# Land use and management intensity shape nitrogen cycling and microbial functions, driving environmental impacts in French urban soils

Fadwa Khalfallah, Jennifer Harris, Sebastien Bonthoux, Amélie a M Cantarel, Jean-Christophe Clement, Arnaud Foulquier, Catherine Joulian, Xavier Le Roux, Agnès Richaume, Nicolas Legay

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## To cite this version:

Fadwa Khalfallah, Jennifer Harris, Sebastien Bonthoux, Amélie a M Cantarel, Jean-Christophe Clement, et al.. Land use and management intensity shape nitrogen cycling and microbial functions, driving environmental impacts in French urban soils. Applied Soil Ecology, 2026, 219, pp.106751. 10.1016/j.apsoil.2025.106751. hal-05440621

HAL Id: hal-05440621

https://hal.science/hal-05440621v1

Submitted on 5 Jan 2026

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# Land use and management intensity shape nitrogen cycling and microbial functions, driving environmental impacts in French urban soils☆

Fadwa Khalfallah<sup>a, *,</sup> , Jennifer Harris<sup>a</sup> , Sebastien Bonthoux<sup>b, f, g</sup> , Amélie A.M. Cantarel<sup>c</sup> , Jean-Christophe Clement<sup>d</sup> , Arnaud Foulquier<sup>e</sup> , Catherine Joulian<sup>a</sup> , Xavier Le Roux<sup>c</sup> , Agnès Richaume<sup>c</sup> , Nicolas Legay<sup>b, g</sup>

ARTICLE INFO

Keywords:

Mineralization

Denitrification

Nitrification

Land-use

Management intensity

Urban green spaces

Microbial ecotoxicology

A B S T R A C T

Urbanization profoundly alters soil through changes in land use and management intensity, affecting both soil functions and microbial communities. These shifts can degrade soil fertility and disrupt ecosystem processes. This study investigates how different public urban land uses—including showcase gardens, parks, tree-covered areas, roadsides, residential zones, sports fields and unused areas—influence nitrogen dynamics and microbial functions. We used a multifaceted approach combining biogeochemical measurements (NH₄⁺, NO₃⁻, total N), functional assays (potential mineralization, potential nitrification, potential denitrification), and molecular techniques (functional gene abundances and bacterial community profiling using Illumina MiSeq).

Results showed that land use significantly influenced nitrogen cycle processes. Showcase garden areas were characterized by elevated nitrate levels, driven by fertilization and higher nitrification activity, whereas denitrification and related gene abundances were higher in parks and roadside soils. Structural equation modeling (SEM) revealed that soil organic carbon was a major driver of denitrification in most of the land uses, and pH positively influenced the abundance of ammonia-oxidizing archaea (AOA), whereas soil water retention, often reduced in compacted soils, was negatively correlated with most nitrogen variables.

In terms of bacterial communities, this study revealed that bacterial functions linked to the nitrogen cycle varied according to land use. Lightly managed areas harboured more nitrogen-fixing bacteria, while intensely managed areas showed a predominance of bacteria involved in nitrification. Furthermore, the absence of any significant correlation between the multiple stages of the nitrogen cycle (mineralization, nitrification, denitrification) in intensively managed areas suggested a breakdown in nitrogen cycling, probably linked to excessive aeration, tillage and low organic matter accumulation or to differences in organic matter decomposition and stability.

These findings highlight the sensitivity of nitrogen cycling to urban land management. Crucially, the role of organic matter dynamics in this context requires deeper investigation.

1. Introduction

Urbanization has significantly disrupted global biogeochemical cycles, particularly the nitrogen cycle (Kinzig and Socolow, 1994). Urban

drivers profoundly alter soil abiotic conditions (Pouyat et al., 2010; Pickett et al., 2011) through pollution, water contamination, soil impermeability and organic or inorganic fertilization. These altered abiotic conditions can directly modify key soil biogeochemical functions

<footnote><sup>a</sup> BRGM, F-45071, Orléans, France</footnote>

<footnote><sup>b</sup> INSA Centre Val de Loire, Université de Tours, UMR 7324 CITERES, Blois, France</footnote>

<footnote><sup>c</sup> Université Claude Bernard Lyon 1, Laboratoire d'Ecologie Microbienne, UMR CNRS 5557, UMR INRAE 1418, 69622, Villeurbanne, France</footnote>

<footnote><sup>d</sup> Univ. Savoie Mont Blanc, INRAE, CARRTEL, Thonon-Les-Bains, France</footnote>

<footnote><sup>e</sup> Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Grenoble, France</footnote>

<footnote><sup>f</sup> UMR 6554 LETG, Rennes, France</footnote>

<footnote><sup>g</sup> LTSER, Zone Atelier Loire, France</footnote>

<footnote>* This article is part of a Special issue entitled: 'Soil health III-ADV & CHLG' published in Applied Soil Ecology.</footnote>

<footnote>* Corresponding author.
E-mail address: f.khalfallah@brgm.fr (F. Khalfallah).</footnote>
(e.g., nutrient cycling, carbon storage and microbial activity; Groffman et al., 2017), and also indirectly influence soil functions by reshaping microbial communities and their functions (Ramirez et al., 2014; Cavicchioli et al., 2019). However, the relative contribution of these biotic versus abiotic factors in shaping soil microbial communities and influencing biogeochemical functioning remains poorly quantified in urban areas.

A key factor underlying these differences is the composition and activity of microbial communities that control nitrogen availability in ecosystems though the denitrification, nitrification and mineralization processes. Recent research has also emphasized the importance of microbial necromass in soil organic nitrogen and carbon pools, highlighting the dual role of microbes in both nitrogen mineralization and immobilization (Liang et al., 2017; Zhu et al., 2020). Moreover, disruptions to microbial composition and function—such as those induced by urbanization, land-use change, and climate variability—can significantly alter nitrogen dynamics. Recent research has highlighted how urbanization can alter the structure and function of soil microbial communities, thereby influencing nitrogen cycling processes (Baruch et al., 2020, 2021; Stephanou et al., 2021; Grierson et al., 2023). Changes in microbial community structure — such as shifts in the relative abundance of key functional groups (e.g., ammonia-oxidizing archaea and bacteria, denitrifiers), lifestyle strategies (oligotrophic or copiотrophic bacteria), fungal guilds (saprotrophic fungi, ectomycorrhizal fungi, ...) or in the diversity of functional genes (e.g., amoA, nirK, nosZ) — can directly impact the efficiency, direction, and magnitude of nitrogen transformations. These structural shifts often lead to altered enzymatic capacities and dominance of certain pathways.

Urban ecology has become an important focus in ecological and environmental science, but large gaps remain in our understanding of not only what species are found in cities, but also what are their adaptation strategies and functions within the urban networks (Rega-Brodsky et al., 2022). Urban soil nitrogen dynamics remain poorly understood, especially across different land use types (e.g., gardens, parks, unused areas), even though the effects of specific land use in urban areas on microbial communities have been demonstrated (Christel et al., 2023). Yet, most of the urban studies (80 %) focus on the urban area through the gradient: “urban”, “peri-urban”, and “rural” area (Rega-Brodsky et al., 2022). In a recent review, the authors have identified the needed of studies in residential and non-sports-related urban grasslands to elucidate management effects, since non-sports dedicated turf comprises most of this ecosystem type (Thompson and Kao-Kniffin, 2019). Indeed, a finer understanding is crucial, as land use directly shapes the physicochemical conditions and biological drivers that regulate nitrogen transformations (Groffman et al., 2004). This highlights the need to better integrate soil function metrics into urban land management frameworks. Urban areas are often specific due to their heterogeneity across land uses and their unique combination of characteristics: soil inputs, compaction, fertilization, pesticides, pollution, ... Such variations in soil properties and environmental variables can influence soil functional communities that could, in turn, affect nitrogen cycling and retention.

Management of urban green spaces plays a critical role not only in maintaining usable groundcover, reducing weed encroachment of weedy intolerant of mowing, and sustaining healthy turf (Thompson and Kao-Kniffin, 2019), but also in regulating key soil processes such as nitrogen cycling. Well-managed green spaces can enhance soil structure, microbial activity, and nutrient retention—factors that directly influence nitrogen dynamics. Moreover, effective green space management contributes to the overall functionality of urban ecosystems, improving their capacity to deliver multiple ecosystem services (Thompson and Kao-Kniffin, 2019; Eldridge et al., 2024). Despite this, studies examining urban management effects have produced mixed results. While some have focused on specific management practices and detected significant differences in soil nitrogen processes, others that considered urban areas more broadly have found little distinction from nearby natural

ecosystems (Eldridge et al., 2024). This variability highlights the essential role of land management as a determinant in nitrogen cycling studies and underscores the need to account for site-specific practices when assessing urban ecosystem functioning.

Urban environments create challenging conditions for nitrogen dynamics, making nitrogen a critical and frequently limiting element (Stephanou et al., 2021). One of the key issues in urban areas is nitrogen leaching, mainly driven by poor water retention. These alterations can lead to reduced soil nitrogen retention, limited plant growth, and increased risk of surface and ground water nitrate pollution. Therefore, understanding nitrogen cycling across various urban land-use types is essential to adapt sustainable green space management—especially under the dual pressures of rapid urban expansion and climate change.

This study aims to investigate how public urban land use and management intensity influence nitrogen cycling processes and associated microbial functions. We tested three hypotheses: (i) Land uses with high management intensity leads to decreased nutrient retention and altered microbial functions. (ii) Land that receives fertilizer inputs (e.g., showcase garden or sport field) influence nitrogen cycling differently compared to unmanaged or low-impact land uses (e.g., unused areas), which may more closely reflect natural nitrogen dynamics. (iii) Microbial functional profiles and functional groups are strongly associated with nitrogen cycling, even under highly disturbed or artificial land uses.

## 2. Material and methods

### 2.1. Experimental site

Both the study site and the methods are described in detail in the data paper Norini et al., 2025. Briefly, soil sampling was carried out in the public space of the city of Blois (46,660 inhabitants in 2020), located in central France (47° 35' 38" N, 1° 19' 41" E). Mean annual temperature is 11.6 °C and annual rainfall is 640 mm. The experimental design was developed in collaboration with the city's management service to represent the entire public urban territory and all relevant management intensities. We focused on public herbaceous green spaces to avoid confounding effects from other vegetation types, except for the “Tree-covered” condition, where samples were taken less than one meter from trees. The sampling design targeted 135 sites which covered 8 different land uses across the city (Fig. 1): 16 City Parks, 8 showcase Gardens (further referred to as “Gardens”), 10 Parklands, 33 Collective Residential areas, 29 Roadsides (further referred to as “road.co”), 5 Sports fields (further referred to as “Sport”), 7 Tree-covered areas (further referred to as “trees”) and 27 Unused areas). City parks are green spaces such as small parks and squares with intermediate management intensity; showcase Gardens are highly maintained public gardens located in the historic old town; Parklands are large natural public green spaces; collective Residential areas are all green spaces associated to large social housing; Roadsides are road components (road verges, roundabouts, traffic islands); Sport fields are highly modified soils with amendments and fertilizer inputs dedicated mainly to playing football and rugby; Tree-covered areas are lines of trees associated to pedestrian paths; Unused area are unused green areas with low management intensity reserved by the municipality for future buildings or flood absorption. The sites were assigned into four management intensity categories ranging from: (1) corresponding to the lowest level of human intervention, grass mowing 1–2 times per year; (2) corresponding to grass mowing 3–4 times per year; (3) corresponding to grass mowing 10–15 times per year and (4) corresponding to the highest level of human influence, grass mowing 18–25 times per year with residue export, lawn watering and scarification (e.g. scratching lawns to aerate the soil and remove the layer of thatch and moss that forms around the roots). Only category 4 green spaces receive fertilizers. None are used in management categories 1, 2, and 3.

Soil sampling was carried out in spring 2020 at 0–15 cm depth using
<figure><img src="image_3.png" /><figcaption>Fig. 1. Localization (A) and land use (B) of the 135 sampled sites in Blois city (France).</figcaption></figure>

a manual auger. For each sample site, on an area of approximately 40 square meters we collected nine cores in a large green space representative of the land use type and as close as possible to the center of the green space to limit edge effects.

The 135 fresh composite soil samples were sieved through a 2 mm mesh and subsamples were stored at -20 °C for microbial related analysis (extracellular enzymatic activities, DNA extraction and quantification of 16S, 18S rRNA), and at 4 °C for the determination of soil moisture, soil texture, bulk density, total soil porosity, total carbon, total nitrogen and pH.

## 2.2. Soil properties

Gravimetric soil water content (SWC) was measured after drying subsamples at 105 °C for 48 h. Bulk density was measured using a 250 cm³ soil core. Water holding capacity (WHC) was calculated after saturating soil cores with 100 mL of distilled water. Soil pH was measured in a 1:4 soil-to-water solution (Smith and Doran, 1997). Soil subsamples were air-dried, ground to <80 µm, and analysed for total carbon (C) and nitrogen (N) content using a FlashEA 1112 elemental analyzer (Thermo Scientific, Flash EA 1112, Milan, Italy). This content was used to calculate the C:N ratio as an indicator of soil functioning. Ammonium was analysed by colorimetry according to the NF ISO 15923-1 (ISO, 2014) method with Thermo Fisher Gallery™ for ammonium (references 984,362 and 984,363) and nitrate were quantified by ionic chromatography (Dionex IC3000-SP-EG-DC system equipped with an AS50 autosampler and a conductimetric detector) according to the standard EN ISO 10304-1 (ISO, 2009) method. Soil texture (sand, silt and clay fraction determination) was measured using a soil texture test kit (Model 1067; LaMotte, Chestertown, MD, USA) using the general method of mechanical analysis or soil separation.

## 2.3. Enzymatic activities

The potential activities of seven extracellular enzymes (α-Glucosidase (AG), β-1,4-Glucosidase (BG), β-D-cellobiosidase (CB), β-Xylosidase (XYL), leucine aminopeptidase (LAP), N-acetyl-β-glucosaminidase (NAG) and neutral phosphatase (PHOS)) were measured using standardized fluorimetric techniques (Bell et al., 2013).

## 2.4. Soil microbial functions

Potential nitrification enzyme activity (NEA) was measured as NO₂⁻

+ NO₃⁻ production using 3 g (dry weight equivalent) of soil placed in an airtight flask with a 21 % O₂ atmosphere. A water solution containing (NH₄)₂SO₄ was added in order to provide 50 µg N-(NH₄)₂SO₄ per gram of dry soil to ensure non-limiting amounts of NH₄⁺ for NEA activity. The flasks were shaken at 28 °C. The soil suspension was sampled every 2 h for 10 h and filtered through a pore size of 0.22 µm. The amount of NO₂⁻ + NO₃⁻ was measured using a Smartchem 200 photometer (AMS Alliance, Villeneuve-la-Garenne, France). The slope of the linear regression was used to estimate the potential nitrification enzyme activity as a measure of NO₂⁻ + NO₃⁻ production (µg N-NO₂⁻ + NO₃⁻·g⁻¹ dry soil·h⁻¹) (for more details on the procedure, see Béraud et al., 2025).

Potential denitrification enzyme activity (DEA) was measured as N₂O production using the procedure described by Béraud et al. (2025). Potentially mineralizable nitrogen (PMN) and potentially nitrifiable nitrogen (PNN) were determined on fresh soil samples using the protocol taken and modified from previous studies by Laffite et al. (2020).

NEA and PNN were analysed to assess both the microbial potential to perform nitrification (NEA) and the amount of nitrogen available in the form that can be nitrified (PNN), thereby capturing both the functional enzymatic capacity and the substrate availability necessary for nitrification.

## 2.5. DNA extraction and molecular analyses of soil microbial communities

DNA was extracted in duplicate from 0.5 g of fresh soil, using the FastDNA® SPIN Kit for soil (MP Biomedicals, United States) per the manufacturer's instructions with a FastPrep® -24 instrument (MP Biomedicals, United States). The purified DNA was eluted in a buffer solution, quantified by the Quantifluor® dsDNA sample kit fluorimetry and a Quantus Fluorometer (Promega, United States) following the manufacturer's protocol and stored at -20 °C until further analysis.

The abundances of the bacterial universal marker (16S rRNA gene) and fungal marker (18S rRNA gene) were assessed by real-time quantitative PCR (qPCR) in a CFX Connect (BioRad) with primer sets 341F/ 515R (5'-CCTACGGGAGGCAGCAG-3'; 5'-ATTACCGCGGCTGCTGGCA-3') for the bacterial abundance (López-Gutiérrez et al., 2004). Data were expressed as gene copy numbers per gram of dry weight soil.

Abundances of microbial functional groups involved in N cycling were quantified from the DNA extracted from each soil sample by measuring the gene copy numbers present per g of soil (expressed for dry soil). The PCR Primers and method references are given in detail in the data paper Norini et al., 2025. The abundance of ammonia
monooxygenase genes AOA (*amoA*) and AOB (*amoB*) were measured as a proxy for the abundance of ammonium oxidizer archaea and bacteria, respectively. The abundance of nitrite-oxidizer genes (NOB) was quantified using 16S rRNA gene for Nitrospira (NS) and for Nitrobacter (*nxrA*).

Quantification was performed by quantitative real-time PCR (qPCR), using iTaq™ Universal SYBR® Green Supermix (Bio-Rad) as the fluorescent dye on a CFX Connect Real-Time PCR Detection System (Bio-Rad). Primer pairs and related qPCR conditions are detailed in Béraud et al. (2025).

A portion of the 16S rRNA gene (V4-V5 region) was amplified using the barcoded, universal 16S primer set (515WF/918WR; 5'-CCTACGG-GAGGCAGCAG-3'/5'-ATTACCGCGGCTGCTGGCA-3') (Wang et al., 2009). PCR reactions were performed using the AccuStart II PCR ToughMix kit, followed by cleaning (HighPrep PCR beads, Mokascience). Pooled triplicates were submitted for sequencing on Illumina MiSeq instrument at GeT-PlaGe (Auzeville, France). Sequences were processed by INRA Transfert (Narbonne, France) using Mothur (version 1.36.1) according to MiSeq SOP pipeline (Schloss et al., 2009).

## 2.6. Bioinformatics analysis

Bioinformatic analyses of the DNA sequences were performed using FROGS (i.e. Find Rapidly ASV with Galaxy Solution; Escudié et al., 2018). Single-linkage clustering on sequences was performed using Swarm (with a read size of 250, aggregation distance clustering of 1) after merging, denoising and dereplication of Illumina sequencing with Vsearch. PCR chimeras were identified using VSEARCH, and both singletons and chimeras were subsequently removed from the dataset. ASVs were filtered based on several criteria, in particular the minimum ASV abundance as proportion or count defined to keep ASVs with at least 0.003 % of all sequences. The taxonomic affiliation of each ASV's seed was assessed by BLAST and filtered with a minimum identity of 0.97 and a minimum coverage of 0.99. We obtained in total 3,195,315 reads normalised at 23669 reads per sample for a total of 2413 ASVs for the 16S. Bacterial ASV were assigned to copiotrophic and oligotrophic groups based on Ho et al. (2017).

## 2.7. Statistical analysis

All statistical analyses were conducted in RStudio (R Core Team, 2020) and all the graphical representations were performed using the ggplot2 package. Differences in nitrogen-cycle-related variables between land-use types were assessed using the Wilcoxon rank-sum test, with Bonferroni correction applied for multiple comparisons. To assess the influence of biotic and abiotic factors on the nitrogen cycle, a Pearson correlation heatmap was generated. In addition to soil physicochemical variables, predicted bacterial richness was included based on the model developed by Terrat et al. (2017).

Bacterial community diversity and richness (based on ASV data) were assessed using the vegan package in R, specifically calculating the Shannon diversity index and observed ASV counts (Oksanen, 2010). Then, to identify the bacterial species specific to each land use, the indicator species analysis was conducted on the bacterial ASV using the *indicspecies* package.

To characterize ecological strategies, we applied Levin's niche breadth index and calculated the randomized occurrence ratio of ASVs, following the method described by Ren et al. (2023), to classify ASVs as generalists, intermediates, or specialists. Finally, the functional potential of bacterial communities related to nitrogen cycling was inferred using the FAPROTAX database on the package *microeco* (Liu et al., 2021) and the FROGSFUNC tool (Escudié et al., 2018). We calculated the relative abundance of bacterial taxa associated with key nitrogen-related functions, including nitrification, nitrate and nitrite denitrification, nitrate transport and reductase activity, as well as leucine phototrophy.

A structural equation model was developed to explore the relationships among key microbial processes driving nitrogen cycling in soils using the lavaan package (Rosseel, 2012). The model included latent and observed variables associated with ammonification, nitrification, and denitrification.

Ammonification was modelled as the transformation of organic nitrogen into ammonium ($NH_4^+$), influenced by enzymatic activity (e.g., *N*-acetylglucosaminidase [NAG], leucine aminopeptidase [LAP]). $NH_4^+$ served as a precursor for nitrification, represented through a latent nitrification factor integrating the shared variance among microbial indicators of ammonia oxidation, including *NS*, ammonia-oxidizing archaea (AOA), and ammonia-oxidizing bacteria (AOB). Nitrate ($NO_3^-$) was further linked to denitrification processes, modelled through a latent denitrification factor defined by the gene abundances of *nirS*, *nirK*, and *nosZI*, which collectively capture the overall microbial potential for nitrogen oxide reduction. The model also incorporated covariances among key microbial genes to capture potential functional redundancy and co-occurrence patterns.

To assess how environmental and soil physicochemical properties mediate nitrogen cycling processes, we extended the initial structural model by including a set of abiotic soil variables as predictors of key process rates and microbial activities. Specifically, the model incorporated soil carbon stocks, water holding capacity, soil pH, soil C:N, and clay content as exogenous variables influencing multiple stages of the nitrogen cycle.

For example, potential mineralizable nitrogen (PMN) was modelled as a function of both enzymatic activity (LAP, NAG) and soil properties, including organic carbon content, pH and texture (clay %). The production of ammonium ($NH_4^+$) was similarly linked to PMN and the same set of soil variables. Nitrification processes (as represented by the formation of $NO_3^-$ via NEA and PNN) were modelled as dependent not only on microbial markers (AOA, AOB, *nxrA*) and $NH_4^+$ but also on these environmental factors. Finally, denitrification enzyme activity (DEA) was predicted by $NO_3^-$ availability, gene abundances (*nirS*, *nirK*, *nosZI*, *nosZII*), and the suite of soil variables described above.

These additions allowed us to evaluate both the direct and indirect effects of soil properties on nitrogen transformations, and to test whether microbial or physicochemical factors were more influential in driving the observed patterns. Covariances were retained between microbial genes with similar functional roles (e.g., *nirS* ~ ~ *nirK*, *nosZI* ~ ~ *nosZII*), and between DEA and NEA, representing potential feedback between nitrification and denitrification activities. Additional residual correlations were allowed between closely related variables ($NH_4^+$ with LAP and NAG), capturing empirically supported biochemical links within the soil nitrogen network.

Standardized path coefficients (β) were extracted and only statistically significant paths were retained. The sum of the absolute values of significant standardized coefficients ($Σ|β|$) was used as a quantitative measure of effective connectivity, representing the overall strength of functional linkages within the nitrogen transformation network. This approach follows the conceptual framework of De Marco et al. (2009), where the integration of significant path strengths reflects the degree of coordinated activity within a functional system.

## 3. Results

To better understand how urban land use and management intensity shape nitrogen cycling in soils, we analysed the relationships between nitrogen-related variables, microbial communities, and environmental factors. This integrative approach aimed to identify the main drivers of nitrogen transformations across contrasting urban contexts. Overall, the analysis revealed that management intensity and microbial diversity are key determinants of nitrification and denitrification processes, with distinct patterns emerging between unused and managed areas. These findings highlight the central role of microbial functional traits and soil properties in regulating nitrogen dynamics in urban ecosystems.
Significant differences of nitrogen variables were found between the land use and the management intensity (Table 1; Table S1; S2). Although ammonium concentrations were not statistically different across different land uses, nitrate concentrations were higher in the garden area compared to the unused and parkland areas. Additionally, total soil nitrogen concentration was higher in city parks than in parkland and tree-covered areas. Potentially mineralizable nitrogen (PMN) showed no variation among land uses, while potentially nitrifiable nitrogen (PNN) was lower in residential and roadside areas compared to unused land. Denitrification enzyme activity (DEA) was higher in city parks, residential areas, roadsides, trees and sports fields compared to unused areas. Nitrification enzyme activity (NEA) was more pronounced in garden, roadside and tree areas than in unused land.

The genes involved in ammonia oxidation— *amoA* for archaea (*AOA*) and *amoB* for bacteria (*AOB*)—were more abundant in garden areas compared to unused areas. Among the genes involved in denitrification, the *nirS* and *nirK* genes (coding for nitrite reductases) showed distinct

land-use responses. The *nirK* gene was less abundant in residential areas compared to city parks and unused areas, whereas *nirS* abundance was lower in residential areas compared to gardens and tree-covered areas. Regarding nitrous oxide reductase genes, *nosZI* was more abundant in residential areas compared to roadside areas. In contrast, *nosZII* abundance was higher in garden areas and sports fields than in unused areas.

Leucine aminopeptidase activity was higher in the garden compared to the unused areas (Table S1). However, overall, the analysis of extracellular enzymatic activities linked to nitrogen cycling revealed only minor differences between land uses.

The heatmap of N-related variables (i.e. gene abundances, potential activities, nitrate content, ...) revealed how they were impacted by management intensity, bacterial richness, soil and root properties (Fig. 2). It showed a positive correlation between management intensity and AOA, NS, NEA, NO3, nosZII, and DEA, while a negative correlation was observed with *nrxA* and *nirK*.

Potentially nitrifiable nitrogen was negatively correlated with most

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

Overview of nitrogen cycles with (a) the chemical variables, (b) the microbial processes (c) the nitrogen cycle genes, across different land uses. Letters denote statistically significant differences based on the Wilcoxon test and the bolding represent the extreme values.

<table><thead><tr><th rowspan="2">(a)</th><th colspan="3">Inorganic N pool</th></tr><tr><th>NH<sub>4</sub><br>(µgN·g<sub>dry</sub> soil<sup>-1</sup>)</th><th>NO<sub>3</sub><sup>-</sup><br>(µgN·g<sub>dry</sub> soil<sup>-1</sup>)</th><th>% Nitrogen</th></tr></thead><tbody><tr><td>City park</td><td>11,1 ± 5,1 a</td><td>2,6 ± 1,6 abc</td><td>0,4 ± 0,1 a</td></tr><tr><td>Garden</td><td>11,6 ± 2,6 a</td><td>7,1 ± 4,2 a</td><td>0,3 ± 0,1 ab</td></tr><tr><td>Parkland</td><td>11,9 ± 4,7 a</td><td>1,8 ± 2 bc</td><td>0,2 ± 0,1 b</td></tr><tr><td>Residential</td><td>10,3 ± 4,6 a</td><td>3,1 ± 2,4 ab</td><td>0,3 ± 0,1 ab</td></tr><tr><td>Road.co</td><td>10,6 ± 4,6 a</td><td>4,4 ± 7,7 ab</td><td>0,3 ± 0,1 ab</td></tr><tr><td>Sport</td><td>7,6 ± 0,5 a</td><td>4 ± 2 abc</td><td>0,3 ± 0 ab</td></tr><tr><td>Trees</td><td>10,4 ± 1,9 a</td><td>1,8 ± 1,4 abc</td><td>0,2 ± 0,1 b</td></tr><tr><td>Unused</td><td>8 ± 4,6 a</td><td>1,4 ± 1,1 c</td><td>0,3 ± 0,1 ab</td></tr></tbody></table><table><thead><tr><th rowspan="2">(b)</th><th colspan="2">Mineralization</th><th colspan="2">Denitrification</th></tr><tr><th>PMN<br>(mgN·Kg<sub>dry</sub><sup>-1</sup> soil·day<sup>-1</sup>)</th><th>NPN<br>(mgN·Kg<sub>dry</sub><sup>-1</sup> soil·day<sup>-1</sup>)</th><th>DEA<br>(µgN·h<sup>-1</sup>·g<sub>dry</sub><sup>-1</sup> soil)</th><th>NEA<br>(µgN·h<sup>-1</sup>·g<sub>dry</sub><sup>-1</sup> soil)</th></tr></thead><tbody><tr><td>City park</td><td>-0,04 ± 0,1 a</td><td>0,5 ± 1,3 abc</td><td>3,7 ± 1,7 a</td><td>0,7 ± 0,4 abc</td></tr><tr><td>Garden</td><td>0,04 ± 0,1 a</td><td>1,6 ± 2,3 abc</td><td>4,1 ± 1,3 ab</td><td>1,4 ± 0,63 a</td></tr><tr><td>Parkland</td><td>-0,1 ± 0,9 a</td><td>0,2 ± 2,3 abc</td><td>4,1 ± 1,5 ab</td><td>0,5 ± 0,2 bc</td></tr><tr><td>Residential</td><td>-0,02 ± 0,2 a</td><td>-0,3 ± 1,2 a</td><td>3,7 ± 2,2 a</td><td>0,7 ± 0,5 bc</td></tr><tr><td>Road.co</td><td>-0,01 ± 0,3 a</td><td>0,6 ± 1,9 ab</td><td>4,3 ± 1,7 a</td><td>0,8 ± 0,3 ab</td></tr><tr><td>Sport</td><td>-0,06 ± 0,2 a</td><td>3 ± 0,9 bc</td><td>6,3 ± 1,4 a</td><td>0,8 ± 0,3 abc</td></tr><tr><td>Trees</td><td>-0,08 ± 0,1 a</td><td>0,1 ± 0,4 abc</td><td>5,7 ± 0,6 a</td><td>1,2 ± 0,6 ab</td></tr><tr><td>Unused</td><td>-0,05, ± 0,1 a</td><td>1,8 ± 1,3 c</td><td>1,9 ± 1,9 b</td><td>0,4 ± 0,5 c</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">(c) *</th><th colspan="4">Nitrification - genes</th></tr><tr><th>AOA</th><th>AOB</th><th>nrxA</th><th>NS</th></tr></thead><tbody><tr><td>City park</td><td>19,8 ± 0,6 ab</td><td>16,5 ± 0,3 ab</td><td>10,7 ± 0,9 a</td><td>18,3 ± 0,3 ab</td></tr><tr><td>Garden</td><td>20,4 ± 0,6 a</td><td>17 ± 0,5 a</td><td>11,0 ± 1,0 a</td><td>19,1 ± 0,4 a</td></tr><tr><td>Parkland</td><td>19,1 ± 0,6 b</td><td>16,3 ± 0,8 ab</td><td>11,2 ± 1,2 a</td><td>18 ± 1 b</td></tr><tr><td>Residential</td><td>19,3 ± 0,9 b</td><td>16,3 ± 0,7 ab</td><td>11 ± 1,5 a</td><td>17,9 ± 0,7 b</td></tr><tr><td>Road.co</td><td>19,6 ± 0,8 ab</td><td>16,5 ± 0,9 ab</td><td>11,6 ± 1,5 a</td><td>18,2 ± 0,5 b</td></tr><tr><td>Sport</td><td>19,9 ± 0,3 ab</td><td>17 ± 0,2 ab</td><td>10,4 ± 1,3 a</td><td>18,7 ± 0,5 ab</td></tr><tr><td>Trees</td><td>20 ± 0,8 ab</td><td>16,8 ± 0,6 ab</td><td>11,4 ± 1,2 a</td><td>18,4 ± 0,5 ab</td></tr><tr><td>Unused</td><td>19 ± 1,4 b</td><td>16,1 ± 1 b</td><td>11,6 ± 1,5 a</td><td>18 ± 0,8 b</td></tr></tbody></table>

<table><thead><tr><th rowspan="2"></th><th colspan="4">Denitrification - genes</th></tr><tr><th>nirK</th><th>nirS</th><th>nosZI</th><th>nosZII</th></tr></thead><tbody><tr><td>City park</td><td>20,7 ± 0,5 a</td><td>16,2 ± 0,6 ab</td><td>15,7 ± 0,4 ab</td><td>15 ± 1 ab</td></tr><tr><td>Garden</td><td>20,7 ± 0,7 ab</td><td>17,3 ± 0,8 a</td><td>15,9 ± 0,6 ab</td><td>17 ± 0 a</td></tr><tr><td>Parkland</td><td>20,3 ± 1,2 ab</td><td>15,9 ± 1,3 ab</td><td>15,4 ± 0,9 ab</td><td>15 ± 1 abc</td></tr><tr><td>Residential</td><td>19,9 ± 0,8 b</td><td>15,4 ± 1,1 b</td><td>15,2 ± 0,8 a</td><td>16 ± 1 ab</td></tr><tr><td>Road.co</td><td>20,7 ± 0,8 a</td><td>16,4 ± 0,9 a</td><td>15,9 ± 0,8 b</td><td>15 ± 1 bc</td></tr><tr><td>Sport</td><td>20,1 ± 0,3 ab</td><td>16,9 ± 0,8 ab</td><td>15,6 ± 0,5 ab</td><td>17 ± 0 a</td></tr><tr><td>Trees</td><td>20,9 ± 0,4 ab</td><td>16,8 ± 0,7 a</td><td>16 ± 0,5 ab</td><td>15 ± 1 abc</td></tr><tr><td>Unused</td><td>20,7 ± 0,7 a</td><td>16 ± 1 ab</td><td>15,5 ± 0,9 ab</td><td>14 ± 1 c</td></tr></tbody></table>

<footnote>* (gene copies·g dry soil⁻¹).</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Heatmap of the effects of soil and bacterial diversity on nitrogen cycle variables. The white cells indicate insignificant correlations (P.value <0.05).</figcaption></figure>

variables, including bacteria with generalist or oligotrophic strategies, potential richness, pH, and location, but positively correlated with the proportion of specialist bacteria. This variable is the only one of the N-related variables to show a positive correlation with specialist bacteria, with all the others showing a negative correlation.

Some N-related variables—AOB, NS, *nosZI*, *nirS*, NO₃, and *nrxA*—were not correlated with soil parameters but were linked to microbial richness and diversity, root traits, and/or management intensity. Among all variables, NAG exhibited the least correlations.

The global structural equation model (SEM) integrating all land uses (Fig. 3A) provided a comprehensive overview of nitrogen cycling pathways across the urban landscape (RMSEA = 0.14; CFI = 0.82; TLI = 0.76). The model confirmed the central role of nitrification (NEA) as a key driver of nitrate (NO₃⁻) accumulation, significantly influenced by both microbial groups and functional genes (e.g., *nrxA*, NS, AOA and AOB). Denitrification enzyme activity (DEA) was also significantly linked to denitrifier gene abundances (*nirS*, *nosZI*, *nirK*), suggesting robust interactions between nitrification and denitrification processes across urban soils.

The SEM model of the nitrogen cycle across the different land uses demonstrated the impact of land use and management intensity on most N-cycle components (Fig. 3B-D). To better understand how management intensity shapes nitrogen cycling, we focused on two contrasting contexts: the best-performing model corresponded to the unused land data (RMSEA = 0.21; CFI = 0.78) and the poorest performance was observed in managed areas (e.g. gardens, sports fields, and city parks; RMSEA = 0.26; CFI = 0.45; TLI = 0.31).

In unused areas, potentially mineralizable nitrogen (PMN) was significantly decreased by leucine aminopeptidase (LAP; -0.58) and negatively influenced soil ammonium content (-0.37). Nitrification (NEA) emerged as the main driver of nitrate production, positively influenced by AOB and AOA as well as *nrxA* while potential nitrification (PNN) was not significantly associated with NO₃⁻. Nitrification (0.58) positively influenced nitrate content (NO₃⁻). Denitrification was also strongly connected to *nirS*, *nosZI*, and *nirK* (≥0.58).

On the other hand, in managed areas, N-acetylglucosaminidase

(NAG) negatively influenced mineralization (-0.73), while leucine aminopeptidase (LAP) had a positive effect (0.38). The primary pathway for nitrogen dynamics was nitrification (path with the highest correlation), driven by nitrification enzyme activity (NEA). Ammonium negatively influenced potentially nitrifiable nitrogen (PNN; -0.36), while AOB and AOA had strong positive effects on NEA (0.88 and 0.76, respectively). NEA in turn enhanced NO₃⁻ (0.68). Denitrification was positively influenced by *nirS*, *nosZI*, and *nirK*.

Other land uses showed intermediate or mixed patterns. For instance, roadside areas (model E) resembled unused areas in the weak coupling between mineralization and DEA. However, in these soils, denitrification was negatively influenced by nitrate (NO₃⁻; -0.59) and primarily driven by the denitrifier genes *nirS* (1.06) and *nosZI* (0.94), suggesting that despite active nitrification, denitrification remained partially constrained. In contrast, residential areas (model D) exhibited stronger nitrification-denitrification coupling, similar to managed zones. Here, nitrification activity (NEA) was mainly supported by AOB (0.56) and AOA (0.83), while denitrification was closely linked to *nirS* (0.82) and *nosZI* (0.94), indicating efficient nitrogen transfer between the two processes.

The models revealed that soil variables influence nitrogen dynamics, with effects modulated by land use (Fig. 4). On average, carbon stock positively influenced denitrification but only significantly for the unused area and residential area. Regarding nitrification, carbon had a positive influence on residential and managed areas but a negative influence on roadside areas. Clay content significantly promoted denitrification and nitrification in the unused area but had a negative effect on denitrification in managed and roadside areas. Overall, pH negatively affected potential nitrification (PNN) and mineralization across all areas but positively influenced nitrification (NEA). Nitrate concentration was negatively impacted by environmental factors but only significantly in the unused areas. Water-holding capacity reduced nitrification in residential areas and decreased ammonium levels across all data. The C:N ratio had a significant negative influence on the ammonium content and nitrification rates for most of the land uses, as well as on denitrification in the unused and managed areas.
<figure><img src="image_5.png" /></figure>

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

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 3.** Structural Equation Modeling of the different land uses (A) all the data; (B) the unused area; (C) managed groups; (D) the residential area and (E) the roadside area. Number associations are partial regression coefficients of multiple regression. The color pathways represent are significant (*P* < 0.05; **P** < 0.01; ***P* < 0.001).</figcaption></figure>

The analysis of microbial functions revealed a significant influence of land use on microbial abundance (Fig. 5). Bacteria with nitrogen-fixing capabilities were more abundant in the unused area compared to other land-use types. Similarly, a higher abundance of leucine-producing

bacteria in the unused areas than in other land-use types.

Bacteria associated with nitrate-related activities exhibited varying distributions: those involved in nitrate transport were more abundant in the unused areas compared to the sports fields, while those associated
<figure><img src="image_10.png" /><figcaption>**Fig. 4.** The standardized total effect of each environmental factor involved in the SEM for each group (Fig. 3). The stars represent the significant effect of the environmental variable (*P* < 0.05; **P** < 0.01; ***P** < 0.001).</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 5.** Bubble plot illustrating microbial functions based on bacterial composition, analysed using FAPROTAX and FROGS FROGSFUNC tool (Escudié et al., 2018). Colours indicate different land uses, while bubble size represents the number of occurrences of nitrogen-related functions. Letters denote statistically significant differences based on the Wilcoxon test.</figcaption></figure>

with nitrate reductase (denitrifying bacteria) were more prevalent in the unused areas than in the residential zones and sports fields.

Conversely, bacteria capable of denitrification and nitrite denitrification were less abundant in the garden areas compared to other land-use types.

4. Discussion

This study investigated how urban land use and management intensity shape nitrogen cycling processes and the associated microbial functions, addressing three central hypotheses. Our findings show that: (i) Managed areas (e.g., residential zones, roadsides) had lower levels of
potentially nitrifiable nitrogen (PNN) and reduced abundances of certain denitrification genes (*nirK*), but displayed strong positive relationships between management intensity and nitrification-related variables (*AOA*, NEA, $NO_3^-$) (ii) Garden areas exhibited higher nitrate concentrations and greater abundances of ammonia-oxidizing genes (*amoA*, *amoB*) than unused areas, suggesting enhanced nitrification under fertilized conditions. In contrast, unused areas were characterized by pathways dominated by natural nitrification and mineralization, highlighting distinct nitrogen dynamics between managed and unmanaged sites. (iii) Structural equation modeling revealed strong connections between nitrification and denitrification processes and the abundance of functional genes (*nxrA*, *nirS*, *nosZI*, *nosZII*) across all land-use types. Despite disturbances, microbial communities maintained their functional roles, with nitrification emerging as a key driver of nitrate accumulation and denitrification closely linked to gene abundance patterns. Together, these findings provide a foundation for understanding urban nitrogen dynamics and their ecological implications. The following sections examine these results in greater detail.

## 1. Key descriptors of urban soils

Urban areas are often studied as a homogenous ensemble; however, multiple disturbances linked to management induce heterogeneities between land-uses which impact microbial communities and their functions (Grierson et al., 2023; Christel et al., 2023). The present study provides insights into how land use and management intensity specifically influence nitrogen dynamics.

In our study, we found a significant correlation of soil and microbial parameters (richness, life strategy and function specialist and strategy) with nitrogen cycle variables. Most of these variables were correlated positively with bacterial richness and geographics locations (indicating a possible effect of the land use). In urban areas, human activities can alter environmental conditions and microbial communities (Grierson et al., 2023; Christel et al., 2023) ultimately resulting in modifications of nitrogen dynamics.

In addition to being a source of atmospheric $CO_2$, urban areas are also important sources for reactive N compounds, from fossil fuel combustion in automobile engines (Zhu et al., 2004) to mowing, irrigation and fertilization activities (Hall et al., 2008). Thus, soil changes associated with urbanization may have effects on processes that produce and consume nitrate (Groffman et al., 2003, 2006). A recent review by Thompson and Kao-Kniffin (2019) emphasized how urban management practices—including mowing, the handling of grass clippings, fertilization, and irrigation—affect soil nitrogen dynamics in urban environments linking to the soil microbiome. Consistent with some of their findings, we observed a greater nitrogen pool in soils subjected to regular mowing and effects of fertilization on soil enzymatic activities.

However, these perturbations are known to depend on nitrogen-related processes. For instance, fertilization has been shown to influence denitrification, while grass removal affects nitrogen mineralization (Thompson and Kao-Kniffin, 2019). Therefore, it is essential to examine each process individually to better understand nitrogen dynamics in urban areas.

## 2. Nitrification vs. Mineralization: Functional shifts across land use

Urban land use significantly alters nitrogen cycling, with garden soils and unused soils representing two ends of a management spectrum. Gardens, characterized by active maintenance and fertilization, contrast with unused areas, which are little managed and nutrient limited.

Our results confirm that nitrification dominates in residential and more managed areas, likely due to favourable conditions such as aeration, fertilization and irrigation (Prosser and Nicol, 2012; Zhang et al., 2012). Interestingly, both the potential nitrification (PNN) and nitrification (NEA) were positively correlated to nitrate only in the unused areas. In contrast, nitrogen mineralization was comparatively lower in

compacted or polluted soils, such as roadsides, where organic matter inputs are limited and microbial decomposition is hindered (Booth et al., 2005; Allison and Martiny, 2008).

Interestingly, soil physicochemical properties (e.g., pH, clay content) had limited influence on mineralization rates, suggesting that biotic or site-specific factors, such as microbial community composition or vegetation type, may play a more significant role. Water-holding capacity emerged as a key negative driver of nitrogen cycling in SEM models, indicating that soil moisture availability is a critical constraint in urban environments (Schimel et al., 2007). In urban soils, soil compaction, and impermeable surfaces reduce infiltration and increase runoff, thereby exacerbating nutrient leaching (Groffman et al., 2004). These processes contribute to reduced water retention, elevated nitrate losses, and disrupted microbial functioning, which collectively impair ecosystem services in urban landscapes (Pickett et al., 2011).

The main drivers of mineralization are microbial biomass, followed by climate, precipitation and soil parameters. (Li et al., 2019). Urban land use is a major drivers of the microbial biomass (Christel et al., 2023; Grierson et al., 2023), thus it is not surprising that soil physicochemical properties had a limited effect on the mineralization rates. Additionally, some urban perturbation (mowing and clipping) can increase the potential N mineralization making inorganic soil N ($NH_4^-$-N and $NO_3^-$-N) more available, which could lead to greater net photosynthesis and higher N content in shoots (Thompson and Kao-Kniffin, 2019). In comparison, the variation of soil nitrification have been demonstrated to be much related to the pedoclimatic characteristics (total soil nitrogen content, Mean Annual Temperature, soil pH) (Li et al., 2020). Thus, the higher nitrification activity in the managed area could simply be explained by the fertilization and the higher total nitrogen concentration.

## 3. Denitrification and nitrogen availability

Long-term N fertilization simultaneously influences plants, soil N and C availability, and soil properties (Carrara et al., 2018). In our study, the unused area may have experienced fertilization due to historical agricultural practices prior to the 1950s, whereas the garden and sports field areas are currently subject to ongoing fertilization. As results, we found a clear difference between these for both the nitrogen availability and the denitrification.

Indeed, the garden areas had higher nitrate concentrations compared to the unused and parkland areas. Our data suggests that fertilization in the garden area likely contributed to increased nitrate levels, influencing both soil chemistry and microbial communities. As shown by the study of Stuchiner et al. (2024), the distribution of particulate organic matter (POM) can strongly influence denitrification potential at the field scale. Indeed, particulate organic matter fuels heterotrophic respiration, which creates anoxic conditions that promote denitrification, a process that also utilizes particulate organic matter as a substrate. Additionally, the proportion of bacteria capable of denitrification was lower in the garden areas compared to other land uses. Moreover, the Structural Equation Modeling (SEM) analysis revealed similar patterns, showing higher rates of nitrification and mineralization relatively to denitrification. In support of this, recent work (Schlüter et al., 2025) showed that hydrological and structural constraints reducing soil moisture and the development of anaerobic microsites can limit denitrification even when nitrification/mineralization are high, thereby decoupling availability of nitrate from its removal via denitrification. Additionally, N additions can cause increases in the abundances of *amoA* (Dong et al., 2022). A higher abundance of *AOA* genes in the garden area compared to other land uses, could also explain the higher concentration of nitrate in the garden areas.

The denitrification potential showed some variation between managed and unused sites. While the relative abundance of the *nosZII* gene was higher in gardens compared to unused areas. In this study, denitrification potential was not uniformly enhanced across managed
sites. While previous research has shown that organic nitrogen inputs promote denitrification gene abundance (e.g., *nirk*, *nirS*, *nosZ*) (Hallin et al., 2009; Yang et al., 2024), our findings suggest that inorganic nitrogen fertilization in urban gardens may not have the same effect. This aligns with agricultural studies indicating that synthetic fertilizers can suppress denitrification when applied at high rates (>250 kg N ha⁻¹; Wang et al., 2018). Excessive fertilization may lead to nitrate accumulation, which can inhibit microbial activity and alter redox conditions (Firestone and Davidson, 1989).

In contrast, unused areas, though nitrogen-limited, exhibited higher relative abundances of nitrogen-fixing bacteria and a more connected nitrogen cycle, from mineralization to denitrification. This suggests that lightly managed soils, despite lower nutrient inputs, may support a more integrated microbial nitrogen network, possibly due to stable micro-habitats and reduced disturbance (Fierer et al., 2012).

The difference between the land uses could result in major fluctuations in ecosystem functioning. Indeed, denitrification increases nitrate concentrations in soils, enhancing nitrogen availability for plant uptake in urban green spaces and parks but can lead to nitrate leaching, affecting groundwater, while denitrification removes excess nitrate, preventing water pollution and reducing nitrogen-related greenhouse gas emissions (Jarvis, 1999). Groundwater pollution in urban areas is a significant issue due to urban activities and can seep into the groundwater and pose serious health risks (Howard et al., 2023).

It is important to note that an increase in potential denitrification activity (DEA) does not necessarily correspond to a proportional rise in N₂O emissions. N₂O production also depends on the molar ratio of N₂O/(N₂O + N₂). The DEA assay, conducted under laboratory-optimal conditions, measures the potential denitrification but inhibits the further reduction of N₂O to N₂, leading to N₂O accumulation. The spatial distribution of particulate organic matter and oxygen gradients within the heterogeneous soil matrix influences whether denitrification terminates ends with N₂O or proceeds to complete reduction to N₂ (Lucas et al., 2024). Thus, both the micro-scale organization of organic matter and local oxygen availability play crucial roles in determining denitrification outcomes.

## 4. Microbial gene dynamics and environmental stability

The abundance of genes involved in the denitrification process of the nitrogen cycle fluctuated according to land use. For example, *nosZI* only showed differences between two areas of the city, whereas *nosZII* showed many more significant differences between different areas. These differences of genes involved in similar processes could indicate variations in the environments of the different land use. The *nosZI* gene has been demonstrated to be more stable when faced with different land uses, while the *nosZII* gene could be more sensitive (Liu et al., 2023). However, high concentrations of nitrate could inhibit the microbial activity of the micro-organism that can express *nozII* (Zhang et al., 2024). We did not find this trend regarding our data; maybe the forms of nitrate could have an impact on the inhibition.

Correlations between nitrogen dynamics and microbial and/or soil physico-chemical parameters indicate an increase of certain processes depending on the urban area (e.g., nitrification linked to AOA, NS, NEA, NO₃⁻, *nosZII*, and DEA). For instance, previous studies have demonstrated an association between fertilization and increased abundances of AOA and AOB (Wang et al., 2019). Others have highlighted differences between AOA and AOB genes due to soil acidification. Indeed, previous model also showed that soil pH exerted an important control on amoA abundance in croplands (Dong et al., 2022). Similarly, we found a significant positive correlation between pH and the genes AOA but not AOB. AOA gene abundance was the key driver of nitrification rates in managed and residential areas while AOB gene abundance had more influence in unused and roadside areas. The results we obtained suggest that changes in pH caused by different land uses may influence the contribution of AOA and AOB to nitrification in urban soils. Overall, our

findings highlight that land-use-driven variations in soil pH and nitrate availability may selectively shape the structure and activity of nitrogen-cycling microbial communities, ultimately modulating the relative contribution of different functional genes (e.g., AOA, AOB, *nosZI*, *nosZII*) from mineralization to nitrification and denitrification processes in urban soils.

## 5. Microbial functional specialization and cycle connectivity

As previously demonstrated the bacterial community's diversity and composition is influenced by land use (Christel et al., 2023). Land use also influenced microbial functional specialization. Nitrogen-fixing bacteria were more abundant in unused areas, while denitrifying-bacteria were prevalent in residential zones. This shift in microbial function specialist and strategy could also be supported by the Levin's niche breadth index that showed a significantly higher proportion of specialist bacteria in the highest management intensity (level 4) compared to the lowest one (level 3; Table S3) This could suggests some degree of specialization in microbial communities along a gradient from nutrient-poor to nutrient-rich soils as observed previously in microbial ecology and in urban ecology with birds and plants (Nemergut et al., 2014; Lokatis and Jeschke, 2022).

Despite the lower abundance of denitrifying bacteria in gardens, denitrification potential was not significantly reduced, possibly due to disturbance effects from tilling, mulching, and aeration. These practices enhance oxygen diffusion, limiting anaerobic microsites necessary for denitrification (Philippot et al., 2009). This decoupling of microbial abundance and function was evident in SEM models, where nitrogen cycle steps appeared disconnected in gardens but more integrated in unused soils. This supports the hypothesis that urban management disrupts the links between bacterial community structure and function, while unmanaged soils maintain a more complete nitrogen cycle (Ouyang et al., 2018).

## 6. Other land use comparisons and functional implications

Beyond the garden–unused soil dichotomy, other urban land uses—such as city parks, tree-covered areas, sports fields, and residential zones—demonstrate distinct nitrogen cycling dynamics shaped by their specific management regimes and environmental conditions.

City parks exhibited elevated total nitrogen concentrations, likely due to the dense herbaceous cover and the retention of grass cuttings, which contribute to nitrogen accumulation through decomposition. However, denitrification potential in these areas was relatively low. This may be attributed to well-aerated soils maintained through regular mowing and drainage systems (Deparis et al., 2025), which limit the formation of anaerobic microsites necessary for denitrification (Philippot et al., 2009).

Tree-covered areas, often located adjacent to impervious surfaces such as sidewalks and roads, showed higher denitrification potentials. These areas tend to have compacted and moist soils, conditions favourable for denitrifying bacteria. However, the routine removal of leaf litter and woody debris—common in urban forestry practices—may reduce microbial enzymatic activities and organic matter inputs, potentially limiting long-term nitrogen retention (Prescott and Grayston, 2013; Maillard et al., 2023).

Sports fields, characterized by intensive maintenance and fertilization, support microbial communities associated with nitrogen transformation. In particular, these areas showed high abundances of *nosZII* genes. The frequent disturbances and nutrient inputs in these environments likely select fast-growing, nitrogen-responsive microbial taxa.

Residential areas demonstrated dominant nitrification activity, likely driven by fertilizer use, irrigation, and soil aeration. However, nitrogen mineralization was comparatively low, possibly due to soil compaction, pollution, and limited organic matter inputs (Booth et al., 2005; Allison and Martiny, 2008). These findings suggest that while
residential soils may support rapid nitrogen turnover through nitrification, they may be less effective at sustaining long-term nitrogen availability through mineralization.

Collectively, these patterns highlight the importance of land use in shaping the structure and function of urban nitrogen cycles. Each land use-type creates a unique set of environmental filters that influence microbial community composition, gene expression, and nitrogen transformation pathways.

## 5. Conclusion

The nitrogen cycle plays a crucial role in soil functioning. In this study, we examined how both land use and management intensity influence nitrogen cycle dynamics. While we emphasize the significant impact of high management intensity, we also demonstrate how different public land uses affect the nitrogen cycle, particularly in relation to variations in microbial community composition. Notably, the influence of organic matter stocks in urban areas suggests that organic matter dynamics may evolve depending on land use patterns. These results further highlight the necessity to regulate management for different land uses to assure optimal soil functioning. Indeed, our results suggest that management practices promoting stable microhabitats in less intensively managed areas—or reducing mowing frequency—may improve the connectivity and efficiency of nitrogen cycling processes. Additionally, discontinuing litter removal beneath trees could enhance soil nitrogen availability and stimulate denitrification processes. Finally, we recommend monitoring nitrogen-related functional genes to better assess the impact of public management practices on nitrogen cycling, rather than relying solely on potential enzymatic activities, which show limited correlation with actual nitrogen transformations.

## CRediT authorship contribution statement

**Fadwa Khalfallah:** Writing – original draft, Formal analysis. **Jen-nifer Harris:** Writing – review & editing, Supervision, Investigation, Conceptualization. **Sebastien Bonthoux:** Writing – review & editing, Investigation, Conceptualization. **Amélie A.M. Cantarel:** Writing – review & editing, Investigation, Conceptualization. **Jean-Christophe Clement:** Writing – review & editing, Investigation, Conceptualization. **Arnaud Foulquier:** Writing – review & editing, Investigation, Conceptualization. **Catherine Joulian:** Writing – review & editing, Investigation, Conceptualization. **Xavier Le Roux:** Investigation, Conceptualization. **Agnès Richaume:** Writing – review & editing, Investigation, Conceptualization. **Nicolas Legay:** Writing – review & editing, Supervision, Investigation, Conceptualization.

## Declaration of competing interest

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

## Acknowledgements

The authors acknowledge the financial support provided by the following programs: the French National program EC2CO through its FORME project (FORME – N°12991), the Centre Val de Loire region through its BIENSUR project (convention 2019 - 001131766 and convention N° 2023 00155715), the Labex VOLTAIRE (ANR-10-LABX-100-01), the PRIME-PIVOTS projects (ARD 2020 of the Centre Val de Loire region, CPER 2015-2020, and FEDER). This work was performed within the framework of the ZAL (LTSER Zone Atelier Loire). The authors would like to thank the AME platform (Microbial Ecology Laboratory, Villeurbanne, France) and Jonathan Gervaix for the nitrification and denitrification analyses. Abigaïl Delort is thanked for performing qPCR on nitrogen cycle communities and Mickaël Charron for the PNN

and PMN analyses.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.apsoil.2025.106751.

## Data availability

Data that support the findings of this study are available at doi: 10.48579/PRO/MTZ0KB. Sequences data are available on the European Nucleotide Archive system under project accession numbers PRJEB52287 (16S sequences).

## References

Allison, S.D., Martiny, J.B.H., 2008. Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. 105, 11512–11519. https://doi.org/10.1073/pnas.0801925105.

Baruch, Z., Liddicoat, C., Laws, M., Kiri Marker, L., Morelli, H., Yan, D., et al., 2020. Characterising the soil fungal microbiome in metropolitan green spaces across a vegetation biodiversity gradient. Fungal Ecol. 47, 100939. https://doi.org/10.1016/j.funeco.2020.100939.

Baruch, Z., Liddicoat, C., Cando-Dumancela, C., Laws, M., Morelli, H., Weinstein, P., et al., 2021. Increased plant species richness associates with greater soil bacterial diversity in urban green spaces. Environ. Res. 196, 110425. https://doi.org/10.1016/j.envres.2020.110425.

Bell, C.W., Fricks, B.E., Rocca, J.D., Steinweg, J.M., McMahon, S.K., Wallenstein, M.D., 2013. High-throughput fluorometric measurement of potential soil extracellular enzyme activities. J. Vis. Exp., 50961 https://doi.org/10.3791/50961.

Béraud, C., Piola, F., Gervaix, J., Boisselet, C., Creuze Des Chatelliers, C., Defour, P., et al., 2025. Unravelling knotweed clonal control of soil microbial activities related to the nitrogen cycle through plant growth phases and ramet positions within the patch. Plant Soil. https://doi.org/10.1007/s11104-024-07190-9.

Booth, M.S., Stark, J.M., Rastetter, E., 2005. Controls on nitrogen cycling in terrestrial ecosystems: a synthetic analysis of literature data. Ecol. Monogr. 75, 139–157. https://doi.org/10.1890/04-0988.

Carrara, J.E., Walter, C.A., Hawkins, J.S., Peterjohn, W.T., Averill, C., Brzostek, E.R., 2018. Interactions among plants, bacteria, and fungi reduce extracellular enzyme activities under long-term N fertilization. Glob. Chang. Biol. 24 (6), 2721–2734.

Cavicchioli, R., Ripple, W.J., Timmis, K.N., Azam, F., Bakken, L.R., Baylis, M., et al., 2019. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586. https://doi.org/10.1038/s41579-019-0222-5.

Christel, A., Dequiedt, S., Chemidlin-Prevost-Bouré, N., Mercier, F., Tripied, J., Comment, G., et al., 2023. Urban land uses shape soil microbial abundance and diversity. Sci. Total Environ. 883, 163455. https://doi.org/10.1016/j.scitotenv.2023.163455.

De Marco, G., Vrignaud, P., Destrieux, C., De Marco, D., Testelin, S., Devauchelle, B., Berquin, P., 2009. Principle of structural equation modeling for exploring functional interactivity within a putative network of interconnected brain areas. Magn. Reson. Imaging 27 (1), 1–12.

Deparis, M., Legay, N., Castellanos, A., Duque, C., Guilloteau, U., Isselin-Nondedeu, F., et al., 2025. Linking plant diversity and urban uses at the city-block scale to inform urban planning. Land 14, 3. https://doi.org/10.3390/land14010003.

Dong, J., Zhang, J., Liu, Y., Jing, H., 2022. How climate and soil properties affect the abundances of nitrogen-cycling genes in nitrogen-treated ecosystems: a meta-analysis. Plant Soil 477, 389–404. https://doi.org/10.1007/s11104-022-05420-6.

Eldridge, D.J., Cui, H., Ding, J., Berdugo, M., Sáez-Sandino, T., Duran, J., Delgado-Baquerizo, M., 2024. Urban greenspaces and nearby natural areas support similar levels of soil ecosystem services. npj Urban Sustainability 4 (1), 15.

Escudié, F., Auer, L., Bernard, M., Mariadassou, M., Cauquil, L., Vidal, K., et al., 2018. FROGS: find, rapidly, OTUs with galaxy solution. Bioinformatics 34, 1287–1294. https://doi.org/10.1093/bioinformatics/btx791.

Fierer, N., Lauber, C.L., Ramirez, K.S., Zaneveld, J., Bradford, M.A., Knight, R., 2012. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 6, 1007–1017. https://doi.org/10.1038/ismej.2011.159.

Firestone, M.K., Davidson, E.A., 1989. Microbiological basis of NO and N2O production and consumption in soil. In: Andreae, M.O., Schimel, D.S. (Eds.), Exchange of Trace Gases between Terrestrial Ecosystems and the Atmosphere. John Wiley & Sons Ltd, pp. 7–21.

Grierson, J., Flies, E.J., Bissett, A., Ammitzboll, H., Jones, P., 2023. Which soil microbiome? Bacteria, fungi, and protozoa communities show different relationships with urban green space type and use-intensity. Sci. Total Environ. 863, 160468. https://doi.org/10.1016/j.scitotenv.2022.160468.

Groffman, P.M., Bain, D.J., Band, L.E., Belt, K.T., Brush, G.S., Grove, J.M., et al., 2003. Down by the riverside: urban riparian ecology. Front. Ecol. Environ. 1, 315–321. https://doi.org/10.1890/1540-9295(2003)001[0315:DBTRUR]2.0.CO;2.

Groffman, P.M., Law, N.L., Belt, K.T., Band, L.E., Fisher, G.T., 2004. Nitrogen fluxes and retention in urban watershed ecosystems. Ecosystems 7, 393–403. https://doi.org/10.1007/s10021-003-0039-x.
Groffman, P.M., Pouyat, R.V., Cadenasso, M.L., Zipperer, W.C., Szlavecz, K., Yesilonis, I. D., et al., 2006. Land use context and natural soil controls on plant community composition and soil nitrogen and carbon dynamics in urban and rural forests. For. Ecol. Manag. 236, 177-192. https://doi.org/10.1016/j.foreco.2006.09.002.

Groffman, P.M., Cadenasso, M.L., Cavender-Bares, J., Childers, D.L., Grimm, N.B., Grove, J.M., et al., 2017. Moving towards a new Urban Systems science. Ecosystems 20, 38-43. https://doi.org/10.1007/s10021-016-0053-4.

Hall, S.J., Huber, D., Grimm, N.B., 2008. Soil N2O and NO emissions from an arid, urban ecosystem. J. Geophys. Res. Biogeosci. 113. https://doi.org/10.1029/2007JG000523.

Hallin, S., Jones, C.M., Schloter, M., Philippot, L., 2009. Relationship between N-cycling communities and ecosystem functioning in a 50-year-old fertilization experiment. ISME J. 3, 597-605. https://doi.org/10.1038/ismej.2008.128.

Ho, A., Di Lonardo, D.P., Bodelier, P.L.E., 2017. Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol. Ecol. 93, fix006. https://doi.org/10.1093/femsec/fix006.

Howard, J.K., Dooley, K., Brauman, K.A., Klausmeyer, K.R., Rohde, M.M., 2023. Ecosystem services produced by groundwater dependent ecosystems: a framework and case study in California. Front. Water 5, 1115416.

Jarvis, S.C., 1999. 1 - Nitrogen Dynamics in Natural and Agricultural Ecosystems. In: Wilson, W.S., Ball, A.S., Hinton, R.H. (Eds.), Managing Risks of Nitrates to Humans and the Environment. Woodhead Publishing, pp. 2-20. https://doi.org/10.1533/9781845693206.2

Kinzig, A.P., Socolow, R.H., 1994. Human impacts on the nitrogen cycle. Phys. Today 47, 24-31. https://doi.org/10.1063/1.881423.

Laffite, A., Florio, A., Andrianarisoa, K.S., Creuze des Chatelliers, C., Schloter-Hai, B., Ndaw, S.M., et al., 2020. Biological inhibition of soil nitrification by forest tree species affects *Nitrobacter* populations. Environ. Microbiol. 22, 1141-1153. https://doi.org/10.1111/1462-2920.14905.

Li, Z., Tian, D., Wang, B., Wang, J., Wang, S., Chen, H.Y., Niu, S., 2019. Microbes drive global soil nitrogen mineralization and availability. Glob. Chang. Biol. 25 (3), 1078-1088.

Li, Z., Zeng, Z., Tian, D., Wang, J., Fu, Z., Zhang, F., Niu, S., 2020. Global patterns and controlling factors of soil nitrification rate. Glob. Chang. Biol. 26 (7), 4147-4157.

Liang, C., Schimel, J.P., Jastrow, J.D., 2017. The importance of anabolism in microbial control over soil carbon storage. Nat. Microbiol. 2, 1-6. https://doi.org/10.1038/nmicrol.2017.105.

Liu, C., Cui, Y., Li, X., Yao, M., 2021. Microeco: an R package for data mining in microbial community ecology. FEMS Microbiol. Ecol. 97, fiaa255. https://doi.org/10.1093/femsec/fiaa255.

Liu, C., Zhang, W., Hou, H., Liao, R., Wei, W., Sheng, R., 2023. Effect of long-term land use on the nosZI- and nosZII-containing microbial communities. Appl. Soil Ecol. 189, 104961. https://doi.org/10.1016/j.apsoil.2023.104961.

Lokatis, S., Jeschke, J.M., 2022. Urban biotic homogenization: approaches and knowledge gaps. Ecol. Appl. 32, e2703. https://doi.org/10.1002/eap.2703.

López-Gutiérrez, J.C., Henry, S., Hallet, S., Martin-Laurent, F., Catroux, G., Philippot, L., 2014. Quantification of a novel group of nitrate-reducing bacteria in the environment by real-time PCR. J. Microbiol. Methods 57, 399-407. https://doi.org/10.1016/j.jimet.2004.02.009.

Lucas, M., Rohe, L., Apelt, B., Stange, C.F., Vogel, H.J., Well, R., Schlüter, S., 2024. The distribution of particulate organic matter in the heterogeneous soil matrix—balancing between aerobic respiration and denitrification. Sci. Total Environ. 951, 175383. https://doi.org/10.1016/j.scitotenv.2024.175383.

Maillard, F., Leduc, V., Bach, C., Thébault, E., Reichard, A., Morin, E., et al., 2023. Aboveground organic matter removal reshapes soil microbial functional group balance in temperate forests. Appl. Soil Ecol. 184, 104776. https://doi.org/10.1016/j.apsoil.2022.104776.

Nemergut, D.R., Shade, A., Violle, C., 2014. When, where and how does microbial community composition matter? Front. Microbiol. 5. https://doi.org/10.3389/fmicb.2014.00497. Available at: Accessed September 29, 2023.

Norini, M.-P., Hellal, J., Bonthoux, S., Arnoldi, G., Boscardin, R., Cantarel, A., et al., 2025. Dataset Of Physicochemical, Microbiological, And Plant Root Parameters Of 135 Soils From Various Urban Land Uses in Blois City. Data Brief, in press, France.

Oksanen, J., 2010. Vegan: Community Ecology Package. Httpvegan-Forg.-Proj. Available at https://cir.nii.ac.jp/crid/1570291225091856896. (Accessed 6 September 2023).

Ouyang, Y., Evans, S.E., Friesen, M.L., Tiemann, L.K., 2018. Effect of nitrogen fertilization on the abundance of nitrogen cycling genes in agricultural soils: a meta-analysis of field studies. Soil Biol. Biochem. 127, 71-78. https://doi.org/10.1016/j.soilbio.2018.08.024.

Philippot, L., Cuhel, J., Saby, N.P.A., Chêneby, D., Chronáková, A., Bru, D., et al., 2009. Mapping field-scale spatial patterns of size and activity of the denitrifier community. Environ. Microbiol. 11, 1518-1526. https://doi.org/10.1111/j.1462-2920.2009.01879.x.

Pickett, S.T.A., Cadenasso, M.L., Grove, J.M., Boone, C.G., Groffman, P.M., Irwin, E., et al., 2011. Urban ecological systems: scientific foundations and a decade of progress. J. Environ. Manag. 92, 331-362. https://doi.org/10.1016/j.jenvman.2010.08.022.

Pouyat, R.A., Szlavecz, K., Yesilonis, I.D., Groffman, P.M., Schwarz, K., 2010. Chemical, physical, and biological characteristics of urban soils. In: Urban Ecosystem Ecology. John Wiley & Sons, Ltd, pp. 119-152. https://doi.org/10.2134/agronmonogr55.c7.

Prescott, C.E., Grayson, S.J., 2013. Tree species influence on microbial communities in litter and soil: current knowledge and research needs. For. Ecol. Manag. 309, 19-27. https://doi.org/10.1016/j.foreco.2013.02.034.

Prosser, J.J., Nicol, G.W., 2012. Archaeal and bacterial trends in soil: the quest for niche specialisation and differentiation. Trends Microbiol. 20, 523-531. https://doi.org/10.1016/j.tim.2012.08.001.

R Core Team, 2020. R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria.

Ramirez, K.S., Leff, J.W., Barberán, A., Bates, S.T., Betley, J., Crowther, T.W., et al., 2014. Biogeographic patterns in below-ground diversity in new York City's Central Park are similar to those observed globally. Proc. R. Soc. B Biol. Sci. 281, 20141988. https://doi.org/10.1098/rspb.2014.1988.

Rega-Brodsky, C.C., Aronson, M.F.J., Piana, M.R., Carpenter, E.-S., Hahs, A.K., Herrera-Montes, A., et al., 2022. Urban biodiversity: state of the science and future directions. Urban Ecosyst. 25, 1083-1096. https://doi.org/10.1007/s11252-022-01207-w.

Ren, Y., Shao, Q., Ge, W., Li, X., Wang, H., Dong, C., et al., 2023. Assembly processes and biogeographical characteristics of soil bacterial sub-communities of different habitats in urban green spaces. Curr. Microbiol. 80, 309. https://doi.org/10.1007/s00284-023-03428-4.

Rosseel, Y., 2012. lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, 1-36.

Schimel, J., Balser, T.C., Wallenstein, M., 2007. Microbial stress-response physiology and its implications for ecosystem function. Ecology 88, 1386-1394. https://doi.org/10.1890/06-0219.

Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., et al., 2009. Introducing moth: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537-7541. https://doi.org/10.1128/AEM.01541-09.

Schlüter, S., Lucas, M., Grosz, B., Ippisch, O., Zawallich, J., He, H., Well, R., 2025. The anaerobic soil volume as a controlling factor of denitrification: a review. Biol. Fertil. Soils 61 (3), 343-365. https://doi.org/10.1007/s00374-024-01819-8.

Smith, J.L., Doran, J.W., 1997. Measurement and use of pH and electrical conductivity for soil quality analysis. Methods for assessing soil quality 49, 169-185.

Stephanou, C., Omirou, M., Philippot, L., Zissimos, A.M., Christoforou, I.C., Trajanoski, S., et al., 2021. Land use in urban areas impacts the composition of soil bacterial communities involved in nitrogen cycling. A case study from Lefkosia (Nicosia) Cyprus. Sci. Rep. 11, 8198. https://doi.org/10.1038/s41598-021-87623-y.

Stuchiner, E.R., Jernigan, W.A., Zhang, Z., Eddy, W.C., DeLucia, E.H., Yang, W.H., 2024. Particulate organic matter predicts spatial variation in denitrification potential at the field scale. Geoderma 448, 116943. https://doi.org/10.1016/j.geoderma.2024.116943.

Terrat, S., Horrigue, W., Dequietd, S., Saby, N.P.A., Lelièvre, M., Nowak, V., et al., 2017. Mapping and predictive variations of soil bacterial richness across France. PLoS One 12, e0186766. https://doi.org/10.1371/journal.pone.0186766.

Thompson, G.L., Kao-Kniffin, J., 2019. Urban grassland management implications for soil C and N dynamics: a microbial perspective. Front. Ecol. Evol. 7, 315.

Wang, J., Chadwick, D.R., Cheng, Y., Yan, X., 2018. Global analysis of agricultural soil denitrification in response to fertilizer nitrogen. Sci. Total Environ. 616-617, 908-917. https://doi.org/10.1016/j.scitotenv.2017.10.229.

Wang, Y., Ji, H., Wang, R., Guo, S., 2019. Responses of nitrification and denitrification to nitrogen and phosphorus fertilization: does the intrinsic soil fertility matter? Plant Soil 440, 443-456. https://doi.org/10.1007/s11014-019-04108-8.

Wang, Z., Tollervey, J., Briese, M., Turner, D., Ule, J., 2009. CLIP: construction of cDNA libraries for high-throughput sequencing from RNAs cross-linked to proteins in vivo. Methods 48, 287-293. https://doi.org/10.1016/j.jymeth.2009.02.021.

Yang, J., Masoudi, A., Li, H., Gu, Y., Wang, C., Wang, M., et al., 2024. Soil Total nitrogen mediated the impact of climatic factors on urban soil organic matter under different land uses. J. Soil Sci. Plant Nutr. 24, 5487-5504. https://doi.org/10.1007/s42729-024-01921-8.

Zhang, L.-M., Hu, H.-W., Shen, J.-P., He, J.-Z., 2012. Ammonia-oxidizing archaea have more important role than ammonia-oxidizing bacteria in ammonia oxidation of strongly acidic soils. ISME J. 6, 1032-1045. https://doi.org/10.1038/ismej.2011.168.

Zhang, S., Xia, X., Yu, L., Liu, S., Li, X., Wang, J., et al., 2024. Biogeography and impact of nitrous oxide reducers in rivers across a broad environmental gradient on emission rates. Environ. Microbiol. 26, e16622. https://doi.org/10.1111/1462-2920.16622.

Zhu, W.-X., Dillard, N.D., Grimm, N.B., 2004. Urban nitrogen biogeochemistry: status and processes in green retention basins. Biogeochemistry 71, 177-196. https://doi.org/10.1007/s10533-004-9683-2.

Zhu, X., Jackson, R.D., DeLucia, E.H., Tiedje, J.M., Liang, C., 2020. The soil microbial carbon pump: from conceptual insights to empirical assessments. Glob. Chang. Biol. 26, 6032-6039. https://doi.org/10.1111/gcb.15319.