Sara Gallego
email: sara.gallego.blanco@gmail.com

José María Esbrí

Juan Antonio Campos

Jesús Daniel Peco

Fabrice Martin-Laurent

Pablo Higueras

Microbial diversity and activity assessment in a 100-year-old lead mine

Keywords: Potentially toxic elements, mining soil, enzyme activities, high-throughput sequencing, microbial ecotoxicology

Mining activities frequently leave a legacy of residues that remain in the area for long periods causing the pollution of surroundings. We studied on a 100 year-old mine, the behaviour of potentially toxic elements (PTEs) and their ecotoxicological impact on activity and diversity of microorganisms. The PTEs contamination assessment allowed the classification of the materials as highly (reference-and contaminated-samples) and very highly polluted (illegal spill of olive mill wastes (OMW), tailings and dumps). OMW presented the lowest enzymatic activities while tailings and dumps had low dehydrogenase and arylsulfatase activities. All the alpha diversity indices studied were negatively impacted in dumps. Tailings had lower Chao1 and PD whole tree values as compared to those of reference-samples. Beta diversity analysis showed similar bacterial community composition for reference-and contaminated-samples, significantly differing from that of tailings and dumps. The relative abundance of Gemmatimonadetes, Bacteroidetes and Verrucomicrobia was lower in OMW, tailings and dumps as compared to reference-samples. Fifty-seven OTUs were selected as responsible of the changes observed between samples. This study highlights that assessing the relationship between physicochemical properties and microbial diversity and activity gives clues about ongoing regulating processes that can be helpful for stakeholders to define an appropriate management strategy.

Introduction

Mining activities are found all over the world because they provide access to mineral resources that fuel various industrial activities in both developed and in developing countries.

Although recognized of great importance for the world gross domestic product, mining operations are often viewed as an important source of pollution with negative impact on the environment. During the processes of mineral extraction and preparation, large amounts of ore wastes and debris are commonly accumulated in the proximity of the mining operation site. These materials are essentially fractured rocks and soil devoid of vegetation, characterized by high concentrations of heavy metals and metalloids. Consequently, the environment is drastically transformed in highly polluted barren areas [START_REF] Martín Duque | A descriptive and quantitative approach regarding erosion and development of landforms on abandoned mine tailings: New insights and environmental implications from SE Spain[END_REF][START_REF] Sánchez-Donoso | Tailing's geomorphology of the San Quintín mining site (Spain): landform catalogue, aeolian erosion and environmental implications[END_REF], which can be toxic to human health and other life, including plants and microorganisms [START_REF] Giller | Toxicity of heavy metals to microorganisms and microbial processes in agricultural soils: A review[END_REF][START_REF] Nagajyoti | Heavy metals, occurrence and toxicity for plants: A review[END_REF][START_REF] Tchounwou | Heavy metal toxicity and the environment[END_REF]. Furthermore, potentially toxic elements (PTEs) from these polluted areas can transfer to surrounding aquatic and terrestrial compartments via leaching or runoff [START_REF] Elmayel | Evolution of the Speciation and Mobility of Pb , Zn and Cd in Relation to Transport Processes in a Mining Environment[END_REF][START_REF] Fernández-Martínez | Comparison of mercury distribution and mobility in soils affected by anthropogenic pollution around chloralkali plants and ancient mining sites[END_REF][START_REF] Jung | Heavy metal contamination of soils and plants in the vicinity of a lead-zinc mine, Korea[END_REF][START_REF] Kisková | The Bacterial Population of Neutral Mine Drainage Water of Elizabeth's Shaft (Slovinky, Slovakia)[END_REF], disperse in the atmosphere [START_REF] Esbrí | 4D dispersion of total gaseous mercury derived from a mining source: identification of criteria to assess risks related with high concentrations of atmospheric mercury[END_REF][START_REF] Naharro | Experimental assessment of the daily exchange of atmospheric mercury in Epipremnum aureum[END_REF][START_REF] Naharro | Assessment of mercury uptake routes at the soil-plant-atmosphere interface[END_REF], and indirectly contribute to pollutant dissemination. The recent interest in the reclamation of abandoned mining sites in arid and semiarid regions for agricultural purposes highlights the need to understand the biogeochemical processes contributing to soil health and fertility [START_REF] Favas | Abandoned Mine Land Reclamation-Challenges and Opportunities (Holistic Approach)[END_REF]Higueras et al., 2019b;[START_REF] Krzaklewski | Selected physico-chemical properties of zinc and lead ore tailings and their biological stabilisation[END_REF][START_REF] Mendez | Characterization of a bacterial community in an abandoned semiarid lead-zinc mine tailing site[END_REF] Microorganisms such as bacteria and fungi are key players in soil ecosystem services.

They are involved in multiple geochemical cycles, influence plant growth and contribute to climate regulation and soil restoration, among others [START_REF] Ayangbenro | Sulfate-reducing bacteria as an effective tool for sustainable acid mine bioremediation[END_REF][START_REF] Cavicchioli | Scientists' warning to humanity: microorganisms and climate change[END_REF][START_REF] Domeignoz-Horta | Peaks of in situ N 2 O emissions are influenced by N 2 O-producing and reducing microbial communities across arable soils[END_REF], 2015;[START_REF] Van Der Heijden | The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems[END_REF]. Toxicity from heavy metals and metalloids can compromise their growth and survival, with enormous consequences on ecosystem functioning [START_REF] Bell | The contribution of species richness and composition to bacterial services[END_REF][START_REF] Delgado-Baquerizo | Multiple elements of soil biodiversity drive ecosystem functions across biomes[END_REF], 2017, 2016b, 2016a;[START_REF] Domeignoz-Horta | Microbial diversity drives carbon use efficiency in a model soil[END_REF][START_REF] Griffiths | Insights into the resistance and resilience of the soil microbial community[END_REF]Philippot et al., 2013;[START_REF] Wagg | Soil biodiversity and soil community composition determine ecosystem multifunctionality[END_REF]. However, some microorganisms have developed several resistance mechanisms to cope with their toxic effects such as extra and intracellular sequestration, exclusion by permeability barriers, enzymatic detoxification, efflux-pumps and specific reduction of cellular targets' sensibility [START_REF] Hobman | Transcriptomic Responses of Bacterial Cells to Sublethal Metal Ion Stress[END_REF][START_REF] Nies | Efflux-mediated heavy metal resistance in prokaryotes[END_REF][START_REF] Rouch | Understanding cellular responses to toxic agents: a model for mechanism-choice in bacterial metal resistance[END_REF]. In this frame, soil microorganisms constitute useful bio indicators of soil quality [START_REF] Alvarenga | Indicators for Monitoring Mine Site Rehabilitation[END_REF][START_REF] Thiele-Bruhn | Identification of new microbial functional standards for soil quality assessment[END_REF]. Measurement of microbial activity through analysis of soil extracellular enzymatic activities has long been used as indicator of soil disturbance and can allow the assessment of the impact of environmental contaminates on soil microbial processes (Campos et al., 2018a(Campos et al., , 2018b;;[START_REF] Chu | Soil microbial biomass, dehydrogenase activity, bacterial community structure in response to long-term fertilizer management[END_REF][START_REF] Elmayel | Evolution of the Speciation and Mobility of Pb , Zn and Cd in Relation to Transport Processes in a Mining Environment[END_REF]Higueras et al., 2019a;[START_REF] Hinojosa | Soil moisture pre-treatment effects on enzyme activities as indicators of heavy metal-contaminated and reclaimed soils[END_REF]. Additionally, the implementation of state of the art high throughput sequencing approaches could provide a high-resolution analysis of the ecotoxicological effects of PTEs on the composition and diversity of soil microbial populations [START_REF] Fernandes | Bacterial communities in mining soils and surrounding areas under regeneration process in a former ore mine[END_REF][START_REF] Gallego | Assessment of the effects of oxamyl on the bacterial community of an agricultural soil exhibiting enhanced biodegradation[END_REF][START_REF] Jacquiod | Long-term soil metal exposure impaired temporal variation in microbial metatranscriptomes and enriched active phages[END_REF][START_REF] Kisková | The Bacterial Population of Neutral Mine Drainage Water of Elizabeth's Shaft (Slovinky, Slovakia)[END_REF].

The province of Ciudad Real, in the center of the Iberian Peninsula, is scattered with abandoned and decommissioned mines [START_REF] Bravo | Geochemical distribution of major and trace elements in agricultural soils of Castilla-La Mancha (central Spain): finding criteria for baselines and delimiting regional anomalies[END_REF][START_REF] García-Lorenzo | Assessment of Potentially Toxic Elements in Technosols by Tailings Derived from Pb-Zn-Ag Mining Activities at San Quintín (Ciudad Real, Spain): Some Insights into the Importance of Integral Studies to Evaluate Metal Contamination Pollution Hazards[END_REF][START_REF] Higueras | Potentially harmful elements in soils and holm-oak trees (Quercus ilex L.) growing in mining sites at the Valle de Alcudia Pb-Zn district (Spain)-Some clues on plant metal uptake[END_REF][START_REF] Higueras | Low-cost geochemical surveys for environmental studies in developing countries: Testing a field portable XRF instrument under quasi-realistic conditions[END_REF][START_REF] Martín-Crespo | Abandoned mine tailings in cultural itineraries: Don Quixote Route (Spain)[END_REF][START_REF] Rodríguez | Heavy metal distribution and chemical speciation in tailings and soils around a Pb-Zn mine in Spain[END_REF][START_REF] Ruiz | Assessment of metal availability in soils from a Pb-Zn mine site of South-central spain[END_REF]. In this regard, the San Quintín mine site (Cabezarados, Ciudad Real, Spain) offers an unprecedented opportunity to shed light on this topic. This area covers about 100 Ha, with a complex long-lasting history of mining and mineralurgical operations (Fig. 1). Between the years 1888 and 1923, three mine shafts and a rudimentary froth flotation plant operated to obtain Ag-rich galena (PbS) concentrates, leaving a first legacy of dumps, very heterogeneous in grain size and containing high concentrations of Pb, and Zn (García-Lorenzo et al., 2019).

In 1973, a second froth flotation plant aimed to recuperate the sphalerite (ZnS) as well as residual Ag-rich galena was established. During this period, the froth flotation process favored the conversion of the dumps in wastes dams, which accumulated the residues from this reprocessing. Additionally, cinnabar (HgS) from the adjacent Hg mines of Almadén [START_REF] Higueras | The Almadén district (Spain): Anatomy of one of the world's largest Hg-contaminated sites[END_REF][START_REF] Tejero | An estimation of mercury concentrations in the local atmosphere of Almadén (Ciudad Real Province, South Central Spain) during the twentieth century[END_REF] was accumulated in the area during the last years of activity of the froth flotation plant (1988)(1989)(1990). In 2000, olive-oil milling wastes (OMW) were illegally applied over the soils without any remediation interest. Currently, soil and waste materials from the area are used in local road pavements, resulting in a redistribution of the soil. Consequently, the area is deeply polluted with Pb, Zn and associated elements [START_REF] García-Lorenzo | Assessment of Potentially Toxic Elements in Technosols by Tailings Derived from Pb-Zn-Ag Mining Activities at San Quintín (Ciudad Real, Spain): Some Insights into the Importance of Integral Studies to Evaluate Metal Contamination Pollution Hazards[END_REF][START_REF] Higueras | Potentially harmful elements in soils and holm-oak trees (Quercus ilex L.) growing in mining sites at the Valle de Alcudia Pb-Zn district (Spain)-Some clues on plant metal uptake[END_REF][START_REF] Martín-Crespo | Abandoned mine tailings in cultural itineraries: Don Quixote Route (Spain)[END_REF]Rodríguez et al., 2009;[START_REF] Ruiz | Assessment of metal availability in soils from a Pb-Zn mine site of South-central spain[END_REF].

The characterization of the behavior of PTEs from heavily degraded mining soils and their impact on the environment is the first step for planning restoration strategies in contaminated areas. We hypothesized that decades of exposure to different levels of pollutants have exerted a selective pressure on microbial communities in the San Quintín mine area. Within this context, this study aims to investigate the link between geochemical characteristics and microbial communities in a long-term polluted mining site and assess the effects of PTEs on the activity, composition and diversity of microbial communities through enzymatic analysis and high throughput sequencing.

Materials and Methods

Samples collection

Twenty samples of circa 800 g corresponding to the first 10-15 cm and relative to five materials were collected (four independent replicates per material, each replicate was a composite made out of three subsamples collected and homogenized in situ). The five different materials included: non-directly polluted soil taken from the surrounding area and used as local background (reference-samples); PTEs polluted soil from mine operations (via leaching and runoff) and collected within the precincts of the mining site (contaminatedsamples); olive-oil mill wastes residues spilled in the area (OMW); processed waste accumulated in the dams (tailings); and mine wastes without any treatment (dumps). Samples were immediately transported to the laboratory and sieved (<2 mm) within 24 hours. Samples were divided in three aliquots and stored at -4ºC (physicochemical and enzyme analysis) [START_REF] Peoples | Considerations in the storage of soil samples for enzyme activity analysis[END_REF] or -20ºC (DNA extraction) until use. Location and main characteristics of sampling points are described in Fig. 1 and Table 1.

Physicochemical analysis

Physicochemical analyses were conducted in the Laboratory of Soil Biogeochemistry at the EIMIA-UCLM, Almadén (Spain). Electric conductivity and pH were determined using ISO-10390 (ISO-10390, 2005) and ISO-7888 (ISO-11265, 1994) protocols, respectively.

Analyses were performed on 2 g dwt of sieved samples. Texture was determined on dried samples treated with a solution of 3% (v/v) H2O2 for 48 h to remove organic matter. Samples were then wet and sieved at 2 mm and analyzed with a Fritsch Analysette MicroTec Plus 22 (Fritsch, Germany). A textural classification triangle plot was used to identify the granulometry of samples [START_REF] Gerakis | A Computer Program for Soil Textural Classification[END_REF]. Organic matter (OM) was determined using the Walkley-Black method [START_REF] Walkley | Estimation of soil organic carbon by the chromic acid titration method[END_REF].

Elemental concentration analyses were performed from ground dried samples with particle length minor to 100 µm determined by means of X-Ray Fluorescence spectroscopy, using an Epsilon 1 device (Malvern Panalytical, England). Pollution Load Index (PLI) [START_REF] Jorfi | Pollution load index for heavy metals in Mian-Ab plain soil, Khuzestan, Iran[END_REF] was calculated according to the equation:

= / = √ 1ñ 2 …
Where PI is the single factor pollution index of each metal, Cn and Bn is the concentration of metal in the sample and background, respectively (mg Kg -1 ). Reference average values from this study were used as local background level. The values of PLI allow the qualification of samples as follows: PLI < 2: moderately to unpolluted; 2 ≤ PLI < 4: moderately polluted; 4 ≤ PLI < 6: highly polluted; PLI > 6: very highly polluted.

Enzyme analysis

Enzyme activities were measured through colorimetric techniques using a Biochrom Libra S60 spectrophotometer (Biochrom, United Kingdom). Dehydrogenase (DHA) activity was measured according to the method described by [START_REF] Casida | Microbial metabolic activity in soil as measured by dehydrogenase determinations[END_REF], with slight modifications.

Acid and alkaline phosphomonoesterase (PhA 6.5 and PhA 11, respectively) activities were determined following [START_REF] Tabatabai | Use of p-nitrophenyl phosphate for assay of soil phosphatase activity[END_REF] method. Arylsulfatase (ARS) activity was measured as described by Tabatabai and Bremmer, (1970) and β-galactosidase (β-Gal) activity was assayed according to [START_REF] Eivazi | Glucosidases and galactosidases in soils[END_REF]. Functional diversity from selected enzymes was assessed with the geometric mean index (GMean) [START_REF] Lessard | Toxicity response of a new enzyme-based functional diversity methodology for Zn-contaminated field-collected soils[END_REF] calculated as follow:

=

Where yi is the mean value for each enzyme activity, n is the total number of enzymes.

Microbiological analysis

DNA was extracted using the Power soil DNA isolation kit (Qiagen). The extracted DNA was quantified by using fluorescent dye of a Quant-iT™ PicoGreen® dsDNA assay kit (Invitrogen). The diversity of the bacterial community was determined by high throughput sequencing of 16S rRNA amplicons generated in a two-step PCR. First PCR reaction was performed using the universal bacterial primers U341_F-805_R with overhang adapters (forward adapter: TCGTCGGCAGCGTC AGATGTGTATAAGAGACAG, reverse adapter: GTCTCGTGGGCTCGGAGATGTGTATA AGAGACAG). The reaction mixture contained 2 ng of DNA as template, 7.5 µL of 2X Phusion High Fidelity PCR Mastermix (Thermo Scientific, Waltham, MA, USA), 250 ng of T4 gene 32 protein (MP Biomedicals, Santa Ana, CA, USA), 0.375 µL of each primer (10 µM) and ultrapure sterile water to a total volume of 15 µL. Thermal conditions were 3 min at 98°C, 25 cycles at 98° C for 30 sec, 55° C for 30 sec and 72° C for 30 sec followed by a final extension of 10 min at 72ºC. Duplicates of each amplicon were pooled and then 6 µL aliquot was used as template in a second PCR carried out with multiplexed primers containing the universal overhang adaptors and specific barcodes and using a 384 Nextera XT index kit (Illumina, San Diego, CA, USA). The reaction mixture was carried out in 30 μL reaction volumes containing 2.5 μL sterile water, 15 μL 2X Phusion HF master mix (Thermo Scientific, Waltham, MA, USA), 250 ng of T4 gene 32 protein (MP Biomedicals, Santa Ana, CA, USA), 3 μL of each primer (10 μM) and 6 μL of the step-one PCR product. The thermal cycling was 98°C for 3 min, followed by eight cycles of 98° C for 30 sec, 55ºC for 30 sec and 72° C for 30 sec, with a final extension of 72º C for 10 min. The size of the amplicons was verified by electrophoresis on a 2 % agarose gel.

Amplicons were purified (amplicon library purification, PicoGreen® quantification and pooling) and sequenced (Illumina MiSeq 2 x300bp) by Microsynth (Balgach, Switzerland). Amplicons were normalized (SequalPrep TM kit), purified (Pippin prep) and sequenced by Microsynth (Switzerland). The sequence data was analyzed using an in-house developed Phyton notebook pipeline together with different bioinformatics tools: 16S rDNA sequences were assembled using the PEAR software [START_REF] Zhang | PEAR: a fast and accurate Illumina Paired-End reAd mergeR[END_REF] with the default settings; further quality checks were conducted using the QIIME 1 pipeline (Caporaso et al., 2010a); sequences shorter than 350 bp were removed; reference-based and de novo chimera detection, as well as clustering in OTUs were performed with the VSEARCH software [START_REF] Rognes | VSEARCH: a versatile open source tool for metagenomics[END_REF] using appropriate reference databases (Greengenes' representative set of sequences for 16S rDNA) with a threshold placed at 94 % identity; representative sequences for each OTU were aligned using PyNAST (Caporaso et al., 2010b); phylogenetic trees were constructed using FastTree [START_REF] Price | Fasttree: Computing large minimum evolution trees with profiles instead of a distance matrix[END_REF]; taxonomy was assigned using UCLUST [START_REF] Edgar | Search and clustering orders of magnitude faster than BLAST[END_REF] and the latest released Greengenes database (v.05/2013(v.05/ , McDonald et al., 2012) ) for 16S rDNA sequences; sequences were deposited in the GenBank to the sequence read archive (SRA) under the accession number PRJNA646888. A range of bacterial α-diversity indices pertaining to richness (Chao1, observed species), evenness (Simpson reciprocal, equitability, dominance, Shannon) and relatedness (PD whole tree) were calculated based on rarefied tables (13,000 sequences per sample). Bray Curtis distance matrices were also computed to detect changes in the composition of microbial communities. Canonical Analysis of Principal Coordinates (CAP) of OTU Bray Curtis distance matrices were also performed and plotted.

The relative abundance of the different bacterial phyla was also determined using a comparative bar chart.

Statistical analysis

All statistical analyses were performed in R (http://www.r-project.org). The normality of the data and residuals was checked (Shapiro Wilk's with p>0.05) and the homogeneity of variances was verified (Levene's test with p>0.05). Root square, inverse and log-transformations on the data were performed when necessary. For parametric distributions, ANOVA followed by Tukey's test and t-student were used to determine differences. For nonparametric distributions, data was compared using Kruskal Wallis test. To detect significant differences in communities' structure among sample types, Permutational multivariate analysis of variance (PermANOVA) was used on Bray Curtis dissimilarity matrix using Adonis function from R package "vegan" [START_REF] Oksanen | Package 'vegan'community ecology package[END_REF].

Integration and visualization of amplicon sequencing and physicochemical and enzymatic analysis data sets were performed through the R package mixOmics [START_REF] Rohart | mixOmics: An R package for 'omics feature selection and multiple data integration[END_REF] using DIABLO (Data Integration Analysis for Biomarker discovery using a Latent component method for Omics studies) in order to identify correlated key omics variables in both datasets [START_REF] Singh | DIABLO: An Integrative Approach for Identifying Key Molecular Drivers From Multi-Omics Assays -PubMed[END_REF].

Results

Physicochemical and geochemical characterization of samples

Table 2 shows the physicochemical parameters of the samples of the five selected materials. According to their granulometry, samples were classified with some minor variability in sandy loam (reference-, contaminated-samples and dumps), silt loam (tailings) and loamy sand (OMW) (Table 2, Table S1 and Fig. S1). Reference-, contaminated-samples and tailings presented a slightly acidic character while OMW and dumps had lower pH values. EC varied from 62.4 ± 27.8 (reference-samples) to 2282.3 ± 1786.4 mV (dumps).

Overall, no significant differences were found in the pH and EC (p>0.055), except for the dumps, which presented significant lower pH (p=0.00371) and significant higher EC values (p=0.004) as compared to the reference-samples. OM ranged from 1.57% ± 0.54 (dumps) to 60.60 % ± 19.67 (OMW). Similar organic content was found in reference-samples, tailings and dumps, while higher values were found in contaminated-samples, although not significant (p>0.5006), probably due to their high heterogeneity. On the contrary, significant higher organic matter was found in OMW as compared to the rest of the samples (p=0.0000271).

The concentration of PTEs for the different samples is shown in Table 3. Among the different PTEs studied, those which were part of the ore (Pb, Zn and Hg) were found at very high concentrations in all samples except reference-samples, with significantly higher concentrations for Pb (p<0.0043) and Zn (p<0.00577) in dumps and OMW as compared to reference-samples. Similarly, Sb, Co and Cu concentrations were significantly higher in dumps and OMW than in the three other samples (p<0.0407). Regarding Mn, Ni and Th, similar concentrations were recorded in all samples and no significant differences were observed (p>0.05). A high variability was found for all the PTEs studied. The pollution index for each PTE was calculated (Table S2) and yielded values higher than 100 for Hg (in all samples except reference-samples), Pb (dumps and OMW) and Zn (OMW). Pollution load index (PLI) allowed to classify the samples as follows: reference-and contaminated-samples as highly polluted (4 ≤ PLI < 6) and OMW, dumps and tailings (from most polluted to less polluted respectively) as very highly polluted (PLI > 6) (Table 3).

Enzymatic analysis

Five enzymatic activities including the dehydrogenase (DHA), arylsulfatase (ARS), the acid and alkaline phosphomonoesterase (PhA 6.5 and PhA 11, respectively) and βgalactosidase (β-Gal) were measured in the samples collected from the five sites studied in mining area (Fig. 2 and Table S3). Reference-and contaminated-samples showed similar enzyme activities (p>0.05). The lowest enzymatic activities (ARS, PhA 6.5, PhA 11 and β-Gal) were recorded in the OMW, which were significantly different from the values recorded in reference-and contaminated-samples (p<0.0398351) Tailings and dumps had significantly lower DHA value (reduction of about 96 %) and ARS activities than reference-samples (p<0.0018687). Tailings had PhA 6.5 activities significantly lower than that of the referencesamples (p=0.0223). The geometric mean index was significantly lower in OMW samples (p=0.0000002) than that of all the other samples, which were similar (p>0.06922) (Fig. S2).

Bacterial composition and diversity

The composition and diversity of the bacterial community was assessed by high throughput sequencing of 16S rRNA amplicons amplified from DNA extracts. In total, after de-multiplexing and removal of low-quality raw sequence reads, high throughput sequencing generated 1,796,830 high quality sequences with an average sequence length of 461 ± 11bp.

Using a threshold at 94% nucleotide sequence identity, these sequences were grouped in 16,299 different OTUs.

A range of α-diversity indices pertaining richness (Chao1), relatedness (PD whole tree) and evenness (Simpson reciprocal) were calculated using rarefied data (Fig. 3). Overall, Chao1, PD whole tree and Simpson reciprocal α-diversity indices were higher in referencesamples than in contaminated-samples, OMW, tailings and dumps, ranked from the highest to the lowest values. Chao1 values were significantly lower in OMW, tailings and dumps (p<0.027) while significant differences were found for tailings and dumps in PD whole tree values as compared to reference-samples values (p<0.019). Regarding Simpson reciprocal, no significant differences were found between reference-, contaminated-samples, OMW and tailings, and only significant lower values were found in dumps (p=0.024).

Differences in the composition of bacterial communities measured in the different samples were assessed by multivariate beta-diversity analysis using Canonical Analysis of Principal Coordinates (CAP) based on Bray Curtis distances matrices (Fig. 4 & Fig. S3). A good reproducibility between replicates of each sample type was observed regardless the dataset used as explanatory factors. CAP ordinations using a range of PTEs (Co, Cu, Hg, Mn, Ni, Sb, Th, Pb, Zn) as explanatory factors, explained up to 78 % of the variance observed, of which 29% is explained in the first two axes (17% and 12%, respectively) (Fig. 4a). The sample type was the most explanatory variable (36.96%) followed by the Hg concentration (6.41%), both associated with the first axis. All the other variables (Mn, Co, Ni, Cu, Zn, Sb, Pb and Th) analyzed were not significant (Table S4). Likewise, CAP ordinations using physicochemical parameters and enzymatic activities as variables explained 73% of the variance observed (17% and 12% respectively for the first two axes) (Fig. 4b). None of the physicochemical parameters nor the enzymatic activities were significant explanatory variables of the composition of the bacterial community. For all CAP ordinations, reference-and contaminated-samples showed similar bacterial community composition (p=0.734) that was differing from those of tailings and dumps which were similar (p=0.056). The composition of the bacterial community of OMW was markedly different from that of all the other samples analyzed no matter the variables considered (p<0.036).

Taxonomic analysis of the 16S rRNA amplicon sequences at phylum level showed that the relative abundance of the major bacterial phyla was very similar in reference-and contaminated-samples. On the contrary, it markedly differed between reference-samples and dumps. All samples were dominated by bacteria belonging to Proteobacteria, Actinobacteria, Chloroflexi, Acidobacteria, Gemmatimonadetes, Bacteroidetes, Firmicutes, TM7, Verrucomicrobia and Cyanobacteria (Fig. 5). Proteobacteria, Actinobacteria, Chloroflexi, Acidobacteria, Firmicutes, TM7 and Cyanobacteria were observed in similar abundance in all the sample types (p>0.05). Gemmatimonadetes and Bacteroidetes were found in significantly lower amount in dumps than in reference-samples (p<0.035). Verrucomicrobia had significantly lower relative abundances in dumps, tailings and OMW than in the referencesamples (p<0.024).

To further identify the OTUs responsible of differences in the composition of the bacterial community between samples, a correlation analysis was performed with OTUs represented at least once in all four replicates of a given sample type (a total of 76 different OTUs) using the diablo R package. Among these 76 OTUs, 57 responsible of the differences observed between substrate types (Pearson's correlation > 0.4) were plotted in a heatmap (Fig 6). All discriminant OTUs affiliated to 7 of the 10 most abundant bacterial phyla detected (Actinobacteria, Chloroflexi, Acidobacteria, Bacteroidetes, Cyanobacteria, Gemmatimonadetes and Proteobacteria).

Similar abundances were found in discriminant OTUs for reference-and contaminatedsamples, confirming the results from α-and β-diversity analyses. Microorganisms belonging to the orders Actinomycetales, Sphingomonadales, Burkholderiales, Rhodospirillales, Stramenopiles, RB41, Solibacterales, Ellin 329 and some microorganisms belonging to the order Rhizobiales were highly represented in reference-and contaminated-samples.

Microorganisms belonging to the families Acidobacteriaceae, Sphingobacteriaceae and

Xanthomonadaceae, the order Solirubrobacterales, N1423WL, some Actinomycetales and some Rhizobiales; and the genera Sphingomonas and Segetibacter were highly represented in OMW but scarcely detected in reference-and contaminated-samples. Regarding the dumps, five OTUs belonging to Acidomicrobiales, B12-WMSP1, Acidobacteriaceae and Enterobacteriaceae phylotypes were found in high abundance. Finally, a high heterogeneity was observed for the discriminant OTUs in tailings.

Discussion

Mining soils are generally rich in PTEs that are known to have ecotoxicological effects on soil microorganisms by disrupting their metabolism, affecting soil enzymatic activity and having consequence on the abundance, composition and diversity of soil microbial communities [START_REF] Zhao | Effects of soil heavy metal pollution[END_REF]. The analysis of the PTEs concentrations in the five different sample types allowed the classification of the samples as highly polluted (reference-and contaminated-samples) and very highly polluted (OMW, tailings and dumps). Although the potential value of OMW as fertilizer or metal immobilization amendment in polluted soils was shown in previous studies [START_REF] Hmid | Olive mill waste biochar: a promising soil amendment for metal immobilization in contaminated soils[END_REF][START_REF] Paredes | Characterization of olive mill wastewater (alpechin) and its sludge for agricultural purposes[END_REF], here the application of OMW showed no remediation activity. It is noteworthy that a number of recent studies have shown the negative impact of OMW on aquatic and terrestrial ecosystems [START_REF] Ntougias | The microbiology of olive mill wastes[END_REF]. Soil enzymes are mainly produced by microorganisms, which play a crucial role in nutrient cycling and consequently, in soil fertility [START_REF] Adetunji | The biological activities of βglucosidase, phosphatase and urease as soil quality indicators: A review[END_REF]. Microbial enzymes are highly sensitive to metals, and their use as standard biochemical indicators has been widely proposed to evaluate the quality of polluted soils [START_REF] Pająk | Restoration of Vegetation in Relation to Soil Properties of Spoil Heap Heavily Contaminated with Heavy Metals[END_REF][START_REF] Tang | Diagnosis of soil contamination using microbiological indices: A review on heavy metal pollution[END_REF].

However, several studies have showed no effect of PTEs contamination on some soil enzymatic activities, such as dehydrogenase, catalase, acid phosphomonoesterase and amylase (Campos et al., 2018a;[START_REF] Liu | Heavy metal concentration, potential ecological risk assessment and enzyme activity in soils affected by a lead-zinc tailing spill in Guangxi, China[END_REF][START_REF] Yang | A proposal of "core enzyme" bioindicator in long-term Pb-Zn ore pollution areas based on topsoil property analysis[END_REF]. This phenomenon could be due to three reasons: (I) the long-term presence of metals in the soil, which might promote the development of heavy metal resistant microorganisms [START_REF] Ciarkowska | Enzyme activity as an indicator of soil-rehabilitation processes at a zinc and lead ore mining and processing area[END_REF]; (II) variations in soil physicochemical properties, which may influence the activity of soil microbial communities [START_REF] Xian | Quantitative assessment on soil enzyme activities of heavy metal contaminated soils with various soil properties[END_REF], the bioavailability and speciation of the pollutants [START_REF] Rieuwerts | Factors influencing metal bioavailability in soils: Preliminary investigations for the development of a critical loads approach for metals[END_REF]; and (III) antagonistic effects between various PTEs [START_REF] Tang | Diagnosis of soil contamination using microbiological indices: A review on heavy metal pollution[END_REF]. Our results showed that enzyme activities were similar in reference-and contaminatedsamples. This might be explained by the high organic matter content in these two sample types, which may on the one hand, provide nutrient source for the development of microorganisms and the production of enzymes and on the other hand, reduce the bioavailability of metals and metalloids, limiting their toxicity towards microbial enzymatic activities [START_REF] Santiago-Martín | Metal contamination disturbs biochemical and microbial properties of calcareous agricultural soils of the Mediterranean area[END_REF][START_REF] Lair | Sorption of heavy metals on organic and inorganic soil constituents[END_REF]. It is important to note, that organic matter can sometimes increase the mobility and bioavailability of pollutants [START_REF] Lindsay | Iron oxide solubilization by organic matter and its effect on iron availability[END_REF][START_REF] Meunier | Effects of organic matter and ageing on the bioaccessibility of arsenic[END_REF][START_REF] Wang | Effect of natural organic matter on arsenic release from soils and sediments into groundwater[END_REF]. Consequently, further research is needed to better understand the processes governing the complex interactions between organic matter and pollutants. Additionally, all enzymatic activities were very low in OMW as confirmed by the geometric mean index. The important amount of phenols with powerful antimicrobial properties and high content of PTEs, might explained the low enzymatic activities recorded in OMW samples [START_REF] Siles | Assessing the impact of biotransformed dry olive residue application to soil: Effects on enzyme activities and fungal community[END_REF]. DHA activities measured in tailings and dumps were significantly lower than those of reference-samples. Keeping in mind the fact that the inhibition of DHA activity in response to PTEs such as Pb, Cu, Cd, Zn has been previously described [START_REF] Santiago-Martín | Metal contamination disturbs biochemical and microbial properties of calcareous agricultural soils of the Mediterranean area[END_REF][START_REF] Pająk | Restoration of Vegetation in Relation to Soil Properties of Spoil Heap Heavily Contaminated with Heavy Metals[END_REF][START_REF] Parelho | Assessing microbial activities in metal contaminated agricultural volcanic soils -An integrative approach[END_REF][START_REF] Tang | Diagnosis of soil contamination using microbiological indices: A review on heavy metal pollution[END_REF], one could hypothesize that the high concentration of PTEs in tailings and dumps would explain the observed low enzyme activities. Similar results were found for ARS activity, that is a key player in the sulfur cycle and known to be inhibited by PTEs (De Santiago-Martín et al., 2013;[START_REF] Gülser | The effects of heavy metal pollution on enzyme activities and basal soil respiration of roadside soils[END_REF][START_REF] Stefanowicz | Soil organic matter prevails over heavy metal pollution and vegetation as a factor shaping soil microbial communities at historical Zn-Pb mining sites[END_REF]. No-matter the sample type, acid PhA activities were higher than those of alkaline PhA probably due to the acidic nature of all the samples [START_REF] Adetunji | The biological activities of βglucosidase, phosphatase and urease as soil quality indicators: A review[END_REF]. Acid PhA activity was lower in tailings than in the reference-samples which is in accordance with known sensitivities of these enzymes to heavy metals [START_REF] Angelovičová | Effect of heavy metals on soil enzyme activity at different field conditions in middle spis mining area (Slovakia)[END_REF][START_REF] Santiago-Martín | Metal contamination disturbs biochemical and microbial properties of calcareous agricultural soils of the Mediterranean area[END_REF][START_REF] Gao | Assessment of effects of heavy metals combined pollution on soil enzyme activities and microbial community structure: Modified ecological dose-response model and PCR-RAPD[END_REF]. Despite the fact that β-Gal has been reported as a sensitive bioindicator of metal contamination of soils [START_REF] Santiago-Martín | Metal contamination disturbs biochemical and microbial properties of calcareous agricultural soils of the Mediterranean area[END_REF][START_REF] Martínez-Iñigo | Bulk soil and rhizosphere bacterial community PCR-DGGE profiles and βgalactosidase activity as indicators of biological quality in soils contaminated by heavy metals and cultivated with Silene vulgaris (Moench) Garcke[END_REF], all samples showed similar β-Gal activities except in dumps where slightly lower activities were recorded.

As observed with microbial enzymatic activities, the exposure to PTEs can also affect key microbial processes, which reflects on changes in microbial composition and diversity. On the one hand, PTEs such as Pb, Zn or Hg can exert toxic effects compromising the survival and growth of specific microbial guilds [START_REF] Abdu | Heavy metals and soil microbes[END_REF][START_REF] Doelman | Short-term and long-term effects of cadmium, chromium, copper, nickel, lead and zinc on soil microbial respiration in relation to abiotic soil factors[END_REF]. On the other hand, PTEs can promote the emergence of specific resistant microorganisms able to cope with the ecotoxicity of these contaminants [START_REF] Gillard | Heavy-metal-resistant microorganisms in deep-sea sediments disturbed by mining activity: An application toward the development of experimental in vitro systems[END_REF][START_REF] Martinez | Horizontal gene transfer of PIB-type ATPases among bacteria isolated from radionuclide-and metal-contaminated subsurface soils[END_REF][START_REF] Sobolev | Effects of heavy metal contamination upon soil microbes: Lead-induced changes in general and denitrifying microbial communities as evidenced by molecular markers[END_REF]. Here, the lowest values of all the α-diversity indices (Chao1, PD whole tree and Simpson reciprocal) were recorded in dumps, probably as consequence of the very high concentrations of Pb, Zn, Sb, Co and Cu present in these samples and low organic matter. Indeed, the reduction in soil bacterial α-diversity as consequence of Pb, Zn, Sb, Co or Cu toxic effects has previously been reported [START_REF] Luo | Effects of Pb smelting on the soil bacterial community near a secondary lead plant[END_REF][START_REF] Nunes | Coping with copper: legacy effect of copper on potential activity of soil bacteria following a century of exposure[END_REF][START_REF] Stefanowicz | Metals affect soil bacterial and fungal functional diversity differently[END_REF][START_REF] Xie | Effect of heavy metals pollution on soil microbial diversity and bermudagrass genetic variation[END_REF]. Similarly, Chao1 and PD whole tree values were lower in tailings than in the reference-samples. It is noteworthy that PTEs concentrations in tailings were not significantly different from that of reference-samples, suggesting that differences observed in α-diversity may result from other physicochemical parameters governing the bioavailability of PTEs. Chao1 diversity indices in OMW samples were significantly lower than that of reference-samples. As observed for the enzymatic activities, the high content of organic matter rich in phenols in OMW could have ecotoxicological effects on survival and growth of phenol-sensitive microorganisms and therefore decreased the microbial diversity. In this regard, changes in the structure of ammonium oxidizers bacteria and Actinobacteria have been previously reported [START_REF] Karpouzas | Olive mill wastewater affects the structure of soil bacterial communities[END_REF][START_REF] Mekki | Changes in microbial and soil properties following amendment with treated and untreated olive mill wastewater[END_REF]. All alpha diversity indices were similar in both reference-and contaminatedsamples.

Multivariate β-diversity analysis based on Bray Curtis distance matrix confirmed the results observed from α-diversity: reference-and contaminated-samples clustered together no matter the explanatory factors (PTEs, enzymatic activities or texture). These two samples were clearly separated from tailings and dumps. OMW was discriminated from all others whatever the factors considered. Besides the sample type, which explained 37% of the variance observed, concentration of Hg was the only significant explanatory factor (6% of the variance). In line with our work, the influence of soil properties and heavy metals on the structure of bacterial communities has been previously showed in contaminated soils [START_REF] Li | Response of soil microbial communities and microbial interactions to long-term heavy metal contamination[END_REF][START_REF] Liu | Diversity of microbial communities potentially involved in mercury methylation in rice paddies surrounding typical mercury mining areas in China[END_REF][START_REF] Nunes | Coping with copper: legacy effect of copper on potential activity of soil bacteria following a century of exposure[END_REF][START_REF] Tipayno | The bacterial community structure and functional profile in the heavy metal contaminated paddy soils, surrounding a nonferrous smelter in South Korea[END_REF] Phylogenetic analysis revealed that the majority of bacteria from all samples affiliated to the Proteobacteria, Actinobacteria, Chloroflexi, Acidobacteria, Gemmatimonadetes, Bacteroidetes, Firmicutes, TM7, Verrucomicrobia and Cyanobacteria phylotypes, in line with previous studies performed in a range of PTEs polluted soils [START_REF] Chen | Long-term and highconcentration heavy-metal contamination strongly influences the microbiome and functional genes in Yellow River sediments[END_REF][START_REF] Guo | Understanding the variation of microbial community in heavy metals contaminated soil using high throughput sequencing[END_REF][START_REF] Pacwa-Płociniczak | Effect of Silene vulgaris and Heavy Metal Pollution on Soil Microbial Diversity in Long-Term Contaminated Soil[END_REF][START_REF] Tipayno | The bacterial community structure and functional profile in the heavy metal contaminated paddy soils, surrounding a nonferrous smelter in South Korea[END_REF]Zhen et al., 2019). Overall, their relative abundances were similar in all samples, with the exception of the bacteria belonging to Gemmatimonadetes, Bacteroidetes and Verrucomicrobia, whose relative abundance were significantly lower in dumps as compared to reference-samples. A significant reduction in the relative abundance of Verrucomicrobia was also observed in tailings and OMW. The significantly higher concentrations of PTEs found in in tailings and OMW may explain the reduction of these phylotypes. This assumption is in line with the reduction of Verrucomicrobia previously reported in a study performed on an agricultural paddy soil polluted with cadmium [START_REF] Luo | Bacterial community response to cadmium contamination of agricultural paddy soil[END_REF] and Yellow river sediments heavily polluted with cadmium, arsenic, lead and mercury [START_REF] Chen | Long-term and highconcentration heavy-metal contamination strongly influences the microbiome and functional genes in Yellow River sediments[END_REF]. Similarly, the relative abundance of members of several phyla including Verrucomicrobia and Bacteroidetes was found to significantly decrease in a long-term Cu polluted site [START_REF] Berg | Selection for Cu-tolerant bacterial communities with altered composition, but unaltered richness, via long-term cu exposure[END_REF]. In this regard, our findings are consistent with a recent study that proposes that changes in the response of some bacterial groups (the Verrucomicrobia/Chlamydiae ratio) can be used as bioindicators of heavy metal pollution [START_REF] Schneider | Response of bacterial communities to Pb smelter pollution in contrasting soils[END_REF]. Contrary to our results, studies from a metal polluted forest soil showed an increase in the Gemmatimonadetes [START_REF] Azarbad | Microbial community composition and functions are resilient to metal pollution along two forest soil gradients[END_REF] but a negative correlation between the relative abundance of Verrucomicrobia and pollution levels as observed in our study.

Further analysis at lower taxonomical level led to the identification of 57 OTUs responsible of α-and β-diversity changes observed between different sample types.

Discriminant OTUs were found in similar abundances in reference-and contaminatedsamples. Interestingly, OTUs barely found in reference-and contaminated-samples were significantly more abundant in OMW, suggesting that these OTUs may be able to grow in a PTEs contaminated environment rich in organic matter. OTUs related to the B12-WMSP1 group described by [START_REF] Costello | Microbial diversity in alpine tundra wet meadow soil: Novel Chloroflexi from a cold, water-saturated environment[END_REF] in fumarolic soils [START_REF] Costello | Fumarolesupported islands of biodiversity within a hyperarid, high-elevation landscape on socompa volcano, puna de atacama, andes[END_REF][START_REF] Schmidt | Life at extreme elevations on Atacama volcanoes: the closest thing to Mars on Earth? Antonie van Leeuwenhoek[END_REF][START_REF] Tebo | Microbial communities in dark oligotrophic volcanic ice cave ecosystems of Mt. Erebus, Antarctica[END_REF] were found in higher amount in dumps than in the reference-samples.

Conclusions

The importance of soil health and quality in ecosystem services underlines the need to understand the processes governing the behavior and effects of PTEs on soil microorganisms and supported functions. This assumption is especially relevant in highly polluted areas such as mining areas. Here, we evaluated the degree of contamination in a long-term mining site and assessed the ecotoxicological effects of PTEs on bacterial communities. We observed that low PTEs concentrations can have a strong impact on the diversity and composition of microbial communities while physicochemical parameters such as organic content can either help counteract or enhance their negative effects. Understanding the relationship between physicochemical parameters and diversity and composition of microorganisms can contribute to the environmental risk assessment of long-term polluted sites and help create strategies to restore contaminated sites and mitigate the pollution dissemination.

Funding Sources

Spanish Ministry of Economy and Competitiveness (MINECO) funded this work, Project CGL2015-67644-R.

ACKNOWLEDGEMENT

The authors thank Sofía Rivera, Efrén García-Ordiales, Sandra Viso and Eva Mª García-Noguero for their contribution in collecting samples and edaphological sample characterization.

ABBREVIATIONS

FiguresFigure 1 .

 1 Figures

Figure 2 .Figure 3 .Figure 4 .Figure 5 .Figure 6 .

 23456 Figure2. Enzymatic activities (mean ± S.D): dehydrogenase (DHA) (µg TPF g -1 DW day -1 ), arylsulfatase (ARS) (µg PNF g -1 DW day -1 ), acid phosphomonoesterase (PhA 6.5) (µg PNF g - 1 DW day -1 ), alkaline phosphomonosterase (PhA 11) (µg PNF g -1 DW day -1 ) and β-Galactosidase (β-Gal) (µg PNF g -1 DW day -1 ) for reference-, contaminated-samples, olive mill waste (OMW), tailings and dumps. ANOVA followed by Tukey's test and Kruskal Wallis tests (*) were performed. For DHA, ANOVA was performed on log-transformed data.For each parameter, different letters within the same line indicate significant differences (p <0.05). n=4

Table 1 .

 1 Distribution (geographic coordinates) and main characteristics of samples

	REFERENCE	CONTAMINATED	OMW	TAILINGS	DUMPS

1 Tables

Table 2 .

 2 Physicochemical characteristics of reference-, contaminated-samples, olive mill waste, tailings and dumps. Values were mean ± standard deviation. ANOVA followed by Tukey's test were performed. For EC and OM, ANOVA was performed on log-transformed data. For each parameter, different letters within the same line indicate significant differences (p <0.05). For EC and OM, ANOVA performed on log-transformed data. n=4

		Reference	Contaminated	Olive Mill Waste	Tailings	Dumps
	pH	6.1 ± 0.3a	6.8 ± 1.0a	5.2 ± 0.6ab	6.7 ± 1.7a	3.9 ± 0.5b
	EC (mV)	62.4 ± 27.8a	90.6 ± 49.9a	651.3 ± 489.7ab	415.6 ± 378.8ab	2282.3 ± 1786.4b
	OM (%)	2.9 ± 1.9a	8.1 ± 8.9a	60.6 ± 19.7b	2.0 ± 0.4a	1.6 ± 0.5a
	Sample type	sandy loam	sandy loam	loamy sand	silt loam	sandy loam

EC: electric conductivity, OM: organic matter

Table 3 .

 3 PTEs concentrations (mg/kg) (mean values ± standard deviation) and Pollution Load Index (PLI) in reference-, contaminated-samples, olive mill waste, tailings and dumps.ANOVA test followed by Tukey's test and Kruskal Wallis followed by pairwise comparisons with Bonferroni correction (*) were performed. Values indicated by different letters are significantly different within the same line. For Ni and Cu ANOVA was performed on root square transformed data. For Sb, ANOVA was performed on inverse transformed data, respectively. For Zn, Hg and Th, ANOVA performed on log-transformed data, respectively.

	n=4
		Reference	Contaminated	Olive Mill Waste	Tailings	Dumps
	Pb	315.8 ± 332.6a	3696.5 ± 4406.2a	38890 ± 20687.0b	4030.2 ± 7170.8a	48597.5 ± 16835.1b
	Zn	204.8 ± 153.8a	1522.4 ± 2165.2ab	86050 ± 99094.2c	2024.5 ± 2492.1ab	4640.0 ± 2259.5b
	Hg	0 ± 0a	55.4 ± 92.1a	3605.8 ± 6752.0a	557.1 ± 430.8a	130.8 ± 142.5a
	Sb	16.8 ± 1.8a	37.4 ± 31.0abc	330.9 ± 435.2bc	65.5 ± 87.7ab	433.6 ± 198.9c
	Co	221.7 ± 76.4a	268.3 ± 80.0ab	451.7 ± 78.4bc	192.5 ± 27.9a	503.9 ± 180.6c
	Cu	17.3 ± 10.0a	165.6 ± 297.6a	963.7 ± 466.2b	113.4 ± 196.7a	1000.6 ± 805.0b
	Mn*	688.2 ± 238.9ab	1572.5 ± 908.6a	1637.4 ± 1866.4ab	423.5 ± 136.5ab	210.4 ± 90.8b
	Ni	31.4 ± 18.7a	38.9 ± 31.4a	132.1 ± 95.6a	37.0 ± 14.0a	27.6 ± 8.1a
	Th	9.7 ± 3.9ab	4.6 ± 4.1ab	0 ± 0b	3.8 ± 6.9ab	35.1 ± 23.7a
	PLI	1.0 ± 0.1	3.2 ± 2.8	21.1 ± 23.9	10.5 ± 4.8	7.4 ± 2.4
	PLI: Pollution Load Index