A protocol for data exploration to avoid common statistical problems[END_REF]. We also checked that the selected model was the one with the lowest corrected AIC value (AICc). The goodness-of-fit was investigated by calculating the conditional coefficient of determination for Generalised Mixed-Effect Models (R 2 C) (Nakagawa & Schielzeth, 2013). Finally, the model assumptions were validated by plotting the residuals against fitted values and the Q-Q plots of the random effects [START_REF] Zuur | A protocol for conducting and presenting results of regression-type analyses[END_REF]. We ran Linear Mixed-Effects Models (LMMs) on bat and insect diversity, with a normal error [START_REF] Crawley | The R Book[END_REF], using the function lmer. The model that best explained the data variability was the one with no multicollinearity (VIF <3), the lowest AICc value and a plot of residuals versus fitted values without pattern.

Models were run separately for each response variable: first for the 36 sites in Paris, Zurich and Antwerp (12 each) including the city as a fixed factor in order to identify biodiversity differences between the cities, and secondly for the 32 sites in Zurich. Overall, the graphics of the residuals versus the fitted values of the GLMMs and LMMs pointed out to a good fit of the model, as the point clouds did not exhibit any particular trend and were well dispersed over the axis (see Supplementary

Fig. A.2).

RESULTS

We sampled a total of 12,714 insects and recorded 283,126 bat passes containing 5 million echolocation calls. Small Diptera and Trichoptera represented 56% of the sampled individuals, while medium-sized Lepidoptera and small-sized Coleoptera represented 20%. The remaining groups accounted for less than 10% of total abundance each. Around 99% of the bat calls belonged to Pipistrellus pipistrellus followed by Pipistrellus nathusii (0.6%). Thus, Mid-range echolocators (MRE) were significantly more present than the remaining guilds.

Nocturnal insect diversity showed a significant negative effect with increasing isolation. Conversely, insect abundance was reduced with increasing SRE, quadratic MRE and quadratic total bat relative activity, as well as with bat group diversity (Figure 1). We found artificial light (i.e. ALAN RGB) to have a city-specific effect on bat group diversity. Moreover, the range of ALAN RGB and the response of bat diversity differed among cities (Figure 2). In Antwerp and Paris bat diversity was strongly reduced with increasing values of ALAN RGB, despite having a different range of illumination. Conversely, Zurich had the shortest range of illuminance values, which translated into a small increase of bat diversity (Figure 2). In addition, Zurich harboured a more diverse bat community than Antwerp and Paris (Supplementary Table B.1). The total bat relative activity in Antwerp was significantly higher than in the other cities (Supplementary Table B.1). Moreover, we found the relative activity of the guild of LRE to be significantly decreased with increasing ALAN RGB in all cities, but not with any other category of predictors. Similarly, no significant effects were found for the connectivity, landscape or remote sensing predictors on MRE. The model only selected an increase of the MRE relative activity with insect diversity gain. Finally, SRE activity was negatively affected by the proportion of impervious cover in the 100 m radius (Figure 1).

The analyses of bat and insect responses in an extended set of sampling sites in Zurich unveiled the influence of new predictors (Figure 3). Insect responses were enhanced by the size of the UGA and constrained by the activity of bat guilds. Strikingly, we found a scale-dependent response with increasing vegetation heterogeneity, which was positive at 100 m and negative at 350 m radius. Bat diversity and activity responses depended on a combination of predictors including connectivity (distance to water), bottom-up (insect diversity) and landscape (impervious surfaces at different radii) variables. Overall, bat diversity and activity increased together with insect diversity gain but was constrained by increasing distance to water and the amount of impervious surfaces at 100 m. The land use heterogeneity predictors, both from the Urban Atlas and from the ecologically-based land-cover map of Zurich, only showed significant effects for bat response variables.

Detailed model results (e.g. fitted model, p-values) can be found in Supplementary Tables B.2 and B.3.

DISCUSSION

The multi-city approach revealed some consistent responses of nocturnal biodiversity to urbanisation across cities. Isolation had a negative influence on nocturnal insect diversity, which might be related to the limited mobility of insects and their dependence on vegetation for reaching new patches [START_REF] Concepción | Impacts of urbanisation on biodiversity: The role of species mobility, degree of specialisation and spatial scale[END_REF]. Contrastingly, [START_REF] Turrini | A landscape ecology approach identifies important drivers of urban biodiversity[END_REF] found that patch isolation plays a limited role in urban ecosystems and arthropods can be diverse when sufficient vegetated space is provided. On the other hand, connectivity metrics did not show significant effects on bats, likely because they are highly mobile organisms and can reach distant patches [START_REF] Krauel | Patterns of bat distribution and foraging activity in a highly urbanized temperate environment[END_REF].

Especially for MRE, their generalist detection range -compared to LRE and SRE -could explain their tolerance to fragmentation and might make the most present guild in our study prone to cross less suitable areas (Frey-Ehrenbold et al., 2013).

We identified ALAN to be a prominent factor lowering bat group diversity. Particularly, Paris and Antwerp showed high radiance levels and low bat diversity, suggesting for the filtering out of lightaverse species. ALAN can modify the foraging behaviour of bat communities [START_REF] Russo | Sensitivity of bats to urbanization: A review[END_REF], enhancing a subset of light-tolerant species (e.g. Pipistrellus pipistrellus, the most common bat in our study) that can take advantage of insect resources at the expense of light-averse ones. This might indicate an ongoing behavioural change of the urban bat community, possibly caused by adaptation or plasticity towards light pollution as noted in moths [START_REF] Altermatt | Reduced flight-to-light behaviour of moth populations exposed to long-term urban light pollution[END_REF]), yet evidence is lacking. Future studies delving into the eco-evolutionary dynamics of urban fauna could provide important insights to better inform nocturnal biodiversity management [START_REF] Lambert | Urban biodiversity management using evolutionary tools[END_REF]. Finally, recent trends to replace traditional street lighting with LED might help to rebalance this filtering effect, as LEDs presumably reduce the photo-taxis of insects and their availability [START_REF] Lewanzik | Transition from conventional to light-emitting diode street lighting changes activity of urban bats[END_REF], especially when combined with appropriate choice of longer wave emission spectra (i.e. >500 nm, see [START_REF] Bennie | Artificial light at night causes top-down and bottom-up trophic effects on invertebrate populations[END_REF][START_REF] Bolliger | Low impact of two LED colors on nocturnal insect abundance and bat activity in a peri-urban environment[END_REF]. Moreover, we found a top-down control of insect abundance by both bat relative activity and group diversity, pointing out the importance of assessing and considering urban trophic dynamics [START_REF] Shochat | From patterns to emerging processes in mechanistic urban ecology[END_REF].

Our single-city approach revealed that insect diversity increased both bat relative activity and diversity in Zurich, similarly as shown by [START_REF] Lewanzik | Transition from conventional to light-emitting diode street lighting changes activity of urban bats[END_REF]. These results emphasise a bottom-up regulation in the nocturnal insects-bats trophic system in Zurich. In addition, vegetation heterogeneity appeared to have a scale-dependent effect on nocturnal insects, positive at 100 m and negative at 350 m radius. Thus, our results are consistent with previous works on arthropods [START_REF] Turrini | A landscape ecology approach identifies important drivers of urban biodiversity[END_REF] and limited mobile organisms, which more likely rely on local patch characteristics (i.e. 100 m radius) [START_REF] Concepción | Impacts of urbanisation on biodiversity: The role of species mobility, degree of specialisation and spatial scale[END_REF]Threlfall et al., 2012a). On the other hand, we were not able to assess an effect of vegetation structure metrics on bats although their significance has been proven on prior studies in urban environments [START_REF] Suarez-Rubio | Insectivorous bats respond to vegetation complexity in urban green spaces[END_REF]. Furthermore, [START_REF] Threlfall | Increasing biodiversity in urban green spaces through simple vegetation interventions[END_REF] have pointed out that native plant species in UGA increase bat diversity, likely because of bats' insectivorous diet. Similarly, land use heterogeneity derived from the detailed landcover map of Zurich did not show substantial effects. In this regard high resolution predictors did not present major contributions to our data understanding. Interestingly, no model selected ALAN or ALAN RGB, likely because of the low radiance values within the city (Figure 2). Studying European cities with intermediate radiance levels might help finding the tipping point at which ALAN starts decreasing nocturnal biodiversity.

Our study provides evidence of the role of UGA sustaining nocturnal biodiversity in European cities and sheds light on the importance of proxies used to infer the effects of urban intensification.

Nocturnal habitats are main ecological niches in cities responsible for several ecosystem services such as insect regulation and pollination, but have been so far neglected in urban management (Pinho et al., 2020 in this issue). We developed a standardized multi-city design to consistently compare responses of bats and nocturnal insects to urban intensification. Our results may set a starting point for planning and managing nocturnal biodiversity in UGA of Western and Central Europe. Overall, our findings showed an urgent need of regulating and reducing ALAN, but accounting also for the existing trophic controls. The specific responses of our studied groups to urbanisation highlight the need for manifold management strategies in UGA that reflect the different traits of the nocturnal species. Currently, European cities face a scenario of urban densification to avoid the sprawl to other ecosystems, leading to increasing levels of stressors and thus threatening biodiversity within cities. In this context, UGA are key elements to preserve and enhance present and future urban biodiversity.

Figures and captions

Table 1. Classification of predictors into categories. The table depicts the seven categories (i.e. City, Connectivity, Landscape, Remote Sensing, Bottom-up, Top-down and statistical interactions) used to aggregate the 39 predictors included in the models in order to facilitate reporting of the results (see Figure 123). Each category is described and the number and type of predictors included and their availability in each city for analysis is reported (A = Antwerp, P = Paris, Z = Zurich). [START_REF] Green | Next generation insect light traps: The use of led light technology in sampling emerging aquatic macroinvertebrates[END_REF][START_REF] Infusino | Assessing the efficiency of UV LEDs as light sources for sampling the diversity of macro-moths (Lepidoptera)[END_REF]Price & Baker, 2016;[START_REF] Van Grunsven | Spectral composition of light sources and insect phototaxis, with an evaluation of existing spectral response models[END_REF]. The 20 traps were made from plastic 2-liter soda bottles, with a 120° window cut into the side (A) and suspended upside-down with a wire hook. The cap of the bottle was modified to screw a 200 ml vial where insects fell and could be easily collected. This simple structure contained a LED device [START_REF] Elekon | Detect and observe bats successfully[END_REF] scotched to the inside wall of the bottle (B). The device contained 4 battery operated LED lights: one blue (λmax ~ 440 nm), one green (λmax ~ 530 nm) and two UV (λmax ~ 350 nm), as most phototactic insects have photoreceptors at these wavelengths [START_REF] Briscoe | The evolution of color vision in insects[END_REF]. The device was also equipped with a light sensor adjusted to 20-25 lx, so it automatically switched on at sunset and switched off at sunrise. Light-traps were prepared with fully charged batteries and a solution of water and dishwashing solution in the 200 ml vial to break the surface tension.

Table A.2. Insect orders found in Antwerp, Paris and Zurich were assigned a relative factor according to their length, and a biomass index was calculated per sample. Some orders were divided in classes by length (i.e. I, II, III).

Order

Fig

  Fig. A.1).To minimize interferences, they were installed in a suitable tree 10-20 m distant from the

Figure 1 .

 1 Figure 1. Heatmap of the significant results of Generalised Linear Mixed-Effects Models for the three

Figure 2 .

 2 Figure 2. Relationship between the RGB values of nocturnal rasters of the cities (ALAN RGB) and

Figure 3 .

 3 Figure 3. Heatmap of the significant results of Generalised Linear Mixed-Effects Models in the city

Figure A. 1 .

 1 Figure A.1. Intercept LED light-trap used for sampling nocturnal flying insects in Antwerp, Paris and Zurich, based on the assumption that nocturnal insects possess positive phototaxis (e.g.[START_REF] Green | Next generation insect light traps: The use of led light technology in sampling emerging aquatic macroinvertebrates[END_REF][START_REF] Infusino | Assessing the efficiency of UV LEDs as light sources for sampling the diversity of macro-moths (Lepidoptera)[END_REF] Price & Baker, 2016;[START_REF] Van Grunsven | Spectral composition of light sources and insect phototaxis, with an evaluation of existing spectral response models[END_REF]. The 20 traps were made from plastic 2-liter soda bottles, with a 120° window cut into the side (A) and suspended upside-down with a wire hook. The cap of the bottle was modified to screw a 200 ml vial where insects fell and could be easily collected. This simple structure contained a LED device[START_REF] Elekon | Detect and observe bats successfully[END_REF] scotched to the inside wall of the bottle (B). The device contained 4 battery operated LED lights: one blue (λmax ~ 440 nm), one green (λmax ~ 530 nm) and two UV (λmax ~ 350 nm), as most phototactic insects have photoreceptors at these wavelengths[START_REF] Briscoe | The evolution of color vision in insects[END_REF]. The device was also equipped with a light sensor adjusted to 20-25 lx, so it automatically switched on at sunset and switched off at sunrise. Light-traps were prepared with fully charged batteries and a solution of water and dishwashing solution in the 200 ml vial to break the surface tension.

  Figure A.2. The residuals versus the fitted values graphics of the GLMMs and LMMs for every response variable, for the multi-city (Paris, Antwerp and Zurich) and single-city (Zurich) approaches. Overall, they point out to a good fit of the model, as the point clouds do not exhibit any particular trend and are well dispersed over the axis. SRE: short-range echolocators, MRE: mid-range echolocators, LRE: long-range echolocators(Frey-Ehrenbold et al., 2013).

Acknowledgments

We would like to thank all collaborators of BioVEINS project (https://www.biodiversa.org/1012).

We thank H. Eggenberg for designing the trap-nests. A special thank goes to A. Zanetta for scientific advice, T. Hallikma and K. Kilchhofer for logistical support, Ch. Ginzler for preparing LiDAR data, M. Noguera and C. Perez-Mon for making fieldwork and data processing more pleasant and A. Groll for his precious statistical help. We also acknowledge the authorities of Antwerp, Paris and Zurich for the permissions to sample their UGA and 'Grün Stadt Zürich' (GSZ) for the permission to use their high resolution habitat map of the city of Zurich. This work was partially funded by the European ERA Net BiodivERsA project "BioVEINS: Connectivity of green and blue infrastructures: living veins for biodiverse and healthy cities" (H2020 BiodivERsA32015104), the French Research Agency

Data accessibility

Data is available via the EnviDat data portal (www.envidat.ch).

Appendices A and B. Supplementary data

Supplementary material related to this article can be found, in the online version, at Appendix B. Detailed results of the exploratory analysis and the fitted models.

Table B.1. Summary of results of exploratory analysis for the three cities. Mean value (mean), the standard deviation (s.d.), the minimum value (min) and the maximum value (max) are shown.

Responses

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: