# Assembly processes of bacterial and fungal communities in metal(loid)s smelter soil

Miaomiao Li, Jun Yao, Geoffrey Sunahara, Robert Duran, Bang Liu, Ying Cao, Hao Li, Wancheng Pang, Houquan Liu, Shun Jiang, et al.

► To cite this version:

Miaomiao Li, Jun Yao, Geoffrey Sunahara, Robert Duran, Bang Liu, et al. Assembly processes of bacterial and fungal communities in metal(loid)s smelter soil. Journal of Hazardous Materials, 2023, 451, pp.131153. 10.1016/j.jhazmat.2023.131153. hal-04036925

HAL Id: hal-04036925

https://univ-pau.hal.science/hal-04036925

Submitted on 20 Mar 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Assembly processes of bacterial and fungal communities in metal(loid)s
smelter soil

Miaomiao Li<sup>a</sup>, Jun Yao<sup>a*</sup>, Geoffrey Sunahara<sup>a,b</sup>, Robert Duran<sup>c</sup>, Bang Liu<sup>a</sup>, Ying Cao<sup>a</sup>, Hao Li<sup>a</sup>, Wancheng Pang<sup>a</sup>, Houquan Liu<sup>a</sup>, Shun Jiang<sup>a</sup>, Junjie Zhu<sup>a</sup>, Qinghua Zhang<sup>d</sup>

<sup>a</sup> Science and Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing 100083, China

<sup>b</sup> Department of Natural Resource Sciences, McGill University, 21111 Lakeshore Drive, Ste-Anne-de-Bellevue, Quebec, H9X 3V9, Canada

<sup>c</sup> Universite de Pau et des Pays de l'Adour, UPPA/E2S, IPREM CNRS 5254, Pau, France

<sup>d</sup> College of Bioscience and Engineering, Jiangxi Agricultural University, Nanchang 330045, PR China

* Corresponding author:

Jun Yao (E-mail: yaojun@cugb.edu.cn)

School of Water Resource and Environment, Research Center of Environmental Science and Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, 100083, Beijing, China
The assembly processes of microbial communities governing the metal(loid)s biogeochemical cycles at smelters are yet to be solved. Here, a systematic investigation combines geochemical characterization, co-occurrence patterns, and assembly mechanisms of bacterial and fungal communities inhabiting soil around an abandoned arsenic smelter.

*Acidobacteriota*, *Actinobacteriota*, *Chloroflexi*, and *Pseudomonadota* were dominant in the bacterial communities, whereas *Ascomycota* and *Basidiomycota* dominated the fungal communities. The random forest model indicated the bioavailable fractions of Fe were the main positive factor driving the beta diversity of bacterial communities (9.58%) and the negative impact of total N on fungal communities (8.09%). The microbial-contaminant interactions demonstrate the positive impact of the bioavailable fractions of certain metal(loid)s on bacteria (*Comamonadaceae* and *Rhodocyclaceae*) and fungi (*Meruliaceae* and *Pleosporaceae*). The fungal co-occurrence networks exhibited higher connectivity and complexity than the bacterial networks. Interestingly, positive links dominated all networks.

The keystone taxa were identified in bacterial (including the family *Diplorickettsiaceae*, *norank_o_Candidatus_Woesebacteria*, *norank_o_norank_c_AT-s3-28*, *norank_o_norank_c_bacteriap25*, and *Phycisphaeraceae*) and fungal (including *Biatriosporaceae*, *Ganodermataceae*, *Peniophoraceae*, *Phaeosphaeriaceae*, *Polyporaceae*, *Teichosporaceae*, *Trichomeriaceae*, *Wrightoporiaceae*, and *Xylariaceae*) communities.

Meanwhile, community assembly analysis revealed that deterministic processes (especially variable selection) dominated the microbial community assemblies, which were highly impacted by the pH, total N, and total and bioavailable metal(loid) content. This study
provides a deeper insight into the organization of microbial communities in response to
long-term metal(loid)s pollution and helpful information to develop bioremediation strategies
for the mitigation of metal(loid)s-polluted soils.

47

48 **Keywords:** Metal(loid)s, Bacterial and fungal community, Co-occurrence pattern, Assembly mechanism

50
51

# 1. Introduction

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

More than 10 million sites present metal(loid)s polluted soils worldwide, being a major concern for society and the scientific community (Fei et al., 2019; Zhang et al., 2020; Xu et al., 2021a). Metal smelting activity is considered one of the main anthropogenic sources of metal(loid)s pollution in soil (Yun et al., 2018). Smelting operations generate large amounts of hazardous solid waste (e.g., smelting slag, combustion residues, and mine tailings) that are deposited near the smelters (Xu et al., 2021a; Liu et al., 2022a). Thus, the dispersion of smelter waste can affect the stability of surrounding ecosystems causing important ecological disturbances (Zhang et al., 2022b). The presence of metal(loid)s can decrease the soil microbial biomass, as well as disrupt the composition and function of soil microbial communities (Park et al., 2021). To date, most studies have investigated the ecological processes of microbial communities under metal(loid)s pressure, usually focusing on only one microbial domain (Liu et al., 2021; Park et al., 2021; Liu et al., 2022a; Zhang et al., 2022c). In addition to bacteria, fungi can play vital roles in soil, including biogeochemical cycling of metal(loid) elements, decomposition of organic matter, and maintenance of ecosystem stability and productivity (Wang et al., 2022b; Zhang et al., 2022c). However, the simultaneous exploration of the responses of bacterial and fungal communities to long-term metal(loid)s contamination in smelting environments has been little studied.

Co-occurrence network analysis has proved to be an efficient tool to explore the responses of microbial communities to environmental stress (Price et al., 2021). Also, the investigation of microbial interactions is critical to understand the complexity and diversity of microbial communities (Zhou et al., 2020). The co-occurrence, competition, and antagonism
between different microbial compartments can be examined using network analyses based on
molecular data (Zhang et al., 2022c). Furthermore, the network topological features can
identify the keystone taxa. Although extensive studies have explored the complex microbial
interactions in metal(loid)s contaminated environments (Sun et al., 2020; Chun et al., 2021;
Liu et al., 2022c), only a few studies have investigated the differences between bacterial and
fungal communities from the perspective of co-occurrence patterns under metal(loid)s
contamination. Investigating the relative importance of assembly-regulating processes in
microbial communities under metal(loid)s exposure can help reveal the adaptive responses of
microbial communities to environmental stress (Zhang et al., 2022b), such as bacterial
communities living in mining and smelting sites (Liu et al., 2022a). However, there remains a
knowledge gap in the microbial community assembly processes, particularly when comparing
the ecological assembly process of bacterial and fungal communities under diverse
metal(loid)s exposure. Therefore, a holistic approach investigating all the components of the
microbial community (prokaryotes and eukaryotes) under long-term metal(loid)s
contamination is required for a better understanding of the assembly rules involved in the
microbial communities in response to metal(loid)s exposure.

We suspected that bacterial and fungal communities will have similar adaptive responses
to long-term metal(loid)s exposure. In the present study, soil samples were collected from the
Huilong nonferrous metal smelter, which produced mainly arsenic and arsenic trioxide, and
was closed for the past 20 years. Studies were conducted to reveal the differences in adaptive
responses between bacterial and fungal communities under metal(loid)s exposure. The
objectives of this study were to (1) investigate the effects of selected geochemical properties
95

on bacterial and fungal communities; (2) characterize the co-occurrence patterns of bacterial

96

and fungal communities, and (3) determine the ecological assembly processes of bacterial

97

and fungal under metal(loid)s exposure.

98

## 2. Materials and methods

99

### 2.1 Study site description

100

The study area (111°16′48.64″E, 24°27′48.41″N) has a total area of about 16,617 m² and is located at Zhongshan City in Guangxi Province, southwestern China (Fig. 1). The study region was divided into different areas, including site XY (belonging to the original roasting reverberatory furnace production workshop, which was later used by another factory), site YBS (the main production workshop, which was since demolished and leveled to the ground; its topography was changed greatly), and site ZD (consisting mainly of large piles of arsenic-containing waste slag and soot slag from the production plant). The surrounding areas proximal to the Huilong factory were each identified numerically at site Y. The climate in this region is subtropical monsoon with distinct wet and dry seasons. The mean annual precipitation is 1576.7 mm, with 47% precipitation occurring between April and June. The mean annual air temperature is 19.7°C, with 39.8°C maximal (in 2003 and 2007) and -3.7°C minimal (in 1969) temperatures.

101

102

103

104

105

106

107

108

109

110

111

112

### 2.2 Soil sampling

113

A total of 18 topsoil samples (10-20 cm) were collected in October 2020 using a sterilized shovel. Three soil samples were taken from the Y site, and five soil samples were taken from XY, YBS, and ZD sites (Fig. 1). Each sample was a composite of five sub-samples (each approximately 500 g from the four corners and the center point of a 4 m ×

114

115

116
4 m quadrat). Large-grain gravel and plant residues were removed during the sampling process. Then these samples were stored in separate polyethylene bags placed on ice and transported immediately to the laboratory. After a preliminary treatment (air-dried and pass through a 10-mm mesh sieve), each composite sample was divided into two aliquots (each approximately 50 g), one for the analysis of soil chemical properties at 4°C, and the other was stored at -80°C for DNA extraction.

## 2.3 Geochemical measurements

Samples were air-dried at room temperature and passed through a 10-mm mesh sieve. The moisture content (MC) was measured using a published procedure (Dettmann et al., 2021). The soil pH and oxidation-reduction potential (ORP) were determined by suspending soil in distilled water (1:3, solid to water, w/v), shaking for 10 min, and equilibrating for 30 min, before the supernatant was measured using a standard PE-28 pH meter (Mettler Toledo, Shanghai). The electrical conductivity (EC) was determined by a Model DDS-11A conductivity meter (Leici, Shanghai) using soil suspended in distilled water (1:5, w/v) according to a previous study (Aponte et al., 2020). Soil total nitrogen (TN) contents were measured by a Vario MACRO cube elemental analyzer (Elementar, Hanau, Germany). For the total organic carbon (TOC) measurements, 5% HCl was added to digest the carbonates of each soil sample and dried at 105°C, then the TOC was measured using a Shimadzu TOC-L Analyzer (Japan). The sulfates were measured on an ICS-600 ion chromatograph (Thermo, USA). The soil metal(loids)s were first digested with a mixture of aqua regia and HF (5:3, v/v) and then the total metal concentrations (for Al, As, Cd, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sb, Ti, V, and Zn) were determined by inductively coupled plasma optical emission spectrometry
(ICP-OES, Thermo iCAP 7000 SERIES, USA). In addition, the bioavailable fractions of metals (Kim et al., 2014) were obtained using the modified BCR sequential extraction method according to Zhu et al. (Zhu et al., 2018). Four metal(loid)s fractions were collected and detailed information was consistent with a previous paper (Li et al., 2022b). Here the bioavailable fractions are expressed as "element symbolmetal(loid)s_bio," such as As_bio and Cd_bio, etc. Each sequential digested filtrate was rinsed with distilled water and filtered before ICP-OES analysis. All samples were analyzed in triplicate for the quality of data.

## 2.4 DNA extraction, amplification, and sequencing

DNA extraction was conducted using the MoBio PowerSoil® DNA isolation kit (MO BIO Laboratories, Carlsbad, CA, USA) according to the manufacturer's instructions. The quality of extracted DNA was evaluated by the NanoDrop 2.0 spectrophotometer (Thermo Fisher Scientific, CA, USA). A Quant-IT Pico Green dsDNA Kit (Invitrogen Molecular Probes Inc, Oregon, USA) was used to examine the final DNA concentration. Primer pairs 515F (GAGCCAGCMGCCGCGGTA) and 806R (GGACTACHVGGGTWTCTAAT) were used to amplify the V4 region of the bacterial 16S rRNA gene. Primer pairs ITS1F (CTTGGTCATTTAGAGGAAGTAA) and ITS2R (GCTGCGTTCTTCATCGATGC) were used to amplify the ITS1 region of the fungal rRNA gene (Liu et al., 2022b). The products of PCR amplification and tag-encoded high-throughput sequencing of the 16S and ITS1 region were conducted by MajorBio (Shanghai, China) using the Illumina platform (Miseq, USA) and quantified by QuantiFluor™-ST microfluorometry (Promega, USA).

The paired-end sequences were merged by the FLASH program based on the default parameters of the equipment (Magoc and Salzberg, 2011). Then the merged sequences were
further filtered using QIIME 1.9 for quality, with a size greater than 20 as a threshold of the
average Phred quality score. The sequences of chimeric were removed using the USEARCH.
The sequences were classified into the operational taxonomic units (OTUs) at a similarity
level of 97%, and all singleton OTUs were removed. The representative OTUs of bacteria
were classified with the taxonomic information in the SILVA (v138.1) database and the
representative OTUs of fungi were classified with the taxonomic information in the UNITE
database. Finally, subsampling was performed to normalize the dataset to the sample with the
lowest number of reads. The alpha diversity indices (Shannon, Simpson, Chao) were
calculated from these samples to evaluate the microbial diversity and richness of bacteria and
fungi. The sequence reads of bacteria and fungi obtained here have been submitted to the
database of the (National Center of Biotechnology Information) NCBI (Accession No.
PRJNA853628 and PRJNA853717, respectively).

## 2.5 Network construction

The network was adopted to build the co-occurrence patterns of bacterial and fungal
communities, exploring the potential relationships among microbes. Initially, the OTU
taxonomy file, the relative abundance of OTUs, and the geochemical parameters were
assembled for future analysis. Only those genera with the sum of relative abundances higher
than 0.005 were kept and the OTUs were filtered that simultaneously appeared in five
samples. Subsequently, the Spearman's correlation coefficient analysis ($R > |0.6|$ and $P < 0.05$)
was performed to determine the interactions with each of the two genera (Barberan et al.,
2012). The correlation matrix was then transformed into the similarity matrix based on the
Spearman's correlation coefficient. The global network properties (the average path length,
connectivity, average clustering coefficient, and modularity) were calculated. Among these
properties, the average path length is the average distance between two nodes and the average
clustering coefficient represents the interconnection of nodes. Connectivity reflects the
connection strength between two nodes, and modularity is a measure to divide the network
into modules, which is identified as a group of nodes connected more densely than the nodes
outside the group (Olesen et al., 2007). The topological properties are commonly used to
describe the complex pattern of inter-relationships between OTUs (Wei et al., 2020). Network
hubs and connectors are considered the keystone nodes in a network, which play important
roles in the stability and resistance of microbial communities. Therefore, the OTUs related to
these nodes were defined as keystone species. To investigate the interactions between
environmental factors and microorganisms in the samples with different contaminant
concentrations, the networks between OTUs and the environmental factors were also
constructed. All the networks in this investigation were constructed and analyzed by Gephi
0.9.2 (http://igraph.org).

## 2.6 *Modeling microbial community assembly*

Microbial assembly processes are essential for establishing the co-occurrence patterns of
community composition within a given ecosystem (Feng et al., 2018). Two complementary
mechanisms regulate the assemblages of a microbial community, namely the deterministic
(niche-based theory) and stochastic (neutral theory) processes (Zhou and Ning, 2017). The
deterministic process emphasizes the dominant role of environmental selection (including
biological and non-biological factors) in the microbial assembly process, which largely
affects the abundance, composition, and distribution patterns of the microbial communities
(Zhang et al., 2022b). Whereas the stochastic process emphasizes the role of dispersal and
ecological drift (Zheng et al., 2021).

The deterministic and stochastic assembly processes of microbial communities observed
here were calculated based on the beta nearest taxon index (βNTI) and the Bray-Curtis-based
Raup-Click (RC<sub>bray</sub>). These data were analyzed using the online tool of the Majorbio Cloud
Platform (https://cloud.majorbio.com/page/tools/). The value of |βNTI| > 2 indicates that a
deterministic process (βNTI > 2: variable selection; whereas βNTI < -2: homogeneous
selection) dominates the community assembly process. Otherwise, it indicates the stochastic
process (|βNTI| < 2) dominates the community assembly process (specifically, |βNTI| < 2 and
RC<sub>bray</sub> < -0.95: homogeneous dispersal; |βNTI| < 2 and RC<sub>bray</sub> > 0.95: dispersal limitation;
|βNTI| < 2 and |RC<sub>bray</sub>| < 0.95: undominated).

## 2.7 Statistical analyses

The alpha diversity (Shannon, Simpson, or Chao) was estimated using rarified libraries.
The Principal Component Analysis (PCoA, beta diversity) was determined by the Bray-Curtis
distance matrix to explain the difference of sample groups based on the R package 'vegan'.
The Canonical Correspondence Analysis (De Agostini et al.) was also performed by R
package 'vegan' to analyze the relationships between geochemical parameters and the
microbial community in different groups. The Random Forest (Numberger, #24) prediction
was conducted in R using the 'randomForest' package. The comparison between different
groups was determined using one-way analysis of variance (Dell'Anno et al.) and SPSS
software (v8.0) and was considered significant at *P* < 0.05. All the statistical tests were
performed using R 4.1.0 unless otherwise stated.
227

## 3. Results

228

### 3.1 *Geochemical characteristics of soil*

229

230

231

232

233

234

235

236

237

238

The geochemical parameters, including selected soil properties and heavy metal(loid) concentrations of 18 soil samples (from Y, XY, YBS, and ZD sites) are summarized in Fig. 2 and Fig. S1. The pH values of soil samples ranged from pH 3.88 to pH 8.37 and were not significantly different (ANOVA, *P* > 0.05) among the four sites. The oxidation-reduction potential (ORP) was significantly higher (ANOVA, *P* < 0.05) in Y and YBS sites than in XY and ZD sites. The total nitrogen content in the Y site was significantly higher (ANOVA, *P* < 0.05) compared to the other sites. In addition, the total organic carbon (TOC) concentration in the XY site was significantly lower (*P* < 0.05) than those in the Y and ZD sites. No significant differences in moisture content (MC) or sulfate concentrations were observed among the four sites.

239

240

241

242

243

244

245

246

247

248

The total concentrations of 14 heavy metal(loid)s, including Al, As, Cd, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sb, Ti, V, and Zn were determined (Fig. 2). Specifically, the Cd, Cr, Fe, Mn, Ti, V, and Zn concentrations in Y samples were significantly higher (ANOVA, *P* < 0.05) than those in XY and ZD sites. There were no significant differences in concentrations of Al, As, Cu, Mg, Ni, Pb, and Sb among the four sites. It is worth noting that the total concentrations of As, Cr, and Sb in all samples greatly exceeded the background limits of the Chinese soil standard (CSL, GB15618-2018), confirming that the Huilong plant (XY, YBS, and ZD sites) and its nearby areas (Y site) were highly polluted by various heavy metal(loid)s; especially As, Cr, and Sb being the main pollutants. BCR sequential extraction showed that the bioavailable fraction of metal(loid)s in the ZD site was higher than those in the other three sites, except for
249 the As and Cd species (Fig. S2).

250 **3.2 Diversity and taxonomic characteristics of microbial communities**

251 The sequences of 16S rRNA and ITS1 genes were amplified by Illumina Miseq
252 sequencing to analyze the bacterial and fungal compositions. After quality filtration, chimeras
253 were removed and sequences were assembled, a total of 1,211,767 high-quality reads were
254 obtained from the 18 samples for bacteria, and 1,003,712 high-quality reads were obtained
255 from the 17 samples for fungi (sample YBS5 did not successfully amplify fungi, maybe
256 because the fungal biomass was too low). The bacterial reads ranged from 37,345 to 74,602,
257 whereas the reads of fungi ranged from 39,272 to 74,314. Based on the 97% sequence
258 similarity, a total of 9,704 and 2,638 OTUs were identified with valid reads for the bacterial
259 and fungal communities, respectively. All coverage indices were > 0.98, indicating that the
260 reads libraries were reliable for the subsequent study and that the sequences typified the
261 actual bacterial and fungal communities (Fig. S3). For the bacterial community, the Chao
262 index was 2301.93 ± 897.79 (mean ± SD) (ranging from 3195.94 to 225.60), the Simpson
263 index was 0.02 ± 0.02 (from 0.005 to 0.084) and the Shannon index was 5.32 ± 0.92 (from
264 6.31 to 3.50). Although slight biases were observed in these indices, there were no significant
265 differences (ANOVA, P > 0.05) in bacterial community diversity and richness between these
266 four sites. Fungal community diversity showed that the Chao index (ranging from 1061.85 to
267 35.50) and Shannon index (from 4.74 to 1.81) in the XY site were significantly higher than
268 those in the Y site (ANOVA, P < 0.05). No significant differences in the Simpson index
269 (ranging from 0.35 to 0.03) were observed among the four sites (ANOVA, P > 0.05). These
270 results indicated that the alpha diversity of bacterial communities in different regions was not
271

significantly different, whereas a slight difference was observed in fungal communities.

272

PCoA analysis was calculated based on the sequences to determine the differences in the microbial communities among different sites (Fig. S4). PCoA1 and PCoA2 explained 15.81% and 13.22% of the variation of the bacterial community, respectively. Similarly, PCoA1 and PCoA2 explained 13.06% and 9.36% of the variation of fungal community, respectively. It was worth noting that the Y samples clustered separately, compared to the other three site samples (XY, YBS, and ZD sites) that were closely clustered. These results demonstrated that microbial communities were clustered by their corresponding origin. Taxonomic assignment analysis showed that a total of 65 bacterial and 14 fungal phyla were identified in the OTUs dataset. Among these, 14 bacterial phyla comprised > 90% of the whole raw reads (Fig. S6A), which included four bacterial dominant phyla (the relative abundance > 5%) of

273

274

275

276

277

278

279

280

281

282

*Pseudomonadota* (24.43%-48.84%), *Chloroflexi* (8.92%-19.94%), *Actinobacteriota* (7.05%-15.74%), and *Acidobacteriota* (5.96%-10.38%). The dominant phyla

283

284

*Pseudomonadota* were composed of *Alphaproteobacteria* and *Gammaproteobacteria* classes, which contributed to 20.57% and 21.48% of the bacterial community, respectively. And the dominant phyla *Actinobacteriota* in the Y site had higher relative abundances than the ZD site (*P* < 0.05) (Fig. S7A). Also, the relative abundances of some bacteria (such as the *Bacillota* phylum) in the YBS site were significantly higher (*P* < 0.05) than those at the XY and ZD sites (Fig. S7A). In addition, other phyla with low relative abundances were also identified among samples (Fig. S7A), such as *Bacteroidota*, *Desulfobacterota*, *Bacillota*, *Gemmatimonadota*, *Methylomirabilota*, *Patescibacteria*, *Planctomycetota*, etc.

285

286

287

288

289

290

291

292

For the fungal community, *Ascomycota* (21.40%-57.32%) and *Basidiomycota*
(3.42%-17.13%) were the dominant fungal phyla among four sites (except for the Y site) (Fig. 293 S6B). The dominant phylum *Ascomycota* was significantly lower in the Y site than in the other three sites (*P* < 0.05). Also, the relative abundance of fungi-like *Basidiomycota* phylum in the Y site was significantly lower (*P* < 0.05) than that in the ZD site. In contrast, the relative abundances of some fungi (such as *Monoblepharomycota* and *unclassified_k_Fungi* phyla) in the Y site were significantly higher (*P* < 0.05) than those at the YBS and ZD sites (Fig. S7B). Additionally, the phyla *unclassified_k_Fungi* accounted for the fungal sequences with a relative abundance of 59.66% in the Y site, 32.38% in the XY site, 14.74% in the YBS site, and 18.92% in the ZD site, indicating that many unknown species existed in the fungal community. The above results indicated that there were significant differences in the distribution of bacterial and fungal communities in different smelting areas.

### 3.3 The importance of geochemical properties on microbial communities

RDA (Redundancy analysis) analysis (Fig. S4 and Fig. S5) and the RF (Random Forest) model (Fig. 3 and Fig. S8) were used to determine the role of geochemical properties on bacterial and fungal communities, respectively. According to the RDA analysis, the geochemical variables (RDA1 39.5%, RDA2 25.12% for the edaphic variables; RDA1 24.34%, RDA2 17.04% for the total metal(loid)s; RDA1 25.5%, RDA2 20.16% for the bioavailable metal(loid)s) explained the variations of bacterial communities (Fig. S4c,e and Fig. S5A), whereas the geochemical variables (RDA1 40.83%, RDA2 19.89% for the edaphic variables; RDA1 26.49%, RDA2 11.49% for the total metal(loid)s; RDA1 24.23%, RDA2 18.03% for the bioavailable metal(loid)s) explained the variations of the fungal communities (Fig. S4d,f and Fig. S5B). The relative abundance of the geochemical variables that affected
the beta diversity (NMDS1) was also predicted by the RF model (Fig. 3 and Fig. S8), which explained 50.69% and 31.74% of the total variations of bacterial and fungal communities, respectively. For bacteria, the Fe_bio and the TOC were the most important geochemical variables on the beta diversity, which explained more than 15% of the variations, followed by Cr_bio, Al_bio, Ni_bio, Zn_bio, and other environmental factors (Fig. 3). Many bioavailable metal(loid)s showed positive effects on the beta diversity, such as Fe_bio, Ni_bio, Zn_bio, V_bio, Mn_bio, As_bio, Sb_bio, and Ti_bio, whereas edaphic properties such as TN showed negative impacts on the beta diversity of bacterial communities. The effects of other geochemical variables on beta diversity were not clear and will not be further discussed. For fungi, the dominant environmental factors were TN and As_bio, which explained more than 10% of the variations. Sulfate, as well as the Pb_bio, Cu_bio, and Cr_bio also influenced the beta diversity of fungal communities (the relative abundance was more than 2%). The As_bio, Ni_bio, and Pb_bio were positively related to the beta diversity, whereas the TN and Cd_bio were negatively related to the beta diversity (Fig. S8).

The interactions between geochemical variables and microbial taxa (the top 500 abundant OTUs of bacterial (Table S1) and fungal (Table S2) communities) were further analyzed using co-occurrence networks, with Spearman's correlations ($|R| > 0.6$ and $P < 0.05$) (Fig. 4). For the bacterial community, Fe_bio had the most important impact on 25 OTUs, which agreed well with the results of the Random Forest model. Cr_bio also had important impacts on the bacterial community (13 OTUs), followed by Ni_bio (12 OTUs), the moisture content (MC) (12 OTUs), and Fe_tot (10 OTUs) (Fig. 4A). A total of 88 OTUs displayed significant correlations with geochemical variables, and the network was divided into ten
modules. The microbial taxa of *Burkholderiaceae* (2 OTUs), *Chitinophagaceae* (2 OTUs), *Comamonadaceae* (4 OTUs), *Hyphomonadaceae* (1 OTUs), *Nocardiaceae* (3 OTUs), *norank_o_IMCC26256* (1 OTUs), *norank_o_norank_c_P2-11E* (1 OTUs), *Oxalobacteraceae* (2 OTUs), *Rhodocyclaceae* (3 OTUs), *UBA12409* (1 OTUs), and *unclassified_c_Acidimicrobiia* (1 OTUs) families displayed the highest correlations with geochemical variables (Table S2). Some groups, such as *Chitinophagaceae*, *Comamonadaceae*, *Nocardiaceae*, *Oxalobacteraceae*, *Rhodocyclaceae*, and *UBA12409*, showed positive correlations with the bioavailable fractions of metals (Table S3). For the fungal community, a total of 135 OTUs displayed strong correlations with the geochemical variables, and the network was divided into eight modules. *Mn_tot* had the most important impact on 85 OTUs, followed by *Cr_tot* (79 OTUs), *Ni_tot* (62 OTUs), *TOC* (57 OTUs), *V_tot* (43 OTUs), and *MC* (30 OTUs) (Fig. 4B). The OTUs affiliated with the *Didymosphaeriaceae* (5 OTUs), *Fomitopsidaceae* (3 OTUs), *Lophiostomataceae* (2 OTUs), *Lycoperdaceae* (3 OTUs), *Meruliaceae* (12 OTUs), *Phaeosphaeriaceae* (5 OTUs), *Pleosporaceae* (4 OTUs), *Steccherinaceae* (3 OTUs), and *Xenasmataceae* (2 OTUs) families were strongly influenced by multiple environmental parameters. These families (except for *Lophiostomataceae* and *Xenasmataceae*) showed positive correlations with the bioavailable fractions of metal(loid)s, whereas negative correlations were found with the total metal(loid) content and the other environmental parameters (Table S4).

### 3.4 Co-occurrence patterns and keystone taxa analysis of microbial communities

In the present study, the co-occurrence patterns of bacterial and fungal communities were individually constructed by strong correlations (Spearman $R > |0.6|$) and significant ($P < 0.05$)
0.05) among the top 500 abundant OTUs in the Huilong smelter regions. For bacteria, the networks of the Huilong smelter regions contained 473 nodes (OTUs) and 5120 edges (connection between OTUs). Whereas for fungi, the networks of the Huilong smelter regions contained 490 nodes and 16559 edges. Analogously, the average degree, clustering coefficient, and density in fungal communities were higher than those in bacterial communities. It was worth noting that, the proportions of positive edges tended to be higher than negative edges in the networks of bacterial and fungal communities (Table 1). Moreover, the nodes were assigned by phylum (Fig. 5A and C) and modularity class (Fig. 5B and D).

A total of 11 dominant taxa (at the phylum level) were identified from the co-occurrence patterns of bacterial communities, which accounted for 86.26% of total nodes, and the remaining modes (65 nodes) were classified as others (13.74%). *Pseudomonadota* was the most dominant phylum, which accounted for 32.98% of total nodes, followed by *Actinobacteriota* (10.99%), *Chloroflexi* (7.82%), *Acidobacteriota* (5.92%), *Planctomycetota* (5.5%), and *Bacteroidota* (5.5%) in the bacterial networks (Fig. 5A). All nodes were divided into 11 modules, including 5 major modules (Fig. 5B). Additionally, the OTUs with nodes degree > 30 and betweenness centrality < 100 were typically identified as the keystone phylotypes in the present study. Here a total of 5 nodes were identified as the keystone taxa, which are mainly distributed in Module III and Module IV. The keystone OTUs were affiliated to *Diplorickettsiaceae*, *norank_o_Candidatus_Woesebacteria*, *norank_o_norank_c_AT-s3-28*, *norank_o_norank_c_bacteriap25*, and *Phycisphaeraceae* families.

Accordingly, for fungi, a total of 11 phyla (Fig. 5C), including *Ascomycota* (51.84%),
381 *Basidiomycota* (41.63%), *Glomeromycota* (2.24%), *Chytridiomycota* (2.04%), *Rozellomycota*
382 (0.61%), *Mucoromycota* (0.41%), *Olpidiomycota* (0.41%), *Mortierellomycota* (0.2%),
383 *Kickxellomycota* (0.2%), *Calcarisporiellomycota* (0.2%) and *Monoblepharomycota* (0.2%),
384 were identified from the co-occurrence patterns of fungal communities. All nodes were
385 divided into 9 modules, including 6 major modules (Fig. 5D). Here a total of 9 nodes were
386 identified as the keystone OTUs, which were affiliated to *Biatriosporaceae*,
387 *Ganodermataceae*, *Peniophoraceae*, *Phaeosphaeriaceae*, *Polyporaceae*, *Teichosporaceae*,
388 *Trichomeriaceae*, *Wrightoporiaceae*, *Xylariaceae* families, mainly distributing in Module I,
389 Module III and Module IV.

390 **3.5 Ecological assembly processes of microbial communities**

391 The calculated βNTI and RC<sub>bray</sub> were compared between bacterial and fungal
392 communities, and the assembly processes were determined. There were slight differences in
393 the scale of the assembly process at each site. The assembly process was judged according to
394 the criteria described in the above method. For the whole bacterial communities, the majority
395 of the βNTI in the whole microbial community (91.5%) was greater than 2, indicating that the
396 deterministic process controlled the bacterial community assembly (Fig. 6A). The Y and YBS
397 sites had similar assembly processes, with the variable selection accounting for 94.5% and
398 undominated process accounting for 5.5%. Similarly, the variable selection process (90.1%
399 and 87.7%) also governed the bacterial community assembly of XY and ZD sites, whereas
400 the dispersal limitation (4.4% and 2.3%) and undominated (5.5% and 10%) incurred
401 variations in bacterial communities of the XY and ZD sites, respectively. For the whole
402 fungal communities, the relative contribution of the deterministic process (90.2%) was higher
than that of the stochastic process (9.8%) (Fig. 6B). Within fungal communities of each site, variable selection accounted for a high percentage of community assembly, including Y (82.3%), XY (92.9%), YBS (91.2%), and ZD sites (90.5%). The stochastic process contained dispersal limitation (7.9%), homogenizing dispersal (3.9%), and undominated (5.9%) in the Y site, whereas only undominated processes (7.1%, 8.8%, and 9.5%) were present in the XY, YBS, and ZD sites, respectively. These results indicated that deterministic processes dominated the assembly of microbial communities regardless of bacterial or fungal communities.

A correlation matrix between edaphic variables and the βNTI was assembled to investigate the factors influencing the ecological assembly process of microbial communities. The assembly process of bacterial communities was more affected by the tested geochemical properties than the fungal communities (Table S1). In particular, the pH, TN, total metal(loid)s, and the bioavailable fractions of metal(loid)s (except for As, Cd, and Ti) had significant impacts on either the bacterial or fungal assembly processes in this study (Table S1-1).

## 4. Discussion

Metal(loid)s pollution of soil is a high-priority research topic in which microbial strategies for stabilizing metal(loid)s are recognized as a promising alternative relative to traditional remediation technologies. Microbial communities are identified as potential indicators of soil function and health in contaminated and surrounding environments (Banerjee and van der Heijden, 2022). Most recent studies of metal(loid)s contamination focus on only one microbial domain at a time (Park et al., 2021; Liu et al., 2022a; Liu et al.,
2022c; Zhang et al., 2022c). However, the co-occurrence patterns and ecological assembly
processes of both bacterial and fungal communities under diverse metal(loid)s exposures
have not been investigated so far. Therefore, it is necessary to comprehensively compare the
responses of indigenous bacterial and fungal communities to metal(loid)s pollution.

## 4.1 *Geochemical characterizations*

In the Huilong smelter area, the concentrations of total As, Cr, and Sb exceeded the
Chinese *Soil Environmental Quality Construction Land Soil Pollution Risk Management and Control Standard (Trial)* (GB36600-2018), which is the second land use standard for
screening purposes. Although no significant differences in these total metal(loid)s
concentrations (except for total Cr) were found among the four studied sites. In contrast,
significant differences were observed in the bioavailable fraction of these metal(loid)s among
the four contaminated areas. For example, the bioavailable fraction proportion of As (As_bio)
in three sites (XY, YBS, and ZD) was higher than that in the Y site (Fig. S2). Recent studies
of the evaluation of metal(loid)s pollution risk at other sites of the Huilong smelter (Li et al.,
2022a) showed that the high-risk metal elements were As and Sb. These results suggested
that smelting activities released these metal(loid)s to the local community, despite being shut
down more than two decades ago. Additionally, both TN and TOC were significantly higher
in the Y site than in the other three sites. It was likely that the Y site was located near
farmland, as the levels of TN and TOC in agricultural soils tend to be higher than in other soil
types (Compton et al., 2004). Therefore, the above results indicated that significant
differences were observed in metal(loid)s and environmental factors between the Huilong
smelter area (XY, YBS, and ZD sites) and the surrounding area (Y site), which might be
related to the artificial smelting activities.

## 4.2 Effects of geochemical characteristics on bacterial and fungal communities

There were no significant differences in bacterial diversity and richness (Fig. S3A).
Such observations demonstrate that the alpha diversity of bacterial communities was not
significantly affected by the variations of metal(loid)s and environmental factors, which is
similar to previous studies of metal(loid)s contaminated environments near mining and
smelting sites (Pereira et al., 2014; Chun et al., 2021; Liu et al., 2021). In contrast, significant
differences in alpha diversity (except the Simpson indices) and richness were observed for
fungi. From the perspective of microbial diversity, the toxic effects of metal(loid)s can often
reduce microbial diversity during the initial phase of metal(loid)s exposure (Liu et al., 2022a).
However, the microbes could then develop an adaptive response mechanism to metal(loid)s
contamination, as evidenced by an increase in their diversity and richness (Yan et al., 2020).
In particular, the diversity and richness of fungal communities in the XY site (in Huilong
smelter area) were significantly higher than in the Y site (in the surrounding area), which
demonstrated that smelting activities may increase the fungal diversity and richness.

Notably, beta-diversity patterns were distinguished in both bacterial and fungal
communities, with significant differences among the four sites as revealed by PCoA (Fig. S4)
and RDA analyses (Fig. S4 and S5). Such observations may be due to the combined effect of
metal(loid)s and other geochemical parameters. The RF model prediction showed that the
bioavailability of certain metal(loid)s (Al, As, Cr, Fe, Ni, Pb, Sb, Ti, and Zn) had positive
effects on the beta-diversity of bacterial communities (Fig. 3). Similarly, bioavailable As, Ni,
and Pb had positive effects on the beta diversity of fungal communities (Fig. S8). These
results suggest that the bioavailable fractions of metal(loid)s have substantial impacts on the beta diversities of the bacterial and fungal communities (Wang et al., 2018), although the dominant factors differed for the bacterial and fungal communities. As previously reported, bioavailable Al, As, Fe, and Sb have strong negative effects on microbial diversity (Liu et al., 2022c) and significant decreases in community richness in As- and Cr-contaminated soils (Sheik et al., 2012). In contrast, the bioavailable fractions of certain metal(loid)s presented here generally showed positive effects on the beta diversity of microbial communities, as shown in the RF model, probably due to sensitive indigenous microorganisms that were stimulated by the metal(loid)s.

The microbial-contaminant interactions demonstrated that some bacteria and fungi were responsive to some but not all bioavailable metals in soil. For example, three OTUs associated with *Comamonadaceae* showed positive relationships with the bioavailable Fe, Cr, and Ni. Members of *Comamonadaceae* have been identified as Fe(III)-reducing bacteria, which could adapt to the detrimental environment by changing their metabolic ways (Wang et al., 2021; Zhang et al., 2022a). Consistently, two OTUs associated with *Rhodocyclaceae* showed positive relationships with the bioavailable Fe and Cr. It also has been reported that Pb exhibited a strong correlation with the Fe(III)-reducing bacterial lineages *Rhodocyclaceae* (Moberly et al., 2016; Jia et al., 2022). Apart from these bacteria, the fungi, e.g., *Meruliaceae* and *Pleosporaceae*, showed similar trends. Members of *Meruliaceae* were the most influenced by multiple environmental parameters, three OTUs associated with *Meruliaceae* showed positive relationships with the bioavailable Fe, Ni, and V. Although *Meruliaceae* often grows on rotten gymnosperm wood (Liu et al., 2022e), the relationship of *Meruliaceae*
with metal(loid)s has not been studied. Additionally, two OTUs associated with *Pleosporaceae* showed positive relationships with the bioavailable Fe and V. It was consistent with the observation of rhizosphere fungal communities in a Cu tailings area (Jia et al., 2020).

The observations presented here demonstrate the adaptation of the promising candidates (e.g., bacteria, *Comamonadaceae*, *Rhodocyclaceae*, and fungi, *Meruliaceae*, *Pleosporaceae*) to the conditions prevailing at Huilong nonferrous metal smelter.

### 4.3 The co-occurrence patterns of bacterial and fungal communities

Soil microbes do not thrive well in isolated environments; instead, they prefer complex networks of interactions. In the present study, the co-occurrence patterns of bacterial and fungal communities were established using networks based on the random matrix theory to reveal the interactions between microbes (Faust and Raes, 2012). The topological characteristics of networks generally represent the levels of interactions and connectedness (Zheng et al., 2018). Compared to bacterial networks, the fungal networks had more links (Table 1 and Fig. 5), which indicated that inter-species relationships among different adaptive fungal species would be strengthened and stabilized in metal(loid)s contaminated areas. On the other hand, the bacterial communities were less adapted and were weaker as evidenced by their few inter-species relationships. These data suggest that fungal communities were less sensitive and more resistant to HM toxicity than the bacterial communities residing at this abandoned smelter site. These results indicated that the frequency of connections for fungal communities was higher than those in bacterial communities. Generally, there were similar trends in network topological properties for both bacterial and fungal communities.

The keystone species inhabiting the heavily polluted areas may indicate that they have
special ecological roles in harsh environments. In the present study, the OTU with nodes degree > 30 and betweenness centrality < 100 were typically identified as the keystone phylotypes. In the bacterial networks, the members of the family *Diplorickettsiaceae*, *norank_o_Candidatus_Woesebacteria*, *norank_o_norank_c_AT-s3-28*, *norank_o_norank_c_bacteriap25*, and *Phycisphaeraceae* were considered as the keystone taxa in the bacterial communities. Within these keystone taxa, members of *Diplorickettsiaceae*, belonging to the phylum of *Pseudomonadota*, have been identified as Pb-sensitive taxa in the soil microbial community (Wan et al., 2022). Consistently, several previous studies have shown that the members of *Pseudomonadota* possess a large number of metal-related operons, which are responsible for the detoxification of metal(loid)s (Qiao et al., 2021; Zou et al., 2021). The *Pseudomonadota* was abundant in the Huilong area, suggesting that this phylum mitigates metal(loid)s toxicity through a special adaptive mechanism (Keshri et al., 2015; Fashola et al., 2016). Moreover, members of *norank_o_Candidatus_Woesebacteria* belong to the phylum of *Patescibacteria*, which phyla has As, Fe, and Ag resistance genes (Zhou et al., 2022) and can transport various heavy metals from massive soils to the rhizosphere (Tian et al., 2022). Consistent with previous studies (Flieder et al., 2020; Liu et al., 2022c), the members of *norank_o_norank_c_AT-s3-28* family, associated with the phylum *Acidobacteriota*, play a key role in bacterial communities in areas such as Huilong, which exhibited a resistance capacity to metal(loid)s pollution (Wang et al., 2022a). It is not surprising that the family *norank_o_norank_c_bacteriap25* (*Myxococcota*) and *Phycisphaeraceae* (*Planctomycetota*) were identified as keystone taxa in the Huilong site, owing to their ability to tolerate and
535 resist metals ions using the complexation and adsorption mechanisms (Dell'Anno et al., 2021;
536 Liu et al., 2022d; Marghoob et al., 2022; Gao et al., 2022). These findings could help develop
537 remediation strategies for Huilong contaminated areas, for example, by purposefully
538 screening key species for biological materials.

539 The fungi with a strong resistance to metal(loid)s pollution can play a key role in the
540 restoration of metal(loid)s polluted ecosystems. In the fungal networks, OTUs affiliated to the
541 *Biatriosporaceae* (*Ascomycota*), *Ganodermataceae* (*Basidiomycota*), *Peniophoraceae*
542 (*Basidiomycota*), *Phaeosphaeriaceae* (*Ascomycota*), *Polyporaceae* (*Basidiomycota*),
543 *Teichosporaceae* (*Ascomycota*), *Trichomeriaceae* (*Ascomycota*), *Wrightoporiaceae*
544 (*Basidiomycota*), *Xylariaceae* (*Ascomycota*) families were considered as the keystone taxa.
545 These OTUs mainly belonged to the phylum of *Ascomycota* and *Basidiomycota*. *Ascomycota*,
546 the most abundant and widely existing soil fungi in the world, was the keystone phylum in
547 the fungal networks of Huilong sites, consistent with the previous studies (Lin et al., 2019;
548 De Agostini et al., 2020; Xie et al., 2021). This phylum has a strong tolerance to metal(loid)s;
549 in fact, even its abundance was increased with metal(loid)s concentrations (Lin et al., 2019).
550 Furthermore, *Basidiomycota* was also a dominant phylum in the networks of fungal
551 communities. *Basidiomycota* (filamentous fungi) can be used to remove metal(loid)s (Chen et
552 al., 2012). Therefore, *Basidiomycetes* could be associated with their adaptation and function
553 in metal(loid)s contaminated environments. These results agree with a previous report (Chen
554 et al., 2014) that *Ascomycota* and *Basidiomycota* were the most important components in
555 metal(loid)s polluted soils.

556 In addition, positive and negative links (representing positive and negative correlations)
may reflect cooperation (or niche overlap) and competition (or niche separation) among
species (Deng et al., 2016; Ghoul and Mitri, 2016; Zhang et al., 2018). Here there were more
positive links than negative links in both bacterial and fungal networks (Table 1). This
reflects the importance of microbial synergy, where most microbial species potentially
cooperate to withstand metal(loid)s pollution (Hoek et al., 2016). It is worth noting that the
proportion of positive connections is significantly higher in fungal networks than in bacterial
networks. This indicates that environmental pollution at the studied Huilong factories, as well
as other anthropogenic sources, increased the competition of species among the fungal
community (Zhou et al., 2020). Competition can stabilize the co-oscillation of the community
and promote the stability of the network (Coyte et al., 2015).

4.4 *Ecological assembly processes of bacterial and fungal communities*

Revealing the assembly process of the microbial community in ecological niches can
uncover the responses of a microbial community to perturbations by metal(loid) exposures
(Jiao et al., 2016; Zhang et al., 2021; Liu et al., 2022a). In the current study, variable selection
accounted for the highest contribution rate (82.3-94.5%) in the deterministic process among
bacterial and fungal communities (Fig. 6). However, the stochastic processes contribute
differently to the assemblies of fungal and bacterial communities. For example, the fungal
community showed a stronger dispersal limitation than the bacterial community in the
influenced area (Y site), which was consistent with previous studies (Jiang et al., 2016). That
it is because the body size of fungi hindered their spread or spatial aggregation, and limited
long distance dispersal compared with the smaller bacteria (Xu et al., 2021b). An opposite
trend occurred in Huilong factories area (XY, YBS, and ZD sites), in which high levels of
dispersal limitation were found in bacterial communities, possibly due to the differences of metal(loid) concentrations and geochemical properties between the influenced area (Y site) and Huilong factories area (XY, YBS, and ZD sites). Above observations agreed with several previous studies (Zhou and Ning, 2017; Mori et al., 2018; Zheng et al., 2021) reporting that the deterministic and stochastic processes could jointly regulate the natural community assembly, and their relative importance varies with the environmental context. Furthermore, the present study showed that the pH, TN, total metal(loid) concentrations, and the bioavailable fractions of metal(loid)s (except for the As, Cd, and Ti) had significant impacts on the microbial assembly process. These results confirm the combined effect of metal(loid) exposures and geochemical properties on the microbial community. However, ecological deterministic and stochastic processes exist that can jointly contribute to the soil microbial community assembly in response to metal(loid)s exposure. Their detailed contributions can vary in bacterial and fungal communities depending on the environmental context.

## 5. Conclusion

The response of bacterial and fungal communities to different geochemical variables, as well as their ecological assembly processes, were investigated in the present study. The smelting activities had indeed brought metal(loid)s pollution, especially As and Sb, to the locality, although Huilong smelter has been shut down for decades. Both metal(loid)s and edaphic variables drive the microbial communities, but very different responses of innate microbiota were observed. The bioavailable Fe and TOC had considerable impacts on the beta diversity of bacterial communities, whereas TN and bioavailable As mainly influenced the fungal communities. The inter-species interactions of fungal co-occurrence networks were
more complex than the bacterial networks, suggesting inter-species relationships among different fungal species were more stable than the bacterial species in this metal(loid)s contaminated area. Moreover, the relatively higher positive interaction of microbiota indicated the importance of microbial synergy among species with stronger resistance to metal(loid)s polluted environments. The keystone taxa were investigated and revealed their adaptive superiority to metal(loid)s stress. Additionally, the deterministic process (variable selection) dominated all bacterial and fungal assemblies, which was highly impacted by the pH, TN, total metal(loid)s, and the bioavailable fractions of certain metal(loid)s. These findings enhance our understanding of the microbial responses to anthropogenically induced smelting activities and provide a vital guideline for developing the bioremediation practice of metal(loid)s contaminated sites.

## Acknowledgments

This work has been supported partly by grants received from the Major National R & D Projects from the Chinese Ministry of Science and Technology (2019YFC1803500), the National Science Foundation of China (42230716, 41720104007), the 111 Project (B21017), and the 1000-Talents Plan project (WQ2017110423).

## Author contributions

**Miaomiao Li**: Conceptualization, Methodology, Resources, Formal analysis, Writing - original draft, Writing - review & editing. **Jun Yao**: Writing - review & editing, Funding acquisition. **Geoffrey Sunahara**, **Robert Duran** and **Qinghua Zhang**: Conceptualization, Data Curation, Visualization, Writing - review & editing. **Bang Liu**, **Ying Cao**, **Hao Li**,
622

Wancheng Pang, Houquan Liu, Shun Jiang, Junjie Zhu: Writing – review & editing.

623

Conflicts of interest

624

The authors declare that there are no conflicts of interest.

625

626

References

627

Aponte, H., Herrera, W., Cameron, C., Black, H., Meier, S., Paolini, J., Tapia, Y., Cornejo, P., 2020. Alteration of enzyme activities and functional diversity of a soil contaminated with copper and arsenic. Ecotoxicol Environ Saf. 192, 110264. 10.1016/j.ecoenv.2020.110264

629

630

631

Banerjee, S., van der Heijden, M.G.A., 2022. Soil microbiomes and one health. Nat Rev Microbiol. 10.1038/s41579-022-00779-w

632

633

Barberan, A., Bates, S.T., Casamayor, E.O., Fierer, N., 2012. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6(2), 343-51. 10.1038/ismej.2011.119

634

635

636

Chen, G., Fan, J., Liu, R., Zeng, G., Chen, A., Zou, Z., 2012. Removal of Cd(II), Cu(II) and Zn(II) from aqueous solutions by live Phanerochaete chrysosporium. Environ Technol. 33(22-24), 2653-9. 10.1080/09593330.2012.673015

637

638

639

Chen, J., He, F., Zhang, X., Sun, X., Zheng, J., Zheng, J., 2014. Heavy metal pollution decreases microbial abundance, diversity and activity within particle-size fractions of a paddy soil. FEMS Microbiol Ecol. 87(1), 164-81. 10.1111/1574-6941.12212

640

641

642

Chun, S.J., Kim, Y.J., Cui, Y., Nam, K.H., 2021. Ecological network analysis reveals distinctive microbial modules associated with heavy metal contamination of abandoned mine soils in Korea. Environ Pollut. 289, 117851. 10.1016/j.envpol.2021.117851

643

644

645

646

Compton, J.E., Watrud, L.S., Arlene Porteous, L., DeGrood, S., 2004. Response of soil microbial biomass and community composition to chronic nitrogen additions at Harvard forest. Forest Ecology and Management. 196(1), 143-158. 10.1016/j.foreco.2004.03.017

647

648

649
650

Coyte, K.Z., Schluter, J., Foster, K.R., 2015. The ecology of the microbiome: networks, competition, and stability. Science. 350, 663-666.

651

652

De Agostini, A., Caltagirone, C., Caredda, A., Cicatelli, A., Cogoni, A., Farci, D., Guarino, F., Garau, A., Labra, M., Lussu, M., Piano, D., Sanna, C., Tommasi, N., Vacca, A., Cortis, P., 2020. Heavy metal tolerance of orchid populations growing on abandoned mine tailings: A case study in Sardinia Island (Italy). Ecotoxicol Environ Saf. 189, 110018. 10.1016/j.ecoenv.2019.110018

653

654

655

656

657

Dell'Anno, A., Beolchini, F., Rocchetti, L., Luna, G.M., Danovaro, R., 2012. High bacterial biodiversity increases degradation performance of hydrocarbons during bioremediation of contaminated harbor marine sediments. Environmental Pollution. 167, 85-92. 10.1016/j.envpol.2012.03.043

658

659

660

661

Dell'Anno, F., Rastelli, E., Tangherlini, M., Corinaldesi, C., Sansone, C., Brunet, C., Balzano, S., Ianora, A., Musco, L., Montereali, M.R., Dell'Anno, A., 2021. Highly Contaminated Marine Sediments Can Host Rare Bacterial Taxa Potentially Useful for Bioremediation. Front Microbiol. 12, 584850. 10.3389/fmicb.2021.584850

662

663

664

665

Deng, Y., Zhang, P., Qin, Y., Tu, Q., Yang, Y., He, Z., Schadt, C.W., Zhou, J., 2016. Network succession reveals the importance of competition in response to emulsified vegetable oil amendment for uranium bioremediation. Environ Microbiol. 18(1), 205-18. 10.1111/1462-2920.12981

666

667

668

669

Dettmann, U., Kraft, N.N., Rech, R., Heidkamp, A., Tiemeyer, B., 2021. Analysis of peat soil organic carbon, total nitrogen, soil water content and basal respiration: Is there a 'best' drying temperature? Geoderma. 403. 10.1016/j.geoderma.2021.115231

670

671

672

Fashola, M.O., Ngole-Jeme, V.M., Babalola, O.O., 2016. Heavy Metal Pollution from Gold Mines: Environmental Effects and Bacterial Strategies for Resistance. Int J Environ Res Public Health. 13(11). 10.3390/ijerph13111047

673

674

675

Faust, K., Raes, J., 2012. Microbial interactions: from networks to models. Nat Rev Microbiol. 10(8), 538-50. 10.1038/nrmicro2832

676

677

Fei, X., Christakos, G., Xiao, R., Ren, Z., Liu, Y., Lv, X., 2019. Improved heavy metal mapping and pollution source apportionment in Shanghai City soils using auxiliary information. Sci Total Environ. 661, 168-177. 10.1016/j.scitotenv.2019.01.149

678

679
680 Feng, M., Adams, J.M., Fan, K., Shi, Y., Sun, R., Wang, D., Guo, X., Chu, H., 2018.
681 Long-term fertilization influences community assembly processes of soil diazotrophs.
682 Soil Biology and Biochemistry. 126, 151-158. 10.1016/j.soilbio.2018.08.021

683 Flieder, M., Buongiorno, J., Herbold, C.W., Hausmann, B., Rattei, T., Lloyd, K.G., Loy, A.,
684 Wasmund, K., 2020. Novel taxa of Acidobacteriota involved in seafloor sulfur cycling.
685 bioRxiv. 10.1101/2020.10.01.322446

686 Gao, W., Liu, P., Ye, Z., Zhou, J., Wang, X., Huang, X., Deng, X., Ma, L., 2022. Divergent
687 prokaryotic microbial assembly, co-existence patterns and functions in surrounding
688 river sediments of a Cu-polymetallic deposit in Tibet. Sci Total Environ. 851(Pt 1),
689 158192. 10.1016/j.scitotenv.2022.158192

690 Ghoul, M., Mitri, S., 2016. The Ecology and Evolution of Microbial Competition. Trends
691 Microbiol. 24(10), 833-845. 10.1016/j.tim.2016.06.011

692 Hoek, T.A., Axelrod, K., Biancalani, T., Yurtsev, E.A., Liu, J., Gore, J., 2016. Resource
693 Availability Modulates the Cooperative and Competitive Nature of a Microbial
694 Cross-Feeding Mutualism. PLoS Biol. 14(8), e1002540.
695 10.1371/journal.pbio.1002540

696 Jia, S., Tian, Y., Li, J., Chu, X., Zheng, G., Liu, Y., Zhao, W., 2022. Field study on the
697 characteristics of scales in damaged multi-material water supply pipelines: Insights
698 into heavy metal and biological stability. J Hazard Mater. 424(Pt A), 127324.
699 10.1016/j.jhazmat.2021.127324

700 Jia, T., Guo, T.Y., Wang, R.H., Chai, B.F., 2020. Effects of heavy metal contents on
701 phyllosphere and rhizosphere fungal communities for bothriochloa ischaemum in
702 copper tailings area. Environmental Science. 41(11), 5193-5200.

703 Jiang, Y., Liang, Y., Li, C., Wang, F., Sui, Y., Suvannang, N., Zhou, J., Sun, B., 2016. Crop
704 rotations alter bacterial and fungal diversity in paddy soils across East Asia. Soil
705 Biology and Biochemistry. 95, 250-261. 10.1016/j.soilbio.2016.01.007

706 Jiao, S., Liu, Z., Lin, Y., Yang, J., Chen, W., Wei, G., 2016. Bacterial communities in oil
707 contaminated soils: Biogeography and co-occurrence patterns. Soil. Biol. Biochem.
708 98, 64-73. 10.1016/j.soilbio.2016.04.005

709 Keshri, J., Mankazana, B.B., Momba, M.N., 2015. Profile of bacterial communities in South
710

African mine-water samples using Illumina next-generation sequencing platform.

711

Appl Microbiol Biotechnol. 99(7), 3233-42. 10.1007/s00253-014-6213-6

712

Kim, E.J., Yoo, J.C., Baek, K., 2014. Arsenic speciation and bioaccessibility in arsenic-contaminated soils: sequential extraction and mineralogical investigation.

713

714

Environ Pollut. 186, 29-35. 10.1016/j.envpol.2013.11.032

715

Li, H., Yao, J., Min, N., Chen, Z., Li, M., Pang, W., Liu, B., Cao, Y., Men, D., Duran, R., 2022a. Comprehensive evaluation of metal(loid)s pollution risk and microbial activity characteristics in non-ferrous metal smelting contaminated site. Journal of Cleaner Production. 344. 10.1016/j.jclepro.2022.130999

716

717

718

719

Li, M., Yao, J., Sunahara, G., Hawari, J., Duran, R., Liu, J., Liu, B., Cao, Y., Pang, W., Li, H., Li, Y., Ruan, Z., 2022b. Novel microbial consortia facilitate metalliferous immobilization in non-ferrous metal(loid)s contaminated smelter soil: Efficiency and mechanisms. Environ Pollut. 313, 120042. 10.1016/j.envpol.2022.120042

720

721

722

723

Lin, Y., Ye, Y., Hu, Y., Shi, H., 2019. The variation in microbial community structure under different heavy metal contamination levels in paddy soils. Ecotoxicol Environ Saf. 180, 557-564. 10.1016/j.ecoenv.2019.05.057

724

725

726

Liu, B., Yao, J., Chen, Z., Ma, B., Li, H., Wancheng, P., Liu, J., Wang, D., Duran, R., 2022a. Biogeography, assembly processes and species coexistence patterns of microbial communities in metalloids-laden soils around mining and smelting sites. J Hazard Mater. 425, 127945. 10.1016/j.jhazmat.2021.127945

727

728

729

730

Liu, B., Yao, J., Chen, Z., Ma, B., Liu, J., Li, H., Zhu, X., Li, M., Cao, Y., Pang, W., Zhao, C., Mihucz, V.G., Duran, R., 2022b. Unraveling ecological risk of As/Sb and other metal(loid)s and fungal community responses in As/Sb smelting-intensive zone: A typical case study of Southwest China. Journal of Cleaner Production. 338. 10.1016/j.jclepro.2022.130525

731

732

733

734

735

Liu, B., Yao, J., Ma, B., Chen, Z., Zhao, C., Zhu, X., Li, M., Cao, Y., Pang, W., Li, H., Feng, L., Mihucz, V.G., Duran, R., 2021. Microbial community profiles in soils adjacent to mining and smelting areas: Contrasting potentially toxic metals and co-occurrence patterns. Chemosphere. 282, 130992. 10.1016/j.chemosphere.2021.130992

736

737

738

739

Liu, B., Yao, J., Ma, B., Chen, Z., Zhu, X., Zhao, C., Li, M., Cao, Y., Pang, W., Li, H.,
740

741

742

Mihucz, V.G., Duran, R., 2022c. Metal(loid)s diffusion pathway triggers distinct microbiota responses in key regions of typical karst non-ferrous smelting assembly. J. Hazard. Mater. 423(Pt B), 127164. 10.1016/j.jhazmat.2021.127164

743

744

745

Liu, J., Bao, Z., Wang, C., Wei, J., Wei, Y., Chen, M., 2022d. Understanding of mercury and methylmercury transformation in sludge composting by metagenomic analysis. Water Res. 226, 119204. 10.1016/j.watres.2022.119204

746

747

748

Liu, Z.B., Zhang, J.L., Papp, V., Dai, Y.C., 2022e. Taxonomy and Phylogeny of Meruliaceae with Descriptions of Two New Species from China. J Fungi (Basel). 8(5). 10.3390/jof8050501

749

750

Magoc, T., Salzberg, S.L., 2011. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 27(21), 2957-63. 10.1093/bioinformatics/btr507

751

752

753

754

Marghoob, M.U., Rodriguez-Sanchez, A., Imran, A., Mubeen, F., Hoagland, L., 2022. Diversity and functional traits of indigenous soil microbial flora associated with salinity and heavy metal concentrations in agricultural fields within the Indus Basin region, Pakistan. Front Microbiol. 13, 1020175. 10.3389/fmicb.2022.1020175

755

756

757

Moberly, J., D'Imperio, S., Parker, A., Peyton, B., 2016. Microbial community signature in Lake Coeur d'Alene: Association of environmental variables and toxic heavy metal phases. Applied Geochemistry. 66, 174-183. 10.1016/j.apgeochem.2015.12.013

758

759

Mori, A.S., Isbell, F., Seidl, R., 2018. beta-Diversity, Community Assembly, and Ecosystem Functioning. Trends Ecol Evol. 33(7), 549-564. 10.1016/j.tree.2018.04.012

760

761

Olesen, J.M., Bascompte, J., Dupont, Y.L., Jordano, P., 2007. The modularity of pollination networks. PNAS. 104 (50) 19891-19896.

762

763

764

765

Park, S.C., Boyanov, M.I., Kemner, K.M., O'Loughlin, E.J., Kwon, M.J., 2021. Distribution and speciation of Sb and toxic metal(loid)s near an antimony refinery and their effects on indigenous microorganisms. J Hazard Mater. 403, 123625. 10.1016/j.jhazmat.2020.123625

766

767

768

Pereira, L.B., Vicentini, R., Ottoboni, L.M., 2014. Changes in the bacterial community of soil from a neutral mine drainage channel. PLoS One. 9(5), e96605. 10.1371/journal.pone.0096605

769

Price, G.W., Langille, M.G.I., Yurgel, S.N., 2021. Microbial co-occurrence network analysis
770

771

of soils receiving short- and long-term applications of alkaline treated biosolids. Sci Total Environ. 751, 141687. 10.1016/j.scitotenv.2020.141687

772

Qiao, L., Liu, X., Zhang, S., Zhang, L., Li, X., Hu, X., Zhao, Q., Wang, Q., Yu, C., 2021.

773

Distribution of the microbial community and antibiotic resistance genes in farmland surrounding gold tailings: A metagenomics approach. Sci Total Environ. 779, 146502. 10.1016/j.scitotenv.2021.146502

775

776

Sheik, C.S., Mitchell, T.W., Rizvi, F.Z., Rehman, Y., Faisal, M., Hasnain, S., McInerney, M.J.,

777

Krumholz, L.R., 2012. Exposure of soil microbial communities to chromium and arsenic alters their diversity and structure. PLoS One. 7(6), e40059.

778

779

10.1371/journal.pone.0040059

780

Sun, X., Kong, T., Xu, R., Li, B., Sun, W., 2020. Comparative characterization of microbial communities that inhabit arsenic-rich and antimony-rich contaminated sites:

781

Responses to two different contamination conditions. Environ Pollut. 260, 114052.

782

783

10.1016/j.envpol.2020.114052

784

Tian, Z., Li, G., Tang, W., Zhu, Q., Li, X., Du, C., Li, C., Li, J., Zhao, C., Zhang, L., 2022.

785

Role of Sedum alfredii and soil microbes in the remediation of ultra-high content heavy metals contaminated soil. Agriculture, Ecosystems & Environment. 339.

786

787

10.1016/j.agee.2022.108090

788

Wan, Y., Devereux, R., George, S.E., Chen, J., Gao, B., Noerpel, M., Scheckel, K., 2022.

789

Interactive effects of biochar amendment and lead toxicity on soil microbial community. J Hazard Mater. 425, 127921. 10.1016/j.jhazmat.2021.127921

790

791

Wang, C., Jia, Y., Wang, Q., Yan, F., Wu, M., Li, X., Fang, W., Xu, F., Liu, H., Qiu, Z., 2022a.

792

Responsive change of crop-specific soil bacterial community to cadmium in farmlands surrounding mine area of Southeast China. Environ Res. 214(Pt 1), 113748.

793

794

10.1016/j.envres.2022.113748

795

Wang, C., Liu, S., Wang, P., Chen, J., Wang, X., Yuan, Q., Ma, J., 2021. How sediment

796

bacterial community shifts along the urban river located in mining city. Environ Sci Pollut Res Int. 28(31), 42300-42312. 10.1007/s11356-020-12031-0

797

798

Wang, N., Zhang, S., He, M., 2018. Bacterial community profile of contaminated soils in a

799

typical antimony mining site. Environ Sci Pollut Res Int. 25(1), 141-152.
800 10.1007/s11356-016-8159-y

801 Wang, W., Wang, H., Cheng, X., Wu, M., Song, Y., Liu, X., Loni, P.C., Tuovinen, O.H.,
802 2022b. Different responses of bacteria and fungi to environmental variables and
corresponding community assembly in Sb-contaminated soil. Environmental Pollution.
803 298. 10.1016/j.envpol.2022.118812
804

805 Wei, G., Li, M., Shi, W., Tian, R., Chang, C., Wang, Z., Wang, N., Zhao, G., Gao, Z., 2020. Similar drivers but different effects lead to distinct ecological patterns of soil bacterial
806 and archaeal communities. Soil Biology and Biochemistry. 144.
807 10.1016/j.soilbio.2020.107759
808

809 Xie, Y., Bu, H., Feng, Q., Wassie, M., Amee, M., Jiang, Y., Bi, Y., Hu, L., Chen, L., 2021. Identification of Cd-resistant microorganisms from heavy metal-contaminated soil
810 and its potential in promoting the growth and Cd accumulation of bermudagrass.
811 Environ Res. 200, 111730. 10.1016/j.envres.2021.111730
812

813 Xu, D.M., Fu, R.B., Liu, H.Q., 2021a. Current knowledge from heavy metal pollution in Chinese smelter contaminated soils, health risk implications and associated remediation progress in recent decades: A critical review. J. Clean. Prod. 286.
814
815 10.1016/j.jclepro.2020.124989
816

817 Xu, M., Huang, Q., Xiong, Z., Liao, H., Lv, Z., Chen, W., Luo, X., Hao, X., 2021b. Distinct Responses of Rare and Abundant Microbial Taxa to In Situ Chemical Stabilization of Cadmium-Contaminated Soil. mSystems. 6(5), e0104021.
818
819 10.1128/mSystems.01040-21
820

821 Yan, C., Wang, F., Geng, H., Liu, H., Pu, S., Tian, Z., Chen, H., Zhou, B., Yuan, R., Yao, J., 2020. Integrating high-throughput sequencing and metagenome analysis to reveal the characteristic and resistance mechanism of microbial community in metal contaminated sediments. Sci Total Environ. 707, 136116.
822
823
824
825 10.1016/j.scitotenv.2019.136116

826 Yun, S.W., Baveye, P.C., Kim, D.H., Kang, D.H., Lee, S.Y., Kong, M.J., Park, C.G., Kim, H.D., Son, J., Yu, C., 2018. Analysis of metal(loid)s contamination and their continuous input in soils around a zinc smelter: development of methodology and a case study in South Korea. Environ Pollut. 238, 140-149.
827
828
829
830 10.1016/j.envpol.2018.03.020

831 Zhang, B., Zhang, J., Liu, Y., Shi, P., Wei, G., 2018. Co-occurrence patterns of soybean rhizosphere microbiome at a continental scale. Soil Biology and Biochemistry. 118, 178-186. 10.1016/j.soilbio.2017.12.011

834 Zhang, K., Li, K., Tong, M., Xia, Y., Cui, Y., Liu, Z., Chen, Q., Li, Q., Hu, F., Yang, F., 2022a. Distribution Pattern and Influencing Factors of Heavy Metal Resistance Genes in the Yellow River Sediments of Henan Section. Int J Environ Res Public Health. 19(17). 10.3390/ijerph191710724

838 Zhang, M., Ye, J., He, J.S., Zhang, F., Ping, J., Qian, C., Wu, J., 2020. Visual detection for nucleic acid-based techniques as potential on-site detection methods. A review. Anal Chim Acta. 1099, 1-15. 10.1016/j.aca.2019.11.056

841 Zhang, M., Zhang, T., Zhou, L., Lou, W., Zeng, W., Liu, T., Yin, H., Liu, H., Liu, X., Mathivanan, K., Praburaman, L., Meng, D., 2022b. Soil microbial community assembly model in response to heavy metal pollution. Environ Res. 213, 113576. 10.1016/j.envres.2022.113576

845 Zhang, X., Chen, B., Yin, R., Xing, S., Fu, W., Wu, H., Hao, Z., Ma, Y., Zhang, X., 2022c. Long-term nickel contamination increased soil fungal diversity and altered fungal community structure and co-occurrence patterns in agricultural soils. J Hazard Mater. 436, 129113. 10.1016/j.jhazmat.2022.129113

849 Zhang, Z.F., Pan, J., Pan, Y.P., Li, M., 2021. Biogeography, Assembly Patterns, Driving Factors, and Interactions of Archaeal Community in Mangrove Sediments. mSystems. 6(3), e0138120. 10.1128/mSystems.01381-20

852 Zheng, W., Zhao, Z., Gong, Q., Zhai, B., Li, Z., 2018. Responses of fungal-bacterial community and network to organic inputs vary among different spatial habitats in soil. Soil Biology and Biochemistry. 125, 54-63. 10.1016/j.soilbio.2018.06.029

855 Zheng, W., Zhao, Z., Lv, F., Wang, R., Wang, Z., Zhao, Z., Li, Z., Zhai, B., 2021. Assembly of abundant and rare bacterial and fungal sub-communities in different soil aggregate sizes in an apple orchard treated with cover crop and fertilizer. Soil Biology and Biochemistry. 156. 10.1016/j.soilbio.2021.108222

859 Zhou, H., Gao, Y., Jia, X., Wang, M., Ding, J., Cheng, L., Bao, F., Wu, B., 2020. Network
860

861

862

analysis reveals the strengthening of microbial interaction in biological soil crust development in the Mu Us Sandy Land, northwestern China. Soil Biology and Biochemistry. 144. 10.1016/j.soilbio.2020.107782

863

864

Zhou, J.Z., Ning, D.L., 2017. Stochastic community assembly: does it matter in microbial ecology. Microbiol. Mol. Biol. Rev. 10.1128/MMBR

865

866

867

868

Zhou, L., Zhao, Z., Shao, L., Fang, S., Li, T., Gan, L., Guo, C., 2022. Predicting the abundance of metal resistance genes in subtropical estuaries using amplicon sequencing and machine learning. Ecotoxicol Environ Saf. 241, 113844. 10.1016/j.ecoenv.2022.113844

869

870

871

872

873

Zhu, X., Yao, J., Wang, F., Yuan, Z., Liu, J., Jordan, G., Knudsen, T.S., Avdalovic, J., 2018. Combined effects of antimony and sodium diethyldithiocarbamate on soil microbial activity and speciation change of heavy metals. Implications for contaminated lands hazardous material pollution in nonferrous metal mining areas. J Hazard Mater. 349, 160-167. 10.1016/j.jhazmat.2018.01.044

874

875

876

877

Zou, H.Y., He, L.Y., Gao, F.Z., Zhang, M., Chen, S., Wu, D.L., Liu, Y.S., He, L.X., Bai, H., Ying, G.G., 2021. Antibiotic resistance genes in surface water and groundwater from mining affected environments. Sci Total Environ. 772, 145516. 10.1016/j.scitotenv.2021.145516

878
879 **Highlights**

880 ● The interaction between microbes, metal(loid)s, and soil properties was clarified.

881 ● The links of fungal co-occurrence networks were higher than the bacterial networks.

883 ● The keystone taxa were investigated in bacterial and fungal communities.

884 ● Deterministic assemblies played a vital role in shaping the microbial community.
885 **Figure captions**

886 **Fig. 1.** Location of the abandoned non-ferrous metal(loid)s Huilong smelters (left) and distribution of sampling points (right), located in Zhongshan, Guangxi province, southwest China. The study area is delineated in red and different background colors represent different sites. The red dots represent the impact zone south of the smelter.

891 **Fig. 2.** Boxplots showing the soil total metal(loid)s concentrations in the non-ferrous metal(loid)s smelter and surrounding sites. Different lowercase letters in the boxplots indicate significant differences (*P* < 0.05) among the tested sites.

894 **Fig. 3.** Contributions of bioavailable metal(loid)s and edaphic parameters on bacterial beta diversity (NMDS1) in Huilong smelter and surrounding sites as predicted by the random forest model (A), and partial dependence of the predicted model (B), the black points and blue lines are the partial dependence data and trend, the red lines are the local polynomial regression fitting trends, with the 95% confidence limits (the gray band).

900 **Fig. 4.** Networks analysis between environmental variables and bacterial (A) and fungal (B) species among the top 500 abundant OTUs in the Huilong smelter regions. Edges are shown by strong correlations (Spearman *R* > |0.6|) and significant (*P* < 0.05). The nodes were colored by modularity class. The size of the node corresponds to the number of connections. The thickness of the edges corresponds to the strength of the correlation. Pink lines indicate positive interactions and green lines indicate negative interactions.

907 **Fig. 5.** Co-occurrence patterns of bacterial (A and B) and fungal (C and D) communities among the top 500 abundant OTUs in the Huilong regions. Edges are shown by strong correlations (Spearman *R* > |0.6|) and significant (*P* < 0.05). The nodes were colored by phylum (A and C) and modularity class (B and D). The thickness of the edges corresponds to the strength of the correlation. Light purple lines indicate positive interactions and green lines indicate negative interactions.
914 **Fig. 6.** Relative contributions of deterministic and stochastic processes for bacterial
915 (A) and fungal (B) communities in the Huilong smelter and surrounding
916 regions were determined using the null model framework.

917
918
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Location of the abandoned non-ferrous metal(loid)s Huilong smelters (left) and distribution of sampling points (right), located in Zhongshan, Guangxi province, southwest China. The study area is delineated in red and different background colors represent different sites. The red dots represent the impact zone south of the smelter.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Boxplots showing the soil total metal(loid)s concentrations in the non-ferrous metal(loid)s smelter and surrounding sites. Different lowercase letters in the boxplots indicate significant differences (*P* < 0.05) among the tested sites.</figcaption></figure>
A

<figure><img src="image_4.png" /></figure>

B

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Contributions of bioavailable metal(loid)s and edaphic parameters on bacterial beta diversity (NMDS1) in Huilong smelter and surrounding sites as predicted by the random forest model (A), and partial dependence of the predicted model (B), the black points and blue lines are the partial dependence data and trend, the red lines are the local polynomial regression fitting trends, with the 95% confidence limits (the gray band).</figcaption></figure>
A

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

B

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

931

932

933

934

935

936

937

938

<caption>**Fig. 4.** Networks analysis between environmental variables and bacterial (A) and fungal (B) species among the top 500 abundant OTUs in the Huilong smelter regions. Edges are shown by strong correlations (Spearman *R* > |0.6|) and significant (*P* < 0.05). The nodes were colored by modularity class. The size of the node corresponds to the number of connections. The thickness of the edges corresponds to the strength of the correlation. Pink lines indicate positive interactions and green lines indicate negative interactions.</caption>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Co-occurrence patterns of bacterial (A and B) and fungal (C and D) communities among the top 500 abundant OTUs in the Huilong regions. Edges are shown by strong correlations (Spearman *R* > |0.6|) and significant (*P* < 0.05). The nodes were colored by phylum (A and C) and modularity class (B and D). The thickness of the edges corresponds to the strength of the correlation. Light purple lines indicate positive interactions and green lines indicate negative interactions.</figcaption></figure>
A

<figure><img src="image_9.png" /></figure>

B

<figure><img src="image_10.png" /></figure>

948

949

**Fig. 6.** Relative contributions of deterministic and stochastic processes for bacterial (A) and fungal (B) communities in the Huilong smelter and surrounding regions were determined using the null model framework.

950

951

952
<table><caption>**Table 1.** Topological characteristics of bacterial and fungal networks in Y, XY, YBS, and ZD sites.</caption><thead><tr><th rowspan="2">Topological properties</th><th colspan="2">Bacterial networks</th><th colspan="2">Fungal networks</th></tr><tr><th>1*</th><th>2*</th><th>1*</th><th>2*</th></tr></thead><tbody><tr><td>Nodes</td><td>473</td><td>120</td><td>490</td><td>177</td></tr><tr><td>Total links</td><td>5120</td><td>159</td><td>16559</td><td>520</td></tr><tr><td>Positive links</td><td>4706</td><td>82</td><td>16553</td><td>89</td></tr><tr><td>Negative links</td><td>414</td><td>77</td><td>6</td><td>431</td></tr><tr><td>Average degree</td><td>21.649</td><td>2.65</td><td>67.588</td><td>5.876</td></tr><tr><td>Clustering coefficient</td><td>0.433</td><td>0</td><td>0.584</td><td>0</td></tr><tr><td>Path distance</td><td>3.219</td><td>4.229</td><td>2.656</td><td>3.181</td></tr><tr><td>Density</td><td>0.046</td><td>0.022</td><td>0.138</td><td>0.033</td></tr><tr><td>Modularity</td><td>0.508</td><td>0.63</td><td>0.29</td><td>0.341</td></tr></tbody></table>

* abbreviations: 1, nodes of co-occurrence patterns were colored by phylum and modularity class in the bacterial or fungal communities network analysis; 2, networks analysis between environmental variables and bacterial or fungal species.