Uncovering microbial responses to sharp geochemical gradients in a terrace contaminated by acid mine drainage

Journal Pre-proof Uncovering microbial responses to sharp geochemical gradients in a terrace contaminated by acid mine drainage Rui Xu, Baoqin Li, Enzong Xiao, Lily Y. Young, Xiaoxu Sun, Tianle Kong, Yiran Dong, Qi Wang, Zhaohui Yang, Lei Chen, Weimin Sun PII:

S0269-7491(19)36825-3

DOI:

https://doi.org/10.1016/j.envpol.2020.114226

Reference:

ENPO 114226

To appear in:

Environmental Pollution

Received Date: 19 November 2019 Revised Date:

23 January 2020

Accepted Date: 16 February 2020

Please cite this article as: Xu, R., Li, B., Xiao, E., Young, L.Y., Sun, X., Kong, T., Dong, Y., Wang, Q., Yang, Z., Chen, L., Sun, W., Uncovering microbial responses to sharp geochemical gradients in a terrace contaminated by acid mine drainage, Environmental Pollution (2020), doi: https:// doi.org/10.1016/j.envpol.2020.114226. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Graphical Abstract

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Uncovering microbial responses to sharp geochemical gradients in a

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terrace contaminated by acid mine drainage

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Rui Xu1,2†, Baoqin Li1,2†, Enzong Xiao3, Lily Y. Young4, Xiaoxu Sun1,2, Tianle Kong1,2, Yiran Dong5, Qi

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Wang1,2, Zhaohui Yang6, Lei Chen1,2, and Weimin Sun1,2*

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Guangdong Institute of Eco-Environmental Science & Technology, Guangzhou 510650, China

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China, Guangzhou 510650, China

Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management,

National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South

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Education, Guangzhou University, Guangzhou 510006, China

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Department of Environmental Sciences, Rutgers University, New Brunswick, 08540, USA.

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School of Environmental Studies, China University of Geosciences (Wuhan), Wuhan 430074, China

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College of Environmental Science and Engineering, Hunan University, Changsha 410082, China

Innovation Center and Key Laboratory of Waters Safety & Protection in the Pearl River Delta, Ministry of

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*Corresponding authors:

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Weimin Sun (W.M. Sun)

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Email: [email protected]

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808 Tianyuan Road, Guangzhou, Guangdong, China

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†These authors contribute equally to this paper.

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Abstract

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Acid mine drainage (AMD) is harmful to the environment and human health. Microorganisms-mineral

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interactions are responsible for AMD generation but can also remediate AMD contamination. Understanding

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the microbial response to AMD irrigation will reveal microbial survival strategies and provide approaches

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for AMD remediation. A terrace with sharp geochemical gradients caused by AMD flooding were selected to

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study the microbial response to changes in environmental parameters related to AMD contamination. AMD

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intrusion reduced soil microbial community diversity and further changed phylogenetic clustering patterns

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along the terrace gradient. We observed several genera seldom reported in AMD-related environments (i.e.,

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Corynebacterium, Ochrobactrum, Natronomonas), suggesting flexible survival strategies such as nitrogen

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fixation, despite the poor nutritional environment. A co-occurrence network of heavily-contaminated fields

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was densely connected. The phyla Proteobacteria, Acidobacteria, Chloroflexi, and Euryarchaeota were all

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highly interconnected members, which may affect the formation of AMD. Detailed microbial response to

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different soil characterizations were highlighted by random forest model. Results revealed the top three

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parameters influencing the microbial diversity and interactions were pH, Fe(III), and sulfate. Various

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acidophilic Fe- and S-metabolizing bacteria were enriched in the lower fields, which were heavily

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contaminated by AMD, and more neutrophiles prevailed in the less-contaminated upper fields. Many

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indicator species in the lower fields were identified, including Desulfosporosinus, Thermogymnomonas,

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Corynebacterium, Shewanella, Acidiphilium, Ochrobactrum, Leptospirillum, and Allobaculum, representing

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acid-tolerant bacteria community in relevant environment. The detection of one known sulfate-reducing

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bacteria (i.e., Desulfosporosinus) suggested that biotic sulfate reduction may occur in acidic samples, which

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offers multiple advantages to AMD contamination treatment. Collectively, results suggested that the

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geochemical gradients substantially altered the soil microbiota and enriched the relevant microorganisms

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adapted to the different conditions. These findings provide mechanistic insights into the effects of

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contamination on the soil microbiota and establish a basis for in situ AMD bioremediation strategies. 2

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Keywords: acid mine drainage; terrace; soil microbiota; random forest; co-occurrence network

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Main finding

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This work revealed the soil microbial community diversity and phylogenetic interactions in response to the

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terrace gradient irrigated by AMD irrigation.

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1

Introduction

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Soils represent a critical hotspot for microbially-mediated biogeochemical cycling of critical elements,

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including C, N, and S, and a number of metal(loid)s, such as Fe and As. Microbial behaviors are usually

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influenced by the surrounding environment or geochemical parameters (Liu et al., 2014a), and microbial

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activities may also, in turn, influence the speciation and distribution of geochemical parameters (Konhauser

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et al., 2011). Therefore, understanding the interactions among microbial communities and geochemical

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conditions is a central issue in soil microbial ecology and may provide insights into the management and

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cleanup of environmental contamination.

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Acid mine drainage (AMD) usually refers to the acid wastewater formed by the scouring of abandoned

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mining areas (Méndez-García et al., 2015). AMD is mainly caused by the oxidation of sulfate metals (e.g.,

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mineral pyrite) when the mineral sources are exposed to water, air, and microbial activity (Naidu et al.,

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2019). AMD into waterways can have a severe impact on the environment because of its high acidity and

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levels of toxic metal(loid)s (Evangelou and Zhang, 1995). Due to extensive coal mining, the Southwest Coal

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Basin of China (located in Guizhou province) has become a heavily AMD-contaminated region (Tao et al.,

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2012). Due to the limited availability of arable land in this mountainous area, many crops have been

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cultivated near the mining area and irrigated using AMD-polluted water. 3

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AMD affects ecosystems in numerous ways, including simplifying the food chain, eliminating species, and

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disturbing ecological stability, all of which lead to both direct and indirect selective pressures on ecosystems

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(Gray, 1997). Extreme acidity and high concentrations of sulfate and metals are two general characteristics

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of AMD. Consequently, long-term irrigation with AMD-contaminated water increases soil acidity and

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decreases soil fertility (Choudhury et al., 2017; Sun et al., 2015a). For example, a report on paddy soils

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affected by AMD found that contamination increased available sulfur and extractable heavy metal levels in

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the soil and decreased the availability of nutrients and trace metals critical for rice production (e.g.,

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phosphorus, potassium, and zinc) (Choudhury et al., 2017). More importantly, AMD contamination

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increases soil toxicity and threatens human welfare due to the leaching of metals from nearby water sources

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and sediments (Naidu et al., 2019). Shangba Village, also known as “cancer village,” is located downstream

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of the Hengshi River, which is heavily contaminated with AMD-polluted irrigation water (Larson, 2014;

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Wang et al., 2011). Therefore, attenuation of AMD contamination in arable soils is important for

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environmental protection, food safety, and human health.

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Total soil biomass is an important index of environmental pressure, as microorganisms form a critical part of

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the soil food web (Pietri and Brookes, 2009). Interactions between microorganisms and ecological soil

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niches are one of the most studied aspects of current soil research. Since soils are dynamic reservoirs for

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microorganisms with various metabolic functions (such as NO3-/Fe(III)/SO42- reducing bacteria, fermenters,

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and methanogens (Liesack et al., 2000)), the introduction of AMD can substantially influence the

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composition of the soil microbial community. Our research group has investigated the microbiota of soils

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interacting with AMD-contaminated river water and found elevated abundances of Fe- and S-related

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microorganisms, which may outcompete methanogens (Sun et al., 2015a). Wang et al. (2016) investigated

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the distribution and diversity of bacterial communities in a soil irrigated with AMD-contaminated water and

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observed an increase in the abundance of sulfate-reducing bacteria. Later, the same research group showed 4

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that AMD irrigation considerably enriched the microbial phyla Acidobacteria and Crenarchaeota, while

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fungal communities remained relatively stable (Wang et al., 2017). A similar study also reported the soil

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microbial biomass was reduced due to the low pH and high metal concentrations coincident with AMD

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contamination, but these effects were compensated for by the positive effects of additional organic carbon in

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a field contaminated by AMD field (Sahoo et al., 2010). Recent research successfully predicted the natural

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AMD microbial assemblages at a taxonomic level using an advanced, high-throughput sequencing and

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modeling approach (Kuang et al., 2016). Collectively, although these studies characterized the phylogenies

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of microbial assemblages in AMD-contaminated fields, little is known regarding the innate microbial

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community change (or lack thereof) when exposed to AMD contamination.

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The investigation of microbial interactions across geochemical gradients will shed light on how soil

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microbiota responds to AMD intrusion. In this study, an AMD-contaminated terrace demonstrated sharp

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geochemical gradients, which provided a unique natural case to investigate how microbial community was

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affected by extreme environmental perturbation (i.e., AMD contamination). We hypothesized that the

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microbial communities are niche-driven and strongly correlate with changes in environmental parameters,

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which in turn alter the innate microbial response and interactions. We used an integrated approach combined

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with geochemical analysis, 16S rRNA amplicon sequencing, and statistical tools to elucidate the microbial

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response to AMD contamination.

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2

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2.1 Soil sampling

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The AMD-contaminated terrace located in Fuquan, Guizhou, Southwest China. The terrace is about 80

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meters high and can be divided into five fields according to altitude by a height difference of 2-3 m. Five

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fields were marked as “Field 1” to “Field 5” from low to high. A river, receiving AMD effluents from

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abandoned coal mines, flowing through under the terraces. Between June and August of each year, flooding

Materials and Methods

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of the AMD lake frequently submerges the ‘lower fields’ (Field 1 and Field 2). This seasonal flooding

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condition leads to the geographical characteristics of gradient variation at the lower fields and ‘upper fields’

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(Field 3 to Field 5). Sampling occurred at October after the flooding season. This is an ideal time point for

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sampling because the flooding season ceased around September. Sampling occurred one month after the

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flooding season. Therefore, the innate microbiota may get enough time to respond to the sharp exogenous

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perturbation. For each field, three sampling locations were selected, and soils were sampled from different

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depth of the sampling locations, designated as S for surface soils (ca. 0-10 cm), M for middle soils (ca.

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10-20 cm), and B for bottom soils (ca. 20-30 cm). Each sample was coded as AMD
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number>--. For example, sample AMD4-3-B would be soil sample

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taken from the bottom of #3 sampling site of Field 4. A total of 45 soil samples was taken from the five

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quasi-independent fields for both geochemical and molecular analyses, except for two low-quality samples

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(AMD3-1-M and AMD5-1-M) based on the examination of DNA extraction. Therefore, the total number of

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samples analyzed in this study was 43. The soil samples were held at 4°C immediately after sampling then

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transported to laboratory within 24 hours. Samples were stored at -80°C until analysis.

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2.2 Geochemical parameter measurement

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Geochemical analyses, including pH, total carbon, total nitrogen, ferrous and ferric irons, and metal(loid)s

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(i.e. arsenic and antimony) were performed according to our previous studies (Sun et al., 2016a; Sun et al.,

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2017). Before the measurement, the bulk soil sample was dried at 105°C. Sample was then fully ground and

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passed through a 200-mesh sieve. The soil samples were mixed with distilled water for equilibration. pH

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was measured by a pH meter. Total sulfur (TS), total nitrogen (TN), and total organic carbon (TOC) were

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measured using an elemental analyzer (vario MACRO cube, Elementar, Hanau, Germany) (Lundquist et al.,

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1980; Schumacher, 2002). Sulfate and nitrate were measured by ion chromatography (IC, Dionex, ICS-90,

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USA) (Tabatabai and Dick, 1983). Ferrous iron (Fe(II)) concentration was detected by the 6

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spectrophotometry (UV-9000s, METASH, Shanghai, China) at 510 nm according to the 1, 10 phenanthroline

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method (Rice et al., 2012). Total iron (Fetot) concentration was measured with by an atomic fluorescence

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spectrometer (Haiguang, China) after the soil samples were fully digested with HCl and HNO3 (3:1 in v/v)

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as modified from a previous study (Rahman et al., 2000). Ferric iron (Fe(III)) concentration was calculated

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by the differences between the Fetot and Fe(II) concentrations. Concentrations of arsenic and antimony in the

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soils were determined by an atomic fluorescence spectrometer (Haiguang, China) using the fully digested

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samples as described above.

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2.3 Illumina MiSeq sequencing of 16S rRNA genes

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Total genomic DNA was extracted from 0.5 g of soil samples using PowerSoil® Soil DNA Isolation kit (MO

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BIO Laboratories, Inc. Carlsbad, CA) according to the manufacture’s protocol. High-throughput amplicon

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sequencing of V4-V5 hypervariable region of 16S rRNA genes was performed on Illumina MiSeq platform

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at Ecogene company (Shenzhen, China) as described previously (Sun et al., 2018c). Briefly, the forward and

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reverse raw reads were merged to paired-end reads by FLASH (Magoč and Salzberg, 2011). QIIME (v1.7.0)

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was used to remove the barcoded primer sequences from the paired-end reads (Caporaso et al., 2010;

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Bokulich et al., 2013), and chimeric sequences were then removed using UCHIME (Haas et al., 2011).

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Operational taxonomic units (OTUs) were clustered at 97% sequence similarity using UPARSE. The

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taxonomic assignment of each OTU was performed using the RDP classifier (v2.2) and confirmed by the

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latest version of GreenGenes Database (v13.5) (DeSantis et al., 2006; Wang et al., 2007).

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2.4 Statistical analyses

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Statistical analyses were conducted to discern the differences within the microbial community across fields.

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Alpha-diversity indices were calculated using the scikit-bio of QIIME. Principal Coordinate Analysis (PCoA)

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was calculated using weighted UniFrac distance matrices to compare the microbial community in each

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sample (Lozupone et al., 2011). PCoA analysis was performed by the MicrobiomeAnalyst platform 7

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(Dhariwal et al., 2017). Indicator species analysis (ISA) was performed in R (‘indicspecies’ package) to

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determine the indicator species at the genus (95% sequence similarity) and OTU (97% sequence similarity)

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levels (De Cáceres et al., 2010). ANOSIM (an analog of univariate ANOVA) test was performed in SPSS (v

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12.0).

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2.5 Random forest model

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Random forest (RF) was performed to predict the relationship between environmental variables and

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responding parameters, such as alpha diversity indexes and the relative abundances of specific genus. RF

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was further used to output the variable importance of each environmental parameters on the microbial

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diversity and the dynamics of specific genus. Methods and algorithms of the RF were described previously

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(Wang et al., 2015), which provides a model to obtain average results (Friedman, 2006). RF was performed

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in the ‘RandomForest’ package in R (v. 3.1.2). Receiver operating characteristic (ROC) approach was used

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to valid the RF model as described previously (Díaz-Uriarte and De Andres, 2006). The RF model was then

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further visualized in R based on partial dependence plots and two-dimensional interaction.

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2.6 Co-occurrence network analysis

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The interaction between ‘geochemical parameters-individual microbial taxa’ or ‘microbe-microbe’ was

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visualized by the co-occurrence network (Banerjee et al., 2018). In the network, a connected link indicates a

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significant (p < 0.05) and strong (0.8 < |r| <1) Spearman’s correlation between two variables. The size of

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each node is proportional to the number of connections (i.e., degree). The bigger the size of the node, the

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more connections of the node. The thickness of each link is proportional to the absolute value of Spearman’s

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correlation. Co-occurrence networks were visualized using Cytoscape (v3.6.1) (Xu et al., 2018; Xu et al.,

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2019). Number of nodes and links, centralization of closeness/betweenness/degree, and modularity were

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output in R (‘igraph’ packages) (Csardi and Nepusz, 2006). Raw sequences of 16S rRNA have been

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submitted to NCBI Sequence Read Archive with No. PRJNA431101. 8

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3

Results

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3.1 Geochemical gradients caused by AMD flooding

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Characterization of the geochemical conditions in the five fields showed that flooding of the AMD lake

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examined in this study created a steep geochemical gradient along the terrace (Figure 1). For example, pH

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values were significantly lower in the ‘lower fields’ (including fields 1 and 2) than in the ‘upper fields’

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(including fields 3–5). All samples in Field 1 exhibited pH values of less than 3, suggesting that AMD

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contamination considerably acidified the lower fields. Submersion in AMD also created gradients of

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Fe-related compounds, with the highest concentrations of Fetot (Fe(II) plus Fe(III)) in Field 1 and the lowest

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concentrations in fields 5. Sulfate and TS concentrations were significantly higher in the lower fields due to

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the contact with S-rich AMD water. In contrast, upper fields showed significantly higher concentrations of

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TN and nitrate than the lower fields. No significant differences in TOC were observed, but TOC was

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relatively higher in upper fields. Arsenic (As) was highly enriched in lower fields, indicating that flooding

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from the AMD lake may be the major source of As. However, antimony (Sb), another element belonging to

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the same group in the periodic table as As, only showed slightly higher concentrations in Field 3. Thus, Sb in

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the fields may not have originated from the AMD lake.

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3.2 Diversity of microbial community

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The microbial composition and diversity varied among fields, indicating that the geochemical gradients

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affected the microbial communities. More OTU numbers (at phylum level) were obtained from the upper

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fields as compared to the lower fields, as well other taxonomy levels from class to genus (Table S1). OTUs

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with only one sequence (‘singletons’) were removed before estimation the difference in the alpha diversity.

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Results showed all alpha diversity indices (Chao1, observed species number, Shannon, ACE, and Simpson)

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increased from the lower to the upper fields (Figure S1), indicating that AMD contamination negatively

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affects the microbial diversity in these fields. 9

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A weighted UniFrac dissimilarity matrix was calculated on normalized sequencing data to compare the

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composition of microbial communities within different fields (Figure S2). PC1 and PC2 respectively

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explained 21.9% and 3.6% of the total variation. Beta diversity analyses indicated that the microbial

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members clustered by their corresponding fields. Samples from lower fields 1 and 2 clustered more closely,

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whereas samples from upper fields 4 and 5 clustered together. Samples from Field 3 overlapped with the two

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above-mentioned clusters. ANOSIM was used to statistically support the visual clustering of above PCoA

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analysis, microbial communities in 5 fields were examined (Table S2). Results indicated that all fields were

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significantly different from each other with the P values < 0.05.

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3.3 Composition of microbial community

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The distributions of the top 10 most abundant phyla are shown in Figure S3. A clear filed-specific

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distribution was observed for some phyla. For example, the phyla Acidobacteria (~30%, relative abundance),

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Crenarchaeota (~2.5%), Bacteroidetes (~2.5%), Planctomycetes (~4%), and Verrucomicrobia (~2.5%) were

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significantly enriched in upper fields, particularly in fields 4 and 5, whereas the phyla Firmicutes (~4.5%)

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and Euryarchaeota (~7%) were strongly enriched in Field 1. There were no significant differences for

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Proteobacteria or Actinobacteria among these fields.

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A closer look at the filed-specific distributions of various lineages were performed at genus level. As

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illustrated in Figure 2, Leptospirillum (~3%), Thermogymnomonas (~2%), Ochrobactrum (~1%),

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Corynebacterium (~1%), and Propionibacterium (~0.3%) were significantly enriched in the lower fields 1

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and 2, whereas Rhodoplanes (~4%), Candidatus Solibacter (~3.5%), Candidatus Koribacter (~2.5%),

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Mycobacterium (~0.8%), Burkholderia (~0.5%), Kaistobacter (~0.4%), and Rhodanobacter (~0.3%) showed

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significantly higher relative abundances in the upper fields.

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Because of the apparent differences in taxa between the lower and upper fields, indicator species analysis 10

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(ISA) was performed to determine distinct indicator species in different fields. ISA detected 28 indicator

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genera in the lower fields, 8 in the upper fields. Full lists of indicator species and their corresponding

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indicator values can be found in Table 1. Thermogymnomonas, Corynebacterium, Brevibacterium,

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Propionibacterium, Shewanella, Dietzia, Acidiphilium, Ochrobactrum, Leptospirillum, and Allobaculum,

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were identified as indicator species in the lower fields, representing bacteria adapting to the AMD

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contamination environment. In contrast, many neutrophiles, such as Anaerolinea, Gemmata, Mesorhizobium,

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and Rhodoblastus, were identified as indicators in the upper fields, where was less contaminated by AMD.

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The enrichment of acidophiles in the lower fields and enrichment of neutrophiles in the upper fields indicate

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that the environmental gradients impact the microbial communities.

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3.4 Interaction network of microbiomes

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In the view of whole microbial community level, co-occurrence network was constructed to investigate the

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interaction between ‘microbe-microbe’ in different fields (Figures 3A and 3B). In the networks, only the

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strong (0.8 < |r| < 1) and significant Spearman correlations (p < 0.05) between pair-wise OTUs were

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visualized. The size of each node is proportional to the number of strong and significant correlations with

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other nodes. When considering the taxonomy of each node, a strong dynamic was observed in two networks

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(Figure 3C). In the lower fields, OTUs were mainly correlated within the phylum Proteobacteria,

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Acidobacteria, and Chloroflexi with a node’s ratio of 29%, 20%, and 18%, respectively. While in the upper

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fields, Acidobacteria became the most correlated phylum with a node ratio of 36%, followed by

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Proteobacteria (29%) and Chloroflexi (11%). Notably, two unique correlated-phyla were identified in each

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of the two networks that is Euryarchaeota (5%) in lower fields and Bacteroidetes (4%) in upper fields.

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Number of links/nodes, centralization of closeness and betweenness were normally used to evaluate the

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interactions of OTUs within a network. The higher the value, the stronger the interaction (Banerjee et al.,

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2018). In current study, results support the expectation that potential interactions in lower fields networks 11

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are stronger than those of upper fields networks, as revealed by the number of links and nodes (Figure 3D).

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Centralization of closeness and betweenness, representing the number of paths through a node (Williams et

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al., 2014), were as well as higher in the lower fields than those in the upper fields (Figure 3E).

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3.5 Effects of environmental parameters on microbial communities and diversity

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When considering only significant correlations (r > 0.8, p < 0.05) between environment parameters and

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individual microbial taxa, it is found that the nodes correlated with sulfate were identified as the largest

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nodes in the network, followed by nodes correlated with pH and Fe(III) (Figure 4). In contrast, nodes

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associated with Sb and nitrate were relatively small, suggesting that these parameters may have less effects

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on microbial assemblages.

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RF model was used to further assess the effects of environmental parameters on microbial alpha-diversity

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index metrics. In this study, observed species number produced the best results, where more than 75% of

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variations in the microbial community composition could be explained by the impact of environmental

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parameters (Figure 5). Among the selected environmental parameters, pH, Fe(III), and sulfate ranked as the

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top three parameters influencing the microbial diversity (Figure 5A). Partial dependence plots derived from

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the RF model indicated how these environmental parameters influenced the alpha diversity (in observed

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species number; Figure 5B). pH was positively correlated with the observed species, whereas sulfate

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negatively influenced the observed species, particularly when the concentration was below 2500 mg/L. Both

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Fe(III) and Fetot were negatively correlated with the observed species, whereas environmental parameters

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related to nutrients, such as TOC, TN, and nitrate, were positively correlated. Although As and Sb belong to

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the same group in the periodic table, As was negatively correlated with the observed species, whereas Sb

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showed a positive correlation. TS and Fe(II) did not show predictable trends, which could be attributed to

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the reduced variation explained by these two parameters. Collectively, both RF prediction and network

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analysis demonstrated that the pH, sulfate concentration and Fe(III) concentration were the main factors for 12

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the change of microbial community in these fields.

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The response of individual microbial taxa, especially those identified as the keystone species, is of particular

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interest. In the current study, genera frequently detected in AMD environments were considered as key

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genera from literatures, due to their extensive presence in AMD related environments. Accordingly,

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Acidiphilium, Anaeromyxobacter, Bradyrhizobium, Brevibacterium, Clostridium, Corynebacterium,

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Desulfosporosinus, Geobacter, Halomonas, Leptospirillum, Ochrobactrum, Rhodoplanes, Shewanella,

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Stenotrophomonas, and Thermogymnomonas were mainly discussed in the following analysis (Baker and

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Banfield, 2003; Chen et al., 2016; Huang et al., 2016; Sun et al., 2019; Xiao et al., 2009; Zhang et al.,

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2019a). Consistently, these genera were found to be in good agreement with the ISA results in the lower

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fields (Table 1). Besides, RF model was performed to predict their response to different environmental

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parameters. These selected genera showed an active response to environmental parameters (including pH,

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sulfate, iron ion, TS, nitrate, TN, TOC, Sb, and As) with the total of variation of the dependent variable

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(%IncMSE) ranging from 27% to 73%. Among them, pH, sulfate and Fe(III) concentrations strongly

288

influenced the abundances of these genera (Figure S4). For example, pH mainly affected the Rhodoplanes,

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Bradyrhizobium and Leptospirillum with an IncMSE value above 10. In addition, sulfate and Fe(III) were

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also the most important factors affecting Leptospirillum.

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All statistical analyses indicated that pH, Fe(III) and sulfate as an important driver shaping both the

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microbial communities and diversities. Therefore, the interactions of key genera and pH/Fe(III)/sulfate were

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further investigated by RF prediction (Figures 6, 7 and 8). For example, a large number of key genera

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(except Anaeromyxobacter, Bradyrhizobium, Geobacter, and Rhodoplanes, marked as blue) were found to

295

be negatively correlated with pH based on the RF model (Figure 6). The relative abundance of these genera

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was found to be much higher when pH below 4, which indicating their potential tolerance to an extremely

297

acid soil. In accordance with the RF prediction, these genera were also detected to have higher relative 13

298

abundances in lower fields with a low pH value, such as Leptospirillum, Thermogymnomonas and

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Ochrobactrum (Figure 2). Similarly, the detail response of these genera to Fe(III) (Figure 7) and sulfate

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(Figure 8) were also presented. Collectively, evidences suggested a concentration effect of different Fe(III)

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or sulfate contents on the relative abundance of microorganisms.

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4

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The response of the microbial community in AMD-irrigated fields is in lack of investigation. In this study,

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we used an integrated survey of soil microbial communities combined with high-throughput sequencing,

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geochemical analyses, and statistical tools to characterize the microbial responses to environmental changes

306

along a steep geochemical gradient in five AMD-contaminated fields. Knowledge of the key microbial

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populations and the “microbe-environment” interactions could provide insights into the metabolic potential

308

and physiological traits that may contribute to our future manipulation of these microorganisms in order to

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improve soil productivity and environmental remediation (Sun et al., 2018a).

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4.1 Phylogenetic clustering by geochemical gradients

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AMD contamination posed a significant threat to the adjoining environmental micro-biosphere. Major

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formation of AMD originates from metal sulfide compounds, such as “FeS2(s) + 14Fe(III) + 8H2O →

313

15Fe(II) + 2HSO4

314

dramatically lowered soil pH (< 3) and enriched the concentrations of sulfate, TS, and Fe ions in Field 1

315

(Figure 1). AMD-contamination can also be characterized as a poor nutritional environment with relatively

316

low concentrations of TN and TOC. Therefore, alpha diversity of the microbial community and OTU counts

317

were collectively lower in Field 1 (Figure S1 and Table S1). Decreases in biodiversity will result in a

318

reduction of the ecological services provided by the indigenous microbial community (Xu et al., 2017; Yan

319

et al., 2017). The reduced alpha diversity in the microbial community may be caused by the extremely low

320

TOC, TN, and pH in the lower fields due to AMD intrusion, and the microbial alpha diversity recovers from

Discussion

−

+14H+” (Akcil and Koldas, 2006). Thus, the introduction of AMD contamination

14

321

the impact of the AMD attenuated in higher fields with elevated TOC and TN (Zhang et al., 2019b). From

322

the RF model, we also found that TOC, TN, as well as nitrate, showed positive effects on alpha diversity

323

indices (in terms of observed species number) (Figure 5).

324

The selective pressures of the geochemical gradients created obvious phylogenetic clustering along the

325

terrace (i.e., distinct phylogenetic taxa clustering in different groups). PCoA results suggest that the

326

phylogenetic clustering of microbial populations was affected by AMD contamination (Figure S2). In detail,

327

microbial community dynamics were also observed in all five fields with different levels of AMD

328

contamination (Figures 2 and S3). Microbial community composition in the current study presented high

329

similarity with other AMD-related sites, including many indicator species identified in the lower fields (such

330

as members of Leptospirillum, Thermogymnomonas, Acidiphilium, and Alicyclobacillaceae) that were

331

known to have acclimated to AMD-related environments (Méndez-García et al., 2014; Schrenk et al., 1998;

332

Sun et al., 2015a). However, several phylotypes seldom reported in AMD environments were also found to

333

be dominant in this study (Table 1), such as Stenotrophomonas, Corynebacterium, Ochrobactrum, and

334

Natronomonas. The genus Natronomonas consists of several extremely haloalkaliphilic archaea that can

335

survive in alkaline environments (Cao et al., 2008). These archaea were identified as indicator species in the

336

extremely low pH environment of Field 1 but were not detected in upper fields where the pH was

337

significantly higher. Our observations suggested that these members may be metabolically resilient toward

338

adverse environments and may be able to survive at very low pH values. Autochthonous chemolithotrophs

339

were characterized as the main reason for the acidification of mining-related leachates (Johnson and

340

Hallberg, 2003). The presence of Stenotrophomonas, Corynebacterium, and Ochrobactrum in the lower

341

fields as keystone genera was intriguing, as previous characterizations of these genera include organisms

342

that are chemolithoautotrophic (Hu et al., 2016; Tunail and Schlegel, 1974), which may explain their

343

presence in AMD-contaminated sites. For example, one previous study reported Ochrobactrum and 15

344

Corynebacterium were able to fix nitrogen from the atmosphere and may further compensate for the low N

345

concentration in AMD fields (Giri and Pati, 2004; Ngom et al., 2004). Detection of these members suggested

346

the flexible survival strategies of bacteria, i.e., nitrogen fixation, in order to adapt to poor nutritional

347

environments. Factors affecting the enrichment of these potential chemolithoautotrophic bacteria in lower

348

fields, however, require further investigation.

349

The presence of AMD-related microbiota in niches has no well-established boundaries (Méndez-García et al.,

350

2015). Due to the synergistic/complicated relationships within various microbial groups, co-occurrence

351

network provided an overview to compare the mutual interactions of microorganisms exposed under

352

different acidic conditions. The lower fields’ network was more densely connected than the upper fields’

353

network (Figure 3). The phyla Proteobacteria, Acidobacteria, and Chloroflexi were the most connected

354

nodes in the lower fields’ network. In addition, the presence of Euryarchaeota was found to be unique in the

355

lower fields’ network, which might hold specific interest to tolerance within extremely acidic environments

356

or play a critical role in the biotic interactions. We propose that the highly interconnected network members

357

mentioned above may accelerate the formation of AMD or maintain physiochemical conditions for other

358

groups. For example, Proteobacteria are widely found in acidic environments. Several of the most common

359

genera that inhabit AMD ecosystems have been preliminarily studied, including Acidithiobacillus spp.

360

(which were able to oxidize Fe(II) and sulfur compounds by chemolithotrophic metabolism) (Williams and

361

Kelly, 2013) and Acidithiobacillus ferrooxidans, which were able to fix CO2 and atmospheric nitrogen

362

(Valdés et al., 2008). Additionally, the genus Thermogymnomonas is a typical Euryarchaeota often found in

363

AMD fields, which were suggested to be able to create a more suitable environment for the Fe(II) oxidizers

364

by removing the organic compounds that are toxic to the growth of autotrophs (Hallberg, 2010).

365

4.2 Interactions between environmental parameters and microbial communities

366

Microorganisms involved in the cycling of geochemical material can affect the mobility and bioavailability 16

367

of elements (Sun et al., 2018b). For example, Alicyclobacillus and Ferrovum were frequently described as

368

Fe-oxidizing bacteria in acidic environments (Johnson et al., 2014; Lu et al., 2010). Evidence suggests that

369

they affect the speciation of Fe and the carbon cycle in soils (Sun et al., 2015b). Such biological processes

370

have been applied to the remediation of metal contamination (Gadd, 2004). Although TOC, TN, or ionic

371

composition are also considered to significantly influence the microbial populations thriving in AMD

372

environments (Méndez-García et al., 2015), the pH and the Fe(III) and sulfate concentrations were the top

373

three most important factors affecting the microbiota in this study. Based on the “environment-microbe”

374

interaction network, sulfate and pH had substantial effects on the structure of abundant OTUs (Figure 4).

375

Consistent with the network analysis, the RF also showed that these three factors can explain more than 40%

376

of the variation.

377

4.2.1 pH

378

AMD contamination is ecologically impacted by the acid (H+), which generated directly from the oxidative

379

dissolution of metal sulfide compounds (Chen et al., 2016). Many studies reported the pH as the major factor

380

driving the dynamics of soil microbial community structure in different AMD sites (Baker and Banfield,

381

2003; Kuang et al., 2013; Liu et al., 2014b; Sun et al., 2016b). While pH may directly select certain

382

acidophilic groups and only those adapting to such acidic habitats can grow. pH can also indirectly affect the

383

microbial communities involved in biogeochemical cycles by affecting various physical and chemical

384

conditions of soil, such as solubility and availability of metal(loid)s (Baker and Banfield, 2003).

385

In the current study, pH varied significantly from Field 1 to Field 5. Variation of the pH may pose a strong

386

selective pressure on the innate microbiota of each field (Figure S4). The closer the field was to AMD

387

effluents from abandoned coal mines, the lower the observed pH (pH in Field 1 was < 3). pH was further

388

selected to predict impacts on several genera using the RF model (Figure 6). We observed in the current

17

389

study that microbes responded differently to the acidic environment based on the RF prediction. For

390

example, the relative abundance of some bacteria was very high at low pH (i.e., < 3) and decreased sharply

391

with the increase of pH over 3, evident in Acidophilus, Brevibacterium, Halomonoas, Leptospirillum,

392

Shewanella, Stenotrophomonas, and Thermogymnomonas. These could be considered as “extremely

393

acidophilic bacteria.” Another group of bacteria have higher abundances only in a certain range of lower

394

pHs (3−4), such as Clostridium and Desulfosporosinus, which could be considered as “moderately

395

acidophilic bacteria.” Acid-tolerant microorganisms showed different ecological adaptability. Consistently,

396

we observed enrichment of extreme acidophiles by ISA in lower fields, such as Acidiphilium and

397

Leptospirillum (Table 1), whereas many neutrophiles prevailed in the upper fields with relatively higher pH

398

values, representing the effect of pH on the microbial communities. It has been reported that Leptospirillum

399

(including three species: Leptospirillum ferrooxidans, Leptospirillum ferriphilum, and Leptospirillum

400

ferrodiazotrophum) can adapt to environments where pH is less than 2 (García-Moyano et al., 2008).

401

Similarly, according to the results of a limited pure culture experiment, one Thermogymnomonas species, T.

402

acidicola, was able to grow at an optimum pH of 3 (Itoh et al., 2011), implying its high affinity for acidic

403

conditions and may explain its prevalence in AMD fields. Based on predictions of the RF model on pH, we

404

also found that Thermogymnomonas was negatively correlated with environmental pH (Figure 8). The

405

relative abundance of Thermogymnomonas was stable when pH < 3, and it decreased dramatically when pH

406

was higher than 3. Further investigation into the reasons for their enrichment in low pH environments will

407

be helpful to the bioremediation of AMD fields.

408

4.2.2 Fe(III)

409

Fe(III) was one of the most critical environmental variables affecting the abundance of OTUs within the

410

co-occurrence network (Figure 4). Both Fe(III) and Fetot had adverse effects on microbial alpha diversity

411

(Figure 5 and 7). Fe(II) and Fe(III) were highly soluble when the pH was low (< 2.5) and thus may be 18

412

utilized by microorganisms (Baker and Banfield, 2003). On the one hand, the elevated levels of Fe(II) and

413

Fe(III) in the lower fields promoted the enrichment of typical AMD-resident Fe(II) oxidizers, such as

414

Leptospirillum, Alicyclobacillaceae, and Acidiphilium. This is supported by the frequent identification of

415

Leptospirillum and Acidiphilium in mining areas (Sun et al., 2019). Members of the genus Leptospirillum are

416

suggested to be an acidic-tolerant Fe(II) oxidizing bacteria. Acidiphilium spp. are reported to be

417

metabolically versatile, with the ability to oxidize S or Fe(II) (Gonzalez et al., 2015; Rohwerder and Sand,

418

2003). Collectively, the presence of these Fe-oxidizing bacteria suggested the prevalence of oxidation of

419

iron/sulfur coupled to autotrophic growth in AMD-impacted fields.

420

On the other hand, Fe(III) may also be widely used as an electron acceptor by Fe(III)-reducing bacteria

421

(FeRB) due to the excessive concentrations in AMD-impacted soils. Shewanella and Geobacter were the

422

two most commonly FeRB characterized in the literature. In the current study, an OTU including the genus

423

Shewanella was identified as an indicator species in lower fields (Table 1), whereas Geobacter was

424

predicted to be depleted in low pH environments (Figure 6). Therefore, we proposed that different FeRB

425

may perform Fe(III) reduction in different geochemical gradients: Shewanella was the primary organism

426

performing Fe(III) reduction in the lower fields with extremely low pHs, whereas Geobacter performed

427

Fe(III) reduction in the upper fields with their relatively higher pH values. This was consistent with our

428

previous observation that Shewanella was more active than Geobacter in environments with low pH (Sun et

429

al., 2015b). These findings agree well with the RF prediction, in which Shewanella and Geobacter presented

430

negative and positive correlations with environmental pH, respectively (Figure 6). Therefore, we proposed

431

that Shewanella is likely to be an acidophilic Fe(III) reduction bacteria, whereas Geobacter can perform

432

Fe(III) reduction in weakly acidic conditions with pH > 4.

433

Microbial diversity in a previous AMD-related study included microorganisms mainly belonging to Bacteria

434

and Archaea (Méndez-García et al., 2015). Other than Bacteria, Archaea may also participate in Fe cycling. 19

435

Thermogymnomonas, an extremely acidophilic and Fe(III)-oxidizing archaea, contributes to AMD

436

generation by influencing the cycles of Fe and sulfur. Although they were often found in low pH

437

environments (i.e., metal-rich tailing ponds) (Sakai and Kurosawa, 2016; Yang et al., 2014), little is known

438

about these archaea. Thermogymnomonas, belonging to the order Thermoplasmatales, are heterotrophic and

439

thermophilic. They were able to gain energy during the oxidation-reduction of Fe (Yang et al., 2014).

440

4.2.3 Sulfate

441

Sulfate was predicted as the third most important parameter from the RF model (Figures 5 and 8).

442

Consistent with this, sulfate was also shown to be strongly correlated with abundant taxa (Figure 4). The

443

lower fields also exhibited high concentrations of sulfate (reaching over 10,000 mg/kg, Figure 1). Exposure

444

of water, air, and microbial activity triggers the oxidation of metal sulfide compounds and the generation of

445

AMD, and therefore, sulfate generated. Sulfate reduction is an important process in AMD because it can

446

reduce acidity and precipitate metal(loid)s (Johnson and Hallberg, 2005). However, sulfate-reducing bacteria

447

(SRB) involved in such processes are highly dependent on the pH of the environment. For example, many

448

SRB members, such as Desulfovirga, Desulfotomaculum, Desulfarculaceae, and Desulfobacca, were

449

identified as indicator species in the upper fields (Table 1). Generally, the optimal pH range for the growth

450

of most SRB is 5-9 (Widdel and Bak, 1992). For these SRB, more energy was required to pump protons

451

across the cytoplasmic membrane at low pH conditions (Sánchez-Andrea et al., 2014). Therefore, SRB

452

showed low abundance at acidic environment. Notably, one known SRB (i.e., Desulfosporosinus) were

453

identified as an indicator species in the lower fields (Table 1). RF prediction further showed that

454

Desulfosporosinus spp. has a higher relative abundance only in the pH range of 3−4 (Figure 6). Although

455

the acidic environments in the lower fields may not favor the growth of SRB (Widdel and Bak, 1992) and

456

could have caused the reduced diversity of SRB, the detection of SRB as indicator species in the lower fields

457

suggested that biotic sulfate reduction may occur in acidic Fields 1 and 2. Our previous study also detected a 20

458

relatively high abundance of Desulfosporosinus spp., as well as Syntrophobacter spp., in an AMD creek,

459

suggesting that these SRB may be able to cope with extremely acidic environments (Sun et al., 2015b).

460

Applying SRB to AMD remediation at low pH conditions could offer multiple advantages to the potential to

461

treat AMD contamination. Since sulfate reduction neutralizes the AMD by producing alkalinity, it can also

462

precipitate soluble metals as metal sulfides to simultaneously recover metals (Sánchez-Andrea et al., 2014).

463

5

464

Our current study revealed major microbial responses to a gradient of environmental stress introduced by

465

AMD flooding in a steep terrace. AMD contamination affected the microbiota by reducing the microbial

466

community diversity, changing community composition, and disturbing the network of microbial

467

interactions. Random forest model suggested that pH and Fe(III) and sulfate concentrations are three

468

important environmental factors in shaping the microbial community composition. Potential interactions in

469

the heavily contaminated (the lower) fields networks were stronger than those of less contaminated (the

470

upper) fields networks. Key OTU-nodes were identified as Proteobacteria, Acidobacteria, Chloroflexi, in

471

addition to Euryarchaeota, and strongly interacted in the lower fields’ microbial network. Most of the

472

indicator species found in the lower fields were predicated to tolerate the extremely acidic pH conditions,

473

including members of the genera Shewanella, Acidiphilium, Brevibacterium, Corynebacterium,

474

Desulfosporosinus,

475

Desulfosporosinus in the lower fields suggested that biotic sulfate reduction may occur in extremely acidic

476

environments. Shewanella may be responsible for the Fe(III) reduction in acidic fields, while Geobacter

477

were suggested to able to reduce Fe(III) reduction in a weakly acidic condition (pH > 4). These observations

478

provide preliminary information regarding the effects of environmental stressors introduced by AMD

479

contamination, including low pH and elevated metal(loid) concentrations, on the metabolic traits of the soil

480

microbiota.

Conclusions

Leptospirillum,

Thermogymnomonas,

21

and

Ochrobactrum.

The

presence

of

481

Acknowledgements

482

This work was supported by GDAS' Project of Science and Technology Development (grant nos.

483

2018GDASCX-0601, 2020GDASYL-20200103086, 2019GDASYL-0302006, 2019GDASYL-0301002, and

484

2017GDASCX-0835); the National Natural Science Foundation of China (grant no. 41771301); the

485

High-level Leading Talent Introduction Program of GDAS (grant no. 2016GDASRC-0103); Guangdong

486

Introducing Innovative and Enterpreneurial Talants (grant no. 2017GC010570); the Local Innovative and

487

Research Teams Project of Guangdong Pearl River Talents Program (grant no. 2017BT01Z176); the

488

National Natural Science Foundation of Guangdong (grant no. 2019A1515011559); and the Science and

489

Technology Planning Project of Guangzhou (grant no. 201904010366).

490

Supplementary material

491

The supplementary material for this article contains Figure S1-S4 and Table S1-S2.

492

Conflict of Interest

493

The authors declare that they have no conflict of interest.

494

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Table 1 Indicator species at genus level in the lower fields (1 and 2) versus the upper fields (3-5). Ranked as alpha-beta order. Lower fields Acidiphilium Acidobacterium Acinetobacter Allobaculum Anaerococcus Aquicella Brachybacterium Brevibacterium Brevundimonas Chryseobacterium Corynebacterium Dietzia Enhydrobacter Escherichia Desulfosporosinus Halomonas Kocuria Leptospirillum Lysobacter Micrococcus Natronomonas Ochrobactrum Paracoccus Propionibacterium Proteus Shewanella Staphylococcus Stenotrophomonas Thermogymnomonas

Upper fields Anaerolinea Chthoniobacter Cupriavidus Desulfovirga Desulfotomaculum Desulfarculaceae Desulfobacca Gemmata Mesorhizobium Nitrosotalea Rhodoblastus Steroidobacter

Figure Captions Figure 1. Box plot indicates the distribution of geochemical parameters in the five AMD fields. Figure 2. Box plot indicates the distribution of abundant genera in the five AMD fields. Figure 3. Co-occurrence network of microbe-microbe interactions based on top OTUs (relative abundance > 0.1% in all samples) in the lower fields (A) and upper fields (B). A connection stands for the significant Spearman correlation with 0.8<|r| <1 (p < 0.05). The size of a node is proportional to the number of connections (degree). The color of a node represents the assignment at phylum level. Number of keystones OTUs within two networks were counted at phylum level (C). Network properties, including the number of links and nodes (D) and centralization of closeness and centralization of betweenness (E) were also presented. Figure 4. Co-occurrence network of environment-microbe interactions based on 16S rRNA amplicon sequencing. A connection stands for the Spearman correlation with 0.8<|r| <1 (positive correlation–red lines and negative correlation–green lines) and statistical significance (p < 0.05). Please note that only the strong and significant correlations between environmental parameters and OTUs could be shown in this network. The size of a node is proportional to the number of connections Figure 5. Random forest model output relating environmental variables to alpha diversity indices based on observed species number. Relative influence (represented by %incMSE mean decrease accuracy) of environmental variables on the observed species (A) and partial dependence plots for the model of the Observed species index (B) the red line is the partial dependence data line, the blue line is the local polynomial regression fitting trend line, and the grey band represents the 95% confidence interval. The partial plots show the relationships of selected environmental parameters with the observed species. Figure 6. Prediction of relative abundance of selected genus on pH based on the random forest model. Color of genus’s name was marked as the correlation between relative abundance and pH by negatively (red) or positively (blue). Figure 7. Prediction of relative abundance of selected genus on Fe(III) based on the random forest model. Color of genus’s name was marked as the correlation between relative abundance and Fe(III) by negatively (red) or positively (blue). Figure 8. Prediction of relative abundance of selected genus on sulfate based on the random forest model. Color of genus’s name was marked as the correlation between relative abundance and sulfate by negatively (red) or positively (blue).

Figure 1. Box plot indicates the distribution of geochemical parameters in the five AMD fields.

Figure 2. Box plot indicates the distribution of abundant genera in the five AMD fields.

Figure 3. Co-occurrence network of microbe-microbe interactions based on top OTUs (relative abundance > 0.1% in all samples) in the lower fields (A) and upper fields (B). A connection stands for the significant Spearman correlation with 0.8<|r| <1 (p < 0.05). The size of a node is proportional to the number of connections (degree). The color of a node represents the assignment at phylum level. Number of keystones OTUs within two networks were counted at phylum level (C). Network properties, including the number of links and nodes (D) and centralization of closeness and centralization of betweenness (E) were also presented.

Figure 4. Co-occurrence network of environment-microbe interactions based on 16S rRNA amplicon sequencing. A connection stands for the Spearman correlation with 0.8<|r| <1 (positive correlation–red lines and negative correlation–green lines) and statistical significance (p < 0.05). Please note that only the strong and significant correlations between environmental parameters and OTUs could be shown in this network. The size of a node is proportional to the number of connections

A

B

Figure 5. Random forest model output relating environmental variables to alpha diversity indices based on observed species number. Relative influence (represented by %incMSE mean decrease accuracy) of environmental variables on the observed species (A) and partial dependence plots for the model of the Observed species index (B) the red line is the partial dependence data line, the blue line is the local polynomial regression fitting trend line, and the grey band represents the 95% confidence interval. The partial plots show the relationships of selected environmental parameters with the observed species.

Figure 6. Prediction of relative abundance of selected genus on pH based on the random forest model. Color of genus’s name was marked as the correlation between relative abundance and pH by negatively (red) or positively (blue).

Figure 7. Prediction of relative abundance of selected genus on Fe(III) based on the random forest model. Color of genus’s name was marked as the correlation between relative abundance and Fe(III) by negatively (red) or positively (blue).

Figure 8. Prediction of relative abundance of selected genus on sulfate based on the random forest model. Color of genus’s name was marked as the correlation between relative abundance and sulfate by negatively (red) or positively (blue).

Highlights


A terrace with sharp geochemical gradients caused by AMD flooding were selected.




AMD intrusion reduced soil microbial community diversity and phylogenetic interactions.




Microbe showed flexible survival strategies in the poor nutritional environment.




pH, Fe(III), and sulfate were main factors influencing the soil microbial community.




Many acid-tolerant microorganisms were identified in the heavily contaminated fields.

RX contributed to data analysis and maintext writing. BL, EX, and XS contributed to R script. LY, YD, ZY helped in maintext writing. TK, QW, and LC contributed to samples collection and data measurements. WS designed the experiments.

Declaration of interests

√ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: