# Size doesn't always matter: Greenspace connectivity can offset insufficient habitat patch size to improve urban tits breeding success

François-Marie Martin, Jean-Christophe Foltête, Gilles Vuidel, Stéphane Garnier, Aurélie Khimoun, Nicolas Navarro, Célia Sineau, Bruno Faivre

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## ► To cite this version:

François-Marie Martin, Jean-Christophe Foltête, Gilles Vuidel, Stéphane Garnier, Aurélie Khimoun, et al.. Size doesn't always matter: Greenspace connectivity can offset insufficient habitat patch size to improve urban tits breeding success. Landscape and Urban Planning, 2026, 269, pp.105584. 10.1016/j.landurbplan.2026.105584. hal-05459946

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Research Paper

# Size doesn't always matter: Greenspace connectivity can offset insufficient habitat patch size to improve urban tits breeding success

François-Marie Martin<sup>a,b,*†</sup>, Jean-Christophe Foltête<sup>a</sup>, Gilles Vuidel<sup>a</sup>, Stéphane Garnier<sup>b</sup>, Aurélie Khimoun<sup>b</sup>, Nicolas Navarro<sup>b,c †</sup>, Célia Sineau<sup>b</sup>, Bruno Faivre<sup>b</sup>

## HIGHLIGHTS

* The connectivity of greenspaces is more important than their size for tit breeding.

* Greenspace connectivity could compensate for low habitat availability.

* Appropriate greenspace layout could favour bird population dynamics.

* Small interstitial wooded patches help the maintenance of bird populations.

* Unprecedented use of landscape graphs to explore complex biological processes.

## ARTICLE INFO

Keywords:

Avifauna

Landscape ecology

Food resource distribution

Greenspace layout

Population dynamics

Graph theory

Urban design

## ABSTRACT

Urban landscapes are often highly fragmented, constraining animals to live in and exploit a multitude of habitat patches (e.g., greenspaces) of varying size and isolation. Small greenspaces may not contain enough resources for species to maintain viable populations. Yet, appropriate spatial configuration of the habitat network (i.e., high greenspace connectivity) could theoretically alleviate or even compensate the local food resource limitations by allowing access to additional foraging grounds. Surprisingly, this effect has never been tested to explain the reproductive performances of urban fauna. We hypothesised that higher greenspace connectivity would improve the breeding outputs of two insectivorous bird species (tits), especially with decreasing nesting greenspace area (i.e., the habitat patch where the nest is located).

For four years, we monitored the survival and mass of nestlings of *Parus major* and *Cyanistes caeruleus* using 240 nestboxes located along a multivariate urban gradient in Dijon (France), and analysed their variations with GLMMs while controlling for various confounding factors (e.g., urbanness, noise and light pollution, microclimate, vegetation management). Functional connectivity was measured through graph-based modelling.

Greenspace connectivity was important to explain both nestling survival and mass while the nesting green-space area was not a good predictor of breeding success. Furthermore, the positive effect of connectivity on nestling survival significantly increased with decreasing area of their nesting patches.

Urban tits can maintain successful reproduction dynamics in small greenspaces provided those are well connected to the network of preferred foraging habitats. Promoting the connectivity of interstitial urban greenspaces could thus partly compensate for their frequently insufficient sizes in cities.

<footnote><sup>a</sup> ThéMA, UMR 6049 CNRS, Université Marie et Louis Pasteur, Besançon, France</footnote>

<footnote><sup>b</sup> Biogéosciences, UMR 6282 CNRS, Université Bourgogne Europe, Dijon, France</footnote>

<footnote><sup>c</sup> EPHE, PSL University, Paris 75014 France</footnote>

<footnote>* Corresponding author at: ThéMA lab, 14 rue Mégevand, 25000 Besançon, France.</footnote>

<footnote>E-mail addresses: francois-marie.martin@univ-fcomte.fr (F.-M. Martin), jean-christophe.foltete@univ-fcomte.fr (J.-C. Foltête), gilles.vuidel@univ-fcomte.fr (G. Vuidel), stephane.garnier@u-bourgogne.fr (S. Garnier), aurelie.khimoun@u-bourgogne.fr (A. Khimoun), nicolas.navarro@u-bourgogne.fr (N. Navarro), celia.sineau@u-bourgogne.fr (C. Sineau), bruno.faivre@u-bourgogne.fr (B. Faivre).</footnote>

<footnote n="1">¹ Current address: IUGA, 14bis avenue Marie Reynoard, 38100 Grenoble, France.</footnote>
# 1. Introduction

Urbanisation is one of the most inexorable anthropogenic processes currently altering natural landscapes. Among other things, urbanisation entails a tremendous loss of habitat surfaces as well as their fragmentation, resulting in highly altered spatial configurations of habitat patches like, for instance, greenspaces (i.e., parks, gardens, informal vegetation patches, etc.; Shanahan et al., 2014; Liu et al., 2016). This favours the exclusion of the species unable to maintain themselves in such environmental conditions or that fail to recolonise remnants of suitable habitat patches (Wang et al., 2022), such as many habitat or resource specialists and less mobile species. Identifying the responses at all levels of biological organisation (e.g., genes, species, populations, communities) to variations in the quantity and configuration of urban greenspaces is thus crucial to design cities favouring rich and functional ecosystems (Beninde et al., 2015; Lepczyk et al., 2017; Wang et al., 2022), for the benefit of both biodiversity conservation and the well-being of city-dwellers.

Countless studies have examined the effects of the size and configuration of urban greenspaces on the diversity of various taxonomic groups (e.g., Beninde et al., 2015; La Sorte et al., 2020). However, their effects on the reproductive parameters of urban adapted species remain rarely explored (Melero et al., 2020; Turo & Gardiner, 2021), despite the importance of demographic and reproductive performances for the maintenance of viable populations. Some studies have examined variations in the selection of nesting sites in response to the size of habitat patches (e.g., Langley, 2013), their spatial arrangement (e.g., Kang et al., 2012), or both (e.g., Ding et al., 2023). A few additional studies have also linked the breeding success of various species to both the size and configuration of greenspaces (e.g., Morimoto et al., 2012; Soga & Koike, 2013), however, the combined influence of these factors is usually merely suggested but not actually tested for (but see Turo & Gardiner, 2021).

The size of habitat patches could influence the fitness of species breeding in urban landscapes in various ways. For instance, patch size (and shape) determines the frequency of habitat edges and, as such, it may be linked to variations in noise and light pollutions (Fernández-Juricic & Jokimäki, 2001; Shanahan et al., 2014), predation (Marzluff & Ewing, 2001; Berry, 2002), or parasitism (Chace et al., 2003; Fenoglio et al., 2013; Buxton & Benson, 2015). Moreover, because of their size, larger greenspaces tend to offer resources in higher quantity and diversity than smaller ones, and are thus more likely to provide the food required by breeding animals to rear their offspring successfully (Morimoto et al., 2012; Buxton & Benson, 2015). When parents live in a small habitat patch, however, a higher connectivity to the network of foraging habitats could theoretically compensate for the lower availability of local resources (cf. Hinsley et al., 2008; Dekeukeleire et al., 2019). Yet, to our knowledge, this has never been explicitly investigated in an urban context. The present study aims at exploring this issue.

Because they readily breed in artificial nestboxes and are easy to capture and manipulate, Great Tit (*Parus major* L.) and Blue Tit (*Cyanistes caeruleus* L.) are passerine birds frequently studied in both forest and urban settings, and constitute suitable models to explore the effects of local and landscape factors on their life-history traits. A vast literature reports that the reproductive performances of urban tits are generally lower than those of their forest counterparts (e.g., Wawrzyniak et al., 2015; Gładalski et al., 2017; de Satgé et al., 2019). Among the many drivers proposed to explain such discrepancies, the availability of suitable prey (e.g., caterpillars – which are the dominant food items for the feeding of tit nestlings) seems to be one of the most limiting factors (e.g., Pollock et al., 2017; Seress et al., 2020). As these insectivorous birds preferentially forage in the canopy of deciduous trees (Mackenzie et al., 2014; Seress et al., 2025), whose composition and quantity are linked to arthropods abundance (Jones & Leather, 2012), the access to high-quality prey should be facilitated in large tree covered patches. Accordingly, a positive relationship between the size of the local

foraging habitat of tits and their reproductive performances is frequently reported (Hinsley et al., 1999; Bueno-Enciso et al., 2016), similarly to other bird species (Morimoto et al., 2012; Buxton & Benson, 2015), whereas a negative relationship has been found with the proportion of impervious surface areas (Corsini et al., 2021; Corsini & Szulkin, 2025). In urban landscapes, tit accessibility to high-quality prey could thus be constrained by the fact that contiguous wooded greenspaces are frequently reduced (Shanahan et al., 2014), hence lowering their reproductive outputs.

If the local quality or quantity of prey is low, theory predicts that birds can maintain an optimal growth of their nestlings by foraging farther away for better or bigger prey, at the expense of a higher parental energetic investment (Stauss et al., 2005; Tremblay et al., 2005). A recent radio-tracking study focused on *Parus major* in a medium-sized Hungarian city supports this theory, as breeding females spent more time foraging at much greater distances than those usually reported from richer and less fragmented environments such as continuous forests (Seress et al., 2025). Given that tits are “central-place foragers”, meaning that they exploit first the resources close to their nest before foraging in suitable patches located farther away (Naef-Daenzer, 2000), and considering that the energetic costs incurred by foraging movements quickly increase with the proportion of gap-crossings between resource patches (Hinsley et al., 2008), we can expect their reproductive performances to be positively linked to the connectivity of wooded greenspaces (i.e., their preferred foraging habitats), especially when they nest in small greenspaces.

In this paper, we evaluated the importance of the spatial configuration of the preferred foraging habitat of *Parus major* and *Cyanistes caeruleus* (i.e., shrub- or tree-covered surfaces, hereafter referred to as “wooded greenspaces”) with regard to the local amount of habitat, in explaining the intraurban variations of two postnatal traits strongly related to breeding success: namely, nestling survival and nestling mass. By using the landscape patch-matrix framework, in which habitats are seen as favourable patches within a less favourable landscape matrix (Norton et al., 2016), we here distinguish the *nesting greenspace area* – i. e., the size of the local wooded greenspace patch harbouring a tit pair’s nest and in which they will preferentially forage – from the *greenspace* (functional) connectivity – i.e., a configurational metric quantifying the amount of reachable foraging habitat in the surroundings of the local greenspace patch, considering each species gap-crossing ability (cf. Saura & Rubio, 2010). To monitor the reproduction of these two insectivorous passerine bird species, we used a network of nestboxes located along a multivariate urban gradient in which we controlled for various potentially confounding effects (e.g., temperature, built environment, pollution, vegetation management). As previous works emphasised the relevance of habitat size in explaining breeding success (e.g., Hinsley et al., 1999; Bueno-Enciso et al., 2016), we expected that the *nesting greenspace area* would have a positive effect on both reproductive traits. Additionally, we hypothesised that 1) the reproductive performances of tits should increase with increasing connectivity of their preferred foraging habitats (i.e., greenspace connectivity), and 2) this increase should be more pronounced with decreasing nesting greenspace area, and conversely.

# 2. Materials and methods

## 2.1. Study area and biological data

We focused our analyses on the urban populations of two tit species, *Parus major* and *Cyanistes caeruleus*, located in Dijon, a medium-sized city of central-eastern France (47°19'N, 5°01'E; 210–410 m a.s.l.). The metropolitan population is around 250,000 and population density is about 3900/km². Wooded hills and vineyards cover the city’s western flank while crops surround the other sides. Historical capital of Burgundy, Dijon harbours an old-city characterised by a very mineral landcover (i.e., white limestone buildings and plazas, cobbled streets)
with little vegetation, surrounded by housing buildings and many resi-
dential, industrial and commercial neighbourhoods (Fig. 1).

Reproduction of tits was monitored for four years (2019–2022) using
a network of 240 artificial nestboxes distributed throughout the city.
Nestboxes were installed 3 m high on trees at least 40 m apart. Our
network of nestboxes sampled a large urban gradient: ranging from
densely built and fragmented urban sites with 8 % of woody vegetation
cover within 150 m radiuses (and roughly 90 % of impervious surfaces),
to large and well-connected wooded patches (see also Table 1). Overall,
the woody vegetation surrounding our sites was mostly composed of
deciduous trees and shrubs. We only focused on first broods as the
environmental conditions, notably pertaining to the availability of food
resources, are likely to differ between early spring and late spring.

Every year from mid-March onward, nestboxes were visited at least once a week to determine occupancy and estimate the date of the first-laid egg. If more than one egg was found, laying date was back-calculated assuming a laying rate of one egg per day. Once clutches were completed, we recorded the size of the clutch (i.e., the number of laid eggs) and let birds incubate for 12 days before the next inspection. The nests were then checked every 1–2 days (depending on logistical constraints) until hatching. The brood size (i.e., the number of hatchlings) and the hatching date were then estimated. Thirteen days after hatching, nestboxes were visited again and the number of unhatched eggs was compared to clutch sizes to confirm the brood sizes (as parents usually do not remove unhatched eggs from the nest, brood size should be equal to the number of laid eggs minus the number of unhatched eggs). The surviving nestlings were then counted, weighed (±0.01 g) and ringed. If parents could be caught, they were also identified (or ringed if it was not already the case) and weighed. Because nestlings older than 13 days may prematurely leave the nest if disturbed, broods were left alone for 9 more days. Twenty-two days after hatching, a final inspection of the nestboxes was performed to assess the numbers of nestlings that died between days 13 and 22, as mortality might still

occur during this period (Cramp & Perrins, 1993) with little chance that
parents manage to remove the dead nestlings from the nest. The number
of dead chicks was then subtracted from the number of nestlings at day
13 to obtain the estimated number of survivors. Dividing the latter by
brood size gave us the nestling survival rate, which may be viewed as a
proxy of the fledging success. We then averaged the individual body
mass of nestlings at day 13 for each brood.

As not all nestboxes were occupied each year, we only observed 427 clutches out of a potential pool of 960 reproduction events (240 nestboxes x 4 years). From this raw sample size, data cleaning and statistical diagnostics led us to discard 49 observations deemed invalid (more details in the “Statistical analyses” section).

## 2.2. Environmental data and variables

i. Landcover data and urbanness proxy

In order to characterise our study area and perform our spatial analyses, we produced a landcover map initially based on the BD TOPO® database from IGN (https://geoservices.ign.fr/bdtopo). To improve the discretisation of greenspaces within the city, we further classified a Pleiades image from 2020 using thresholds of the Modified Soil Adjusted Vegetation Index. We also used a LiDAR image from 2020 to estimate vegetation and building heights. We then applied over 14,000 manual corrections, mainly to add woody vegetation patches (e.g., street trees, hedges, bushes and groves) hidden by the projected shadows of buildings. We finally obtained a landcover map with 17 classes at ca. 1 m spatial resolution (Fig. 1).

The reproductive traits of both tit species have been shown to covary with various “urbanness” proxies (e.g., built density, proximity to a residential area; Charmantier et al., 2017; Sprau et al., 2017). To account for this effect, we computed the area of woody, herbaceous, built, bare and impervious landcovers within 150 m buffers around each nestbox. We also computed the mean volume of buildings and its

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Location of the 240 nestboxes (pink dots) within the urban area of Dijon, France. Background map is a raster landcover map with a ca. 1 m spatial resolution. For readability reasons, only 11 of the 17 initial landcover classes are displayed: actually, there are three subclasses (low, medium and high) for the two “woody vegetation” classes (deciduous and coniferous) shown on the map. These were later simply merged together (see text). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<caption>**Table 1**</caption>

Summary of the three groups of variables included in our statistical modelling: response variables (i.e., dependent variables), explanatory variables (i.e., central to test our hypotheses), and covariates (i.e., potentially confounding effects we want to control for). Response variables are presented for each species separately even though they were pooled together during analyses. For more details regarding variables meaning, source and computation, readers are referred to the main text. PM = *Parus major*; CC = *Cyanistes caeruleus*; SD = standard deviation.

<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Variable name</th><th>Unit or type</th><th>Mean (± SD)</th><th>Range</th><th>Meaning and details</th></tr></thead><tbody><tr><td colspan="5"><strong>Response variables</strong></td></tr><tr><td>Nestling survival (PM)</td><td>Rate</td><td>0.73 (± 0.33)</td><td>0 – 1</td><td>Proportion of nestlings that (supposedly) fledged from the nest (proxy for fledging success)</td></tr><tr><td>Nestling survival (CC)</td><td>Rate</td><td>0.61 (± 0.33)</td><td>0 – 1</td><td>Proportion of nestlings that (supposedly) fledged from the nest (proxy for fledging success)</td></tr><tr><td>Nestling mass (PM)</td><td>g</td><td>14.77 (± 1.76)</td><td>9.87 – 18.41</td><td>Mass of nestlings at day 13</td></tr><tr><td>Nestling mass (CC)</td><td>g</td><td>8.89 (± 1.28)</td><td>5.87 – 11.51</td><td>Mass of nestlings at day 13</td></tr><tr><td colspan="5"><strong>Explanatory variables (variables of interest)</strong></td></tr><tr><td>Nesting greenspace area †</td><td>m<sup>2</sup></td><td>6.10<sup>3</sup> (± 9.210<sup>3</sup>)</td><td>12 – 3.310<sup>4</sup></td><td>Area of the foraging habitat patch (i.e., wooded greenspace) harbouring the nestbox</td></tr><tr><td>Greenspace connectivity †</td><td>m<sup>2</sup></td><td>6.110<sup>4</sup> (± 8.510<sup>4</sup>)</td><td>0 – 3.810<sup>5</sup></td><td>Amount of reachable foraging habitat within the landscape (as measured by the Flux metric, a functional connectivity indicator)</td></tr><tr><td colspan="5"><strong>Covariates (control variables)</strong></td></tr><tr><td>Species</td><td>Categorical</td><td>–</td><td>CC or PM</td><td>Species to which belong the brood</td></tr><tr><td>Clutch size (PM)</td><td>Count</td><td>8.21 (± 1.42)</td><td>4 – 12</td><td>Number of eggs laid per clutch</td></tr><tr><td>Clutch size (CC)</td><td>Count</td><td>10.69 (± 1.55)</td><td>7 – 14</td><td>Number of eggs laid per clutch</td></tr><tr><td>Urban intensity</td><td>Unitless</td><td>0 (± 1.68)</td><td>-3.65 – 4.8</td><td>Synthetic variable combining information (e.g., area, volume, SD) on impervious and built surfaces (as proxy of the degree of urbanisation)</td></tr></tbody></table>

<table><thead><tr><th>Variable name</th><th>Unit or type</th><th>Mean (± SD)</th><th>Range</th><th>Meaning and details</th></tr></thead><tbody><tr><td>Management intensity</td><td>Categorical</td><td>–</td><td>0 to > 6·y<sup>-1</sup></td><td>Class of vegetation management frequency (as proxy of disturbances)</td></tr><tr><td>Light pollution</td><td>Unitless</td><td>0 (± 1.74)</td><td>-2.76 – 5.17</td><td>Synthetic variable combining information on light intensity and number of light sources visible at 0 and 10 m above ground</td></tr><tr><td>Noise pollution ◊</td><td>dB</td><td>54.28 (± 4.78)</td><td>31 – 65.12</td><td>Median sound intensity</td></tr><tr><td>Traffic ◊</td><td>vehicle·m day<sup>-1</sup></td><td>2.7·10<sup>6</sup> (± 2.5·10<sup>6</sup>)</td><td>0 – 9.8·10<sup>6</sup></td><td>Mean daily traffic per street length (as proxy of chemical pollution and collision risk)</td></tr><tr><td>Temperature</td><td>°C</td><td>0.23 (± 2.03)</td><td>-3.06 – 6.48</td><td>Minimum daily temperature during nestlings rearing</td></tr><tr><td>Laying day</td><td>Count</td><td>93.78 (± 6.49)</td><td>72 – 113</td><td>Number of days elapsed between January 1st and the first egg laying</td></tr><tr><td colspan="5">◊ indicates that the variable was rescaled by dividing it by a multiple of ten prior to modelling.</td></tr><tr><td colspan="5">† indicates that the variable was log-transformed (base 10) prior to modelling.</td></tr></tbody></table>

standard deviation. Then we performed a normed-PCA on these variables (using R v4.2.2) and used the coordinates on its first axis as an urban intensity covariate (Table 1), since this axis explained 70 % of the variables' variance.

The choice to use buffer radii of 150 m, whether for the computation of the urban intensity proxy or for other environmental variables (see next sections), was deliberate. After observing the rather high variability of the buffer sizes reported in the tit breeding literature, ranging from 15 to 300 m (cf. Shutt et al., 2018; Dekeukeleire et al., 2019), we followed Vaugoyeau et al. (2016) and Capilla-Lasheras et al. (2022) and computed environmental variables using four buffer radii: 50, 100, 150 and 200 m. As the extracted variables were all highly correlated across the four distances (Supplementary Materials 1), we chose to work with the “150 m distance dataset” because it was the closest round number to the mean gap-crossing distance of tits according to a panel of experts surveyed (see next section). Furthermore, while it is known that tits mostly forage close to their nests (i.e., < 50 m away) when they live in continuous forests (e.g., Naef-Daenzer, 1994; Stauss et al., 2005; Tremblay et al., 2005), several authors report that tit foraging trips and home ranges can be hundreds of metres long in unfavourable contexts, such as patchy urban or agricultural landscapes (e.g., Nour et al., 1998; Hinsley, 2000; Tremblay et al., 2005; Seress et al., 2025). The chosen buffer size therefore seemed like a reasonable compromise to encompass most of the daily environment encountered by tits along the fragmentation gradient that constitutes the greenspace
network of a city.

## ii. Connectivity modelling and explanatory variables

Our connectivity analysis of *Parus major* and *Cyanistes caeruleus* foraging habitats was based on a graph-theoretic approach, where habitat networks are modelled as graphs in which nodes represent habitat patches and links represent potential movement paths between them (Urban & Keitt, 2001; Galpern et al., 2011). In these so-called landscape graphs, nodes are weighted by their carrying capacity, usually approximated by their area, while links are often weighted by least-cost distances to account for the permeability of the landscape matrix to the movements of the focal species (Adriaensen et al., 2003). Local connectivity metrics can then be computed at node level. Despite its assumptions regarding species habitat preferences and movement abilities, this spatially-explicit method can satisfyingly reflect actual ecological fluxes when correctly parametrised and is usually thought as a fair compromise between data requirements, modelling complexity and reliability (Calabrese & Fagan, 2004; Daniel et al., 2023).

To evaluate how *Parus major* and *Cyanistes caeruleus* spatially exploit the greenspaces surrounding their nest as well as their willingness to cross certain landcover types, we performed an extensive review of the relevant literature (the list of the 105 references consulted is provided below Table S1 in Supplementary Materials 2) and invited six tit experts to fill a dedicated survey. The collected information was used to approximate the resistance to movement for each class of the landcover map considering the habits and preferences of tits, and to estimate the range of their gap-crossing abilities. However, as much uncertainty remained regarding the favourability to movement for some of our 17 landcover classes, and since several of them were very rare within the intraurban area (e.g., crops, coniferous cover, etc.), we merged these classes to produce a simplified landcover map with only 10 classes (see Table S1 and S2 in Supplementary Materials 2).

We created a specific unpruned landscape graph for each tit species using *GRAPHAB* v.2.8.4 (Foltete et al., 2012). As we were interested in foraging movements and not dispersal, we considered all wooded greenspaces (i.e., shrub- or tree-covered surfaces) larger than 10 m² as potential (foraging) habitat in our study area. The patch area limitation was only chosen for computational reasons. For both graphs (one for each species), a cost value was assigned to every pixel of our simplified landcover map, based on the resistances derived from the most accurate knowledge we could gather: i.e., the combination of a literature review, an expert survey, and our own observations of these birds. Interestingly, our cost values were quite consistent with those used in previous studies that used resistance-based connectivity models for tits or closely related species (e.g., Gil-Tena et al., 2014; Balbi et al., 2021). These cost-values enabled the computation of the least-cost paths between each pair of greenspaces in a minimum planar tree topology (Galpern et al., 2011). Both landscape graphs underwent a very thorough qualitative examination to ensure their reliability.

We then computed the *Flux metric* (Urban & Keitt, 2001; Foltête et al., 2012) for every greenspace in the network. The *Flux metric* is a functional connectivity metric representing, for a given habitat patch (in our case, a wooded greenspace; i.e. any shrub- or tree-covered patch >10 m²), the amount of habitat that an individual of the focal species may reach within the network considering the cost-distances between patches and the probability of moving across a certain distance (Urban & Keitt, 2001). More precisely, this metric includes a decay effect based on a distance parameter that weighs the strength of the decrease in the probability that a bird will cross a gap (between two habitat patches) as calculated by a negative exponential function. In other words, the probability that a tit reaches an isolated habitat patch declines exponentially with increasing distance between the source patch and the destination, and according to the favourability of the in-between landscape matrix. We considered that the 0.5 probability (i.e., median probability) of flying over the matrix occurred for a distance of 140 and 130 m, for *Parus major* and *Cyanistes caeruleus*, respectively. These distances were the average values answered by the six surveyed experts for

the median gap-crossing distances of tits. Finally, additional *Flux metrics* based on two other median probability distances were also computed for later robustness assessment (see “Statistical analyses” section): (i) a smaller *gap-crossing distance* corresponding to the median value given by all six experts (i.e., 60 m for *Parus major* and 30 m for *Cyanistes caeruleus*); and (ii) a larger *gap-crossing distance* corresponding to the average of the three highest values given by the experts (i.e., 270 m for *Parus major* and 250 m for *Cyanistes caeruleus*).

To approximate the local amount of foraging habitat (i.e., shrub- or tree-covered greenspaces) readily available to tit pairs, we computed the area of the wooded greenspace patch in which each nestbox was located (hereafter referred to as the *nesting greenspace area*). In most cases, these wooded greenspaces were isolated patches of various sizes. But, in the rare cases where nestboxes were located in very large patches (e.g., urban forest), we restricted *nesting greenspace area* to a circle with a 200 m radius (that is ca. 33.000 m²), which is sufficient to represent the home range of breeding tits in a continuous forest (Naef-Daenzer, 1994). Concurrently, we used the *Flux metric* (hereafter referred to as *greenspace connectivity*) computed for each of these nesting greenspace patches as a measure of the cumulated amount of foraging habitat reachable by a tit given the spatial configuration of the habitat network within the landscape. These two variables represented our “explanatory variables” (Table 1).

## iii. Vegetation management data

Since vegetation management affects plant diversity and represents a disturbance for birds (Shanahan et al., 2014), we surveyed the various greenspace managers of the area to collect data regarding the frequency of management operations around our nestboxes. We thereby classified our sites according to the most common frequency of mechanical intervention in the neighbourhood, obtaining three classes: “extensive” (once a year or less), “moderate” (2–6 times a year), and “intensive” (over 6 times a year). As we could not survey all private landowners in the surroundings of nestboxes, we assumed that they tended to intensively manage their gardens and parks. This assumption was broadly confirmed by our field observations.

## iv. Pollution data

Noise, chemical and light pollutions can greatly affect tit physiology, behaviour and reproduction (Seress & Liker, 2015; Chatelain et al., 2021). To account for these effects, we calculated pollution proxies from the data described in this section, using 150 m buffers around the nestboxes for all spatial computations.

A noise pollution raster map (in dB) was produced for the study area using *MITHRA*SIG v.4.4.1 (www geomod.fr). This software models the three-dimensional propagation of sounds produced by specified sources, considering both topography and buildings. Input noise sources consisted of traffic data (road, railway and tramway) provided by Atmo-BFC, the regional observatory of environmental pollution. We then used the median sound intensity as a noise pollution covariate.

Road traffic data were generated by the OPSAM model (https://www.atmo-bfc.org/energie/outils/outils-mobilite/opsam/) that computes the annualised mean daily traffic for all main roads and streets in the study area, based on localised traffic counts. It was used to approximate both collision risk and air pollution.

To estimate the artificial light at night pollution experienced by tits, we acquired data regarding the location and characteristics of all public street lights in the city, provided by local authorities. We used the STRIX model (https://darkskylab.com/) to estimate the visible light intensity (in lux) received at regular grid-points located every 20 m around each nestbox, at two different heights: 0 and 10 m above the ground. The model accounted for the number, height, type and power of all public street lights within a 150 m radius that were not masked out by buildings or topography. As we could not obtain data for private lands close to the nestboxes, the model likely underestimated the actual light intensity perceived in some massively illuminated areas, such as industrial and commercial estates. The median light intensity values and number of visible light sources (at both height) were then aggregated using a
normed-PCA (using R v4.2.2), the first axis of which explained 76 % of their variances, to create a light pollution covariate.

### v. Microclimate data

To characterise the local microclimate, we exploited a network of 71 temperature dataloggers distributed across the city. The dataloggers were HOBO Pro V2 probes (PROSENSOR, Amanvillers, France), located in shelters 3 m above the ground, that measure air temperature every hour. We paired each nestbox with the nearest datalogger sharing the same local climate zone, as delineated by the GeoClimate toolbox (https://www.wudapt.org/tools-overview/geoclimate/).

Thermal conditions before and during reproduction are known to affect tits breeding phenology as well as several of their life-history traits such as nestling survival, mass and fledging success (Pipoly et al., 2022). Therefore, for each nestbox, we computed the minimum daily temperature during the nestlings rearing period.

All spatial data manipulations and computations were made using QGIS v3.22.7 or POSTGIS v3.2.1.

## 2.3. Statistical analyses

Exploratory data analyses were performed following Zuur et al. (2010). As a small fraction of our temperature variables included missing values (due to dataloggers malfunctions), we first imputed these missing values using Random Forest regressions with the ‘missForest’ package. Then, although most modelling assumption checks were actually carried out on fitted models, we still checked the data for potential distributional issues as well as univariate and multivariate outliers. Variables that were too strongly skewed were log₁₀ transformed, and some predictors were rescaled to ensure meaningful coefficient estimates (cf. Table 1).

For both response variable (i.e., nestling survival and nestling mass), we built three inferential models: i) a reduced model incorporating the nesting greenspace area and all covariates but excluding the greenspace connectivity; ii) an additive full model similar to the former but including the greenspace connectivity; and iii) a mediated full model including all covariates as well as an interaction term between the nesting greenspace area and greenspace connectivity. To test for the relevance of greenspace connectivity to explain tits reproductive outputs (hypothesis 1), we performed a likelihood-ratio test (LRT) by comparing the reduced model to the additive full model. To test whether the effect of connectivity is mediated by the nesting greenspace area (hypothesis 2), we compared both full models using LRT to evaluate the significance of the interaction term and whether its inclusion increases the likelihood of the model. Note that for the mediated models, the two explanatory variables were median-centred to avoid collinearity issues. We used the “nestbox ID” as a random effect to account for nestboxes with multiple observations across years, while modelling diagnostics indicated that the inclusion of a “site” random effect was not required, probably because our predictors already accounted for most site-related spatial autocorrelation. However, due to the restricted number of levels of our random effect, we used parametric bootstrap LRT to ensure robust p-value calculations (Nakagawa & Schielzeth, 2010). We also computed 95 % confidence intervals for the model coefficients.

The two response variables displayed distinct distributions and thus required different model types. Preliminary diagnostics indicated that the data to model nestling survival showed significant over-dispersion as well as zero-inflation. Following Harrison (2015), we thus fitted zero-inflated beta-binomial GLMMs with logit link functions after attesting that they outperformed binomial models with observation-level random effects. To model the mass of tit nestlings, we used linear mixed models (LMMs). For both sets of models, clutch size was added as covariate. In the nestling survival models though, the inclusion of the clutch size covariate forced the removal of the species factor because of excessive multicollinearity.

From the initially collected sample size ($n_{raw}$ = 427), data cleaning and modelling diagnostics led to the exclusion of 49 problematic

observations (Table 2a). For instance, clutches that completely failed (i. e., not a single hatched egg) were discarded as preliminary diagnostics flagged them as being outliers, possibly because they are linked to stochastic phenomena we could not account for (e.g., adult predation, nest desertion by the parents). Out of the valid set of broods (i.e., with at least one hatched egg) that could be used to model nestling survival ($n_{cleaned}$ = 378), 61 experienced the death of all nestlings before their 13th day, which forced us to remove them from the dataset used to model nestling mass ($n_{cleaned}$ = 317) since, obviously, the masses could not be measured (Table 2b). Given these exclusions, we decided to pool together the observations of both tit species to increase our sample size, thereby adding a species covariate to the models (see also Table 2c).

After having tested our hypotheses, we additionally explored some other model parametrisations. For instance, we tried to tune the zero-inflation component of the nestling survival models, we fitted models with other proxies of the local amount of preferred foraging habitat (e. g., woody vegetation cover area or volume within two buffer sizes: 50 and 150 m) instead of the nesting greenspace area, and we switched our greenspace connectivity proxy with variants of the Flux metric based on two alternative gap-crossing distances (see “Connectivity modelling and explanatory variables” and Supplementary Materials 3).

All fitted models went through thorough modelling diagnostics and

<caption>**Table 2**</caption>

Contingency tables displaying, for each species (PM = *Parus major*; CC = *Cynanistes caeruleus*): a) the number of observations (i.e., reproduction events) that were cleaned out from the raw dataset to obtain the number of valid brood observations, b) the number of broods that failed completely (i.e., all nestlings died before the 13th day after hatching) that could thus not be included in the nestling mass models, and c) the final sample sizes for each response variable broken down by year. For a), the causes for exclusion are also detailed: “late reproduction” (were not directly comparable to other reproduction events), “vandalism” and “predation” (resulted in the destruction of the nests), “success manipulation” (consisted of experimentally manipulated broods as part of another study), and “complete clutch failure” (were likely caused by unaccountable processes, such as parental death or nest desertion).

<table><thead><tr><th colspan="4">a)</th></tr><tr><th colspan="4">Number of observations excluded from the raw dataset</th></tr><tr><th>Cause for exclusion</th><th>PM (n<sub>raw</sub> = 279)</th><th>CC (n<sub>raw</sub> = 148)</th><th>Total (N<sub>raw</sub> = 427)</th></tr></thead><tbody><tr><td>Late reproduction</td><td>1</td><td>1</td><td>2</td></tr><tr><td>Vandalism</td><td>1</td><td>1</td><td>2</td></tr><tr><td>Predation</td><td>1</td><td>2</td><td>3</td></tr><tr><td>Success manipulation</td><td>7</td><td>7</td><td>14</td></tr><tr><td>Complete clutch failure</td><td>15</td><td>13</td><td>28</td></tr><tr><td><strong>Total number of exclusions</strong><br>(relevant for all models)</td><td><strong>25</strong></td><td><strong>24</strong></td><td><strong>49</strong></td></tr></tbody></table><table><thead><tr><th colspan="4">b)</th></tr><tr><th colspan="4">Number of observations excluded from the cleaned dataset</th></tr><tr><th>Cause for exclusion</th><th>PM<br>(n<sub>cleaned</sub> =<br>279 - 25 =<br>254)</th><th>CC (n<sub>cleaned</sub> = 148 - 25 =<br>124)</th><th>Total<br>(N<sub>cleaned</sub> =<br>427 - 49 =<br>378)</th></tr></thead><tbody><tr><td>Complete brood failure (= no mass measurements)</td><td>34</td><td>27</td><td>61</td></tr><tr><td><strong>Overall total number of exclusions (only relevant for mass models)</strong></td><td><strong>(25 + 34) = 59</strong></td><td><strong>(24 + 27) = 51</strong></td><td><strong>(49 + 61) = 110</strong></td></tr></tbody></table><table><thead><tr><th colspan="7">c)</th></tr><tr><th colspan="7">Final sample sizes</th></tr><tr><th></th><th colspan="3">For nestling survival</th><th colspan="3">For nestling mass</th></tr><tr><th></th><th>PM</th><th>CC</th><th>Total</th><th>PM</th><th>CC</th><th>Total</th></tr></thead><tbody><tr><td>2019</td><td>56</td><td>32</td><td>88</td><td>46</td><td>24</td><td>70</td></tr><tr><td>2020</td><td>40</td><td>26</td><td>66</td><td>33</td><td>19</td><td>52</td></tr><tr><td>2021</td><td>67</td><td>36</td><td>103</td><td>62</td><td>33</td><td>95</td></tr><tr><td>2022</td><td>91</td><td>30</td><td>121</td><td>79</td><td>21</td><td>100</td></tr><tr><td><strong>Total</strong></td><td><strong>254</strong></td><td><strong>124</strong></td><td><strong>378</strong></td><td><strong>220</strong></td><td><strong>97</strong></td><td><strong>317</strong></td></tr></tbody></table>
assumption checks, mostly using the 'DHARMa' and 'performance' packages. Conditional and marginal pseudo-R² coefficients were computed following Nakagawa et al. (2017). LMMs and GLMMs were fitted using the 'ade4' and the 'glmmTMB' packages, respectively. Their associated parametric bootstrapping procedures were performed using the 'pbkrltest' and the 'DHARMa' packages. Data pre-processing, assembly of the final dataset as well as all statistical analyses were carried out using R v4.2.2 (R Development Core Team, 2022). All details, data and codes are available (see Data Availability Statement).

### 3. Results

Results for the nestling survival models validated our two hypotheses (Figs. 2 and 3). The greenspace connectivity significantly improved the fit of the additive full model (p-value = 0.016; conditional R²<sub>GLMM</sub> = 0.17 and marginal R²<sub>GLMM</sub> = 0.17) and the mediated model showed a significant crossover interaction between the greenspace connectivity and the nesting greenspace area (p-value = 0.027; conditional R²<sub>GLMM</sub> = 0.18 and marginal R²<sub>GLMM</sub> = 0.18). Fig. 3 indeed shows that the positive effect of the greenspace connectivity on tit nestling survival increases with decreasing nesting greenspace area, while the latter only increases nestling survival when connectivity is very low, otherwise, its effect could even be negative. Clutch size, intensive vegetation management, and laying day all have significant negative effects on tit nestling survival (Fig. 2a). Our exploratory models, furthermore, hinted that urban intensity tends to increase the probability of complete brood failures (Supplementary Materials 3).

Our results for the nestling mass validated our first hypothesis (p-value = 0.011) but not our second (p-value = 0.522), meaning that we found a significant positive effect of greenspace connectivity in the additive model but that the inclusion of an interaction term did not improve the model fit (for both models, the conditional and marginal R²<sub>LMM</sub> = 0.81). Confidence intervals show that species, clutch size, and laying day also have significant effects on the mass of nestlings (Fig. 2b).

Overall, Fig. 2 shows that there were important interannual variations among all the modelled response variables. Interestingly, the only effect of the nesting greenspace area on the response variables that was statistically significant was found in the mediated full model fitted to explain nestling survival. In other words, when we do not account for the interaction effect of connectivity, the area of the greenspace patch in which tit's nest does not seem to be a meaningful driver of neither the

survival or mass of tit nestlings.

Interestingly, the general pattern of results observed for both nestling survival and nestling mass was quite consistent regardless of the variable used to approximate the local amount of preferred foraging habitat or the distances used to approximate greenspace connectivity (Supplementary Materials 3).

### 4. Discussion

Overall, analyses results widely confirmed our expectations regarding the breeding performances of the two investigated tit species, namely Parus major and Cyanistes caeruleus. Firstly, the connectivity of wooded greenspaces, that is the reachability of tit preferred foraging habitats within the urban landscape, as approximated by the Flux metric, appeared to have a strong positive effect on both nestling survival and mass (Fig. 2), the latter being considered a good proxy for later survival and recruitment (Hinsley et al., 2008; Seress & Liker, 2015). This result gives support, unprecedented in an urban setting, to theoretical predictions (e.g., Hinsley et al., 1999; Dolman et al., 2007) and empirical observations (e.g., Tremblay et al., 2005; Hinsley et al., 2008; Bueno-Cenciso et al., 2016) made in patchy rural landscapes suggesting that sufficient connectivity of tit foraging habitats could improve their reproductive outputs, likely by facilitating access to more – or to higher quality – prey. This importance of connectivity had previously been suggested to explain the breeding success of other birds (e.g., Morimoto et al., 2012; Robinson et al., 2018) or insect species (e.g., Turo & Gardiner, 2021), but actual evidence within cities remains rare. Secondly, as hypothesised, it appears that the effect of greenspace connectivity on nestling survival increased with decreasing area of the local greenspace patches, and conversely (Fig. 3). These results therefore suggest that an appropriate spatial configuration of habitat patches could, at least partially, compensate for the lack of large contiguous greenspaces found in most cities. While such a compensation of size by connectivity has occasionally been proposed to explain the reproductive or population dynamics of a few species within fragmented urban landscapes (e.g., Soga & Koike, 2013; López-Uribe et al., 2015), this is, to the best of our knowledge, the first time this idea is actually evaluated (but for an example in a rural setting, see Matesanz et al., 2015). Furthermore, Fig. 3 suggests that the survival of tit nestlings is highest when connectivity is high but the nesting greenspace area is low. This trend could possibly be the sign of a trade-off between resource

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Coefficient estimates and their 95% confidence intervals for the additive models fitted for the two response variables: a) nestling survival (logit scale) and b) nestling mass (response scale). When coefficients exceed the borders of the plotting window, they are indicated with an arrow and their point estimate is given (e.g., that is the case for the intercepts). For species, management intensity and year (the three categorical variables), reference groups for the contrasts are "Parus major", "extensive" and "2019", respectively. If no estimate is shown, then the variable was not included in the model (see text for details).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Marginal plot depicting how the predicted effect of the *greenspace connectivity* (approximated by the *Flux metric*) on tit nestling survival is mediated (crossover interaction) by the *nesting greenspace area* (i.e., the area of the local habitat patch harbouring the nest). Standard errors are also displayed. For readability reasons, the *nesting greenspace area* is only plotted at its minimum, quartiles and maximum values. Note that both variables have been log₁₀-transformed and median-centred before modelling (see text for details).</figcaption></figure>

availability and pressure from competitors or predators in larger greenspace patches (Dhondt, 2012), by which small but well-connected greenspaces would be less affected. To test these effects, new data focused on competition or predation would be required. More frequent observations of nestlings during the rearing period could also be valuable to assess the existence of age-dependent survival effects or to provide alternative clues on the cause of nestling deaths (e.g., gradual death by starvation, complete brood disappearance due to snakes, incremental disappearance due to bird predators). In any case, this surprising result entails rich research perspectives.

The negative effects of some covariates on the examined reproductive traits (Fig. 2) were also to be expected. Broods that are too large or too late often display a lower fitness because of a frequent mismatch between the peak of caterpillar availability and the peak of nestling demand (Visser & Gienapp, 2019; Pipoly et al., 2022), hence the observed effects of clutch size and laying day. The negative effect of intensive vegetation management on these traits is similarly unsurprising as frequent management interventions cause disturbances that may affect the composition of the vegetation and the foraging behaviour of parents (Shanahan et al., 2014; Cox et al., 2016), since vegetation composition strongly modulates the diversity and abundance of prey arthropods (Jones & Leather, 2012).

Interestingly, when the effects of habitat connectivity and other confounding factors are accounted for, the nesting greenspace area did not seem to meaningfully drive the variations of the mass of urban tits offspring. This is rather surprising as proxies of the local amount of habitat (e.g., patch/fragment size or area, amount of vegetation cover) are frequently associated with various responses related to the offspring quality in many species (tits or not): such as fledging success (e.g., Morimoto et al., 2012), offspring health and size (e.g., Bueno-Enciso et al., 2016), mass (e.g., Hinsley et al., 1999), or fitness (e.g., Turo & Gardiner, 2021). Actually, considering that nesting greenspace area has a positive effect on nestling survival only when connectivity is low, and no obvious effect on the mass of nestlings (these patterns remaining globally consistent even when switching to other proxies of the local

amount of habitat: e.g. area or volume of woody vegetation within a 150 m radius; Supplementary Materials 3), it seems that this variable alone is a rather bad proxy to evaluate the provisioning capacity of the local environment experienced by tits, at least in an urban context. In such patchy landscapes, the configuration of reachable greenspaces matters perhaps more than their overall quantity. It may also illustrate the need to account for other unmeasured variables, such as the density of competitors (Matthysen et al., 2001) or the vegetation composition of greenspaces (Mackenzie et al., 2014; Dekeukeleire et al., 2019; Seress et al., 2025). In any case, these aspects definitely require further investigations, and the relationship between the amount of reachable foraging habitat in the landscape (e.g., as approximated by the *Flux metric* computed on wooded greenspaces) and actual food-availability should be formally investigated. Finally, as tits are known to be mobile and quite tolerant species, it would be interesting to see if our results are generalisable to other habitat specialist species, such as many urban avoiders or utilisers, as they should theoretically respond strongly to increased patch size and connectivity (Dayananda et al., 2021).

## 5. Conclusion

Accessibility to resource patches is arguably crucial for the maintenance of viable populations, especially in highly fragmented and heterogeneous urban landscapes. In spite of their importance, reproductive parameters are seldom studied with regard to the spatial configuration of urban greenspaces (Lepczyk et al., 2017; Melero et al., 2020). Our findings show that the functional connectivity of preferred foraging habitats is important to improve some crucial traits related to the breeding success of two insectivorous passerine birds, possibly because it approximates quite well the level of resources available to these habitat specialist taxa, at least better than does the frequently used local amount of vegetation (e.g., patch size, nesting greenspace area, or similar proxies). It turns out that graph-based connectivity metrics can thus be used to approximate other biological processes than those affecting the presence-absence of species or individuals, for which these
metrics are usually used. Ordinarily, urban connectivity metrics are indeed only confronted to occurrence data or diversity indices (e.g., Kang et al., 2015), or no biological data at all (e.g., Zhang et al., 2019). This, therefore, widens their interest as a decision support tool for urban planning.

In the urban ecology literature, recommendations pertaining to the design and planning of greenspaces massively focus on their size and management, given their well-known relationships with biological richness and diversity (e.g., Beninde et al., 2015; Plummer et al., 2020; Gao et al., 2021). Having rich and diverse biotic communities are certainly beneficial, but ensuring that the populations that compose them can maintain viable populations dynamics is arguably even better. Research and recommendations should thus expand beyond community assemblage to account for other biological processes, such as maintenance or reproduction. In this regard, we showed that, when many confounding effects are simultaneously controlled for, the spatial configuration of greenspaces sometimes matters more than proxies of their size. Interestingly, this appears to be true for some human health benefits as well (Ha et al., 2022). It is a reminder that small interstitial greenspaces, whether in private or public space (e.g., gardens, street trees, bush beds, brownfield), also play key ecological functions by providing resources, habitats or stepping-stones to various species within the urban landscape (Hostetler & Drake, 2009; Norton et al., 2016). It means that thoughtful planting designs can help support the maintenance of bird species, along with all the ecosystem services they provide for city-dwellers. We believe this to be of practical relevance for urban planners and landscape designers, especially considering that it is often difficult to increase the size of vegetated patches in highly constrained urban spaces (Beninde et al., 2015).

## CRediT authorship contribution statement

**François-Marie Martin:** Writing – review & editing, Writing – original draft, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Jean-Christophe Foltête:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. **Gilles Vuidel:** Writing – review & editing, Validation, Software, Resources, Methodology, Formal analysis, Data curation. **Stéphane Garnier:** Writing – review & editing, Validation, Supervision, Resources, Investigation. **Aurélie Khimoun:** Writing – review & editing, Validation, Resources, Investigation. **Nicolas Navarro:** Writing – review & editing, Validation, Resources, Investigation. **Célia Sineau:** Writing – review & editing, Validation, Resources, Data curation. **Bruno Faivre:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Investigation, Funding acquisition, Data curation, Conceptualization.

## Declaration of competing interest

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.

## Acknowledgements

This work was supported by the Région Bourgogne – Franche-Comté (Excellence program), the I-SITE BFC project “PubPrivLands”, the CNRS long term monitoring program SEE-Life and the Agence Nationale de la Recherche (Projet ANR-19-CE34-0008-08 “URBANTIT”). We thank Fanny Baillou, Vincent Gardet, Elwenn Le Magoarou, Clémence Pechinot, Maxime Ragué and Loïc Venague for their help during fieldwork. We are also grateful to Yves Richard, Sophie Pujol, Philippe Deverchère, Benjamin Pauc, Dijon Métropole, and the survey respondents for their help in data acquisition.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.landurbplan.2026.105584.

## Data availability

Data and codes can be accessible here: https://github.com/mrelnoob/ppl.fanf_tits.

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