1. Introduction
Soil is an essential component of terrestrial ecosystems and one of the most species-rich habitats, hosting at least one quarter of all living organisms on the planet [1,2,3]. Soils provide a vast set of functions and ecosystem services critical to both human well-being and biodiversity conservation [4,5,6] through the complex interactions that occur between their abiotic and biotic components [3,7]. Soil biota is indeed fundamental in soil formation and functioning [8].
Over the last decades, soils have faced intense pressures driven by human activities worldwide, resulting in irreversible loss and degradation, with significant negative impacts on the services they provide [9]. In urban ecosystems, soils can be particularly affected and profoundly altered by extensive human activities and disturbances [4,10]. Urban land conversion, mainly determined by construction, transportation, and industrial activities, alters existing habitats and gives rise to new ones, leading to profound changes in soil properties. Major disturbances such as excavating, mixing, compacting, and soil relocation disrupt underground ecosystems [6,11]. Additionally, hydrological alterations, pollution, landscaping techniques, and the introduction of alien species further contribute to altering the soil’s biotic composition and functionality [12]. In particular, physical disturbances [13], lawn maintenance practices [14,15], compaction from trampling [16,17], atmospheric deposition [18], higher temperatures [19], and altered organic resource availability for decomposers [10] severely affect the characteristics of urban soils. Consequently, urban soils commonly exhibit heavily modified structures, altered layering, increased alkalinity, lower organic matter, and elevated pollutant concentrations [6]. These alterations often impair their functionality, diminishing their capacity to meet ecological and human needs [4]. Nonetheless, due to this multifaceted human influence, urban soils are highly diversified, ranging from relatively undisturbed soils in native vegetation patches to completely disrupted and transformed soils in the most impacted areas [20,21,22]. However, despite their more or less profoundly altered nature, urban soils are sometimes able to provide many of the same ecosystem services as nonurban soils [4,6,10].
In this context, urban green spaces (e.g., forest remnants, urban parks and gardens, allotments, and cemeteries, as well as agricultural areas and abandoned lands) are recognized for their significant ecological benefits, particularly in high-density cities [6,23]. These spaces serve as primary sources of biodiversity, and some of them may provide locations with minimally altered and relatively well-functioning soils. Such soils are essential for delivering many of the soil-related ecosystem services within urban environments [4,23]. However, even the soil of these areas can be subject to anthropogenic alterations, especially from the surrounding urban matrix. Therefore, the preservation, sustainable management, and restoration of soil habitats are critical for advancing green urban development [4]. For the correct management of urban soils and the preservation of their roles in urban ecosystems, however, accurate assessments of the soil quality of green spaces are essential.
Soil fauna, particularly arthropods and some other invertebrates (e.g., earthworms, enchytraeids and nematodes), have long been recognized and utilized as valuable bioindicators (e.g., [24,25,26,27]). Their pivotal roles in maintaining nutrient cycling, enhancing soil structure and stability, improving soil aeration and water use efficiency, sequestering carbon, and helping regulate energy flow within the soil food web make soil invertebrates useful indicators of ecological change [28,29]. The use of soil invertebrates as bioindicators can therefore provide a comprehensive assessment of the soil’s ecological health, responding rapidly to subtle environmental stressors, and offering insights beyond the scope of the physical, chemical, and microbial evaluation techniques that are traditionally applied in soil monitoring [8,29].
Among soil invertebrates, microarthropods play crucial roles in soil ecosystems. By occupying multiple trophic levels within the soil food webs, they actively contribute to regulating soil functions and maintaining ecosystem health [30,31]. Detritivores and microbivores facilitate organic matter decomposition and nutrient cycling, while predators and parasites help control consumer populations [30]. As integral components of the complex soil food web, microarthropods serve as a food source for higher-level invertebrates and vertebrates and influence key ecosystem processes and soil stability [31]. Additionally, due to their limited mobility and dispersal ability compared to other arthropods, they are particularly sensitive to microenvironmental changes [12]. For these reasons, along with their high density and morphological, taxonomic, and functional diversity, as well as their ease of sampling, microarthropods may serve as particularly valuable biological indicators [7,26,32,33]. The study of microarthropod communities and their use as bioindicators in relation to different land-use types in urban environments has, however, received less attention compared to their use in forests, grasslands, or agricultural settings, despite having proven to be particularly effective (see [12] and the references therein).
To characterize soil invertebrate communities, ecologists frequently use diversity indices, which, however, do not account for the taxon’s ecological role, being therefore of limited utility in assessing the influence of environmental characteristics (especially disturbance) on soil health [33]. To address this limitation, in the past two decades, several integrative indices have been specifically conceived to evaluate the biological quality of the soil [29]. One such index is the QBS-ar (Soil Biological Quality index based on Arthropods) [34,35]. This index, which is based on the concept that the microarthropods highly adapted to the soil environment are those most sensitive to soil degradation, has been widely applied [36,37]. However, studies using the QBS-ar approach have primarily focused on agricultural soils (e.g., [38,39,40,41,42]) or natural soils (e.g., [36,37,42,43,44,45,46]), with particular attention to soil restoration projects (e.g., [47,48]). The application of QBS-ar in peri-urban areas or urban green spaces is less common but has proven to be valuable in assessing the effects of disturbance, management, or contamination on soil preservation [17,33,49,50,51].
Among the largest Mediterranean cities, Rome likely has one of the most complex histories, spanning over 2500 years, making it one of the oldest continuously inhabited human settlements in the world. For almost two millennia, most of the area now covered by urban Rome was occupied by the so-called ‘Campagna Romana’, a semi-natural mosaic of rural (agricultural, seminatural, and natural) landscapes [52]. Until Rome’s annexation into the Kingdom of Italy in the 1860s, the city covered just 14 km2, with only a small portion developed and housing around 200,000 people [53]. In the 20th century, however, Rome’s urban expansion accelerated, transforming what had been a virtually uninhabited rural landscape into a densely populated urban area. This rapid, often uncontrolled, growth embedded remnants of the ‘Campagna Romana’ as green enclaves within the urban matrix of built-up surfaces, with a few green spaces maintaining a direct connection with rural landscapes [54]. The arthropod fauna of Rome has been investigated since the early 19th century [53,55,56], but interest in soil-dwelling arthropods is more recent and has been researched primarily in the last decades. These studies mainly focused on the analysis of the structure and dynamics of communities of Araneae [57]), Chilopoda [58,59,60,61], Collembola [62], Coleoptera Carabidae [60,63,64,65] and Tenebrionidae [65,66,67,68,69], and Isopoda Oniscidea [59,60,62]. However, none of these studies specifically examined the structure and composition of soil-dwelling microarthropod communities or their relationship with the impact of urbanization on soil ecosystems. Additionally, no study aimed to assess the biological soil quality of different land-use types within urban Rome, linking it to the chemo-physical soil properties and landscape characteristics measured at the same sites.
The present study aims to evaluate the influence of urbanization on soil biological quality and soil microarthropod communities in urban Rome by analyzing their diversity and degree of specialization (in terms of biological forms) across a set of green spaces representing different land-use types, along a gradient of disturbance and management intensity from the city center to places outside the urban areas. Specifically, we expect the following: (1) soils from different urban land-use types will vary in key chemical and physical parameters; (2) urbanization, mainly through soil alteration, will generally have a negative impact on microarthropod communities, leading to both taxonomic and functional (eco-morphological) changes in community composition; (3) soil biological quality will decline with increasing disturbance and management intensity in green spaces; and (4) microarthropod responses to urbanization will differ among biological forms, with highly specialized ones being the first to decrease as disturbance intensifies. However, based on findings from other studies [50], we also expect that some urban green spaces, particularly those that are less managed, may retain a relatively high level of biological quality.
2. Materials and Methods
2.1. Study Area, Sampling Sites and Sampling Design
The study was carried out in urban and suburban areas in the Municipality of Rome, Italy’s capital city and one of the largest and most populated southern European cities. The city is located in central Italy (41°54′00″ N, 12°29′00″ E), between the Tyrrhenian Sea and the Central Apennines. The area is characterized by sub-Mediterranean climatic conditions, with moderate drought during the summer period [70]. The mean annual precipitation is 878 mm (570 mm in autumn–winter; 308 mm in spring–summer) and the mean annual temperature is 17.1 °C (12 °C in autumn–winter; 22.1 °C in spring–summer) [71]. Altitude ranges between 15–20 m a.s.l. (SW districts) and 139 m a.s.l. (Monte Mario). The geological substrate is composed of the following two main types: (1) sedimentary deposits from Plio-Pleistocene marine sediments (mainly clay and marl) and Pleistocene alluvial sediments (sand and gravel); (2) volcanic products (mainly tuff, tuffite, and pozzolans) derived from the activity of the Sabatini Volcano District (NW of Rome) and the Albano Volcano District (SE of Rome) [72]. The natural and seminatural forest vegetation of suburban or extra-urban green spaces is mainly characterized by fragments of mixed woods of Quercus cerris L. and Quercus frainetto Ten., sometimes with Quercus suber L. and Quercus robur L., which represent the potential natural vegetation in this low-altitude part of central Italy [73].
Following the most commonly used approach of previous research in the study area, urban Rome was here defined as the area within the ‘Grande Raccordo Anulare’ (GRA; literally, the Great Ring Road), a major circular motorway that encompasses an area of 345 km2, with a population of about 2.9 million inhabitants [53,54,66]. Approximately half of this area is occupied by green spaces, including historical villas, archeological sites, gardens, parks, grasslands, pastures, and uncultivated grounds, even if the portions of territory referring to these last three typologies have further shrunk in the last two decades.
Sampling sites were chosen according to the protocol of the Global Urban Soil Ecology and Education Network (GLUSEEN, [22]), slightly adapted to the specific conditions of the study area. Four soil habitats (land-use types) in four replicates were sampled along a disturbance gradient (Figure S1), as follows: (1) urban unmanaged green spaces (high disturbance–low management; UNMAN, roughly corresponding to RUD in the GLUSEEN protocol), represented by disturbed but unmanaged green spaces with relatively young soils, partly covered with grass and opportunistic arboreal vegetation (<30%); (2) urban managed green spaces (high disturbance–medium management; MAN, roughly corresponding to TURF in the GLUSEEN protocol), represented by public urban green spaces such as recreational areas in highly frequented urban parks with low tree cover (<50%) and prevalence of herbaceous cover, maintained by regular mowing; (3) urban forests (low disturbance–low management; UF, roughly corresponding to REM in the GLUSEEN protocol), represented by sites characterized by a vegetation similar to that of the reference sites, but located in protected areas or in very large historical parks in suburban areas with moderate or low urban influence; (4) reference sites (REF), that is, green spaces located outside the GRA perimeter, representing (semi)natural conditions with Quercus spp. as the dominant species, characteristic of the potential natural vegetation in this low-altitude region of central Italy [73]. Further information on the sampling site is given in Table S1, Supplementary Materials. Sampling was conducted in spring, between 20 April and 5 May, with each site sampled only once. At each sampling site, three soil replicates (10 cm × 10 cm × 10 cm) at least 10 m apart were collected, following the QBS-ar protocol [34,35,74].
2.2. Soil Characterization and Landscape Characteristics
The three replicates from the same site were pooled for physical and chemical analyses. Soil conductivity (dS/m), soil pH, organic matter (OM%), soil organic carbon (SOC%), total N%, assimilable P2O5 (mg/kg), exchangeable CaO and MgO (mg/kg), Na%, and cation exchange capacity (CEC; mEq/100 g) were determined following the standards officially established by the Italian government (D.M. 13/09/1999 GU SO n° 248 21/10/1999 protocol). The carbon-to-nitrogen ratio (C/N) was also included as an indicator of organic matter quality [75]. Soil compaction (kg/cm2; measured with a soil penetrometer ST207, Drill Service, Mosciano Sant’Angelo, Italy, using a 10 mm tip), soil moisture (%), and temperature at 5 cm depth, along with air temperature at the soil surface (measured with a YINMIK YK-S02EU hygrometer, JiNan Huiquan Electronic, Guangzhou, China and an RC-51H Data Logger, ELITechGroup, Turin, Italy, respectively), were recorded in the field with ten measurements per replicate, and these were averaged for each site. Building on previous research [51,76], we considered not only local environmental factors but also the broader landscape context, which can significantly influence the communities of soil-dwelling invertebrates, which are highly sensitive to landscape composition [77]. To quantify the urban intensity around each study site, we calculated an Urban Intensity Index (UI) within a 400 m × 400 m buffer area, integrating elements like vegetation cover, building density, and the proportion of sealed surfaces (e.g., roads and pavements), as described by Liker et al. [78]. Additionally, the UI calculation accounted for the proximity to the Rome city center, measured as the distance to Piazza Barberini (41°54′13″ N, 12°29′18″ E; see Fattorini et al. [63]). Higher UI values indicate higher urbanization. We also estimated the vegetation cover using the averages of 16-day EVI (Enhanced Vegetation Index) data at 250 m of spatial resolution, downloaded from the Application for Extracting and Exploring Analysis Ready Samples for the sampling period (AppEEARS [79]). EVI was preferred over the most widely used Normalized Difference Vegetation Index (NDVI) because it offers several advantages and has been found to be more suitable for assessing urban vegetation [80].
2.3. Microarthropod Collection and Identification
Soil microarthropods were extracted using Berlese–Tullgren funnels [81], equipped with a mesh screen of 3 mm and an incandescent lamp (60 W), for 15 days. Soil arthropods were then sorted under a stereomicroscope into biological forms (BFs), following the QBS-ar protocol [34,35,74]. The three replicates for each site were processed separately and preserved in 70% ethanol.
2.4. Soil Biology Quality Indices
The biological quality of the soil was assessed using the QBS-ar index [34,35] and its modification that also considers abundances (QBS-ab [39]). The QBS-ar index measures soil quality based on the morphological specialization of arthropod groups to the edaphic life, with a larger number of morphologically highly adapted microarthropod groups indicating higher soil quality. In the QBS-ar calculation, soil arthropods are categorized into biological forms (BFs) according to their adaptation to the soil environment. For the taxonomic groups in which all species share a similar level of adaptation, the BF corresponds directly to the taxonomic group (usually at the order level). For groups with a variable degree of specialization, different categories are recognized (epiedaphic, low specialization; emiedaphic, intermediate specialization; euedaphic, high specialization) [74]. For the QBS-ar calculation, each BF receives an Eco-Morphological Index (EMI) score from 1 (low adaptation) to 20 (highest adaptation). The QBS-ar value of a sample is the sum of the EMIs for all collected groups among the three replicates, with the highest EMI value used when multiple eco-morphological forms are present within the same group. The QBS-ab index expands on QBS-ar by incorporating the abundance of biological forms rather than just their presence/absence. QBS-ab is calculated by multiplying a group’s EMI score by its abundance, expressed as the log10(x + 1)-transformed number of individuals to reduce the effect of highly abundant groups (primarily Acari and Collembola) [39].
2.5. Microarthropod Density and Diversity
The diversity and overall density (individuals/m2) were also used to characterize the soil microarthropod communities. Diversity was calculated using Hill’s numbers (qD), a mathematically unified family of diversity indices that differ only by the exponent q (which represents the diversity order) [82]. While Hill numbers are typically used to quantify diversity based on the number of individuals belonging to different species, they can also be applied to other categories (see, for example, their use for chemical diversity [83] and biogeographical diversity [84]). In the present research, they were calculated using the number of individuals belonging to the different biological forms (BFs). Hill’s numbers are always perfectly comparable since they are expressed in units of effective numbers, that is, the number of equally abundant categories that would produce the observed diversity [85]. We used Hill’s number to depict the diversity profiles of microarthropod communities of different land-use types by varying the parameter q from 0 to 5 and using biological forms instead of species [86]. When q = 0, the respective Hill’s number (H0) corresponds to the number of BFs (BF richness) and the contribution of rare BFs is emphasized; when q = 1, the obtained Hill’s number (H1) corresponds to the exponential version of Shannon diversity; when q = 2, the Hill number (H2) corresponds to the inverse of Simpson dominance and the influence of the most abundant BFs is emphasized. At higher q values, the corresponding Hill’s numbers tend to the inverse of the Berger-Parker dominance index, thus overemphasizing the contribution of the most abundant BFs [82,87].
2.6. Statistical Analyses
Prior to the analyses, the dataset was explored following the data exploration framework proposed by Zuur et al. [88]. Boxplots and Cleveland dot plots were used to identify potential outliers in both the dependent and independent variables. No outliers were detected. Since the independent variables were on different scales, they were standardized as z-scores before model construction and multivariate analyses.
To investigate how the chemo-physical properties and landscape characteristics varied among the sampling sites according to the land-use classification, a Principal Components Analysis (PCA) with a correlation matrix approach using the R package FactoMineR [89] was applied. Differences across land-use types for each variable were tested with an Analysis of Variance (ANOVA), followed by Tukey’s HSD tests for pairwise comparisons, using the R package stats [90].
In accordance with previous studies [42,43,51], extremely rare categories with a relative abundance < 0.1% and frequency < 20% (Opiliones, Palpigrada, Diptera, Hymenoptera adults, and Hymenoptera larvae) were retained for the QBS-ar calculation but excluded from all other analyses.
Hill’s numbers and diversity profiles were calculated using the R package hilldiv [91].
The effects of land-use type and of the most relevant environmental properties of soil habitats on the density and diversity of soil microarthropod communities, as well as on the QBS-ar and QBS-ab indices, were assessed by means of generalized linear models (GLMs) using the R package MASS [92]. Prior to model construction, multi-collinearity among covariates was assessed through Pearson’s r and the Variance Inflation Factor (VIF). Highly correlated independent variables (Pearson’s r > 0.70 and VIF ≥ 10) were excluded from the same model, and their effects were eventually tested in separate models. Given the strong and unavoidable correlation between the land-use types and most of the environmental variables, these two predictor classes were tested in separate models. For comparisons across land-use types, the least-squares means (estimated marginal means) were employed to assess the least significant difference between the various land-use types. The environmental properties considered as explanatory variables, retained after multi-collinearity checking, included vegetation cover (EVI), soil conductivity, soil pH, C/N ratio, assimilable P2O5, cation exchange capacity, exchangeable MgO and CaO, soil compaction, temperature, and moisture. To avoid overparameterization, we initially identified the six most significant predictors for every response variable using a random forest algorithm (“randomForest” and “varImpPlot” functions of the R package randomForest [93]). After fitting the full model with the six variables identified through the random forest algorithm, the best model was selected using the automated selection performed by the “dredge” function of the MuMIn R package [94], comparing all candidate models. The final model was chosen as the one with the lowest AICc and the highest weights within a ΔAICc < 2 [95]. The coefficient of determination (R2) was used to evaluate the goodness of fit of the models. The assumptions (normality and homoscedasticity of residuals) were checked using the DHARMa R package [96].
Non-Metric Multidimensional Scaling (NMDS) analyses using Bray–Curtis dissimilarity were performed on the abundance and the EMI scores of biological forms to visualize differences in the microarthropod communities across the four land-use types. The analyses were carried out using the function “metaMDS” of the R package vegan [97]. NMDS analyses were followed by PERMANOVA analyses [98] with 9999 permutations to statistically test the effects of land-use type on the composition of microarthropod communities using the R package vegan.
In order to test and visualize the relationship between the soil microarthropod community structure and the environmental properties, Canonical Correspondence Analyses (CCAs) on the abundance of the biological forms and their EMI scores were performed using the R package vegan. Because of the strong collinearity among some of the measured soil parameters, a subset of them was considered as a constrained variable, based on Pearson’s r values and ecological importance. The soil parameters retained in the model were as follows: EVI, soil conductivity, soil pH, C/N ratio, total N%, assimilable P2O5, exchangeable CaO and MgO, Na%, cation exchange capacity, soil compaction, and soil temperature at a depth of 5 cm.
Finally, an Indicator Taxa Analysis was conducted on the abundance data using the “multipatt” function from the R package indicspecies [99] to identify potential associations between soil microarthropod biological forms (BFs) and land-use types.
All statistical analyses were conducted in RStudio v4.2.2 [100].
3. Results
3.1. Soil and Landscape Characteristics of Urban Green Spaces
Sites belonging to different land-use types differed in their soil chemo-physical and landscape characteristics (Figure 1, Table 1). Highly disturbed urban soils (urban managed and unmanaged) were found to be much more alkaline and sodic compared to soils of urban forests or reference sites and exhibited lower levels of total organic matter, soil organic carbon, and total N. In contrast, the C/N ratio differed significantly only between urban unmanaged and reference sites (Table 1). Significant differences were also observed for cation exchange capacity (CEC), with urban forest sites exhibiting the highest values, as well as for soil compaction and temperature, which showed a clear gradient from disturbed managed soils to reference soils (Table 1). Regarding landscape characteristics (Urban Intensity Index and Enhanced Vegetation Index), significant differences were observed only between predominantly open land-use types (urban managed and urban unmanaged) and woody land-use types (urban forest and reference) (Table 1).
3.2. Soil Microarthropod Diversity and Biological Quality
A total of 18,553 individual microarthropods belonging to 33 biological forms (BFs) were extracted from the 48 soil samples collected at the 16 sampling sites. Of these, 18,508 specimens belonging to 28 biological forms were retained and used for the calculation of the diversity indices and statistical analyses (Table 2).
The most common BFs were Acari, Symphyla, emiedaphic Collembola, and Hymenoptera Formicidae, which were collected at all sites. BFs with a frequency ≥ 80% included Isopoda, Diplopoda, epiedaphic Collembola, Diplura, epiedaphic Coleoptera, Coleoptera larvae, and Thysanoptera. Pseudoscorpiones, Diplopoda Polyxenida, Chilopoda (both Geophilomorpha and other orders), Pauropoda, euedaphic Collembola, Protura, emiedaphic Coleoptera, Diptera larvae, Hemiptera, and Psocoptera had a frequency ≥ 40%. Araneae were the least common BF, being collected at approximately 30% of the sites (Table 2).
Acari were by far the most abundant BF, with a total mean density of 19,941 ± 1366 individuals/m2, accounting for 51.7% of the total number of sampled individual arthropods. They were followed by emiedaphic and epiedaphic Collembola, with total mean densities of 8131 ± 828 (21.1%) and 4102 ± 439 (10.7%) individuals/m2, respectively. The remaining 25 BFs collectively accounted for approximately 16.5% of the total collected microarthropods. The total density per site ranged from 19,967 individuals/m2 (Villa Celimontana, a managed site, MAN2) to 57,100 individuals/m2 (Monte Mario, an urban forest, UF3) (Table S2, Supplementary Materials). The highest mean microarthropod density was observed in urban forests (48,017 ± 5592), while the lowest was observed in urban managed green spaces (26,342 ± 2515). However, we found significant differences (p < 0.05) only between the urban managed spaces and the reference sites and between the urban managed spaces and the urban forests (Table 2). The mean (±SE) densities of microarthropod BFs in the four investigated land-use types are given in Table 2.
The QBS-ar and QBS-ab values significantly differed among the land-use types (Table 2; Figure 2). The lowest mean QBS-ar value was observed in the urban managed land-use type (115.25, range 107–128), while the highest mean value was recorded in the urban forest land-use type (210.75, range 202–219). Significant differences were found among all land-use types (p < 0.01), except between urban forest and reference land-use types (Table 2). The lowest mean QBS-ab value was again observed in the urban managed land-use type (131.71, range 123.3–144.6), while the highest mean value was recorded in the reference land-use type (257.82, range 251.8–267.1). Significant differences were found only between the disturbed (urban managed + unmanaged sites) and undisturbed (urban forest + reference sites) land-use types (p < 0.001) (Table 2).
We found significant differences (p < 0.05) in microarthropod BF diversity between different land-use types (Table 2; Figure 3). Hill’s number H0 (which corresponds to the number of BFs per site) ranged from 14 (urban managed green sites, MAN1 and MAN2) to 28 (an urban forest site, UF1) (Table S2, Supplementary Materials), with significant differences found between all land-use types except between urban forest and reference sites (Table 2). H1 (which is the exponential version of Shannon diversity) ranged between 3.93 (an urban managed site, MAN2) and 7.66 (a reference site, REF3) (Table S2, Supplementary Materials). Both managed and unmanaged land-use types significantly differed from the reference land-use type, while no significant differences were found between them as well as between urban forest and both unmanaged and reference land-use types (Table 2). H2 (which is the inverse of Simpson dominance) ranged between 2.27 (a managed site, MAN2) and 4.52 (a reference site, REF2). Significant differences were found only between the urban managed land-use type and the reference land-use type (Table 2). Diversity profiles based on the mean values for each land-use type indicated that when the contribution of rare groups is emphasized (H0), microarthropod diversity in urban forest communities is higher but comparable to that of the reference land-use type, with both showing greater diversity than the urban unmanaged land-use type, while the urban managed communities had the lowest diversity (Figure 3). When neither rare nor common groups are emphasized (H1), the reference land-use type had a slightly higher (statistically not different) diversity compared to the urban forest. This difference increased when the contribution of common groups was emphasized (H2), though no significant difference was observed. The difference between urban forest, urban managed, and urban unmanaged land-use types decreased as the emphasis on common groups increased (q > 2). For orders of diversity of q = 3 or higher, no significant differences were found among any of the four land-use types. In general, overemphasizing the contribution of common BFs reduces the differences in the diversity of the microarthropod communities of the different land-use types.
Non-Metric Multidimensional Scaling (NMDS) based on the abundance (Figure 4A) and ecomorphological scores (Figure 4B) revealed a clear separation of microarthropod communities of different land-use types, except for urban forest and reference sites, which largely overlapped. Results of PERMANOVA supported the NMDS results on both the abundance and eco-morphological scores, indicating that the land-use type had significant effects on soil microarthropod community structure (p < 0.001, Table S3, Supplementary Materials).
3.3. Environmental Factors and Soil Microarthropod Diversity
The richness of biological forms (Hill’s number H0) across the four land-use types was negatively influenced by soil compaction (p < 0.01) and positively influenced by cation exchange capacity (CEC, p < 0.05), while diversity expressed by the exponential version of Shannon diversity and the inverse of Simpson dominance (Hill’s numbers H1 and H2, respectively) were both positively related to the enhanced vegetation index (EVI, p < 0.001 and p < 0.01, respectively) (Table 3; Figure 5). Similarly to H0, microarthropod density was negatively influenced by soil compaction (p < 0.05) and positively influenced by CEC (p < 0.05).
QBS-ar was found to be sensitive to soil compaction and pH (p < 0.001 and p < 0.01, respectively), while QBS-ab was sensitive to compaction (p < 0.05), cation exchange capacity (CEC, p < 0.01), and soil temperature (p < 0.01) (Table 3; Figure S2).
The relationships between the distribution of microarthropod biological forms (BFs) across land-use types and the associated environmental variables were investigated using Canonical Correspondence Analyses (CCAs) (Figure 6). In a model based on abundance data (number of sampled individuals of each BF per site, Figure 6A), the constrained axis explained 57.2% of the total variance, with CCA1 and CCA2 accounting for 46.0% and 7.4% of the total variance, respectively. The remaining 3.8% was explained by CCA3. Both the first two axes were found to be significant (with p < 0.001 and p < 0.05, respectively) (Table S4, Supplementary Materials). Soil pH, soil compaction, and C/N ratio had significant effects on microarthropod communities (p < 0.05, p < 0.001, p < 0.01, respectively), with the abundances of most of the euedaphic forms (mainly Collembola, Polyxenida, and Pauropoda) being positively correlated with the C/N ratio and negatively correlated with pH and soil compaction. Overall, in the multidimensional space, communities are well separated according to the land-use types. The reference and urban forest sites were mainly influenced by the C/N ratio, while urban managed and unmanaged sites were mainly influenced by soil pH and soil compaction.
In the model based on the ecomorphological scores, in which BFs were weighted by their specialization to the edaphic life (EMI scores) instead of their abundance, the constrained axis explained 36.8% of the total variance, with CCA1 and CCA2 accounting for 28.8% and 8.0% of the total variance, respectively (Figure 6B). Only the first axis was found to be significant (p < 0.001) (Table S4, Supplementary Materials). Soil pH and soil compaction had significant effects on the functional composition of the microarthropod communities (p < 0.001). In general, the four land-use types showed microarthropod communities characterized by different levels of adaptation to edaphic life, with pH and compaction exerting negative effects on the most specialized BFs.
The Indicator Taxa Analysis further highlighted the habitat preference of certain euedaphic forms. Specifically, Pauropoda, Polyxenida, Geophilomorpha, and euedaphic Collembola were significantly associated with less disturbed land-use types (urban forest and reference sites, p < 0.05) (Table S5, Supplementary Materials). Moreover, Isopoda and Diptera larvae were also found to be associated with sites of urban unmanaged land-use type (p < 0.05), in addition to urban forest and reference sites (Table S5, Supplementary Materials).
4. Discussion
To the best of our knowledge, this study represents the first comprehensive analysis of the structure and composition of soil microarthropod communities in relation to the soil conditions and biological soil quality of green spaces across a wide rural–urban gradient in a large Italian city.
Our findings indicate that there are important differences in the soil chemo-physical properties and landscape characteristics among the four land-use types investigated. Specifically, highly disturbed urban soils, such as those found in managed (MAN) and unmanaged (UNMAN) urban green spaces, displayed marked alkalinity, sodicity, compaction, and diminished levels of moisture, organic matter (OM), soil organic carbon (SOC), total nitrogen (N%), and cation exchange capacity (CEC). These results align with previous research that attributes such alterations of urban soils to urbanization-driven factors, including soil compaction, contamination, and vegetation removal [101,102,103,104]. In this regard, it is also true that the markedly higher vegetation cover in the reference forests and urban forests likely plays a crucial role in maintaining specific soil and microclimatic conditions [105]. Notably, CEC was observed to be significantly higher in the urban forest (UF) sites compared to the reference (REF) sites. This may be the result of some direct or indirect effects of urbanization, such as anthropogenic soil amendments, deposition of atmospheric particles, and reduced leaching—factors to which urban forests are still exposed, albeit to a lesser extent than managed and unmanaged green spaces. The combination of high organic matter content and these factors may enhance CEC in urban forests compared to reference forests [106,107].
Our results underscore the influence of land-use types, which represent distinct subsets of human impact on soil ecosystems, on microarthropod communities, and consequently, on biological soil quality as measured using the QBS-ar and QBS-ab indices.
The total mean density of microarthropods in our urban green spaces (see Table 2) aligns with the ranges reported in other studies conducted in Italy [33,49] and in other countries (e.g., [12,51]), although higher densities were reported by Rota et al. [108] for Siena and Naples (central and southern Italy). Microarthropod density, richness (of biological forms), and, to a lesser extent, diversity expressed by Hill’s numbers H1, corresponding to the exponential version of Shannon diversity, and H2, corresponding to the inverse of Simpson dominance, were significantly greater in the less disturbed land-use types (urban forest and reference) compared to the urban managed land-use type, while differences between urban forests and reference versus the urban unmanaged land-use type were generally less pronounced. The lower effectiveness of the exponential Shannon diversity and, particularly, the inverse of Simpson dominance, compared to richness, in distinguishing between different types of urban green spaces can be attributed to the varying weight assigned to taxon abundances in their calculation. In Hill numbers, the influence of abundance on the diversity increases with the q-order as follows: the Simpson index (q = 2) places greater emphasis on the most abundant categories, while the exponential version of Shannon diversity (q = 1) provides a more balanced representation, and richness (q = 0) gives equal weight to all species, highlighting the contribution of rare taxa [39,82]. Given that our samples were predominantly composed of Acari and epiedaphic or hemiedaphic Collembola, which were present in high densities across all land-use types, their dominance increasingly influenced the index calculations as the q-order increased. This likely worsened the ability of these diversity indices to differentiate between land-use types, effectively masking differences that were more evident in the richness-based analyses. This is likely the main limitation of conducting analyses at a coarse resolution, such as biological forms, which roughly correspond to the taxonomic level of order. In any case, our findings are consistent with those of prior studies indicating that anthropogenic disturbances negatively affect soil fauna abundance and diversity at a general level (e.g., [12,109]). Interestingly, urban forest sites exhibited density and diversity metrics comparable to those of reference sites, which contrast with what was observed by Huang et al. [12] in Baltimore (MD, USA), where urban forest microarthropod densities were more similar to those found in turfgrass and ruderal sites than in reference forests. The higher microarthropod density and diversity observed in more vegetated land-use types (reference and urban forest) compared to more open land-use types (managed and unmanaged) suggest that vegetation cover serves as a critical driver of biological activity and biodiversity. This is probably due to its role in regulating soil temperature and moisture, as well as influencing the quality and quantity of litter [50,110,111]. The presence of a well-structured litter layer is known to be crucial for microarthropod communities, providing food and a favorable microenvironment for various taxa [112]. However, Tóth et al. [51] did not observe significant differences among land-use types or between forested and non-forested sites for any of the calculated community metrics (Hill’s numbers and density), nor for soil biological quality indices. This may suggest that, in addition to the separate effects of these factors (land-use type and tree cover), the synergy between them and the local factors directly influenced by urbanization—such as soil chemical and physical parameters, which can vary greatly from city to city—may play a key role in shaping the responses of soil communities. The reduced diversity observed in urban managed and unmanaged land-use types highlight the possible negative impact of urban-induced soil alterations and the differences in vegetation cover on sensitive microarthropod biological forms, particularly those adapted to stable, high-quality soils. For example, euedaphic Collembola, Pauropoda, Polyxenida, and Protura were either absent or found in very low abundance in highly disturbed urban soils (Table 2).
The QBS indices values obtained in our study indicate a general high biological quality of the soils in urban green spaces in Rome (QBS-ar: mean = 168, range = 107–219; QBS-ab: mean = 200, range = 123–285). The QBS-ar values greatly exceed the thresholds of 100 and 93.7, which Parisi [34] and Menta et al. [37] respectively proposed as indicative of well-functioning soils. Furthermore, our QBS-ar values are notably higher than the average values reported for urban green spaces in other studies (e.g., mean = 106, range = 78–145 in Naples, Italy [33]; mean = 101, range = 56–182 in Carpi, Italy [50]; mean = 123, range = 85–168 in Budapest, Hungary [51]). The values recorded for most land-use types within the city (see Table 2) are also comparable to those found in forest ecosystems within the same climatic region (e.g., range: 196–208 [42]; range: 147–230 [43]). The high QBS-ar values may be attributed to the presence of diverse and well-structured microhabitats, which support a variety of soil microarthropod taxa, even in the most disturbed and managed urban green spaces examined in this study. Regarding the QBS-ab index proposed by Mantoni et al. [39], it has been applied to urban green spaces, to the best of our knowledge, only by Tóth et al. [51], who reported values between 91 and 150 (mean: 125). As observed for QBS-ar, these values are much lower than the ones we recovered in this study. Despite the exceptionally high values of soil quality recorded in Rome’s urban green spaces compared to other studies, the QBS indices effectively distinguished between the land-use types investigated, with the lowest values found in urban managed sites and the highest in urban forest and reference sites (Table 2, Figure 2). Overall, the QBS results corroborate the patterns observed in our other analyses, particularly regarding richness. This is consistent with previous research demonstrating that QBS-ar and QBS-ab are strongly correlated with taxon richness and, to a lesser extent, with the exponential version of Shannon diversity [39,42,43]. Specifically, QBS-ar was able to differentiate between all land-use types, except for urban forest and reference, while QBS-ab proved more conservative, effectively discriminating only between the less disturbed vegetated sites (urban forest + reference) and the highly disturbed sites (urban managed + urban unmanaged). This difference can be due to the incorporation of biological form abundance in the calculation of QBS-ab. By considering abundance, the index gives greater weight to highly abundant groups, such as Acari and hemiedaphic Collembola, while reducing the influence of less abundant forms, even if the abundance of biological forms is log-transformed during the QBS-ab calculation. Consequently, QBS-ab may not effectively differentiate between soil types that vary only in the presence of a few biological forms with high values of eco-morphological specialization but occurring at very low densities. Nevertheless, the two indices, QBS-ar and QBS-ab, yielded similar and comparable results, consistent with the observations made by Mantoni et al. [39]. Conversely, Tóth et al. [51] noted that the two indices produced differing results, particularly concerning their sensitivity to environmental variables, which suggests that their behavior may be very sensitive to the characteristics of the study system, providing complementary information.
This general, significant variation in the QBS-ar and QBS-ab values among land use types provides further evidence of the impact of urbanization on soil biological quality. The relatively low-quality values observed in managed urban soils reflect a reduced presence of highly adapted taxa, likely attributable to habitat simplification and disturbance. Conversely, the higher values of urban forests reaffirm their critical role in soil conservation. Notably, urban unmanaged sites demonstrated better biological soil quality than urban managed sites, highlighting their potential as refugia for diverse soil microarthropod communities, as already documented for other arthropod communities [113,114,115].
Non-Metric Multidimensional Scaling (NMDS) and Permutational Multivariate Analysis of Variance (PERMANOVA) analyses, based on both the abundance and the degree of specialization, showed important changes in the composition of microarthropod communities among land-use types. Sites categorized as reference and urban forests exhibited overlapping assemblages, which were clearly distinct from those of managed and unmanaged sites. This pattern is consistent with variation in diversity metrics across land-use types, suggesting that urban forests support faunal communities similar to those found in natural habitats, at least at the coarse level of identification based on biological forms. This phenomenon may be attributed to the ability of remnant forest patches within urban environments to partially mitigate the effects of surrounding urban disturbances, thereby playing a pivotal role in sustaining arthropod populations [116,117]. Notably, unmanaged sites were distinctly separated from managed sites, also showing a higher richness of biological forms and higher QBS-ar values. These findings suggest that management practices and, potentially, people’s frequentation of already disturbed urban green spaces have some further negative influence on the abundance and composition of soil microarthropod communities. Frequent mowing, particularly when conducted with heavy machinery in managed green spaces such as urban parks, may contribute to the diminished abundance and diversity of soil microarthropods, as pointed out by previous studies [118,119,120]. Moreover, the intensity of people frequentation—higher in managed sites than in unmanaged sites—can lead to increased soil trampling, negatively impacting microarthropod communities [17,121].
The most important environmental drivers influencing the densityand diversity of soil microarthropods communities are identified as soil compaction, soil cation exchange capacity (CEC), and vegetation cover, as expressed by the Enhanced Vegetation Index (EVI). The negative correlation between soil compaction and the density and richness (H0) of biological forms underscores the detrimental impact of physical soil disturbance on arthropod communities. Soil compaction is a key factor influencing the structure and composition of soil arthropod communities across various environments, including urban [12,122,123] and natural or seminatural environments [42]. In contrast, CEC emerged as a positive predictor of the density of microarthropod biological forms, highlighting the importance of soil fertility and nutrient availability in sustaining abundant and diverse soil microarthropod communities. Since the investigated sites were not regularly or extensively treated with fertilizers, high CEC values may be related to the presence of organic matter [124], which is most prevalent in urban forest and reference sites. Notably, vegetation cover positively influenced the diversity metrics that consider group abundances (H1 and H2), emphasizing the critical role of aboveground vegetation in influencing soil ecosystems mainly by supplying litter and organic inputs, as well as regulating the microclimate by influencing soil moisture and temperature. Similar findings have been reported in previous studies (e.g., [12,51,125,126]), which highlighted that the structure and abundance of microarthropod communities also strongly depend on the extent of vegetation development.
Both QBS-ar and QBS-ab showed negative relationships with soil compaction. Additionally, QBS-ar was negatively related to soil pH, while QBS-ab was positively associated with CEC and negatively related to soil temperature. These results confirm the associations highlighted by the CCA analyses, showing that high soil pH and compaction are linked to reduced densities or the absence of particularly sensitive euedaphic microarthropods. Furthermore, the positive correlation between QBS-ab and cation exchange capacity underscores the significance of soil fertility and nutrient availability in supporting the abundance of sensitive biological forms. Interestingly, our analyses did not reveal any association between QBS indices and soil organic carbon or the C/N ratio, which contrasts with findings from other studies on soil microarthropods in urban green spaces (e.g., [51,127,128]). However, as previously mentioned, cation exchange capacity (CEC) may, at least in this study, be associated with high organic matter content, potentially serving as a proxy for it.
Canonical Correspondence Analyses (CCAs) further elucidated the relationships between soil biotic and abiotic components, revealing that sites in the less disturbed land-use types (urban forest and reference) were associated with high C/N ratios and supported communities dominated by euedaphic taxa, whereas disturbed sites (urban managed and unmanaged) were characterized by high values of pH and compaction. Specifically, the presence of sensitive and highly specialized euedaphic taxa (e.g., Polyxenida, euedaphic Collembola, Protura, and Pauropoda) was negatively associated with high soil pH and compaction and positively associated with the soil C/N ratio. In particular, soil pH, which was significantly higher in urban unmanaged sites compared to managed sites, was found to have a detrimental effect on the composition of microarthropod communities in terms of biological forms. Other studies have reported the negative impact of elevated pH levels in urban areas on soil arthropod communities, often in combination with other factors such as compaction [122] or pollution [51,129]. In particular, Maisto et al. [129] noted that urban soils frequently contain high levels of contaminants, which, when combined with high pH values, can further reduce litter palatability for arthropods. This decline in food quality may lead to decreased microarthropod populations, particularly in urban settings where litter quality is already compromised. Conversely, the soil C/N ratio—or, more broadly, the quantity and quality of soil organic matter—is well established as a key determinant of soil arthropod abundance and biodiversity, with particularly positive effects on the most sensitive taxa [31]. The positive relationship between highly specialized biological forms and less altered soils is further supported by the Indicator Taxa Analysis, which identified Pauropoda, Polyxenida, Geophilomorpha, and euedaphic Collembola as significantly associated with less disturbed land-use types (urban forest and reference). Nonetheless, some euedaphic taxa, such as Diplura and Symphyla, did not appear to be significantly affected by increased soil pH and compaction, despite their generally high sensitivity to environmental stress [123]. A similar situation was observed by Tóth et al. [51], who suggested that the presence of these taxa in the most disturbed sites indicates a relatively good state of conservation, at least sufficient to support their persistence. Similar to taxonomic composition, functional composition based on eco-morphological traits was influenced by land-use type, soil compaction, and pH, underscoring the importance of these factors in shaping soil microarthropod communities in our study.
5. Conclusions
This study provides a comprehensive analysis of soil microarthropod communities in urban green spaces in Rome, revealing the significant impacts of urbanization on soil conditions and microarthropod communities. The marked differences in soil conditions such as increased alkalinity, compaction, and reduced organic matter in managed and unmanaged urban green spaces negatively impact microarthropod communities, leading to a decline in sensitive taxa. In particular, the less disturbed land-use types—urban forests and reference forest—support high-density and rich microarthropod communities and emphasize the importance of vegetation cover in enhancing biodiversity. In contrast, the more disturbed and managed land-use types showed reduced abundance and diversity of microarthropod biological forms, indicating that management practices, such as frequent mowing and soil trampling, adversely affected these communities. Community composition analyses further illustrate distinct shifts among land-use types, with the urban forest land-use type exhibiting assemblages of biological forms that resemble those found in natural habitats. This suggests that urban forests can partially mitigate the effects of urban disturbances, supporting diverse faunal communities, at least at the level of biological forms. Soil quality assessments using the QBS-ar and QBS-ab indices suggest that urban green spaces in Rome generally exhibit high biological quality, particularly in less disturbed sites. The lower QBS-ar values observed in managed sites reflect a reduced presence of highly adapted taxa, likely attributable to habitat disturbance and alteration. This reinforces the notion that urban forests play a crucial role in maintaining soil biological quality and supporting diverse soil microarthropod communities [12]. At the same time, it also underlines the importance of low-management green spaces as potential areas for the conservation and persistence of diverse microarthropod communities, reinforcing the need for urban policies that protect unmanaged green areas, even small ones, as essential biodiversity refuges within the urban landscape. However, more extensive surveys, ideally encompassing multiple cities and incorporating a finer taxonomic identification of arthropods (possibly at the species level), will be instrumental in drawing more exhaustive conclusions in the future.
Conceptualization, P.G.; methodology, P.G. and S.F.; validation, P.G., S.F., S.S. and P.A.; formal analysis, P.G.; investigation, P.G. and S.S.; resources, P.A.; data curation, P.G.; writing—original draft preparation, P.G.; writing—review and editing, P.G., S.F., S.S. and P.A.; visualization, P.G. and S.F.; supervision, S.S. and P.A.; project administration, P.A. and S.S.; funding acquisition, P.A. All authors have read and agreed to the published version of the manuscript.
The raw data supporting the conclusions of this article will be made available by the authors upon request.
We are deeply grateful to Flavia Sicuriello and Paolo Colangelo (National Research Council—Research Institute on Terrestrial Ecosystems, Montelibretti, Italy) for their logistic support, allowing us to use their Berlese–Tullgren funnels. We would also like to thank Loris Galli (Department of Earth, Environmental and Life Sciences, University of Genoa, Italy) for his valuable advice during the initial phase of this study.
The authors declare no conflicts of interest.
Footnotes
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Figure 1 Principal Component Analysis (PCA, based on correlation matrix) of soil properties and landscape characteristics measured at 16 sampling sites from urban green spaces in Rome, Italy. The diagram shows the first two ordination axes, accounting for 53.4% and 13.7% of the variance, respectively. Abbreviations: CEC, cation exchange capacity; Comp, soil compaction; Conduc, soil conductivity; EVI, Enhanced Vegetation Index; OM, organic matter; SOC, soil organic carbon; Temp_air, air temperature at the soil surface; Temp_soil, soil temperature at 5 cm depth; UI, Urban Intensity Index.
Figure 2 Soil biology quality, expressed by QBS-ar and QBS-ab indices, across land-use types in Rome, Italy. The QBS-ar index does not use abundance, but only the degree of microarthropod specialization to the edaphic life. QBS-ab considers abundance. The mean values ± SE are shown. Different letters indicate significant differences among the four land-use types (p < 0.05) based on the least-squares means with Tukey adjustment (see
Figure 3 Diversity profiles (Hill’s numbers) of the soil microarthropod communities in the four land-use types investigated in Rome, Italy. Asterisks indicate different levels of statistical significance based on the Generalized Linear Model results (*** = p < 0.001, ** = p < 0.01, * = p < 0.05).
Figure 4 Non-Metric Multidimensional Scaling based on the Bray–Curtis distances of abundance data (A) and the ecomorphological scores (B) of soil microarthropods in four land-use types in Rome, Italy.
Figure 5 Relationships of total density (A,B) and diversity (C–F) of microarthropod communities with the soil properties and landscape characteristics in Rome, Italy. Regression lines are shown in green and red depending on the direction of the relationship (positive and negative, respectively). The shaded areas indicate the 95% CI. Each regression line is based on a separate model that considers only one response variable at a time, using the original scale rather than the standardized values employed in the full models of
Figure 6 CCA (Canonical Correspondence Analysis) plots of the soil microarthropod community structure based on the abundance data (A) and ecomorphological scores (B) of soil microarthropods in four land-use types in Rome, Italy. Abbreviations: C_N, C/N ratio; Comp, soil compaction. The groups are labeled using the same codes as in
Local (soil) and landscape characteristics (mean ± SE) of urban green spaces by land-use types investigated in the city of Rome, Italy. Different letters indicate significant differences between land-use types (p < 0.05) based on post hoc Tukey’s HSD tests for pairwise comparisons. Abbreviations: SOC, soil organic carbon; CEC, cation exchange capacity; UI, Urban Intensity Index; EVI, Enhanced Vegetation Index.
| Environmental Variables | Urban Unmanaged | Urban Managed | Urban Forest | Reference |
|---|---|---|---|---|
| Soil pH | 7.95 ± 0.12 a | 7.34 ± 0.05 b | 6.95 ± 0.09 c | 6.61 ± 0.10 c |
| Conductivity (dS/m) | 1.32 ± 0.54 a | 1.48 ± 0.31 a | 1.01 ± 0.18 a | 1.30 ± 0.28 a |
| Organic matter (%) | 4.47 ± 0.99 a | 6.47 ± 0.65 a | 12.47 ± 0.98 b | 11.40 ± 0.73 b |
| SOC (%) | 2.60 ± 0.57 a | 3.75 ± 0.38 a | 7.23 ± 0.57 b | 6.45 ± 0.30 b |
| Total N (%) | 0.31 ± 0.08 a | 0.36 ± 0.03 a | 0.70 ± 0.13 b | 0.49 ± 0.05 ab |
| C/N ratio | 8.90 ± 1.44 a | 10.4 ± 0.40 ab | 10.91 ± 1.12 ab | 13.37 ± 1.01 b |
| P2O5 (mg/kg) | 103.70 ± 40.09 a | 154.8 ± 33.09 a | 151.42 ± 21.1 a | 54.60 ± 22.62 a |
| CaO (mg/kg) | 7903.50 ± 1016.57 a | 9232.25 ± 975.36 ab | 11,835.50 ± 763.9 b | 6861.00 ± 841.36 a |
| MgO (mg/kg) | 563.25 ± 62.81 a | 552.00 ± 70.58 a | 719.50 ± 109.33 a | 656.00 ± 92.68 a |
| Na (%) | 2.13 ± 0.21 a | 2.17 ± 0.23 a | 0.67 ± 0.15 b | 0.63 ± 0.09 b |
| CEC (mEq/100 g) | 19.30 ± 1.85 a | 28.80 ± 3.33 ac | 49.90 ± 4.92 b | 34.88 ± 0.99 c |
| Compaction (kg/cm2) | 2.86 ± 0.24 ab | 4.49 ± 0.53 a | 2.03 ± 0.16 bc | 1.94 ± 0.10 c |
| Moisture (%) | 20.40 ± 0.65 a | 21.55 ± 0.85 a | 22.83 ± 0.74 ab | 24.17 ± 0.10 b |
| Soil temperature (°C) | 21.04 ± 0.45 ab | 21.31 ± 0.54 a | 19.61 ± 0.19 b | 17.90 ± 0.35 c |
| Surface temperature (°C) | 22.83 ± 1.12 ab | 25.30 ± 0.87 a | 21.54 ± 0.33 b | 20.75 ± 0.43 b |
| UI | 1.83 ± 0.76 a | 1.80 ± 0.66 a | −1.41 ± 0.42 b | −2.22 ± 0.09 b |
| EVI | 0.28 ± 0.03 a | 0.29 ± 0.04 a | 0.51 ± 0.04 b | 0.62 ± 0.04 b |
Mean (±SE) densities (individuals/m2) of soil microarthropods with community metrics and biological soil quality indices in the four land-use types investigated in Rome, Italy. Labels for each biological form (BF) are also provided, as well as the scores of the ecomorphological index of specialization (EMI). Asterisks indicate biological forms excluded from the analyses due to the lack of representativeness but included in the table for the sake of data completeness. The community metrics and biological soil quality indices are shown are shown at the bottom of the table. Different letters indicate significant differences among the four land-use types (p < 0.05) based on differences in the least-squares means with Tukey adjustment for multiple comparisons.
| Biological Form | Label | EMI | Urban Unmanaged | Urban Managed | Urban Forest | Reference |
|---|---|---|---|---|---|---|
| Acari | ACA | 20 | 18,658.3 ± 2367.1 | 15,900 ± 1645.3 | 25,125 ± 3514.7 | 20,083.3 ± 1309.0 |
| Araneae | ARA | 1 | 41.7 ± 25.0 | 0 | 75 ± 43.8 | 33.3 ± 33.3 |
| Opiliones * | 10 * | 8.3 ± 8.3 | 0 | 0 | 0 | |
| Pseudoscorpiones | PSEU | 10 | 33.3 ± 33.3 | 0 | 100 ± 49.1 | 58.3 ± 21.0 |
| Palpigrada * | 20 * | 0 | 0 | 41.7 ± 41.7 | 0 | |
| Isopoda | ISO | 10 | 266.7 ± 52.7 | 8.3 ± 8.3 | 975.0 ± 107.5 | 558.3 ± 15.9 |
| Symphyla | SYMPH | 20 | 150.0 ± 61.6 | 216.7 ± 16.7 | 608.3 ± 169.1 | 558.3 ± 45.9 |
| Diplopoda | DIPLO | 10 | 258.3 ± 86.5 | 66.7 ± 27.2 | 1175.0 ± 127.2 | 925.0 ± 207.4 |
| Diplopoda Polyxenida | POLYX | 20 | 0 | 16.7 ± 16.7 | 200 ± 75.8 | 241.7 ± 25.0 |
| Chilopoda | CHILO | 10 | 50.0 ± 16.7 | 75.0 ± 43.8 | 25.0 ± 15.7 | 33.3 ± 33.3 |
| Chilopoda Geophilomorpha | GEOPH | 20 | 41.7 ± 41.7 | 16.7 ± 16.7 | 158.3 ± 53.4 | 191.7 ± 28.5 |
| Pauropoda | PAURO | 20 | 0 | 0 | 158.3 ± 39.4 | 158.3 ± 67.2 |
| Collembola epiedaphic | COLL_EPI | 1 | 391.7 ± 123.5 | 150.0 ± 95.7 | 225.0 ± 142.3 | 33.3 ± 33.3 |
| COLL_EPI | 2 | 1258.3± 622.2 | 1150.0 ± 183.3 | 658.3 ± 217.0 | 166.7 ± 88.2 | |
| COLL_EPI | 4 | 3716.7± 864.7 | 2525.0 ± 291.3 | 3516.7 ± 474.8 | 2616.7 ± 391.9 | |
| Collembola emiedaphic | COLL_EMIED | 6 | 3866.7 ± 524.2 | 2075.0 ± 276.3 | 4075.0 ± 512.9 | 2725.0 ± 653.8 |
| COLL_EMIED | 8 | 1058.3 ± 234.3 | 716.7 ± 161.9 | 2258.3 ± 612.3 | 1900.0 ± 407.8 | |
| COLL_EMIED | 10 | 2325.0 ± 330.1 | 1616.7 ± 427.2 | 2966.7 ± 345.1 | 6941.7 ± 783.4 | |
| Collembola euedaphic | COLL_EUED | 20 | 0 | 0 | 2350.0 ± 474.8 | 5400.0 ± 928.3 |
| Diplura | DIPLUR | 20 | 100.0 ± 33.3 | 108.3 ± 45.9 | 158.3 ± 41.7 | 283.3 ± 44.1 |
| Protura | PROT | 20 | 25.0 ± 15.9 | 25.0 ± 15.9 | 275.0 ± 98.5 | 275.0 ± 36.7 |
| Coleoptera epigeic | COLEO_EPI | 1 | 200.0 ± 60.9 | 58.3 ± 8.3 | 91.7 ± 47.9 | 58.3 ± 39.4 |
| COLEO_EPI | 5 | 41.7 ± 25.0 | 0 | 166.7 ± 36.0 | 58.3 ± 21.0 | |
| Coleoptera emiedaphic | COLEO_EMIED | 10 | 58.3 ± 58.3 | 0 | 25.0 ± 16.0 | 66.7 ± 23.6 |
| Coleoptera larvae | COLEO_L | 10 | 391.7 ± 96.6 | 166.7 ± 83.9 | 316.7 ± 61.6 | 300.0 ± 49.1 |
| Diptera * | 1 * | 16.7 ± 16.7 | 16.7 ± 16.7 | 75.0 ± 75 | 25.0 ± 25.0 | |
| Diptera larvae | DIPT_L | 10 | 216.7 ± 91.8 | 0 | 525.0 ± 153.0 | 375.0 ± 103.1 |
| Hymenoptera * | 1 * | 16.7 ± 16.7 | 0 | 33.3 ± 13.6 | 16.7 ± 9.6 | |
| Hymenoptera Formicidae | FORM | 5 | 866.7 ± 201.8 | 1041.7 ± 535.8 | 1116.7 ± 325.6 | 1241.7 ± 230.7 |
| Hymenoptera larvae * | 1 * | 41.7 ± 25 | 16.7 ± 16.7 | 0 | 0 | |
| Hemiptera | HEMI | 1 | 191.7 ± 87.5 | 250.0 ± 102.3 | 91.7 ± 43.8 | 50.0 ± 39.7 |
| Psocoptera | PSOC | 1 | 33.33 ± 33.3 | 58.3 ± 39.4 | 250.0 ± 115.9 | 208.3 ± 75.0 |
| Thysanoptera | THYS | 1 | 100.0 ± 43 | 66.7 ± 47.1 | 200.0 ± 49.1 | 175.0 ± 62.9 |
| Community metrics | ||||||
| Total density | 34,425.0 ± 1702.4 ab | 26,341.7 ± 2515.0 a | 48,016.7 ± 5591.8 b | 45,758.3 ± 2955.3 b | ||
| H0: BFs richness (q = 0) | 19.75 ± 0.75 a | 15.25 ± 0.75 b | 25.5 ± 1.3 c | 23.25 ± 0.5 bc | ||
| H1: exp(Shannon) (q = 1) | 5.32 ± 0.62 ab | 4.39 ± 0.20 a | 6.66 ± 0.26 bc | 7.01 ± 0.26 c | ||
| H2: 1/Simpson (q = 2) | 3.15 ± 0.47 ab | 2.57 ± 0.16 a | 3.41 ± 0.22 ab | 4.14 ± 0.24 b | ||
| QBS-ar | 137.75 ± 4.21 a | 115.25 ± 4.92 b | 210.75 ± 4.50 c | 208.75 ± 4.15 c | ||
| QBS-ab | 157.92 ± 9.95 a | 131.715 ± 5.03 a | 254.01 ± 14.68 b | 257.82 ± 3.64 b |
Summary statistics of final Generalized Linear Models including the only response variables found to be significant for microarthropod diversity (Hill’s numbers H0, H1, and H2), microarthropod density and soil biology quality (QBS-ar, which does not consider abundance; QBS-ab, which consider abundance) in Rome, Italy. R2 measures the goodness of fit of the best selected models. Abbreviations: CEC, cation exchange capacity; EVI, Enhanced Vegetation Index; SE, standard error.
| Final Model | Estimate | SE | t-Value | p-Value | R 2 |
|---|---|---|---|---|---|
| H0 (richness) | 0.87 | ||||
| Compaction | −2.649 | 0.663 | −3.993 | 0.001 | |
| CEC | 1.674 | 0.663 | 2.523 | 0.03 | |
| H1 (expShannon) | 0.66 | ||||
| EVI | 1.038 | 0.198 | 5.254 | 0.0001 | |
| H2 (1/Simpson) | 0.49 | ||||
| EVI | 0.548 | 0.151 | 3.630 | <0.01 | |
| Density | 0.57 | ||||
| Compaction | −4499.000 | 1804.000 | 2.494 | 0.03 | |
| CEC | 4967.000 | 2266.000 | 2.192 | 0.05 | |
| QBS-ar | 0.82 | ||||
| Compaction | −0.171 | 0.034 | −4.962 | <0.001 | |
| pH | −0.121 | 0.036 | −3.335 | <0.01 | |
| QBS-ab | 0.88 | ||||
| Compaction | −0.020 | 0.008 | −2.442 | 0.03 | |
| CEC | 0.021 | 0.007 | 3.107 | <0.01 | |
| Soil temperature | −0.027 | 0.008 | −3.383 | <0.01 |
Supplementary Materials
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; Fattorini Simone 2
; Audisio Paolo 1
; Sabatelli Simone 1
1 Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Viale dell’Università 32, 00186 Roma, Italy; [email protected], National Biodiversity Future Center (NBFC), Piazza Marina 61, 90133 Palermo, Italy
2 Department of Life, Health and Environmental Sciences, University of L’Aquila, Via Vetoio, 67100 L’Aquila, Italy; [email protected]