Differential effects of tree species identity on
rhizospheric bacterial and fungal community richness
and composition across multiple trace
element-contaminated sites

Stéphane Pfendler, Lisa Ciadamidaro, Shinji Ozaki, Aurélie Bonin, Pierre Taberlet, Cyril Zappelini, François Maillard, Damien Blaudez, Michel Chalot

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To cite this version:

Stéphane Pfendler, Lisa Ciadamidaro, Shinji Ozaki, Aurélie Bonin, Pierre Taberlet, et al. Differential effects of tree species identity on rhizospheric bacterial and fungal community richness and composition across multiple trace element-contaminated sites. Science of the Total Environment, 2024, 912, pp.168600. 10.1016/j.scitotenv.2023.168600. hal-04429741

HAL Id: hal-04429741

https://hal.univ-lorraine.fr/hal-04429741v1

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Differential effects of tree species identity on rhizospheric bacterial and fungal
community richness and composition across multiple trace element-contaminated sites

Stéphane Pfendler ¹, Lisa Ciadamidaro ¹, Shinji Ozaki ¹, Aurélie Bonin ², Pierre

Taberlet ²,³, Cyril Zappelini ¹, François Maillard ¹, Damien Blaudez ⁴, Michel Chalot ¹,⁵

¹ Université de Franche-Comté, CNRS, Chrono-environnement, F-25200 Montbéliard, France

² Université Grenoble Alpes, CNRS, LECA, Laboratoire d'Ecologie Alpine, F-38000

Grenoble, France

³ UiT – The Arctic University of Norway, Tromsø Museum, Tromsø, Norway

⁴ Université de Lorraine, CNRS, LIEC, F-54000 Nancy, France

⁵ Université de Lorraine, Faculté des Sciences et Technologies, F-54000 Nancy, France

Current addresses

Shinji Ozaki: UK Centre for Ecology & Hydrology Lancaster Environment Centre, Library
Avenue, Bailrigg, Lancaster, LA1 4AP, United Kingdom

François Maillard: Microbial Ecology Group, Department of Biology, Lund University, Lund,
Sweden

Cyril Zappelini: Agroécologie, INRAE, Institut Agro, Université de Bourgogne, Univ.
Bourgogne Franche-Comté, F-21000 Dijon, France

* Correspondence: stephane.pfendler@univ-fcomte.fr
**Abstract:**

Soil microbial communities play a key role in plant nutrition and stress tolerance. This is particularly true in sites contaminated by trace metals, which often have low fertility and stressful conditions for woody plants in particular. However, we have limited knowledge of the abiotic and biotic factors affecting the richness and composition of microbial communities inhabiting the rhizosphere of plants in contaminated sites. Using high-throughput amplicon sequencing, we studied the rhizospheric bacterial and fungal community structures of 14 woody plant families planted in three contrasting sites contaminated by metals (Pb, Cd, Zn, Mn, Fe, S). The rhizospheric bacterial communities in the given sites showed no significant difference between the various woody species but did differ significantly between sites. The Proteobacteria phylum was dominant, accounting for over 25% of the overall relative abundance, followed by Actinobacteria, Bacteroidetes and Gemmatimonadetes. Site was also the main driver of fungal community composition, yet unlike bacteria, tree species identity significantly affected fungal communities. The Betulaceae, Salicaceae and Fagaceae families had a high proportion of Basidiomycota, particularly ectomycorrhizal fungi, and the lowest diversity and richness. The other tree families and the unplanted soil harboured a greater abundance of Ascomycota and Mucoromycota. Consequently, for both bacteria and fungi, the site effect significantly impacted their community richness and composition, while the influence of plants on the richness and composition of rhizospheric microbial communities stayed consistent across sites and was dependent on the microbial kingdom. Finally, we highlighted the importance of considering this contrasting response of plant rhizospheric microbial communities in relation to their host identity, particularly to improve assisted revegetation efforts at contaminated sites.

**Keywords:** Phytomanagement, Metabarcoding, Contaminated sites, Microbial communities
# 1. Introduction

In the last two centuries, soil pollution in European countries has increased, and the number of polluted sites has reached approximately three million (Europe's environment - The fourth assessment). Among soil contaminants, mineral oil, trace elements (TEs) and polycyclic aromatic hydrocarbons are the most common (Mench et al., 2010; Quintela-Sabarís et al., 2017). In addition, soil contaminants may be transferred or dispersed to other environmental compartments by several drivers, such as wind (Quintela-Sabarís et al., 2017). Conventional engineering technologies for mitigating soil pollution have often been proven economically prohibitive and ecologically unfriendly (Adriano et al., 2004). Consequently, it is necessary to find new strategies to mitigate and remediate soil pollution (Hullot et al., 2021). New soil management strategies have emerged in recent decades, prominently featuring plant-based alternatives such as phytoremediation (Kidd et al., 2015). This development has led to significant growth in the literature about using native pioneer trees for revegetation efforts on degraded lands (Asensio et al., 2018; Mleczek et al., 2017). Importantly, most studies have focused on contaminated sites planted with a single tree species (Beauchamp et al., 2018; Gonzalez et al., 2018) or various genotypes of the same species, such as poplar (Di Baccio et al., 2003; Laureysens et al., 2004; Pottier et al., 2015; Robinson et al., 2007; Sebastiani et al., 2004). In some other studies, a combination of trees (Al Souki et al., 2022; Hullot et al., 2021) or trees and herbaceous plants (Desjardins et al., 2018) has been investigated, and in some of these studies, plant-associated microorganisms were studied. Plant microbiomes are very diverse and contribute to plant fitness; thus, they deserve attention because they might help plants deal with the often harsh conditions of polluted sites.

Soil contamination, as well as plant-based management, induces changes in fungal and bacterial community structures. It was reported that TEs have several deleterious effects on
microorganisms and play a substantial role as an environmental filter in structuring soil communities (Song et al., 2021). Several authors have used the phytoremediation strategy combined with bacterial or fungal inoculation (Ciadamidaro et al., 2019; Phanthavongsa et al., 2017; Pogrzeba et al., 2017) to enhance plant resistance to soil contamination and improve biomass production. Among the mechanisms involved, microorganisms can enhance plant growth and health by producing phytohormones, cytokinins and gibberellins (Taghavi et al., 2009; Tanimoto, 2005). They can also improve nutrient availability through the production of organic acids and siderophores (Álvarez-López et al., 2020). Furthermore, a large range of woody species are known to select their microbial communities (Zhang et al., 2021) to improve plant resistance to stress (e.g., through production of 1-aminocyclopropane-1-carboxylic acid deaminase), such as drought, insects, diseases, or TEs (Glick, 2014; Meyer et al., 2023, 2023; Taghavi et al., 2009)

Numerous authors have assessed the microbial communities in noncontaminated rhizospheric soils, such as forestry fields (Baldrian, 2016; de Gannes et al., 2016). Most of these studies focused on woody species of biomass interest or noble woods (e.g., oaks, poplars, pines). At contaminated sites, the majority of studies have assessed the rhizospheric fungal (Foulon et al., 2016; Gonzalez et al., 2018) and bacterial communities (Álvarez-López et al., 2020; Durand et al., 2018; Girardot et al., 2020; Zappelini et al., 2018) in monoculture and/or in several species from the same genus/genotype (e.g., Populus, Salix, Betula). However, as contaminants (e.g., TEs) are known to impact microbial communities, it is important to understand the structure of rhizospheric fungi and bacteria. Moreover, the microbial communities from trees growing in several types of soil exhibiting different agronomical and contamination parameters have been poorly studied.

In Leforest, Carrières and Thann, three studied contaminated sites in France, the growth of cultured woody species does not appear to be restricted by the presence of TEs in
the soil (Ciadamidaro et al., 2019). However, at the three sites, the effect of these trace elements on the rhizospheric microbial communities that likely drive plant primary productivity is unknown. It is thus important to determine how site and tree species identity shape the richness and composition of the rhizospheric microbial communities, as it may help in understanding the revegetation processes of contaminated areas. With regard to our previous data (Foulon et al., 2016), we hypothesized that the site characteristics would intensely shape bacterial and fungal communities. In a second hypothesis, we predicted that tree species would be an additional source of variation in microbial communities. We also aimed to determine which of the two factors (soil parameters vs. tree species identity) is the main microbial community driver. To address these hypotheses, the rhizospheric bacterial and fungal communities of twenty-five woody species growing at three phytomanaged sites with contaminants of various origins were assessed and compared using high-throughput amplicon sequencing. In this study, we also address the question of the influence of site physicochemical parameters versus tree selection on microbial communities.

## 2. Materials and Methods

### 2.1. Site description

We conducted our study at three experimental sites in France, namely, Carrières (48°57′39.7″N 2°02′09.6″E), Leforest ((50°25′40.5″N 3°02′40.5″E) and Thann (47°47′48.4″N 7°08′20.8″E) (Ciadamidaro et al., 2019). Carrières and Leforest have agricultural soils with high levels of lead (Pb), cadmium (Cd) and zinc (Zn) due to wastewater and atmospheric deposition, respectively. Thann is an industrial landfill mainly contaminated by manganese (Mn), sulfur (S) and iron (Fe). They were initially planted in autumn 2013 with 38 tree varieties belonging to 14 families (Sapindaceae, Betulaceae, Bignoniaceae, Meliaceae, Moraceae, Elaeagnaceae, Paulowniaceae, Platanaceae, Juglandaceae, Fagaceae,
Anacardiaceae, Salicaceae, Oleaceae, Ulmaceae). Each of the three sites was divided into four plots that were planted with 16 (in Carrières and Leforest) or 18 (in Thann) tree species (31 replicates per tree species in each plot). For the present study, 12 tree species were selected per site based on their initial growth (Table S1). *Pterocarya stenoptera* (PTS, Fagaceae), *Salix aquatica grandis* (SAQ, Salicaceae) and *Ostrya capinifolia* (OCA, Betulaceae) were the common species at all three sites. The species that were planted on one or two sites are ABU (*Acer buergerianum*), ALS (*Alnus subcordata*), ASA (*Acer saccharinum*), CON (Control - unplanted soil), CSP (*Catalpa speciosa*), EUM (*Eleagnus umbellata*), FOR (*Fraxinus ornus*), FOX (*Fraxinus oxyphylla*), HRH (*Hippophae rhamnoides*), MPO (*Maclura pomifera*), OCA (*Ostrya carpinifolia*), PLO (*Platanus orientalis*), POX (*Populus maximowiczii* × *P. berolinensis* (Oxford clone)), PTS (*Pterocarya stenoptera*), QAC (*Quercus acutissima*), QCE (*Quercus cerris*), QPD (*Quercus pedonculata*), QPT (*Quercus petraea*), RCO (*Rhus copallina*), SAC (*Salix acutifolia*), SAL (*Salix alba*), SAQ (*Salix aquatica grandis*), SCA (*Salix caprea*), SFR (*Salix fragilis*), UPA (*Ulmus pumila*) and ZSE (*Zelkova serrata*). The initial sampling campaign (Ciadamidaro et al., 2019) was carried out in 2015, i.e., two growing seasons after plantation establishment. For the present study, the sampling campaign was carried out in early spring 2018, i.e., four growing seasons after planting.

## 2.2. Soil parameters and sampling

For each of the four plots at each site, we randomly selected three trees per species (i.e., 12 replicates/tree species/site) that were uprooted using a mechanical shovel. Directly after uprooting, approximately 2 L of non-rhizospheric soil (i.e. unplanted soil corresponding to the control soil = CON) and rhizospheric soils were collected in sterile plastic bags and kept at 4 °C for further soil analysis of CaCl₂-extractable concentrations of 12 TEs. For community analysis, 16 g of the rhizospheric soil of each tree was sampled as described and
placed in 250 mL plastic tubes with four silica gel bags as described in Taberlet et al. (2012).

In total, 432 rhizospheric soil samples were collected (3 sites x 4 plots/site x 12 species x 3 replicates) and shipped to the Laboratoire d'Écologie Alpine (Grenoble, France) for DNA extraction, PCR amplification and high-throughput amplicon sequencing.

The soil physico-chemical characteristics were analysed in 2013 at the plantation (agronomical characteristics and TE concentrations), and in 2017 at the sampling campaign (TE concentrations). The initial physico-chemical characteristics of the soils were presented in a previous study (Ciadamidaro et al., 2019). The soil samples were dried at 40 °C in a forced air oven to a constant weight and ground with a grinder (agate mortar, Retsch RM100). The samples were sent to a certified laboratory (SADEF, France) for the following analyses: particle size distribution, exchangeable Ca (CaO), Mg (MgO), K (K2O) and Na (Na2O), cation exchange capacity (CEC) (NF X 31-130), organic matter (OM) (NF ISO 14235), total CaCO3 (NF ISO 10693), total nitrogen (Nt) (NF ISO 13878), total phosphorus (Joret-Hebert method) (NF X 31-161) and soluble boron (B) (NF X 31-122). The available element concentrations in the soil were assessed in 2013 and 2017 using a 0.01 M CaCl2-extraction with a sample extraction ratio of 1:10 via ICP-AES. Briefly, in 2013, the soil pH ranged from 5.4 at Leforest to 7.1 at Thann, with Carrières presenting an intermediate pH (pH= 6.8). Other parameters, such as the organic matter (37, 68 and 29 g kg⁻¹ dry weight (DW) in Leforest, Carrières and Thann, respectively), total nitrogen (1.9, 3.8 and 1.3 g kg⁻¹ DW) and organic carbon (21, 39 and 16 g kg⁻¹ DW) concentrations, were higher in Carrières than in the two other sites. The main chemical elements characterizing the soil of Thann were Mn (total Mn concentrations [Mn] tot = 1472 µg g⁻¹), Fe ([Fe] tot = 28267 µg g⁻¹) and S ([S] tot = 85712 µg g⁻¹), while Pb ([Pb] tot = 311 and 195 µg g⁻¹), Zn ([Zn] tot = 685 and 250 µg g⁻¹) and Cd ([Cd] tot = 6 and 3.7 µg g⁻¹) were the main metallic contaminants in Carrières and Leforest at the time of planting.
## 2.3. DNA extraction

Extracellular DNA was extracted from the dried soil following a modified version of the phosphate buffer protocol described by Taberlet et al. (2012). Briefly, each soil sample was combined with 20 ml of saturated phosphate buffer ($Na_2HPO_4$; 0.12 M; pH 8). The resulting mixture was shaken gently (45 rpm) for 15 min and centrifuged at 11,000 g for 10 min. The following DNA extraction steps were carried out using the NucleoSpin Soil Kit (Macherey-Nagel, Düren, Germany) according to the manufacturer's instructions and after skipping the lysis steps. Four hundred microlitres of supernatant was added to 250 µL of SB buffer, loaded onto the extraction column, and washed once with SB buffer, once with SW1 buffer, and twice with SW2 buffer. The final elution was performed with 100 µL of SE buffer.

## 2.4. Molecular methods and sequencing

Microbial diversity was surveyed using the Bact02 (GCCAGCMGCCGCGGTAA and GGACTACCMGGGTATCTAA) and Fung02 (GGAAGTAAAAGTCGTAACAAGG and CAAGAGATCCGTTGYTGAAAGTK) primer pair reported in Taberlet et al. (2018). These markers target the V4 region of the bacterial 16S rDNA and the fungal ITS1 region, respectively. Four independent PCR replicates were carried out for each DNA extract and each marker. To allow bioinformatic discrimination of PCR replicates after sequencing, eight-nucleotide tags were added to the 5' end of both the forwards and reverse primers so that each PCR replicate was represented by a unique combination of forwards and reverse tags. Tags had at least five nucleotide differences among them (Coissac, 2012). Two to four random nucleotides were also added on the 5' end of the tags to ensure sufficient sequence diversity and efficient detection of clusters during sequencing.
PCR amplifications were performed in a 20 µL volume containing 10 µL of AmpliTaq Gold 360 Master Mix (Applied Biosystems, Foster City, CA, USA), each primer at 0.2 µM, 0.16 µL of 20 mg.ml⁻¹ bovine serum albumin (BSA; Roche Diagnostics, Basel, Switzerland), and 2 µL of DNA extract. Thermocycling conditions included an initial DNA polymerase activation step of 10 min at 95 °C, 40 cycles with a 30 s denaturation at 95 °C, a 30 s annealing step at 53 °C for Bact02 and 56 °C for Fung02, and a 60 s elongation step at 72 °C, followed by a final extension at 72 °C for 7 min.

After amplification, PCR products were combined in equal volumes and purified with the MinElute PCR purification kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. Library preparation and sequencing were outsourced to Fasteris SA (Geneva, Switzerland). Four sequencing libraries (two per marker) were prepared according to the PCR-free MetaFast protocol (www.fasteris.com) that limits the formation of chimaeras. All amplicons were sequenced on a HiSeq 2500 sequencing platform (Illumina, San Diego, CA, USA) using the rapid run mode generating 250 bp paired-end sequences.

To monitor potential contaminants and false positives caused by tag jumps (Schnell et al. 2015), the experiment included extraction and PCR negatives, as well as unused tag combinations. Soil extracts from another experiment were also used as positive controls.

## 2.5. Bioinformatic analysis of metabarcoding data

In total, 81,589,286 sequencing clusters were paired-end sequenced. Raw reads were analysed using the OBITools software suite (Boyer et al., 2016), specifically dedicated to handling metabarcoding data. Paired-end reads were first assembled with the program *illumina paired end*, keeping only sequences with an alignment score higher than 40. Aligned sequences were assigned to the corresponding sample using the program *ngsfilter* by allowing two and zero mismatches for primers and tags, respectively. After sequence dereplication
using *obiuniq*, poor-quality sequences (i.e., containing “N”), sequences whose length fell outside the expected size interval (shorter than 40 bp for Bact02 or 66 bp for Fung02) and sequences observed less than ten times in at least one PCR replicate were filtered out. The remaining unique sequences were clustered into molecular operational taxonomic units (MOTUs) as follows. First, the Sumatra algorithm (Mercier et al., 2013) was run to compute pairwise similarities between sequences based on the length of the longest common subsequence (LCS). Then, pairwise similarities were used to cluster the sequences with a similarity threshold of 97% using Sumaclust (Mercier et al., 2013). Abundances of sequences belonging to the same cluster (i.e., MOTU) were summed, and the cluster centre was kept as the MOTU representative of the cluster. The final datasets contained 2,440 MOTUs and 7,130,286 sequences for Bact02 and 8,119 MOTUs and 25,772,129 sequences for Fung02. Finally, the taxonomic assignment of each MOTU was performed using the program *ecotag* of OBITools, which compares the MOTU sequence to a database of full-length reference metabarcodes. The reference database was built for each marker by running the *ecoPCR* program (Ficetola et al., 2010). More specifically, *ecoPCR in-silico* PCR was carried out on the EMBL public database (release 133) with the primer pair used for the experiment and allowing three mismatches per primer. The obtained reference databases were further curated by keeping only sequences assigned at the species, genus and family levels.

## 2.7. Statistical analysis of metabarcoding data

The community composition of all three sites was first analysed separately for bacterial and fungal OTUs. A Bray–Curtis dissimilarity matrix was built for both fungal and bacterial community compositions and graphically represented by principal coordinate analysis (PCoA) (Borcard et al., 2018). Significant differences in community composition were assessed by permutational multivariate analysis of variance (PERMANOVA) between
the three sites (Anderson, 2001). The numbers of OTUs observed in more than one site ("shared OTUs") and those in only one site ("specific OTUs") were represented by Venn diagrams. The same approach (PCoA followed by PERMANOVA and Venn diagram construction) was used for the OTUs observed in the rhizosphere of the three common woody species (PTS, SAQ and OCA).

Richness, abundance and community composition were then separately analysed per site for both bacterial and fungal OTUs. Richness was estimated by the number of different OTUs, whereas abundance was estimated by the number of OTUs per unique OTU or per phylum. Richness was compared between planted woody species per site using the nonparametric Kruskal-Wallis test. When the Kruskal-Wallis test showed a significant difference, multiple comparisons by Fisher's least significant difference with the Bonferroni correction were also carried out. The numbers of shared and specific OTUs between planted woody species were visualized by a graphic composed of a matrix encoding all set intersections and bar plots representing the number of OTUs in each intersection (Lex and Gehlenborg, 2014). Abundance was estimated by the number of OTUs in different phyla. Compositions at the phylum level were graphically represented by PCoA and tested by PERMANOVA.

Redundancy analysis (RDA) was carried out per site to assess the effects of the environmental conditions on the microbial communities (Borcard et al., 2018). Fungal OTU composition was considered the response matrix. A Hellinger transformation was applied to each of the bacterial and fungal community compositions before carrying out RDA. CaCl₂-extractable concentrations of 10 TEs (Al, Cd, Fe, K, Mg, Mn, P, Pb, S, Zn) were logarithmically transformed and applied to RDA as explanatory variables. Significant explanatory variables were determined by forward selection (Borcard et al., 2018). Each of all variables was added to the null model and tested its significance by permutation. When there
were several significant variables, the most significant one was added. Then, another selection
was carried out to check the significance of the remaining variables. This process was
repeated until there was no significant variable. The proportion of variance explained by the
selected explanatory variables was indicated by an adjusted R² (Borcard et al., 2018).

All statistical analyses were performed using R software (version 4.1.0; R
Development Core Team). The Kruskal-Wallis tests and multiple comparisons were
performed by the “kruskal” function in the “agricolae” package. Set intersections were
visualized by the “upset” function in the “UpSetR” package. PERMANOVA and tests for
multivariate homogeneity were carried out by the “adonis” and the “betadisper” functions in
the “vegan” package, respectively. The “ordiplot” and “ordiellipse” functions in the “vegan”
package were used to carry out PCoA and draw confidence ellipses, respectively. RDA and
forward selection were carried out by the “rda” and “ordistep” functions in the “vegan”
package.

**3. Results and discussion**

3.1. *Site is the main factor affecting the composition of rhizospheric bacterial and fungal communities*

The soil properties of the three study sites (Carrières, Leforest and Thann) were
significantly different at the planting time (Fig. 1), as shown in Ciadamidaro et al. (2019).
Here, we further analysed the TE bioavailable fractions, as well as pH (Table S2). In general,
the extractable TE concentrations in rhizospheric soils were lower than the total TE
concentrations. Additionally, the bioavailable TE concentrations were slightly higher than the
extractable TE concentrations in the bulk soil measured in a previous experiment conducted at
the same sites in 2015 (Ciadamidaro et al. 2019).
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Soil parameters and microbial features of the three sites. (a) Principal component analysis (PCA) of agronomic parameters measured at the Carrières, Leforest and Thann sites at the plantation (in 2013). All parameters were standardized before PCA because of their different units. Principal coordinate analysis (PCoA) of the bacterial (b) and fungal (c) communities from rhizospheric and non-rhizospheric soils sampled at the three sites, Leforest (LEF), Carrières (CAR) and Thann (THA) in 2017. The numbers of specific and shared</figcaption></figure>

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bacterial and fungal OTUs are shown in Venn diagrams. Abbreviations: CEC (cation exchange capacity); OM organic matter); Tot (total); Ass_phos (assimilable phosphorus). Squares: bulk soils; circles: rhizospheric soils.

At the three study sites, the CaCl₂ extractable concentrations of Cd, Pb, Zn and Mn in the rhizospheric soils were compared between the 12 tree species and the control soil. No significant difference was observed for Cd, Pb and Mn, while Zn showed few differences. Significantly higher concentrations of Zn were measured for the three *Quercus* species at Carrières, but the control soil showed a significantly lower Zn concentration (p value < 0.05) in comparison to all the rhizospheric soils. In general, the pH values at the three sites slightly decreased from those measured in our previous experiment in 2015: from 5.4 to 5.0 at Leforest and from 6.8 and 7.1 to 6.7 at Carrières and Thann, respectively. Given the ability of certain woody species to accumulate TE in their aerial tissues, changes in total TE concentrations may have occurred between 2013 and 2017, but these were not measured, given the large number of samples. A new sampling campaign is planned for 2024, which will provide a more detailed answer to this very pertinent question.

The bacterial (p value < 0.01, Fig. 1b) and fungal (p value < 0.01, Fig. 1c) communities significantly differed between the three study sites. For bacterial communities, the Venn diagram shows that the Leforest and Carrières communities shared more OTUs with each other than with the Thann community, which had more specific OTUs (563 specific OTUs compared with 41 and 107 OTUs at Carrières and Leforest, respectively). For fungal communities, twice as many specific OTUs (1508, 1531 and 1993 OTUs in Carrières, Leforest and Thann, respectively) were found at each site compared with their shared OTUs (714 OTUs).
The differences in soil structure and physico-chemical composition may explain the
difference in microbial community composition. Several studies have reported that soil
chemistry strongly influences microbial communities (Van Horn et al., 2013; Urbanová et al.,
2015; Pinot, 2016; Xue et al., 2018). For instance, Dequiedt et al. (2009) showed a highly
significant correlation between microbial communities and soil physico-chemical parameters
such as soil texture, pH, CEC, Corg, N, CaCO3, P, Ca, Mg and K. It is also known that one of
the most prominent factors affecting the microbial community in the soil is pH (Foulon et al.,
2016; Rousk et al., 2009), which controls numerous biotic factors, such as soil bacterial
biomass and composition (Fierer and Jackson, 2006a). However, pH seemed to be less
important for soil fungi (Lauber et al., 2009; Rousk et al., 2009; Urbanová et al., 2015). The
impact of tree species is further discussed in Section 3.2 below.

Figure 2 represents the number of fungal (Fig. 2 a, b and c) and bacterial (Fig. 2 d, e and f) OTUs that were specific to one soil sample (red bars) or shared (green bars) by all soil samples at the three sites. At the Leforest site, 48% of the total fungal OTUs (1399/2910) recorded were specific to one soil sample, i.e., to one tree species (Fig. 2a), whereas 52% (1511/2910) of the OTUs were shared by at least two tree species (not shown). In detail, *Salix fragilis* (SFR) had the fewest specific OTUs (39/2910 = 1.3%), while *Platanus orientalis* (PLO) presented the most specific OTUs (7.4%). The number of fungal OTUs shared by all the tree species accounted for 5.4% (156/2910), whereas the control soil (unplanted soil) presented 7.9% of the total OTUs in the whole Leforest soil (229/2910). Similarly, at the Carrières site, 46.4% of the total fungal OTUs (1465/3158) were specific to a single tree species sample (Fig. 2b), whereas 53.6% (1693/3158) of the OTUs were shared by at least two tree species (not shown). The *Quercus*, *Ostrya*, *Salix* and *Poplar* species showed the fewest specific OTUs. A total of 4.8% of the total OTUs (152/3158) were shared by all tree species. Finally, 38% of the total fungal OTUs (1471/3872) recorded at the Thann site were
specific to one soil sample (Fig. 2c), whereas 62% (2401/3872) of the OTUs were shared by at least two tree species (not shown). As at the Leforest and Carrières sites, the same tree species (*Salix*, *Ostrya*, *Quercus*) accounted for the fewest specific OTUs, whereas 5.4% of OTUs were shared by all the tree species. These results are in agreement with those of Buée et al. (2011), who demonstrated that more than one-third of the dominant fungal species were restricted to one or two tree species.

For the bacterial communities, 33% of the OTUs were shared by all tree species at the Leforest site, whereas 12.2% of bacterial OTUs were specific, with a maximum of 1.6% for *Pterocarya stenoptera* (PTS) and a minimum of 0.3% for *Salix caprea* (SCA), *Ostrya carpinifolia* (OCA) and *Quercus cerris* (QCE; Fig. 2d). The same trends were observed at the Carrières site (maximum 2.5% for Populus “Oxford” (POX) and minimum 0.5% for *Acer saccharinum* (ASA), Fig. 2e) and the Thann site (maximum 2.1% for *Salix alba* (SAL) and minimum 0.2% for OCA and PLO, Fig. 2f). The results obtained for bacterial communities show a different pattern in comparison to those of the fungal communities, especially because of the high number of bacterial OTUs that are shared by all the tree species and the low number of specific OTUs.
<figure><img src="image_4.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(e)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(f)</figcaption></figure>

<figure><img src="image_9.png" /></figure>
**Figure 2.** Shared and specific fungal (a, b and c) and bacterial (d, e and f) OTUs recorded from the rhizosphere of different tree species at the Leforest (a and d), Carrières (b and e) and Thann (c and f) sites. The horizontal blue bars correspond to the number of OTUs recorded for each tree species. The green bars indicate the number of OTUs common to all tree species designated with green dots (e.g., in Fig. 2 (a) there are 156 OTUs that are shared by all the tree species). The red bars represent the number of OTUs specific to a tree species designated by the red dots (e.g., in Fig. 2 (a) there are 215 OTUs specific to the PLO rhizospheric soil). The OTUs shared by more than 2 sampled trees (and less than 12) are not shown for clarity.

## 3.2. Tree species and soil characteristics have different impacts on bacterial and fungal communities

At each of the three sites, the Proteobacteria phylum was dominant, accounting for more than 25% of the total abundance (Fig. 3). The Bacteroidetes and Chloroflexi phyla further dominated at the Leforest site (Fig. 3a), whereas Actinobacteria dominated at the Carrières (Fig. 3b) and Thann (Fig. 3c) sites. The obtained results allow us to extend our knowledge for several tree species that have not yet been reported. For tree species that have already been studied (e.g., Populus, Salix), the present results are in agreement with those obtained in several other studies. Fierer (2017) reported that Bacteroidetes, Proteobacteria, and Actinobacteria are usually dominant in soils. Álvarez-López et al. (2020) found a predominance of Actinobacteria and Proteobacteria in the *Betula pendula* rhizosphere at the Thann site. These two phyla also dominated under poplar trees at the Pierrelaye site (Ile-de-France, France), of which the contamination features were similar to those of the Carrières site (Foulon et al., 2016). The Proteobacteria and Actinobacteria phyla were also predominant in ultramafic rhizosphere soils (Lopez et al., 2022), in an Indian Mn-contaminated mine
tailing (Ghosh and Das, 2018) and in a Cu-polluted mining soil in China (Jia et al., 2022).

Actinobacteria exhibit diverse physiological and metabolic features, such as the production of extracellular enzymes and the formation of a wide variety of secondary metabolites, which makes them relevant microbial resources for bioremediation (Narsing Rao et al., 2022).

Proteobacteria isolated from a highly polluted coal mine drainage showed capacities for toxic metal sequestration that render them useful for bioremediation (Oyetibo et al., 2021).

Additionally, the PCoA of the composition of the bacterial communities highlights significant differences between communities inhabiting the rhizospheric soil of planted trees compared to unplanted controls at the Leforest (Fig. 3d), Carrières (Fig. 3e) and Thann (Fig. 3f) sites. Tree species greatly affect the composition, diversity and activity of soil bacterial communities (Dukunde et al., 2019; Pfeiffer et al., 2013). However, bacterial richness did not differ between planted and unplanted soils in the present study (Table 1).

Regarding the richness (Table 1) and abundance (data not shown) of bacterial OTUs, our analyses revealed no significant difference between the different tree species, regardless of the site. Similar trends were observed for the bacterial communities of various poplar cultivars grown on contaminated soils, with little variation in bacterial richness and abundance (Foulon et al., 2016). No difference in richness was found between the planted and unplanted soils at our sites. Foulon et al. (2016) also found that bacterial species richness and diversity in soil habitats were not different between planted and unplanted plots in a poplar plantation in Pierrelaye (France). The lack of a significant relationship between plant diversity and soil bacterial diversity was observed by (Fierer and Jackson, 2006b) in a field study.

Previous studies on grassland and agricultural sites (Buckley and Schmidt, 2001; Felske et al., 2000; Kowalchuk et al., 2002) also yielded similar conclusions. Different tree species ( *Acer pseudoplatanus*, *Fagus sylvatica*, *Fraxinus excelsior*, *Carpinus betulus*, *Picea abies* and *Tilia cordata*) also hosted similar bacterial communities (Pfeiffer et al., 2013; Uroz et al., 2016).
<figure><img src="image_10.png" /><figcaption>**Figure 3.** Composition of the bacterial communities at the Leforest (a), Carrières (b) and Thann (c) sites at the phylum level based on 16S rRNA gene sequencing. Bacterial communities were further analysed using PCoA at Leforest (d), Carrières (e) and Thann (f).</figcaption></figure>

<table><caption>**Table 1.** Bacterial and fungal richness at the three sites Leforest, Carrières and Thann for all 25 planted woody species (n=12). Significant differences are indicated in brackets. “CON” corresponds to the control (unplanted plot).</caption><thead><tr><th></th><th colspan="2">Carrières</th><th colspan="2">Leforest</th><th colspan="2">Thann</th></tr><tr><th></th><th>Bacteria<br>926<br>OTUs</th><th>Fungi<br>3158<br>OTUs</th><th>Bacteria<br>984 OTUs</th><th>Fungi<br>2910<br>OTUs</th><th>Bacteria<br>1657 OTUs</th><th>Fungi<br>3872 OTUs</th></tr></thead><tbody><tr><th>CON</th><td>235 (n.s)</td><td>226 (a)</td><td>CON</td><td>239 (n.s)</td><td>CON</td><td>267 (n.s)</td></tr></tbody></table>
<table><tr><td>ASA</td><td>251 (n.s)</td><td>239 (a)</td><td>PLO</td><td>225 (n.s)</td><td>191 (ab)</td><td>ABU</td><td>297 (n.s)</td><td>302 (a)</td></tr><tr><td>EU<br>M</td><td>245 (n.s)</td><td>227 (a)</td><td>PTS</td><td>241 (n.s)</td><td>188 (b)</td><td>MPO</td><td>271 (n.s)</td><td>292 (a)</td></tr><tr><td>FO<br>X</td><td>247 (n.s)</td><td>229 (a)</td><td>OCA</td><td>217 (n.s)</td><td>165 (bc)</td><td>PLO</td><td>292 (n.s)</td><td>322 (a)</td></tr><tr><td>HR<br>H</td><td>262 (n.s)</td><td>243 (a)</td><td>SAC</td><td>220 (n.s)</td><td>165 (bcd)</td><td>PTS</td><td>278 (n.s)</td><td>269 (a)</td></tr><tr><td>FO<br>R</td><td>250 (n.s)</td><td>223 (ab)</td><td>SCA</td><td>234 (n.s)</td><td>162<br>(bcde)</td><td>UPA</td><td>284 (n.s)</td><td>284 (a)</td></tr><tr><td>PTS</td><td>252 (n.s)</td><td>185 (bc)</td><td>SAL</td><td>236 (n.s)</td><td>155 (cdef)</td><td>ZSE</td><td>272 (n.s)</td><td>297 (a)</td></tr><tr><td>SAQ</td><td>246 (n.s)</td><td>166 (cd)</td><td>SAQ</td><td>244 (n.s)</td><td>142<br>(cdefg)</td><td>RCO</td><td>280 (n.s)</td><td>317 (a)</td></tr><tr><td>OC<br>A</td><td>258 (n.s)</td><td>135 (de)</td><td>SFR</td><td>236 (n.s)</td><td>143<br>(cdefg)</td><td>CSP</td><td>285 (n.s)</td><td>274 (a)</td></tr><tr><td>PO<br>X</td><td>256 (n.s)</td><td>147 (de)</td><td>QPD</td><td>235 (n.s)</td><td>136<br>(defg)</td><td>OCA</td><td>287 (n.s)</td><td>170 (b)</td></tr><tr><td>QPT</td><td>244 (n.s)</td><td>152 (de)</td><td>POX</td><td>232 (n.s)</td><td>135 (efg)</td><td>QAC</td><td>263 (n.s)</td><td>144 (b)</td></tr><tr><td>QC<br>E</td><td>248 (n.s)</td><td>126 (e)</td><td>QCE</td><td>231 (n.s)</td><td>128 (fg)</td><td>SAL</td><td>273 (n.s)</td><td>130 (b)</td></tr><tr><td>QP<br>D</td><td>249 (n.s)</td><td>120 (e)</td><td>ASL</td><td>229 (n.s)</td><td>117 (g)</td><td>SAQ</td><td>263 (n.s)</td><td>196 (b)</td></tr></table>

In contrast to bacterial communities, fungal communities showed contrasting patterns among sites and tree species. Ascomycota, Basidiomycota and Mucoromycota sequences dominated at the Leforest (Fig. 4a), Carrières (Fig. 4b) and Thann (Fig. 4c) sites. The
proportion of Basidiomycota sequences (≥ 50%) was significantly higher in tree species of the Fagaceae, Salicaceae and Betulaceae (referred to as 'tree species Group 1'), together with lower proportions of Ascomycota (< 35%) and Mucoromycota (< 15%) sequences, than in tree species from the Sapindaceae, Bignoniaceae, Meliaceae, Moraceae, Elaeagnaceae, Paulowniaceae, Platanaceae, Juglandaceae, Anacardiaceae, Oleaceae, and Ulmaceae families (referred to as 'tree species Group 2'). Soils collected under the latter group of tree species, as well as the control soils, showed higher proportions of Ascomycota (≥ 50%) and Mucoromycota (> 20%), contrasting with a much lower abundance of Basidiomycota sequences (<20%), than those collected under Group 1. Mucoromycota sequences from the Thann site even reached 45% of the sequenced dataset for some tree species of these Group 2 families (e.g., UPA). Regarding the fungal richness index (Table 1), the woody species could also be clustered into the two groups described above. Group 1 exhibited the lowest fungal richness, while the highest fungal richness occurred in Group 2. The low fungal richness observed for Group 1 may be explained by the tree species-specific nature of ectomycorrhizal fungi (ECMFs), to which the majority of Basidiomycota fungi belong. In fact, Urbanová et al. (2015) reported the complex nature of plant-fungal symbiosis of ECMFs, which explained the stronger effects of trees on the fungal community than on the bacterial community.
<figure><img src="image_11.png" /><figcaption>**Figure 4.** Composition of the fungal communities at the Leforest (a), Carrières (b) and Thann (c) sites at the phylum level based on ITS rRNA gene sequencing. Fungal communities were further analysed using PCoA at Leforest (d), Carrières (e) and Thann (f).</figcaption></figure>

The PCoA revealed that tree species might be classified into the two groups described above based on their fungal communities. For instance, at the Leforest site, the Betulaceae and Salicaceae families (Group 1) did not show significantly different fungal communities, which were similar to the communities under the Fagaceae family (Group 1) (Fig. 4d). The unplanted soil, PTS and PLO species (Group 2) exhibited similar communities. Finally, specific fungal communities for the ALS species were recorded at the Leforest site. Clear clusters of tree species (Group 1 versus Group 2) were also noticed for the Carrières (Fig. 4e) and Thann (Fig. 4f) sites. In agreement with the present results, Lindhe et al. (2004) showed that the highest similarities for fungal communities were found between a *Betula* sp. and a *Populus* sp., followed by a *Quercus* sp., compared to spruce. It has been hypothesized that the
richness and diversity of natural fungal communities are affected by tree species (Buée et al., 2011; Tedersoo et al., 2015).

### 3.3. *Trees from the two tree species groups host distinct fungal communities*

The previous datasets described above highlighted the occurrence of two tree family groups, namely, Group 1 (Basidiomycota-enriched samples from the Fagaceae, the Salicaceae and the Betulaceae) and Group 2 (Ascomycota-enriched samples from all the other tree families). The ecological functions of the fungal species in these two groups were further assessed using FunGuild (Nguyen et al., 2016), and the abundance of DNA reads was calculated and expressed as a percentage of the total reads. Our analysis indicated that 74.9%, 72.9% and 49.2% of the specific fungal reads of Group 1 tree families were assigned to the ECMF guild for the Leforest, Carrières and Thann samples, respectively (Fig. 5, Fig. S1 and Fig. S2). In a previous study at a set of post-mining sites recultivated with six different trees, 80% of the fungal sequences were composed of only 23 to 28 OTUs, which led to the conclusion that the fungal taxa showed a greater tendency to be tree-specific (Urbanová et al., 2015). It can be hypothesized that tree species with few specific OTUs are selecting their symbionts in a more restrictive way and thus exhibit lower microbial richness.
<figure><img src="image_12.png" /><figcaption>**Figure 5.** Distribution of shared and specific OTUs for the two groups of trees, as well as the main fungal guilds on the Leforest site. The common OTUs, the specific OTUs (Group 1) and the specific OTUs (Group 2) represented 1,116 OTUs (38.4 %), 1,161 OTUs (39.9 %) and 631 OTUs (21.7 %), respectively.</figcaption></figure>

The dominant ECMFs associated with Group 1 tree species in the present work (Tuber, Tomentella, Hymenogaster, Geopora, and Hebeloma) were also identified in Salix roots in a previous study carried out at a TE- and PCB-contaminated site (Fortin Faubert et al., 2022) and in some Quercus species in other studies in tropical forests and in the Taihang Mountains (China) (Morris et al., 2009; Wei et al., 2021). Members of the Hebeloma mycorrhizal genus (e.g., H. mesophaeum) were frequently detected within forest soils, and their presence was correlated with a high growth rate of host trees in soils contaminated with metals (Durand et al., 2017; Foulon et al., 2016). Moreover, a peculiar trend was noticed for
*Alnus subcordata* planted at the Leforest site, of which 76% of dominant OTUs belonged to the *Tomentella* and *Alnicola* genera. *Alnus* species are known to contract specific symbiotic relationships with a few fungal genera (Schwob et al., 2017).

In contrast, only 0.32%, 0.21% and 4.8% of reads of Group 2 tree families were related to the ECMF guild at Leforest, Carrières and Thann, respectively, while saprotrophs and unidentified OTUs were dominant at these sites. The OTUs shared between the two groups that were identified in Leforest, Carrières and Thann samples were distributed within various fungal guilds (e.g., orchid mycorrhizal, plant pathogen, wood saprotroph, ectomycorrhizal, fungal parasite, soil saprotroph, undefined saprotroph). However, none of these guilds were dominant in terms of read abundance.

### 3.4. Effect of environmental conditions on the microbial communities

At the Leforest site, no TE significantly explained fungal community composition. However, the first two principal axes of the RDA represented 11.0% and 6.5% of the fungal community variation, respectively (Fig. S3a). A group of fungi associated with Group 1 tree species (SAC, SAL, SAQ, SCA, SFR, QCE, QPD, OCA), mainly composed of *Hymenogaster griseus* and *H. vulgaris* (ectomycorrhiza), was positively correlated with soil Zn, Cd, Pb, Fe and Al, and to a lesser extent, with Mn and K. Moreover, three OTUs belonging to the *Tomentella* and *Alnicola* genera, which were associated with ASL, were positively correlated with Mn. As already discussed above, *Tomentella* and *Alnicola* represented more than two-thirds of the *Alnus* community in terms of abundance. *Alnus* species are known to establish symbiotic relations with these ectomycorrhizal fungi. For instance, (Tedersoo et al., 2009) demonstrated that *Tomentella*, *Alnicola* spp. and the *Lactarius cyathuliformis* groups were among the most frequent taxa in *Alnus* samples collected from various forests. *Alnus* is the only member of the Betulaceae family that has evolved root symbiotic relationships with
nitrogen-fixing actinobacteria from the genus *Frankia* (Benson & Clawson, 2000; Pujic et al., 2022) that were recorded in the present study. Nitrogen fixation requires abundant sources of carbon and phosphorus, and mycorrhizal fungi provide mineral nutrition to plants and are a supplier of phosphorus for both leguminous and actinorhizal plants (Benson & Clawson, 2000; Tedersoo et al., 2009).

At the Carrières site (Fig. S3b), the first two principal RDA axes represent 24.1 % and 8.0 % of the variation of fungal communities. The results showed that the communities are significantly explained by the presence of Zn (adjusted R²= 0.15). Thus, fungal communities associated with certain tree species of Group 1, such as QPD, QCE, and QPT, were correlated with Cd and Zn. Fungi associated with OCA were correlated with Al and Fe, and fungi associated with SAQ are correlated with Pb. In contrast, the communities associated with Group 2 tree species (PTS, FOR, FOX, HRH, ASA and EUM) were correlated with soil Mg and K contents. A large proportion of these OTUs belongs to Ascomycota-rich group. For example, the OTU 4 corresponding to *Hebeloma mesophaeum* is a Zn-accumulating ectomycorrhizal (ECM) species (*Sácký et al.*, 2014) and is frequently associated with metal-disturbed sites (Foulon et al., 2016; Lacercat-Didier et al., 2016). The association of roots with metal-resistant ECM fungi would enhance plant fitness in metal-contaminated environments by improving plant nutrition, reducing the transfer of TEs to the plant and stimulating the response of trees to abiotic stress (de Moura et al., 2022; Dowarah et al., 2022).

Similarly to Leforest site, no TE significantly explained the fungal communities at Thann site. The first two principal axes of RDA represent 20.3 % and 3.6 % of the variation of fungal communities, respectively (Fig. S3c). The fungal OTUs associated with Group 1 tree species (SAQ, SAL, OCA and QAC) corresponded to ectomycorrhizal fungi and were positively correlated with K and Mg. The same fungal OTUs, composed by various fungal
guilds, were shared by the tree species of the Group 2 and were positively correlated with the S content in the soil.

### 3.5. Microbial communities of OCA, PTS and SAQ change as a function of the study site

As described in the Materials and Methods section, OCA, PTS and SAQ were planted at the three sites, which allowed us to assess the impact of site on microbial communities. The results indicated that the three tree species exhibited different bacterial communities, depending on the site (Fig. 6, Fig. S4 and Fig. S5). The Venn diagram indicates that 262 OTUs (22.9%) were common at Leforest, Carrieres and Thann for SAQ. For OCA, 291 OTUs (25.8%) were recorded, and 293 OTUs (24.2%) were recorded for PTS. Similarly, the dataset for SAQ (Fig. 6), PTS (Fig. S4) and OCA (Fig. S5) shows that the fungal communities were specific to the site where the trees were grown. Only 6% of the fungal OTUs were shared by all three sites (106 OTUs for SAQ, 115 OTUs for PTS and 144 OTUs for OCA).

The ten most abundant bacterial (Table S3) and fungal (Table S4) OTUs of the three common tree species OCA, PTS and SAQ and the unplanted soil (CON) were compared at the three study sites. For OCA, only one fungal OTU identified at the species level (*Tuber maculatum*) was present at the three sites. For SAQ, PTS and control soils, no fungal OTUs were shared among the three sites (Table S4). For bacterial communities, one OTU was recorded for the unplanted soil, PTS and OCA (Sphingomonadaceae for the unplanted soil and *Gemmatimonas* sp. for PTS and OCA), while SAQ did not share any OTUs (Table S3).
<figure><img src="image_13.png" /><figcaption>**Figure 6.** PCoA comparing the bacterial (a) and fungal (b) OTU communities inhabiting the SAQ rhizosphere at the three study sites. The numbers of specific and common OTUs are shown by Venn diagrams.</figcaption></figure>

**5. Conclusions**

This study aimed to compare the rhizospheric microbiomes of 25 woody species that were planted at an industrial landfill (Thann), an agricultural soil contaminated by wastewater (Carrières) and an agricultural soil contaminated by atmospheric deposition (Leforest) in France. Despite the strong anthropization of the study sites, especially at Thann (*industrial landfill-technosoil*), microbial colonization of tree roots after four years of phytomanagement occurred, but strong variations were observed between the sites and tree species. The bacterial communities associated with plants differed from those in the unplanted soil, but no difference was observed between the different woody species families. In contrast, the data
obtained from the sequencing of fungal DNA showed significant differences between groups of trees, highlighting the selection of fungal communities by some tree species. Indeed, the Betulaceae, Salicaceae and Fagaceae families had the highest proportion of Basidiomycota, especially ECMFs, and the lowest diversity and richness. The other tree families, as well as the unplanted soils, exhibited larger Ascomycota and Mucoromycota communities. Overall, site (and hence soil) and tree species were the main drivers of the microbial communities. The potential for root colonization by microbes was high and led to successful revegetation, improving our phytomanagement strategy. Further studies on the functionality of microbial communities would provide a complete view of these plant-soil-microorganism systems.

**Funding:**

This work was supported by the French National Research Agency [PHYTOCHEM ANR-13-CDII-0005-01], the French Environment and Energy Management Agency [PROLIPHYT ADEME-1172C0053], the Région Franche-Comté [Environnement-Homme-Territoire 2014-069] and the Pays de Montbéliard Agglomération [13/070-203-2015]. C.Z. received a PhD grant from the French Ministry of Higher Education and Research.

**Acknowledgments**

We acknowledge Dr. Nadia Morin-Crini and Caroline Amiot of the PEAT2 platform for ICP analyses. We are also indebted to the Pépinière SOUPE for their precious help in planting and harvesting the trees.

**Conflicts of Interest:** The authors declare no conflict of interest.
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Graphical abstract

Assessment of fungal and bacterial communities
on 468 soil samples

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

Three sites, three distinguished
bacterial and fungal communities

<figure><img src="image_16.png" /></figure>

Microbial communities is driven by soil
parameters and tree species

<figure><img src="image_17.png" /></figure>