# Exploring the relationships between landscape connectivity and urban biodiversity: Insights from citizen science on pollinators and birds in Paris, France

Tanguy Louis-Lucas, Joanne Clavel, Sarah Bortolamiol, Nathalie Blanc, Etienne Grésillon, Céline Clauzel

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## To cite this version:

Tanguy Louis-Lucas, Joanne Clavel, Sarah Bortolamiol, Nathalie Blanc, Etienne Grésillon, et al. "Exploring the relationships between landscape connectivity and urban biodiversity: Insights from citizen science on pollinators and birds in Paris, France." Biological Conservation, 2025, 309, pp.111271. 10.1016/j.biocon.2025.111271. halshs-05103645

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# Exploring the relationships between landscape connectivity and urban biodiversity: Insights from citizen science on pollinators and birds in Paris, France

Tanguy Louis-Lucas<sup>a</sup>, Joanne Clavel<sup>a,b</sup>, Sarah Bortolamiol<sup>a,c</sup>, Nathalie Blanc<sup>a,b</sup>, Etienne Grésillon<sup>a,b</sup>, Céline Clauzel<sup>a,c,d,*</sup>

## ARTICLE INFO

### Keywords:

Functional connectivity

Graph theory

Urban greening

Biodiversity

SPIPOLL

Oiseaux des Jardins

## ABSTRACT

Urban greening has emerged as a vital strategy to address biodiversity loss and promote sustainability. Enhancing landscape connectivity in cities is often cited as essential for facilitating species movement and supporting biodiversity. However, empirical validation remains scarce, particularly regarding the relative roles of habitat quantity and connectivity.

This study investigates the effects of landscape connectivity and vegetation amount on urban biodiversity in Paris, France, using citizen science data on passerine birds and pollinators. Using citizen science data, we modeled ecological networks with graph theory to test three hypotheses: (H1) increased connectivity positively correlates with species richness and abundance, (H2) the connectivity-abundance relationship depends on species specialization, and (H3) connectivity influences bird community composition, favoring specialists in highly connected areas.

Our results show that pollinator and bird richness is positively influenced by functional connectivity and vegetation cover. However, bird abundance is driven primarily by vegetation cover, with no overall effect of connectivity. Importantly, functional connectivity only has a significant positive effect when habitat patch size is considered; otherwise, its impact disappears or becomes negative. Species-level analyses revealed that some urban-adapted birds respond negatively to high connectivity, suggesting fragmentation may favor them.

These findings underline the complexity of biodiversity responses to urban landscapes and the need to clarify connectivity estimation methods. The study highlights the importance of integrating habitat amount and species-specific ecology in urban planning to optimize green infrastructure for biodiversity.

## 1. Introduction

Urban greening policies have received growing attention worldwide, as cities face the twin challenges of biodiversity loss and environmental sustainability (Bowler et al., 2010). Continued erosion of biodiversity through habitat destruction, climate change and urbanization has led to the recognition of urban ecosystems as critical spaces for conservation efforts (Aronson et al., 2014; Seto et al., 2012). Urban biodiversity supports ecological resilience, and provides essential ecosystem services - such as pollination, pest control and improved air quality - that directly

benefit urban dwellers (Elmqvist et al., 2013). Once viewed as biodiversity deserts, cities are now seen as potential refuges for species, provided they are designed and managed to enhance landscape connectivity and green infrastructure (Ives et al., 2016). This shift highlights the importance of integrative approaches that link urban planning to ecological principles and the valuable role of citizen science in advancing our understanding of urban biodiversity dynamics. In densely built environments, the challenge lies in creating functional and resilient green infrastructure composed of small vegetated elements that collectively form corridors linking larger green spaces. Such infrastructure

<footnote><sup>a</sup> UMR 7533 CNRS LADYSS, France</footnote>

<footnote><sup>b</sup> Université Paris Cité, France</footnote>

<footnote><sup>c</sup> Université Paris 1 Panthéon-Sorbonne, France</footnote>

<footnote><sup>d</sup> Institut universitaire de France (IUF), France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: celine.clauzel@univ-paris1.fr (C. Clauzel).</footnote>
must not only improve urban living conditions but also enhance biodi-
versity by fostering landscape connectivity (Ersoy, 2016).

The concept of connectivity has been studied extensively since the
1970s (Dunning et al., 1992) in response to the landscape fragmentation
and the need for individuals to move between their habitats to ensure
their life cycle. The persistence of populations requires reproduction
success and genetic mixing, which is made possible by the flow of genes
and therefore of individuals across the landscape (Levins, 1969). There
is an abundance of literature on connectivity and several reviews have
been produced (see for example, Fahrig et al., 2021 on the definition of
landscape connectivity; Correa Ayram et al., 2016 in biodiversity con-
servation; Lookingbill et al., 2022 in urban landscapes). The concept of
connectivity is defined in different ways depending on the scales
considered (Calabrese and Fagan, 2004). In metapopulation ecology,
connectivity is an attribute of a habitat patch (Moilanen and Hanski,
2001), while in landscape ecology, connectivity is a property of an entire
landscape. To clarify this scale, Merriam (1984) followed by Taylor et al.
(1993) proposed the concept of landscape connectivity, defined as “the
degree to which a landscape facilitates or impedes organismal move-
ment between patches of ressources” (Tischendorf and Fahrig, 2000).

According to Fahrig et al. (2021), connectivity can be measured in different ways, leading to methodological challenges and varying interpretations of its ecological role. The main difference is between structural and functional connectivity. Structural connectivity is defined as “the extent to which patches are connected to one another by similar habitat or corridors” (Noss, 1987). The focus is on the physical arrangement of habitat patches, regardless of the movement capabilities of the species. Despite this limitation, it has been the focus of many studies on connectivity because of the simplicity of its measurement using landscape metrics. Functional connectivity goes further by explicitly integrating the movements of organisms and postulating that they are interacting with landscape structures (Merriam, 1984). It integrates species-specific movement abilities and takes into account that a species may move through an area that does not match its habitat. Functional connectivity is estimated in two ways depending on the objectives of the study and the available data (Calabrese and Fagan, 2004). Using empirical data to track individuals or estimate genetic distances between populations, it is possible to measure the *actual* (or *empirical*) functional connectivity for a target species. While this approach is valuable for estimating the success of movements and thus the persistence of populations, it is difficult to implement it widely due to the technical, human and financial cost of collecting empirical movement data. To overcome this difficulty, many studies (Lookingbill et al., 2022) have focused on *potential* functional connectivity, which is estimated based on landscape configuration and assumptions about the movement behaviour of species in that landscape. Estimated potential functional connectivity requires modelling approaches often based on graph theory (Urban and Keitt, 2001) or circuit theory (McRae and Beier, 2007) to model the potential movements of a species across a landscape, as a function of landscape element resistance.

Measuring connectivity raises the question of the distinction be-
tween the amount of habitat and its spatial configuration, and the spatial
scale considered (patch vs landscape connectivity). Landscape ecology
studies have shown the importance of varying scales to account for the
spatial and temporal overlap of ecological processes, from daily foraging
to dispersal over several generations (Saura and Rubio, 2010). In this
study, we focus on the potential functional landscape connectivity and
define it as the area of habitat reachable when individuals move within
and between patches. It is measured from each habitat patch in the study
area at a spatial scale defined by the dispersal capabilities of the species,
to account for its role in the connectivity of the habitat network as a
whole (Savary, 2021).

Landscape connectivity is often cited by stakeholders as a justifica-
tion for urban green infrastructure, as connectivity is assumed to benefit
urban biodiversity (Lookingbill et al., 2022). Some features, like green
roofs and walls, may contribute to ecological networks because of their

strategic placement in urban settings (Louis-Lucas et al., 2022; Mayrand and Clergeau, 2018). While numerous studies indicate that connectivity positively impacts biodiversity (Fletcher et al., 2016), caution is warranted. The effects of connectivity vary across geographic settings, species, and how connectivity is estimated (Lookingbill et al., 2022). This variability is particularly true in urban environments, where studies often lack clear definitions and rely on diverse metrics of connectivity, mainly based on distance and surface area (LaPoint et al., 2015; McDonnell and Hahs, 2013). Many studies tested the effects of proximity to a favourable habitat or the area of a favourable habitat on biodiversity (Beninde et al., 2015; Callaghan et al., 2018). However, urbanization profoundly impacts biodiversity by altering habitat availability and creating new ecological opportunities for certain species. Some bird species, for instance, exhibit a particular affinity for human habitation, which influences their distribution and ecological traits (for example *Columba livia*) (Møller and Díaz, 2018). This raises questions about the potential positive role that connectivity could play in such altered environments. While connectivity is often assumed to enhance population survival by facilitating movement and gene flow, its benefits may be context-dependent. Classical metapopulation theory suggests that connectivity is particularly beneficial for small, relatively isolated patches (Hanski, 1998).

The recent literature review of Lookingbill et al. (2022) pointed out a greater recognition of functional connectivity, a diversification of the urban geographic contexts and a greater taxonomic diversity. Functional connectivity has been modeled for various groups, including large mammals, birds, and invertebrates, based on the assumption that it is beneficial to these taxa (Fletcher et al., 2016). Some studies demonstrated the effectiveness of corridors in urban contexts using capture for shrews (Vergnes et al., 2013) and mark and recapture methods for moths and playback recalls for passerines (Balbi et al., 2021). Despite these advances, Lookingbill et al. (2022) highlighted the limited use of empirical data to validate connectivity models, an observation already made in LaPoint et al. (2015). Although nearly two-thirds of the papers included a measure of functional connectivity, very few attempted to test these measures against biological data, reflecting practices in studies of various types of environments (Foltete et al., 2020). In addition, Lookingbill et al. (2022) stressed the need to improve geographic information on urban landscapes and to mobilize more complex modelling approaches to go beyond the typical Euclidean or least-cost distance used to assess connectivity.

Our paper aims to clarify the effect of landscape connectivity on
biodiversity by analyzing its relationship with different estimation
methods in a dense urban context. As we have no empirical multi-species
data with which to measure actual connectivity, we worked on the basis
of an estimate of potential connectivity. By employing both graph the-
ory—an approach widely used in landscape connectivity studies
(Fletcher et al., 2016; Foltete et al., 2020) to estimate functional con-
nectivity -, and Euclidean distance to estimate structural connectivity,
we assessed how different estimation approaches of landscape connec-
tivity influence biodiversity patterns. Additionally, we examined the
relative importance of habitat quantity (vegetation cover) compared to
connectivity, aiming to disentangle their respective roles in shaping
urban biodiversity. We mobilized citizen science data to cover a wide
range of species of two taxa: birds, representing taxonomic diversity,
and pollinators, reflecting functional diversity. Citizen science data has
greatly multiplied in recent years with participatory science pro-
grammes. The quantity of data makes them very interesting for moni-
toring biodiversity on a large scale, provided that the various biases
inherent in these data are taken into account (Johnston et al., 2023).

This study provides a refined understanding of the interplay between
habitat amount and connectivity in urban ecosystems, contributing to
ongoing debates on how to better quantify and integrate these factors
into conservation strategies. Our first hypothesis (H1) stated that func-
tional connectivity is strongly and positively correlated with species
richness and abundance, and that its influence would be stronger than
structural connectivity. Consequently, highly connected areas were ex-
pected to support more individuals and greater species richness than
poorly connected ones, before leveling off. Furthermore, as the method
for estimating functional connectivity associates habitat quantity and
connections between habitats, we expected that even if habitat quantity
was a determining factor, the influence of estimated connections alone
was also a significant factor. The second hypothesis (H2) was that the
relationship between abundance and functional connectivity varies with
species specialization, reflecting their specialist or generalist status. We
expected specialist species to benefit more from functional connectivity
than generalist species. Moreover, given the negative relationship be-
tween specialism and urban tolerance (Callaghan et al., 2020), our third
hypothesis (H3) was that bird community composition shifts along a
functional connectivity gradient, with more urban-averse species
appearing in highly connected areas.

## 2. Materials and methods

### 2.1. Study area and biodiversity data

Our study focuses on Paris, France, and its surrounding areas,
covering approximately 460 km². While built-up areas dominate, the
city contains two significant anthropized woodlands—the Bois de Bou-
logne and the Bois de Vincennes—and is bordered by suburban national
forests, such as the Forêt de Meudon (Fig. 1). Vegetation covers roughly
35 % of the study area, with buildings occupying over 32 % (APUR,
2015; IGN, 2022; Institut Paris Région, 2021). Roads and railways
constitute 16 %, while artificial open spaces account for 12 %. Water-
ways and water bodies, primarily the Seine, represent just over 2 %, and
cropland covers <1 %.

Since 2015, Paris has implemented an ambitious greening policy to
adapt to climate change. This strategy includes the creation of large
green spaces, such as urban “forests” in public squares, alongside smaller

green initiatives, including micro-green spaces in the courtyards of
apartment blocks and schools.

We used biodiversity data from two national standardized citizen
science programs (Fig.1). These data were collected according to a
protocol that ensures comparability over time and space. In both cases,
observers must report all species (or morphospecies) observed in a well-
defined area during a defined period. The first, SPIPOLL (Suivi Photo-
graphique des insectes pollinisateurs), is a photographic survey of pol-
linators (https://www.spipoll.org/). The SPIPOLL program is a
recognized initiative, highlighting the effects of urbanization on polli-
nator communities (Deguines et al., 2012) and the importance of private
gardens for pollinators (Levé et al., 2019). Volunteers select a flowering
plant and capture images of every insect visiting its flowers within a 20-
minute timeframe. Participants then identify plants and insects using an
online identification tool that provides a list of predefined taxa (i.e.,
morphospecies defined as a group of species distinguishable from all
other groups by externally noticeable features). Identifications are then
validated by professional entomologists (Deguines et al., 2012). While
this method ensures a standardized identification process, it may include
some non-pollinating flower visitors, such as nectar thieves or incidental
visitors. We extracted 1281 sampling points located within our study
area from 2015 to 2022, encompassing a total of 263 morphospecies.
Observation sessions covered diverse settings, with 60 % conducted in
vegetated areas, 16 % in buildings, 13 % in artificialized open spaces,
and 11 % beside roads and railways (Supplementary 1).

The second program, Oiseaux des jardins, monitors birds in private
and public gardens (https://www.oiseauxdesjardins.fr/). Volunteers
choose a location and record all bird species observed, along with their
respective abundance, during a 10-minute session. Only “landed” birds
are counted, excluding raptors hunting over the garden. Following Balbi
et al. (2021), we focused on passerines, which use wooded and shrub
areas as habitats. From 2015 to 2020, 1387 sampling points were
recorded within our study area, encompassing 36 bird species.

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Land-use map of the study area, centred on Paris's city boundaries (France) (APUR, 2015; Institut Paris Région, 2021; IGN BD topo, 2022) and observation points for pollinators collected through the SPIPOLL program (n = 1281) and for birds recorded by the Oiseaux des Jardins initiative (n = 1387). These two sets of data are derived from national citizen science programs managed by the National Museum of Natural History.</figcaption></figure>
Observation sessions were distributed across vegetated areas (48 %), buildings (37 %), artificialized open spaces (8 %), and roadsides (7 %) (Supplementary 2). Our dataset includes generalist species, such as robins (*Erithacus rubecula*), as well as more specialist species like swallows (*Hirundo rustica*). As we believe that functional connectivity may influence species differently depending on their habitat specialization and urban tolerance, we characterized these criteria using different databases to test our hypothesis (Callaghan et al., 2020; Morelli et al., 2019) (Supplementary 3).

To reduce potential learning bias, we excluded participants with fewer than 10 sampling sessions from the two datasets (Deguines et al., 2018). For the bird dataset, we kept only 10-minute passerine observation sessions, in line with the protocol. Furthermore, for bird abundance, only species with at least 10 different counts were retained for more robust statistical analysis.

## 2.2. Land-use map and graph modelling

We created a land-use map (Fig. 1) by aggregating multiple geographical datasets. Linear features, such as roads and railways, were sourced from IGN BD TOPO 2021 (https://geoservices.ign.fr/bdtopo). Other land uses, except vegetation, were derived from the Institut Paris Region (2021) land-use map. Vegetation height data, obtained from APUR (2015) at a 50 cm resolution, was categorized into three strata: herbaceous vegetation (0–1 m), shrub vegetation (1–10 m), and tree canopy (over 10 m). Where roads overlapped with vegetation, we prioritized roads due to their fragmentation effects, as highlighted by Bennett (2017). The final land-use map was compiled into a raster format with a spatial resolution of 3 m.

From the land-use map, we modeled ecological networks for pollinators and birds through a landscape graph approach implemented in Graphab 2.8 software (Foltête et al., 2021). Landscape graphs consist of habitat patches (nodes) connected by functional links representing potential movement flows between patches (Urban et al., 2009).

Herbaceous vegetation and grassland were identified as optimal habitats for pollinators (Matteson et al., 2013). While trees, shrubs, and crops can provide supplementary floral resources, we excluded them as habitats. Concerning birds, shrub and tree strata were selected as optimal habitat for passerines, as in Balbi et al. (2021). They offer essential resources for feeding and nesting (Marlès Magre et al., 2019). For both species' groups, habitat patches were defined as clusters of adjacent vegetation pixels spanning a minimum area of 36 m² (equivalent to four pixels at 3 m resolution). This threshold was selected to include small habitat patches, which are particularly vital for supporting urban biodiversity (Vega and Küffer, 2021).

The graph links were determined in planar topology and calculated via least-cost distances, considering the resistance to movement of each land-use category. The lowest resistance value (1) was assigned to optimal habitat or foraging areas, while the highest value (100) was assigned to the most movement-restrictive features, such as buildings (Table 1). Since pollinating insects fly, we assigned a lower resistance value to shrub vegetation (5) than to tree canopy or crops (10). Despite their herbaceous nature, crop areas were penalized due to the adverse effects of agricultural practices on bees and other Hymenoptera (Theodorou et al., 2020). Artificial open spaces (e.g., public areas), minor roads, and waterways were assigned a resistance value of 10, as they pose minimal movement barriers. Resistance values increased for secondary roads (20) and major roads, such as motorways (50), correlating with their width and traffic density. Buildings represented the greatest obstacle to movement, receiving the maximum resistance value of 100. For birds, we assigned a resistance value of 5 to grassland and 10 to herbaceous vegetation because of higher predation and disturbance risks. For all other land uses, we adopted the same resistance values as for pollinators, following Balbi et al. (2021).

Not all links in the graph can be considered usable by species, due to their length and/or cumulative cost. The graph is therefore pruned to

<caption>**Table 1**</caption>

Resistance values for each land-use category for pollinators and birds. Habitat categories for each group are assigned the value of 1.

<table><thead><tr><th>Land use categories</th><th>Resistance values for pollinators</th><th>Resistance values for birds</th><th>Percentage of study area (%)</th></tr></thead><tbody><tr><td>Major roads</td><td>50</td><td>50</td><td>0.83</td></tr><tr><td>Secondary roads</td><td>20</td><td>20</td><td>3.73</td></tr><tr><td>Minor roads</td><td>10</td><td>10</td><td>11.6</td></tr><tr><td>Buildings</td><td>100</td><td>100</td><td>32.55</td></tr><tr><td>Railways</td><td>10</td><td>10</td><td>2.11</td></tr><tr><td>Waterways & water bodies</td><td>10</td><td>10</td><td>2.26</td></tr><tr><td>Herbaceous vegetation</td><td>1</td><td>10</td><td>10.05</td></tr><tr><td>Shrub vegetation</td><td>5</td><td>1</td><td>15.29</td></tr><tr><td>Tree canopy</td><td>10</td><td>1</td><td>9.60</td></tr><tr><td>Artificial open areas</td><td>10</td><td>10</td><td>11.92</td></tr><tr><td>Grasslands</td><td>1</td><td>5</td><td>&lt;0.001</td></tr><tr><td>Crops</td><td>10</td><td>10</td><td>0.05</td></tr></tbody></table>

keep only those links below a certain distance, generally the dispersal distance. A maximum dispersal distance of 500 m was set for pollinators, based on observations by Osborne et al. (2008). For birds, we applied a median dispersal distance of 500 m, guided by studies on the most abundant species in our dataset (Matthysen et al., 2005; Ortego et al., 2011). While this distance might seem conservative given that many bird species are capable of flying much farther, it reflects findings from multiple studies that report significantly reduced mobility in urban bird populations due to fragmentation and urban stressors (Hahs et al., 2023; Møller, 2008).

## 2.3. Habitat quantity and connectivity estimation

### 2.3.1. Functional connectivity estimation

Once the ecological network was modeled, the graph was used to calculate connectivity metrics for each habitat patch, in order to estimate their level of connection with neighboring patches. The metrics classically used in landscape ecology incorporate a double weighting, by the area of the habitat patch and the distance between patches (Keeley et al., 2021). Among the many connectivity metrics available, we selected the Flux metric (Urban and Keitt, 2001), which is an indicator of the potential dispersal from/to patches. This metric is widely used in the literature and assesses the importance of a patch's connections with other patches. It entails summing the areas of patches linked to a focal patch (Fi) and weighting them based on their minimum distance to the focal patch through the graph:

$$F_i = \sum_{j=1} a_j e^{-\alpha d_{ij}} \quad j \neq i$$

with $a_j$ being the potential recruitment capacity of the patch $j$, $d_{ij}$ is the least-cost path between patches $i$ and $j$, and $\alpha$ ($0 < \alpha < 1$) expresses the decrease in dispersal probabilities resulting from an exponential function. By default, the capacity ($a$) of a patch is equal to its surface area. To disentangle the effects of habitat quantity and connection between patches, the Flux metric was calculated with and without patch surface weighting. In the latter case, all patches are considered to be of equivalent minimal size ($a = 1$). In this way, the metric calculation accounts only for the connections between the neighboring patches and the focal patch, without integrating their surface.

Then, we interpolated connectivity values to the entire spatial continuum, so that each observation point is associated with a value of estimated connectivity. The connectivity value for a given point $i$ was derived by averaging the weighted values of the metric from multiple habitat patches, as outlined by Tarabon et al. (2022). The weighting function applied was a decreasing exponential function, as described previously.
### 2.3.2. Structural connectivity estimation

Unlike functional connectivity, which accounts for species-specific movement behaviors, the proximity to a habitat patch represents a simple and generic (i.e., species-nonspecific) structural connectivity metric. It was calculated as the Euclidean distance to the nearest patch, derived from a binary map (habitat/non-habitat) (Keeley et al., 2021). This metric quantifies the minimum straight line distance an individual must travel to reach the closest suitable habitat.

### 2.3.3. Contextual variable

In addition to connectivity metrics, we accounted for the broader habitat context by incorporating vegetation cover, which is considered as a fundamental determinant of biodiversity in urban environments (Beninde et al., 2015; Ibáñez-Álamo et al., 2020). This contextual variable is defined as the proportion of vegetated area within a 150 m-Euclidean buffer as a proxy for habitat quantity around each counting point. The 150 m buffer was determined after several tests to reduce collinearity (see Supplementary 4 and 5). By integrating this variable, we aimed to disentangle the respective contributions of habitat quantity and connectivity to biodiversity patterns.

## 2.4. Data statistical analysis

To test our first hypothesis (H1), we ran three statistical models to examine the relationships between estimated structural and functional connectivity, habitat quantity, and three response variables: (1) pollinator richness, (2) bird richness, and (3) bird abundance. The analyses were conducted using R 4.2.1 (2022). We used the DHARMa package (Hartig and Hartig, 2017) to check model residuals and assess potential violations of model assumptions, the MuMln package (Barton, 2020) for model selection and multimodel inference, and glmmTMB (Brooks et al., 2017) to fit generalized linear mixed models (GLMMs).

The first two models, analyzing species richness, were generalized linear models (GLMs) with negative binomial distributions. GLMs were chosen for species richness because observations at each sampling site were independent, and species richness was treated as a single aggregated response variable per site. Additionally, temperature was included in the first model to account for its effect on pollinator morphospecies richness. In contrast, for bird abundance (H2), we used a generalized linear mixed model (GLMM) with a negative binomial distribution to account for species-specific variation in abundance. For bird abundance, we integrated species as a fixed effect in interaction with functional connectivity to evaluate the influence of connectivity on the abundance of each species. Month and Year have been added as a fixed effect. To reduce sampling bias, the observer and the site were included as random effects in the model. We correlated this effect with the bird specialization database from Morelli et al. (2019), which assigns scores ranging from 0 to 1 for diet, foraging, nesting, and habitat specialization, as well as an aggregate specialization index.

For all models, the Akaike Information Criterion (AIC) was used to identify the best fit (Burnham and Anderson, 2004). We tested both AIC and the corrected AIC (AICc), however, given our large sample sizes, AIC and AICc converged to similar values. Residuals were checked using the DHARMa package (Hartig and Hartig, 2017) (Supplementary 6, 7, 8). Multicollinearity was assessed using the Variance Inflation Factor (VIF) from the car package in R. We ensured that all predictor variables had a VIF value below 1, indicating minimal collinearity and reducing the risk of redundancy in our models. (Supplementary 4, 5). We accounted for the potential spatial bias by testing for spatial autocorrelation in the model residuals to ensure statistical robustness. Despite these filters, spatial sampling remains uneven, as citizen science data tend to be clustered around populated or accessible areas, which may limit coverage of less-visited habitats (Castagneyrol et al., 2020; Johnston et al., 2023).

Finally, to test our third hypothesis (H3), we used the urban score developed by Callaghan et al. (2020). For each sampling point, we

calculated the community weighted mean (CWM) of the urban score and compared it to the functional connectivity value (Flux metric).

# 3. Results

## 3.1. Richness and abundance

For pollinators, an average of 6.23 morphospecies were observed per count. Within Paris, this average increased to 6.84, while it was slightly lower outside the city at 5.87. The most frequently observed morphospecies were small bees, such as Halictidae, which were present at 55 % of observation points (Supplementary 9). For birds, the average number of species per count was 1.86. Outside Paris, this rose marginally to 1.88, while within the city limits, it decreased slightly to 1.81. The most frequently observed bird species was the great tit (Parus major), found at 38 % of observation locations. The house sparrow (Passer domesticus) was the most abundant species, representing 16 % of total observations (Supplementary 10, 11).

## 3.2. Ecological network identification

The ecological network for pollinators comprised 220,000 habitat patches with an average size of 185 m², covering a total of 40 km². The highest connectivity values were found in the Bois de Boulogne and Bois de Vincennes, as well as in areas west of Paris with abundant herbaceous vegetation (Fig. 2a). In contrast, most of Paris exhibited low connectivity values.

The ecological network for birds included 218,894 habitat patches, averaging 510 m² in size, with a total area of 111 km². A notable contrast in connectivity values emerged between the northern and southern parts of the study area. The highest connectivity values were concentrated in the western and southwestern areas, characterized by extensive wooded regions (Fig. 2b). In contrast, the northern part was denser, with smaller patches of shrub and tree vegetation. Within Paris, the highest connectivity values were observed in the largest urban parks and along tree-lined roads, particularly in the southern half area.

## 3.3. Relationships between landscape connectivity and species richness

The model retained based on AIC (6892), indicates that multiple factors positively influence the number of pollinator morphospecies (Table 2). We observe a positive effect for the functional connectivity (estimate = 7.83e-07), the proportion of vegetated area within a 150 m buffer (estimate = 2.9e-01), and temperature (estimate = 3.15e-01). Consequently, as these factors increase, so does the number of morphospecies. All these variables were statistically significant, underscoring their robust influence on the observed patterns.

When the weighting by patch area is removed from the calculation of the F metric (a = 1), functional connectivity no longer has a significant effect on pollinator morphospecies diversity (Table 3, p-value = 0.6).

For birds (Table 4), the model retained (AIC = 4187) reveals that both functional connectivity and the proportion of vegetated area within a 150 m buffer have a significant positive influence on the number of bird species. The distance from the nearest patch does not show a statistically significant relationship with bird species richness.

When the weighting by the patch area is removed from the calculation of the F metric (a = 1) (Connectivity maps are available in the Supplementary 16), functional connectivity has a negative significant effect on bird richness (Table 5, p-value = 0.0003), as does structural connectivity (p = 0.017).

## 3.4. Relationships between landscape connectivity and bird abundance

The best model (Table 6) reveals no relationship between connectivity, whether structurally or functionally defined, and bird abundance. However, consistent with previous analyses, the proportion of vegetated
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Spatial distribution of connectivity values (Flux metric, with *a* = patch surface area) interpolated from habitat patches, for pollinators (a) and birds (b). The values were discretized by quantile. Note that the range of values differs significantly between the two taxa, with birds showing a broader range.</figcaption></figure>

**Table 2**

Generalized linear model (GLM) explaining pollinator morphospecies richness with functional connectivity accounting for habitat patch size (a = patch surface area), AIC = 6892. *** p ≤ 0.001; ** 0.001 < p ≤ 0.01; * 0.01 < p ≤ 0.05

<table><thead><tr><th></th><th>Estimate</th><th>Std error</th><th>Z value</th><th>Pr (&gt;|z|)</th><th>Significance level</th></tr></thead><tbody><tr><td>Intercept</td><td>7.47e-01</td><td>9.44e-02</td><td>7.91</td><td>2.50e-15</td><td>***</td></tr><tr><td>Functional connectivity</td><td>7.83e-07</td><td>1.83e-07</td><td>4.28</td><td>1.87e-05</td><td>***</td></tr><tr><td>Temperature</td><td>3.15e-01</td><td>2.91e-02</td><td>10.84</td><td>&lt;2e-16</td><td>***</td></tr><tr><td>150 m vegetation</td><td>2.906e-01</td><td>1.14e-01</td><td>2.55</td><td>0.01</td><td>*</td></tr><tr><td>Structural connectivity</td><td>-9.47e-03</td><td>2.63e-03</td><td>-3.6</td><td>0.0003</td><td>***</td></tr></tbody></table>

**Table 3**

Generalized linear model (GLM) explaining pollinator morphospecies richness with functional connectivity that does not account for habitat patch size *a* = 1, AIC = 6911. *** p ≤ 0.001; ; no markings indicate non-significance

<table><thead><tr><th></th><th>Estimate</th><th>Std error</th><th>Z value</th><th>Pr (&gt;|z|)</th><th>Significance level</th></tr></thead><tbody><tr><td>Intercept</td><td>7.29e-01</td><td>9.69e-02</td><td>7.53</td><td>5.22e-14</td><td>***</td></tr><tr><td>Functional connectivity</td><td>2.78e-05</td><td>5.28e-05</td><td>0.53</td><td>0.6</td><td>***</td></tr><tr><td>Temperature</td><td>3.21e-01</td><td>2.94e-02</td><td>10.94</td><td>&lt;2e-16</td><td>***</td></tr><tr><td>150 m vegetation</td><td>5.28e-01</td><td>1.04e-01</td><td>5.08</td><td>3.79e-07</td><td>***</td></tr><tr><td>Structural connectivity</td><td>-9.69e-03</td><td>2.68e-03</td><td>-3.62</td><td>0.0003</td><td>***</td></tr></tbody></table>

area within a 150 m buffer exhibits a positive correlation with bird abundance (estimate = 0.68).

The results indicate that functional connectivity does not influence overall bird abundance (*p* = 0.35). However, certain species, such as *Passer domesticus* (House Sparrow), show a significant negative response to increasing functional connectivity (*p* = 0.03), suggesting that more connected habitats may not favor some urban-adapted species and that these species might be more abundant in fragmented habitats. Similarly, *Fringilla coelebs* (Chaffinch), *Sylvia atricapilla* (Blackcap), and *Turdus merula* (Blackbird) exhibit near-significant negative trends, indicating. In contrast, vegetation amount has a strong positive effect on bird abundance (*p* = 0.015), reinforcing the idea that habitat quantity, rather than connectivity alone, plays a key role in urban bird populations.

Distance to the nearest patch, year, and month do not show significant effects, suggesting that seasonality and isolation are less influential factors in this dataset (Table 6).

Notably, functional connectivity accounting for habitat patch size does not appear to significantly influence the abundance of 18 out of the 19 bird species analyzed. Results are almost identical with functional connectivity without taking habitat size into account (17 species out of 19 with no effect of functional connectivity, Table 7). Furthermore, a comparative analysis of specialization indices (including habitat, foraging, diet, nesting, and overall specialization) revealed no significant correlation with functional connectivity, suggesting that other ecological or behavioral traits may play a more critical role in determining species responses to urban landscapes.
**Table 4**

Generalized linear model (GLM) explaining bird species richness with functional connectivity accounting for habitat patch size (a = patch surface area), AIC = 4187. *** p ≤ 0.001; ** 0.001 < p ≤ 0.01; * 0.01 < p ≤ 0.05; no markings indicate non-significance

<table><thead><tr><th></th><th>Estimate</th><th>Std error</th><th>Z value</th><th>Pr (&gt;|z|)</th><th>Significance level</th></tr></thead><tbody><tr><td>Intercept</td><td>2.87e-01</td><td>8.82e-02</td><td>3.26</td><td>0.001</td><td>**</td></tr><tr><td>Functional connectivity</td><td>7.93e-09</td><td>3.62e-09</td><td>2.19</td><td>0.029</td><td>*</td></tr><tr><td>150 m vegetation</td><td>4.89e-01</td><td>1.33e-01</td><td>3.69</td><td>0.0002</td><td>***</td></tr><tr><td>Structural connectivity</td><td>-1.34e-03</td><td>1.91e-03</td><td>-0.70</td><td>0.54</td><td></td></tr></tbody></table>

**Table 5**

Generalized linear model (GLM) explaining bird species richness with functional connectivity that does not account for habitat patch size (a = 1), AIC = 4178.

<table><thead><tr><th></th><th>Estimate</th><th>Std error</th><th>Z value</th><th>Pr (&gt;|z|)</th><th></th></tr></thead><tbody><tr><td>Intercept</td><td>5.80e-01</td><td>6.42e-02</td><td>9.03</td><td>&lt;2e-16</td><td>***</td></tr><tr><td>Functional connectivity</td><td>-6.73e-05</td><td>1.85e-05</td><td>-3.64</td><td>0.0003</td><td>***</td></tr><tr><td>150 m vegetation</td><td>8.74e-01</td><td>1.48e-01</td><td>5.92</td><td>3.21e-09</td><td>***</td></tr><tr><td>Structural connectivity</td><td>-4.9e-03</td><td>2.05e-03</td><td>-2.38</td><td>0.017</td><td>*</td></tr></tbody></table>

**Table 6**

Fixed effects of the generalized linear mixed model (GLMM) explaining bird abundance (AIC = 8355) with functional connectivity accounting for habitat patch size (a = surface area). Species with *p*-values below 0.1 are included in the table. * 0.01 < p ≤ 0.05; . 0.05 < p ≤ 0.1; no markings indicate non-significance

<table><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>z- Value</th><th>Pr (&gt;|z|)</th><th>Significance level</th></tr></thead><tbody><tr><td>Intercept</td><td>0.09</td><td>59.06</td><td>0.002</td><td>0.99</td><td></td></tr><tr><td>Functional connectivity (log)</td><td>0.67</td><td>0.72</td><td>0.93</td><td>0.35</td><td></td></tr><tr><td>150 m vegetation</td><td>0.68</td><td>0.28</td><td>2.44</td><td>0.015</td><td>*</td></tr><tr><td>Structural connectivity</td><td>0.004</td><td>0.004</td><td>0.93</td><td>0.36</td><td></td></tr><tr><td>Year</td><td>-0.005</td><td>0.03</td><td>-0.19</td><td>0.85</td><td></td></tr><tr><td>Month</td><td>0.014</td><td>0.011</td><td>1.38</td><td>0.17</td><td></td></tr><tr><td>Functional connectivity: <i>Passer domesticus</i></td><td>-1.54</td><td>0.71</td><td>-2.16</td><td>0.03</td><td>*</td></tr><tr><td>Functional connectivity: <i>Fringilla coelebs</i></td><td>-1.47</td><td>0.81</td><td>-1.82</td><td>0.07</td><td>.</td></tr><tr><td>Functional connectivity: <i>Sylvia atricapilla</i></td><td>-2.08</td><td>1.21</td><td>-1.72</td><td>0.08</td><td>.</td></tr><tr><td>Functional connectivity: <i>Turdus merula</i></td><td>-1.24</td><td>0.73</td><td>-1.70</td><td>0.09</td><td>.</td></tr></tbody></table>

We found no significant relationship between the species-specific model coefficients and Morelli's specialization indices. Thus, there is no relationship between the degree of specialization of a species and the benefit a species derives from estimated functional connectivity (for abundance). Interestingly, we observed a significant negative correlation between the community-weighted mean urban score and estimated functional connectivity (Flux metric). The correlation is -0.16 (p-value = 6.375e-09) for functional connectivity including patch size, and -0.14 (p-value = 6.355e-08) for functional connectivity without patch size. As functional connectivity increases, the bird community composition shifts towards species that are less specialized for urban environments (Fig. 3).

**Table 7**

Fixed effects of the generalized linear mixed model (GLMM) explaining bird abundance (AIC = 8367) with functional connectivity that does not account for habitat patch size (a = 1). Species with *p*-values below 0.1 are included in the table. * 0.01 < p ≤ 0.05; . 0.05 < p ≤ 0.1; no markings indicate non-significance

<table><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>z- Value</th><th>Pr (&gt;|z|)</th><th>Significance level</th></tr></thead><tbody><tr><td>Intercept</td><td>-14.46</td><td>61.48</td><td>-0.24</td><td>0.81</td><td></td></tr><tr><td>Functional connectivity (log)</td><td>0.72</td><td>0.50</td><td>1.44</td><td>0.15</td><td></td></tr><tr><td>150 m vegetation</td><td>0.61</td><td>0.33</td><td>1.83</td><td>0.07</td><td>.</td></tr><tr><td>Structural connectivity</td><td>0.01</td><td>0.01</td><td>1.05</td><td>0.3</td><td></td></tr><tr><td>Year</td><td>0.01</td><td>0.03</td><td>0.15</td><td>0.9</td><td></td></tr><tr><td>Month</td><td>0.02</td><td>0.01</td><td>1.42</td><td>0.16</td><td></td></tr><tr><td>Functional connectivity: <i>Passer domesticus</i></td><td>-1.60</td><td>0.56</td><td>-2.85</td><td>0.004</td><td>*</td></tr><tr><td>Functional connectivity: <i>Fringilla coelebs</i></td><td>-0.98</td><td>0.5</td><td>-1.97</td><td>0.049</td><td>*</td></tr><tr><td>Functional connectivity: <i>Sylvia atricapilla</i></td><td>-1.12</td><td>0.58</td><td>-1.95</td><td>0.051</td><td>.</td></tr><tr><td>Functional connectivity: <i>Turdus merula</i></td><td>-0.96</td><td>0.5</td><td>-1.93</td><td>0.054</td><td>.</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>Fig. 3. Relationship between bird community-weighted mean urban score and functional connectivity as measured by the Flux metric.</figcaption></figure>

**4. Discussion**

To better understand the ecological drivers of urban biodiversity, we discuss here the effects of landscape connectivity and habitat quantity on species richness and abundance across two taxonomic groups. This discussion is structured around the interpretation of our main findings, a comparison with existing literature, and a reflection on methodological limitations and implications for urban conservation planning.

Urban ecology seeks to bridge the gap between ecological principles and urban planning. Our study contributes to this aim by investigating the relationship between landscape connectivity (structural, functional, per species) and biodiversity in urban environments, offering insights
for enhancing urban greening strategies (Heymans et al., 2019).

Our results demonstrate a positive influence of herbaceous habitat quantity and connectivity (functional and structural) on urban pollinator richness (H1) when habitat size is considered in the connectivity calculation. The observed decline in morphospecies numbers with increasing distance from habitat patches highlights the importance of proximity to suitable habitats, aligning with previous findings (Turrini and Knop, 2015). Our results support the notion that both structural and functional connectivity are critical to sustaining pollinator communities in urban areas. These findings reinforce earlier studies showing the benefits of corridors and functional connectivity for urban invertebrates, such as moths (Balbi et al., 2021) and forest specialist butterflies (Plarbona et al., 2022). Nevertheless, as functional connectivity is classically estimated by taking into account both habitat quantity and connections between habitats (Fahrig et al., 2021), we sought to disentangle these two contributions. Our results, which showed that when only the connections between patches were included in the metric ponderation (i.e., without the patch surface), functional connectivity was no longer significant, led us to relativize the role of the connections between patches on pollinator richness compared to habitat quantity.

For birds, vegetation quantity in a 150 m neighborhood significantly influences richness, consistent with previous studies (Matthies et al., 2017; Morelli et al., 2021). However, contrary to pollinators, we did not find a significant effect of habitat proximity on bird richness and abundance except when habitat size is not taken into account in calculating functional connectivity. When using a functional connectivity metric that accounts for patch area, functional connectivity had significant positive effects on bird richness (Table 4) echoing findings from smaller-scale studies (e.g., Shanahan et al., 2011 in Brisbane, Australia). However, when functional connectivity was estimated without weighting by patch area (i.e., focused on connections between patches), its effect on richness became significantly negative (Table 5). This inversion suggests that when habitat size is not taken into account in calculating connectivity, it leads to higher connectivity values in areas with many small habitat patches, i.e., areas with low habitat quantity and relatively low bird species richness. Ignoring the amount of habitat in connectivity measures may lead to overemphasis on connections between small patches. As bird richness, bird abundance is mainly influenced by the amount of vegetation within a 150 m-Euclidean perimeter. The results showed no clear relationship with functional connectivity for 18 of the 19 species studied. Thus, our first hypothesis (H1) is not validated for birds, as these results highlight the importance of habitat quantity rather than the landscape connectivity. Our results are consistent with those of Ibáñez-Alamo et al. (2020) who found a positive relationship between tree cover and bird diversity, and those of Morelli et al. (2021) who found a positive relationship between vegetation cover and bird taxonomic diversity. Our results suggest that an optimized and theoretically highly connected organization of small urban green spaces does not enhance birds' richness.

Our hypothesis (H2) that specialist species would benefit more from functional connectivity was not supported. Nevertheless, functional connectivity significantly influences urban bird communities by favoring species less adapted to urban environments (low urbanophilia), aligning with our third hypothesis (H3). This suggests that enhancing functional connectivity may particularly benefit urban avoiders that depend on larger, diverse vegetated areas (Chiron et al., 2024).

Given the importance of the breeding season for bird movement (Marshall, 2013), we compared our results using only observations between March and August (94 % of points). Concerning bird richness, functional connectivity was near-significant (estimate = 7.291e-09, *p* value = 0.055) during the breeding season, whereas the proportion of vegetation is no longer significant. Concerning bird abundance, the results are similar except for the distance to the nearest patch, which has a negative effect. No difference in results is observed on the links between connectivity and specialization and urban scores. This seasonal variability deserves to be explored further and highlights the importance of

taking temporal factors into account in connectivity studies. Similar to our findings, Ibáñez-Alamo et al. (2020) reports seasonal variation in results depending on the breeding period. They observe higher functional diversity in land-sharing areas exclusively during the winter season.

## 4.1. Methodological insights

Our study contributes to the growing body of research exploring the relationship between landscape connectivity and urban biodiversity. However, methodological choices inherently shape the outcomes and interpretations of such research. Below, we detail key considerations and their implications for urban greening and biodiversity conservation.

We used a graph-based approach using least-cost distances to model ecological networks and to estimate functional connectivity. These methods, validated in urban contexts for species such as moths and birds (Balbi et al., 2021), provides a practical framework for estimating connectivity in heterogeneous urban landscapes. We are going much further than just least-cost paths, since the graph structure facilitates advanced analyses, such as the calculation of functional connectivity metrics (Galpern et al., 2011). As other models (e.g. circuit theory, McRae and Beier, 2007), integrating the permeability of the landscape matrix to calculate distances requires assumptions about the permeability of different land-use types for species movement. This requires a good knowledge of the movement patterns of the species, which is not always available. Resistance values are therefore often determined on the basis of scientific literature and expert knowledge (Dutta et al., 2022). While these assumptions are a standard practice, they underscore the need for empirical data on species-specific movement behaviors. In our study, we chose a hypothetico-deductive approach in which the model is constructed from relatively generic data without biological data. Once the connectivity metrics are calculated, biological data (e.g., abundance or richness) are considered as target variables in a statistical analysis in which the connectivity metrics are used as explanatory variables. The objective of this approach is to provide knowledge on the influence of connectivity on species and to assess the strength of this relationship (Foltête et al., 2021). Future studies could integrate complementary methods, such as circuit theory, which evaluates multiple potential movement pathways and might offer alternative perspectives on connectivity patterns. For well-documented species, it may also be useful to build connectivity models based on biological data (e.g., GPS tracks, genetic), with a training validation approach. Despite the inherent limitations of any model, our results also emphasize the sensitivity of connectivity to methodological choices and call for caution in its use. A major limitation in urban connectivity research is the lack of detailed knowledge about species movement patterns in urban environments. Movement data obtained from methods such as GPS tracking, mark-recapture studies, and genetic analyses could greatly enhance our ability to model connectivity accurately (Savary et al., 2022). These approaches could help identify how different urban features—such as building height, density, and configuration—influence species movements.

Selecting habitats to include in connectivity models is also a critical step, influenced by ecological knowledge and data availability. Burdine and McCluney (2019) claim that bee abundance (though not diversity) declined with increasing urbanization and diminished with tree canopy cover in public green spaces in Toledo (Ohio, USA). Winfree et al. (2011) report that the habitat of most pollinator species is unknown, but pollinator richness and abundance is often higher in open habitat. While forests can serve as nesting areas for pollinators, and certain urban trees can provide floral resources (Gerner and Sargent, 2022), we chose herbaceous areas because of their significance as primary resources for pollinating insects. A more integrated approach considering multiple habitat types could provide a comprehensive view of urban biodiversity dynamics. The minimum size of habitat patches can be quite large in some studies (e.g., 1 or 2 ha in Balbi et al., 2019). Given the accuracy of
our land-use map, we opted to integrate small vegetation features that can host urban small biodiversity (Vega and Küffer, 2021). However, considering these small patches as functional may overestimate connectivity. Consequently, it is important to conduct sensitivity studies of the importance of habitat size and land-use map accuracy. For example, we tested our models with higher minimal habitat patch size thresholds (Supplementary 12, 13, 14, 15) and observed that functional connectivity became non-significant for explaining pollinator richness (Supplementary 15, *p*-value = 0.09). This finding highlights the trade-offs between ecological realism and spatial resolution in connectivity studies. The accuracy of land-use maps also affects connectivity analyses. We used high-resolution mapping to capture fine-scale habitat features, which can significantly influence operational decisions (Hernando et al., 2017; Lin et al., 2021). Moreover, understanding temporal variations in species movement is essential. For example, breeding seasons or migration periods may alter species' connectivity requirements. Our seasonal analyses revealed differences in the significance of connectivity and habitat amount for birds during the breeding season, suggesting that connectivity studies must account for temporal dynamics to ensure robust conclusions.

The SPIPOLL and Oiseaux des Jardins programs appear well-suited to our objective of assessing biodiversity patterns at the community level, especially in the context of evaluating the effects of landscape connectivity on species richness and abundance. These citizen science initiatives have already demonstrated their scientific relevance in studies on the impacts of urbanization and pesticide use (e.g., Bergerot et al., 2011; Deguines et al., 2016; Muratet and Fontaine, 2015). More broadly, concerns about the reliability of citizen science data are increasingly counterbalanced by growing evidence that such data, when collected through structured or semi-structured protocols, are not only robust but often irreplaceable for large-scale ecological monitoring (Castagneyrol et al., 2020). While citizen science datasets may raise issues related to observer expertise, spatial bias, or detectability, these limitations can be addressed through adequate protocols, training materials, and post-collection validation systems (Johnston et al., 2023). In our study, both programs rely on standardized or semi-standardized procedures, include complete species lists, and ensure repeated observations at fixed sites—design characteristics that enhance data quality and analytical reliability. Furthermore, the involvement of citizens in biodiversity research also provides substantial societal benefits, including increased ecological literacy and stronger community engagement with conservation issues (Castagneyrol et al., 2020). Thus, rather than viewing data from these programs as inherently flawed, we align with recent literature in considering them as a powerful tool for investigating ecological patterns, particularly where traditional data are lacking or impractical to obtain at scale. Citizen science proves especially powerful in the context of urban ecology, given the vast number of potential observers.

## 4.2. Recommendations for future greening schemes to support urban biodiversity

Our results show that it is not always easy to accurately identify the separate influence of habitat quantity and habitat connectivity. Urban greening policies therefore probably need to work on these two dimensions together by moving beyond isolated green spaces to focus on creating interconnected networks of habitats. This aligns with the growing recognition that urban landscapes function as socio-ecological systems, where biodiversity outcomes depend on spatial planning, governance, and community involvement (Aronson et al., 2017). The city offers many opportunities to vegetate the private and public space. Small features such as schoolyards, tree-lined streets, vegetated railway embankments, green roofs can serve as stepping stones between larger green spaces (Francis and Lorimer, 2011; Vergnes et al., 2013). The overarching goal is to transition from land-sparing to land-sharing urban models, thereby fostering a more integrated and continuous ecological

landscape (Ibáñez-Álamo et al., 2020). Our results align with the findings of Beninde et al. (2015) on the importance of habitat patch size, as well as with the research of Hadi et al. (2024) who demonstrate that maintaining large, intact green areas alongside smaller, well-connected patches can optimize habitat availability for bird species.

However, urban greening alone is insufficient. Stakeholders must remain attentive to local factors that influence urban biodiversity (Barr et al., 2021; Philpott et al., 2014; Theodorou et al., 2020) and adopt tailored management practices for supporting pollinators (Fauviau et al., 2022) and birds (Threlfall et al., 2016). The successful implementation of greening strategies requires collaboration between policymakers, scientists, urban planners, and local communities. Aiman et al. (2022) highlight the need to integrate social dimensions into conservation planning by fostering awareness, participatory governance, and community-led initiatives. Citizen science initiatives, such as biodiversity monitoring programs, can engage residents in conservation efforts and provide valuable data to inform connectivity planning (Bonney et al., 2016). Additionally, interdisciplinary frameworks are essential for integrating ecological connectivity into urban development (Lookingbill et al., 2022). For example, the Biophilic Cities Network promotes urban designs that incorporate nature into every aspect of city life, enhancing both human well-being and biodiversity (Beatley, 2011). Such approaches could be adapted and scaled to European cities, where dense urban fabrics require innovative solutions for greening and connectivity.

## 5. Conclusion

One of the major challenges for cities is to further vegetate urban space. This meets the needs of humans, but also of other living things. The challenge is not only to refresh cities and improve the well-being of urban dwellers, but also to slow the decline in biodiversity. However, greening operations must be designed in a way that is consistent with the ecological needs of the species, because habitat quality and quantity is the main factor in abundance and species richness. Our results highlight the need to account for species-specific requirements in connectivity planning. While generalist species may benefit from small-scale interventions, such as roadside vegetation, urban avoiders and specialists require larger, more contiguous habitats. Thus, a tiered approach to connectivity—combining micro-scale interventions with the conservation and expansion of large green spaces—can maximize biodiversity outcomes.

### CRediT authorship contribution statement

**Tanguy Louis-Lucas:** Writing – original draft, Visualization, Methodology, Formal analysis, Conceptualization. **Joanne Clavel:** Writing – review & editing, Validation, Supervision, Methodology, Formal analysis. **Sarah Bortolamiol:** Writing – review & editing, Visualization, Project administration. **Nathalie Blanc:** Writing – review & editing, Project administration, Funding acquisition. **Etienne Grésillon:** Writing – review & editing, Visualization. **Céline Clauzel:** Writing – review & editing, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Conceptualization.

### Declaration of competing interest

All authors declare no conflict of interest.

### Acknowledgement

This work builds on the COOLSCHOOLS project (www.coolschools.eu) and has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101003758, the Spanish Research Agency (AEI), Innoviris (Brussels
Capital Region), Dutch Research Council (NWO), The Research Foun-
dation—Flanders (FWO), and Agence Nationale de la Recherche (ANR).
Computations were performed thanks to the TGIR Huma-num (http
s://www.huma-num.fr) and the CCIN2P3 cluster (https://cc.in2p3.
fr/). The authors particularly thank Christopher Sutcliffe for the revi-
sion of the English manuscript. We express our gratitude to the Vigie
Nature and Cesco teams for providing valuable feedback on this work.
Additionally, we extend our thanks to the individuals involved in col-
lecting the data.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.biocon.2025.111271.

Data availability

Data from open-access databases are available on the data producers'
websites. Processed data are available on request.

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