Sensitivity variations on soil health indicators related to arbuscular mycorrhizae in a highly disturbed urban basin of Buenos Aires, Argentina

Sensitivity variations on soil health indicators related to arbuscular mycorrhizae in a highly disturbed urban basin of Buenos Aires, Argentina | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sensitivity variations on soil health indicators related to arbuscular mycorrhizae in a highly disturbed urban basin of Buenos Aires, Argentina Mailen Guerra Moreno, Vanesa Analía Silvani, Alicia Margarita Godeas, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9107710/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The Matanza-Riachuelo river basin (MRB) is considered one of the most polluted sites in the world, with very high levels of heavy metal(loid)s (HMs). However, as in almost all urban basins, studies on biological indicators of soil health are limited. In this work, we explore the potential of arbuscular mycorrhizal fungi (AMF) diversity, plant mycorrhizal status, and glomalin-related soil proteins (GRSP) concentration as bioindicators of soil health. We sampled three regions of the MRB: upper, middle and lower basins. The highest concentrations of HMs were detected in the lower basin, because it is exposed to a high input of pollutants and anthropogenic impact. Community composition and diversity were compared across the different soil samples. The Glomeraceae family was the most represented in the MRB, and Rhizoglomus intraradices was the only AMF species detected at every sampled site. This work constitutes the first report of 8 of the 16 AMF species described so far in the MRB. GRSP differences were strongly related to HMs concentrations. Both AMF species richness and GRSP concentrations were reliable biological indicators of soil health; while GRSP increases with soil toxicity, AMF richness decreases with soil disturbance. Matanza-Riachuelo basin arbuscular mycorrhizal fungi anthropogenic impact heavy metal(loid)s glomalin bioindicators soil health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Urban basins are geographic areas where water dynamics are impacted by the infrastructure of the city and the urban expansion. Proper urban watershed management is essential for flood control and prevention of environmental pollution. The Matanza-Riachuelo is a low-gradient plain river. Surface run-off within its basin gives rise to a network of streams that converge to form the main course, called Matanza in its upper section and Riachuelo in its final stretch. This main course (64 km long) discharges into the Río de La Plata estuary, which separates Argentina from Uruguay and serves as the principal water source for the Buenos Aires Metropolitan area (ACUMAR 2025 ). The Matanza-Riachuelo river basin (MRB) is located in the northeast of Buenos Aires province (Argentina), with a surface of 2047 km 2 . It includes the most densely populated and industrialized region in Argentina, with petrochemical industries, tanneries, slaughterhouses and meat-packing plants situated along its riverside (Mernlinsky et al. 2016). In some areas of the basin, the concentrations of potentially toxic elements (PTEs), such as heavy metal(loid)s (HMs), exceed the thresholds established by national (Argentine hazardous waste legislation - Law N° 24051) and international (FAO 2004 ) environmental standards (Ratto et al. 2004; Mendoza et al. 2015; Colombo et al. 2020 ; Pereyra et al. 2022). Despite being considered one of the most polluted basins in the world (Bernhardt and Gysi, 2013 ), studies on biological indicators of soil health remain scarce. This is a recurring issue in almost all urban basins. Ratto et al. (2004) conducted the first study on the extent and origin of organic and inorganic contamination in MRB soils. Subsequent studies have estimated the potential risk on human health, associated with soil contamination, on urban settlements across the basin (Pasqualini et al. 2019; Cittadino et al. 2020 ; Ceballos et al. 2021 ). Far less attention has been given to the characterization of the flora or soil microorganisms, despite being biological indicators of soil heath and potential agents in future bioremediation programs (Mendoza et al. 2015; Colombo et al. 2020 ; Garcia et al. 2022). Among these understudied microorganisms, arbuscular mycorrhizal fungi (AMF) (Phylum Glomeromycota) are particularly important. They form mutualistic associations with roots of most land plants and play key roles in ecosystem functioning and plant survival under stressful conditions (González-Chavez et al. 2004; Gonzalez et al. 2020 ). AMF improves soil quality through the development of extraradical hyphal networks that bind soil particles together; they also stabilize soil aggregates by the production of glomalin (technically referred to as glomalin-related soil protein, GRSP). GRSP are a family of thermo-stable and water-insoluble glycoproteins, produced in the cell walls of hyphae and spores, and considered a “biological cement” of soil aggregates (Driver et al. 2005 ; Rillig and Mummey 2006; Chauhan et al. 2023 ). The functional groups present in the molecular structure of GRSP interact with various ions and chelate them, leading to a decrease in HMs bioavailability (Malekzadeh et al. 2016; Son et al. 2024). Wang et al. (2020) reported an increased release of GRSP in soils contaminated with HMs, attributing this to a possible response of AMF against stressful conditions. Previous reports revealed that soils of the MRB exhibited a longitudinal contamination gradient that increased from the upper to the lower basin (Mendoza et al. 2015; Paredes del Puerto et al. 2021). However, the lack of studies addressing soil toxicity and its effects on beneficial soil microorganisms, both in the MRB and in other worldwide urban basins. limits our knowledge of how AMF symbionts respond to the environmental pressures on these highly anthropized soils. Mendoza et al. (2015) found variations in mycorrhizal colonization through the basin and correlated this to HMs contamination; however, AMF species were not identified. Later, Colombo et al. ( 2018 , 2020 ) provided the first reports of AMF species in one highest polluted site within the MRB, highlighting the predominance of the Claroideoglomeraceae family. In this context, the present study evaluated the potential of arbuscular mycorrhizae as bioindicators of soil health in highly disturbed urban basins. We assessed total GRSP content, AMF richness, and mycorrhizal root colonization, and examined their relationships with typical soil health indicators (pH, electric conductivity, organic matter, HMs, among others), used as indicators of soil quality. By integrating microbial, biochemical, and physicochemical variables, this study aims to determine whether these AMF-related parameters can provide sensitive indicators of soil condition in polluted wetland environments. Material and Methods Study area The MRB is divided into three regions: upper, middle, and lower basin (Fig. 1 ). The upper basin is predominantly rural and shaped by primary and agro-industrial activities. The middle basin exhibits a transitional landscape that combines urban and rural areas. The lower basin is highly urbanized, with a concentration of industrial and service-oriented activities. This gradient, from rural to increasingly urbanized and industrialized environments, drives the progressive degradation of soil and water quality, towards the river mouth in the Río de La Plata estuary (ACUMAR 2025 ). The region has a humid temperate climate with four different seasons, a mean annual temperature of 17.3°C, mean annual precipitation of 1236 mm, and a relative humidity of 71% (Servicio Meteorológico Nacional, n.d). Soil and root samples were assessed from six sites distributed throughout the three sections of the basin: lower basin (Site A, Site P, and Site C), middle basin (Site M and Site O), upper basin (Site S) (Fig. 1 , Figure S1 in the Online Resource ESM_1). Each site was subsequently characterized (Table 1 ). Sampling was conducted between April 2022 and April 2023. All sampled soils were located in wetlands and were selected based on direct field observations of flooding, as they were found to be covered by water during at least one of the site visits (each site was visited at least twice to document hydrological dynamics). Table 1 Characteristics of the study sites along three different sections in the Matanza-Riachuelo Basin. Site and associated watercourse Location Basin section Watercourse history Soil type /Land use Site A: Riachuelo River (Sup. Figure 1a) Avellaneda Borough, Central Town (-34.657722, -58.375586) Lower Natural Natural soil (Mollisol). Industrial/ residential; near busy highway Site P: Riachuelo River (Sup. Figure 1b) Avellaneda Borough, Piñeyro Town (-34.662376, -58.390899) Lower Modified Backfill soil. Industrial/ residential; near busy highway Site C: Cildañez Stream (Sup. Figure 1c) Buenos Aires Capital City (-34.678558, -58.442985) Lower Modified. Mostly a piped stream. Backfill soil. Industrial. Sampling site adjacent to a former dump, busy highway and racetrack. Site O: Ortega Stream (Sup. Figure 1d) Esteban Echeverria Borough (-34.806102, -58.506269) Middle Natural. Natural soil (Mollisol). Residential. Close to a concrete manufacturing establishment. Near a busy highway. Site M: Matanza River (Sup. Figure 1e) Ezeiza Borough (-34.763925, -58.549772) Middle Natural Natural soil (Mollisol). Green recreational area. Close to the international airport. Site S: San Vicente Lagoon (Sup. Figure 1f) San Vicente Borough (-35.014402, -58.419423) Upper Natural Natural soil (Mollisol). Green recreational area/ Residencial Soil and root sampling Three rhizospheric soil cores (15 cm deep) and roots of the most abundant mycorrhizal plant species (Table S1 , in the Online Resource ESM_1) were collected at each site. Roots were used to evaluate mycorrhizal colonization and establish pot trap cultures. Each soil sample was separated in four fractions for physical and chemical analysis, GRSP extraction, AMF spores’ isolation and characterization, and pot trap cultures establishment. To minimize field-scale heterogeneity, subsamples were homogenized into a single composite sample per site, which was used for HMs analysis. All samples were air-dried and stored at 4°C until further analysis. Soil analysis The following soil characteristics were measured: HMs concentrations, percentage of organic matter (OM), available phosphates (P), pH, total dissolved salts (TDS), and electrical conductivity (EC). Each soil sample was subjected to acid digestion, and Zn, Cu, Ni, Cd, Cr, Fe, Mn and Pb concentrations were quantified by atomic absorption spectroscopy (EPA Method 3050B). The analyses were conducted at the Laboratory of Inorganic and Analytical Chemistry (Qualified Service - Faculty of Agronomy, UBA). Contamination levels were then estimated using the Contamination Factor ( Cf ) and the Degree of contamination ( Dc ) indices. The indices were calculated according to Magni et al. (2021): Cf = \(\:\frac{Cm}{Bn}\) Dc = \(\:\sum\:_{i=1}^{n}Cf\) where Cm is the measured concentration of a particular HMs, and Bn is the concentration of the same HMs in a reference soil. In this study, the reference soil corresponded to San Vicente lagoon soils (Site S) located in the upper basin and characterized by non-toxic concentrations of HMs (according to FAO, 2004 ). The Degree of Contamination ( Dc ) is defined as the sum of all Cf values calculated for each site ( n ). OM percentage (OM%) was estimated using the ignition method (Page et al. 1982). Available P content was estimated by colorimetry using the Visocolor ECO reagent kit (Machery-Nagel). Soil pH and EC were measured in a 1:2.5 (wt./vol) soil/water solution using a pH, EC/TDS and temperature meter (Hanna, model: HI98129). Total GRSP quantification Total GRSP was extracted from soil samples following Cornejo et al. ( 2008 ). Briefly, a 50 mM citrate buffer was added to soil autoclaved and centrifugated. Supernatant was collected and extraction was repeated until supernatant lost its typical reddish colour. All the supernatants were pooled and brought to a final volume with a citrate buffer. Quantification was performed by spectrophotometry following Bradford ( 1976 ). AMF richness With the aim of knowing the specific richness of AMF in the MRB, spores were isolated from soil samples by wet sieving and decanting (Gerdermann and Nicolson 1963). Healthy spores were selected under a stereomicroscope (Olympus SZ61) and mounted in Polyvinyl-Lacto-Glycerol and Melzer reagents (1:1, v/v). Spore morphological characteristics were examined under microscope (Olympus BX51), and AMF species were identified following Blaszkowski ( 2012 ) and the International Collection of Vesicular Arbuscular Mycorrhizal Fungi (INVAM, n.d.). Vouchers were deposited in the Banco de Glomeromycota in Vitro (BGIV, n.d.). Taxonomic assignments were done according to the Index Fungorum (n.d.). One trap culture per site was established to facilitate the observation and identification of AMF species. Briefly, soil samples (containing spores and roots) were inoculated into pots filled with tindalized substrate (soil:perlite:vermiculite; 1:1:1). Surfaced-sterilized seeds of Lactuca sativa , Pisum sativum , Linum usitatissimum and Zea mays were sown and maintained under greenhouse conditions. Trap cultures were sampled at 4, 6, and 8 months of growth to evaluate AMF colonization and spore development. Spores were isolated and identified as previously described. Mycorrhizal root colonization In order to evaluate AMF colonization roots (sampled at field or from pot trap cultures) were clarified with KOH (10% w/v), acidified with HCl (0.1 N) and stained with Trypan blue in lactic acid (0.05% w/v) (modified from Phillips and Hayman, 1970). The frequency (%F) and intensity (%I) of colonization were estimated (modified from Declerck et al., 2004 ). For each sample, fifty randomly selected root segments (0.5 cm length) were examined under microscope (Olympus BX51, 200x and 400x magnification). Colonization frequency was calculated as the percentage of root segments containing any arbuscular mycorrhizal structure. Colonization intensity was estimated by sorting out the root segments into different intensity classes (1–20, 21–40, 41–60, 61–80, and 81–100%) of AMF intraradical cover (Declerck et al. 1996 ; Plenchette and Morel, 1996). Statistical analysis For parameters with replicates (pH, EC, TDS, and total GRSP), statistical models were fitted using basin and site as explanatory variables. Normality and homoscedasticity assumptions were verified. Differences in total GRSP concentration among basins were assessed with linear mixed-effects models (LMM) including basin as a fixed effect and site as a random intercept. Differences among sites were tested with a separate LMM using log-transformed GRSP as the response variable, site as a fixed effect, and basin as a random intercept. Model diagnostics were performed using residual and Q–Q plots, as well as DHARMa simulated residuals. Fixed-effect significance was evaluated using Type III ANOVA (Satterthwaite approximation), and pairwise comparisons were obtained via estimated marginal means (emmeans package). Generalized linear models (GLMs) with a Gamma distribution and log link were used to test site effects on TDS and EC. Pearson correlations among heavy metals, physicochemical properties, mycorrhization, and GRSP were visualized with a heatmap. Principal component analysis (PCA) was performed on standardized variables, and contributions to PC1 and PC2 were evaluated using a 1000-iteration permutation test. AMF richness (presence/absence) was analysed with binomial GLMs and a logit link. Predictor significance was assessed with likelihood ratio tests (Type II ANOVA), and Tukey-adjusted pairwise comparisons were obtained using emmeans. Model assumptions were checked with DHARMa, and predictive performance was evaluated with ROC curves and AUC. Non-metric multidimensional scaling (NMDS) based on Bray–Curtis distances was used to visualize microbial community dissimilarities and assess the influence of environmental factors on community composition. Frequency (%F) and intensity (%I) were analysed using LMMs with basin as a fixed effect and site nested within basin as a random effect (1 | Basin/Site). Tukey-adjusted comparisons of estimated marginal means were used to compare basins. Results Soil properties From the upper to the lower basin (Table 2 ), the soil samples showed a clear trend of increasing HMs concentrations. Site C, located in the lower basin, exhibited the highest concentrations of Zn (1229 mg/kg), Cu (617 mg/kg), Cr (202.26 mg/kg) and Cd (7.47 mg/kg), all of which exceeded the regulatory limits established for soils designated for agricultural or residential use under national and international environmental standards. Notably, Cu was the only element that also exceeded the limit set for industrial use of soil. Table 2 Heavy metal(loid)s concentration, percentage of organic matter (OM%), total phosphates (Total P), pH, electrical conductivity (EC), and total dissolved salts (TDS) in soil samples from the MRB, collected near the water body. Site Zn (mg/kg) Cu (mg/kg) Ni (mg/kg) Cd (mg/kg) Cr (mg/kg) Fe (mg/kg) Mn (mg/kg) Pb (mg/kg) OM % Total P (mg/kg) pH EC (mS/cm) TDS (mg/kg) A 227.4 150.00 19.70 ND 84.27 26000.00 360.00 140.00 3.96 20 7.6 \(\:\pm\:\) 0.5a 0.76 \(\:\pm\:\) 0.08 ab 380 \(\:\pm\:\) 40 ab P 669.44 213.99 24.83 4.25 41.26 31666.84 630.37 172.68 5.6 20 7.7 \(\:\pm\:\) 0.2a 0.41 \(\:\pm\:\) 0.12 a 180 \(\:\pm\:\) 60 a C 1229.20 617.11 52.7 7.47 202.26 23686.83 433.84 39.59 21.05 60 7.3 \(\:\pm\:\) 0.3a 0.91 \(\:\pm\:\) 0.09 ab 450 \(\:\pm\:\) 50 ab M 57.10 26.19 19–56 3.04 23.90 10947.75 210.15 59.84 7.51 20 7.75 \(\:\pm\:\) 0.5a 0.70 \(\:\pm\:\) 0.46 ab 350 \(\:\pm\:\) 230 ab O 270.12 62.00 28.89 3.68 47.61 25630.89 524.47 68.77 10.26 40 5.6 \(\:\pm\:\) 0.1b 1.56 \(\:\pm\:\) 0.03 b 780 \(\:\pm\:\) 10 b S 72.62 36.46 19.53 3.33 21.08 17344.34 280.09 38.16 9.37 40 7 \(\:\pm\:\) 0.4a 0.46 \(\:\pm\:\) 0.12 a 230 \(\:\pm\:\) 60 a Different letters between columns indicate significant differences within sites according to the Tukey test (alpha = 0.05). ND: Not detected (< 2 mg/kg). Concentrations exceeding the permitted values according to national and international environmental standards (Argentinean Law and FAO Maximum Limits) are indicated in bold. Data are presented as mean ± standard deviation. Lower basin (Site A, Site P, and Site C), middle basin (Site O and Site M), upper basin (Site S). The contamination factor (Cf) showed that the highest contamination levels occurred in the lower basin, particularly for Zn, Cu, Cr and Pb, which ranged from elevated to very high. In contrast, the middle basin exhibited lower contamination (Table 3 ). Specifically, Sites P and C registered the highest Cf and Dc values, both associated with a high degree of contamination (Dc ≥ 24) (Table 3 ). The Dc values for Sites A and O indicated an important contamination level (12 ≤ Dc > 24), whereas Site M showed a moderate level (6 ≤ Dc < 12) (Table 3 ). Total P ranged from 20 mg/kg to 60 mg/kg, with the highest concentration at Site C. Organic matter values decreased from upper to lower basin, except for Site C, which registered the highest value (21.05%). For pH, all the soil samples had neutral values, except Site O, which showed a significant difference with acid pH (5.56 0.1) (p value < 0.05). EC values in the MRB varied from 0.46 \(\:\pm\:\) 0.12 mS/cm to 1.56 \(\:\pm\:\:\) 0.03 mS/cm, while TDS varied from 180 \(\:\pm\:\) 60 mg/kg to 780 \(\:\pm\:\) 10 mg/kg. Statistical analysis indicated significant differences (p < 0.05) among Site O (highest values of EC and TDS) and Sites P and S (Table 2 ). Table 3 Contamination Factor (Cf) and Degree of Contamination (Dc) for each heavy metal(loid) at the sampling sites (lower, middle, and upper basin), near the watercourse. Site S (Upper Basin) was used as the reference soil (Bn). Site Cf Zn Cu Ni Cd Cr Fe Mn Pb Dc A 3.13* 4.11* 1.01 0.60 4.00* 1.50 1.29 3.67* 19.31* P 9.22** 5.87* 1.27 1.28 1.96 1.83 2.25 4.53* 28.19** C 16.93** 16.93** 2.70 2.24 9.59** 1.37 1.55 1.04 52.34** M 0.79 0.72 1.00 0.91 1.13 0.63 0.75 1.57 7.50 O 3.72* 1.70 1.48 1.11 2.26 1.48 1.87 1.80 15.42* Cf < 1: low contamination; 1 ≤ Cf < 3: moderate contamination; 3 ≤ Cf < 6: high contamination (*); Cf ≥ 6: very high contamination (**); Dc < 6: low degree of contamination; 6 ≤ Dc < 12: moderate contamination; 12 ≤ Dc < 24: considered contamination (*); Dc ≥ 24: high degree of contamination (**). Lower basin (Site A, Site P and Site C), middle basin (Site O and Site M). GRSP and HMs concentrations in soil The total GRSP concentration in soil samples varied among basins and sampling sites. The highest concentration was recorded in the lower basin (2.47 ± 0.83 mg GRSP/g soil), followed by the middle basin (0.98 ± 0.38 mg GRSP/g soil) and the upper basin (0.89 ± 0.23 mg GRSP/g soil). Although these differences were not statistically significant, the increasing trend was clear (Fig. 2 ). Significant differences in GRSP concentration were observed among sites (p < 0.05). Site M had the lowest GRSP concentration, differing significantly from all other study sites (p < 0.05) except Site S. The highest concentrations were detected in the lower basin sites, particularly at Site C, which showed significantly higher values (p < 0.05) (Fig. 2 b). AMF richness assessment A total of 16 AMF species were identified across the MRB from soil samples and pot trap cultures (Table S2 , in the Online Resource ESM_1). These species belonged to five families: Glomeraceae, Gigasporaceae, Entrophosporaceae, Diversisporaceae, and Paraglomeraceae. In all sites, Glomeraceae was the most abundant family (72.95%), followed by Entrophosporaceae family (16.22%). When assessing AMF richness, clear differences were found among sites and basins. The statistical model showed moderate predictive performance (AUC = 0.62), revealing a significantly (p < 0.05) higher probability of AMF richness in the upper basin (Fig. 3 ). At site level, the significantly highest species richness was detected at Site S, which hosted 11 AMF species, followed by Site M with 9 species. Site A, located in the lower basin, also showed relatively high richness (8 AMF species). In contrast, Site C and O showed markedly lower richness, with only 2 and 1 species, respectively (Table S2 , in the Online Resource ESM_1). The proportion of AMF families at each site was calculated (Fig. 4 ). Site S was the only site where all identified families were represented. At this site, 54.55% of species (6 species) belonged to the Glomeraceae family, 18.18% (2 species) to the Gigasporace, and each of the remaining families comprised 9.1% of the total AMF richness (one species each) (Table S2 , in the Online Resource ESM_1). At Sites A and P, AMF species were restricted to the Glomeraceae (87.50% and 85.71%, respectively) and Entrophosporaceae (12.50% and 14.29%, respectively) families. Similarly, AMF species at Site M belonged to the Glomeraceae (66.67%) and Entrophosporaceae (33.33%). At Sites C and O, only species from the Glomeraceae family were detected (Fig. 4 ). Significant differences in AMF richness probability were found between Site S and Sites O and C (p < 0.05) (Figure S2 , Table S3, in the Online Resource ESM_1), based on a model with moderate predictive capacity (AUC 0.62). No significant differences were found among the remaining sites. The most widely distributed AMF species was Rhizoglomus intraradices , which was found at all the sampled sites, followed by Funneliformis mosseae , Rhizoglomus microaggregatum , Septoglomus viscosum , and Entrophospora etunicata (Table S2 , in the Online Resource ESM_1) Mycorrhizal root colonization The frequency and intensity of AMF root colonization were estimated to evaluate the relationship of these parameters with the environmental conditions at each sampling site. Both mycorrhizal parameters showed significant differences among sites (Fig. 5 ), although no significant differences were found when comparing basin sections. Sites M, P and A exhibited the highest %F values, with no significant differences among them. In contrast, Sites S, C and O showed lower frequencies, which differed significantly from the high-frequency group (p < 0.05) (Fig. 5 a). Regarding %I, Sites M and A reached the highest values, showing significant differences (p < 0.05) from sites O and C, which exhibited the lowest ones (Fig. 5 b). At the basin scale, significant differences were found in %F between the upper and lower basins (p < 0.05), with higher values in the latter. However, no significant differences were observed in I% among basins. Correlation among soil health indicators To further understand the correlations among soil health indicators, a heatmap was generated (Fig. 6 ). GRSP showed a positive correlation with HMs (Zn, Cu, Cr, Ni, Cd), with particularly strong and significant correlations with Cu (rho = 0.84, p < 0.05) and Cr (rho = 0.89, p < 0.05). Weak positive correlations were observed among total GRSP and Fe, Mn, Pb, and OM% (Fig. 6 ). OM% was also positively correlated with all HMs except Pb, which displayed a negative association. Significant positive correlations were found between organic matter and Ni (rho = 0.93, p-value = 0.01), Cd (rho = 0.91, p < 0.05) and P (rho = 0.94, p < 0.05) (Fig. 6 ). AMF richness presented a strong and significant negative correlation with TDS and EC (rho= -0.82, p < 0.05). Meanwhile, the highest concentrations of HMs and total P tended to negatively correlate with the variables corresponding to mycorrhizal colonization (richness, %F and %I), although these correlations were not statistically significant, except for the effect of total P on %F, which was significantly negative (rho= -0.88; p < 0.05) (Fig. 6 ). Soil analysis and AMF communities The PCA analysis explained the associations between the physicochemical and biological parameters (Figure S3). The first two principal components (PC) accounted for 73.40% of the total variance. Monte Carlo permutation tests revealed that six variables (Zn, Cu, Ni, Cd, P and OM%) were significantly associated with the first PC (PC1). Although no variables were identified as significant contributors to PC2 based on permutation tests, the parametric dimdesc () analysis suggested that PC2 primarily reflects variation in soil pH (Figure S2 , in the Online Resource ESM_1). NMDS (Fig. 7 ) and Pearson correlation analysis (Figure S4, in the Online Resource ESM_1) were conducted to further elucidate relationships among soil parameters (chemical properties, HMs and GRSP) and AMF species. Both analyses consistently showed a strong positive correlation between Rhizophagus irregularis and HMs, including Zn (r = 0.87), Cu (r = 0.95), Ni (r = 0.96), Cd (r = 0.92), Cr (r = 0.94), as well as with OM% (r = 0.92) either. The NMDS analysis also associated R. irregularis and Rh. intraradices with TDS, EC and GRSP. Rhizophagus fasciculatus was significantly correlated with Pb (r = 0.96). F. mosseae , S. viscosum , Rh microaggregatum, Septoglomus constrictum and E. etunicata were positively correlated with AMF colonization values. E. etunicata was negatively correlated with total P contents. Funneliformis geosporus and Entophospora claroidea showed strong negative correlations with Fe concentration (Figure S4). PCA and NMDS analyses revealed a distinct separation of Sites C, O and S from the remaining sampling locations. Site C was strongly associated with HMs and with the occurrence of R. irregularis . In contrast, Site S occupied the opposite quadrant to Site C, mainly supported by the presence of AMF species belonging to Diversisporales order. The separation of Site O was mainly explained by its higher EC and TDS values. The spatial proximity of Sites P, A, and M in the ordination diagrams was largely explained by mycorrhizal colonization percentages observed. Discussion There is broad consensus that indicators used to assess soil quality and health should be sensitive to management practices, responsive to environmental variation, and accurately quantifiable. These indicators comprise physical, chemical, and biological attributes that reflect soil functioning. Among them, total soil concentrations of HMs remain one of the most widely used indicators for environmental risk assessment, whereas changes in the microbial community structure are commonly employed as biological indicators. Microbial communities respond to nutritional and environmental change, with more sensitive populations declining or disappearing under altered conditions (van Bruggen and Semenov 2000; Gonzalez et al. 2020 ; Nunes et al. 2020). In this study the effectiveness of typical (HMs concentration, pH, EC, TDS, OM%) and AMF-related (total GRSP concentration, AMF richness and root colonization) indicators was evaluated as predictors of soil health in disturbed urban watersheds. Chemical indicators of soil pollution Previous studies in the MRB have reported an increasing gradient of HMs pollution from the upper to the lower basin (Mendoza et al. 2015). However, land use, soil disturbance, population density, and chemicals effluents thrown into streams and rivers have generated exceptions from this expected pattern. In our study, Site O, located in the middle basin near a concrete manufacturing plant and a highway, revealed Cf and Df values similar to those observed in the lower basin, demonstrating the importance of local land use in shaping pollution patterns. High Cd concentrations were expected in the MRB, mainly in the lower basin. It is known that Cd pollution in urban soils is predominantly of anthropogenic origin, with major inputs arising from atmospheric deposition associated with industrial activities and vehicle emissions, as well as from the use of phosphate fertilizers in agriculture and urban green spaces (Kubier et al. 2019; Paredes del Puerto et al. 2021). Nevertheless, although most sites showed elevated Cd levels ​​(exceeding legal thresholds), soils near the river mouth (Site A) exhibited Cd levels below the detection level as found by Colombo et al. ( 2018 , 2020 ). This site has a slightly alkaline pH, low OM% content (the lowest among the sampled sites), and clay-rich texture (Colombo et al. 2018 ), conditions that may prevent soil Cd stabilization (Kubier et al. 2019). After comparing HMs concentrations in the MRB with other soil quality indicators (OM%, total P, EC and TDS), Sites C and O showed the highest values across all parameters. OM% content displayed a strong positive correlation with Cd and Ni concentrations. This pattern aligns with previous studies reporting that soils influenced by polluted water and enriched in organic matter tend to accumulate higher levels of HMs and nutrients such as P, largely due to the capacity of organic matter to form complexes with both (Violante et al. 2010; Zhang et al. 2017; Li G et al. 2018). These findings corroborate that organic matter plays a key role in the retention and immobilization of these elements within the soil matrix by forming stable complexes, adsorbing metal ions through functional groups, and thereby reducing their bioavailability (Stevenson & Fitch, 1986; Tipping 2002; Basta et al. 2005 ). Regarding P concentration, Indris et al. (2020) reported a positive association between elevated P levels and factors such as urbanization, heavy vehicular traffic, and additives present in unleaded fuels. These conditions align with what was observed at Sites C and O, both situated next to major highways, with Site C additionally bordering a racetrack. Soil pH is a critical factor that influences the solubility and bioavailability of HMs (Kicińska et al. 2021). Acidic pH conditions promote the dissolution and mobilization of soluble compounds into the soil solution, leading to higher TDS, an elevated EC and a higher bioavailability of HMs (Mohd-Aizat et al. 2014; Mylavarapu et al. 2020). In the MRB, previous studies have reported contrasting pH patterns with depth: some describe acidic values at the surface transitioning to neutral conditions below the first meter (Ceballos et al. 2021 ), while others document near‑neutral pH throughout the soil profile (Pereyra et al. 2022), consistent with the values observed in this study. Among the sampled sites, Site O exhibited the most acidic pH, along with the highest EC and TDS levels. Overall, these differences highlight the heterogeneous nature of the MRB in terms of pH variation, shaped by both natural processes and anthropogenic inputs. AMF- related indicators GRSP Although the complete molecular structure of GRSP remains partially resolved, their capacity to immobilize cations, including HMs and nutrients, has been attributed to the presence of negatively charged functional groups and surface-attached sugars that provide active sites for the adsorption of charged or partially charged elements (Lin et al. 2023). These interactions reduce the bioavailability of toxic elements in polluted soils, thereby promoting their stabilization (Son et al. 2024). Cornejo et al. ( 2008 ) reported an increased release of GRSP in contaminated soils with Cu, attributing this response to a strategy of native AMF communities to cope with environmental stress caused by excess HMs. In our study, the highest GRSP concentrations were reported in the lower basin at Site C, where the HMs concentrations also reached the highest values. Conversely, the site with the lowest HMs concentrations exhibited the lowest GRSP concentrations. Consistent with this pattern, significant positive correlations were detected between GRSP and several HMs, with particularly strong associations observed for Cr and Cu. AMF richness This research represents the first study to assess AMF richness across the MRB. While previous studies have focused on specific locations within the basin (Colombo et al. 2018 , 2020 ), our work successfully reported a wide assessment of these beneficial fungal species throughout the basin and correlated their presence/absence with indicative parameters of soil health. Of the sixteen AMF species identified in the MRB, eight of them ( R. fasciculatus, F. geosporus, Glomus sinuosum, E. etunicata, Entrophospora claroidea, Dentiscutata heterogama, Sieverdingia tortuosa , and Paraglomus laccatum) are reported for the first time, thereby increasing the number of AMF species known in this area (specifically, these species were found at sites A, S, and M). The most widely distributed AMF species was Rh. intraradices , found in all the sampled sites, followed by F. mosseae , Rh. microaggregatum and S viscosum , showing a prevalence of the Glomeraceae family in soils highly contaminated with HMs. Several species identified in our study matched with those previously found by Colombo et al. ( 2020 ) in the MRB (closed to Site A) using DNA sequencing approaches, including Rh. intraradices, R. irregularis, F. mosseae, S. constrictum, S. viscosum, Rh. microaggregatum, Enthophospora infrequens, and Gigaspora decipiens. In our study also, significant differences in AMF richness were observed among the three sections of the MRB. Non-contaminated sites showed greater AMF richness, both at the genus and family levels, than polluted ones. In the MRB, AMF species were indicators of soil health conditions, in relation to land use and the watercourse history. Green recreational areas with natural streams or lagoons (Sites S and M), showed higher species richness than more anthropologically impacted sites (Sites C and O). Additionally, Site A, located in an urban-industrial area but preserves its original watercourse and soil characteristics (Colombo et al. 2018 ), exhibited higher AMF richness than Sites P and O. Many studies have evaluated the effects of HMs contamination on AMF diversity, particularly in mining-impacted soils (Yang et al. 2015; Sanchez-Castro et al. 2016; Suarez et al. 2023; Utge Perri et al. 2025) and to a lesser extent, in natural metalliferous or urban polluted soils (Colombo et al. 2020 ; Silvani et al. 2017). In each case, the Glomeraceae family and the AM fungus Rh. intraradices were the most dominant. In our analysis of the presence/absense of AMF species as biological indicators of soil health, species occurring at the most contaminated sites, such as Rh. intraradices and R. irregularis , showed significant positive correlations with HMs concentrations (Fe, Cu, Cd, Ni, Cr, Zn). Conversely, species associated with less contaminated sites displayed negative correlations with HMs. This pattern reflects the environmental selective pressures shaping microbial community composition. The increased tolerance in fungi inhabiting contaminated soils is likely mediated by adaptive mechanisms such as metal sequestration and detoxification strategies (Benavidez et al. 2024 ; Colombo et al. 2024). Accordingly, the absence of certain AMF species or families may be considered as an indicator of soil toxicity or, in turn, their presence may be indicative of good soil health. The results presented so far, regarding AMF richness, indicate that contamination not only reduces species richness but also alters community composition, favouring those taxa with ruderal life strategies ( Rhizophagus sp. Rh. intraradices and F. mosseae ). These species are characterized by high rates of intraradical growth, short life cycles, early spore production, and better protection of the host plant against all types of stress (Chagnon et al. 2013 , Utge Perri et al. 2025). Finally, AMF richness showed a strong and significant negative correlation with TDS and EC, consistent with previous studies, demonstrating that high EC values adversely affect AMF development, species diversity, and community structure by delaying spore germination, hyphal growth, and root colonization (Fang et al. 2023 ). AMF root colonization The results obtained in our study revealed a negative correlation between mycorrhizal colonization and HMs concentration, as sites with the highest levels of contamination (Site C and Site O) registered the lowest levels of mycorrhization (assessed as frequency and intensity of colonization). Mendoza et al. (2015) reported that AMF colonization decreased with increasing HMs concentrations throughout the contamination gradient of the MRB. However, Site S, despite being one of the least polluted locations, presented colonization levels similar to those observed at Site C. This discrepancy may be explained by unmeasured factors of contamination or by local conditions. Site S is located near a populated area and is subject to increased soil disturbance due to its use as a recreational space. On the other hand, Site A, although highly polluted with Cu y Cr, showed high mycorrhization levels. Differences between the expected and observed mycorrhization percentages may also be related to the AMF species present and the competition for intraradical space with other root endophytes (Hardoim et al. 2015 ), specific to each site and each sampled plant. Conclusions The highest concentrations of HMs were detected in the lower basin, an industrial area characterized by a greater exposure to pollution and anthropogenic disturbance. GRSP exhibited a strong positive correlation with HMs concentrations, therefore, it may serve as a reliable indicator of contamination by potentially toxic elements. AMF species richness was more strongly influenced by the history of soil disturbance than by pollution levels, with the most disturbed environments exhibiting the lowest diversity. So, AMF species richness may function as an indicator of the history of anthropogenic disturbance, mainly through the loss of species belonging to the Paraglomeraceae, Diversisporaceae and Gigasporaceae families. This study provides the first report of R. fasciculatus , F. geosporus , G. sinuosum , E. etunicata , E. claroidea , D. heterogama , Si. tortuosa , and P. laccatum at MRB. Even when total GRSP concentration and diversity of AMF proved to be reliable biological indicators of soils health, mycorrhization percentages did not reflect the degree of soil degradation in the MRB. Therefore, this parameter does not appear to be a suitable indicator of soil health under the conditions evaluated. Finally, we emphasize the need for further research on soil microbiology in urban watersheds to improve understanding of anthropogenic impacts on biodiversity and to better define the role of microorganisms as soil health indicators in these particularly and highly degraded environments. Declarations Acknowledgments Authors acknowledge Universidad de Buenos Aires (UBA), Universidad Nacional de Avellaneda (UNDAV) and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) for their financial support (ODS-UNDAV-SIVTI 2021; PIP 11220200102192CO). We thank the Environmental Secretariat of the Province of Buenos Aires and the Autonomous City of Buenos Aires, and the Lago Lugano Ecological Reserve for the sampling permits granted. We also acknowledge Lic. Sofía Yasmín Utge Perri (IBBEA, CONICET-UBA) and Dr. Scarano (UNLP) for their technical support. Funding Universidad Nacional de Avellaneda (ODS-UNDAV-SIVTI 2021); Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET - PIP 11220200102192CO) Author Contribution Mailen Guerra Moreno : Investigation, Data curation, Formal analysis, Visualization, Writing - original draft, Writing - review & editing. Vanesa Analía Silvani: Conceptualization, Supervision, Writing - review & editing. Alicia Margarita Godeas: Funding acquisition, Project administration, Supervision, Writing - review & editing. Roxana Paula Colombo: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Writing - original draft, Writing - review & editing. Ethical Approval This is not applicable Consent to Participate This is not applicable Consent to Publish This is not applicable Competing Interests The authors declare no conflict of interest Data Availability Statement The data that support the findings are all included within the article and/or the supporting material Supporting information The supplemental material, including figures, tables and measured data that support the findings is available online for this article. References ACUMAR (2025) Matanza Riachuelo Basin Authority. https://www.acumar.gob.ar/ Accessed 9 February 2026 (in Spanish) Basta NT, Ryan JA, Chaney RL (2005) Trace element chemistry in residual‐treated soil: Key concepts and metal bioavailability. J Environ Qual 34:49-63. https://doi.org/10.2134/jeq2005.0049dup Benavidez ME, de la Fournière EM, Colombo RP, Silvani VA, Debray ME, Scotti A, Godeas AM (2024) Micro PIXE mapping proves a differential distribution and concentration of trace elements in fungal structures of Rhizophagus intraradices. Fungal Biol 128:2089–2093. https://doi.org/10.1016/j.funbio.2024.08.008 . Bernhardt A, Gysi N (2013) The world’s worst 2013: the top ten toxic threats. Cleanup, progress and ongoing challenges. Blacksmith Institute, New York and Green Cross Switzerland, Zurich. https://www.worstpolluted.org/docs/TopTenThreats2013.pdf. Accessed 9 February 2026 BGIV Banco de Glomeromycota in vitro (2026) BGIV Banco de Glomeromycota in vitro database. http://bgiv.com.ar/ Accessed 9 February 2026 Blaszkowski J (2012) Glomeromycota. W. Szafer Institute of Botany. Polish Academy of Sciences. Kraków, Poland. 303pp Bradford MM (1976) A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem 72:248–254. https://doi.org/10.1016/0003-2697(76)90527-3 Ceballos E, Dubny S, Othax N, Zabala ME, Peluso F (2021) Assessment of human health risk of Chromium and Nitrate pollution in groundwater and soil of the Matanza‑Riachuelo river basin, Argentina. Expo Health 13:323–336. https://doi.org/10.1007/s12403-021-00386-9 Chagnon PL, Bradley RL, Maherali H, Klironomos JN (2013) A trait-based framework to understand life history of mycorrhizal fungi. Trends Plant Sci 18:484–491. http://doi.org/10.1016/j.tplants.2013.05.001 Chauhan P, Sharma N, Tapwal A, Kumar A, Verma GS, Meena M, Seth CS, Swapnil P (2023) Soil microbiome: diversity, benefits and interactions with plants. Sustainability 15:14643. https://doi.org/10.3390/su151914643 Cittadino A, Ocello N, Majul MV, Ajhuacho R, Dietrich P, Igarzabal MA (2020) Heavy metal pollution and health risk assessment of soils from open dumps in the Metropolitan Area of Buenos Aires, Argentina. Environ Monit Assess 192:291. https://doi.org/10.1007/s10661-020-8246-x Colombo RP, Benavidez M, Fernández Bidondo L, Silvani VA, Bompadre MJ, Statello M, Scorza MV, Scotti A, Godeas AM (2020) Arbuscular mycorrhizal fungi in heavily metal-polluted soil in the Riachuelo river basin. Rev Argent Microbiol 52:145–149. https://doi.org/10.1016/j.ram.2019.05.001. Colombo RP, Fernandez Bidondo L, Patitucci M, Stephan BI, Dias J, Bompadre MJ, Benavidez ME, Pereyra FX (2018) First physical, chemical, biological and contamination description of three soil profiles of the coastal marginal forest of Avellaneda. In: Díaz Porres M, Falco L (eds) CONEBIOS V – National Congress of Ecology and Soil Biology. EdUnLu, Luján, Buenos Aires. https://www.sabes.org.ar/files/actas-CONEBIOS-V.pdf Accessed 9 Feb 2026 (in Spanish) Cornejo P, Meier S, Borie G, Rilling MC, Borie F (2008) Glomalin-related soil protein in a Mediterranean ecosystem affected by a copper smelter and its contribution to Cu and Zn sequestration. Sci Total Environ 406:154–160. https://doi.org/ 10.1016/j.scitotenv.2008.07.045 Declerck S, Strullu DG, Plenchette C (1996) In vitro mass-production of the arbuscular mycorrhizal fungus, Glomus versiforme , associated with Ri T-DNA transformed carrot roots. Mycol Res 100:1237–1242. https://doi.org/10.1016/S0953-7562(96)80186-9 Declerck S, D’Or D, Bivort C, de Souza F (2004) Development of extraradical mycelium of Scutellospora reticulata under root-organ culture: spores production and function of auxiliary cells. Mycol Res 108:84–92. http://doi.org/10.1017/S0953756203008761 Driver JD, Holben WE, Rillig MC (2005) Characterization of glomalin as a hyphal wall component of arbuscular mycorrhizal fungi. Soil Biol Biochem 37:101–106. https://doi.org/10.1016/j.soilbio.2004.06.011 Fang LL, Liu YJ, Wang ZH, Lu X., Li JH, Yang CX (2023) Electrical conductivity and pH are two of the main factors influencing the composition of arbuscular mycorrhizal fungal communities in the vegetation succession series of Songnen Saline-Alkali grassland. J Fungi 9:870. https://doi.org/10.3390/jof9090870 FAO (2004) Maximum limits for dangerous substances in soil and groundwater. Regulation of the Minister of the Environment No. 12 (RTL 2004, 40, 662). https://faolex.fao.org/docs/pdf/est97999E.pdf Accessed 9 February 2026 García I, Eigandt C, Fabrizio de Iorio A, de Cabo L (2022) A tool for the restoration of degraded riparian environments of the Matanza-Riachuelo Basin: Lotus tenuis Waldst. & Kit. in symbiosis with arbuscular mycorrhizal fungi. Agron Ambient 42:56–65. http://agronomiayambiente.agro.uba.ar/index.php/AyA/article/view/207 Accessed 9 Feb 2026 (in Spanish) Gerdemann J, Nicolson T (1963) Spores of mycorrhizal endogone species extracted from the soil by wet sieving and decanting. Trans Br Mycol Soc 46:235–244. https://doi.org/10.1016/S0007-1536(63)80079-0 Gonzalez M, Arango C, Pastorino G, Ruscitti M (2020) Arbuscular mycorrhizae and the restoration of degraded ecosystems. In: Saparrat MCN, Ruscitti MF, Arango MC (eds) Arbuscular mycorrhizae: biology and applications in the agro-forestry sector. Universidad Nacional de La Plata. https://libros.unlp.edu.ar/index.php/unlp/catalog/book/1504 Accessed 9 February 2026 González-Chávez M, Carrillo-González R, Wright S, Nichols K (2004) The role of glomalin, a protein produced by arbuscular mycorrhizal fungi, in sequestering potentially toxic elements. Environ Pollut 130:317-323. https://doi.org/10.1016/j.envpol.2004.01.004 Hardoim PR, Berg G, Pirttilä AM, Compant S, Campisano A, Döring M, Sessitsch A (2015) The hidden world within plants: ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol Mol Biol Rev 79:293. https://doi.org/10.1128/MMBR.00050-14 Index Fungorum (2026) Index Fungorum database. https://www.indexfungorum.org/names/names.asp Accessed 15 February 2026 Indris SN, Rudolph DL, Glass BK, Cappellen PV (2020) Evaluating phosphorus from vehicular emissions as a potential source of contamination to ground and surface water. Cogent Environ Sci 6:1794702. https://doi.org/10.1080/23311843.2020.1794702 INVAM (International Culture Collection of (Vesicular) Arbuscular Mycorrhizal Fungi) (2026). INVAM database . West Virginia University, Morgantown, WV. https://invam.ku.edu/ Accessed 9 February 2026 Kicińska A, Pomykała R, Izquierdo‐Diaz M (2022) Changes in soil pH and mobility of heavy metals in contaminated soils. Eur Journal Soil Sci 73: e13203. https://doi.org/10.1111/ejss.13203 Kubier A, Wilkin RT, Pichler T (2019) Cadmium in soils and groundwater: A review. Appl Geochem 108:104388. https://doi.org/10.1016/j.apgeochem.2019.104388. Li G, Lu N, Wei Y, Zhu D (2018) Relationship between heavy metal content in polluted soil and soil organic matter and pH in mining areas. In IOP conference series: materials science and engineering. 394: p052081. https://doi.org/ 10.1088/1757-899X/394/5/052081 Magni LF, Castro LN, Rendina AE (2021) Evaluation of heavy metal contamination levels in river sediments and their risk to human health in urban areas: A case study in the Matanza-Riachuelo Basin, Argentina. Environ Res 197:110979. https://doi.org/10.1016/j.envres.2021.110979 Malekzadeh E, Aliasgharzad N, Majidi J, Aghebati-Maleki L, Abdolalizade J (2016) Cd-induced production of glomalin by arbuscular mycorrhizal fungus (Rhizophagus irregularis) as estimated by monoclonal antibody assay. Environ Sci Pollut Res Int 23:20711–20718. http://doi .org/10.1007/s11356-016-7283-z Mendoza RE, García IV, de Cabo L, Weigandt C, Fabrizio de Iorio A (2015) The interaction of heavy metals and nutrients present in soil and native plants with arbuscular mycorrhizae on the riverside in the Matanza-Riachuelo River Basin (Argentina). Sci Total Environ 505:555–564. http://dx.doi.org/10.1016/j.scitotenv.2014.09.105 Merlinsky MG (2016) Mists of the Riachuelo: River basins and climate change in Buenos Aires. Lat Am Perspect 43:43–55. https://doi.org/10.1177/0094582X15623764. Mohd-Aizat A, Mohamad-Roslan MK, Sulaiman WNA, Karam DS (2014) The relationship between soil pH and selected soil properties in 48 years logged-over forest. Int J Environ Sci 4:1129. https://doi.org/ 10.6088/ijes.201404060000 Mylavarapu R, Bergeron J, Wilkinson N, Hanlon EA (2020) Soil PH and Electrical Conductivity: A county extension soil laboratory manual: CIR1081 SS118, Rev. 1:2020. Gainesville, Florida. https://doi.org/10.32473/edis-ss118-2020 National Meteorology Service (2026). Normal climate statistics. https://datos.gob.ar/dataset/smn-estadisticas-climaticas-normales Accessed 9 February 2026 (in Spanish) Nunes MR, Karlen DL, Veum KS, Moorman TB, Cambardella CA (2020) Biological soil health indicators respond to tillage intensity: A US meta-analysis. Geoderma 369:114335. https://doi.org/10.1016/j.geoderma.2020.114335 Page AL, Miller RH, Keeney D (1982) Methods of soil analysis. Part 2. American Society of Agronomy. Soil Science Society of America Madison, Wisconsin, 1143 pp Paredes del Puerto JM, Paracampo AH, García ID, Maiztegui T, Garcia de Souza JR, Maroñas ME, Colautti DC (2021) Fish assemblages and water quality in pampean streams (Argentina) along an urbanization gradient. Hydrobiologia 848:4493–4510. https://doi.org/10.1007/s10750-021-04657-z(0123456789 Pasqualin MF, Faure Montania E, Hepp Y, Antolini L, Finkelstein JZ, García SI (2019) Environmental health risk map of the Matanza-Riachuelo Basin (Argentina): A methodology to prioritize interventions. Rev Salud Ambient 19:148–158. https://ojs.diffundit.com/index.php/rsa/article/view/955 (in Spanish) Pereyra FX, Ragas DB (2022) Urban soils: Case study of the Buenos Aires metropolitan region. Ciencia Hoy 180:34–42. https://untref.edu.ar/muntref/cienciahoy/public/es/archivo-ciencia-hoy Accessed 9 Feb 2026 (in Spanish) Phillips JM, Hayman DS (1970) Improved procedures for clearing roots and staining parasitic and vesicular-arbuscular mycorrhizal fungi for rapid assessment of infection. Trans Br Mycol Soc 55:158–160. https://doi.org/10.1016/S0007-1536(70)80110- 3 Plenchette C, Morel C (1996) External phosphorus requirements of mycorrhizal and non-mycorrhizal barley and soybean plants. Biol Fertil Soils 21:303–308. https://doi.org/10.1007/BF00334907 R Development Team (2018) R: a language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. URL http://www.R-project.org Accessed 9 February 2026 Ratto S, Marceca E, Moscatelli G, Abbruzese D, Bardi H, Bossi M, Bres P, Cordón G, Di Nano MP, Murruni L, Potarsky K, Williams F (2004) Evaluation of organic and inorganic contamination in an alluvial soil on the coast of the Riachuelo River, Buenos Aires, Argentina. Ecol Austral 14:179–190 (in Spanish). Rillig MC, Mummey DL (2006) Mycorrhizas and soil structure. New Phytol 171:41–53. https://doi.org/10.1111/j.1469-8137.2006.01750.x Sánchez-Castro I, Gianinazzi-Pearson V, Cleyet-Marel JC, Baudoin E, van Tuinen D (2017) Glomeromycota communities survive extreme levels of metal toxicity in an orphan mining site. Sci Total Environ 598:121–128. https://doi. org/ 10. 1016/j. scito tenv. 2017. 04. 084 . Silvani VA, Colombo RP, Scorza MV, Fernandez Bidondo L, Rothen CP, Scotti A, Fracchia S, Godeas AM (2017) Arbuscular mycorrhizal fungal diversity in high-altitude hypersaline Andean wetlands studied by 454-sequencing and morphological approaches. Symbiosis 72:143–152. https://doi.org/10.1007/s13199-016-0454-3 Son Y, Martínez CE (2024) Three important roles and chemical properties of glomalin-related soil protein. Front Soil Sci 4:1418072. https://doi.org/10.3389/fsoil.2024.1418072 Suarez JP, Herrera P, Kalinhoff C, Vivanco-Galval O, Thangaswamy S (2023) Generalist arbuscular mycorrhizal fungi dominated heavy metal polluted soils at two artisanal and small−scale gold mining sites in southeastern Ecuador. BMC Microbiol 23:42. https://doi.org/10.1186/s12866-022-02748-y Stevenson FJ, Fitch A (1986) Chemistry of complexation of metal ions with soil solution organics. In: Huang PM, Schnitzer M. Interactions of Soil minerals with natural organics and microbes. Volume 17, SSSA Special Publications https://doi.org/10.2136/sssaspecpub17.c2 Tipping E (2002) Cation binding by humic substances. Cambridge University Press, Cambridge, UK, Vol. 12 Utge Perri SY, Valerga Fernández MV, Scotti A, Colombo RP, González F, Valenzuela L, Godeas AM, Silvani VA (2025) Responses of arbuscular mycorrhizal fungi and plant communities to long-term mining and passive restoration. Plants 14:580. https://doi.org/10.3390/plants14040580 Van Bruggen AH, Semenov AM (2000) In search of biological indicators for soil health and disease suppression. Appl Soil Ecol 15:13–24. https://doi.org/10.1016/S0929-1393(00)00068-8 Violante A, Cozzolino V, Perelomov L, Caporale AG, Pigna M (2010) Mobility and bioavailability of heavy metals and metalloids in soil environments. J Soil Sci Plant Nutr 10:268–292. https://doi.org/10.4067/S0718-951620100001000 Wang Q, Lu H, Chen J, Jiang Y, Williams MA, Wu S, Li S, Liu J, Yang G, Yan C (2020) Interactions of soil metals with glomalin-related soil protein as soil pollution bioindicators in mangrove wetland ecosystems. Sci Total Environ 709:136051. https://doi.org/10.1016/j.scitotenv.2019.136051 Yang Y, Scheller HV, Ghosh A, Chen H, Tang M (2015) Community structure of arbuscular mycorrhizal fungi associated with Robinia pseudoacacia in uncontaminated and heavy metal contaminated soils. Soil Biol Biochem 86:146–158. https://doi.org/10.1016/j.soilbio.2015.03.018. Zhang C, Nie XP, Liang J, Zeng GM (2017) Spatial and seasonal variation of heavy metals and nutrients in the sediments of a contaminated river in China. Environ Sci Pollut Res 24:11867–11878. https://doi.org/10.1007/s11356-017-8813-6 Supplementary Files Authorsresponse.docx ESM1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 28 Mar, 2026 Editor invited by journal 26 Mar, 2026 Editor assigned by journal 23 Mar, 2026 First submitted to journal 17 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9107710","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613807807,"identity":"ea0ffadb-371d-4981-b41a-cfa06322ff4c","order_by":0,"name":"Mailen Guerra Moreno","email":"","orcid":"","institution":"Univeridad de Buenos Aires (UBA) Facultad de Ciencias Exactas y Naturales, Departamento de Biodiversidad y Biología Experimental, Laboratorio de Microbiología del Suelo, Buenos Aires, Argentina","correspondingAuthor":false,"prefix":"","firstName":"Mailen","middleName":"Guerra","lastName":"Moreno","suffix":""},{"id":613807808,"identity":"bb945451-619d-4110-a558-e5de46e03d2c","order_by":1,"name":"Vanesa Analía Silvani","email":"","orcid":"","institution":"Consejo Nacional de Investigaciones Cientificas y Tecnicas, Instituto de Biodiversidad y Biología Experimental y Aplicada (CONICET-UBA), Buenos Aires, Argentina","correspondingAuthor":false,"prefix":"","firstName":"Vanesa","middleName":"Analía","lastName":"Silvani","suffix":""},{"id":613807809,"identity":"71b14190-ef08-455e-b459-356c7771dcba","order_by":2,"name":"Alicia Margarita Godeas","email":"","orcid":"","institution":"Consejo Nacional de Investigaciones Cientificas y Tecnicas, Instituto de Biodiversidad y Biología Experimental y Aplicada (CONICET-UBA), Buenos Aires, Argentina","correspondingAuthor":false,"prefix":"","firstName":"Alicia","middleName":"Margarita","lastName":"Godeas","suffix":""},{"id":613807810,"identity":"48102cea-1f34-4a53-850d-c51d932bf2e5","order_by":3,"name":"Roxana Paula Colombo","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-2856-7976","institution":"Consejo Nacional de Investigaciones Cientificas y Tecnicas, Instituto de Biodiversidad y Biología Experimental y Aplicada (CONICET-UBA), Buenos Aires, Argentina.","correspondingAuthor":true,"prefix":"","firstName":"Roxana","middleName":"Paula","lastName":"Colombo","suffix":""}],"badges":[],"createdAt":"2026-03-12 18:53:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9107710/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9107710/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106074209,"identity":"9d5e708b-c83c-4e8c-ab22-1dac04966296","added_by":"auto","created_at":"2026-04-03 07:11:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1064931,"visible":true,"origin":"","legend":"\u003cp\u003eMap and location of the Matanza Riachuelo river basin (A and B) and sampling sites (red) in the lower (C), middle (D) and upper (E) sections of the basin. ARG: Argentina, UY: Uruguay. Map created using QGIS. Satellite imagery © Microsoft Bing Maps (accessed September 2025)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9107710/v1/a6a43915f2aa840b3d772a5c.png"},{"id":106094581,"identity":"4906afb1-6241-49df-a7b0-d3cf3e7b5a23","added_by":"auto","created_at":"2026-04-03 11:42:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41466,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of glomalin concentration (mg GRSP/g soil) (A) per basin; (B) per site. Different letters represent statistically significant differences (p \u0026lt; 0.05) among basin/sites based on the model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9107710/v1/8a9f177289f7497236917ee4.png"},{"id":106074207,"identity":"45465ec9-8ea1-4c66-a7fb-d3d5bf21b709","added_by":"auto","created_at":"2026-04-03 07:11:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50191,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probability of AMF richness across upper, middle, and lower areas of the Matanza-Riachuelo basin. Error bars represent 95% confidence intervals. Different letters indicate significant differences (p\u0026lt;0.05)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9107710/v1/0be229af809db01def0936b6.png"},{"id":106094519,"identity":"c2c47501-6eb9-45ac-a277-55619708b538","added_by":"auto","created_at":"2026-04-03 11:42:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43449,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of AMF families per site (%). Lower basin (Site A, Site P, and Site C), middle basin (Site O and Site M), upper basin (Site S)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9107710/v1/242867ff1eeab8a7786de133.png"},{"id":106074220,"identity":"74b5005c-b4b5-451f-a83c-e1c926b404d8","added_by":"auto","created_at":"2026-04-03 07:12:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":115212,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated marginal means (± 95% confidence intervals) of mycorrhizal (A) frequency (%) and (B) intensity (%) for each sampling site. Different letters denote statistically significant differences among sites based on Tukey-adjusted pairwise comparisons (p\u0026lt;0.05). Lower basin in yellow (Site A, Site P, and Site C), middle basin in green-blue (Site O and Site M), upper basin in violet (Site S)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9107710/v1/2cfd5ddad774e12da62fa1f8.png"},{"id":106074210,"identity":"a0d0d539-476e-479b-bf71-25835b6cefe1","added_by":"auto","created_at":"2026-04-03 07:11:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":115937,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix plot of the studied parameters, with the colour scale indicating the r values. Squares with black borders represent significant p-values (p \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9107710/v1/e97c9a6090d92d6b46a2dc40.png"},{"id":106074217,"identity":"6dbf548b-9c47-44eb-b375-e8d06064b6c1","added_by":"auto","created_at":"2026-04-03 07:12:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":163604,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric multidimensional scaling (NMDS) of sites based on AMF species composition. Sites are represented by coloured circles (S, O, M, C, P and A). Species are labelled in italic text. Environmental variables are shown as arrows with red labels\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9107710/v1/61a171645eb845cc583b6def.png"},{"id":106095798,"identity":"48e541c5-2b5e-4dec-8be8-c3aeb0459608","added_by":"auto","created_at":"2026-04-03 11:51:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2606981,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9107710/v1/d5ba255b-808a-4698-af49-2d73bf492ed4.pdf"},{"id":106074222,"identity":"dea20bf1-c9bc-47b2-a430-04fa62666822","added_by":"auto","created_at":"2026-04-03 07:12:02","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":15191,"visible":true,"origin":"","legend":"","description":"","filename":"Authorsresponse.docx","url":"https://assets-eu.researchsquare.com/files/rs-9107710/v1/f415eb10ca933b0b6d272303.docx"},{"id":106074225,"identity":"0aefcf10-e12a-4e2c-a01c-f1578d1ad422","added_by":"auto","created_at":"2026-04-03 07:12:03","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":4342747,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9107710/v1/8b0b4bdbeab08f18ef7feab9.docx"}],"financialInterests":"","formattedTitle":"Sensitivity variations on soil health indicators related to arbuscular mycorrhizae in a highly disturbed urban basin of Buenos Aires, Argentina","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrban basins are geographic areas where water dynamics are impacted by the infrastructure of the city and the urban expansion. Proper urban watershed management is essential for flood control and prevention of environmental pollution. The Matanza-Riachuelo is a low-gradient plain river. Surface run-off within its basin gives rise to a network of streams that converge to form the main course, called Matanza in its upper section and Riachuelo in its final stretch. This main course (64 km long) discharges into the R\u0026iacute;o de La Plata estuary, which separates Argentina from Uruguay and serves as the principal water source for the Buenos Aires Metropolitan area (ACUMAR \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The Matanza-Riachuelo river basin (MRB) is located in the northeast of Buenos Aires province (Argentina), with a surface of 2047 km\u003csup\u003e2\u003c/sup\u003e. It includes the most densely populated and industrialized region in Argentina, with petrochemical industries, tanneries, slaughterhouses and meat-packing plants situated along its riverside (Mernlinsky et al. 2016). In some areas of the basin, the concentrations of potentially toxic elements (PTEs), such as heavy metal(loid)s (HMs), exceed the thresholds established by national (Argentine hazardous waste legislation - Law N\u0026deg; 24051) and international (FAO \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) environmental standards (Ratto et al. 2004; Mendoza et al. 2015; Colombo et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pereyra et al. 2022).\u003c/p\u003e \u003cp\u003eDespite being considered one of the most polluted basins in the world (Bernhardt and Gysi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), studies on biological indicators of soil health remain scarce. This is a recurring issue in almost all urban basins. Ratto et al. (2004) conducted the first study on the extent and origin of organic and inorganic contamination in MRB soils. Subsequent studies have estimated the potential risk on human health, associated with soil contamination, on urban settlements across the basin (Pasqualini et al. 2019; Cittadino et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ceballos et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Far less attention has been given to the characterization of the flora or soil microorganisms, despite being biological indicators of soil heath and potential agents in future bioremediation programs (Mendoza et al. 2015; Colombo et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Garcia et al. 2022).\u003c/p\u003e \u003cp\u003eAmong these understudied microorganisms, arbuscular mycorrhizal fungi (AMF) (Phylum Glomeromycota) are particularly important. They form mutualistic associations with roots of most land plants and play key roles in ecosystem functioning and plant survival under stressful conditions (Gonz\u0026aacute;lez-Chavez et al. 2004; Gonzalez et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AMF improves soil quality through the development of extraradical hyphal networks that bind soil particles together; they also stabilize soil aggregates by the production of glomalin (technically referred to as glomalin-related soil protein, GRSP). GRSP are a family of thermo-stable and water-insoluble glycoproteins, produced in the cell walls of hyphae and spores, and considered a \u0026ldquo;biological cement\u0026rdquo; of soil aggregates (Driver et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Rillig and Mummey 2006; Chauhan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The functional groups present in the molecular structure of GRSP interact with various ions and chelate them, leading to a decrease in HMs bioavailability (Malekzadeh et al. 2016; Son et al. 2024). Wang et al. (2020) reported an increased release of GRSP in soils contaminated with HMs, attributing this to a possible response of AMF against stressful conditions.\u003c/p\u003e \u003cp\u003ePrevious reports revealed that soils of the MRB exhibited a longitudinal contamination gradient that increased from the upper to the lower basin (Mendoza et al. 2015; Paredes del Puerto et al. 2021). However, the lack of studies addressing soil toxicity and its effects on beneficial soil microorganisms, both in the MRB and in other worldwide urban basins. limits our knowledge of how AMF symbionts respond to the environmental pressures on these highly anthropized soils. Mendoza et al. (2015) found variations in mycorrhizal colonization through the basin and correlated this to HMs contamination; however, AMF species were not identified. Later, Colombo et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) provided the first reports of AMF species in one highest polluted site within the MRB, highlighting the predominance of the Claroideoglomeraceae family.\u003c/p\u003e \u003cp\u003eIn this context, the present study evaluated the potential of arbuscular mycorrhizae as bioindicators of soil health in highly disturbed urban basins. We assessed total GRSP content, AMF richness, and mycorrhizal root colonization, and examined their relationships with typical soil health indicators (pH, electric conductivity, organic matter, HMs, among others), used as indicators of soil quality. By integrating microbial, biochemical, and physicochemical variables, this study aims to determine whether these AMF-related parameters can provide sensitive indicators of soil condition in polluted wetland environments.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe MRB is divided into three regions: upper, middle, and lower basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The upper basin is predominantly rural and shaped by primary and agro-industrial activities. The middle basin exhibits a transitional landscape that combines urban and rural areas. The lower basin is highly urbanized, with a concentration of industrial and service-oriented activities. This gradient, from rural to increasingly urbanized and industrialized environments, drives the progressive degradation of soil and water quality, towards the river mouth in the R\u0026iacute;o de La Plata estuary (ACUMAR \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The region has a humid temperate climate with four different seasons, a mean annual temperature of 17.3\u0026deg;C, mean annual precipitation of 1236 mm, and a relative humidity of 71% (Servicio Meteorol\u0026oacute;gico Nacional, n.d).\u003c/p\u003e \u003cp\u003eSoil and root samples were assessed from six sites distributed throughout the three sections of the basin: lower basin (Site A, Site P, and Site C), middle basin (Site M and Site O), upper basin (Site S) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in the Online Resource ESM_1). Each site was subsequently characterized (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSampling was conducted between April 2022 and April 2023. All sampled soils were located in wetlands and were selected based on direct field observations of flooding, as they were found to be covered by water during at least one of the site visits (each site was visited at least twice to document hydrological dynamics).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study sites along three different sections in the Matanza-Riachuelo Basin.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite and associated watercourse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBasin section\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWatercourse history\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSoil type /Land use\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite A: Riachuelo River (Sup. Figure\u0026nbsp;1a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvellaneda Borough,\u003c/p\u003e \u003cp\u003eCentral Town\u003c/p\u003e \u003cp\u003e(-34.657722, -58.375586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNatural soil (Mollisol).\u003c/p\u003e \u003cp\u003eIndustrial/ residential; near busy highway\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite P: Riachuelo River (Sup. Figure\u0026nbsp;1b)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvellaneda Borough, Pi\u0026ntilde;eyro Town\u003c/p\u003e \u003cp\u003e(-34.662376, -58.390899)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBackfill soil.\u003c/p\u003e \u003cp\u003eIndustrial/ residential; near busy highway\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite C: Cilda\u0026ntilde;ez Stream (Sup. Figure\u0026nbsp;1c)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuenos Aires Capital City\u003c/p\u003e \u003cp\u003e(-34.678558, -58.442985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModified. Mostly a piped stream.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBackfill soil.\u003c/p\u003e \u003cp\u003eIndustrial. Sampling site adjacent to a former dump, busy highway and racetrack.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite O: Ortega Stream (Sup. Figure\u0026nbsp;1d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEsteban Echeverria Borough\u003c/p\u003e \u003cp\u003e(-34.806102, -58.506269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNatural.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNatural soil (Mollisol).\u003c/p\u003e \u003cp\u003eResidential. Close to a concrete manufacturing establishment. Near a busy highway.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite M: Matanza River (Sup. Figure\u0026nbsp;1e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEzeiza Borough\u003c/p\u003e \u003cp\u003e(-34.763925, -58.549772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNatural soil (Mollisol).\u003c/p\u003e \u003cp\u003eGreen recreational area. Close to the international airport.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite S: San Vicente Lagoon (Sup. Figure\u0026nbsp;1f)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSan Vicente Borough\u003c/p\u003e \u003cp\u003e(-35.014402, -58.419423)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNatural soil (Mollisol).\u003c/p\u003e \u003cp\u003eGreen recreational area/ Residencial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoil and root sampling\u003c/h3\u003e\n\u003cp\u003eThree rhizospheric soil cores (15 cm deep) and roots of the most abundant mycorrhizal plant species (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, in the Online Resource ESM_1) were collected at each site. Roots were used to evaluate mycorrhizal colonization and establish pot trap cultures. Each soil sample was separated in four fractions for physical and chemical analysis, GRSP extraction, AMF spores\u0026rsquo; isolation and characterization, and pot trap cultures establishment. To minimize field-scale heterogeneity, subsamples were homogenized into a single composite sample per site, which was used for HMs analysis. All samples were air-dried and stored at 4\u0026deg;C until further analysis.\u003c/p\u003e\n\u003ch3\u003eSoil analysis\u003c/h3\u003e\n\u003cp\u003eThe following soil characteristics were measured: HMs concentrations, percentage of organic matter (OM), available phosphates (P), pH, total dissolved salts (TDS), and electrical conductivity (EC).\u003c/p\u003e \u003cp\u003eEach soil sample was subjected to acid digestion, and Zn, Cu, Ni, Cd, Cr, Fe, Mn and Pb concentrations were quantified by atomic absorption spectroscopy (EPA Method 3050B). The analyses were conducted at the Laboratory of Inorganic and Analytical Chemistry (Qualified Service - Faculty of Agronomy, UBA). Contamination levels were then estimated using the Contamination Factor (\u003cem\u003eCf\u003c/em\u003e) and the Degree of contamination (\u003cem\u003eDc\u003c/em\u003e) indices. The indices were calculated according to Magni et al. (2021):\u003c/p\u003e \u003cp\u003e \u003cem\u003eCf\u003c/em\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Cm}{Bn}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eDc\u003c/em\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{n}Cf\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eCm\u003c/em\u003e is the measured concentration of a particular HMs, and \u003cem\u003eBn\u003c/em\u003e is the concentration of the same HMs in a reference soil. In this study, the reference soil corresponded to San Vicente lagoon soils (Site S) located in the upper basin and characterized by non-toxic concentrations of HMs (according to FAO, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The Degree of Contamination (\u003cem\u003eDc\u003c/em\u003e) is defined as the sum of all \u003cem\u003eCf\u003c/em\u003e values calculated for each site (\u003cem\u003en\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eOM percentage (OM%) was estimated using the ignition method (Page et al. 1982). Available P content was estimated by colorimetry using the Visocolor ECO reagent kit (Machery-Nagel). Soil pH and EC were measured in a 1:2.5 (wt./vol) soil/water solution using a pH, EC/TDS and temperature meter (Hanna, model: HI98129).\u003c/p\u003e\n\u003ch3\u003eTotal GRSP quantification\u003c/h3\u003e\n\u003cp\u003eTotal GRSP was extracted from soil samples following Cornejo et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Briefly, a 50 mM citrate buffer was added to soil autoclaved and centrifugated. Supernatant was collected and extraction was repeated until supernatant lost its typical reddish colour. All the supernatants were pooled and brought to a final volume with a citrate buffer. Quantification was performed by spectrophotometry following Bradford (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1976\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAMF richness\u003c/h3\u003e\n\u003cp\u003eWith the aim of knowing the specific richness of AMF in the MRB, spores were isolated from soil samples by wet sieving and decanting (Gerdermann and Nicolson 1963). Healthy spores were selected under a stereomicroscope (Olympus SZ61) and mounted in Polyvinyl-Lacto-Glycerol and Melzer reagents (1:1, v/v). Spore morphological characteristics were examined under microscope (Olympus BX51), and AMF species were identified following Blaszkowski (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and the International Collection of Vesicular Arbuscular Mycorrhizal Fungi (INVAM, n.d.). Vouchers were deposited in the Banco de Glomeromycota in Vitro (BGIV, n.d.). Taxonomic assignments were done according to the Index Fungorum (n.d.).\u003c/p\u003e \u003cp\u003eOne trap culture per site was established to facilitate the observation and identification of AMF species. Briefly, soil samples (containing spores and roots) were inoculated into pots filled with tindalized substrate (soil:perlite:vermiculite; 1:1:1). Surfaced-sterilized seeds of \u003cem\u003eLactuca sativa\u003c/em\u003e, \u003cem\u003ePisum sativum\u003c/em\u003e, \u003cem\u003eLinum usitatissimum\u003c/em\u003e and \u003cem\u003eZea mays\u003c/em\u003e were sown and maintained under greenhouse conditions. Trap cultures were sampled at 4, 6, and 8 months of growth to evaluate AMF colonization and spore development. Spores were isolated and identified as previously described.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMycorrhizal root colonization\u003c/h2\u003e \u003cp\u003eIn order to evaluate AMF colonization roots (sampled at field or from pot trap cultures) were clarified with KOH (10% w/v), acidified with HCl (0.1 N) and stained with Trypan blue in lactic acid (0.05% w/v) (modified from Phillips and Hayman, 1970). The frequency (%F) and intensity (%I) of colonization were estimated (modified from Declerck et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). For each sample, fifty randomly selected root segments (0.5 cm length) were examined under microscope (Olympus BX51, 200x and 400x magnification). Colonization frequency was calculated as the percentage of root segments containing any arbuscular mycorrhizal structure. Colonization intensity was estimated by sorting out the root segments into different intensity classes (1\u0026ndash;20, 21\u0026ndash;40, 41\u0026ndash;60, 61\u0026ndash;80, and 81\u0026ndash;100%) of AMF intraradical cover (Declerck et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Plenchette and Morel, 1996).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor parameters with replicates (pH, EC, TDS, and total GRSP), statistical models were fitted using basin and site as explanatory variables. Normality and homoscedasticity assumptions were verified. Differences in total GRSP concentration among basins were assessed with linear mixed-effects models (LMM) including basin as a fixed effect and site as a random intercept. Differences among sites were tested with a separate LMM using log-transformed GRSP as the response variable, site as a fixed effect, and basin as a random intercept. Model diagnostics were performed using residual and Q\u0026ndash;Q plots, as well as DHARMa simulated residuals. Fixed-effect significance was evaluated using Type III ANOVA (Satterthwaite approximation), and pairwise comparisons were obtained via estimated marginal means (emmeans package). Generalized linear models (GLMs) with a Gamma distribution and log link were used to test site effects on TDS and EC. Pearson correlations among heavy metals, physicochemical properties, mycorrhization, and GRSP were visualized with a heatmap. Principal component analysis (PCA) was performed on standardized variables, and contributions to PC1 and PC2 were evaluated using a 1000-iteration permutation test. AMF richness (presence/absence) was analysed with binomial GLMs and a logit link. Predictor significance was assessed with likelihood ratio tests (Type II ANOVA), and Tukey-adjusted pairwise comparisons were obtained using emmeans. Model assumptions were checked with DHARMa, and predictive performance was evaluated with ROC curves and AUC. Non-metric multidimensional scaling (NMDS) based on Bray\u0026ndash;Curtis distances was used to visualize microbial community dissimilarities and assess the influence of environmental factors on community composition. Frequency (%F) and intensity (%I) were analysed using LMMs with basin as a fixed effect and site nested within basin as a random effect (1 | Basin/Site). Tukey-adjusted comparisons of estimated marginal means were used to compare basins.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSoil properties\u003c/h2\u003e \u003cp\u003eFrom the upper to the lower basin (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the soil samples showed a clear trend of increasing HMs concentrations. Site C, located in the lower basin, exhibited the highest concentrations of Zn (1229 mg/kg), Cu (617 mg/kg), Cr (202.26 mg/kg) and Cd (7.47 mg/kg), all of which exceeded the regulatory limits established for soils designated for agricultural or residential use under national and international environmental standards. Notably, Cu was the only element that also exceeded the limit set for industrial use of soil.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeavy metal(loid)s concentration, percentage of organic matter (OM%), total phosphates (Total P), pH, electrical conductivity (EC), and total dissolved salts (TDS) in soil samples from the MRB, collected near the water body.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOM\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTotal P\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003cp\u003e(mS/cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e227.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e150.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26000.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e360.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e140.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.6\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.76\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.08 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e380\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e40 ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e669.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e213.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31666.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e630.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e172.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.7\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.41\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.12 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e180\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e60\u003c/p\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1229.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e617.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e202.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23686.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e433.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e21.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.91\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.09 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e450\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e50 ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u0026ndash;56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10947.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e210.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e59.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.75\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.70\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.46 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e350\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e230 ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e270.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25630.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e524.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e68.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.6\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.56\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.03 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e780\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003c/p\u003e \u003cp\u003eb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17344.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e280.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.4a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.46\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.12 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e230\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e60\u003c/p\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDifferent letters between columns indicate significant differences within sites according to the Tukey test (alpha\u0026thinsp;=\u0026thinsp;0.05). ND: Not detected (\u0026lt;\u0026thinsp;2 mg/kg). Concentrations exceeding the permitted values according to national and international environmental standards (Argentinean Law and FAO Maximum Limits) are indicated in bold. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Lower basin (Site A, Site P, and Site C), middle basin (Site O and Site M), upper basin (Site S).\u003c/p\u003e \u003cp\u003eThe contamination factor (Cf) showed that the highest contamination levels occurred in the lower basin, particularly for Zn, Cu, Cr and Pb, which ranged from elevated to very high. In contrast, the middle basin exhibited lower contamination (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, Sites P and C registered the highest Cf and Dc values, both associated with a high degree of contamination (Dc\u0026thinsp;\u0026ge;\u0026thinsp;24) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Dc values for Sites A and O indicated an important contamination level (12\u0026thinsp;\u0026le;\u0026thinsp;Dc\u0026thinsp;\u0026gt;\u0026thinsp;24), whereas Site M showed a moderate level (6\u0026thinsp;\u0026le;\u0026thinsp;Dc\u0026thinsp;\u0026lt;\u0026thinsp;12) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTotal P ranged from 20 mg/kg to 60 mg/kg, with the highest concentration at Site C. Organic matter values decreased from upper to lower basin, except for Site C, which registered the highest value (21.05%).\u003c/p\u003e \u003cp\u003eFor pH, all the soil samples had neutral values, except Site O, which showed a significant difference with acid pH (5.56 0.1) (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). EC values in the MRB varied from 0.46 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.12 mS/cm to 1.56 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\:\\)\u003c/span\u003e\u003c/span\u003e0.03 mS/cm, while TDS varied from 180 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 60 mg/kg to 780 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 10 mg/kg. Statistical analysis indicated significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) among Site O (highest values of EC and TDS) and Sites P and S (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContamination Factor (Cf) and Degree of Contamination (Dc) for each heavy metal(loid) at the sampling sites (lower, middle, and upper basin), near the watercourse. Site S (Upper Basin) was used as the reference soil (Bn).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSite\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eZn\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCu\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNi\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCd\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCr\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eFe\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eMn\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ePb\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDc\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.11*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.67*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e19.31*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.87*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.53*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28.19**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.93**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.93**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.59**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e52.34**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.72*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e15.42*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCf\u0026thinsp;\u0026lt;\u0026thinsp;1: low contamination; 1\u0026thinsp;\u0026le;\u0026thinsp;Cf\u0026thinsp;\u0026lt;\u0026thinsp;3: moderate contamination; 3\u0026thinsp;\u0026le;\u0026thinsp;Cf\u0026thinsp;\u0026lt;\u0026thinsp;6: high contamination (*); Cf\u0026thinsp;\u0026ge;\u0026thinsp;6: very high contamination (**); Dc\u0026thinsp;\u0026lt;\u0026thinsp;6: low degree of contamination; 6\u0026thinsp;\u0026le;\u0026thinsp;Dc\u0026thinsp;\u0026lt;\u0026thinsp;12: moderate contamination; 12\u0026thinsp;\u0026le;\u0026thinsp;Dc\u0026thinsp;\u0026lt;\u0026thinsp;24: considered contamination (*); Dc\u0026thinsp;\u0026ge;\u0026thinsp;24: high degree of contamination (**). Lower basin (Site A, Site P and Site C), middle basin (Site O and Site M).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGRSP and HMs concentrations in soil\u003c/h2\u003e \u003cp\u003eThe total GRSP concentration in soil samples varied among basins and sampling sites. The highest concentration was recorded in the lower basin (2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83 mg GRSP/g soil), followed by the middle basin (0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 mg GRSP/g soil) and the upper basin (0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 mg GRSP/g soil). Although these differences were not statistically significant, the increasing trend was clear (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Significant differences in GRSP concentration were observed among sites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Site M had the lowest GRSP concentration, differing significantly from all other study sites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) except Site S. The highest concentrations were detected in the lower basin sites, particularly at Site C, which showed significantly higher values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAMF richness assessment\u003c/h2\u003e \u003cp\u003eA total of 16 AMF species were identified across the MRB from soil samples and pot trap cultures (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, in the Online Resource ESM_1). These species belonged to five families: Glomeraceae, Gigasporaceae, Entrophosporaceae, Diversisporaceae, and Paraglomeraceae. In all sites, Glomeraceae was the most abundant family (72.95%), followed by Entrophosporaceae family (16.22%). When assessing AMF richness, clear differences were found among sites and basins.\u003c/p\u003e \u003cp\u003eThe statistical model showed moderate predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.62), revealing a significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) higher probability of AMF richness in the upper basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt site level, the significantly highest species richness was detected at Site S, which hosted 11 AMF species, followed by Site M with 9 species. Site A, located in the lower basin, also showed relatively high richness (8 AMF species). In contrast, Site C and O showed markedly lower richness, with only 2 and 1 species, respectively (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, in the Online Resource ESM_1).\u003c/p\u003e \u003cp\u003eThe proportion of AMF families at each site was calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Site S was the only site where all identified families were represented. At this site, 54.55% of species (6 species) belonged to the Glomeraceae family, 18.18% (2 species) to the Gigasporace, and each of the remaining families comprised 9.1% of the total AMF richness (one species each) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, in the Online Resource ESM_1).\u003c/p\u003e \u003cp\u003eAt Sites A and P, AMF species were restricted to the Glomeraceae (87.50% and 85.71%, respectively) and Entrophosporaceae (12.50% and 14.29%, respectively) families. Similarly, AMF species at Site M belonged to the Glomeraceae (66.67%) and Entrophosporaceae (33.33%). At Sites C and O, only species from the Glomeraceae family were detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSignificant differences in AMF richness probability were found between Site S and Sites O and C (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Table S3, in the Online Resource ESM_1), based on a model with moderate predictive capacity (AUC 0.62). No significant differences were found among the remaining sites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe most widely distributed AMF species was \u003cem\u003eRhizoglomus intraradices\u003c/em\u003e, which was found at all the sampled sites, followed by \u003cem\u003eFunneliformis mosseae\u003c/em\u003e, \u003cem\u003eRhizoglomus microaggregatum\u003c/em\u003e, \u003cem\u003eSeptoglomus viscosum\u003c/em\u003e, and \u003cem\u003eEntrophospora etunicata\u003c/em\u003e (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, in the Online Resource ESM_1)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMycorrhizal root colonization\u003c/h2\u003e \u003cp\u003eThe frequency and intensity of AMF root colonization were estimated to evaluate the relationship of these parameters with the environmental conditions at each sampling site. Both mycorrhizal parameters showed significant differences among sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), although no significant differences were found when comparing basin sections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSites M, P and A exhibited the highest %F values, with no significant differences among them. In contrast, Sites S, C and O showed lower frequencies, which differed significantly from the high-frequency group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Regarding %I, Sites M and A reached the highest values, showing significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from sites O and C, which exhibited the lowest ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eAt the basin scale, significant differences were found in %F between the upper and lower basins (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with higher values in the latter. However, no significant differences were observed in I% among basins.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation among soil health indicators\u003c/h2\u003e \u003cp\u003eTo further understand the correlations among soil health indicators, a heatmap was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). GRSP showed a positive correlation with HMs (Zn, Cu, Cr, Ni, Cd), with particularly strong and significant correlations with Cu (rho\u0026thinsp;=\u0026thinsp;0.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Cr (rho\u0026thinsp;=\u0026thinsp;0.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Weak positive correlations were observed among total GRSP and Fe, Mn, Pb, and OM% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOM% was also positively correlated with all HMs except Pb, which displayed a negative association. Significant positive correlations were found between organic matter and Ni (rho\u0026thinsp;=\u0026thinsp;0.93, p-value\u0026thinsp;=\u0026thinsp;0.01), Cd (rho\u0026thinsp;=\u0026thinsp;0.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and P (rho\u0026thinsp;=\u0026thinsp;0.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAMF richness presented a strong and significant negative correlation with TDS and EC (rho= -0.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Meanwhile, the highest concentrations of HMs and total P tended to negatively correlate with the variables corresponding to mycorrhizal colonization (richness, %F and %I), although these correlations were not statistically significant, except for the effect of total P on %F, which was significantly negative (rho= -0.88; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSoil analysis and AMF communities\u003c/h2\u003e \u003cp\u003eThe PCA analysis explained the associations between the physicochemical and biological parameters (Figure S3). The first two principal components (PC) accounted for 73.40% of the total variance. Monte Carlo permutation tests revealed that six variables (Zn, Cu, Ni, Cd, P and OM%) were significantly associated with the first PC (PC1). Although no variables were identified as significant contributors to PC2 based on permutation tests, the parametric dimdesc () analysis suggested that PC2 primarily reflects variation in soil pH (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, in the Online Resource ESM_1).\u003c/p\u003e \u003cp\u003eNMDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) and Pearson correlation analysis (Figure S4, in the Online Resource ESM_1) were conducted to further elucidate relationships among soil parameters (chemical properties, HMs and GRSP) and AMF species. Both analyses consistently showed a strong positive correlation between \u003cem\u003eRhizophagus irregularis\u003c/em\u003e and HMs, including Zn (r\u0026thinsp;=\u0026thinsp;0.87), Cu (r\u0026thinsp;=\u0026thinsp;0.95), Ni (r\u0026thinsp;=\u0026thinsp;0.96), Cd (r\u0026thinsp;=\u0026thinsp;0.92), Cr (r\u0026thinsp;=\u0026thinsp;0.94), as well as with OM% (r\u0026thinsp;=\u0026thinsp;0.92) either. The NMDS analysis also associated \u003cem\u003eR. irregularis\u003c/em\u003e and \u003cem\u003eRh. intraradices\u003c/em\u003e with TDS, EC and GRSP. \u003cem\u003eRhizophagus fasciculatus\u003c/em\u003e was significantly correlated with Pb (r\u0026thinsp;=\u0026thinsp;0.96). \u003cem\u003eF. mosseae\u003c/em\u003e, \u003cem\u003eS. viscosum\u003c/em\u003e, \u003cem\u003eRh microaggregatum, Septoglomus constrictum\u003c/em\u003e and \u003cem\u003eE. etunicata\u003c/em\u003e were positively correlated with AMF colonization values. \u003cem\u003eE. etunicata\u003c/em\u003e was negatively correlated with total P contents. \u003cem\u003eFunneliformis geosporus\u003c/em\u003e and \u003cem\u003eEntophospora claroidea\u003c/em\u003e showed strong negative correlations with Fe concentration (Figure S4).\u003c/p\u003e \u003cp\u003ePCA and NMDS analyses revealed a distinct separation of Sites C, O and S from the remaining sampling locations. Site C was strongly associated with HMs and with the occurrence of \u003cem\u003eR. irregularis\u003c/em\u003e. In contrast, Site S occupied the opposite quadrant to Site C, mainly supported by the presence of AMF species belonging to Diversisporales order. The separation of Site O was mainly explained by its higher EC and TDS values. The spatial proximity of Sites P, A, and M in the ordination diagrams was largely explained by mycorrhizal colonization percentages observed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere is broad consensus that indicators used to assess soil quality and health should be sensitive to management practices, responsive to environmental variation, and accurately quantifiable. These indicators comprise physical, chemical, and biological attributes that reflect soil functioning. Among them, total soil concentrations of HMs remain one of the most widely used indicators for environmental risk assessment, whereas changes in the microbial community structure are commonly employed as biological indicators. Microbial communities respond to nutritional and environmental change, with more sensitive populations declining or disappearing under altered conditions (van Bruggen and Semenov 2000; Gonzalez et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nunes et al. 2020).\u003c/p\u003e \u003cp\u003eIn this study the effectiveness of typical (HMs concentration, pH, EC, TDS, OM%) and AMF-related (total GRSP concentration, AMF richness and root colonization) indicators was evaluated as predictors of soil health in disturbed urban watersheds.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eChemical indicators of soil pollution\u003c/h2\u003e \u003cp\u003ePrevious studies in the MRB have reported an increasing gradient of HMs pollution from the upper to the lower basin (Mendoza et al. 2015). However, land use, soil disturbance, population density, and chemicals effluents thrown into streams and rivers have generated exceptions from this expected pattern. In our study, Site O, located in the middle basin near a concrete manufacturing plant and a highway, revealed Cf and Df values similar to those observed in the lower basin, demonstrating the importance of local land use in shaping pollution patterns.\u003c/p\u003e \u003cp\u003eHigh Cd concentrations were expected in the MRB, mainly in the lower basin. It is known that Cd pollution in urban soils is predominantly of anthropogenic origin, with major inputs arising from atmospheric deposition associated with industrial activities and vehicle emissions, as well as from the use of phosphate fertilizers in agriculture and urban green spaces (Kubier et al. 2019; Paredes del Puerto et al. 2021). Nevertheless, although most sites showed elevated Cd levels ​​(exceeding legal thresholds), soils near the river mouth (Site A) exhibited Cd levels below the detection level as found by Colombo et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This site has a slightly alkaline pH, low OM% content (the lowest among the sampled sites), and clay-rich texture (Colombo et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), conditions that may prevent soil Cd stabilization (Kubier et al. 2019).\u003c/p\u003e \u003cp\u003eAfter comparing HMs concentrations in the MRB with other soil quality indicators (OM%, total P, EC and TDS), Sites C and O showed the highest values across all parameters. OM% content displayed a strong positive correlation with Cd and Ni concentrations. This pattern aligns with previous studies reporting that soils influenced by polluted water and enriched in organic matter tend to accumulate higher levels of HMs and nutrients such as P, largely due to the capacity of organic matter to form complexes with both (Violante et al. 2010; Zhang et al. 2017; Li G et al. 2018). These findings corroborate that organic matter plays a key role in the retention and immobilization of these elements within the soil matrix by forming stable complexes, adsorbing metal ions through functional groups, and thereby reducing their bioavailability (Stevenson \u0026amp; Fitch, 1986; Tipping 2002; Basta et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding P concentration, Indris et al. (2020) reported a positive association between elevated P levels and factors such as urbanization, heavy vehicular traffic, and additives present in unleaded fuels. These conditions align with what was observed at Sites C and O, both situated next to major highways, with Site C additionally bordering a racetrack.\u003c/p\u003e \u003cp\u003eSoil pH is a critical factor that influences the solubility and bioavailability of HMs (Kicińska et al. 2021). Acidic pH conditions promote the dissolution and mobilization of soluble compounds into the soil solution, leading to higher TDS, an elevated EC and a higher bioavailability of HMs (Mohd-Aizat et al. 2014; Mylavarapu et al. 2020). In the MRB, previous studies have reported contrasting pH patterns with depth: some describe acidic values at the surface transitioning to neutral conditions below the first meter (Ceballos et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while others document near‑neutral pH throughout the soil profile (Pereyra et al. 2022), consistent with the values observed in this study. Among the sampled sites, Site O exhibited the most acidic pH, along with the highest EC and TDS levels. Overall, these differences highlight the heterogeneous nature of the MRB in terms of pH variation, shaped by both natural processes and anthropogenic inputs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAMF- related indicators\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003eGRSP\u003c/h2\u003e \u003cp\u003eAlthough the complete molecular structure of GRSP remains partially resolved, their capacity to immobilize cations, including HMs and nutrients, has been attributed to the presence of negatively charged functional groups and surface-attached sugars that provide active sites for the adsorption of charged or partially charged elements (Lin et al. 2023). These interactions reduce the bioavailability of toxic elements in polluted soils, thereby promoting their stabilization (Son et al. 2024). Cornejo et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) reported an increased release of GRSP in contaminated soils with Cu, attributing this response to a strategy of native AMF communities to cope with environmental stress caused by excess HMs. In our study, the highest GRSP concentrations were reported in the lower basin at Site C, where the HMs concentrations also reached the highest values. Conversely, the site with the lowest HMs concentrations exhibited the lowest GRSP concentrations. Consistent with this pattern, significant positive correlations were detected between GRSP and several HMs, with particularly strong associations observed for Cr and Cu.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eAMF richness\u003c/h2\u003e \u003cp\u003eThis research represents the first study to assess AMF richness across the MRB. While previous studies have focused on specific locations within the basin (Colombo et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), our work successfully reported a wide assessment of these beneficial fungal species throughout the basin and correlated their presence/absence with indicative parameters of soil health.\u003c/p\u003e \u003cp\u003eOf the sixteen AMF species identified in the MRB, eight of them (\u003cem\u003eR. fasciculatus, F. geosporus, Glomus sinuosum, E. etunicata, Entrophospora claroidea, Dentiscutata heterogama, Sieverdingia tortuosa\u003c/em\u003e, and \u003cem\u003eParaglomus laccatum)\u003c/em\u003e are reported for the first time, thereby increasing the number of AMF species known in this area (specifically, these species were found at sites A, S, and M). The most widely distributed AMF species was \u003cem\u003eRh. intraradices\u003c/em\u003e, found in all the sampled sites, followed by \u003cem\u003eF. mosseae\u003c/em\u003e, \u003cem\u003eRh. microaggregatum\u003c/em\u003e and \u003cem\u003eS viscosum\u003c/em\u003e, showing a prevalence of the Glomeraceae family in soils highly contaminated with HMs. Several species identified in our study matched with those previously found by Colombo et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in the MRB (closed to Site A) using DNA sequencing approaches, including \u003cem\u003eRh. intraradices, R. irregularis, F. mosseae, S. constrictum, S. viscosum, Rh. microaggregatum, Enthophospora infrequens, and Gigaspora decipiens.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eIn our study also, significant differences in AMF richness were observed among the three sections of the MRB. Non-contaminated sites showed greater AMF richness, both at the genus and family levels, than polluted ones. In the MRB, AMF species were indicators of soil health conditions, in relation to land use and the watercourse history. Green recreational areas with natural streams or lagoons (Sites S and M), showed higher species richness than more anthropologically impacted sites (Sites C and O). Additionally, Site A, located in an urban-industrial area but preserves its original watercourse and soil characteristics (Colombo et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), exhibited higher AMF richness than Sites P and O.\u003c/p\u003e \u003cp\u003eMany studies have evaluated the effects of HMs contamination on AMF diversity, particularly in mining-impacted soils (Yang et al. 2015; Sanchez-Castro et al. 2016; Suarez et al. 2023; Utge Perri et al. 2025) and to a lesser extent, in natural metalliferous or urban polluted soils (Colombo et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Silvani et al. 2017). In each case, the Glomeraceae family and the AM fungus \u003cem\u003eRh. intraradices\u003c/em\u003e were the most dominant. In our analysis of the presence/absense of AMF species as biological indicators of soil health, species occurring at the most contaminated sites, such as \u003cem\u003eRh. intraradices\u003c/em\u003e and \u003cem\u003eR. irregularis\u003c/em\u003e, showed significant positive correlations with HMs concentrations (Fe, Cu, Cd, Ni, Cr, Zn). Conversely, species associated with less contaminated sites displayed negative correlations with HMs. This pattern reflects the environmental selective pressures shaping microbial community composition. The increased tolerance in fungi inhabiting contaminated soils is likely mediated by adaptive mechanisms such as metal sequestration and detoxification strategies (Benavidez et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Colombo et al. 2024).\u003c/p\u003e \u003cp\u003eAccordingly, the absence of certain AMF species or families may be considered as an indicator of soil toxicity or, in turn, their presence may be indicative of good soil health. The results presented so far, regarding AMF richness, indicate that contamination not only reduces species richness but also alters community composition, favouring those taxa with ruderal life strategies (\u003cem\u003eRhizophagus sp. Rh. intraradices\u003c/em\u003e and \u003cem\u003eF. mosseae\u003c/em\u003e). These species are characterized by high rates of intraradical growth, short life cycles, early spore production, and better protection of the host plant against all types of stress (Chagnon et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Utge Perri et al. 2025).\u003c/p\u003e \u003cp\u003eFinally, AMF richness showed a strong and significant negative correlation with TDS and EC, consistent with previous studies, demonstrating that high EC values adversely affect AMF development, species diversity, and community structure by delaying spore germination, hyphal growth, and root colonization (Fang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eAMF root colonization\u003c/h2\u003e \u003cp\u003eThe results obtained in our study revealed a negative correlation between mycorrhizal colonization and HMs concentration, as sites with the highest levels of contamination (Site C and Site O) registered the lowest levels of mycorrhization (assessed as frequency and intensity of colonization). Mendoza et al. (2015) reported that AMF colonization decreased with increasing HMs concentrations throughout the contamination gradient of the MRB. However, Site S, despite being one of the least polluted locations, presented colonization levels similar to those observed at Site C. This discrepancy may be explained by unmeasured factors of contamination or by local conditions. Site S is located near a populated area and is subject to increased soil disturbance due to its use as a recreational space. On the other hand, Site A, although highly polluted with Cu y Cr, showed high mycorrhization levels.\u003c/p\u003e \u003cp\u003eDifferences between the expected and observed mycorrhization percentages may also be related to the AMF species present and the competition for intraradical space with other root endophytes (Hardoim et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), specific to each site and each sampled plant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe highest concentrations of HMs were detected in the lower basin, an industrial area characterized by a greater exposure to pollution and anthropogenic disturbance. GRSP exhibited a strong positive correlation with HMs concentrations, therefore, it may serve as a reliable indicator of contamination by potentially toxic elements.\u003c/p\u003e \u003cp\u003eAMF species richness was more strongly influenced by the history of soil disturbance than by pollution levels, with the most disturbed environments exhibiting the lowest diversity. So, AMF species richness may function as an indicator of the history of anthropogenic disturbance, mainly through the loss of species belonging to the Paraglomeraceae, Diversisporaceae and Gigasporaceae families. This study provides the first report of \u003cem\u003eR. fasciculatus\u003c/em\u003e, \u003cem\u003eF. geosporus\u003c/em\u003e, \u003cem\u003eG. sinuosum\u003c/em\u003e, \u003cem\u003eE. etunicata\u003c/em\u003e, \u003cem\u003eE. claroidea\u003c/em\u003e, \u003cem\u003eD. heterogama\u003c/em\u003e, \u003cem\u003eSi. tortuosa\u003c/em\u003e, and \u003cem\u003eP. laccatum\u003c/em\u003e at MRB.\u003c/p\u003e \u003cp\u003eEven when total GRSP concentration and diversity of AMF proved to be reliable biological indicators of soils health, mycorrhization percentages did not reflect the degree of soil degradation in the MRB. Therefore, this parameter does not appear to be a suitable indicator of soil health under the conditions evaluated.\u003c/p\u003e \u003cp\u003eFinally, we emphasize the need for further research on soil microbiology in urban watersheds to improve understanding of anthropogenic impacts on biodiversity and to better define the role of microorganisms as soil health indicators in these particularly and highly degraded environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors acknowledge Universidad de Buenos Aires (UBA), Universidad Nacional de Avellaneda (UNDAV) and Consejo Nacional de Investigaciones Cient\u0026iacute;ficas y T\u0026eacute;cnicas (CONICET) for their financial support (ODS-UNDAV-SIVTI 2021; PIP 11220200102192CO). We thank the Environmental Secretariat of the Province of Buenos Aires and the Autonomous City of Buenos Aires, and the Lago Lugano Ecological Reserve for the sampling permits granted. We also acknowledge Lic. Sof\u0026iacute;a Yasm\u0026iacute;n Utge Perri (IBBEA, CONICET-UBA) and Dr. Scarano (UNLP) for their technical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUniversidad Nacional de Avellaneda (ODS-UNDAV-SIVTI 2021); Consejo Nacional de Investigaciones Cient\u0026iacute;ficas y T\u0026eacute;cnicas (CONICET - PIP 11220200102192CO)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMailen Guerra Moreno\u003c/strong\u003e: Investigation, Data curation, Formal analysis, Visualization, Writing - original draft, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVanesa Anal\u0026iacute;a Silvani:\u0026nbsp;\u003c/strong\u003eConceptualization, Supervision, Writing - review \u0026amp; editing. \u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Alicia Margarita Godeas:\u0026nbsp;\u003c/strong\u003eFunding acquisition, Project administration, Supervision, Writing - review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRoxana Paula Colombo:\u0026nbsp;\u003c/strong\u003eConceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Writing - original draft, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings are all included within the article and/or the supporting material\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe supplemental material, including figures, tables and measured data that support the findings is available online for this article.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eACUMAR (2025) Matanza Riachuelo Basin Authority. https://www.acumar.gob.ar/ Accessed 9 February 2026 (in Spanish)\u003c/li\u003e\n \u003cli\u003eBasta NT, Ryan JA, Chaney RL (2005) Trace element chemistry in residual‐treated soil: Key concepts and metal bioavailability. J Environ Qual 34:49-63. https://doi.org/10.2134/jeq2005.0049dup\u003c/li\u003e\n \u003cli\u003eBenavidez ME, de la Fourni\u0026egrave;re EM, Colombo RP, Silvani VA, Debray ME, Scotti A, Godeas AM (2024) Micro PIXE mapping proves a differential distribution and concentration of trace elements in fungal structures of \u003cem\u003eRhizophagus intraradices.\u0026nbsp;\u003c/em\u003eFungal Biol 128:2089\u0026ndash;2093. \u003cu\u003ehttps://doi.org/10.1016/j.funbio.2024.08.008\u003c/u\u003e.\u003c/li\u003e\n \u003cli\u003eBernhardt A, Gysi N (2013) The world\u0026rsquo;s worst 2013: the top ten toxic threats. Cleanup, progress and ongoing challenges. Blacksmith Institute, New York and Green Cross Switzerland, Zurich. https://www.worstpolluted.org/docs/TopTenThreats2013.pdf. Accessed 9 February 2026\u003c/li\u003e\n \u003cli\u003eBGIV Banco de Glomeromycota \u003cem\u003ein vitro\u003c/em\u003e (2026) BGIV Banco de Glomeromycota \u003cem\u003ein vitro\u003c/em\u003e database. http://bgiv.com.ar/ Accessed 9 February 2026\u003c/li\u003e\n \u003cli\u003eBlaszkowski J (2012) Glomeromycota. W. Szafer Institute of Botany. Polish Academy of Sciences. Krak\u0026oacute;w, Poland. 303pp\u003c/li\u003e\n \u003cli\u003eBradford MM (1976) A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem 72:248\u0026ndash;254. \u003cu\u003ehttps://doi.org/10.1016/0003-2697(76)90527-3\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eCeballos E, Dubny S, Othax N, Zabala ME, Peluso F (2021) Assessment of human health risk of Chromium and Nitrate pollution in groundwater and soil of the Matanza‑Riachuelo river basin, Argentina. Expo Health 13:323\u0026ndash;336. https://doi.org/10.1007/s12403-021-00386-9\u003c/li\u003e\n \u003cli\u003eChagnon PL, Bradley RL, Maherali H, Klironomos JN (2013) A trait-based framework to understand life history of mycorrhizal fungi. Trends Plant Sci 18:484\u0026ndash;491. http://doi.org/10.1016/j.tplants.2013.05.001\u003c/li\u003e\n \u003cli\u003eChauhan P, Sharma N, Tapwal A, Kumar A, Verma GS, Meena M, Seth CS, Swapnil P (2023) Soil microbiome: diversity, benefits and interactions with plants. Sustainability 15:14643. https://doi.org/10.3390/su151914643\u003c/li\u003e\n \u003cli\u003eCittadino A, Ocello N, Majul MV, Ajhuacho R, Dietrich P, Igarzabal MA (2020) Heavy metal pollution and health risk assessment of soils from open dumps in the Metropolitan Area of Buenos Aires, Argentina. Environ Monit Assess 192:291. https://doi.org/10.1007/s10661-020-8246-x\u003c/li\u003e\n \u003cli\u003eColombo RP, Benavidez M, Fern\u0026aacute;ndez Bidondo L, Silvani VA, Bompadre MJ, Statello M, Scorza MV, Scotti A, Godeas AM (2020) Arbuscular mycorrhizal fungi in heavily metal-polluted soil in the Riachuelo river basin. Rev Argent Microbiol 52:145\u0026ndash;149. \u003cu\u003ehttps://doi.org/10.1016/j.ram.2019.05.001.\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eColombo RP, Fernandez Bidondo L, Patitucci M, Stephan BI, Dias J, Bompadre MJ, Benavidez ME, Pereyra FX (2018) First physical, chemical, biological and contamination description of three soil profiles of the coastal marginal forest of Avellaneda. In: D\u0026iacute;az Porres M, Falco L (eds) CONEBIOS V \u0026ndash; National Congress of Ecology and Soil Biology. EdUnLu, Luj\u0026aacute;n, Buenos Aires. https://www.sabes.org.ar/files/actas-CONEBIOS-V.pdf Accessed 9 Feb 2026 (in Spanish)\u003c/li\u003e\n \u003cli\u003eCornejo P, Meier S, Borie G, Rilling MC, Borie F (2008) Glomalin-related soil protein in a Mediterranean ecosystem affected by a copper smelter and its contribution to Cu and Zn sequestration. Sci Total Environ 406:154\u0026ndash;160. \u003cu\u003ehttps://doi.org/\u003c/u\u003e10.1016/j.scitotenv.2008.07.045\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eDeclerck S, Strullu DG, Plenchette C (1996) \u003cem\u003eIn vitro\u003c/em\u003e mass-production of the arbuscular mycorrhizal fungus, \u003cem\u003eGlomus versiforme\u003c/em\u003e, associated with Ri T-DNA transformed carrot roots. Mycol Res 100:1237\u0026ndash;1242. \u003cu\u003ehttps://doi.org/10.1016/S0953-7562(96)80186-9\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eDeclerck S, D\u0026rsquo;Or D, Bivort C, de Souza F (2004) Development of extraradical mycelium of \u003cem\u003eScutellospora reticulata\u003c/em\u003e under root-organ culture: spores production and function of auxiliary cells. Mycol Res 108:84\u0026ndash;92. \u003cu\u003ehttp://doi.org/10.1017/S0953756203008761\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eDriver JD, Holben WE, Rillig MC (2005) Characterization of glomalin as a hyphal wall component of arbuscular mycorrhizal fungi. Soil Biol Biochem 37:101\u0026ndash;106. https://doi.org/10.1016/j.soilbio.2004.06.011\u003c/li\u003e\n \u003cli\u003eFang LL, Liu YJ, Wang ZH, Lu X., Li JH, Yang CX (2023) Electrical conductivity and pH are two of the main factors influencing the composition of arbuscular mycorrhizal fungal communities in the vegetation succession series of Songnen Saline-Alkali grassland. J Fungi 9:870. https://doi.org/10.3390/jof9090870\u003c/li\u003e\n \u003cli\u003eFAO (2004) Maximum limits for dangerous substances in soil and groundwater. Regulation of the Minister of the Environment No. 12 (RTL 2004, 40, 662). https://faolex.fao.org/docs/pdf/est97999E.pdf Accessed 9 February 2026\u003c/li\u003e\n \u003cli\u003eGarc\u0026iacute;a I, Eigandt C, Fabrizio de Iorio A, de Cabo L (2022) A tool for the restoration of degraded riparian environments of the Matanza-Riachuelo Basin: \u003cem\u003eLotus tenuis\u003c/em\u003e Waldst. \u0026amp; Kit. in symbiosis with arbuscular mycorrhizal fungi. Agron Ambient 42:56\u0026ndash;65. http://agronomiayambiente.agro.uba.ar/index.php/AyA/article/view/207 Accessed 9 Feb 2026 (in Spanish)\u003c/li\u003e\n \u003cli\u003eGerdemann J, Nicolson T (1963) Spores of mycorrhizal endogone species extracted from the soil by wet sieving and decanting. Trans Br Mycol Soc 46:235\u0026ndash;244. https://doi.org/10.1016/S0007-1536(63)80079-0\u003c/li\u003e\n \u003cli\u003eGonzalez M, Arango C, Pastorino G, Ruscitti M (2020) Arbuscular mycorrhizae and the restoration of degraded ecosystems. In: Saparrat MCN, Ruscitti MF, Arango MC (eds) Arbuscular mycorrhizae: biology and applications in the agro-forestry sector. Universidad Nacional de La Plata. https://libros.unlp.edu.ar/index.php/unlp/catalog/book/1504 Accessed 9 February 2026\u003c/li\u003e\n \u003cli\u003eGonz\u0026aacute;lez-Ch\u0026aacute;vez M, Carrillo-Gonz\u0026aacute;lez R, Wright S, Nichols K (2004) The role of glomalin, a protein produced by arbuscular mycorrhizal fungi, in sequestering potentially toxic elements. Environ Pollut 130:317-323. https://doi.org/10.1016/j.envpol.2004.01.004\u003c/li\u003e\n \u003cli\u003eHardoim PR, Berg G, Pirttil\u0026auml; AM, Compant S, Campisano A, D\u0026ouml;ring M, Sessitsch A (2015) The hidden world within plants: ecological and evolutionary considerations for defining functioning of microbial endophytes.\u003cem\u003e\u0026nbsp;\u003c/em\u003eMicrobiol Mol Biol Rev 79:293. https://doi.org/10.1128/MMBR.00050-14\u003c/li\u003e\n \u003cli\u003eIndex Fungorum (2026) Index Fungorum database. https://www.indexfungorum.org/names/names.asp Accessed 15 February 2026\u003c/li\u003e\n \u003cli\u003eIndris SN, Rudolph DL, Glass BK, Cappellen PV (2020) Evaluating phosphorus from vehicular emissions as a potential source of contamination to ground and surface water. Cogent Environ Sci 6:1794702. https://doi.org/10.1080/23311843.2020.1794702\u003c/li\u003e\n \u003cli\u003eINVAM (International Culture Collection of (Vesicular) Arbuscular Mycorrhizal Fungi) (2026). \u003cem\u003eINVAM database\u003c/em\u003e. West Virginia University, Morgantown, WV. https://invam.ku.edu/ Accessed 9 February 2026\u003c/li\u003e\n \u003cli\u003eKicińska A, Pomykała R, Izquierdo‐Diaz M (2022) Changes in soil pH and mobility of heavy metals in contaminated soils. Eur Journal Soil Sci 73: e13203.\u003cu\u003e\u0026nbsp;\u003c/u\u003ehttps://doi.org/10.1111/ejss.13203\u003c/li\u003e\n \u003cli\u003eKubier A, Wilkin RT, Pichler T (2019) Cadmium in soils and groundwater: A review. Appl Geochem 108:104388. https://doi.org/10.1016/j.apgeochem.2019.104388.\u003c/li\u003e\n \u003cli\u003eLi G, Lu N, Wei Y, Zhu D (2018) Relationship between heavy metal content in polluted soil and soil organic matter and pH in mining areas. In IOP conference series: materials science and engineering. 394: p052081. https://doi.org/\u003cu\u003e10.1088/1757-899X/394/5/052081\u0026nbsp;\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eMagni LF, Castro LN, Rendina AE (2021) Evaluation of heavy metal contamination levels in river sediments and their risk to human health in urban areas: A case study in the Matanza-Riachuelo Basin, Argentina. Environ Res 197:110979. https://doi.org/10.1016/j.envres.2021.110979\u003c/li\u003e\n \u003cli\u003eMalekzadeh E, Aliasgharzad N, Majidi J, Aghebati-Maleki L, Abdolalizade J (2016) Cd-induced production of glomalin by arbuscular mycorrhizal fungus (Rhizophagus irregularis) as estimated by monoclonal antibody assay. Environ Sci Pollut Res Int 23:20711\u0026ndash;20718. http://doi\u003cu\u003e.org/10.1007/s11356-016-7283-z\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eMendoza RE, Garc\u0026iacute;a IV, de Cabo L, Weigandt C, Fabrizio de Iorio A (2015) The interaction of heavy metals and nutrients present in soil and native plants with arbuscular mycorrhizae on the riverside in the Matanza-Riachuelo River Basin (Argentina). Sci Total Environ 505:555\u0026ndash;564. http://dx.doi.org/10.1016/j.scitotenv.2014.09.105\u003c/li\u003e\n \u003cli\u003eMerlinsky MG (2016) Mists of the Riachuelo: River basins and climate change in Buenos Aires. Lat Am Perspect 43:43\u0026ndash;55. \u003cu\u003ehttps://doi.org/10.1177/0094582X15623764.\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eMohd-Aizat A, Mohamad-Roslan MK, Sulaiman WNA, Karam DS (2014) The relationship between soil pH and selected soil properties in 48 years logged-over forest. Int J Environ Sci 4:1129. \u003cu\u003ehttps://doi.org/\u003c/u\u003e10.6088/ijes.201404060000\u003c/li\u003e\n \u003cli\u003eMylavarapu R, Bergeron J, Wilkinson N, Hanlon EA (2020) Soil PH and Electrical Conductivity: A county extension soil laboratory manual: CIR1081 SS118, Rev. 1:2020. Gainesville, Florida. https://doi.org/10.32473/edis-ss118-2020\u003c/li\u003e\n \u003cli\u003eNational Meteorology Service (2026). Normal climate statistics. https://datos.gob.ar/dataset/smn-estadisticas-climaticas-normales Accessed 9 February 2026 (in Spanish)\u003c/li\u003e\n \u003cli\u003eNunes MR, Karlen DL, Veum KS, Moorman TB, Cambardella CA (2020) Biological soil health indicators respond to tillage intensity: A US meta-analysis.\u003cem\u003e\u0026nbsp;\u003c/em\u003eGeoderma 369:114335. https://doi.org/10.1016/j.geoderma.2020.114335\u003c/li\u003e\n \u003cli\u003ePage AL, Miller RH, Keeney D (1982) Methods of soil analysis. Part 2. American Society of Agronomy. Soil Science Society of America Madison, Wisconsin, 1143 pp\u003c/li\u003e\n \u003cli\u003eParedes del Puerto JM, Paracampo AH, Garc\u0026iacute;a ID, Maiztegui T, Garcia de Souza JR, Maro\u0026ntilde;as ME, Colautti DC (2021) Fish assemblages and water quality in pampean streams (Argentina) along an urbanization gradient. Hydrobiologia 848:4493\u0026ndash;4510. https://doi.org/10.1007/s10750-021-04657-z(0123456789\u003c/li\u003e\n \u003cli\u003ePasqualin MF, Faure Montania E, Hepp Y, Antolini L, Finkelstein JZ, Garc\u0026iacute;a SI (2019) Environmental health risk map of the Matanza-Riachuelo Basin (Argentina): A methodology to prioritize interventions. Rev Salud Ambient 19:148\u0026ndash;158. https://ojs.diffundit.com/index.php/rsa/article/view/955 (in Spanish)\u003c/li\u003e\n \u003cli\u003ePereyra FX, Ragas DB (2022) Urban soils: Case study of the Buenos Aires metropolitan region. Ciencia Hoy 180:34\u0026ndash;42. https://untref.edu.ar/muntref/cienciahoy/public/es/archivo-ciencia-hoy Accessed 9 Feb 2026 (in Spanish)\u003c/li\u003e\n \u003cli\u003ePhillips JM, Hayman DS (1970) Improved procedures for clearing roots and staining parasitic and vesicular-arbuscular mycorrhizal fungi for rapid assessment of infection. Trans Br Mycol Soc 55:158\u0026ndash;160. https://doi.org/10.1016/S0007-1536(70)80110-\u003cu\u003e3\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003ePlenchette C, Morel C (1996) External phosphorus requirements of mycorrhizal and non-mycorrhizal barley and soybean plants. Biol Fertil Soils 21:303\u0026ndash;308. \u003cu\u003e\u0026nbsp;https://doi.org/10.1007/BF00334907\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eR Development Team (2018) R: a language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. URL http://www.R-project.org Accessed 9 February 2026\u003c/li\u003e\n \u003cli\u003eRatto S, Marceca E, Moscatelli G, Abbruzese D, Bardi H, Bossi M, Bres P, Cord\u0026oacute;n G, Di Nano MP, Murruni L, Potarsky K, Williams F (2004) Evaluation of organic and inorganic contamination in an alluvial soil on the coast of the Riachuelo River, Buenos Aires, Argentina. Ecol Austral 14:179\u0026ndash;190 (in Spanish).\u003c/li\u003e\n \u003cli\u003eRillig MC, Mummey DL (2006) Mycorrhizas and soil structure. New Phytol 171:41\u0026ndash;53. https://doi.org/10.1111/j.1469-8137.2006.01750.x\u003c/li\u003e\n \u003cli\u003eS\u0026aacute;nchez-Castro I, Gianinazzi-Pearson V, Cleyet-Marel JC, Baudoin E, van Tuinen D (2017) Glomeromycota communities survive extreme levels of metal toxicity in an orphan mining site. Sci Total Environ 598:121\u0026ndash;128. \u003cu\u003ehttps://doi. org/ 10. 1016/j. scito tenv. 2017. 04. 084\u003c/u\u003e.\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eSilvani VA, Colombo RP, Scorza MV, Fernandez Bidondo L, Rothen CP, Scotti A, Fracchia S, Godeas AM (2017) Arbuscular mycorrhizal fungal diversity in high-altitude hypersaline Andean wetlands studied by 454-sequencing and morphological approaches. Symbiosis 72:143\u0026ndash;152. \u003cu\u003ehttps://doi.org/10.1007/s13199-016-0454-3\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eSon Y, Mart\u0026iacute;nez CE (2024) Three important roles and chemical properties of glomalin-related soil protein. Front Soil Sci 4:1418072. https://doi.org/10.3389/fsoil.2024.1418072\u003c/li\u003e\n \u003cli\u003e\u003cspan lang=\"EN-GB\"\u003eSuarez JP, Herrera P, Kalinhoff C, Vivanco-Galval O, Thangaswamy S (2023) Generalist arbuscular mycorrhizal fungi dominated heavy metal polluted soils at two artisanal and small\u0026minus;scale gold mining sites in southeastern Ecuador. BMC Microbiol\u003c/span\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e 23:42. https://doi.org/10.1186/s12866-022-02748-y\u003c/li\u003e\n \u003cli\u003eStevenson FJ, Fitch A (1986) Chemistry of complexation of metal ions with soil solution organics. In: Huang PM, Schnitzer M.\u003cem\u003e\u0026nbsp;\u003c/em\u003eInteractions of Soil minerals with natural organics and microbes. Volume 17, SSSA Special Publications \u003cu\u003ehttps://doi.org/10.2136/sssaspecpub17.c2\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eTipping E (2002) Cation binding by humic substances. Cambridge University Press, Cambridge, UK, Vol. 12\u003c/li\u003e\n \u003cli\u003eUtge Perri SY, Valerga Fern\u0026aacute;ndez MV, Scotti A, Colombo RP, Gonz\u0026aacute;lez F, Valenzuela L, Godeas AM, Silvani VA (2025) Responses of arbuscular mycorrhizal fungi and plant communities to long-term mining and passive restoration. Plants 14:580. https://doi.org/10.3390/plants14040580\u003c/li\u003e\n \u003cli\u003eVan Bruggen AH, Semenov AM (2000) In search of biological indicators for soil health and disease suppression. Appl Soil Ecol 15:13\u0026ndash;24. https://doi.org/10.1016/S0929-1393(00)00068-8\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eViolante A, Cozzolino V, Perelomov L, Caporale AG, Pigna M (2010) Mobility and bioavailability of heavy metals and metalloids in soil environments. J Soil Sci Plant Nutr 10:268\u0026ndash;292. https://doi.org/10.4067/S0718-951620100001000\u003c/li\u003e\n \u003cli\u003eWang Q, Lu H, Chen J, Jiang Y, Williams MA, Wu S, Li S, Liu J, Yang G, Yan C (2020) Interactions of soil metals with glomalin-related soil protein as soil pollution bioindicators in mangrove wetland ecosystems. Sci Total Environ 709:136051. https://doi.org/10.1016/j.scitotenv.2019.136051\u003c/li\u003e\n \u003cli\u003eYang Y, Scheller HV, Ghosh A, Chen H, Tang M (2015) Community structure of arbuscular mycorrhizal fungi associated with \u003cem\u003eRobinia pseudoacacia\u003c/em\u003e in uncontaminated and heavy metal contaminated soils. Soil Biol Biochem 86:146\u0026ndash;158. https://doi.org/10.1016/j.soilbio.2015.03.018.\u003c/li\u003e\n \u003cli\u003eZhang C, Nie XP, Liang J, Zeng GM (2017) Spatial and seasonal variation of heavy metals and nutrients in the sediments of a contaminated river in China. Environ Sci Pollut Res 24:11867\u0026ndash;11878. https://doi.org/10.1007/s11356-017-8813-6\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Matanza-Riachuelo basin, arbuscular mycorrhizal fungi, anthropogenic impact, heavy metal(loid)s, glomalin, bioindicators, soil health","lastPublishedDoi":"10.21203/rs.3.rs-9107710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9107710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Matanza-Riachuelo river basin (MRB) is considered one of the most polluted sites in the world, with very high levels of heavy metal(loid)s (HMs). However, as in almost all urban basins, studies on biological indicators of soil health are limited. In this work, we explore the potential of arbuscular mycorrhizal fungi (AMF) diversity, plant mycorrhizal status, and glomalin-related soil proteins (GRSP) concentration as bioindicators of soil health. We sampled three regions of the MRB: upper, middle and lower basins. The highest concentrations of HMs were detected in the lower basin, because it is exposed to a high input of pollutants and anthropogenic impact. Community composition and diversity were compared across the different soil samples. The Glomeraceae family was the most represented in the MRB, and \u003cem\u003eRhizoglomus intraradices\u003c/em\u003e was the only AMF species detected at every sampled site. This work constitutes the first report of 8 of the 16 AMF species described so far in the MRB. GRSP differences were strongly related to HMs concentrations. Both AMF species richness and GRSP concentrations were reliable biological indicators of soil health; while GRSP increases with soil toxicity, AMF richness decreases with soil disturbance.\u003c/p\u003e","manuscriptTitle":"Sensitivity variations on soil health indicators related to arbuscular mycorrhizae in a highly disturbed urban basin of Buenos Aires, Argentina","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 07:11:08","doi":"10.21203/rs.3.rs-9107710/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-20T10:26:31+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-28T15:38:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2026-03-26T08:57:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T04:55:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2026-03-17T20:05:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"dc1c51bf-b18b-44ad-9511-ae3432843df3","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T07:11:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 07:11:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9107710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9107710","identity":"rs-9107710","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}