OR I G I N A L A R T I C L E

Biodiversity and species competition regulate the resilience ofmicrobial biofilm community

Kai Feng1,2,3,4 | Zhaojing Zhang1,5 | Weiwei Cai1,6 | Wenzong Liu1 | Meiying Xu7 |

Huaqun Yin8 | Aijie Wang1,9 | Zhili He10,11 | Ye Deng1,9

1CAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

2Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark

3Sino-Danish Center for Education and Research, Beijing, China

4University of Chinese Academy of Sciences, Beijing, China

5State Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education, China), School of Environmental Science and Technology,

Dalian University of Technology, Dalian, China

6State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (SKLUWRE, HIT), Harbin, China

7Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Institute of Microbiology, Guangzhou, China

8School of Minerals Processing and Bioengineering, Central South University, Changsha, China

9College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

10School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, China

11Department of Microbiology and Plant Biology, Institute for Environmental Genomics, University of Oklahoma, Norman, OK, USA

Correspondence

Ye Deng, CAS Key Laboratory for

Environmental Biotechnology, Research

Center for Eco-Environmental Sciences,

Chinese Academy of Sciences, Beijing,

China.

Email: yedeng@rcees.ac.cn

Funding information

Key Research Program of Frontier Sciences,

CAS, Grant/Award Number: (QYZDB-SSW-

DQC026); National Water Pollution Control

and Treatment Science and Technology

Major Project, Grant/Award Number:

(2015ZX07206); Strategic Priority Research

Program of the Chinese Academy of

Sciences, Grant/Award Number:

(XDB15010302); CAS 100 talent program

Abstract

The relationship between biodiversity and ecosystem stability is poorly understood in

microbial communities. Biofilm communities in small bioreactors called microbial electrol-

ysis cells (MEC) contain moderate species numbers and easy tractable functional traits,

thus providing an ideal platform for verifying ecological theories in microbial ecosystems.

Here, we investigated the resilience of biofilm communities with a gradient of diversity,

and explored the relationship between biodiversity and stability in response to a pH

shock. The results showed that all bioreactors could recover to stable performance after

pH disturbance, exhibiting a great resilience ability. A further analysis of microbial com-

position showed that the rebound of Geobacter and other exoelectrogens contributed to

the resilient effectiveness, and that the presence of Methanobrevibacter might delay the

functional recovery of biofilms. The microbial communities with higher diversity tended

to be recovered faster, implying biofilms with high biodiversity showed better resilience

in response to environmental disturbance. Network analysis revealed that the negative

interactions between the two dominant genera of Geobacter and Methanobrevibacter

increased when the recovery time became longer, implying the internal resource or spa-

tial competition of key functional taxa might fundamentally impact the resilience perfor-

mances of biofilm communities. This study provides new insights into our understanding

of the relationship between diversity and ecosystem functioning.

K E YWORD S

biofilm community, environmental disturbance, microbial diversity, microbial electrolysis cells,

resilience, species competition

Received: 12 December 2016 | Revised: 30 July 2017 | Accepted: 5 September 2017

DOI: 10.1111/mec.14356

6170 | © 2017 John Wiley & Sons Ltd wileyonlinelibrary.com/journal/mec Molecular Ecology. 2017;26:6170–6182.

1 | INTRODUCTION

Many ecosystems associated with microbial communities generally

face different environmental stressors or perturbations, such as cli-

mate change and various antibiotic conditions (Bissett, Brown, Sicil-

iano, & Thrall, 2013; Shade et al., 2012). To maintain the microbial

community at a comparably stable state is pivotal for ecosystem

functions and services. Although most microbial communities can

exhibit remarkable stability over time in response to perturbations

(Fuhrman, Cram, & Needham, 2015), there were still some communi-

ties which could not recover either their composition or functions

(Shade et al., 2012). Understanding the mechanism of microbial com-

munities in response to disturbances and their resilience is critical

for ecologists concerning the functions of ecosystems (Steudel et al.,

2012).

The stability of ecosystems can be described in two concepts:

one is resilience, which is defined that an ecosystem of composition

and function rebounds to the original state or close to original state

after a perturbation, and the other is resistance, which describes that

an ecosystem remains unchanged in response to a disturbance (Alli-

son & Martiny, 2008). The resilience can also be described as the

recovery process following a disturbance to an alternative stable

state (Griffiths & Philippot, 2013; Hodgson, McDonald, & Hosken,

2015). Such a relationship between stability and biodiversity has

been a long-term debate among ecologists (Ives & Carpenter, 2007;

May, 2001; McCann, 2000), and the general consensus is that biodi-

versity exhibits positive effects on ecosystem stability and functions,

which has been confirmed in plant ecosystems (Bezemer & van der

Putten, 2007; Tilman, Reich, & Knops, 2006) and animal ecosystems

(Hovick, Elmore, Fuhlendorf, Engle, & Hamilton, 2015; Kuhsel &

Bluthgen, 2015). In a decade-long grassland experiment, the results

showed this relationship was related to portfolio and overyielding

effects (Tilman et al., 2006). Portfolio effects or statistical averaging

shows that the diverse species were more likely to fluctuate asyn-

chronously with each other, providing opportunities to maintain

ecosystem stability (Doak et al., 1998), while overyielding effects

perform the stability due to greater growth for a species in species-

rich communities than in monocultures (Hector et al., 2010). Other

mechanisms including increased asynchrony, selection effects, facili-

tation and weak interactions have been well summarized previously

(Downing, Brown, & Leibold, 2014). Some of these effects were sim-

ulated using ecological models to explain the intrinsic relationships

between stability and biodiversity (Loreau & de Mazancourt, 2013;

de Mazancourt et al., 2013; Thibaut & Connolly, 2013). However,

this generic relationship was poorly understood in microbial ecosys-

tems due to their intrinsic traits, including high complexity, huge

numbers of species and inability to directly manipulate individuals of

microbial species.

The microbial community of bioreactors is an ideal model to

explore the relationship of biodiversity and stability of microbial

ecosystem in response to environmental disturbances. Microbial

electrolysis cell (MEC), as a kind of bioelectrochemical system, can

convert biodegradable materials into hydrogen through a series of

redox reactions with the aid of external voltage (Ditzig, Liu, & Logan,

2007; Liu, Grot, & Logan, 2005). Micro-organisms in anodic biofilms,

mainly anaerobic species, play important roles in the utilization of

substrates and the release of electrons and protons which are trans-

ported to cathode and synthesized to hydrogen. A biofilm is an

assemblage of any groups of micro-organisms attached on natural or

artificial surfaces to function as an integrated whole (Chew et al.,

2014; McDougald, Rice, Barraud, Steinberg, & Kjelleberg, 2012).

Most microbial species in biofilm are coordinated, spatially organized

and metabolically integrated together, providing homoeostasis to

ensure their survival in harsh and diverse environmental conditions

(Hall-Stoodley, Costerton, & Stoodley, 2004). These interspecies

cooperations have positive impacts on resource utilization (Jagmann,

von Rekowski, & Philipp, 2012), shaping expression patterns and

genetic organization of bacterial species (Rosenberg et al., 2016),

and optimizing spatial organization (Wessel, Hmelo, Parsek, & White-

ley, 2013). Additionally, the whole system is a closed microbial reac-

tor, with measurable performance characteristics, such as hydrogen

production and relevant bioelectrochemical efficiency, provides func-

tional data of biofilm communities. Meanwhile, the moderate num-

bers of species and their functional genes can be easily detected

with novel metagenomics tools (Liu et al., 2010; Lu, Xing, & Ren,

2012; Zhou et al., 2013). This closed ecosystem with relatively sim-

ple microbial community and easy measurements of functional per-

formance is quite appropriate for microbial ecological model system.

However, how the various species in this ecosystem interact, both

cooperatively and competitively, which each other affects the stabil-

ity of biofilm communities is largely unknown.

Network analysis is an insightful way to explore the microbial

interactions and/or cooccurrence patterns among different microbial

taxa from a wide range of environments (Banerjee et al., 2016; Deng

et al., 2012; Zhou, Deng, Luo, He, & Yang, 2011). By determining

the most connected microbial taxa or analysing node and link influ-

ence with different methods, networks can also identify the key-

stone organisms or other important microbes, which might have

largest effect on microbial community structure and potential func-

tions (Banerjee et al., 2016; Berry & Widder, 2014; Layeghifard,

Hwang, & Guttman, 2017). Network analysis can also be used to

explore the alteration of species interactions or microbial responses

under environmental stresses or anthropogenic disturbances (Bissett

et al., 2013; Zhou et al., 2010, 2011). For example, the microbial

species interactions and functional gene interactions became denser

at elevated CO2 as compared to ambient CO2 (Zhou et al., 2010,

2011). The propagation of stresses or disturbances through the

microbial community could be influenced by species interactions,

thus altering the molecular networks inference strategy, and then

networks were employed to explore the disturbance transmission

through microbial community, showing potentials to understand the

microbial dynamics in response to disturbances (Hunt & Ward,

2015). Recently, a study showed that predominant negative interac-

tions or competition became more important to the decrease of

FENG ET AL. | 6171

biodiversity within a microbial community with increased nutrient

input in groundwater (Deng et al., 2016). However, how the species

interaction network responds to environmental perturbations and

further influence on the community stability is still unclear.

In this study, we constructed a series of biofilm communities

with different levels of biodiversity by diluting the input source com-

munity, and the responses of the different biofilm communities to

pH shock were monitored by several methods. High-throughput

sequencing of 16S ribosomal RNA (rRNA) gene amplicons was used

to assess the diversity of anodic microbial community in MEC, and a

network analysis was conducted to explore the microbial interactions

associated with microbial dynamics under this environmental distur-

bance. Specially, we mainly focused on following questions: (i) How

did the different biofilms, with their gradient of biodiversity, respond

to pH disturbance? (ii) What were the dynamics of biofilm commu-

nity in response to environmental disturbance? (iii) What were the

potential mechanisms of stability for biofilm community? Our results

indicated that biofilms with high biodiversity showed better resili-

ence in response to pH disturbance, and the difference of recovery

time among biofilms was mainly due to the microbial interactions of

key functional taxa associated with different levels of biodiversity.

2 | MATERIALS AND METHODS

2.1 | Bioreactor setup, operation and sampling

Single-chamber microbial electrolysis cells (MECs) were used in this

study with membrane-less reactor introduced previously (Logan,

Cheng, Watson, & Estadt, 2007) (Fig. S1). They were constructed

using polycarbonate with a cylindrical chamber (4 cm 9 Ø3 cm) and

total empty volume of 28 ml following previous research (Call &

Logan, 2008). Anode electrode was graphite brush

(25 mm 9 Ø25 mm) with 3.4 mm diameter of core titanium metal

wire (Logan et al., 2007), which was immersed in acetone for 24 hr

and treated at 450°C for 1 hr in a muffle furnace. Cathode electrode

was wet-proof carbon cloth of 7 cm2 coated with Pt catalyst

(0.5 mg/cm2). On the top of the reactor, an anaerobic gas tube of

10 ml was glued and connected to a gas sampling bag (100 ml) with

a needle to collect gases produced by the MEC. A rubber ring and

gasket were used to seal the MEC, maintaining anaerobic conditions

inside the reactor. All materials were sterilized and assembled in a

super clean bench (SW-CJ-2FD, Boxun Industry & Commerce Co.

Ltd) to avoid contamination.

The activated sludge (AS) was collected from Beixiaohe

Reclaimed Water Plant (Beijing, China), which was sieved twice

through stainless mesh (30 mesh) to discard large particles. Later the

AS mixture was diluted and pretreated following a floc disaggrega-

tion procedure reported previously (Abzazou et al., 2015). This

diluted activate sludge was used as inocula for MEC startup. The

anode brush of MEC enriched with a mixture (1:1 V/V) of inocula

and a buffer solution (50 mM phosphate buffer solution, PBS; Na2H-

PO4�12H2O, 11.55 g/L; NaH2PO4�2H2O, 2.77 g/L; NH4Cl, 0.31 g/L;

KCl, 0.13 g/L; pH, 7.0) (Logan et al., 2007). Sodium acetate (1.5 g/L)

was added as substrates. The dilution to extinction approach was

used for inoculation here. The inocula for Group A, Group B and

Group C was diluted pretreated activated sludge with 1, 10 and 100

folds, respectively. MEC was applied with an external voltage of

0.8V and connected with a resistor (10 Ω). The current in the circuit

of MEC was monitored every 15 min using a multimeter (model

2700; Keithley Instruments, Inc.) and recorded on a computer auto-

matically. All reactors were placed in a room with constant tempera-

ture of approximately 30°C.

After the MECs were started up and were performing normally,

the reactors were fed with 28 ml mixture of sodium acetate

(1.5 g/L) and neutral PBS every 24 hr (a cycle). All feeding solu-

tions and relevant vessels were sterilized, and this feeding process

was conducted in a super clean bench to avoid external contamina-

tions. Group A, with highest concentration of inoculation, took

nearly 7 days for initial startup, while Group C, with 100 times

diluted inoculation, took nearly three weeks for startup. After the

bioreactors were operating for a certain period (approx. 14 cycles),

MEC obtained comparably stable performance (predisturbance).

Then pH shock was applied to MEC as an environmental distur-

bance. This was achieved by changing the feeding with PBS of pH

4.0 with similar compounds to originals (Na2HPO4�2H2O, 5.58 g/L;

NH4Cl, 0.31 g/L; KCl, 0.13 g/L; NaAc, 1.5 g/L; dropping H3PO4

until pH to 4.0, nearly 2 ml; measured with pH meter, Mettler

Toledo International Inc.) without changing sodium acetate concen-

tration or other operational parameters. After four cycles of pH

disturbance, the feeding solutions were changed back to previous

ones with pH of 7.0 (postdisturbance). Afterwards, all bioreactors

were operated to obtain stable performance for nearly 14 cycles

(recovery period).

For each reactor, six samples of anode biofilms were collected,

at predisturbance, postdisturbance and recovery period, with two

pseudoreplicates for each time point, respectively. In total 54 sam-

ples from three group reactors, and two samples from initial inocula-

tion solution were collected. All anodic microbial samples were

collected by cutting approximately 1 cm2 of the graphic brush in the

super clean bench, without affecting reactor performance, and then

stored at �80°C for further DNA extractions.

2.2 | Resilience of bioreactors measurement andevaluation

Concentration of the produced hydrogen, as collected in the

attached gas bag, was analysed using gas chromatography (GC, GC-

4000A, East & West Analytical Instruments, Inc.) equipped with ther-

mal conductivity detector (TCD). Additionally, the gas volume in the

bag was recorded to calculate hydrogen productivity. Concentration

of residual sodium acetate at end of each cycle was analysed using

high performance liquid chromatography (HPLC, LC-20A, Shimadzu

Co. Ltd.) equipment. Fluidic samples were filtered by a 0.45 lm filter

and adding 25 ll 2.5% wt H2SO4 to 1 ml of the filtrates prior to

analysis in HPLC. Total liquid volume was assumed to be the same

as feeding volume (28 ml).

6172 | FENG ET AL.

The MEC performance was evaluated in terms of hydrogen pro-

ductivity, coulombic efficiency, hydrogen recovery rate, electrical

energy recovery and total energy recovery. Hydrogen productivity or

hydrogen production rate was obtained by dividing the produced

hydrogen for each cycle with the reactor volume, and the unit is ml

H2/ml of reactor per day (cycle). The total energy efficiency is the

ratio of output energy evaluated by produced hydrogen to the total

input energy which composed of input electricity and consumed

acetate. The formula is shown below:

gEþS ¼DHH2nH2

WE þ DHSnS(1)

The subscripts E and S are corresponding to electricity and sub-

strate. nH2 is the moles of produced hydrogen, and nS is moles of

consumed acetate. DHH2 is the standard higher heating value of

hydrogen 285.83 kJ/mol (Heidrich et al., 2013), and DHS is the heat

combustion value of acetate 870.27 kJ/mol (Call & Logan, 2008). WE

is the integral based on the relationship of recorded current and

applied voltage with time. Other terms of MEC performance were

calculated according to detailed introduction in previous study (Call

& Logan, 2008; Heidrich et al., 2013).

To identify comparatively complete recovery time and avoid the

lagging of different performance parameters during recovery, five

parameters related to monitoring and evaluation were selected to

calculate the recovery time for each group, involving continuous cur-

rent record, hydrogen production rate, coulombic efficiency, hydro-

gen recovery rate, maximum current value (Fig. S3). Among the

selected five parameters, continuously recorded current reflected

reactor status timely and directly related to anodic biofilm commu-

nity performance, thus providing more precise recovery time and

was therefore selected as default recovery time for further analysis.

2.3 | DNA extraction, amplicon sequencing

DNA of anodic biofilm samples was extracted with FastDNA™ SPIN

Kit for Soil (MP Biomedicals). DNA quality and concentration were

assessed based on absorbance ratios of 260/280 nm (~1.8) and

260/230 nm (>1.7) detected by a NanoDrop Spectrophotometer

(Nano-100, Aosheng Instrument Co Ltd.).

For 16S rRNA gene, the V4 region was amplified with a pairwise

common primer 515F (50-GTGCCAGCMGCCGCGGTAA-30) and 806R

(50-GGACTACHVGGGTWTCTAAT-30) combined with self-designed

barcodes to distinguish samples. The polymerase chain reaction (PCR)

amplification was conducted in a 50 ll reaction system containing

0.5 ll Taq DNA Enzyme (TaKaRa), 5 ll 109 PCR buffer, 1.5 ll dNTP

mixture, 1.5 ll of both 10 lM forward and reverse primers, 1 ll of

template DNA within 20–30 ng/ll and 39 ll ddH2O. The thermal

cycle conditions were as follows: denaturation at 94°C for 1 min, 30

cycles of 94℃for 20 s, 57°C for 25 s and 68°C for 45 s, thereafter

extension at 68°C for 10 min and finally keep systems at 4°C before

purification on SelectCycler II (Select BioProduct).

The positive PCR amplicons were confirmed by agarose gel elec-

trophoresis and purified using Gel Extraction Kit (D2500-02,

OMEGA BioTek). The purified DNA was quantified with NanoDrop

Spectrophotometer, and the optical density of the gel was confirmed

with Gel Image System (Taxon-1600). Thereafter, a standard regres-

sion model was used to fit the net optical density and DNA concen-

tration. A reference value that was the closest value to the

regression curve was selected to obtain the required volume of

150 ng DNA, and others were referred to this value to obtain their

volume based on their net optical reference. All samples were

pooled together for library preparation of connecting Illumina adap-

ters, and the pooled mixture was quantified using Qubit assay with

Qubit 2.0 Fluorometer (Life Technologies). The library preparation

was conducted following the protocol of VAHTS™ Nano DNA

Library Prep Kit for Illumina� (Vazyme Biotech Co., Ltd). The com-

bined library was diluted to 2 nM before sequencing.

Sample library for sequencing was prepared according to the

MiSeq Reagent Kit Preparation Guide (Illumina). The sequencing run

was conducted on Miseq sequencing machine (Illumina) at Central

South University, China, for 251, 12 and 251 cycles for forward,

index and reverse reads, respectively.

2.4 | Sequence preprocessing and bioinformaticsapproaches

The raw reads were aligned to samples according to different bar-

codes, allowing for one mismatch. Both forward and reverse primers

were trimmed, also with one mismatch. Paired end reads of sufficient

length were combined with at least 30 bp overlap into full-length

sequences, average fragment length of 253 bp, by FLASH program

(Magoc & Salzberg, 2011). The Btrim program (Kong, 2011), with

threshold of Quality Score >20 and 5 as window size, was used to fil-

ter out unqualified sequences. Any sequences with either an ambigu-

ous base or <200 bp were discarded, only sequences within the range

of 245~260 bp were retained as targeted sequences. Thereafter,

UPARSE (Edgar, 2013) was used to remove chimeras and classify the

sequences into operational taxonomy units (OTUs) at a similarity of

97% without any singletons being discarded. A large matrix with all 56

samples as columns and all OTUs as rows was generated as OTU table

from which a randomly resample OTU table was obtained to normalize

total reads. All the sequence preprocessing was conducted by an in-

house pipeline (http://mem.rcees.ac.cn:8080) integrated with these

bioinformatics tools. The sequencing data are available in Sequence

Read Archive with Accession no. SRP091356.

2.5 | Ecological and statistical analysis

In this study, we calculated five kinds of a-diversity to measure the

biodiversity of microbial community in MEC. Richness was obtained

by counting the observed species numbers in resampled OTU table.

Chao1 values (Chao, 1984) associated with rarefaction curve were cal-

culated using Mothur program (Schloss et al., 2009). Shannon and

Inverse Simpson indexes were calculated according to species abun-

dance using vegan package (v.2.3-5) in R (v.3.2.5). Phylogenetic diver-

sity (PD) was measured based on Faith’s approach (Faith, 1992), which

FENG ET AL. | 6173

is the sum of total phylogenetic branch length of OTUs in each sample.

The selected representative OTU sequences were aligned using

PyNAST (Caporaso et al., 2010) against with GreenGene data set, and

tree file was generated using FastTree program (Price, Dehal, & Arkin,

2009). The PD was calculated using Picante package in R (v.3.2.5)

(Kembel et al., 2010). Unweighted principal coordinate analysis (PCoA)

based on UniFrac matrix (Lozupone, Hamady, Kelley, & Knight, 2007;

Lozupone, Hamady, & Knight, 2006; Lozupone & Knight, 2005) was

used for microbial community structure changes. Permutational multi-

variate analysis of variance (PERMANOVA) was used to test the dis-

similarity among three groups using both Bray–Curtis and Jaccard

distance methods via vegan package (v.2.3-5) in R (v.3.2.5). Three cor-

relation approaches involving Pearson, Kendall and Spearman were

applied to correlate the recovery time with a-diversity. The signifi-

cance between two groups was determined by two-tailed Student’s t

test, and significance comparing three groups was obtained using one-

way analysis of variance (ANOVA).

2.6 | Network construction with RMT-basedapproach and network analysis

To elucidate microbial interactions in biofilms in response to pH distur-

bance, we constructed three groups of phylogenetic molecular ecologi-

cal networks (pMENs) via random matrix theory-based interface

approach in molecular ecological network analysis pipeline (MENA,

http://ieg2.ou.edu/MENA/) (Deng et al., 2012; Zhou et al., 2010,

2011). The whole process was described in a previous study compre-

hensively (Deng et al., 2012) and we briefly introduce the procedure

used here. First, only the OTUs appeared in more than nine samples

(18 samples totally for each group) were kept without log-transferring

prior to obtaining Spearman rank correlation matrix (r value). A series

of thresholds from 0.01 to 0.95 with 0.01 intervals were obtained and

applied to the matrix. Only the correlations above a specific threshold

were kept for calculating the network eigenvalues. The finest threshold

was selected when the nearest-neighbour spacing distribution fol-

lowed Poisson distribution well, which is related to specific and non-

random properties of a complex system (Luo et al., 2007). In addition,

to compare these three networks under same conditions, a uniform

threshold (0.79) was determined to generate three networks. And fur-

ther network properties including R2 of power law, average connectiv-

ity, average path length, average clustering coefficient and modularity

were all calculated in MENA pipeline (Deng et al., 2012) (Table S5).

Then the constructed networks were visualized using Cytoscape 3.3.0

software (Shannon et al., 2003) for three different networks of MEC

anodic biofilm communities in response to pH disturbance.

3 | RESULTS

3.1 | The resilience of bioreactors to pHdisturbance

The generated microbial communities with different diversity were

shocked by pH disturbance for four cycles, and the resilience of

these communities was evaluated using measures of functional per-

formance and observed recovery time. Two representative parame-

ters of performance (hydrogen production rate and total energy

efficiency) are shown in Figure 1. The average hydrogen production

rates for Group A, B and C were, respectively, 1.38 � 0.17,

1.28 � 0.14 and 1.26 � 0.11 ml/(ml reactor) per day before the pH

disturbance (One-way ANOVA, p < .01). Group A had a higher

hydrogen production rate as compared with Group B (Student’s t

test, t = 2.425, p = .020) and Group C (t = 2.833, p = .007), while

Group B and Group C obtained similar hydrogen production rates

(t = 0.311, p = .757). After pH disturbance and recovering to stable

state close to original predisturbance status, similar hydrogen pro-

duction rates were obtained, 1.05 � 0.05, 0.98 � 0.05, and

1.02 � 0.05 ml/(ml reactor) per day for Group A, B and C, respec-

tively (One-way ANOVA, p = .24) (Figure 1a). In addition, pH distur-

bance significantly decreased the H2 production rate for each group

(Figure 1a). Other performance parameters (total energy efficiency,

hydrogen recovery rate and electrical energy recovery) exhibited

similar patterns in response to pH disturbance while less effect of

F IGURE 1 Hydrogen production rate (a) and total energyefficiency (b) of MEC groups in response to pH disturbance,subsidiary with one-way ANOVA analysis and Student’s t testresults, t value (p-value). Bold font means the significance at p < .05level. Pre: predisturbance; ***p < .001 of Student’s t test

6174 | FENG ET AL.

pH disturbance was shown on coulombic efficiency (Figure 1b;

Fig. S2). Therefore, the three groups of MECs performed with little

difference in reactor effectiveness during the same period (predistur-

bance or recovery), and pH disturbance had negative effects on

MEC performance indicated by their effectiveness indexes.

Recovery time was used to assess the resilience of MECs in a time

scale. Five parameters related to monitoring and effectiveness were

selected to calculate the recovery time for each group (Fig. S3). Group

A with the highest initial concentration of inocula took the shortest

amount of time to reach stable stage after the pH disturbance com-

pared to Group C with lower concentration of inocula. Group B might

experience a transitional state from Group A to Group C in this experi-

ment. Additionally, the Student’s t test exhibited that four of five

recovery time showed significant difference between Group A and

Group C (p < .05, Table S1). The recovery time was 82.6 � 0.1 hr,

117.8 � 17.7 hr and 126.0 � 25.4 hr for Group A, B and C, respec-

tively, which was comparably precise and was determined as default

recovery time by statistical analysis and other calculations.

3.2 | Sequencing statistics and microbial diversity

To determine the biodiversity of biofilm communities of MECs, the V4

region of 16S rRNA gene was amplified and sequenced using high-

throughput sequencing. After quality control, a total of 2,992,109

sequences were classified into 54 biofilm samples and two activated

sludge samples (inocula). An average of 53,431 � 15,485 sequences

per sample was obtained, and the rarefaction curves indicated these

numbers of sequences were fairly enough (Fig. S4). When the opera-

tional taxonomy unit table was generated, we randomly resampled

23,741 sequences per sample for further analyses, for example micro-

bial diversity, composition and structure.

Shannon and Inverse Simpson indexes were calculated to evaluate

the a-diversity of anodic microbial communities under different condi-

tions. Group A obtained the highest a-diversity (Figure 2) and Group C

obtained the lowest, showing a significant difference for the predistur-

bance period (t = 3.585, p = .005) as the experiment was designed.

However, this significant difference was not observed between Group

A and C at the end of recovery period (t = 2.200, p > .05), indicating

that the pH shock might eliminate the difference in a-diversity among

three groups (Figure 2). Besides, fewer effects of pH disturbance were

observed in Group B and Group C (p > .05) at the average level, but

still exhibited a decreasing tendency in response to pH disturbance

(Figure 2). The other three diversity characteristics (Chao1, Richness,

Phylogenetic diversity [PD]) all showed similar patterns to Shannon

and Inverse Simpson indexes in response to pH disturbance (Fig. S5).

These results indicated a-diversity of biofilm community was signifi-

cantly decreased by pH disturbance.

3.3 | Structure and composition of biofilm microbialcommunities

To explore the structure variance of biofilm microbial communities,

b-diversity-based statistical tools were employed. The principal

coordinate analysis (PCoA) showed that microbial communities

belonging to the same group were more closely clustered with each

other, but more distinctly separated from other groups (Figure 3).

Group A and Group C were almost separated with PCoA, while they

were totally divergent by nonmetric multidimensional scaling analysis

(NMDS) (Fig. S6). In addition, along with the arrow direction from

Group A to Group C (Figure 3), microbial communities at the end of

recovery period exhibited a tendency to cluster more tightly, which

implied that less difference was observed across groups after micro-

bial community recovered from the pH shock. It implied that pH dis-

turbance decreased the difference among groups in microbial

structure in b-diversity as well as a-diversity.

Further dissimilarity tests revealed that the significant difference

was observed within groups and across three periods. Permutational

multivariate analysis of variances (PERMANOVA) was used to con-

firm the significance results (Table 1). This method based on both

Jaccard and Bray–Curtis distance showed significant differences

F IGURE 2 Comparisons of two a-diversity indexes, Shannonindex (a) and Inverse Simpson index (b), for Group A, B and Cbiofilms at different periods in response to pH disturbance.*p < .05; **p < .01; ***p < .001 based on Student’s t test. Thecolours of asterisk correspond to different periods for pHdisturbance. The column value is the mean of the indices withineach group (n = 6), and error bars stand for standard deviation (SD)

FENG ET AL. | 6175

among groups across all three periods. On the other hand, compar-

ing the difference among three groups at same period, the significant

difference also decreased from predisturbance period

(FPERMANOVA = 6.7591, p < .001) to the end of recovery period

(FPERMANOVA = 3.6889, p = .002) based on the Bray–Curtis distance

(Table S2). Moreover, the difference between each group as indi-

cated by Bray–Curtis and Jaccard index also exerted similar decreas-

ing tendency in response to pH disturbance (Table S3)

To determine the underlying mechanisms for shedding light on

a-diversity and b-diversity previously performed across three biofilm

groups, relative abundance of micro-organisms was conducted at the

phylum/class and genus levels with a similarity of 97% for OTU clas-

sifications (Fig. S7). Obviously, all microbes at the phylum/class level

showed a dynamic equilibrium in response to pH disturbance, espe-

cially a large fluctuation at the end of disturbance compared to the

original states (Fig. S7a). For example, Deltaproteobacteria, which

was the most abundant class in MEC anodic biofilms, decreased

from 67.9% � 13.9% to 44.0% � 9.9% after the four-day pH distur-

bance in Group A. Even so, it rebounded back to 68.3% � 5.4%

after totally recovery. Similar resilient trend was observed in Group

C as well. Other phyla/classes showed a range of resilient variation

in response to pH disturbance, such as Euryarchaeota, Firmicutes

and Bacteroidetes.

With higher resolution of the taxonomy for micro-organisms in

biofilms, there were mainly two dominant genera, Geobacter and

Methanobrevibacter except Unclassified taxa (Fig. S7b). Geobacter pre-

dominated in all three groups and across different periods (>1% in

relative abundance), showing their potential functions for maintaining

functional stability of MECs in response to pH disturbance, and this

genus in three groups across the periods was resilient in relative

abundance. Other exoelectrogens (Pseudomonas and Arcobacter) all

varied in response to pH disturbance and their relative abundances

kept stable in each group at the end of recovery. For the other dom-

inant genus, Methanobrevibacter, a group of nonexoelectrogen, its

relative abundance increased during the pH disturbance to become a

more significant member of the community at the end of recovery in

each group. Thus, this unusual trend of Methanobrevibacter might

relate to different MEC resilience across the three groups. Moreover,

the final relative abundance across the three groups exhibited less

difference (Fig. S7b) and was supported by the comparison of simi-

larity indexes which showed less dissimilarity among groups

(Table S3), confirming that biofilm microbial communities tended to

be clustered together at the genus level after the pH disturbance

(Figure 3).

3.4 | Correlations of MEC resilience and microbialdiversity

To explore the relationship between biodiversity of biofilm microbial

communities and MEC resilient performance, the correlations of the

recovery time with five a-diversity indexes were tested using three

methods (Table 2). Five a-diversity indexes describe the biodiversity

of microbial communities from qualitative description (Richness and

Chao1), quantitative evaluation (Shannon and Inverse Simpson) and

phylogenetic perspective (PD) thus could provide comprehensive

correlation results. Even though the correlation coefficients varied

differently for each column (across three correlation methods), the

correlation results all showed significantly negative interactions

between recovery time and diversity indexes (p < .05, Table 2). This

correlation indicated that MECs with higher diversity biofilm commu-

nities could be more resilient in response to pH disturbance. For

example, Group A with higher diversity needed a shorter recovery

time compared to Group C (Figure 2, Fig. S3).

The relative abundance of the genera (Fig. S7b) was tested with

recovery time as well (Table S4). For the two dominant genera,

Geobacter did not show correlations with the recovery time

(p > .05), while Methanobrevibacter exhibited significantly a positive

correlation with the recovery time (p < .05). This result confirmed

that Methanobrevibacter might be associated with recovery of the

biofilm community in response to pH disturbance. A high relative

F IGURE 3 Principal coordinate analysis (PCoA) of biofilmmicrobial communities for Group A (red), B (blue) and C (orange) inresponse to pH disturbance based on unweighted UniFrac distanceof detected OTUs. The sample colours from light to dark stand fortime scaling coupled with different shapes: predisturbance (■),postdisturbance (●), the end of recovery (▲). The associated arrowsindicate the clustering directions for microbial samples of eachbiofilm community structure

TABLE 1 Dissimilarity tests of biofilm microbial communitiesusing PERMANOVA based on Jaccard and Bray–Curtis distance

PERMANOVA test

Jaccard Bray–Curtis

F p F p

Group (A, B, C) 6.3928 .001*** 13.3703 .001***

Period (Pre, Post, Recovery) 3.6011 .001*** 7.6736 .001***

Group*Period 1.4872 .005** 2.265 .008**

*Difference is significant at 0.05 level.

**Difference is significant at 0.01 level.

***Difference is significant at 0.001 level.

6176 | FENG ET AL.

abundance of Methanobrevibacter may increase the time it takes for

the whole community to recovery. For example, Group C with high

abundance of Methanobrevibacter showed a delayed recovery in

response to pH disturbance (Fig. S3, Fig. S7b).

3.5 | Networks reveal the recovery mechanismswith microbial interactions

To explore the microbial interactions within biofilm microbial com-

munities, phylogenetic molecular ecological networks (pMENs) were

constructed. Three groups of anodic microbial communities were

analysed, and the topological properties of networks are summarized

(Table S5). The overall topology indices showed that all network con-

nectivity distributions fitted well with the power-law model (R2 of

power law from 0.771 to 0.843), indicating that most of nodes had

fewer neighbours while fewer nodes had many neighbours in a net-

work (scale-free). Average connectivity (avgK, from 4.235 to 7.191)

measured the complexity of a network, thus Group A obtained a

more complex network than the other two groups (Fig. S8). Also,

average path lengths (GD) were varied from 2.765 to 5.362, which

were nearly close to the logarithm of total number of network size

and significantly comparable to other networks showing typical

small-world network properties (Watts & Strogatz, 1998). This indi-

cated all nodes were well connected in the networks. Finally, the

modularity value (M) for all groups varied from 0.455 to 0.682,

which were all significantly higher than the M values of correspond-

ing randomized networks, indicating all pMENs appeared to be mod-

ular. Moreover, by generating random networks from the empirical

networks, the results indicated that network indices (e.g., average

path length, average clustering coefficient, modularity) were all sig-

nificant different between any two networks of those three groups

(Table S5).

To better understand the interactions among different micro-

organisms, the three networks were visualized, showing quite differ-

ent network structures (Fig. S8). Group A had the most complex

network with a large number of Proteobacteria (mainly Geobacter),

and the Euryarchaeota (mainly Methanobrevibacter) were almost

entirely clustered into a single module with fewer connections to

other modules. Group B exhibited a transient state for Eur-

yarchaeota and Proteobacteria interactions. Group C showed that

the group of Euryarchaeota had negative interactions with other

groups especially the predominant Proteobacteria, indicating consid-

erable negative interactions in the network. For each group of bio-

films, all Geobacter and Methanobrevibacter and their interactions

were split into three subnetworks (Figure 4). It was clear that the

group of Geobacter with high connected interactions took important

positions in the network of Group A and the interactions between

these major nodes were mainly positive. As for Group B, members

of the Methanobrevibacter were shown up gradually and became

essential nodes with an increasing number of interactions in the net-

work. More importantly, all the interactions between Geobacter and

Methanobrevibacter were negative, which implied competitive rela-

tionships for these two genera in biofilms. This pattern was more

obvious in the network of Group C with largest negative interac-

tions between these two genera (Figure 4d). The number of Geobac-

ter nodes in the networks decreased from Group A to Group C,

while it was the opposite for Methanobrevibacter, and the number of

negative interactions between Geobacter and Methanobrevibacter

increased (Figure 4d). Positive interactions were kept inside the gen-

era, and negative interactions were the only relationship between

these two essential groups of microbes. Moreover, the positive

interactions between the predominant Geobacter and those fermen-

tative bacteria that included Geothrix, Oscillospira, Acetobacterium,

Paludibacter, Anaerovorax, Bacteroides and Proteiniclasticum were

observed in the network analysis across three groups as well

(Fig. S9), which might facilitate the functional resilience of biofilm

community.

4 | DISCUSSION

The relationship of biodiversity and ecosystem stability has been

debated in macro ecosystems for many years (Bezemer & van der

Putten, 2007; May, 2001; Steudel et al., 2012). The appropriate

experiments on this ecological theory in microbial ecosystems remain

less explored, due to the high complexity, high species diversity and

intractability; thus, tractable model systems are needed to explore

mechanisms of perturbing microbial communities in situ (Alivisatos

et al., 2015). Biofilm community of microbial electrolysis cell pro-

vides an ideal ecological model to study the relationship between

diversity and stability in response to environmental disturbances.

MECs are generally operated at room temperature and under mild

TABLE 2 Correlations between recovery time of MECs in response to pH disturbance and a-diversity of anodic microbial community forthree periods

Correlation methods

Richness Shannon Inverse Simpson Chao1 PD

Correlation p Correlation p Correlation p Correlation p Correlation p

Pearson �.582*** .000 �.468*** .000 �.449*** .001 �.611*** .000 �.570*** .000

Spearman �.547*** .000 �.403** .003 �.369** .006 �.671*** .000 �.658*** .000

Kendall �.389*** .000 �.268** .007 �.237* .016 �.499*** .000 �.468*** .000

*Difference is significant at 0.05 level.

**Difference is significant at 0.01 level.

***Difference is significant at 0.001 level.

FENG ET AL. | 6177

conditions. The operation is relatively easy, requiring only a change

of the feeding substrate when used in a batch mode. Additionally,

the species composition of anodic communities was of only moder-

ate complex as compared to the initial activated sludge inoculum

(Fig. S7). More importantly, the change of operation conditions might

influence the composition of microbial communities (Fig. S7), and

such an alteration could reversely affect the bioreactor performance

(Figure 1), which bridges microscope biofilms and macroscopic reac-

tor performance together. As observed in the recovery time compar-

ison (Fig. S3), the biofilms with higher diversity showed better

resilience than low diversity ones (Figure 2), supporting the general

perspective of stability and biodiversity relationship. Therefore, as

we expected, biofilms of MECs were good systems for testing micro-

bial ecology theories.

Although pH disturbance decreased the reactor performance,

MECs still exhibited relatively high resilience that could take certain

time to rebound back to the comparably stable state (Figure 1).

Interestingly, comparing the performance among three groups of

MECs, less difference was observed after the recovery than before

the disturbance (Figure 1). This is most likely associated with the

changes of biodiversity of biofilm communities and as designed, with

Group A having significantly higher biodiversity as compared to

Group B and Group C (Figure 2). This was also confirmed by the

composition of microbial community among the three groups show-

ing a convergent trend after the pH disturbance (Figure 3, Fig. S7),

indicating the distance between each two groups of microbial com-

position decreased due to pH disturbance. A conceptual scheme was

summarized to better comprehend such observations in this study

(Figure 5). This diversity, or community composition convergence,

may be observed when the community gains or loses similar species,

especially rare species, when facing various disturbances (Avolio

et al., 2015). In a low-productivity grassland in Michigan, the species

composition was analysed in response to a one-time removal of

grassland as disturbance following solarization and herbicide applica-

tion. The community dispersion was decreased, showing a conver-

gence in a grassland ecosystem following such a disturbance

(Houseman, Mittelbach, Reynolds, & Gross, 2008). This was consis-

tent with the disturbance effect on tallgrass responses to grazing

and frequent fire, which resulted in decreased community dispersion

(Collins & Smith, 2006). Additionally, the fire could increase remark-

able similarity of dominant shrubs and subshrub density (Keeley,

Fotheringham, & Baer-Keeley, 2005), and invasive grass litter addi-

tions also changed the composition of scrubland arthropod commu-

nities with decreased community variance (Wolkovich, 2010).

Moreover, trophic forests exhibited decreased group distance over

time after cyclone disturbance (Murphy, Metcalfe, Bradford, & Ford,

2014) and a more similar or homogenized community of hoverfly

and carabid beetles was increased within agricultural intensification

(Gagic et al., 2014). In activated sludge bioreactors, an increased sim-

ilarity of bioreactor communities relative to influent was observed in

response to decreased solid retention time (SRT), which might be

due to the washing effect on established species of lower SRT

(Vuono, Munakata-Marr, Spear, & Drewes, 2016). The convergence

trend of microbial communities to steady composition was simulated

in a nitrifying bioreactor after inoculating competitive species (Louca

& Doebeli, 2016). Therefore, the ecological pattern of community

convergence has been observed widely in ecosystems in response to

disturbance, suggesting a wide application of our conceptual model.

F IGURE 4 Networks to visualize interactions between Geobacterand Methanobrevibacter in three groups of biofilm microbialcommunities in response to pH disturbance. (a), (b), (c) arecorresponding to interactions within Group A, B, C respectively. Allnodes of Geobacter (red nodes) and Methanobrevibacter (purplenodes) and their interactions were remained. Node sizes areproportional to their own node degrees (connected number withother nodes). Blue links stand for positive interactions betweennodes and red links stand for negative interactions. (d) Briefsummary of nodes and links numbers is shown in histogram forthree groups of subnetworks

6178 | FENG ET AL.

However, the biodiversity convergence was seen less in biofilm com-

munity responses to environmental disturbances, thus what we per-

formed here might complement and spread such an ecological

pattern in microbial ecosystems.

The functional stability of microbial communities for MECs could

be a result of multispecies cooperation, containing both functional

exoelectrogens and nonexoelectrogens, such as fermentative bacteria

(Fig. S7b). The functional groups in anodic biofilms were affected to a

lesser degree by pH shock, implying that they could play a positive role

in the establishment of stable microbial communities (Liu et al., 2012).

The resilient performance of biofilms might be correlated with resilient

composition of the microbial communities for each group (Fig. S7b).

The Geobacter, as a group of well-known functional microbes in MECs

related to electron delivery (Logan, 2009), were dominant and could

contribute to the functional stability and resilience in biofilm commu-

nity. Other exoelectrogens including Pseudomonas and Arcobacter (Lu

et al., 2012) showed similar trends across groups and might assist the

resilience of biofilms in response to pH disturbance. Fermentative

groups including Geothrix, Oscillospira, Sedimentibacter, Anaerovorax

and Paludibacter could contribute to the utilization of carbohydrates

and produce organic acids (Lu et al., 2012). These fermentative species

may potentially be involved in hydrogen production, but it is not yet

clear (Varrone et al., 2014). Different species occupy specific niches

within biofilms, possibly influencing reactor performance (Logan &

Regan, 2006; Varrone et al., 2014). More importantly, not only the

above mentioned fermentative groups, but also other rare genera with

low relative abundance including Acetobacterium, Bacteroides and

Proteiniclasticum are involved in positive interactions with the domi-

nant functional genus Geobacter (Fig. S9). The resilience of the func-

tional genera could be a result of multispecies cooperations, involving

self-recovery and other species’ facilitation, thus performing stable

functions in response to environmental disturbance. These positive

interactions might contribute to the hypothesis that nonexoelectro-

gens (such as fermentative bacteria) played essential roles in the func-

tional performance of biofilm community in this specific bioreactor.

The cooperation of these complex species within biofilms in MECs

might contribute to the stable biofilms in response to pH disturbance

and the more specifically interspecies interactions required further

analysis.

Obviously, the three groups of MECs exhibited different recov-

ery time in response to pH disturbance (Figure 5, Fig. S3). Biofilms

with higher diversity took less time for recovery, while the reactors

with lower diversity took a longer time to reach stable performance

after pH disturbance. Moreover, the recovery time was significantly

correlated to a-diversity of anodic microbial communities (Table 2,

p < .05), providing potential evidence that biofilm community stabil-

ity could be maintained in response to environmental disturbance by

high biodiversity inoculations. The composition of anodic microbial

communities might be good to explain this phenomenon (Fig. S7).

Methanobrevibacter as unexpected and detrimental micro-organisms

for hydrogen production in MECs (Lee, Torres, Parameswaran, &

Rittmann, 2009) was a predominant genus in this study following

with Geobacter. The relative abundance of Methanobrevibacter varied

from Group A to Group C at end of recovery, which might correlate

with the different recovery times observed across the three groups.

Thereafter, the correlation tests showed that the high component of

Methanobrevibacter may result in the delayed recovery in Group C

(Table S4, p < .05). However, neither the ecological analysis nor the

composition of microbial community analysis could demonstrate the

intrinsic interactions between the diversity and resilience of biofilms

in response to pH disturbance, which motivated us for network anal-

ysis of those biofilm communities.

Network analysis could provide another view of microbial interac-

tions and ecological rules for community assembly (Barberan, Bates,

Casamayor, & Fierer, 2012). It could be used to reveal the intrinsic

mechanisms of microbial interactions in response to environmental

perturbations as well (Bissett et al., 2013; Deng et al., 2016; Hunt &

Ward, 2015). In a constructed network, the positive interaction is

mostly resulted from mutualism or commensalism, while negative

interaction is due to competition, predation, amensalism and so on

(Faust & Raes, 2012). But multispecies interactions might also be dri-

ven indirectly by external environmental stresses (Deng et al., 2016).

The negative interaction in microbial systems was mainly regarded as

competition (Deng et al., 2016; Faust & Raes, 2012) and in this study,

microbial interactions especially negative ones exerted significant

effects on biofilm community recovery. Our results indicated that

although the composition of microbial communities among three

groups of MECs tended to be similar (Figure 3, Fig. S7), the overview

of the three networks still showed different patterns from a holistic

view of microbial communities (Fig. S8). The group of Euryarchaeota

(mainly Methanobrevibacter) showed fewer interactions with other

taxa in Group A and more negative interactions to Proteobacteria

(mainly Geobacter) in Group C, which indicated the competitive rela-

tionship might be reinforced in lower diversity communities. This pat-

tern was more clearly observed by selecting the two major genera in

each group associated with their own interactions (Figure 4). Geobac-

ter was always present at vital position in each network, which was

consistent with the composition analysis (Fig. S7b), while Methanobre-

vibacter gradually grew from a marginal position in Group A to the

F IGURE 5 Schematic of conceptual visions of resilience relatedto reactor performance and biodiversity of biofilm communities inresponse to pH disturbance

FENG ET AL. | 6179

central position in Group C (Figure 4d). In addition, there were fewer

negative interspecies interactions between the two genera in Group A,

but with the increased presence ofMethanobrevibacter in Group C, the

negative interactions between Geobacter and Methanobrevibacter

increased and the competition might become fierce, therefore the

alteration of this negative interaction or competition might be a poten-

tial mechanism in biofilm communities in response to pH disturbance

associated with the diversity. As performed previously, the relative

abundance of Methanobrevibacter increased from Group A to Group C,

opposite the overall trend of microbial diversity (Figure 2, Fig. S7b),

which could reinforce the internal resource or spatial competition with

Geobacter (Figure 4), thus the functional recovery of biofilm communi-

ties in Group C was delayed by this competitive interaction between

the dominant genera in response to environmental disturbance. Simi-

larly, the competitive interactions of microbial populations were also

enhanced by emulsified vegetable oil injection and showed the impor-

tance of groundwater microbial community responses to environmen-

tal perturbations (Deng et al., 2016). Moreover, the environmental

alteration like pH disturbance might affect the microbial fitness or sur-

vival following with competitors in the formation of biofilms (Rendue-

les & Ghigo, 2015). Through this result, networks exhibited the

potential importance in individual species interactions to reveal intrin-

sic mechanisms and were confirmed as complementary analysis tools

for ecological theory exploration.

Overall, the resilience of biofilm community responses to envi-

ronmental disturbances might be highly correlated with a-diversity

and the composition of microbial communities coupling with micro-

bial interactions. The similar performance across three groups of

MECs might be due to the resilient exoelectrogens during the recov-

ery period. Moreover, the presence or the high abundance of

Methanobrevibacter showed negative effects on resilience of biofilms

as revealed by network analysis. The community with high diversity

might lessen the competitive effect of Methanobrevibacter for sub-

strate resources or spatial positions. Thus, this study implies that

improving the biodiversity may enhance the resilience of microbial

biofilm community especially in response to environmental distur-

bances, providing theoretical and experimental evidence for biofilm

assembly and colonization establishment in the future.

ACKNOWLEDGEMENTS

This project was supported by Key Research Program of Frontier

Sciences, CAS (QYZDB-SSW-DQC026), National Water Pollution

Control and Treatment Science and Technology Major Project

(2015ZX07206), Strategic Priority Research Program of the Chinese

Academy of Sciences (XDB15010302) and CAS 100 talent program.

The author is very grateful to Dr. James Walter Voordeckers for

careful edition on the final version.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

DATA ACCESSIBILITY

Raw sequence data were submitted to NCBI with the Sequence

Read Archive (SRA) Accession no. SRP091356.

AUTHOR CONTRIBUTION

Y.D. and K.F. designed the experiments. K.F., Z.Z., W.C., W.L. H.Y.

and A.W. performed the experiment. K.F., Y.D., M.X. and Z.H. con-

tributed to the data analysis. K.F., Y.D. and Z.H. wrote the manu-

script. All authors read and approved the manuscript.

ORCID

Ye Deng http://orcid.org/0000-0002-7584-0632

REFERENCES

Abzazou, T., Salvado, H., Bruguera-Casamada, C., Simon, P., Lardin, C., &

Araujo, R. M. (2015). Assessment of total bacterial cells in extended

aeration activated sludge plants using flow cytometry as a microbial

monitoring tool. Environmental Science and Pollution Research, 22,

11446–11455.

Alivisatos, A. P., Blaser, M. J., Brodie, E. L., Chun, M., Dangl, J. L., Dono-

hue, T. J., . . . Taha, S. A. (2015). A unified initiative to harness Earth’smicrobiomes. Science, 350, 507–508.

Allison, S. D., & Martiny, J. B. (2008). Resistance, resilience, and redun-

dancy in microbial communities. Proceedings of the National Academy

of Sciences of the USA, 105(Suppl 1), 11512–11519.

Avolio, M. L., Pierre, K. J. L., Houseman, G. R., Koerner, S. E., Grman, E.,

Isbell, F., . . . Wilcox, K. R. (2015). A framework for quantifying the

magnitude and variability of community responses to global change

drivers. Ecosphere, 6, 280.

Banerjee, S., Baah-Acheamfour, M., Carlyle, C. N., Bissett, A., Richardson,

A. E., Siddique, T., . . . Chang, S. X. (2016). Determinants of bacterial

communities in Canadian agroforestry systems. Environmental Micro-

biology, 18, 1805–1816.

Barberan, A., Bates, S. T., Casamayor, E. O., & Fierer, N. (2012). Using

network analysis to explore co-occurrence patterns in soil microbial

communities. ISME Journal, 6, 343–351.

Berry, D., & Widder, S. (2014). Deciphering microbial interactions and

detecting keystone species with co-occurrence networks. Frontiers in

Microbiology, 5, 219.

Bezemer, T. M., & van der Putten, W. H. (2007). Ecology: Diversity and

stability in plant communities. Nature, 446, E6–E7.

Bissett, A., Brown, M. V., Siciliano, S. D., & Thrall, P. H. (2013). Microbial

community responses to anthropogenically induced environmental

change: towards a systems approach. Ecology Letters, 16, 128–139.

Call, D., & Logan, B. E. (2008). Hydrogen production in a single chamber

microbial electrolysis cell lacking a membrane. Environmental Science

& Technology, 42, 3401–3406.

Caporaso, J. G., Bittinger, K., Bushman, F. D., DeSantis, T. Z., Andersen,

G. L., & Knight, R. (2010). PyNAST: a flexible tool for aligning

sequences to a template alignment. Bioinformatics, 26, 266–267.

Chao, A. (1984). Nonparametric-Estimation of the Number of Classes in

a Population. Scandinavian Journal of Statistics, 11, 265–270.

Chew, S. C., Kundukad, B., Seviour, T., van der Maarel, J. R., Yang, L.,

Rice, S. A., . . . Kjelleberg, S. (2014). Dynamic remodeling of microbial

biofilms by functionally distinct exopolysaccharides. mBio, 5, e01536–

01514.

6180 | FENG ET AL.

Collins, S. L., & Smith, M. D. (2006). Scale-dependent interaction of fire

and grazing on community heterogeneity in tallgrass prairie. Ecology,

87, 2058–2067.

Deng, Y., Jiang, Y. H., Yang, Y. F., He, Z. L., Luo, F., & Zhou, J. Z. (2012).

Molecular ecological network analyses. BMC Bioinformatics, 13, 113.

Deng, Y., Zhang, P., Qin, Y. J., Tu, Q. C., Yang, Y. F., He, Z. L., . . . Zhou, J.

Z. (2016). Network succession reveals the importance of competition

in response to emulsified vegetable oil amendment for uranium

bioremediation. Environmental Microbiology, 18, 205–218.

Ditzig, J., Liu, H., & Logan, B. E. (2007). Production of hydrogen from domes-

tic wastewater using a bioelectrochemically assisted microbial reactor

(BEAMR). International Journal of Hydrogen Energy, 32, 2296–2304.

Doak, D. F., Bigger, D., Harding, E. K., Marvier, M. A., O’Malley, R. E., &

Thomson, D. (1998). The statistical inevitability of stability-diversity

relationships in community ecology. American Naturalist, 151, 264–

276.

Downing, A. L., Brown, B. L., & Leibold, M. A. (2014). Multiple diversity-

stability mechanisms enhance population and community stability in

aquatic food webs. Ecology, 95, 173–184.

Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from

microbial amplicon reads. Nature Methods, 10, 996–998.

Faith, D. P. (1992). Conservation Evaluation and Phylogenetic Diversity.

Biological Conservation, 61, 1–10.

Faust, K., & Raes, J. (2012). Microbial interactions: from networks to

models. Nature Reviews Microbiology, 10, 538–550.

Fuhrman, J. A., Cram, J. A., & Needham, D. M. (2015). Marine microbial

community dynamics and their ecological interpretation. Nature

Reviews Microbiology, 13, 133–146.

Gagic, V., H€anke, S., Thies, C., Tscharntke, T., Leather, S. R., & Bezemer,

M. (2014). Community variability in aphid parasitoids versus preda-

tors in response to agricultural intensification. Insect Conservation and

Diversity, 7, 103–112.

Griffiths, B. S., & Philippot, L. (2013). Insights into the resistance and resi-

lience of the soil microbial community. FEMS Microbiology Reviews,

37, 112–129.

Hall-Stoodley, L., Costerton, J. W., & Stoodley, P. (2004). Bacterial bio-

films: from the natural environment to infectious diseases. Nature

Reviews Microbiology, 2, 95–108.

Hector, A., Hautier, Y., Saner, P., Wacker, L., Bagchi, R., Joshi, J., . . . Lor-

eau, M. (2010). General stabilizing effects of plant diversity on grass-

land productivity through population asynchrony and overyielding.

Ecology, 91, 2213–2220.

Heidrich, E. S., Dolfing, J., Scott, K., Edwards, S. R., Jones, C., & Curtis, T.

P. (2013). Production of hydrogen from domestic wastewater in a

pilot-scale microbial electrolysis cell. Applied Microbiology and Biotech-

nology, 97, 6979–6989.

Hodgson, D., McDonald, J. L., & Hosken, D. J. (2015). What do you

mean, ‘resilient’? Trends in Ecology & Evolution, 30, 503–506.

Houseman, G. R., Mittelbach, G. G., Reynolds, H. L., & Gross, K. L.

(2008). Perturbations alter community convergence, divergence, and

formation of multiple community states. Ecology, 89, 2172–2180.

Hovick, T. J., Elmore, R. D., Fuhlendorf, S. D., Engle, D. M., & Hamilton,

R. G. (2015). Spatial heterogeneity increases diversity and stability in

grassland bird communities. Ecological Applications, 25, 662–672.

Hunt, D. E., & Ward, C. S. (2015). A network-based approach to distur-

bance transmission through microbial interactions. Frontiers in Micro-

biology, 6, 1182.

Ives, A. R., & Carpenter, S. R. (2007). Stability and diversity of ecosys-

tems. Science, 317, 58–62.

Jagmann, N., von Rekowski, K. S., & Philipp, B. (2012). Interactions of bacteria

with different mechanisms for chitin degradation result in the formation

of a mixed-species biofilm. FEMS Microbiology Letters, 326, 69–75.

Keeley, J. E., Fotheringham, C. J., & Baer-Keeley, M. (2005). Determi-

nants of postfire recovery and succession in Mediterranean-climate

shrublands of California. Ecological Applications, 15, 1515–1534.

Kembel, S. W., Cowan, P. D., Helmus, M. R., Cornwell, W. K., Morlon, H.,

Ackerly, D. D., . . . Webb, C. O. (2010). Picante: R tools for integrating

phylogenies and ecology. Bioinformatics, 26, 1463–1464.

Kong, Y. (2011). Btrim: A fast, lightweight adapter and quality trimming

program for next-generation sequencing technologies. Genomics, 98,

152–153.

Kuhsel, S., & Bluthgen, N. (2015). High diversity stabilizes the thermal

resilience of pollinator communities in intensively managed grass-

lands. Nature Communications, 6, 7989.

Layeghifard, M., Hwang, D. M., & Guttman, D. S. (2017). Disentangling

Interactions in the Microbiome: A Network Perspective. Trends in

Microbiology, 25, 217–228.

Lee, H.-S., Torres, C. I., Parameswaran, P., & Rittmann, B. E. (2009). Fate

of H2 in an Upflow Single-Chamber Microbial Electrolysis Cell Using

a Metal-Catalyst-Free Cathode. Environmental Science & Technology,

43, 7971–7976.

Liu, H., Grot, S., & Logan, B. E. (2005). Electrochemically assisted micro-

bial production of hydrogen from acetate. Environmental Science &

Technology, 39, 4317–4320.

Liu, W. Z., Wang, A. J., Cheng, S. A., Logan, B. E., Yu, H., Deng, Y., . . .

Zhou, J. Z. (2010). Geochip-Based Functional Gene Analysis of Ano-

dophilic Communities in Microbial Electrolysis Cells under Different

Operational Modes. Environmental Science & Technology, 44, 7729–

7735.

Liu, W. Z., Wang, A. J., Sun, D., Ren, N. Q., Zhang, Y. Q., & Zhou, J. H.

(2012). Characterization of microbial communities during anode bio-

film reformation in a two-chambered microbial electrolysis cell (MEC).

Journal of Biotechnology, 157, 628–632.

Logan, B. E. (2009). Exoelectrogenic bacteria that power microbial fuel

cells. Nature Reviews Microbiology, 7, 375–381.

Logan, B., Cheng, S., Watson, V., & Estadt, G. (2007). Graphite fiber brush

anodes for increased power production in air-cathode microbial fuel

cells. Environmental Science & Technology, 41, 3341–3346.

Logan, B. E., & Regan, J. M. (2006). Microbial Fuel Cells—Challenges and

Applications. Environmental Science & Technology, 40, 5172–5180.

Loreau, M., & de Mazancourt, C. (2013). Biodiversity and ecosystem stabil-

ity: a synthesis of underlying mechanisms. Ecology Letters, 16, 106–

115.

Louca, S., & Doebeli, M. (2016). Transient dynamics of competitive exclu-

sion in microbial communities. Environmental Microbiology, 18, 1863–

1874.

Lozupone, C. A., Hamady, M., Kelley, S. T., & Knight, R. (2007). Quantita-

tive and qualitative beta diversity measures lead to different insights

into factors that structure microbial communities. Applied and Envi-

ronmental Microbiology, 73, 1576–1585.

Lozupone, C., Hamady, M., & Knight, R. (2006). UniFrac - An online tool

for comparing microbial community diversity in a phylogenetic con-

text. BMC Bioinformatics, 7, 371.

Lozupone, C., & Knight, R. (2005). UniFrac: a new phylogenetic method

for comparing microbial communities. Applied and Environmental

Microbiology, 71, 8228–8235.

Lu, L., Xing, D. F., & Ren, N. Q. (2012). Pyrosequencing reveals highly

diverse microbial communities in microbial electrolysis cells involved

in enhanced H-2 production from waste activated sludge. Water

Research, 46, 2425–2434.

Luo, F., Yang, Y. F., Zhong, J. Z., Gao, H., Khan, L., Thompson, D. K., &

Zhou, J. Z.a (2007). Constructing gene co-expression networks and

predicting functions of unknown genes by random matrix theory.

BMC Bioinformatics, 8, 299.

Magoc, T., & Salzberg, S. L. (2011). FLASH: fast length adjustment of

short reads to improve genome assemblies. Bioinformatics, 27, 2957–

2963.

May, R. M. (2001). Stability and complexity in model ecosystems, 1st

Princeton landmarks in biology edn. Princeton: Princeton University

Press.

FENG ET AL. | 6181

de Mazancourt, C., Isbell, F., Larocque, A., Berendse, F., De Luca, E.,

Grace, J. B., . . . Loreau, M. (2013). Predicting ecosystem stability from

community composition and biodiversity. Ecology Letters, 16, 617–

625.

McCann, K. S. (2000). The diversity-stability debate. Nature, 405, 228–

233.

McDougald, D., Rice, S. A., Barraud, N., Steinberg, P. D., & Kjelleberg, S.

(2012). Should we stay or should we go: mechanisms and ecological

consequences for biofilm dispersal. Nature Reviews Microbiology, 10,

39–50.

Murphy, H. T., Metcalfe, D. J., Bradford, M. G., & Ford, A. J. (2014).

Community divergence in a tropical forest following a severe cyclone.

Austral Ecology, 39, 696–709.

Price, M. N., Dehal, P. S., & Arkin, A. P. (2009). FastTree: Computing

Large Minimum Evolution Trees with Profiles instead of a Distance

Matrix. Molecular Biology and Evolution, 26, 1641–1650.

Rendueles, O., & Ghigo, J. M. (2015). Mechanisms of Competition in Bio-

film Communities. Microbiology Spectrum, 3, MB-0009-2014.

Rosenberg, G., Steinberg, N., Oppenheimer-Shaanan, Y., Olender, T.,

Doron, S., Ben-Ari, J., . . . Kolodkin-Gal, I. (2016). Not so simple, not

so subtle: the interspecies competition between Bacillus simplex and

Bacillus subtilis and its impact on the evolution of biofilms. NPJ Bio-

films and Microbiomes, 2, 15027.

Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollis-

ter, E. B., . . . Weber, C. F. (2009). Introducing mothur: Open-Source,

Platform-Independent, Community-Supported Software for Describ-

ing and Comparing Microbial Communities. Applied and Environmental

Microbiology, 75, 7537–7541.

Shade, A., Peter, H., Allison, S. D., Baho, D. L., Berga, M., Burgmann, H.,

. . . Handelsman, J. (2012). Fundamentals of microbial community

resistance and resilience. Frontiers in Microbiology, 3, 417.

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D.,

. . . Ideker, T. (2003). Cytoscape: A software environment for inte-

grated models of biomolecular interaction networks. Genome

Research, 13, 2498–2504.

Steudel, B., Hector, A., Friedl, T., Lofke, C., Lorenz, M., Wesche, M., . . .

Gessner, M. (2012). Biodiversity effects on ecosystem functioning

change along environmental stress gradients. Ecology Letters, 15,

1397–1405.

Thibaut, L. M., & Connolly, S. R. (2013). Understanding diversity-stability

relationships: towards a unified model of portfolio effects. Ecology

Letters, 16, 140–150.

Tilman, D., Reich, P. B., & Knops, J. M. H. (2006). Biodiversity and

ecosystem stability in a decade-long grassland experiment. Nature,

441, 629–632.

Varrone, C., Van Nostrand, J. D., Liu, W. Z., Zhou, B., Wang, Z. S., Liu, F.

H., . . . Wang, A. J. (2014). Metagenomic-based analysis of biofilm

communities for electrohydrogenesis: From wastewater to hydrogen.

International Journal of Hydrogen Energy, 39, 4222–4233.

Vuono, D. C., Munakata-Marr, J., Spear, J. R., & Drewes, J. E. (2016). Dis-

turbance opens recruitment sites for bacterial colonization in acti-

vated sludge. Environmental Microbiology, 18, 87–99.

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393, 440–442.

Wessel, A. K., Hmelo, L., Parsek, M. R., & Whiteley, M. (2013). Going

local: technologies for exploring bacterial microenvironments. Nature

Reviews Microbiology, 11, 337–348.

Wolkovich, E. M. (2010). Nonnative grass litter enhances grazing arthro-

pod assemblages by increasing native shrub growth. Ecology, 91,

756–766.

Zhou, J. Z., Deng, Y., Luo, F., He, Z. L., & Yang, Y. F. (2011). Phylogenetic

molecular ecological network of soil microbial communities in

response to elevated CO2. mBio, 2, e00122–00111.

Zhou, J. Z., Deng, Y., Luo, F., He, Z. L., Tu, Q. C., & Zhi, X. Y. (2010).

Functional molecular ecological networks. mBio, 1, e00169–00110.

Zhou, J. Z., Liu, W. Z., Deng, Y., Jiang, Y. H., Xue, K., He, Z. L., . . . Wang,

A. J. (2013). Stochastic Assembly Leads to Alternative Communities

with Distinct Functions in a Bioreactor Microbial Community. mBio,

4, e00584–00512.

SUPPORTING INFORMATION

Additional Supporting Information may be found online in the sup-

porting information tab for this article.

How to cite this article: Feng K, Zhang Z, Cai W, et al.

Biodiversity and species competition regulate the resilience of

microbial biofilm community. Mol Ecol. 2017;26:6170–6182.

https://doi.org/10.1111/mec.14356

6182 | FENG ET AL.

本文献由“学霸图书馆-文献云下载”收集自网络，仅供学习交流使用。

学霸图书馆（www.xuebalib.com）是一个“整合众多图书馆数据库资源，

提供一站式文献检索和下载服务”的24 小时在线不限IP

图书馆。

图书馆致力于便利、促进学习与科研，提供最强文献下载服务。

图书馆导航：

图书馆首页 文献云下载 图书馆入口 外文数据库大全 疑难文献辅助工具