# Inter- and intra-site variation of microarthropod communities in urban parks

Tania de Almeida, Eva Baraud, Sophie Joimel

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## To cite this version:

Tania de Almeida, Eva Baraud, Sophie Joimel. Inter- and intra-site variation of microarthropod communities in urban parks. Pedobiologia, 2025, 111, pp.151061. 10.1016/j.pedobi.2025.151061. hal-05200522

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# Inter- and intra-site variation of microarthropod communities in urban parks

**Article** in *Pedobiologia* - May 2025

DOI: 10.1016/j.pedobi.2025.151061

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# Inter- and intra-site variation of microarthropod communities in urban parks

Tania De Almeida<sup>a,*</sup>, Eva Baraud<sup>b</sup>, Sophie Joimel<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Collembola
Fragmentation
Patch
Soil fauna
Urban soil

## ABSTRACT

Urban parks are one of the most represented green spaces in cities and can support biodiversity. However, most of the studies demonstrating the role of urban biodiversity do not take into account soil biodiversity, which represents at least ¼ of the world's taxonomic diversity. Among these soil organisms, microarthropods are recognised bioindicators because of their role in soil functioning but also because of their high abundance and sensitivity to a variety of environmental and anthropogenic factors. With high heterogeneity due to soil, management or localisation, nothing looks less like a park than another park. In this study, our aim is rather to understand how soil biodiversity is affected by the spatial configuration of the parks. To do this, we studied inter- and intra-park variation in microarthropod communities using taxonomic and functional approaches based on traits. Our results show communities dominated by Acari, with high abundances of Collembola but low specific richness. Inter-park variations were recorded for the species richness and functional composition of Collembola, Oribatidae abundance, texture and metal levels. That may be linked to the age of the sites in relation with soil physicochemical parameters. Intra-park variations were also observed, particularly for biodiversity indices, which could be explained by the fragmentation of the site and patch size. Despite their small size, soil organisms should be considered in park studies and design (configuration and connectivity). Indeed, understanding the distribution patterns of these organisms is important for improving the role of parks within a functional green and brown urban network.

## 1. Introduction

More than half of the world's population lives in cities (United Nations, 2017). This urbanisation, reflected in the concentration of the population in cities and the extension of the urban fabric, generates numerous negative externalities for both people and ecosystems (e.g. pollution, heat island). One of the major issues raised in relation to the loss of biodiversity in cities is landscape fragmentation (Fahrig, 2003), a key attribute of the structure of cities (Angel et al., 2012). The process of fragmentation results in the conversion of a habitat into several smaller fragments, following the creation of impermeable surfaces that are poorly suited to the survival of species (Blanc and Clergeau, 2013). By creating islands of isolated and separate habitats, fragmentation results in a loss of habitat and, above all, connectivity.

Green infrastructure, described as “natural or semi-natural networks of green (soil covered or vegetated) and blue (water covered) spaces and corridors that maintain and enhance ecosystem services” (World Green

Building Council, 2015, p15), are promoted with the aim of encouraging ecological continuity in the city, also known as the green and blue network. Less well known and studied than the green and blue infrastructure, the brown infrastructure corresponds to networks of interconnected soil reservoirs and corridors within the city (Cluzeau et al., 2020).

Analysing the role of blue, green and brown infrastructures for a diversity of organisms is therefore essential for implementing actions aimed at restoring connectivity within cities. Infrastructures are said to be functional if they have the capacity to ensure the maintenance and perpetuation of the species chosen (Taylor et al., 2006). Their functionality therefore depends on the infrastructure itself, but also on the organism's response to the structure (Taylor et al., 2006). The nature of the fragmenting obstacle, such as the degree of impermeability of the physical barrier (e.g. asphalt surface vs. bare soil) and its size (e.g. from tens of metres to centimetres) also affects the functionality of the infrastructure (Taylor et al., 2006, Cluzeau et al., 2020). The notion of

<footnote><sup>a</sup> Univ. Lille, IMT Nord-Europe, Univ. Artois, JUNIA, ULR 4515 – LGCgE, Laboratoire de Génie Civil et géo-Environnement, Lille 59000, France</footnote>

<footnote><sup>b</sup> Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Palaiseau 91120, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: tania.dealmeida@univ-lille.fr (T. De Almeida).</footnote>
fragmentation may therefore vary depending on the organism to study. Each infrastructure can be described by its patch composition. A patch is ‘a relatively discrete and homogeneous unit of the landscape, detectable at a certain scale, for the ecological phenomenon we wish to assess’ (McGarigal, 2006). While an urban area may be considered continuous or homogeneous for a highly mobile species, for others this same area may present an immensity of habitats with potentially uncrossable barriers.

Urban parks are often the most represented green spaces in cities. They are major pillars of both green and brown urban networks, in particular because they are hotspots of urban biodiversity (Nielsen et al., 2014) and provide numerous ecosystem services (e.g. improving air quality, recreational aspect). In order to develop a functional green and brown urban network, the habitats and corridors of which it is composed must be able to accommodate this biodiversity in the best possible way. However, parks turn out to be very heterogeneous, including at soil level (Sauerwein, 2011) and thus present a diverse patch composition, so called intra-site variation (e.g. spatial differences within a single park). On another scale, the parks could also vary greatly from one to another (e.g. inter-site variation illustrating differences among parks) due to park size, park age or management differences.

The effects of infrastructures on urban biodiversity have mainly been studied for aerial organisms such as butterflies and bees (Nielsen et al., 2014). However, it has been shown that the effects of the urban environment can depend on the taxonomic group (Liu et al., 2014). For example, in parks, while the paths probably have little influence on the movement of butterflies, the same will not necessarily be true for soil organisms. Soil biodiversity is an essential component of urban ecosystems, because it play a major role in the vegetation of parks by regulating pests or recycling carbon (Nielsen et al., 2011) and retaining water (van Vliet and Hendrix, 2007). Moreover, soils organisms represent at least ¼ of terrestrial taxonomic diversity (Decaëns et al., 2006). Among soil organisms, microarthropods (mainly Collembola and Acari) are bioindicators of soil fauna, in the same way as earthworms or nematodes. Collembola, which are highly abundant, can colonise a wide range of habitats, including urban parks (Milano et al., 2017) and are sensitive to various factors (e.g. pollution or practices). Moreover, Collembola play a major role in the functioning of urban soils by contributing to the transformation of organic matter (e.g. carbon, nitrogen) (Giller, 1996) or by being the prey of numerous organisms.

The response of soil organisms, and especially microarthropods, to urbanisation remains a subject that needs to be addressed in depth, as it is largely under-studied (<1% of scientific articles on soils deal with urban soils, according to Guilland et al., 2018). According to these few studies, the effects of urbanisation on microarthropods depend on the type of habitat (e.g. parks, urban agriculture) and seem to affect both dispersal and colonisation (Joimel et al., 2022). Dispersal is defined as the mobility or transport of organisms from a source location to a park whereas colonisation is related to the spatio-temporal dynamics in the studied park.

Soil organisms are often considered to have a low dispersal and colonisation capacity due to their reduced mobility (Cluzeau et al., 2020), linked both to their small size and to specific characteristics that sometimes limit their movements (e.g. absence of wings, undeveloped arthropodic appendages) such as earthworms (Cluzeau et al., 2020) or springtails. With only 10 studies on the subject, the dispersal of Collembola in an urban context is still little known (Joimel et al., 2022). However, Collembola are known to disperse through various mechanisms but only at small distances (Dunger et al., 2002). Dispersal can occur actively—when the Collembola controls its own movement—or passively, relying on external forces such as wind, water currents, animals, or materials used in the construction of Technosols (Clobert, 2012). Collembola are known to disperse actively, potentially covering distances of several hundred metres (Ponge, 2013) but we don't know how far Collembola can disperse passively. Moreover, this dispersal can be slowed or facilitated by the configuration of the parks. However,

there are very few studies on the best configuration of parks (e.g. size and type of path, size and shape of patches) to promote dispersal and colonisation but also soil biodiversity within parks (Milano et al., 2017, 2018).

Besides encouraging taxonomic diversity, it may also be appropriate to encourage functional diversity in parks. The functional trait approach, where traits are “a measurable characteristic of an individual organism or its colony that has a link to the organism’s fitness and/or its effect on other organisms and/or the environment” (Joimel et al., 2024) seems particularly interesting for studying biodiversity. Numerous studies in recent years have demonstrated the value of this approach in explaining the distribution patterns of soil biodiversity (Hedde et al., 2018; Santorufo et al., 2021; Bonfanti et al., 2022). For springtails, some traits are more relevant for studying dispersal and colonisation capacity. Ponge (2020) has shown in particular that springtails with a large body size, long furca and legs, and fully developed visual organs have a higher dispersal capacity. These functional traits can even be linked to the active or passive dispersal capacity of springtails. For example, organisms with a furca, big size, ocelli and sexual reproduction are easily transported by air and therefore have a greater capacity for colonisation (Joimel et al., 2018).

In this context, we wish to investigate how colonisation and the dispersal of the microarthropod biodiversity communities in urban parks could be affected by the spatial configuration of the parks. To do this, we are going to analyse inter- and intra-park variation in microarthropod communities and discuss potential factors influencing variations such as park age, park age or physicochemical characteristics. As previously discuss, these organisms have little active dispersal, so we hypothesis that inter-site variation will be greater than intra-site variation. A better understanding of the factors modulating soil biodiversity in urban parks is necessary in order to preserve ordinary biodiversity in the city.

## 2. Material and methods

### 2.1. Study sites

In 2023, four parks located in Paris (France) were selected (Table 1). They were chosen for their similar urban environment, topography (between 35 and 75 m) and management. Indeed, these large-scale municipal parks (> 5 ha) are all managed by the Paris City Council and include a landscaped setting consisting of wooded areas (mostly plane, chestnut, lime and maple), lawns, ponds and leisure facilities (e.g. sports ground, farm). The soils are pseudo-natural, with no known contamination. Although chemical plant protection products may have been used in the past, today no pesticides are used or only authorised products for use in organic farming. Differentiated management of the lawns is applied in all the parks, but is specifically adapted to each park.

### 2.2. Sampling strategy

The parks are characterised by a high degree of spatial heterogeneity (e.g. small or large areas of lawns, areas with a continuous tree cover) highlighting the intra-park variation. We mapped each park (e.g. trees, lawns, paths) using GIS with ArcGis software coupled with the management maps supplied by the managers. Also, we identify several patches, i.e. an area within the parks bounded by paths. We choose to

<table><caption>**Table 1**
Summary of investigated parks.</caption><thead><tr><td>Park</td><td>Age (years)</td><td>Previous land use</td><td>Area (ha)</td></tr></thead><tbody><tr><td>1</td><td>46</td><td>Military land</td><td>10.6</td></tr><tr><td>2</td><td>86</td><td>Stream bed « La Bièvre »</td><td>5.6</td></tr><tr><td>3</td><td>145</td><td>Stone quarries</td><td>15.5</td></tr><tr><td>4</td><td>254</td><td>Wasteland</td><td>8.3</td></tr></tbody></table>
focus only on patches with a uniform cover of herbaceous strata (i.e. lawn). Patches with more than 30 % tree cover were also excluded. Also, in each of the four parks, five uniform patches were selected and four replicates of microarthropods were sampled in the centre of each patch. However, the management and the patch size differ between them.

## 2.3. Soil sampling and analysis

A total of 150 g of soil were taken from the top 5 cm for each replicate, then mixed and dried to obtain one composite sample per patch. The following analyses were carried out by the Teyssier laboratory on the 2 mm sieved fraction: texture (standard X 31–107), Metson CEC (Cation Exchange Capacity) (X 31–130), water pH (standard ISO 10390), percentage of organic matter (standard ISO 10693), total nitrogen (standard ISO 13878), Olsen phosphorus (standard X 31–160) and total carbon. Total metals contents (Cd, Cu, Cr, Hg, Pb, Ni, Zn) were analysed by Teyssier laboratory. Bulk density (NF EN 13041) and moisture content were measured using an undisturbed sample from each replicate.

## 2.4. Microarthropods sampling and identification

Microarthropods were sampled for each replicate by coring the soil (5 cm) using a 6 cm diameter cylinder. In park 1, one sample is missing (19 replicates vs 20 replicates for other parks). Soil cores were then extracted in accordance with standard NF/ISO 23611-2, using a McFayden high temperature gradient extractor for eight days. All organisms were counted and identified to Order. Moreover, Acari were classified in the suborder (Acarididae, Actinididae, Gamasidae, Oribatidae) and Collembola were identified to species.

Using this data, a total density (individuals per m²) was obtained for all groups (Acari, Collembola and other microarthropods) for each replicate. For Collembola, the species richness, Shannon and evenness index (Pielou) and Beta diversity (Jaccard) were also obtained.

In addition to the taxonomic approach, a functional trait approach was also used for Collembola. The eight traits selected provide information on their dispersal capacity (presence of furca, size, presence of ocelli), colonisation (mode of reproduction) and vertical distribution (body shape, presence of PAO, pigmentation and scale in addition to body size, furca and ocelli) (Gisin, 1943; Potapov et al., 2016). These traits were extracted from the BETSI database (https://portail.betsi.cnr.sfr/) (Pey et al., 2014). Functional richness, functional evenness and functional divergence (Villeger et al., 2008) and community-weighted mean (CWM) by trait attribute were used. The CWM corresponding to $\sum [i = 1 \text{ à } N]$ pi tik, where N is the number of taxa, pi the proportional abundance of species i in each sample and tik the attribute of trait t for the i taxa.

## 2.5. Statistical analysis

### 2.5.1. Statistical analyses were carried out using R 4.2.2 software

Comparisons between parks were made using Generalised Linear Models (GLM) with quasipoisson distribution for all calculated indices (e.g. species richness, density) and with gaussian distribution for physicochemical parameters (e.g. pH, Cd). GLMs with gamma distribution were applied for data that violated the assumption of a normal distribution. Models were followed by Tukey HSD post-hoc tests using the glht function with P < 0.05 from the "multcomp" package (Hothorn et al., 2016). To study the composition of Collembola communities, we used NMDS (non-metric multidimensional scaling) based on the Bray-Curtis dissimilarity index (R package 'vegan') (Oksanen et al., 2013). Differences in Collembola community composition between parks were tested by analysis of similarities (ANOSIM) using the Anosim function (R package 'vegan'). For functional trait composition, a

Principal Components Analysis (PCA) was carried out on the CWMs, retaining only a single attribute for binary traits and weighting attributes by the total number of attributes for each functional trait to avoid autocorrelations (R packages "FactoMineR" and "FD") (Laliberté et al., 2014; Lé et al., 2008). Differences in Collembola functional traits between parks were tested by using the Adonis function (R package 'vegan') and pairwise.adonis for the post-hoc test (R package 'pairwiseAdonis'). Comparisons of functional diversity data between parks were made using GLMs with gamma, gaussian or quasibinomial distribution.

The data are available at the following link: https://doi.org/10.57745/DGVDTK

## 3. Results

### 3.1. Soil properties

Sands content was significantly higher in parks 3 and 4 than in parks 1 and 2, while silts content was significantly higher in parks 1 and 2. Park 1 had the highest clay content, while Park 2 had the lowest (Table 2). Organic matter and total nitrogen were significantly higher in Park 3 (Table 2). The bulk density was significantly higher in park 4 than in park 1. The CEC was significantly higher in park 3 than in park 2. The amount of lead in the soil in parks 2 and 3 was two times higher than in parks 1 and 2. The amount of mercury was between 2.5 and 3.8 times higher in the soils of parks 3 and 4 than in parks 1 and 2. Conversely, the amount of nickel is significantly higher in the soil of park 1 than in park 3. Cadmium and copper content were significantly higher in Park 3 (Table 2). No other significant differences were found.

### 3.2. Microarthropods in parks

The microarthropods sampled in the four study parks were mainly Collembola and Acari. Acari represented the largest proportion of microarthropods organisms, 46 % and 82 %, respectively for parks 2 and 4. Collembola are the second most represented organisms, 17 % and 44 %, respectively for parks 4 and 2 (Fig. 1). Other organisms were also sampled, subsequently referred to as "others" and including Araneae, caterpillars, Coleoptera, Hemiptera, Isopoda, larvae of Coleoptera and Diptera, and Pauropoda. Ants were found in all the parks but, as they are social insects, they were not included in the "others" category for the analyses of abundance data. The other organisms always represent 15 % or less of the sampled specimens (Fig. 1; Sup. Mat 1).

Total abundance of Acari was highest in park 1 (up to 30,000 individuals per m²), with 2.7 times more Acari than in park 3, where total abundance was significantly lower than in the other parks (around 11,000 individuals per m²). The abundance of Oribatidae was between 2.5 and 3.9 times higher in the soils of parks 1 and 2 than in parks 3 and 4. Total abundance of Actinedidae was significantly lower in park 3 than in the other three parks. Park 1 was the park with the highest Gamasidae abundance (Table 3).

### 3.3. Focus on Collembola among parks

The species richness of Collembola was about 2.6 times higher in the soils of parks 1 (7.2 species) and 2 (6.2 species) than in parks 3 (2.5 species) and 4 (2.6 species). Similarly, the Shannon index was between 2.4 and 3.8 times higher in the soils of parks 1 and 2 than in parks 3 and 4. The Pielou index was about 2.6 times higher in the soils of parks 1 and 2 than in parks 3. The Jaccard index was significantly higher in park 2 than in the other three parks (Table 4).

The NMDS ordination (stress = 0.14) discriminates the composition of the Collembola community in the different parks studied ($R^2$ = 0,4; p-value = 0.001) (Fig. 2). Park 1 was characterised by the presence of
<caption>**Table 2**</caption>

Soil physico-chemical analyses for the four parks. Values are means ± standard errors. For each park, n = 20 except for park 1, n = 19. F and P values were obtained from GLM with gamma or Gaussian distribution. Two values in the same row with a different letter are significantly different, according to Tukey post-hoc tests. Significance thresholds were set at a risk of 5%.

<table><thead><tr><th></th><th>df</th><th>F</th><th>P-value</th><th>Park 1</th><th>Park 2</th><th>Park 3</th><th>Park 4</th></tr></thead><tbody><tr><td colspan="8"><strong>Soil physical parameters</strong></td></tr><tr><td>Clays (%)</td><td>16</td><td>5.38</td><td>0.009**</td><td>21 ± 0.7 a</td><td>16.3 ± 0.9 b</td><td>19.3 ± 0.8 ab</td><td>20.8 ± 1.2 a</td></tr><tr><td>Sands (%)</td><td>16</td><td>7.49</td><td>0.002**</td><td>34.3 ± 2.8 a</td><td>37.3 ± 4.7 a</td><td>51.1 ± 2.8 b</td><td>50.5 ± 1.9 b</td></tr><tr><td>Silts (%)</td><td>16</td><td>10.66</td><td>&lt; 0.001***</td><td>44.8 ± 2.2 a</td><td>46.4 ± 4.6 a</td><td>29.6 ± 2 b</td><td>28.7 ± 1.9 b</td></tr><tr><td colspan="8"><strong>Soil chemical parameters</strong></td></tr><tr><td>Available phosphorus (mg/Kg of soil)</td><td>16</td><td>2.83</td><td>0.071</td><td>131 ± 23.5</td><td>70.6 ± 11.1</td><td>104.4 ± 15.8</td><td>121 ± 7.9</td></tr><tr><td>Bulk density (g.cm<sup>-3</sup>)</td><td>16</td><td>6.33</td><td>0.005**</td><td>1.11 ± 0.004 b</td><td>1.28 ± 0.02 ab</td><td>1.29 ± 0.2 ab</td><td>1.44 ± 0.1 a</td></tr><tr><td>Cation Exchange Capacity (meq/100 g of soil)</td><td>16</td><td>3.34</td><td>0.046*</td><td>16.6 ± 0.6 ab</td><td>14.5 ± 0.6 b</td><td>17.5 ± 1 a</td><td>16.4 ± 0.6 ab</td></tr><tr><td>Organic matter (%)</td><td>16</td><td>4.96</td><td>0.013*</td><td>7.5 ± 0.4 b</td><td>7.8 ± 0.3 b</td><td>10.2 ± 1 a</td><td>7.4 ± 0.2 b</td></tr><tr><td>pH</td><td>16</td><td>2.74</td><td>0.078</td><td>7.68 ± 0.06</td><td>7.54 ± 0.06</td><td>7.44 ± 0.11</td><td>7.68 ± 0.04</td></tr><tr><td>Soil humidity (%)</td><td>16</td><td>1.26</td><td>0.321</td><td>17.1 ± 0.4</td><td>20.2 ± 2.3</td><td>21.3 ± 2</td><td>19.45 ± 1.1</td></tr><tr><td>Total nitrogen (mg/Kg of soil)</td><td>16</td><td>5.35</td><td>0.010**</td><td>3461.8 ± 201.7 b</td><td>3833.8 ± 252.1 b</td><td>5207.8 ± 617.3 a</td><td>3498.2 ± 142.1 b</td></tr><tr><td>C/N ratio</td><td>16</td><td>2.08</td><td>0.144</td><td>12.7 ± 0.5</td><td>11.8 ± 0.4</td><td>11.4 ± 0.3</td><td>12.3 ± 0.3</td></tr><tr><td colspan="8"><strong>Soil contamination</strong></td></tr><tr><td>Cadmium (mg/Kg of soil)</td><td>16</td><td>11.21</td><td>&lt; 0.001***</td><td>0.44 ± 0.03 b</td><td>0.42 ± 0.03 b</td><td>0.68 ± 0.04 a</td><td>0.52 ± 0.04 b</td></tr><tr><td>Chromium (mg/Kg of soil)</td><td>16</td><td>2.46</td><td>0.101</td><td>43.1 ± 2.7</td><td>45.8 ± 4.2</td><td>33.8 ± 2.6</td><td>40.4 ± 3.3</td></tr><tr><td>Copper (mg/Kg of soil)</td><td>16</td><td>11.14</td><td>&lt; 0.001***</td><td>37.2 ± 3.1 b</td><td>40.5 ± 6.2 b</td><td>63 ± 2 a</td><td>62.1 ± 4 a</td></tr><tr><td>Lead (mg/Kg of soil)</td><td>16</td><td>18.35</td><td>&lt; 0.001***</td><td>97.4 ± 13.1 b</td><td>112.4 ± 15.6 b</td><td>200.8 ± 8.9 a</td><td>202.6 ± 14 a</td></tr><tr><td>Mercury (mg/Kg of soil)</td><td>16</td><td>30.5</td><td>&lt; 0.001***</td><td>0.34 ± 0.05 b</td><td>0.46 ± 0.12 b</td><td>1.17 ± 0.09 a</td><td>1.3 ± 0.08 a</td></tr><tr><td>Nickel (mg/Kg of soil)</td><td>16</td><td>3.67</td><td>0.035*</td><td>25.3 ± 1.5 b</td><td>22.4 ± 0.7 ab</td><td>18.8 ± 1.1 a</td><td>22.7 ± 2 ab</td></tr><tr><td>Zinc (mg/Kg of soil)</td><td>16</td><td>3.17</td><td>0.053</td><td>130 ± 13.9</td><td>122.7 ± 12.8</td><td>165.4 ± 9.2</td><td>171.3 ± 17.9</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Proportion of different microarthropods groups sampled in the 4 parks. The “Others” category includes all sampled organisms not classified as Collembola or Acari (e.g. spiders, isopods, beetles, hemipterans, larvae). Ants have been excluded for this illustration.</figcaption></figure>

*Parisotoma notabilis* (Schäffer, 1896), *Lepidocytus violaceus* (Geoffroy, 1762), *Pseudosinella alba* (Packard, 1873), *Metaphorura affinis* (Börner, 1902), *Lepidocytus cyaneus* Tullberg, 1871 and *Brachystomella parvula* (Schaffer, 1896). Park 2 was characterised by the presence of *Ceratophysella denticulata* (Bagnall, 1941), *Isotoma anglicana* (Lubbock, 1862) and *Isotomurus plumosus* (Bagnall, 1940). Park 1 and 2 were both characterised by the presence of *Sphaeridia pumilis* (Krausbauer, 1898) and *Sminthurinus elegans* (Fitch, 1863). In contrast, park 3 was characterised by the presence of *Sminthurus viridis* (Linnaeus, 1758), *Folsomia candida* (Willem, 1902), *Protaphorura armata* (Tullberg, 1869) and *Frieza truncata* (Cassagnau, 1958). Park 3 and 4 were both characterised by the presence of *Isotomodes productus* (Axelson, 1906) (Fig. 2).

### 3.4. A functional approach

Functional dispersion (FDis) was about 2 times higher in parks 1 and 2 than in park 3. Rao's quadratic entropy indices of diversity (RaoQ) was significantly higher in park 1 than in parks 3 and 4. No significant differences were found between parks for functional richness (FRic) (Table 5).

Functional traits were discriminated among the four parks along the axes 1 and 2 (ADONIS, F = 6; p-value < 0.001; Fig. 3). Axis 1 (23 %) was correlated with reproduction mode, PAO, scales or body size. Axis 2 (18 %) was correlated with the number of ocelli or the pigmentation. Park 2 was notably opposed to parks 1 and 4 (p = 0.006 for both
<caption>**Table 3**</caption>

Abundance of soil Acari and Collembola (in ind.m⁻²) in the 4 parks surveyed. Values are means ± standard errors. P and F values were obtained from GLM with quasipoisson distribution. Two values in the same row with a different letter are significantly different according to Tukey post-hoc tests. Significance thresholds were set at a risk of 5 %.

<table><thead><tr><th></th><th>df</th><th>F</th><th>P-value</th><th>Park 1</th><th>Park 2</th><th>Park 3</th><th>Park 4</th></tr></thead><tbody><tr><td><strong>Acari</strong></td><td>3</td><td>12.24</td><td>&lt; 0.001***</td><td>29860 ± 15476 a</td><td>19339 ± 5705 b</td><td>10901 ± 7617 c</td><td>13047 ± 10926 bc</td></tr><tr><td>Oribatidae</td><td>3</td><td>14.24</td><td>&lt; 0.001***</td><td>13889 ± 10282 a</td><td>10164 ± 4918 a</td><td>3977 ± 4161 b</td><td>3598 ± 3544 b</td></tr><tr><td>Acarididae</td><td>3</td><td>1.7</td><td>0.173</td><td>1617 ± 2110</td><td>1284 ± 2028</td><td>3451 ± 5244</td><td>3157 ± 5534</td></tr><tr><td>Actinedidae</td><td>3</td><td>8.7</td><td>&lt; 0.001***</td><td>6380 ± 2729 a</td><td>4335 ± 1998 a</td><td>1599 ± 1403 b</td><td>5072 ± 5705 a</td></tr><tr><td>Gamasidae</td><td>3</td><td>18.48</td><td>&lt; 0.001***</td><td>7974 ± 6618 a</td><td>3556 ± 2810 b</td><td>1873 ± 1376 bc</td><td>1221 ± 1280 c</td></tr><tr><td><strong>Collembola</strong></td><td>3</td><td>3.7</td><td>0.011*</td><td>16796 ± 13805 a</td><td>19975 ± 11926 a</td><td>17702 ± 44856 a</td><td>3512 ± 6913 b</td></tr></tbody></table>

Total abundance of Collembola was five times lower in park 4 than in the other three parks (Table 4).

<caption>**Table 4**</caption>

Collembola community data for the four parks studied. Values are means ± standard errors. P and F values were obtained from GLM with quasipoisson or quasibinomial distribution. Two values in the same row with a different letter are significantly different according to Tukey post-hoc tests. Significance thresholds were set at a risk of 5 %.

<table><thead><tr><th></th><th>df</th><th>F</th><th>P-value</th><th>Park 1</th><th>Park 2</th><th>Park 3</th><th>Park 4</th></tr></thead><tbody><tr><td>Species richness</td><td>3</td><td>16.58</td><td>&lt; 0.001***</td><td>7.2 ± 1.4 a</td><td>6.2 ± 1.7 a</td><td>2.5 ± 2.7 b</td><td>2.6 ± 2.4 b</td></tr><tr><td>Shannon index</td><td>3</td><td>13.76</td><td>&lt; 0.001***</td><td>1.63 ± 0.28 a</td><td>1.30 ± 0.28 a</td><td>0.43 ± 0.64 b</td><td>0.65 ± 0.70 b</td></tr><tr><td>Evenness index</td><td>3</td><td>12.3</td><td>&lt; 0.001***</td><td>0.83 ± 0.1 a</td><td>0.73 ± 0.1 ab</td><td>0.27 ± 0.39 c</td><td>0.46 ± 0.46 bc</td></tr><tr><td>Jaccard index</td><td>3</td><td>70.67</td><td>&lt; 0.001***</td><td>0.26 ± 0.14 a</td><td>0.35 ± 0.17 b</td><td>0.09 ± 0.20 c</td><td>0.08 ± 0.15 c</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Non-metric multidimensional scaling (NMDS) (stress = 0.14) performed on the Collembola communities of the four parks studied. The 20 samples from each park (19 for park 1) are grouped in polygons, green for park 1, blue for park 2, yellow for park 3 and orange for park 4. Dots represent the 69 samples and crosses represent the spatial location of each species. For clarity, only the species most correlated to the two first axes are shown.</figcaption></figure>

comparisons) on axis 1. In contrast, the park 1 was differentiated from park 3 on axis 2 (p-value = 0.012).

## 4. Discussion

### 4.1. Urban parks: high abundance but low species richness of microarthropods

Urban parks, as green infrastructure, can play a role in providing habitat and connectivity between green spaces in the city. They are often considered to be biodiversity hotspots, although the reality is often more

complex (Brown, 2003). The actual capacity of parks to fulfil this role will depend both on the park and on the type of organism considered. With values of up to 20,000 Collembola or even 30,000 Acari per m², the studied parks do seem to have a role to play in the biodiversity of urban soils, with values close to those found in other uses such as forests or rural meadows (Joimel et al., 2017). However, these conclusions need to be tempered by a comparison with species richness. With a maximum diversity of only seven species, significantly lower than those previously recorded (Santorufo et al., 2014; Rzeszowski and Sterzyńska, 2016), the diversity of Parisian parks is not only lower than natural soils (Joimel et al., 2017) but also than other urban soils (Guilland et al., 2018; Joimel
<caption>**Table 5**</caption>

Functional diversity data for the four parks studied. Values are means ± standard errors. P and F values were obtained from GLM with gamma, gaussian or quasi-binomial distribution. Two values in the same row with a different letter are significantly different according to Tukey post-hoc tests. Significance thresholds were set at a risk of 5%.

<table><thead><tr><th></th><th>df</th><th>F</th><th>P-value</th><th>Park 1</th><th>Park 2</th><th>Park 3</th><th>Park 4</th></tr></thead><tbody><tr><td>Functional dispersion (FDis)</td><td>3</td><td>12.3</td><td>&lt; 0.001***</td><td>0.2 ± 0.05 a</td><td>0.14 ± 0.04 b</td><td>0.07 ± 0.09 c</td><td>0.1 ± 0.09 bc</td></tr><tr><td>Functional richness (FRic)</td><td>3</td><td>2.5</td><td>0.072</td><td>0.14 ± 0.054</td><td>0.092 ± 0.063</td><td>0.15 ± 0.065</td><td>0.079 ± 0.078</td></tr><tr><td>Rao's quadratic entropy indices of diversity (RaoQ)</td><td>3</td><td>7.8</td><td>&lt; 0.001***</td><td>0.05 ± 0.02 a</td><td>0.03 ± 0.01 ab</td><td>0.02 ± 0.02 b</td><td>0.02 ± 0.03 b</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Principal component analysis (PCA) performed on Collembola functional traits of the four parks studied. The 20 samples from each park (19 for park 1) are grouped in ellipses, green for park 1, blue for park 2, yellow for park 3 and orange for park 4. Dots represent the 69 samples. Sizes are in mm.</figcaption></figure>

et al., 2022). This variation in response between density and species richness is probably due to strong disturbances in the parks linked to current or past management practices that tend to affect diversity (Heiniger et al., 2015; Milano et al., 2017). The four parks studied have a high abundance of Collembola, mainly due to species that are more tolerant to these disturbances, as shown by the low equitability values for some parks (0.3 for park 3 for example) illustrating the dominance of a few species. Even when diversity increases, functional richness remains stable, illustrating a high degree of functional redundancy in the parks.

## 4.2. Inter-site variation linked to the age of the sites

The variation between parks is observed on various physicochemical and biological indicators, according to two distribution patterns: (i) Parks 1 and 2 vs. Parks 3 and 4 and (ii) Park 3 vs. the other parks.

The significant differences between parks 1 and 2 vs. parks 3 and 4 were recorded in particular for the species richness and functional composition of Collembola, the abundance of Oribatidae and the texture and levels of metals (Cu, Pb, Hg). This difference between these parks may be linked to the age of the sites. Parks 1 and 2 are less than 100 years old, while the other two sites are older. For physicochemical parameters, the 'age' effect for metal content is often demonstrated in urban environments (Chen et al., 1997; Joimel et al., 2020; Zhong et al., 2022). It may be linked to practices (e.g. pesticides) or to the urban environment. Although pesticides have not been used in Parisian parks since the 1990s, their historical use, along with that of fertilisers, may be responsible for the increase in Cd, Cu and Pb levels observed, as has been

demonstrated in agricultural soils (Chen et al., 1997) or in allotments (Joimel et al., 2020). Moreover, the presence of roads and industrial activities are also sources of diffuse atmospheric pollution, and their proximity to parks can lead to an increase in heavy metal contamination in soils (Councell et al., 2004; Clark et al., 2006). For biodiversity indices, this increase in species richness as a function of the age of urban areas has already been highlighted by different authors (Sattler et al., 2010; Milano et al., 2018). It is linked to the gradual arrival of new species in these systems. The differences in communities in parks 3 and 4 and the presence of species that are not very mobile in these older parks, such as *P. armata*, illustrate this succession. However, a high proportion of species remained common to all the parks, demonstrating that microarthropods can move from one site to another.

The second distribution is linked to the particularity of park 3 in terms of community composition and soil fertility (higher levels of organic matter and CEC). This differentiation from the other parks in terms of communities is certainly linked to this variation in the soil. The higher fertility in park 3 is probably not linked to the original soil (quarry soil), which is potentially poor in OM. On the other hand, it is common for compost to be added to the land. It is therefore possible that, despite joint management by the Paris City Council, park 3 has received more compost input. The presence and high abundance of *F. candida* and *P. armata*, species often found in the composts in urban environments, also seems to support this fact. Park 3 is also the largest park. It is commonly accepted that the size of urban green spaces affects biodiversity (Lepczyk et al., 2017).

## 4.3. Intra-site variation

Biodiversity indices were highly variable within each park. For example, the abundance of Oribatidae reached 14,000 ± 10,000 individuals per m² in park 1, while park 4 had 3500 ± 7000 Collembola per m². This variation could be explained by (i) fragmentation across the site. Microarthropods are organisms with a low active dispersal rate. Any element such as roads or paths separating the different patches could influence the communities. Bellino et al. (2021) showed that there could be variations on a small spatial scale linked to a lack of connectivity between the different areas of the parks. (ii) Patch size. As mentioned above, relationships between the size of green spaces and soil biodiversity have often been demonstrated (Lepczyk et al., 2017). It is possible that beyond the size of the parks, it is the size of the patches that will influence intra-park variation. This variable is difficult to test because the size of the patches on each site will also depend on the size of the park. Although this variation in the structure indices of the taxonomic diversity of Collembola and Acari does not seem to be linked to the same distribution patterns as the inter-site variation, it should nevertheless be noted that the age of the sites has an effect on the variation of the taxonomic and functional composition of Collembola. Thus, the oldest sites also show the most variation in terms of composition.

In the end, despite intra-site variation, it was still possible to observe strong differentiation between the different parks, due to the age of the sites or their management. The question remains, however, as to where this distinction between 'young' and 'old' parks begins. Milano et al.
(2017) place this limit at 50 years. Here, it would be closer to 100 years. However, when we study Technosols, common soil type for parks, community stabilisation seems to occur much quickly, in the order of a few years for soil communities (Hedde et al., 2018; Santorufo et al., 2021). In reality, these communities are certainly evolving in ways that are still difficult to predict.

## 5. Conclusion

The role of parks in hosting microarthropod biodiversity depends on a number of factors linked to the configuration of the parks, their management and also their history (e.g. type of soil, age of the park). The result is a high degree of variation in terms of biodiversity between parks and within parks. Understanding the distribution patterns of communities as a function of these factors is important for improving the role of parks within a functional green and brown network.

## Funding

This study was supported by the Lab Recherche Environnement Vinci ParisTech.

## CRediT authorship contribution statement

**Sophie Joimel:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Tania De Almeida:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Formal analysis, Data curation, Conceptualization. **Eva Baraud:** Writing – review & editing, Writing – original draft, Investigation, Formal analysis, Data curation.

## Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Sophie Joimel reports financial support was provided by Lab Recherche Environnement Vinci ParisTech. If there are other authors, they 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

The authors wish to thank Cyprien Boyer, Sékou Coulibaly, Nolwen Levaillant, Julia Oblin and Antoine Tardif for their technical help in the field or in the lab, always with a big smile. We would also like to thank the City of Paris and its employees who gave us access to the parks.

## Data availability

I have share the link to my data in the manuscript.

## References

Angel, S., Parent, J., Civco, D.L., 2012. The fragmentation of urban landscapes: global evidence of a key attribute of the spatial structure of cities, 1990–2000. Environ. Urban 24 (1), 249–283. https://doi.org/10.1177/0956247811433536.

Bellino, A., Baldantoni, D., Milano, V., Santorufo, L., Cortet, J., Maisto, G., 2021. Spatial patterns and scales of collembola taxonomic and functional diversity in urban parks. Article 23. Sustainability 13 (23). https://doi.org/10.3390/su132313029.

Blanc, N., Clergeau P., 2013. *Trames vertes urbaines De la recherche scientifique au projet urbain.*

Bonfanti, J., Hedde, M., Cortet, J., Krogh, P.H., Larsen, K.S., Holmstrup, M., 2022. Communities of Collembola show functional resilience in a long-term field experiment simulating climate change. *Pedobiologia* 90, 150789. https://doi.org/10.1016/j.pedobi.2022.150789.

Brown, A.L., 2003. Increasing the utility of urban environmental quality information. Landsc. Urban Plan 65, 85–93.

Chen, T.B., Wong, J.W.C., Zhou, H.Y., Wong, M.H., 1997. Assessment of trace metal distribution and contamination in surface soils of Hong Kong. Environ. Pollut. 96 (1), 61–68. https://doi.org/10.1016/S0269-7491(97)00003-1.

Clark, H.F., Brabander, D.J., Erdil, R.M., 2006. Sources, sinks, and exposure pathways of lead in urban garden soil. J. Environ. Qual. 35 (6), 2066–2074.

Clobert, J., 2012. Dispersal Ecology and Evolution. OUP Oxford. (https://books.google.fr/books?id=s3EVDAAAQBAJ).

Cluzeau, D., Marié, X., Lénack, E., 2020. « Intégration de la Trame Brune et de la biodiversité lombricienne dans les projets d'aménagement urbain à l'échelle des quartiers ».

Councell, T.B., Duckenfield, K.U., Landa, E.R., Callender, E., 2004. Tire-wear particles as a source of Zn to the environment. Environ. Sci. Technol. 38, 4206–4214.

Decaëns, T., Jiménez, J.J., Gioia, C., Measey, G.J., Lavelle, P., 2006. The values of soil animals for conservation biology. Eur. J. Soil Biol. 42 (1), S23–S38. https://doi.org/10.1016/j.ejsobiol.2006.07.001.

Dunger, W., Schulz, H.J., Zimdars, B., 2002. Colonization behaviour of Collembola under different conditions of dispersal: proceedings of the Xth international Colloquium on Apteroygota, České Budějovice 2000: Apteroygota. Environ. Chem. Lett. 16, 1267–1282. https://doi.org/10.1007/s10311-018-0751-6.

Hedde, M., Nahmani, J., Séré, G., Auclerc, A., Cortet, J., 2018. Early colonization of constructed technosols by macro-invertebrates. J. Soils Sediment. 19, 3193–3203. https://doi.org/10.1007/s11368-018-2142-9.

Heiniger, C., Barot, S., Ponge, J.-F., Salmon, S., Meriguet, J., Carmignac, D., Suillerot, M., Dubs, F., 2015. Collembolan preferences for soil and microclimate in forest and pasture communities. Soil Biol. Biochem 86, 181–192. https://doi.org/10.1016/j.soilbio.2015.04.003.

Hothorn, T., Bretz, F., Westfall, P., Heibberger, R.M., Schuetzenmeister, A., Scheibe, S., Hedde, M., Nahmani, J., Séré, G., Auclerc, A., 2018. Early colonization of constructed technosols by macro-invertebrates. J. Soils Sediment. 19, 3193–3203. https://doi.org/10.1007/s11368-018-2142-9.

Joimel, S., Cortet, J., Consalès, J.N., Branchu, P., Haudin, C.-S., Morel, J.L., Schwartz, C., 2020. Contribution of chemical inputs on the trace elements concentrations of surface soils in urban allotment gardens. J. Soils Sediment. 21, 328–337. https://doi.org/10.1007/s11368-020-02784-z.

Joimel, S., Grard, B., Auclerc, A., Hedde, M., Le Doaré, N., Salmon, S., Chenu, C., 2018. Are Collembola “flying” onto green roofs? Ecol. Eng. 111, 117–124.

Joimel, S., Jules, A., Vieublé Gonod, L., 2022. Collembola dispersion, selection, and biological interactions in urban ecosystems: a review. Environ. Chem. Lett. 20 (3), 2123–2133. https://doi.org/10.1007/s10311-022-01406-z.

Joimel, S., Potapov, A., Pey, B., Bonfanti, J., Cortet, J., De Almeida, T., Di Lonardo, S., Hackenberger, D.K., Krogh, P.H., Laskowski, R., Loureiro, S., Hedde, M., 2024. Trait concepts, categories, and databases in soil invertebrate ecology – ordering the mess. Soil Org. 96 (3), 151–166. https://doi.org/10.25674/431.

Joimel, S., Schwartz, C., Hedde, M., Kiyota, S., Krogh, P.H., Nahmani, J., Pérès, G., Vergnes, A., Cortet, J., 2017. Urban and industrial land uses have a higher soil biological quality than expected from physicochemical quality, 614–621 Sci. Total Environ. 584–585. https://doi.org/10.1016/j.scitotenv.2017.01.086.

Laliberté, E., Legendre, P., Shipley, B., Laliberté, M.E., 2014. Package ‘fd’. Meas. Funct. Divers. Mult. Traits Other Tools Funct. Ecol. 1, 0–12.

Lé, S., Josse, J., Husson, F., 2008. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18.

Lepczyk, C.A., Aronson, M.F.J., Evans, K.L., Goddard, M.A., Lerman, S.B., MacIvor, J.S., 2017. Biodiversity in the city: fundamental questions for understanding the ecology of urban green spaces for biodiversity conservation. *BioScience* 67 (9), 799–807. https://doi.org/10.1093/biosci/bix079.

Liu, Z., He, C., Zhou, Y., Wu, J., 2014. How much of the world's land has been urbanized, really? A hierarchical framework for avoiding confusion. Landsc. Ecol. 29 (5), 763–771. https://doi.org/10.1007/S10980-014-0034-Y/FIGURES/3.

McGarigal, K., 2006. Landscape Pattern Metrics. In Encyclopedia of Environmetrics. John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470057339.val006.

Milano, V., Cortet, J., Baldantoni, D., Bellino, A., Dubs, F., Nahmani, J., Strumia, S., Maisto, G., 2017. Collembolan biodiversity in Mediterranean urban parks: impact of history, urbanization, management and soil characteristics. Appl. Soil Ecol. 119 (ement C), 428–437. https://doi.org/10.1016/j.apsoil.2017.03.022.

Milano, V., Maisto, G., Baldantoni, D., Bellino, A., Bernard, C., Croce, A., Dubs, F., Strumia, S., Cortet, J., 2018. The effect of urban park landscapes on soil Collembola diversity: a Mediterranean case study. Landsc. Urban Plan 180, 135–147. https://doi.org/10.1016/j.landurbplan.2018.08.008.

Nielsen, U.N., Ayres, E., Wall, D.H., Bardgett, R.D., 2011. Soil biodiversity and carbon cycling: a review and synthesis of studies examining diversity–function relationships. Eur. J. Soil Sci. 62 (1), 105–116. https://doi.org/10.1111/j.1365-2389.2010.01314.x.

Nielsen, A.B., van den Bosch, M., Maruthaveeran, S., van den Bosch, C.K., 2014. Species richness in urban parks and its drivers: a review of empirical evidence. Urban Ecosyst. 17 (1), 305–327. https://doi.org/10.1007/s11252-013-0316-1.
Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O'hara, R.B., Oksanen, M.J., 2013. Package 'vegan'. Community Ecol. Package, Version 2 (9), 1-295.

Pey, B., Nahmani, J., Auclerc, A., Capowiez, Y., Cluzeau, D., Cortet, J., Hedde, M., 2014. Current use of and future needs for soil invertebrate functional traits in community ecology. Basic Appl. Ecol. 15 (3), 194-206.

Ponge, J.F., 2013. Disturbances, organisms and ecosystems: a global change perspective. Ecol. Evol. 3 (4), 1113-1124. https://doi.org/10.1002/ECE3.505

Ponge, J.F., 2020. Move or change, an eco-evolutionary dilemma: the case of Collembola. Pedobiologia 79, 150625.

Potapov, A.A., Semenina, E.E., Korotkevich, A.Y., Kuznetsova, N.A., Tiunov, A.V., 2016. Connecting taxonomy and ecology: trophic niches of collembolans as related to taxonomic identity and life forms. Soil Biol. Biochem 101, 20-31. https://doi.org/10.1016/j.SOILBIO.2016.07.002

Rzeszowski, K., Sterzyńska, M., 2016. Changes through time in soil Collembola communities exposed to urbanization. Urban Ecosyst. 19 (1), 143-158. https://doi.org/10.1007/s11252-015-0478-0

Santorufo, L., Cortet, J., Arena, C., Goudon, R., Rakoto, A., Morel, J.-L., Maisto, G., 2014. An assessment of the influence of the urban environment on collembolan communities in soils using taxonomy- and trait-based approaches. Appl. Soil Ecol. 78, 48-56. https://doi.org/10.1016/j.apsoil.2014.02.008

Santorufo, L., Joimel, S., Auclerc, A., Deremiens, J., Grisard, G., Hedde, M., Nahmani, J., Pernin, C., Cortet, J., 2021. Early colonization of constructed technosol by microarthropods. Ecol. Eng. 162, 106174. https://doi.org/10.1016/j.ecoleng.2021.106174

Sattler, T., Duelli, P., Obrist, M.K., Arlettaz, R., Moretti, M., 2010. Response of arthropod species richness and functional groups to urban habitat structure and management. Landsc. Ecol. 25 (6), 941-954. https://doi.org/10.1007/s10980-010-9473-2

Sauerwein, M., 2011. Urban Soils—Characterization, Pollution, and Relevance in Urban Ecosystems. In: Niemelä, J., Breuste, J.H., Elmqvist, T., Guntenspergen, G., James, P., McIntyre, N.E. (Eds.), Urban Ecology: Patterns, Processes, and Applications. Oxford University Press. (https://doi.org/10.1093/acprof:oso/9780199563562.003.0006).

Taylor, P.D., Fahrig, L., With, K.A., 2006. Landscape connectivity: A return to the basics. In: Crooks, K.R., Sanjayan, M. (Eds.), Connectivity Conservation. Cambridge University Press, pp. 29-43. (https://doi.org/10.1017/CBO9780511754821.003)

United Nations, 2017. World Population Prospects: The 2017 Revision, Key Findings and Advance Tables (No. Working Paper No. ESA/P/WP/248).

vanVliet, P.C.J., Hendrix, P.F., 2007. Role of Fauna in Soil Physical Processes. In: Abbott, L.K., Murphy, D.V. (Eds.), Soil Biological Fertility: A Key to Sustainable Land Use in Agriculture. Springer, Netherlands, pp. 61-80.

Villéger, S., Mason, N.W., Mouillot, D., 2008. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89 (8), 2290-2301.

World Green Building Council - Annual report 2015/2016. https://worldgbc.org/wp-content/uploads/2022/03/P578-WGBC-Annual-Report_LR4.pdf (accessed 12 May 2025).

Zhong, X., Joimel, S., Schwartz, C., Sterckeman, T., 2022. Assessing the future trends of soil trace metal contents in French urban gardens. Environ. Sci. Pollut. Res. 29 (3), 3900-3917.