# Urban earthworm communities driven more by climate and elevation than urbanisation intensity

Helen R P Phillips, Kevin Hoeffner, Bart Muys, Renée-Claire Le Bayon, Daniel Cluzeau, Kevin R Butt, Jeanne Maréchal, Maria J I Briones, Thibaud Decaëns, Mickaël Hedde, et al.

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Helen R P Phillips, Kevin Hoeffner, Bart Muys, Renée-Claire Le Bayon, Daniel Cluzeau, et al. Urban earthworm communities driven more by climate and elevation than urbanisation intensity. Urban Ecosystems, 2025, 28 (6), pp.210. 10.1007/s11252-025-01826-z. hal-05308271

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# Urban earthworm communities driven more by climate and elevation than urbanisation intensity

Helen R. P. Phillips¹ ID · Kevin Hoeffner²,³,⁴ ID · Bart Muys⁵ ID · Renée-Claire Le Bayon⁶ ID · Daniel Cluzeau² ID · Kevin R. Butt⁷ ID · Jeanne Maréchal² · Maria J. I. Briones⁸ ID · Thibaud Decaëns⁹ ID · Mickaël Hedde¹⁰ ID · Sylvain Gérard¹,¹⁰ ID · Guénola Pérès³ ID · Lucas Petit-Dit-Grézériat¹¹,¹²,¹³ ID · Céline Pelosi¹² ID

Received: 24 June 2025 / Accepted: 16 September 2025
© The Author(s) 2025

## Abstract

Soil biodiversity, particularly earthworms, plays a crucial role in ecosystem functions and services but remains largely understudied in urban environments. In this context, we conducted a synthesis analysis of 41 studies from urban environments (726 records, 1995–2024) to (1) describe patterns of earthworm communities, (2) identify the main drivers shaping urban earthworm communities, focusing on the effects of climate, degree of urbanization, soil properties, and local land use, with the expectation that climate would be the strongest driver, and (3) assess the occurrence of exotic earthworm species. Urban earthworm communities showed considerable variation in their abundance, biomass and species richness. Within our dataset, exotic species were detected in North America, South America, and Asia, but no records from European and African cities, despite their known presence there. However, there was a strong geographical bias, with 75% of records from Europe. Our results reveal that at the global scale, elevation, and annual variation in temperature and potential evapotranspiration are the most important predictors shaping total earthworm abundance. For total earthworm biomass, variation in potential evapotranspiration is the key predictor. At the plot scale, we found both positive and negative correlations between earthworm community metrics, soil properties, and specific land uses, namely urban forests and grasslands. We recommend additional standardised sampling, broader geographical and temporal coverage, and the investigation of urban-specific direct and indirect stressors, as crucial steps for understanding urban soil biodiversity and promoting beneficial management practices. Overall, this analysis indicates that urbanisation intensity showed no consistent effects on earthworm communities once climate and soil variables were accounted for. Given the importance of climate impacts, mitigation of climate change effects would be valuable for the maintenance of earthworm communities.

**Keywords** Oligochaeta · Urban ecology · Soil · Climate change · Bio-indicator · Occurrence · Diversity

## Introduction

More than half of the world's population lives in cities, and the urban population is expected to double by 2050 (Angel et al. 2011), driving the expansion of urban environments often at the expense of rural areas. Here, we define 'urban environments' as ecosystems shaped by built infrastructure and high human population density occurring within an urbanisation intensity gradient, such as an urban-rural gradient. The increase in built infrastructure results in

fragmentation of the natural habitats and diversification of habitat types and land uses (Grimm et al. 2008). But there are also similar impacts belowground; native soil is often dramatically reduced, fragmented and compacted due to increasing impervious sealing compared to natural and agricultural areas (Botkin and Beveridge 1997; De Carvalho and Szlafsztain 2019; Gong et al. 2020). However, soil biodiversity plays a crucial role in supporting key ecosystem functions and sustaining Nature's Contributions to People (Schwarz et al. 2017; Weiskopf et al. 2024; Ziter 2016). Yet,

Helen R. P. Phillips and Kevin Hoeffner contributed equally to this work.

Extended author information available on the last page of the article
the biodiversity of urban soils and the ecosystem services they provide remain largely understudied (Beninde et al. 2015; Guilland et al. 2018; Weiskopf et al. 2024), particularly for key organisms such as earthworms.

Studies on earthworm communities have reported positive, neutral, or negative effects of urban environments (Guilland et al. 2018; Schmidt 2024). For example, Pižl and Josens (1995a) observed decreasing earthworm biomass with increasing urbanisation intensity in Brussels, whereas Steinberg et al. (1997) reported higher earthworm abundance and biomass in urban areas than in rural forest stands in New York City. Pelosi et al. (2021) and Xie et al. (2022) found no significant effect of urbanisation intensity, near Paris and in Beijing respectively. While many studies have focused on abundance and biomass of earthworm communities, understanding the identity of the species within the local communities, as well as the species pool, (Aronson et al. 2016) is important to uncover shifts in community composition which can be hidden by aggregate metrics. Both the local earthworm community and regional species pool may include native and introduced species (Aronson et al. 2016; Francis and Chadwick 2015; Tóth et al. 2020). Given the poor dispersal capabilities of most earthworm species, urbanisation, and the associated impacts, may make native species particularly vulnerable to local extinction (Maréchal et al. 2021; Xie et al. 2018), whilst adaptations to harsh environments and human-aided dispersal mechanisms found in many wide-ranging exotic earthworm species (Baumann et al. 2024) may result in their dominance across urban habitats. There is a clear need for a synthesis of available data to ascertain generalities of the impacts on earthworm communities of increasing urbanisation across a range of soils, habitats, countries and climatic conditions.

Across the globe, urban environments are placed within different contexts and experience a variety of different drivers, some of which can have contrasting impact on earthworm communities. For example, the omnipresent concrete results in a higher soil pH which can benefit earthworms, but soil compaction from the construction machinery may increase bulk density reducing earthworm abundance and biomass (Smetak et al. 2007). The effect of other management practices found in urban environments, such as irrigation, fertilization and mowing, on earthworm communities are less well known. One recent study found that management practices (mowing, irrigation, mulching and weed removal) had minimal influence on the earthworm communities (Eydoux et al. 2024). However, in non-urban landscapes, management practices that increase soil carbon (e.g., mulching, organic fertilizers), increase water availability (e.g., irrigation) and avoid pesticides (e.g., manual weed removal) have positive effects on earthworms (Lee 1985; Torppa et al. 2024). Additionally, local socioeconomic and

cultural factors influence present and past human activities such as cultivation, recreation, and ornamental landscaping which, in turn, shape soil properties and earthworm species pool (Aronson et al. 2016; Guilland et al. 2018). The diverse soils and land uses, ranging from relatively undisturbed sites (e.g., old parks, remnant patches of semi-natural vegetation) to highly modified environments (e.g., compacted or more polluted areas), provides heterogeneous and diversified habitat conditions for earthworms, leading to contrasting patterns of population abundance and assemblage diversity (Schmidt 2024); Tóth et al. 2020; Xie et al. 2022). For instance, high abundance and diversity have been observed in small urban gardens and grass strips functioning as refuges (Maréchal et al. 2021; Pižl and Josens 1995a), whereas severe soil disturbance in residential areas may lead to lower earthworm populations despite the presence of green spaces (Li et al. 2020; Steinberg et al. 1997).

Previous large-scale studies have shown that climate is a prominent driver in shaping earthworm communities (Philips et al. 2019; Rutgers et al. 2016). It is well established that urban areas, and the soil within them, experience shifts in climates, specifically higher temperatures and changes in precipitation regimes (Grimm et al. 2008; Shi et al. 2012). And while small increases in temperature may be beneficial to earthworm communities in regions with lower average temperatures and sufficient precipitation, it is unlikely to be beneficial if the temperature exceeds the species' thermal limits or if precipitation is reduced (Singh et al. 2019). Therefore, impacts of urbanisation on earthworm communities will likely also depend on the climatic environment.

Overall, there is a lack of clear, generalisable trends regarding urban earthworm communities. A comprehensive synthesis of urban earthworm studies worldwide is needed to identify common patterns and key environmental drivers. In this context, the systematic literature review presented here aims at synthesising cross-continental data on earthworm communities in urban environments, including their abundance, biomass, species richness, ecological categories and the dominance of native species. Quantifying, for the first time, the relative contribution of climate, topography, soil and urbanisation drivers to shaping earthworm communities. The main goal is to investigate the primary drivers structuring these communities, following the hypotheses that: (1) earthworm communities in cities are primarily influenced by the climate of where the city is located, and furthermore by soil and land use characteristics, and position along an urbanisation gradient and (2) earthworm communities in cities are dominated by exotic species as a result of introductions through human activities. These results will provide bases for effective management practices to maintain or promote beneficial earthworm communities.
# Materials and methods

## Literature search

A systematic literature review was conducted in December 2024 to find publications reporting original sampling data of earthworms in urban environments. This literature review was carried out querying ISI Web of Knowledge, using the “All Databases” option, applying the following search string: (earthworm*) in the ‘topic’ field and (urban* OR town* OR cities OR city OR agglomerat* OR conurbat* OR garden* OR park* OR avenue* OR allotment* OR road* OR highway* OR embankment* OR lawn* OR roof* OR wasteland* OR industr* OR technosol* OR anthrosol* OR brownfield* OR landfill* OR dump* OR metropolit* OR turf* OR golf* OR sward* OR greenspace* OR playing OR graveyard* OR cemeter*) in the ‘title’ field. This search returned 350 references, from which all the titles and abstracts were read to select papers potentially containing original field data on earthworms in urban environments. This screening of titles and abstracts resulted in 90 seemingly suitable articles (See PRISMA Diagram Fig. 1).

The full text of the resulting 90 articles was checked in more detail, and preselected for data extraction if they contained the following information:

1. Original data on at least one of the three earthworm community response variables (abundance, biomass, species richness) from multiple sites across an urban environment.

2. geographical coordinates or detailed info that allowed georeferencing of the study location.

3. the sampling protocol, including the sample area, land use and land cover information.

When the article appeared relevant for the review but some of the data was lacking, the authors were contacted by email and asked to send their original data (on earthworm abundance, biomass, species diversity, soil properties, coordinates, etc.) for each sampling site. Supplied data were incorporated into the database. Following screening of the full text and extraction of appropriate data (see Sect. ‘Data from a set of selected papers’), the full corpus for data extraction contained 41 studies (Fig. 1), including studies focused only on urban areas or incorporating an urban-rural gradient. In cases where multiple earthworm sampling time records were available on the same site, only the most suitable time record was retained, e.g., fall or spring in temperate regions, minimising temporal pseudo-replication. The full list of references for the 41 selected articles is given in Table S1.

<figure><img src="image_2.png" /><figcaption>**Fig. 1** PRISMA Flow Diagram for the literature search and the synthesis analysis on urban earthworms. N and n represent the number of articles and records, respectively</figcaption></figure>
# Data gathering

## Data from a set of selected papers

From the 41 selected studies, the following information was gathered:

- Metadata: Author, year of publication, year of field sampling, sampling period, extraction method, and hierarchical level of aggregation (subplot, plot, site).

- Location: Coordinates in decimal degrees (latitude, longitude), quality of the georeferencing (3 classes: coordinates reported by the study, coordinates derived from maps based on toponyms provided by the authors or assigned to one of several aggregated sites within the studied urban area). Additionally, we included information on the site's classification as urban, peri-urban, or rural, either reported by the authors or inferred using the coordinates.

- Land use and land cover: Local land cover and use intensity from information provided by the authors (9 classes: arable high intensity, arable medium intensity, grass high intensity, grass medium intensity, grass low intensity, lawn, ruderal, trees managed, and trees unmanaged). Land-use intensity was classified based on reported management practices using the following criteria: Low=no biocide use, no artificial fertilizer, and no soil disturbance, and for grasslands a maximum of 1–2 mowing interventions per year; Medium=no biocide use, low fertilizer application, and no soil disturbance, or for grasslands frequent mowing; High=intensive and/or frequent physical interventions (e.g., soil work) and/or chemical interventions. Corine land cover class derived from geodata using the coordinates (3 aggregated classes: 100=urban fabric, 200=agricultural areas, 300=forests and semi-natural areas).

- Earthworm data: Total abundance (individuals per m²), biomass (grams per m²) and species richness (number, not corrected for sampling plot size). Where abundance and biomass were not provided per m², these values were converted based on the sampling area. Species richness was calculated when not directly reported, based on species presence. Where possible, abundance and biomass were also categorized by ecological category (anecic, endogeic, epigeic; explained below), based on species-level data from Bouché (1972) and the sWorm database (Phillips et al. 2021).

- Soil data: Soil texture (percentages of sand, silt, and clay), pH (in H₂O), and soil organic matter content (in %). When reported pH was measured in a different solution, values were converted to pHₐ焉 (see Table S2).

Whilst other soil variables were also captured when present, data was rarely available.

Historically, earthworms have been divided into three main ecological categories (Bouché 1972): epigeics, endogeics and anecics. Epigeic earthworms live on the soil surface, ingest decaying organic matter and do not create deep or permanent burrows. Endogeic earthworms live mainly within the first 20 cm of the soil and feed on organic matter by ingesting the soil creating horizontal burrows. Finally, anecic earthworms create deep vertical burrows and ingest decaying organic matter at the soil surface. All three ecological categories contribute significantly to soil health and ecosystem services (Blouin et al. 2013; Le Bayon et al. 2021), even in urban landscapes and other ecosystems (Steinberg et al. 1997). However, according to their preferential habitats and behaviour, they may be differentially affected by disturbances occurring in urban environments, potentially because of their differing dispersal abilities (Caro et al. 2013).

Additional information on all variables and how they were derived from the selected articles are presented in Table S2. Following extraction of data, 726 records were obtained from the 41 studies, where a record is a single observation of the earthworm community (e.g., abundance, biomass, richness) at the sub-plot, plot or site level, depending on the sampling design of the original study. The extracted data are available in Table S1.

## Data from other sources

The dataset was completed with the following geo-data from external sources based on the coordinates of the sampling sites:

- climate data: mean and standard deviation (SD) of annual temperature, annual precipitation, annual potential evapotranspiration ('PET'). Mean and standard deviations were calculated for the months within the year of sampling (Fig. S1). Additionally, the Global Aridity Index and Köppen climate classification were added (Table S3).

- topographic data: elevation (m above sea level), topographic index (the tendency for the soil to become saturated with water based on topographic position), and landform (a classification system based on the slope gradient, slope convexity and surface texture).

- urbanisation metrics: human population density ('HPD'; humans/km²), proportion of urban land use within one km² ('Urban 1km'), road density (m/km²), distance to urban core (m), and size of the urban centre (km²). The impact of the urban core on surrounding ecosystems
is dependent on a variety of factors, including the size
of the urban core (Alberti 2008). We expect that at the
same distance, a larger urban core will result in larger
negative impacts on biodiversity than smaller urban
cores. Therefore, the ratio of these two variables was
calculated ('Dist: Urban area ratio'), to maintain the in-
teraction between these two variables. Where a ratio of
<1 indicates the site is outside the urban centre, and >1
indicates it is within the urban core, with the highest ra-
tios indicating a larger urban centre and/or less distance
to the centre of the core.

Detailed information about the variables, data sources, how
they were derived, and their units are presented in Table S4.
Prior to all analysis, all continuous predictor variables (clite-
mate variables, urbanisation variables, topography variables
and soil properties) were standardised to ensure comparabil-
ity and improve model fit. Standardisation was performed
by centring (subtracting the mean) and scaling (dividing
by the standard deviation), which allows each variable to
contribute equally to the analysis regardless of its unit or
magnitude.

Data analysis

All data analysis was conducted in R version 4.4.0 (R Core
Team 2024).

The completeness of the data varied across studies, par-
ticularly with regards to sampled soil properties, leading us
to conduct analyses on different subsets of the database. In
brief, first, a descriptive analysis of earthworm communi-
ties and related soil properties in urban environments was
performed (described in ‘Descriptive analysis’; Fig. 1).
Second, a multivariate exploration was carried out using a
co-inertia analysis to assess the co-structure between envi-
ronmental variables (soil properties, land cover, land use
intensity) and earthworm community metrics. This analysis
was performed on a subset of records for which all these
variables were simultaneously available from the original
studies (described in ‘Co-inertia analysis of environmen-
tal variables and earthworm community metrics’; Fig. 1).
Finally, we developed regression models incorporating
additional geospatial data on urbanisation, climate, soil pH
and topography. For this, we used a subset of records con-
taining earthworm community data, land cover, soil pH, and
geographical coordinates (described in ‘Mixed effects mod-
elling’; Fig. 1).

Descriptive analysis

To characterise earthworm communities and soil proper-
ties in urban environments, we calculated mean, median,

standard deviation, and range for earthworm and soil vari-
ables. For earthworm communities, these variables were
total abundance, abundance by ecological category, total
biomass, biomass by ecological category, and total spe-
cies richness. When available, we also gathered and anal-
ysed data on species' abundances. For soil properties, these
included soil pH, soil texture (silt, clay and sand) and soil
organic matter. We restricted our analysis to urban and peri-
urban sites, excluding rural sites (n=60) for this descriptive
analysis, which resulted in a dataset of 40 articles and 666
records (Fig. 1). Analyses were conducted for each variable
based on data availability across all studies.

Co-inertia analysis of environmental variables and
earthworm community metrics

A co-inertia analysis was performed to explore the
co-structure between soil properties, land cover and
land use intensity, and earthworm community metrics.
To ensure a robust analysis, we kept variables whose
combinations, after excluding missing values, allowed
us to perform an analysis on a maximum number of
records across the largest possible number of articles.
Environmental variables included soil properties (pH,
organic matter content, sand and clay), Corinne land cover,
and land use intensity. Due to uneven representation, some
land use intensity categories were merged (e.g., low and
medium grass intensity; managed and unmanaged trees),
resulting in five final categories out of the original nine
(see Table S2). The community variables included total
earthworm richness and total abundance, which were not
highly correlated (R²=0.5). This selection resulted in a
total of 252 records across 8 articles (Amossé et al. 2016;
Maréchal et al. 2021, 2024; Pelosi et al. 2021; Tiho and
Josens 2000; Tresch et al. 2019; Vergnes et al. 2017; Xie
et al. 2018) (Fig. 1). Including total biomass or ecological
category abundances would have further reduced the
dataset to only 3 articles and 192 records, which was
not considered sufficiently representative. A co-inertia
analysis was then conducted to assess the co-structure
between these community and environmental variables.
Statistical significance was tested using a Monte Carlo
permutation test with 999 repetitions. All analyses were
performed using the ade4 package in R (Dray and Dufour
2007; Thioulouse et al. 2018).

Mixed effects modelling

To understand in more detail how urbanisation, climate,
land cover and land use intensity, as well as the environment
topography influenced earthworm communities, generalised
linear mixed effects regression models were created. To be
suitable for the statistical model, the data had to meet the following criteria:

- Within each study, the sampling methodology needed to be uniform. There was one study where sampling methodology varied (as sites were located across multiple countries; Tóth et al. 2020), therefore this publication was considered as 4 studies, with each study including the records from one country.

- Studies needed to have coordinates at sampling site level, not aggregated at the study level. Studies with aggregated coordinates only were excluded from the analysis, unless the coordinates at the site level could be derived from mentioned toponyms or unless the authors provided them after email correspondence.

- Within each study, sites needed to span more than one km. Studies were excluded if all sites were clustered within less than one km of each other. This condition is due to geographically close sites having identical geodata (urbanisation, climate, and topographical data), preventing variation with nearly all predictor variables in the mixed effects model.

- For each model, all predictor variables were checked to ensure representativity. For categorical predictors, each category needed to have an adequate number of records. Underrepresented categories, and the associated records, were removed from the model's dataset. Where values were missing (NAs) from global data layers, these records were also removed.

- Sites that reported extreme values in urbanisation metrics (road density > 20,000 m/km², n=4; HPD > 40000 humans/km², n=3; dist: urban area ratio > 20, n=5), earthworm abundances (> 700 ind/m², n=2) or biomasses (> 300 g/m², n=5) were removed, as they were considered statistical outliers and decreased model robustness. These cut-offs were determined based on the distribution of the data.

- Sites needed to have measured soil pH. Sites with no information on soil pH were removed. While the dataset contained other soil property data (e.g., organic matter), these variables were not well represented across the sites and their inclusion would have resulted in too small datasets for modelling.

- For the species richness model, the number of replicates per site needed to be identical within each study (sampled area is already consistent within a study, due to consistent methodology) to help control for the nonlinear relationship between species richness and sampling intensity.

Subsetting the database to ensure all data were suitable for the mixed effects modelling resulted in 536 records from 22

articles. As not all records included earthworm abundance, biomass and richness, the dataset was further split into subsets to build three separate models (Fig. 1). For each of the three community metrics (total abundance, total biomass, species richness), a mixed effects model was constructed. In the ‘full model’ for each community metric, the fixed effects included the four urbanisation metrics (dist: urban area ratio, road density, HPD and Urban 1 km), 8 climate variables (mean and SD of annual temperature, precipitation, and PET, as well as the aridity index and Köppen classification), two land cover and use intensity variables (CLC level 1 and local land use intensity), and four variables related to the physical soil environment (soil pH, topographic index, elevation, and landform) (Table S2). All continuous variables were in their scaled and centred form to aid model fitting. Due to the limited number of data points (Fig. 1) no interactions were considered within the model. To account for the heterogeneity across different studies (as different methodologies were used across all the studies), a random intercept was included for each study.

The abundance and biomass model used a ‘tweedie’ distribution (using the ‘glmmTMB’ function from the ‘glmmTMB’ package; Brooks et al. 2017), and the species richness model was modelled with Poisson distribution (using the ‘glmer’ function from the lme4 package; Bates et al. 2015).

Multicollinearity between variables in the full model was checked based on variance inflation factors (VIFs), using the ‘check_collinearity’ function from the ‘performance’ package (Lüdecke et al. 2021). Variables were removed, starting with variables with the highest VIFs, until the remaining variables had VIFs < 10. However, in the biomass and species richness model, removing the variables with the highest VIFs resulted in the remaining variables having VIFs < 5. Following removal of the multicollinear variables, the residuals were checked to ensure all model assumptions were met using the DHARMa package (Hartig 2024).

All possible models were then constructed using the remaining predictor variables as described previously, using the ‘dredge’ function from the MuMln package (Barton 2024). From all possible models only the top subset of models were used for the model averaging, those with deltaA-ICc of < 2. The importance of all predictor variables in the subset of models was calculated based on the cumulative AICs weights (‘sw’ function in the MuMln package). Model estimates and predictions based on the average models were calculated based on the ‘full’ coefficients, also referred to as the zero method (Burnham and Anderson 2002), where, if a parameter is absent from a model in the subset, a zero is substituted before calculation of the parameter estimate is obtained by averaging over all models in the model subset. This results in a more conservative estimate of the coefficient
but is recommended when determining the strength of dif-
ferent predictors (Nakagawa and Freckleton 2011). Predic-
tion abilities of the averaged model were evaluated using
the root mean square errors (RMSE), using the models to
predict the original dataset. RMSE are easily interpretable
as they are on the same scale as the original data. Goodness
of fit of the top subset of models was assessed using the
conditional-R². (the variance explained by both the random
and fixed effects) using the ‘r.squaredGLMM’ function in
the MuMin package. For the species richness models, the
trigamma method is reported (Nakagawa et al. 2017).

Results

Status of urban earthworm studies

The studied articles dated from the period 1995 to 2024,
with most articles (54%) of our dataset published in the last
15 years (Table S1). Different methods were used to sample
earthworm communities in urban areas, including chemi-
cal extraction (19% of the articles), hand-sorting combined
with chemical extraction (22%), hand-sorting only (37%),
and electrical extraction (22%). Most records are concen-
trated in Europe (542 records, with 52% in 11 articles from
France and Switzerland), with fewer in North America (36
records; 5 articles) and Asia (58 records; 3 articles), while
South America and Africa were underrepresented, with only
27 (in 3 articles) and 3 (in one article) records, respectively

(Fig. 2). Most articles focus exclusively on urban records
(81%), although several explored a rural-urban gradient
(Hubert et al. 2011; Pelosi et al. 2021; Steinberg et al. 1997),
while others incorporated rural records as controls (Francini
et al. 2018; Richardson 2019; Szlavecz et al. 2006; Tóth et
al. 2020). Some articles contributed prominently to the data-
set, in particular Tresch et al. (2019) accounting for 22%,
with Francini et al. (2018) and Audusseau et al. (2020) each
accounting for 12% of the total number of records. Among
the 726 records, 47% (7 articles) are reported at the indi-
vidual replicate level, 45% are aggregated at the site level
(25 articles), and 8% represent averages across different
sites but within the same land use or habitat type (9 articles;
Table S1).

Earthworm occurrence and diversity metrics in
urban environments

Total earthworm abundance in urban areas ranged from 0 to 1,177.8 ind/m², with an average of 148.6 ind/m² (SD=152.3) across 502 records (Table 1). When focusing on ecological categories, endogeic earthworms were the most abundant group, accounting for 66% of the total abundance, with an average of 56.5 ind/m² (SD=83.0), followed by anecics accounting for 23% of the abundance (mean=19.6, SD=27.2) and epigeics accounting for 11% of the abundance (mean=9.8, SD=23.1, Table 1). Total earthworm biomass in cities ranged from 0 to 608.9 g/m², with a mean of 575.6 g/m² (SD=81.9) over 427 records.

<figure><img src="image_3.png" /><figcaption>**Fig. 2** Central locations of the selected studies reporting urban earthworm communities (with detailed European map inserted), dot size is continuous, and is proportional to the number of records in the study. Blue points indicate records with earthworm community data only</figcaption></figure>

(N=37 articles), while brown points represent sites with both earth-
worm and soil data [soil texture, soil pH and organic matter] (N=28
articles). Dot transparency is to aid in visibility, as some studies are
closely-located
<table><caption>**Table 1** Summary statistics of total earthworm abundance (ind/m²), biomass (g/m²), ecological categories (epigeic, anecic, endogeic), and species richness in urban environments. The table includes median (Median), mean (Mean), standard deviation (SD), minimum (Min), maximum (Max), the number of records (n), and the number of articles (N)</caption><thead><tr><th></th><th>Median</th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th><th>n</th><th>N</th></tr></thead><tbody><tr><td>Total abundance</td><td>103.7</td><td>148.6</td><td>152.3</td><td>0</td><td>1177.8</td><td>502</td><td>30</td></tr><tr><td>Epigeic abundance</td><td>0</td><td>9.8</td><td>23.1</td><td>0</td><td>173.8</td><td>377</td><td>17</td></tr><tr><td>Anecic abundance</td><td>11.1</td><td>19.5</td><td>27.2</td><td>0</td><td>188.9</td><td>377</td><td>17</td></tr><tr><td>Endogeic abundance</td><td>28.0</td><td>56.5</td><td>83.0</td><td>0</td><td>611.1</td><td>377</td><td>17</td></tr><tr><td>Total biomass</td><td>50.7</td><td>75.6</td><td>81.9</td><td>0</td><td>608.9</td><td>427</td><td>23</td></tr><tr><td>Epigeic biomass</td><td>0</td><td>1.5</td><td>5.9</td><td>0</td><td>53.1</td><td>258</td><td>5</td></tr><tr><td>Anecic biomass</td><td>56.1</td><td>68.5</td><td>66.4</td><td>0</td><td>533.3</td><td>258</td><td>5</td></tr><tr><td>Endogeic biomass</td><td>16.7</td><td>26.6</td><td>31.2</td><td>0</td><td>170.0</td><td>258</td><td>5</td></tr><tr><td>Species richness</td><td>3.0</td><td>3.4</td><td>2.1</td><td>0</td><td>12</td><td>500</td><td>26</td></tr></tbody></table>

<table><caption>**Table 2** Summary statistics of soil pH, organic matter (%), silt (%), clay (%) and sand (%). The table includes median (Median), mean (Mean), standard deviation (SD), minimum (Min), maximum (Max), the number of records (n), and the number of articles (N)</caption><thead><tr><th></th><th>Median</th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th><th>n</th><th>N</th></tr></thead><tbody><tr><td>pH</td><td>7.2</td><td>7.0</td><td>1.1</td><td>3.1</td><td>8.8</td><td>504</td><td>26</td></tr><tr><td>Organic matter (%)</td><td>7.0</td><td>7.4</td><td>4.4</td><td>&lt;0.1</td><td>34.9</td><td>447</td><td>22</td></tr><tr><td>Silt (%)</td><td>34.8</td><td>39.3</td><td>15.2</td><td>7.8</td><td>86.5</td><td>258</td><td>9</td></tr><tr><td>Clay (%)</td><td>21.1</td><td>19.9</td><td>9.6</td><td>1.1</td><td>49.5</td><td>258</td><td>9</td></tr><tr><td>Sand (%)</td><td>41.1</td><td>40.4</td><td>14.4</td><td>4.3</td><td>85.0</td><td>258</td><td>9</td></tr></tbody></table>

Anecic earthworms accounted for 70.9% of the biomass, with an average biomass of 68.5 g/m² (SD=66.4), followed by endogeics (mean=26.6 g/m², SD=31.2) and epigeics (mean=1.5 g/m², SD=5.9, Table 1). After removing 34 records that represented site averages, earthworm species richness at a site in urban environments ranged from 0 to 12 species, with a mean of 3.4 species (SD=2.1) across 500 records (Table 1).

Of the 423 records with information at the species level, a total of 50 earthworm species were recorded, along with 5 subspecies and 7 undetermined genera or species (Table S1). The species with the highest occurrence were *Aporrectodea rosea* (endogeic, 42%), *Allolobophora chlorotica* (endogeic, 40% of records), *Lumbricus terrestris* (anecic, 39%), *Aporrectodea caliginosa* (endogeic, 35%), and *Aporrectodea longa* (anecic, 33%). In North American cities, two exotic species were recorded in the United States (*Amynthas agrestis* and *Metaphire hilgendorfi*); at the sites where they were observed, they constituted the entire recorded earthworm community. In South American cities, 8 exotic species were found in Curitiba, Brazil, belonging to the genera *Amynthas*, *Aporrectodea*, *Dichogaster*, *Lumbricus*, *Metaphire* and *Pontoscolex* outnumbering the five native species recorded based on presence/absence data. In Asian cities, four exotic species were recorded: one in Beijing, China (*Bimastos parvus*), where it accounted for less than 15% of the earthworm community on average across 16 records, and three in Izmir, Turkey (*Allolobophora chlorotica*, *Aporrectodea caliginosa*, and *Dendrobaena veneta*) which together represented the entire earthworm communities where they were observed. No exotic species were recorded in African and European cities.

Soil properties derived from earthworm sampling showed that the mean soil pH 7.0 (SD=1.1, Table 2), although presence of some highly acidic sites resulted in a larger range of values (3.1 to 8.8). Soil texture variables exhibited substantial variation: sand content ranged from 4.3% to 85.0% (mean=40.4%, SD=14.4), silt from 7.8% to 86.5% (mean=39.3%, SD=15.2), and clay from 1.1% to 49.5% (mean=19.9%, SD=9.6, Table 2). Organic matter content varied from 0.005 to 34.9%, with a mean of 7.4% (SD=4.4, Table 2). A wide variety of soil types were present across the sites (Fig. S2).

## Effect of environmental variables on earthworm communities

### Soil properties and land use intensity

The co-inertia analysis revealed a significant relationship between the structure of environmental and earthworm community metric variables (p-value=0.001), with an RV coefficient of 0.13, indicating a loose co-structure between the two tables (Fig. 3). The first axis of the co-inertia analysis is driven by a gradient of organic matter content (0.43), clay (0.46), soil pH (−0.43), and high-intensity grassland (−0.47), with earthworm abundance (0.73) contributing most strongly to this axis particularly in relation to organic matter and clay content, and inversely with high-intensity grassland and pH (Fig. 3). The second captures a gradient mainly driven by forests cover (−0.69), with additional contributions from sand content (0.34) and low to medium intensity grassland (−0.34). Earthworm richness (−0.44) is
<figure><img src="image_4.png" /><figcaption>**Fig. 3** Co-inertia analysis between environmental variables (brown) and earthworm variables (blue) on the first two principal axes</figcaption></figure>

aligned with this axis, suggesting co-structure with sites characterized by forests and low to medium intensity grassland and an opposite trend to sandy soils (Fig. 3). In total, the two axes explained 98.7% of the variance.

## Effects of environmental variables using measured variables and external geo-data

Across the 22 articles used in mixed effects modelling, the distance to a major urban core ranged from 0.10 to 112.5 km, with a mean of 18.8 km (SD = 26.9). Human population density varied from 0 to 61,024 humans/km², with an average of 5,181 humans/km² (SD = 7,921). Road density ranged from 0 to 131,050 m/km², with a mean of 7,596 m/km² (SD = 8,859). The proportion of urban habitat within a 1-km grid ranged from 0 to 1, with a mean of 0.65 (SD = 0.29). The majority of sites were within Europe (~79%), however this percentage varied within the subset of data used for the three different models.

Following the removal of collinear variables (Table 3), 65,536 models with total abundance as the response variable were created, out of which 13 with a deltaAICc < 2 (with weights varying from 0.51 to 0.06, and an average conditional-R² of 0.59; range: 0.57–0.61) were kept for the averaged model. The averaged model had a RMSE of 130.39; it contained 13 predictor variables, with the SD of the annual temperature, elevation and SD of the annual potential evapotranspiration (PET) jointly being the most important variables and with slopes significantly different from zero (Table 3; Fig. 4).

<table><caption>**Table 3** Slope estimate, standard error (SE) and 95% confidence interval (95%CI) of the significant predictors resulting from the model averaging. Whilst the species richness model had the SD of the annual PET as the most important predictor, the slope was not significantly different from zero and therefore not presented here</caption><thead><tr><th></th><th>Estimate</th><th>SE</th><th>95%CI</th></tr></thead><tbody><tr><td><strong>Abundance Model</strong></td><td></td><td></td><td></td></tr><tr><td>Temperature (SD)</td><td>-0.51</td><td>0.14</td><td>-0.77; -0.24</td></tr><tr><td>Elevation</td><td>0.26</td><td>0.13</td><td>0.01; 0.51</td></tr><tr><td>PET (SD)</td><td>0.20</td><td>0.09</td><td>0.03; 0.38</td></tr><tr><td><strong>Biomass Model</strong></td><td></td><td></td><td></td></tr><tr><td>PET (SD)</td><td>-0.32</td><td>0.13</td><td>-0.57; -0.07</td></tr></tbody></table>

Following removal of the collinear variables, 16,384 models were created to model earthworm biomass (Table 4). Thirty-five models of the full set had deltaAICc < 2, with weights ranging from 0.02 to 0.05 and an average conditional-R² of 0.64 (range: 0.55–0.73). The averaged model had 11 predictor variables, and a RMSE of 80.91, with SD of annual PET as the most important predictor variable with a slope significantly different from zero (Table 3; Fig. 5).

In total, 8,192 models were created to model changes in species richness, following removal of the collinear variables (Table 4). Among these 17 models with a deltaAICc lower than 2 were included in the subset of models, including 11 predictor variables with model weights ranging between 0.04 and 0.11 (and an average conditional-R² of 0.33 (range: 0.30–0.35)). The averaged model had an RMSE of 2.06. The most important variable was the SD of the annual temperature, but this did not have a slope that was significantly different from zero (estimate: -0.14; SE: 0.11; 95%CI: -0.36–0.07).

# Discussion

## Earthworm community patterns in urban environments are mainly shaped by climate and elevation

Mean urban earthworm abundance derived from this analysis (149 ind/m²) is within the range of 100 to 700 ind/m² reported for gardens in some major French cities (Guilland et al. 2018), and in UK urban areas (200 ind/m²; Burton et al. 2024). It also aligns with the predictions of Phillips et al. (2019), where global abundance values typically ranged from 5 to 150 ind/m². Our analysis revealed a mean species richness of 3.4 (range 0–12), which also falls within the range of 1 to 9.5 reported for urban environments by Guilland et al. (2018) and aligns with broader global trends of 1 to 4 species predicted by Phillips et al. (2019). Endogeic earthworms dominated urban communities, representing 66% of total abundance, followed by anecic (23%) and epigeic
**Fig. 4** Prediction of how total abundance (ind/m²) varied with the (a) SD of the annual temperature, (b) elevation and (c) the SD of annual potential evapotranspiration PET (all three centred and scaled). Grey shading indicates the 95% Confidence Interval for the model prediction line. Predictions from the model are calculated when all other continuous variables are at their median value. Black dots are the data points (n=356 from 12 articles). All three variables were the most important within the model averaging framework, with slopes significantly different from zero, based on 95% CIs

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>
<table><caption>**Table 4** Results from the model averaging approach. For each of the three models (abundance, biomass, and species richness), the status of each predictor variable is given. ‘x’ indicates that the variable was removed from the modelling process due to multicollinearity and ‘-’ indicates that the variable was in the full suite of models, but was not included in the subset of models used for the averaged model. For the remaining variables, present in the averaged model, the ranked importance is given (l=most important)</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Dist: Urban Area ratio</th><th colspan="3">URBANISATION</th><th colspan="3">CLIMATE</th><th colspan="3">USE OF LAND</th><th colspan="3">SOIL/PHYSICAL PROPERTIES</th></tr><tr><th>Road Density</th><th>HPD</th><th>Urban 1 km</th><th>PET (mean)</th><th>PET (SD)</th><th>Precipitation (mean)</th><th>Precipitation (SD)</th><th>Temperature (mean)</th><th>Temperature (SD)</th><th>Aridity</th><th>Köppen</th><th>CLC</th><th>Local Land</th><th>soil pH</th><th>Topographic Index</th><th>Elevation</th><th>Landform</th></tr></thead><tbody><tr><td>Abundance</td><td>9</td><td>11</td><td>13</td><td>12</td><td>4</td><td>1</td><td>5</td><td>7</td><td>8</td><td>1</td><td>x</td><td>x</td><td>-</td><td>-</td><td>10</td><td>6</td><td>1</td><td>-</td></tr><tr><td>Biomass</td><td>11</td><td>9</td><td>-</td><td>-</td><td>6</td><td>1</td><td>4</td><td>5</td><td>x</td><td>7</td><td>10</td><td>x</td><td>x</td><td>3</td><td>x</td><td>2</td><td>-</td><td>8</td><td>x</td></tr><tr><td>Richness</td><td>11</td><td>5</td><td>6</td><td>-</td><td>-</td><td>2</td><td>3</td><td>7</td><td>4</td><td>1</td><td>x</td><td>x</td><td>x</td><td>8</td><td>x</td><td>-</td><td>10</td><td>9</td><td>x</td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>**Fig. 5** Prediction of how total biomass (g/m²) varies with the SD of the annual potential evapotranspiration (PET) (centred and scaled). Grey shading indicates the 95% Confidence Interval for the model prediction line. The prediction of the model is calculated when all other continuous variables are at their median value, and CLC is at “100” (artificial land covers). Black dots are the data points (n=338 from 11 articles). PET SD was the most important with the model averaging framework, with a slope significantly different from zero, based on 95% CIs</figcaption></figure>

(11%). This functional composition, with a dominance of endogeic earthworms and smaller proportions of anecic and epigeic species, is similar to that observed in many agricultural and natural soils (Burton et al. 2024; Decaëns et al. 1998; Torppa and Taylor 2022). While this suggests a potentially comparable structure in terms of ecological strategies, one could have expected a higher proportion of anecic species, especially in grassy-like urban environments (Hoeffner et al. 2021). Given their key role in ecosystem services such as soil structure maintenance, nutrient regulation, and support of plant production, increasing the presence of anecic earthworms may represent a lever to further enhance soil functioning in urban environments (Capowiez et al. 2024; Huang et al. 2020; van Groenigen et al. 2014).

As we hypothesised, when we were able to account for soil pH, land cover and use intensity, measures of urbanisation, climate variables and the local physical environment within one modelling framework, we found that earthworm communities (abundance, biomass and species richness) in urban areas are mainly driven by topographic (elevation) and climatic variables (variability in annual temperature and PET). The relative importance of climate variables is in line with the global analysis of Phillips et al. (2019), which also highlighted that climatic variables might be more important in shaping earthworm communities compared with soil properties and habitat cover. However, analyses at sub-global scales (e.g., in Salako et al. 2023) have found variables such as habitat cover, soil moisture and clay content to be important alongside climatic variables.

Elevation was a strong predictor for total abundance of the earthworm communities, with higher elevations having higher number of individuals. This is in line with previous work outside of urban environments, where earthworm
densities have increased with increasing elevation, at least until similar altitudes as those represented within our study (González et al. 2007). Elevational studies of earthworms with longer gradients have found non-linear relationships, with peaks of diversity and density between 1000 and 2000 m (Ahmed et al. 2022; Fontana et al. 2020; Gabriac et al. 2023). These studies determined that the change in diversity with elevation was due to soil conditions, changing habitat (and therefore tree composition), environmental heterogeneity, as well as variations in climate (Gabriac et al. 2023; González et al. 2007)As far as we are aware, no study has previously investigated the role of elevation on earthworm communities within urban environments, and given the small elevational gradient (8 m to 840 m), and limited dataset preventing interactions between variables within the modelling framework, it is difficult to determine whether the effect of elevation on abundance is non-linear and whether the effect is as an indirect result of other drivers, such as climate. This would be an interesting avenue for further investigations in the future, with direct and indirect effects being specifically investigated using, for example, structural equation models (SEMs; Grace 2008).

We expected that the degree of urbanisation would have an impact on earthworm communities, but this was not the case based on the results of our mixed effects model. One possible explanation for this is the use of urbanisation metrics from global data layers. The need to rely on globally available data limits the possible variables that can be incorporated into the models. In addition, there could also be a mismatch in the spatial or temporal scale of the urbanisation and biodiversity metrics. Although the data layers were at a high resolution (~1km²), this is still considerably larger than the typical plot size of earthworm sampling. Similarly, some of the global data layers for the urbanisation metrics were not from the same year as the sampling for each site, despite reducing temporal mismatch as much as possible. Collecting standardised urbanisation metrics concurrently with earthworm sampling would provide the most useful information. Additionally, our analysis is focused on the gradients of urbanisation within urban contexts, and not the differences between urban and non-urban areas. This is an important distinction: some cities in our dataset may represent only medium to high levels of urbanisation. This limits our ability to detect any habitat filtering of the community as a result of a land use change from non-urban to urban (Aronson et al. 2016), potentially reducing the impact of urbanisation within our models. Beyond the degree of urbanisation itself, landscape-level features such as habitat heterogeneity and the configuration of green spaces may also play a critical role. Previous studies have found that these factors are strong predictors of earthworm diversity, abundance and biomass (Eydoux et al. 2024; Xie et al. 2018,

2024), as is often the case in non-urban landscapes as well (Frazão et al. 2017; Hoeffner et al. 2021).

## Interacting effects of land use intensity and soil properties on urban earthworm communities

Although our mixed-effects models did not detect significant effects of land use types, land use intensity, or soil properties on earthworm communities, exploratory patterns from the multivariate analyses provide complementary insights into potential ecological drivers. These findings should be interpreted with caution due to the weak overall relationships (as indicated by a low RV coefficient), but they remain useful for identifying management levels in urban settings.

In terms of land use, urban grasslands with low to moderate management intensity tended to support high earthworm species richness, echoing trends observed in non-urban grasslands (Cluzeau et al. 2012; Decaëns et al. 2008; Rutgers et al. 2016). However, high-intensity urban grasslands in this dataset, which were typically subjected to frequent mowing, had reduced abundance, contrasting with the results of other non-urban grassland studies (Hoeffner et al. 2024). This difference may reflect not only direct effects of management but also indirect ones, such as repeated trampling and machinery use in public parks that is not present in non-urban grassland but can increase soil compaction and reduce habitat quality for earthworms (Cluzeau et al. 1992; Maréchal et al. 2024; Pižl and Schlaghamerský 2007; Smetak et al. 2007). In contrast, the sites with trees and shrubs, which were likely less disturbed and offering more complex microhabitats, were associated with higher species richness. These vegetated areas likely provide greater organic matter input, more stable soil conditions, and thermal and hydric refuges during extreme events, factors known to benefit earthworm populations (Cesarz et al. 2007; De Wandeler et al. 2018; Schwarz et al. 2015). However, the magnitude and direction of these effects may depend on specific vegetation characteristics, such as the C: N ratio of litter (De Wandeler et al. 2018), tree diversity (Cesarz et al. 2007), or tree identity (Schwarz et al. 2015). In contrast, urban arable land showed no consistent effects on earthworm communities. This may be due to the diverse management practices in urban cultivated areas, which vary in terms of tillage, fertilisation (both type and frequency), and other agronomic factors, leading to more variable effects on soil conditions and earthworm communities, both directly and indirectly.

Through the use of co-inertia analysis, soil characteristics also emerged as potentially influential in urban settings, echoing findings from other ecosystems (Decaëns et al. 2008; Joschko et al. 2006; Lee 1985; Rutgers et al. 2016), where factors such as pH, organic matter, and texture have been shown to shape earthworm communities.
Organic matter content was positively correlated with earth-
worm abundance, consistent with the idea that energy-rich
substrates are a major driver of earthworm communities
(Lee 1985; Rutgers et al. 2016). Textural effects were also
apparent: higher sand content was associated with reduced
total abundance, while lower clay content correlated with
lower species richness. These patterns likely reflect the
poor water-holding capacity and reduced structural stabil-
ity of sandy soils, which are less favourable to earthworms
(Hendrix et al. 1992; Lapied et al. 2009; Lee 1985). Finally,
we observed a decline in species richness with increasing
soil pH, contrary to most studies that report positive effects
of pH on richness and abundance (Decaëns et al. 2008;
Joschko et al. 2006; Ma et al. 1990). This discrepancy may
relate to the relatively high average pH in our urban soils
(mean pH=7.0), compared to the more acidic conditions
commonly reported in the literature (Decaëns et al. 2008;
Joschko et al. 2006). Elevated pH in urban contexts can
result from the presence of alkaline construction materials
such as cement, which may alter the availability of essential
nutrients (Ca, P, Fe) and affect microbial processes critical
for organic matter decomposition (Greinert 2015; Malik et
al. 2018).

Urban areas as gateways for exotic earthworms?

Urbanisation is a strong driver towards biotic homogenisa-
tion (McKinney 2006), often resulting in the same set of
species that are adapted to the adverse conditions found
across cities around the world. Across our database, there
was a very high degree of similarity of species across the
different cities. Specifically, a high occurrence of *Apor-
rectodea rosea*, *Allolobophora chlorotica*, *Lumbricus ter-
restris*, *Aporrectodea caliginosa* and *Aporrectodea longa*
within the communities sampled, which primarily reflects
the dominance of data from European cities. However, other
large-scale urban studies have also found low variation of
species across different cities (Tóth et al. 2020). And while
the majority of our cities are from countries within Europe,
which often have similar species within their communities
(Jupke et al. 2024), there is a clear dominance of peregrine/
cosmopolitan species associated with urban areas. Indeed,
other small-scale studies have found that within urban areas
there is a dominance of cosmopolitan species, with endemic
species typically lacking (Eydoux et al. 2024).

However, it is worth noting that despite the overrepresen-
tation of studies from Europe in our dataset, no exotic spe-
cies were observed in Europe, whereas such species were
recorded in studies from America and Asia. This absence
likely reflects the type of data available, as several stud-
ies based on qualitative or site-specific approaches have
reported exotic species in European cities. For instance,

*Microscolex dubuis* was recently found in Montpellier (Mautuit et al. 2024), *Eukerria saltensis* and *Ocnerodrilus occidentalis* have been observed in England and Italy (Rota 2013), Sherlock and Carpenter (2009) reported 27 exotic species in the Royal Botanic Gardens, Kew. These observations, although not part of our quantitative dataset, confirm that urban areas, through activities such as horticulture and plant trade, can act as hotspots for the introduction of exotic earthworm species (Hendrix and Bohlen 2002; Padayachee et al. 2017). Moreover, exotic earthworm species found in urban environments in America and Asia are also present in other natural habitats on these continents, reflecting a broader invasion pattern beyond cities. In these regions, when exotic species were observed, they tended to dominate the earthworm communities in which they occurred, further highlighting their invasive potential (Brown et al. 2006; Chang et al. 2018; Huang et al. 2007; Szederjesi and Mısırlıoğlu 2017).

The ecological consequences of exotic earthworm spe-
cies in urban environments remain poorly documented but
may parallel those observed in natural or semi-natural eco-
systems. All earthworms can alter soil structure, organic
matter decomposition and nutrient cycling rates, however,
when earthworm move into novel environments these
actions can have negative impacts on the ecosystem, often
leading to a decline in native soil fauna and flora (Frelich
et al. 2006; Hale et al. 2006; Hendrix et al. 2008). For
instance, in North American forests, invasive earthworms
have been shown to reduce the litter layer by accelerating
organic matter decomposition which leads to altered soil
structure and nutrient cycling, subsequently affecting plant
community composition. (Bohlen et al. 2004; Hendrix et
al. 2008) and similar processes may occur in urban green
spaces, especially when exotic species become dominant.
Moreover, exotic earthworms can compete with native spe-
cies, potentially reducing native diversity (Eisenhauer et
2007; Migge-Kleian et al. 2006). In urban contexts, where
soils are already under multiple anthropogenic pressures,
such invasions could further alter ecosystem functioning
and compromise services such as carbon storage, soil water
regulation, or plant–microbe interactions.

Recommendations for management practices

Overall, we found that climate is the main driver of earth-
worm communities. Thus, in the face of climate change
(Lee et al. 2023), management practices need to focus on
mitigating the local effects of this global change. As shown
in our analyses, increasing variations in temperature and
changes in PET can impact total abundance and biomass.
As urban soils already have typically higher temperatures
than the surrounding non-urban areas as a result of the heat
island effect (Shi et al. 2012), mitigating any increase in air temperature, as a result of strong seasonality, would also be beneficial to the earthworm communities. This can be done by increasing tree canopy cover (Ziter et al. 2019), but careful selection of plant species would be needed to ensure water use remains low (Schröder and Kiehl 2020).

While some earthworm species are particularly sensitive to changes in temperature (Phillips et al. 2025), changes in climate that affect soil moisture will likely have the largest impact on earthworm communities as a whole. Thus, maintaining appropriate soil moisture conditions should be a key management goal. Thankfully, many interventions for maintaining soil moisture occur through increased soil organic matter (Rawls et al. 2003) – which we also found to directly promote earthworm communities through resource provision (Lee 1985). Increasing soil organic matter through practices such as leaving mowing residues, applying compost, or using mulch will help maintain soil moisture at levels that support earthworm communities, as well as promoting activity of plant roots and microbes (Herrmann et al. 2023; Torppa et al. 2024). Addition of organic matter will also mitigate other anthropogenic impacts that we found to be detrimental to earthworm communities, such as the impact of soil compaction (Percival et al. 2025).

## Recommendations for further studies

Given the current scarcity of studies on urban earthworm communities, with most studies concentrated in Europe, future research should prioritise expanding investigations to a wider range of cities across different regions of the world using standardised methods (such as ISO 23611-1:2018; ISO2018). Furthermore, the high spatial heterogeneity of urban environments, where metapopulation and metacommunity processes may play a key role (Andrade et al. 2021), suggests that current sampling efforts may underestimate true earthworm diversity. Species accumulation curves have rarely been applied in urban contexts, and adapted sampling designs are likely needed to better capture the local or regional species pool (Thompson et al. 2007). This is particularly important to detect rare native species or newly introduced exotics that may serve as early indicators of ongoing ecological change. There is also a need for long-term monitoring of urban earthworm communities. Since some existing data dates back to the 1990s, future studies have the possibility to investigate the impact of climate change on earthworm populations, as well as intra-annual variations linked to urban microclimates. To enhance comparability between studies, researchers should systematically document the characteristics of sampling sites (Xie et al. 2024), including both urbanisation factors and standardised soil property analyses. By increasing the comparability across

studies, it will increase our ability to identify key factors affecting earthworm communities in urban landscapes.

Future studies could explore how variations in the spatial organisation of cities, including differences in size, shape, and the proportion of built versus non-built areas, influence landscape structure, particularly in terms of habitat fragmentation and landscape homogenisation (Krauss et al. 2010; Maréchal et al. 2024; Püttker et al. 2020; Zhang et al. 2024). Investigating how fragmentation isolates earthworm populations and limits resource access, as well as how urban homogenisation modifies dispersal processes, ecological niches and species assembly, will provide important ecological insights. Additionally, examining the historical development of urban areas, including their expansion patterns and land-use transitions, to assess how these long-term changes have influenced soil and earthworm populations (Maréchal et al. 2021) would be an additional future avenue of research. The role of urban land management in shaping earthworm communities is also an area that could be focused on in future studies. The maintenance and use of non-built urban spaces, including mowing, irrigation, fertilisers and chemical applications, amendments and pedestrian activity, can either exclude certain earthworm species or introduce new ones (Baumann et al. 2024; Hoeffner et al. 2024; Pižl and Schlaghamerský 2007). Investigating how intensive management creates unfavourable conditions or how specific practices, such as tree and flower planting, promote earthworm diversity by providing suitable habitats and potentially introducing new species, would be valuable in understanding urban soil ecosystems. Indirect impacts from urbanisation should also be considered as they remain largely unexplored, such as the effects of transport-related vibrations (Caorsi et al. 2019) or artificial lighting at night (Nuutinen et al. 2014) on earthworm behaviour, movement, and survival. Soil pollution, another indirect but potentially significant factor, has been shown in a few studies to negatively affect earthworm abundance and species richness, mainly due to heavy metals and polycyclic aromatic hydrocarbons (PAHs), but overall, research remains scarce, and other pollutants are also likely to contribute to shaping urban soil fauna (Chatelain et al. 2024; Pižl and Josens 1995b; Steinberg et al. 1997). Future studies should aim to address these knowledge gaps to develop a more comprehensive understanding of earthworm ecology in urban environments.

## Conclusion

Urban soils, and the biodiversity they contain, play a critical role in providing essential ecosystem services that contribute to the sustainability and resilience of cities, and are vital
for the health and well-being of urban populations. Therefore, understanding and managing urban soils is crucial for maximising these benefits. By conducting a global synthesis we provide a generalised overview of urban earthworm communities and highlight that cities can host diverse and abundant earthworm communities, as increasing urbanisation intensity does not directly impact the earthworm communities. Instead, our findings underline the importance of climatic, topographic, and local soil factors in shaping urban earthworm communities. We found that elevation, annual variation in temperature and potential evapotranspiration emerged as the main environmental drivers of earthworm abundance at the global scale, while biomass was primarily influenced by annual variation in evapotranspiration. At finer spatial scales, earthworm abundance was positively related to organic matter and clay content and negatively related to soil pH and urban high-intensity grasslands. Species richness was positively associated with forests and grasslands but negatively with sandy soils. While the dataset had a strong geographical bias towards European cities, with limited representation from other continents, exotic species were only represented in North America, South America and Asia, despite qualitative reports of exotic species in European and African cities. This lack of quantitative data emphasises the need to expand research, specifically beyond Europe, to improve our global understanding, and therefore maintenance of urban soil biodiversity. Utilising citizen science approaches would be one way to increase data generation. Additional further research into the long-term effects of urbanisation on soil biodiversity including nature-based approaches to soil restoration, the integration of green infrastructure that supports soil biodiversity and mitigates the impact of climate change, and the design of soil-friendly urban landscapes will be critical in ensuring the health and functionality of urban ecosystems for future generations.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s11252-025-01826-z.

**Acknowledgements** We sincerely thank Alan Vergnes, Justin B. Richardson, Tian Xie and Olexander Zhukov for generously sharing their data and contributing to this work.

**Author contributions** All authors contributed to the study conception and design. Data collation was performed by Helen R. P. Phillips, Kevin Hoeffner, Bart Muys, Renée-Claire Le Bayon, Daniel Cluzeau, Lucas Petit-Dit-Grézériat and Céline Pelosi. Analyses were performed by Helen R. P. Phillips and Kevin Hoeffner. The first draft of the manuscript was written by Helen R. P. Phillips, Kevin Hoeffner, Bart Muys, Renée-Claire Le Bayon, and Céline Pelosi and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

**Funding** Open Access funding provided by University of Helsinki (including Helsinki University Central Hospital). This research is product

of the “LandWorm” group funded by the synthesis center CESAB of the French Foundation for Research on Biodiversity (FRB; https://www.fondationbiodiversite.fr). In addition, HRPP was supported by a Research Council of Finland grant (grant number 362759).

**Data availability** Data is provided as supplementary information files

## Declarations

**Competing interests** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

Ahmed S, Marimuthu N, Tripathy B, Julka JM, Chandra K (2022) Earthworm community structure and diversity in different land-use systems along an elevation gradient in the Western Himalaya, India. Appl Soil Ecol. https://doi.org/10.1016/j.apsoil.2022.104468

Alberti M (2008) Urban patterns and ecosystem function. In Advances in Urban Ecology. https://doi.org/10.1007/978-0-3-87-75510-6_3

Amossé J, Dózsa-Farkas K, Boros G, Rochat G, Sandoz G, Fournier B, Mitchell EAD, Le Bayon RC (2016) Patterns of earthworm, enchytraeid and nematode diversity and community structure in urban soils of different ages. Eur J Soil Biol. https://doi.org/10.1016/j.ejsobi.2016.01.004

Andrade R, Franklin J, Larson KL, Swan CM, Lerman SB, Bateman HL, Warren PS, York A (2021) Predicting the assembly of novel communities in urban ecosystems. Landscape Ecol 15. https://doi.org/10.1007/s10980-020-01142-1

Angel S, Parent J, Civco DL, Blei A, Potere D (2011) The dimensions of global urban expansion: estimates and projections for all countries, 2000–2050. Progress in Planning. https://doi.org/10.1016/j progress.2011.04.001

Aronson MFJ, Nilon CH, Lepczyk CA, Parker TS, Warren PS, Cilliers SS, Goddard MA, Hahs AK, Herzog C, Katti M, La Sorte FA, Williams NSG, Zipperer W (2016) Hierarchical filters determine community assembly of urban species pools. Ecology. https://doi.org/10.1002/ecy.1535

Audusseau H, Vandenbulcke F, Dume C, Deschins V, Pauwels M, Gigon A, Bagard M, Dupont L (2020) Impacts of metallic trace elements on an earthworm community in an urban wasteland: emphasis on the bioaccumulation and genetic characteristics in *lumbricus castaneus*. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.137259

Barton K (2024) MuMln: Multi-Model inference. R Package Version 1.48.4. https://cran.r-project.org/package=MuMln

Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48
Baumann TT, Frelich LE, Van Riper LC, Yoo K (2024) Anthropogenic transport mechanisms of invasive European earthworms: a review. *Biol Invasions* 26(11):3563–3586. https://doi.org/10.1007/s10530-024-03422-2

Beninde J, Veith M, Hochkirch A (2015) Biodiversity in cities needs space: a meta-analysis of factors determining intra-urban biodiversity variation. *Ecol Lett* 18(6):581–592

Blouin M, Hodson ME, Delgado EA, Baker G, Brussaard L, Butt KR, Dai J, Dendooven L, Peres G, Tondoh JE, Cluzeau D, Brun JJ (2013) A review of earthworm impact on soil function and ecosystem services. *Eur J Soil Sci* 64(2):161–182. https://doi.org/10.1111/ejss.12025

Bohlen PJ, Scheu S, Hale CM, McLean MA, Migge S, Groffman PM, Parkinson D (2004) Non-native invasive earthworms as agents of change in Northern temperate forests. *Front Ecol Environ* 2(8):427–435. https://doi.org/10.1890/1540-9295(2004)002[0427:NEIAAO]2.0.CO;2

Botkin DB, Beveridge CE (1997) Cities as environments. *Urban Ecosyst* 1:3–19. https://doi.org/10.1023/A:1014354923367

Bouché MB (1972) Lombriciens de France - Ecologie et systématique. Ann De Zool -Ecologie Animale

Brooks ME, Kristensen K, van Benthem KJ, Magnusson A, Berg CW, Nielsen A, Skaug HJ, Mächler M, Bolker BM (2017) *GlimmTMB* balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. *R J*. https://doi.org/10.3261/4/rj-2017-066

Brown GG, James SW, Pasini A, Nunes DH, Benito NP, Martins PT, Sautter KD (2006) Exotic, peregrine, and invasive earthworms in brazil: diversity, distribution, and effects on soils and plants. *Caribb J Sci* 42(3):339–358

Burnham KP, Anderson DR (2002) Model selection and multimodel inference. Springer, New York. https://doi.org/10.1007/b97636

Burton VJ, Jones AG, Robinson LD, Eggleton P, Purvis A (2024) Earthworm watch: insights into urban earthworm communities in the UK using citizen science. *Eur J Soil Biol* 121:103622. https://doi.org/10.1016/j.ejsobi.2024.103622

Caorsi V, Guerra V, Furtado R, Llusia D, Miron LR, Borges-Martins M, Both C, Narins PM, Meenderink SWF, Márquez R (2019) Anthropogenic substrate-borne vibrations impact anuran calling. *Sci Rep* 9(1). https://doi.org/10.1038/s41598-019-55639-0

Capowiez Y, Marchán D, Decaëns T, Hedde M, Bottinelli N (2024) Let earthworms be functional - definition of new functional groups based on their bioturbation behavior. *Soil Biol Biochem* 188:109209. https://doi.org/10.1016/j.soilbio.2023.109209

Caro G, Decaëns T, Lecarpentier C, Mathieu J (2013) Are dispersal behaviours of earthworms related to their functional group? *Soil Biol Biochem*. https://doi.org/10.1016/j.soilbio.2012.11.019

Cesarz S, Fahrenholz N, Migge-Kleian S, Platner C, Schaefer M (2007) Earthworm communities in relation to tree diversity in a deciduous forest. *Eur J Soil Biol* 43:S61–S67. https://doi.org/10.1016/j.ejsobi.2007.08.003

Chang CH, Johnston MR, Görres JH, Dávalos A, McHugh D, Szlavecz K (2018) Co-invasion of three Asian earthworms, metaphire hilgendorfi, amynthas agrestis and amynthas Tokioensis in the USA. *Biol Invasions*. https://doi.org/10.1007/s10530-017-1607-x

Chatelain M, Nold F, Mathieu J (2024) Metal pollution drives earthworm biodiversity in urban lawns. *Sci Total Environ*. https://doi.org/10.1016/j.scitotenv.2024.169867

Cluzeau D, Binet F, Vertes F, Simon JC, Riviere JM, Trehen P (1992) Effects of intensive cattle trampling on soil-plant-earthworms system in two grassland types. *Soil Biol Biochem*. https://doi.org/10.1016/0038-0717(92)90166-U

Cluzeau D, Guernion M, Chaussod R, Martin-Laurent F, Villenave C, Cortet J, Ruiz-Camacho N, Pernin C, Mateille T, Philippot L, Bellido A, Rougé L, Arrouays D, Bispo A, Pérès G (2012) Integration of biodiversity in soil quality monitoring: baselines for microbial

and soil fauna parameters for different land-use types. *Eur J Soil Biol*. https://doi.org/10.1016/j.ejsobi.2011.11.003

R Core Team (2024) R: A language and environment for statistical computing

De Carvalho RM, Szlafsztein CF (2019) Urban vegetation loss and ecosystem services: the influence on climate regulation and noise and air pollution. *Environ Pollut*. https://doi.org/10.1016/j.envpol.2018.10.114

De Wandeler H, Bruelheide H, Dawud SM, Dănilă G, Domisch T, Finér L, Hermy M, Jaroszewicz B, Joly FX, Müller S, Ratcliffe S, Raulund-Rasmussen K, Rota E, Van Meerbeek K, Vesterdal L, Muys B (2018) Tree identity rather than tree diversity drives earthworm communities in European forests. *Pedobiologia*. https://doi.org/10.1016/j.pedobi.2018.01.003

Decaëns T, Dutoit T, Alard D, Lavelle P (1998) Factors influencing soil macrofaunal communities in post-pastoral successions of Western France. *Appl Soil Ecol*. https://doi.org/10.1016/S0929-1393(98)00090-0

Decaëns T, Margerie P, Aubert M, Hedde M, Bureau F (2008) Assembly rules within earthworm communities in North-Western France-a regional analysis. *Appl Soil Ecol* 39(3):321–335. https://doi.org/10.1016/j.apsoil.2008.01.007

Dray S, Dufour A-B (2007) The ade4 package: implementing the duality diagram for ecologists. *J Stat Softw* 22(4):1–20. https://doi.org/10.18637/jss.v022.i04

Eisenhauer N, Partsch S, Parkinson D, Scheu S (2007) Invasion of a deciduous forest by earthworms: changes in soil chemistry, microflora, microarthropods and vegetation. *Soil Biol Biochem*. https://doi.org/10.1016/j.soilbio.2006.12.019

Eydoux L, Cortet J, Barantal S, Decaens T, Vergnes A (2024) Driving factors of earthworm communities in mediterranean urban parks. *Appl Soil Ecol* 202:105602. https://doi.org/10.1016/j.apsoil.2024.105602

Fontana V, Guariento E, Hilpold A, Niedrist G, Steinwandter M, Spitale D, Nascimbene J, Tappeiner U, Seeber J (2020) Species richness and beta diversity patterns of multiple taxa along an elevational gradient in pastured grasslands in the European Alps. *Sci Rep*. https://doi.org/10.1038/s41598-020-0-69569-9

Francini G, Hui N, Jumpponen A, Kotze DJ, Romantschuk M, Allen JA, Setälä H (2018) Soil biota in boreal urban greenspace: responses to plant type and age. *Soil Biol Biochem*. https://doi.org/10.1016/j.soilbio.2017.11.019

Francis RA, Chadwick MA (2015) Urban invasions: non-native and invasive species in cities. *Geography*. https://doi.org/10.1080/00167487.2015.12093969

Frazão J, de Goede RGM, Brussaard L, Faber JH, Groot JCJ, Pulleman MM (2017) Earthworm communities in arable fields and restored field margins, as related to management practices and surrounding landscape diversity. *Agric Ecosyst Environ* 248. https://doi.org/10.1016/j.agee.2017.07.014

Frelich LE, Hale CM, Scheu S, Holdsworth AR, Heneghan L, Bohlen PJ, Reich PB (2006) Earthworm invasion into previously earthworm-free temperate and boreal forests. *Biol Invasions* 8(6):1235–1245. https://doi.org/10.1007/s10530-006-9019-3

Gabriac Q, Ganault P, Barois I, Aranda-Delgado E, Cimetière E, Cortet J, Gautier M, Hedde M, Marchán DF, Pimentel Reyes JC, Stokes A, Decaëns T (2023) Environmental drivers of earthworm communities along an elevational gradient in the French Alps. *Eur J Soil Biol*. https://doi.org/10.1016/j.ejsobi.2023.103477

Gong P, Li X, Wang J, Bai Y, Chen B, Hu T, Liu X, Xu B, Yang J, Zhang W, Zhou Y (2020) Annual maps of global artificial impenetrative area (GAIA) between 1985 and 2018. *Remote Sens Environ*. https://doi.org/10.1016/j.rse.2019.111510

González G, García E, Cruz V, Borges S, Zalamea M, Rivera MM (2007) Earthworm communities along an elevation gradient in
northeastern Puerto Rico. Eur J Soil Biol 43(SUPPL 1):S24-S32.
https://doi.org/10.1016/j.ejsobi.2007.08.044

Grace JB (2008) Structural equation modeling and natural systems. Cambridge University Press, 361. https://doi.org/10.1086/586991

Greinert A (2015) The heterogeneity of urban soils in the light of their properties. J Soils Sediments. https://doi.org/10.1007/s11368-014-1054-6

Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM (2008) Global change and the ecology of cities. In Science 319:5864. https://doi.org/10.1126/science.1150195

Guilland C, Maron PA, Damas O, Ranjard L (2018) Biodiversity of urban soils for sustainable cities. In Environmental Chemistry Letters 16(4):1267-1282. https://doi.org/10.1007/s10311-018-0751-6

Hale CM, Frelich LE, Reich PB (2006) Changes in hardwood forest understory plant communities in response to European earthworm invasions. Ecology 87(7). https://doi.org/10.1890/0012-9658(2006)87[1637:CIHFUP]2.0.CO;2

Hartig F (2024) DHARMA: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.4.7, [Software]. In http://florianhartig.github.io/DHARMA/

Hendrix PF, Bohlen PJ (2002) Exotic earthworm invasions in North america: ecological and policy implications. Bioscience 52(9):801-811. https://doi.org/10.1641/0006-3568(2002)052[0801:EEIINA]2.0.CO;2

Hendrix PF, Mueller BR, Bruce RR, Langdale GW, Parmelee RW (1992) Abundance and distribution of earthworms in relation to landscape factors on the Georgia Piedmont, U.S.A. Soil Biol Biochem. https://doi.org/10.1016/0038-0717(92)90118-H

Hendrix PF, Callaham MA, Drake JM, Huang C-Y, James SW, Snyder BA, Zhang W (2008) Pandora's box contained bait: the global problem of introduced earthworms. Annu Rev Ecol Evol Syst 39(1):593-613. https://doi.org/10.1146/annurev.ecolsys.39.110707.173426

Herrmann DL, Hillemeier M, Pouyat RV, Day SD, Chen Y, Soorenian L (2023) Urban soil management for climate resilience: A guide for adopting best practices from treepeople and ARLA. TreePeople

Hoeffner K, Santonja M, Monard C, Barbe L, Moing MLE, Cluzeau D (2021) Soil properties, grassland management, and landscape diversity drive the assembly of earthworm communities in temperate grasslands. Pedosphere 31(3):375-383. https://doi.org/10.1016/S1002-0160(20)60020-0

Hoeffner K, Louault F, Lerner L, Pérès G (2024) Management of grassland: a necessary tool to maintain plant and earthworm diversity. Eur J Soil Biol. https://doi.org/10.1016/j.ejsobi.2023.103589

Huang J, Xu Q, Sun ZJ, Tang GL, Li CP, Cui CX (2007) Species abundance and zoogeographic affinities of Chinese terrestrial earthworms. European Journal of Soil Biology, 43 (SUPPL. 1). https://doi.org/10.1016/j.ejsobi.2007.08.036

Huang W, González G, Zou X (2020) Earthworm abundance and functional group diversity regulate plant litter decay and soil organic carbon level: a global meta-analysis. Appl Soil Ecol 150:103473. https://doi.org/10.1016/j.apsoil.2019.103473

Hubert P, Julliard R, Biagianti S, Poulle ML (2011) Ecological factors driving the higher hedgehog (Erinaceus europeaus) density in an urban area compared to the adjacent rural area. Landsc Urban Plann 103(1). https://doi.org/10.1016/j.landurbplan.2011.05.010

ISO (2018) Soil quality - Sampling of soil invertebrates - Part 1: Hand-sorting and extraction of earthworms (ISO/FDIS 23611-1:2018)

Joschko M, Fox CA, Lentzsch P, Kiesel J, Hierold W, Krück S, Timmer J (2006) Spatial analysis of earthworm biodiversity at the regional scale. Agric Ecosyst Environ 112(4):367-380. https://doi.org/10.1016/j.agee.2005.08.026

Jupke JF, Scheu S, Cameron EK, Eisenhauer N, Phillips HRP, Römbke J, Rutgers M, Schäfer RB, Entling MH (2024) Typical earthworm

assemblages of European ecosystem types. Eur J Soil Sci. https://doi.org/10.1111/ejss.13584

Krauss J, Bommarco R, Guardiola M, Heikkinen RK, Helm A, Kuussaari M, Lindborg R, Öckinger E, Pärtel M, Pino J, Pöyry Juha, Raatikainen Katja M., Sang Anu, Stefanescu Constanti, Teder Tiit, Zobel Martin, Steffan-Dewenter Ingolf (2010) Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels. Ecol Lett 13(5):597-605

Lapied E, Nahmani J, Rousseau GX (2009) Influence of texture and amendments on soil properties and earthworm communities. Appl Soil Ecol 43:2-3. https://doi.org/10.1016/j.apsoil.2009.08.004

Le Bayon RC, Bullinger G, Schomburg A, Turberg P, Brunner P, Schlaepfer R, Guenat C (2021) Earthworms, plants, and soils. In hydrogeology, chemical weathering, and soil formation. https://doi.org/10.1002/9781119563952.ch4

Lee KE (1985) Earthworms: their ecology and relationships with soils and land use. Academic Press Inc

Lee H, Calvin K, Dasgupta D, Krinner G, Mukherji A, Working Groups C, II I and to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Lee H III and J. ... In: Arias P, Bustamante M, Elgizouli I, Flato G, Howden M, Méndez-Vallejo C, Pereira JJ, Pichs-Madruga R, Rose SK, Saheb Y, Sánchez Rodríguez R, Urge-Vorsatz D, Xiao C, Yassaa N, Romero J, Kim J, Haites EF, Jung Y, Stavins R, ... Park Y (eds) Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Issue IPCC, Geneva, Switzerland). https://doi.org/10.59327/IPCC/AR6-9789291691647.001

Li J, Zhang Z, Wang H, Wang S, Chen Q (2020) Urban land-use impacts on composition and spatiotemporal variations in abundance and biomass of earthworm community. J Forestry Res. http://doi.org/10.1007/s11676-018-0807-2

Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D (2021) Performance: an R package for assessment, comparison and testing of statistical models. J Open Source Softw. https://doi.org/10.21105/joss.03139

Ma WC, Brussaard L, de Ridder JA (1990) Long-term effects of nitrogenous fertilizers on grassland earthworms (Oligochaeta: Lumbricidae): their relation to soil acidification. Agric Ecosyst Environ 30(1-2). https://doi.org/10.1016/0167-8809(90)90184-F

Malik AA, Puissant J, Buckeridge KM, Goodall T, Jehmlich N, Chowdhury S, Gweon HS, Peyton JM, Mason KE, van Agtmaal M, Blaud A, Clark IM, Whitaker J, Pywell RF, Ostle N, Gleixner G, Griffiths RI (2018) Land use driven change in soil pH affects microbial carbon cycling processes. Nat Commun 9(1). https://doi.org/10.1038/s41467-018-05980-1

Maréchal J, Hoeffner K, Marié X, Cluzeau D (2021) Response of earthworm communities to soil engineering and soil isolation in urban landscapes. Ecol Eng. https://doi.org/10.1016/j.ecoleng.2021.106307

Maréchal J, Hoeffner K, Marié X, Cluzeau D (2024) Impacts of soil engineering processes and anthropogenic barriers on earthworm communities in urban areas. Eur J Soil Biol. https://doi.org/10.1016/j.ejsobi.2024.103598

Mautuit A, Marchán DF, Barantal S, Brand M, Lucas A, Cortet J, Vergnes A, Decaëns T (2024) Environmental drivers of genetic diversity and phylogeographic pattern in urban earthworms. Eur J Soil Biol 121:103620. https://doi.org/10.1016/j.ejsobi.2024.103620

McKinney ML (2006) Urbanization as a major cause of biotic homogenization. Biol Conserv 127(3):247-260

Migge-Kleian S, McLean MA, Maerz JC, Heneghan L (2006) The influence of invasive earthworms on indigenous fauna in
ecosystems previously uninhabited by earthworms. Biol Invasions. https://doi.org/10.1007/s10530-006-9021-9

Nakagawa S, Freckleton RP (2011) Model averaging, missing data and multiple imputation: a case study for behavioural ecology. Behav Ecol Sociobiol 65(1):103–116. https://doi.org/10.1007/s00265-010-1044-7

Nakagawa S, Johnson PCD, Schielzeth H (2017) The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J R Soc Interface. https://doi.org/10.1098/rsif.2017.0213

Nuutinen V, Butt KR, Jauhiainen L, Shipitalo MJ, Sirén T (2014) Dewworms in white nights: High-latitude light constrains earthworm (*Lumbricus terrestris*) behaviour at the soil surface. Soil Biol Biochem. https://doi.org/10.1016/j.soilbio.2014.01.023

Padayachee AL, Irlich UM, Faulkner KT, Gaertner M, Procheş Ş, Wilson JRU, Rouget M (2017) How do invasive species travel to and through urban environments? Biol Invasions. https://doi.org/10.1007/s10530-017-1596-9

Pelosi C, Baudry E, Schmidt O (2021) Comparison of the mustard oil and electrical methods for sampling earthworm communities in rural and urban soils. Urban Ecosyst. https://doi.org/10.1007/s1252-020-01023-0

Percival GC, Graham S, Percival C, Challice D (2025) Evaluation of nature-based and traditional solutions for urban soil decompaction. Arboric Urban Forestry jauf2025012. https://doi.org/10.4804/jauf.2025.012

Phillips HRP, Guerra CA, Bartz MLC, Briones MJI, Brown G, Crowther TW, Ferlian O, Gongalsky KB, van den Hoogen J, Krebs J, Orgiazzi A, Routh D, Schwarz B, Bach EM, Bennett J, Brose U, Decaëns T, König-Ries B, Loreau M, Eisenhauer N (2019) Global distribution of earthworm diversity. Science 366(6464):480–485. https://doi.org/10.1101/587394

Phillips HRP, Bach EM, Bartz MLC, Bennett JM, Beugnon R, Briones MJI, Brown GG, Ferlian O, Gongalsky KB, Guerra CA, König-Ries B, Krebs JJ, Orgiazzi A, Ramirez KS, Russell DJ, Schwarz B, Wall DH, Brose U, Decaëns T, Eisenhauer N (2021) Global data on earthworm abundance, biomass, diversity and corresponding environmental properties. Sci Data 8(1):136. https://doi.org/10.1038/s41597-021-00912-z

Phillips HRP, Brown GG, James SW, Mathieu J, Reynolds JW, Dharmasiri ME, Singer CL, Briones MJI, Proctor HC, Cameron EK (2025) The applicability of regional red list assessments for soil invertebrates: first assessment of five native earthworm species in Canada. Biodivers Conserv 34(6):2163–2176. https://doi.org/10.1007/s10531-025-03068-z

Pižl V, Josens G (1995a) Earthworm communities along a gradient of urbanization. Environ Pollut 90(1):7–14. https://doi.org/10.1016/0269-7491(94)00097-W

Pižl V, Josens G (1995b) The influence of traffic pollution on earthworms and their heavy metal contents in an urban ecosystem. Pedobiologia 39(5):442–453. https://doi.org/10.1016/S0031-4056(24)00211-7

Pižl V, Schlaghamerský J (2007) The impact of pedestrian activity on soil channelids in urban greens. Eur J Soil Biol 43:S68–S71. https://doi.org/10.1016/j.ejsobi.2007.08.004

Püttker T, Crouzeilles R, Almeida-Gomes M, Schmoeller M, Maurenza D, Alves-Pinto H, Pardini R, Vieira MV, Banks-Leite C, Fonseca CR, Metzger JP, Accacio GM, Alexandrino ER, Barros CS, Bogoni JA, Boscolo D, Brancalion PHS. Bueno AA, Cambui ECB, Prevedello JA (2020) Indirect effects of habitat loss via habitat fragmentation: a cross-taxa analysis of forest-dependent species. Biol Conserv. https://doi.org/10.1016/j.biocon.2019.108368

Rawls WJ, Pachepsky YA, Ritchie JC, Sobecki TM, Bloodworth H (2003) Effect of soil organic carbon on soil water retention. Geoderma. https://doi.org/10.1016/S0016-7061(03)00094-6

Richardson JB (2019) Trace elements in surface soils and megascopicidae earthworms in urban forests within four land-uses around Poughkeepsie, New York, USA. Bull Environ Contam Toxicol 103(3):385. https://doi.org/10.1007/s00128-019-02669-z

Rota E (2013) From corsica to britain: new outdoor records of ocnerodrilidae (Annelida: Clitellata) in Western Europe. Biodivers Data J 1(1). https://doi.org/10.3897/BDJ.1.e985

Rutgers M, Orgiazzi A, Gardi C, Römbke J, Jänsch S, Keith AM, Neilson R, Boag B, Schmidt O, Murchie AK, Blackshaw RP, Pérès G, Cluzeau D, Guernion M, Briones MJI, Rodeiro J, Piñeiro R, Cosín DJD, Sousa JP, de Zwart D (2016) Mapping earthworm communities in Europe. Appl Soil Ecol 97:98–111. https://doi.org/10.1016/j.apsoil.2015.08.015

Salako G, Russell DJ, Stucke A, Eberhardt E (2023) Assessment of multiple model algorithms to predict earthworm geographic distribution range and biodiversity in Germany: implications for soil-monitoring and species-conservation needs. Biodivers Conserv. https://doi.org/10.1007/s10531-023-02608-9

Schmidt O (2024) City dwellers: earthworms in urban ecosystems. In Y. Kooch & Y. Kuzyakov (Eds.) Earthworms and Ecological Processes (pp. 243–264)

Schröder R, Kiehl K (2020) Ecological restoration of an urban demolition site through introduction of native forb species. Urban For Urban Green. https://doi.org/10.1016/j.ufug.2019.126509

Schwarz B, Dietrich C, Cesarz S, Scherer-Lorenzen M, Auge H, Schulz E, Eisenhauer N (2015) Non-significant tree diversity but significant identity effects on earthworm communities in three tree diversity experiments. Eur J Soil Biol. https://doi.org/10.1016/j.ejsobi.2015.01.001

Schwarz N, Moretti M, Bugalho MN, Davies ZG, Haase D, Hack J, Hof A, Melero Y, Pett TJ, Knapp S (2017) Understanding biodiversity-ecosystem service relationships in urban areas: a comprehensive literature review. Ecosyst Serv 27:161–171. https://doi.org/10.1016/j.ecoser.2017.08.014

Sherlock E, Carpenter D (2009) An updated earthworm list for the British Isles and two new 'exotic' species to Britain from Kew gardens. Eur J Soil Biol 45(5-6):431–435. https://doi.org/10.1016/j.ejsobi.2009.07.002

Shi B, Tang CS, Gao L, Liu C, Wang BJ (2012) Observation and analysis of the urban heat island effect on soil in Nanjing, China. Environmental Earth Sci. https://doi.org/10.1007/s12665-011-1501-2

Singh J, Schädler M, Demetrio W, Brown GG, Eisenhauer N (2019) Climate change effects on earthworms - a review. Soil Organisms 91(3). https://doi.org/10.25674/S091iss3pp114

Smetak KM, Johnson-Maynard JL, Lloyd JE (2007) Earthworm population density and diversity in different-aged urban systems. Appl Soil Ecol 37(1-2):161–168. https://doi.org/10.1016/j.apsoil.2007.06.004

Steinberg DA, Pouyat RV, Parmelee RW, Groffman PM (1997) Earthworm abundance and nitrogen mineralization rates along an urban-rural land use gradient. Soil Biol Biochem 29(3-4):427–430. https://doi.org/10.1016/S0038-0717(96)00043-0

Szederjesi T, Mısırlıoğlu M (2017) New earthworm records from Turkey (Clitellata: Lumbricidae, Megascolecidae). Opusc Zool. http://doi.org/10.18348/opzool.2017.1.55

Szlavecz K, Placella SA, Pouyat RV, Groffman PM, Csuzdi C, Yesilonis I (2006) Invasive earthworm species and nitrogen cycling in remnant forest patches. Appl Soil Ecol 32(1):54–62. https://doi.org/10.1016/j.apsoil.2005.01.006

Thioulouse J, Dufour AB, Jombart T, Dray S, Siberchicot A, Pavoine S (2018) Multivariate analysis of ecological data with ade4. In Multivariate Analysis of Ecological Data with ade4. https://doi.org/10.1007/978-1-4939-8850-1

Thompson GG, Thompson SA, Withers PC, Fraser J (2007) Determining adequate trapping effort and species richness using species accumulation curves for environmental impact assessments.
Austral Ecol 32(5):570. https://doi.org/10.1111/j.1442-9993.2007.01729.x

Tiho S, Josens G (2000) Earthworm populations of Roosevelt avenue (Brussels, Belgium): composition, density and biomass. Belg J Zool 130(2):131–138

Torppa KA, Taylor AR (2022) Alternative combinations of tillage practices and crop rotations can foster earthworm density and bioturbation. Appl Soil Ecol. https://doi.org/10.1016/j.apsoil.2022.104460

Torppa KA, Castaño C, Glimskär A, Skånes H, Klinth M, Roslin T, Taylor AR, Viketoft M, Clemmensen KE, Maaroufi NI (2024) Soil moisture and fertility drive earthworm diversity in north temperate semi-natural grasslands. Agric Ecosyst Environ. https://doi.org/10.1016/j.agee.2023.108836

Tóth Z, Szlavecz K, Epp Schmidt DJ, Hornung E, Setälä H, Yesilonis ID, Kotze DJ, Dombos M, Pouyat R, Mishra S, Cilliers S, Yarwood S, Csuzdi C (2020) Earthworm assemblages in urban habitats across biogeographical regions. Appl Soil Ecol. https://doi.org/10.1016/j.apsoil.2020.103530

Tresch S, Frey D, Bayon RC, Le, Mäder P, Stehle B, Fliessbach A, Moretti M (2019) Direct and indirect effects of urban gardening on aboveground and belowground diversity influencing soil multifunctionality. Sci Rep 9(1). https://doi.org/10.1038/s41598-019-46024-y

van Groenigen JW, Lubbers IM, Vos HMJ, Brown GG, De Deyn GB, van Groenigen KJ (2014) Earthworms increase plant production: a meta-analysis. Sci Rep 4(2):6365. https://doi.org/10.1038/srep06365

Vergnes A, Blouin M, Muratet A, Lerch TZ, Mendez-Millan M, Rouelle-Castrec M, Dubs F (2017) Initial conditions during technosol implementation shape earthworms and ants diversity.

Landsc Urban Plann 159(PG-32-41):32-41. https://doi.org/10.1016/j.landurbplan.2016.10.002

Weiskopf SR, Lerman SB, Isbell F, Morelli L (2024) T. Biodiversity promotes urban ecosystem functioning. Ecography, 2024(9). http://doi.org/10.1111/ecog.07366

Xie T, Wang M, Chen W, Uwizeyimana H (2018) Impacts of urbanization and landscape patterns on the earthworm communities in residential areas in Beijing. Sci Total Environ 626(PG-1261-1269):1261-1269. https://doi.org/10.1016/j.scitotenv.2018.01.187

Xie T, Li X, Wang M, Chen W, Faber JH (2022) Factors influencing earthworm fauna in parks in megacity Beijing, china: an application of a synthetic and simple index (ESI). Sustain (Switzerland) 14(10). https://doi.org/10.3390/su14106054

Xie T, Wang M, Chen W, Li X, Lyu Y, Sarvajayakesavalu S (2024) Earthworm diversity and community assemblage: influencing factors at plot-scale in urban areas. Soil & Environmental Health. https://doi.org/10.1016/j.seh.2024.100064

Zhang H, Chase JM, Liao J (2024) Habitat amount modulates biodiversity responses to fragmentation. Nat Ecol Evol 8(8):1437–1447. https://doi.org/10.1038/s41559-024-02445-1

Ziter CD (2016) The biodiversity-ecosystem service relationship in urban areas: a quantitative review. Oikos 125(6):761–768. https://doi.org/10.1111/oik.02883

Ziter CD, Pedersen EJ, Kucharik CJ, Turner MG (2019) Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proc Natl Acad Sci U S A. https://doi.org/10.1073/pnas.1817561116

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## Authors and Affiliations

Helen R. P. Phillips¹ ID • Kevin Hoeffner²,³,⁴ ID • Bart Muys⁵ ID • Renée-Claire Le Bayon⁶ ID • Daniel Cluzeau² ID • Kevin R. Butt⁷ ID • Jeanne Maréchal² • Maria J. I. Briones⁸ ID • Thibaud Decaëns⁹ ID • Mickaël Hedde¹⁰ ID • Sylvain Gérard¹,¹⁰ ID • Guénola Pérès³ ID • Lucas Petit-Dit-Grézériat¹¹,¹²,¹³ ID • Céline Pelosi¹² ID

<figure><img src="image_9.png" /></figure>

Helen R. P. Phillips
helen.phillips@helsinki.fi

1 Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland

2 University of Rennes, CNRS, ECOBIO [(Ecosystèmes, biodiversité, évolution)]-UMR 6553, Rennes, France

3 UMR SAS INRAe Institut Agro Rennes-Angers, 65 rue de Saint Brieuc, Rennes Cedex 35042, France

4 FRB-Cesab, 5 rue de l'école de médecine, Montpellier 34000, France

5 Division of Forest, Nature and Landscape, Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E, Box 2411, Leuven 3001, Belgium

6 Institute of Biology, Functional Ecology Laboratory, University of Neuchâtel, Emile Argand 11, Neuchatel 2000, Switzerland

7 Ecological Engineering, University of Lancashire, Preston PR1 2HE, UK

8 Departamento de Ecología e Biología Animal, Universidad de Vigo, Vigo 36310, Spain

9 CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France

10 Eco&Sols, INRAE, IRD, CIRAD, Institut Agro Montpellier, Université Montpellier, Montpellier, France

11 Avignon Univ, Aix Marseille Univ, CNRS, IRD, IMBE, Pôle Agrosciences, 301 rue Baruch de Spinoza, BP 21239, Avignon 84916, France

12 INRAE, UMR EMMAH 1114 INRAE, Université d'Avignon, Avignon, France

13 FiBL France, Research Institute for Organic Agriculture, Eurre 26400, France