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fmicb-11-555795 September 22, 2020 Time: 19:47 # 1
ORIGINAL RESEARCH
published: 24 September 2020
doi: 10.3389/fmicb.2020.555795
Edited by:
Jackie L. Collier,
Stony Brook University, United States
Reviewed by:
Jin Zhou,
Tsinghua University, China
Dominik Forster,
University of Kaiserslautern, Germany
*Correspondence:
Zhenzhen Yi
zyi@scnu.edu.cn
Specialty section:
This article was submitted to
Aquatic Microbiology,
a section of the journal
Frontiers in Microbiology
Received: 26 April 2020
Accepted: 24 August 2020
Published: 24 September 2020
Citation:
Zhu C, Bass D, Wang Y, Shen Z,
Song W and Yi Z (2020)
Environmental Parameters
and Substrate Type Drive
Microeukaryotic Community Structure
During Short-Term Experimental
Colonization in Subtropical Eutrophic
Freshwaters.
Front. Microbiol. 11:555795.
doi: 10.3389/fmicb.2020.555795
Environmental Parameters and
Substrate Type Drive
Microeukaryotic Community
Structure During Short-Term
Experimental Colonization in
Subtropical Eutrophic Freshwaters
Changyu Zhu1,2,3 , David Bass4, Yutao Wang3,5, Zhuo Shen6,7 , Weibo Song1,2 and
Zhenzhen Yi2,3*
1Institute of Evolution and Marine Biodiversity, College of Fisheries, Ocean University of China, Qingdao, China, 2Pilot
National Laboratory for Marine Science and Technology, Qingdao, China, 3Guangzhou Key Laboratory of Subtropical
Biodiversity and Biomonitoring, School of Life Sciences, South China Normal University, Guangzhou, China, 4Department
of Life Sciences, Natural History Museum, London, United Kingdom, 5Dongli Planting and Farming Industrial Co., Ltd.,
Lianzhou, China, 6Institute of Microbial Ecology and Matter Cycle, School of Marine Sciences, Sun Yat-sen University,
Zhuhai, China, 7Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
Microeukaryotes are key components of aquatic ecosystems and play crucial
roles in aquatic food webs. However, influencing factors and potential assembly
mechanisms for microeukaryotic community on biofilms are rarely studied. Here, those
of microeukaryotic biofilms in subtropical eutrophic freshwaters were investigated for
the first time based on 2,585 operational taxonomic units (OTUs) from 41 samples,
across different environmental conditions and substrate types. Following conclusions
were drawn: (1) Environmental parameters were more important than substrate types in
structuring microeukaryotic community of biofilms in subtropical eutrophic freshwaters.
(2) In the fluctuating river, there was a higher diversity of OTUs and less predictability
of community composition than in the stable lake. Sessile species were more likely to
be enriched on smooth surfaces of glass slides, while both free-swimming and attached
organisms occurred within holes inside PFUs (polyurethane foam units). (3) Both species
sorting and neutral process were mechanisms for assembly of microeukaryotic biofilms,
but their importance varied depending on different habitats and substrates. (4) The
effect of species sorting was slightly higher than the neutral process in river biofilms
due to stronger environmental filtering. Species sorting was a stronger force structuring
communities on glass slides than PFUs with more niche availability. Our study sheds
light on assembly mechanisms for microeukaryotic community on different habitat and
substrate types, showing that the resulting communities are determined by both sets of
variables, in this case primarily habitat type. The balance of neutral process and species
sorting differed between habitats, but the high alpha diversity of microeukaryotes in both
led to similar sets of lifecycle traits being selected for in each case.
Keywords: biofilm, habitat, microeukaryotes, neutral process, species sorting, substrate
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Zhu et al. Microeukaryotic Colonization on Substrates
INTRODUCTION
In the aquatic environment, surfaces of submerged materials
often favor the attachment and eventual colonization
by microorganisms, including bacteria, archaea, and
microeukaryotes (Besemer, 2015). These organisms become
enmeshed in a matrix of extracellular polymeric substances to
form what is collectively known as “biofilm” (Sutherland, 2001).
Biofilms are often referred to as microfouling, resulting in an
undesirable accumulation of microorganisms (Zhang et al.,
2014). They provide an effective strategy for microorganisms to
survive in unfavorable environments and to colonize new niches
(Hall-Stoodley et al., 2004). Hence, biofilms are considered to
be good models for understanding processes governing the
structure of communities in nature system. Mechanistic insight
into community assembly is crucial to better understand the
functioning of biofilms, which drive key ecosystem processes in
water (Singer et al., 2010;Peter et al., 2011).
A small number of previous studies on biofilm community
were published, mostly focusing on bacterial communities in
various environments such as streams (Besemer et al., 2012),
pools (Langenheder and Székely, 2011), lakes (Jackson et al.,
2001), rivers (Lyautey et al., 2005), and the deep sea (Zhang
et al., 2014). They indicated that environmental condition and
substrate type were the most important factors in structuring
bacterial communities (Lyautey et al., 2005;Besemer et al.,
2012;Lee et al., 2014;Zhang et al., 2014). For instance,
bacterial communities were mainly influenced by temperature,
light, and hydrodynamic stability in Garonne River (Lyautey
et al., 2005). Strong selection effect of the substrates on the
microbial assembly was reported in the brine pool in Thuwal
cold Seep (Zhang et al., 2014). By contrast, some studies
showed that microbial community assembly can theoretically be
dictated by neutral processes. In this model, random patterns in
species co-occurrence and environmentally independent spatial
autocorrelation (e.g., dispersal) were the main features of
community structure if demographic stochasticity and limited
dispersal alone were driving population dynamics, rather
than species sorting (environmental filtering and interspecific
competition) (Bell et al., 2005;Sloan et al., 2006;Bell, 2010;
Langenheder and Székely, 2011;Li et al., 2019;Liu J. et al., 2019).
For instance, the population dynamics of bacterial communities
in the Palo Alto Regional Water Quality Control Plant were
consistent with neutral community assembly (Ofi¸teru et al.,
2010). The neutral model also explained the distributions
of bacterial communities of water-filled treeholes in large
European beech trees (Woodcock et al., 2007). In addition, some
investigations reported that both species sorting and neutral
processes may shape the bacterial community structure, and
their importance may differ depending on how many generalists
and specialists are present in a community and homogenous
condition of flow landscape (Langenheder and Székely, 2011;
Woodcock et al., 2013;Wang et al., 2014;Li et al., 2018).
As predators, producers, decomposer, and parasites,
microeukaryotes represent the bulk of microbial diversity
and play key roles in the ecological functioning and process of
aquatic biological ecosystems (Bik et al., 2012;Zhang et al., 2018;
Zhao et al., 2018;Chen X. et al., 2019;Chi et al., 2019;Lu
et al., 2019;Wang et al., 2020;Xu et al., 2020). Previous studies
showed that several environmental factors play important roles
in structuring microeukaryotic communities on biofilms. For
instance, temperature, nutrients, and salinity were suggested as
the strongest determinants of community structure of ciliates
colonizing on glass slides in Jiaozhou Bay (Qingdao, China)
(Gong et al., 2005). Apart from the effect of abiotic factors,
predator–prey interactions between bacteria and eukaryotes
were also identified as important factors in structuring
morphology and function of biofilms from River Rhine in
Cologne (Germany) (Wey et al., 2012). However, the extent to
which different substrates determine the eukaryotic microbial
communities growing on them remains unclear (Ragon et al.,
2012;Cutler et al., 2013). For instance, green biofilm varied in
association with major differences in limestone and sandstone
in Belfast (Cutler et al., 2013). In contrast, algal community
compositions were reported to have no significant correlation
with substrate chemistry of exteriors of buildings in Europe
and Latin America (Gaylarde and Gaylarde, 2005). Uher et al.
(2005) found no connection between substrate type and algal
communities on stone in southeastern Spain. Previous studies
of microbial biofilms mostly concentrated on a single type
of aquatic environment such as lakes (Xu et al., 2005;Jiang
et al., 2007) or coastal seas (Xu et al., 2009;Abdullah Al et al.,
2018;Sikder and Xu, 2020;Sikder et al., 2020). Few studies
have compared microeukaryotic community assemblies on
substrates in different aquatic fluctuating conditions (Battin
et al., 2003;Besemer et al., 2007), even though it is recognized
that current and tide play important role in microeukaryotic
colonization (Xu et al., 2009). Additionally, mechanisms
structuring microeukaryotic communities of biofilms on
different substrates were rarely reported. We could find only
one study showing that neutral process was the most influential
process for microeukaryotic community assembly of epilithic
biofilms on mineral composite substrates (Ragon et al., 2012).
In summary, previous studies provided our understanding of
microeukaryotic biofilms to some extent, but possible factors
and potential mechanisms in structuring microeukaryotic
community assembly on different aquatic fluctuating condition
and substrates are still largely unknown.
Most biofilm-dwelling microeukaryotes are primary
consumers and play an important role in controlling the
transfer of energy to higher trophic levels in aquatic microbial
food webs (Abdullah Al et al., 2018). Microeukaryotes are diverse
and abundant in subtropical freshwater systems, because of high
spatial and temporal heterogeneities (Chen W. et al., 2019).
The Pearl River, the third biggest river in China, represents a
diversity hotspot for microeukaryotes (Liu W. et al., 2019). The
river is affected by tides from the Pearl River Estuary, presenting
fluctuating biotic (seed planktonic microeukaryotic species)
and abiotic environments (Huang et al., 2003) between river
water at high and low tide. The Ming Lake, locating in Jinan
University, is a lentic eutrophic lake, representing a stable aquatic
environment. Hence, the Pearl River and Ming lake represent
two typical subtropical freshwater environments, providing a
good opportunity for investigating the microeukaryotic assembly
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Zhu et al. Microeukaryotic Colonization on Substrates
on substrates under different conditions. We hypothesized
that microeukaryotic communities of biofilms are significantly
different in fluctuating river and stable lake environments.
Polyurethane foam units (PFUs) and glass slides, which have
been widely used in enrichment of microorganisms in aquatic
ecosystems for biodiversity assessment (Cairns et al., 1969;Xu
et al., 2005, 2009;Oberbeckmann et al., 2016). We predicted
that microeukaryotic communities of biofilms may be assembled
differently on these two substrates, considering that glass slides
are smooth (two-dimensional) and PFUs possess many holes
(three-dimensional).
In this study, microeukaryotic diversity of biofilms and
water columns from the river and lake were investigated
over time. We aimed to answer the following questions:
(1) Do microeukaryotic communities of biofilms in different
environment and substrates show similar diversity patterns? (2)
Do environmental parameters or substrate types more strongly
affect structuring microeukaryotic communities of biofilms?
MATERIALS AND METHODS
Sample Collection
Microeukaryotic communities were collected in two sites: Ming
Lake (23◦13 N, 113◦34 E, Guangzhou, China), a still freshwater
lake, and Pearl River (23◦11 N, 113◦33 E, Guangzhou, China), the
third biggest river in China (Figure 1). The sampling site in Pearl
River is about 66 km away from river mouth, and semidiurnal
tides are present. Temperature, pH, and dissolved oxygen (DO)
were all measured for each sampling with an ORION 520M-01A
(Thermo Fisher Scientific, MA, United States) multiparametric
probe. About 0.1 L of water was collected at the depth
of 30 cm below the surface water and then taken back to
laboratory (South China Normal University, Guangzhou) within
1 h. In laboratory, total nitrogen (TN), total phosphorus (TP),
ammonium nitrogen (AN), and chemical oxygen demand (COD)
were measured using a DR3900 spectrophotometer (HACH, CO,
United States) according to Water Analysis Handbook (HACH,
CO, United States) (Supplementary Table S1).
Two types of artificial substrates were used to measure
microeukaryotic communities: PFUs and glass slides. Glass slides
offer a robust, inexpensive, and reliable method for allowing
microeukaryotes to form biofilm and have been shown to harbor
microeukaryotic species richness almost as high as those on
natural substrates exposed to the same environmental conditions
(Gong et al., 2005). The PFU method was standardized by the
Environmental Protection Agency of China under number GB/T
12990-9 (Water Quality-Microbial Community Biomonitoring-
PFU Method) [(SBTS) China State Bureau of Quality, and
Technical Supervision, and (EPA) China State Environmental
Protection Administration, 1992] for collecting microeukaryotes
in aquatic ecosystems. Their effectiveness and practicability
were validated by previous studies (Gong et al., 2005;Xu
et al., 2005, 2009). In this study, the PFU method was based
FIGURE 1 | Location of sampling sites.
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Zhu et al. Microeukaryotic Colonization on Substrates
on standard protocol of Water Quality-Microbial Community
Biomonitoring-PFU Method) [(SBTS) China State Bureau of
Quality, and Technical Supervision, and (EPA) China State
Environmental Protection Administration, 1992]. The PFU
blocks were 6.5 ×6.5 ×7.5 cm in size and were soaked in distilled
water for 24 h and squeezed before using. Ten glass slides were
placed into a slide frame. Then the PFU blocks and glass slide
frames were tied with thin ropes and placed at the depth of 30 cm
below the surface water at the two sampling sides (Cairns et al.,
1969). Biofilms on one piece of PFU and 10 pieces of glass slides
were sampled by manual lifting. Water column samples (200 mL)
were collected at the same depth as the biofilms. Previous studies
(Plafkin et al., 1980;Xu et al., 2005, 2009) indicated that the
microeukaryotic community would reach equilibrium within
28 days in lentic water and 15 days in flowing water. According
to standard protocol of GB/T 12990-9 (Water Quality-Microbial
Community Biomonitoring-PFU Method) [(SBTS) China State
Bureau of Quality, and Technical Supervision, and (EPA) China
State Environmental Protection Administration, 1992], sampling
was done in Ming Lake at the 1st, 3rd, 7th, 11th, 15th, 21th, and
28th days (October 28–November 24, 2015) after the substrates
were deployed, and in Pearl River on the 1st, 3rd, 7th, 11th,
and 15th days (January 7–21, 2016). Water column samples were
collected at both low tide and high tide. Totally, we collected
21 samples from Ming Lake and 20 samples from Pearl River
(Supplementary Table S1).
Samples of the PFUs were obtained by manually squeezing as
much water as possible, and those of glass slides were manually
gently scraped in sterile water (approximately 200 mL). After
that, three types of samples (PFUs, glass slides, and water
columns) were filtered with a peristaltic pump (Vacuum Pump
XF5423050; Millipore, MA, United States) through a 0.22-µm
pore size polyethersulfone membranes (47-mm diameter; Pall,
NY, United States). Then, the membranes were stored at −80◦C
until DNA extraction.
DNA Extraction, Polymerase Chain
Reaction, and High-Throughput
Sequencing
Each membrane was cut by scissors and moved into bead
tube. Then total DNA was extracted from the membranes
FIGURE 2 | Venn diagrams displaying the number of unique and shared OTUs in different samples of Ming Lake (A) and Pearl River (B) and the number of unique
and shared OTUs in different environment of glass slides (C) and PFUs (D). Samples are named as follows: G, glass slides in Ming Lake; P, PFUs in Ming Lake; W,
water columns in Ming Lake; GR, glass slides in Pearl River; PR, PFUs in Pearl River; WH, water columns at high tide in Pearl River; WL, water columns at low tide in
Pearl River. Detailed information of samples is given in Supplementary Table S1.
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Zhu et al. Microeukaryotic Colonization on Substrates
using PowerSoilR
DNA Isolation Kit (MOBIO Laboratories,
CA, United States) according to the manufacturer’s instructions.
Total DNA was used as templates for polymerase chain reaction
(PCR) amplification of the V4 region of the SSU rDNA
(∼380 bp) using universal eukaryotic primers (Stoeck et al., 2010)
TAReuk45FWD1 [50-CCAGCA(G/C)C(C/T)GCGGTAATTCC-
30] and TAReuKREV3 [50-ACTTTCGTTCTTGAT(C/T)(A/G)A-
30]. Each PCR reaction (20 µL) contained 5 ×FastPfu buffer,
2.5 mM dNTPs, 1 U of FastPfu polymerase (TRANSGEN
BIOTECH, Beijing, China), 5 µM of each primer, and 10 ng
of target DNA. The amplification protocol consisted of an
initial denaturation step of 95◦C for 5 min, 27 cycles of
denaturation at 95◦C for 30 s, annealing at 55◦C for 30 s,
extension at 72◦C for 45 s, and a final extension step at
72◦C for 10 min. Then sequencing libraries were generated
using TruSeqR
DNA PCR-Free Sample Preparation Kit for
Illumina (San Diego, CA, United States) following manufacturer’s
recommendations, and bar-code indexes were added. The library
quality was assessed on the QubitR
2.0 Fluorometer (Thermo
Fisher Scientific, MA, United States). Finally, PCR products
were sequenced on an Illumina Hiseq instrument using a
paired-end 250-bp sequence read run (Total Genomics Solution,
Shenzhen, China).
Sequence Analysis
The paired-end reads were merged with FLASH (Tanja and
Salzberg, 2011). Raw sequence reads were analyzed and quality
filtered in UPARSE v. 8.1 (Edgar, 2013), pipeline implemented
in USEARCH v. 8.1 (Edgar, 2013), and QIIME v.1.8.0 (Caporaso
et al., 2010). Sequences were filtered in order to generate
high-quality reads through the QIIME quality-filtering pipeline
(Caporaso et al., 2010). Sequences of length <200 or >500,
average quality <20, ambiguous bases >0, or homopolymer
length >6 were removed. Chimeras were identified and removed
using UCHIME (Edgar et al., 2011). Remaining sequences
were grouped into operational taxonomic units (OTUs) at a
97% similarity cutoff using the UPARSE default algorithms
(Edgar, 2013). Afterward, singletons (OTUs with only one
sequence) were discarded before the downstream analysis as
potential sequencing errors. Then, we generated taxonomic
assignment of the OTUs using SILVA 128 (Quast et al.,
2012) using blast with default parameters within the QIIME
FIGURE 3 | The boxplots for richness and Shannon–Wiener index of microeukaryotes in lake (A) and river (B). Samples are named as follows: G, glass slides in
Ming Lake; P, PFUs in Ming Lake; W, water columns in Ming Lake; GR, glass slides in Pearl River; PR, PFUs in Pearl River; WH, water columns at high tide in Pearl
River; WL, water columns at low tide in Pearl River. The global P-value represents the significance among sample types in lake and river. The significant P-value
between two sample types is shown (P<0.05). Significance tests were based on Kruskal–Wallis test.
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Zhu et al. Microeukaryotic Colonization on Substrates
program (Caporaso et al., 2010). Finally, to enable comparisons
between samples, we used a randomly subsampled subset of
29,027 sequences from each sample to standardize sequencing
effort across samples.
Statistical Analysis
Statistical analysis and all graphic visualization were performed
in R (R Core Team, 2019). In order to minimize outlier effects,
logarithmic transformations were applied to the counts of reads
attributed to each OTU and environmental factors (except for
pH) for subsequent analyses. Richness and diversity of each
sample were estimated by the total number of OTUs per sample
and the “Shannon–Wiener” index, respectively, using the Vegan
package (Oksanen et al., 2013). Venn diagrams were generated
to show shared numbers of OTUs between the different sample
types (glass slide, PFU, and water column) within the same
environment and same sample type from different environment
using the “VennDiagram” package (Chen and Boutros, 2011).
Bray–Curtis dissimilarity matrix, which is considered to be one
of the most robust dissimilarity coefficients for ecological studies
(Kent, 2011), was applied to our microeukaryotic OTU relative
abundance of all samples. In order to compare the relative species
abundance among different samples, a heatmap was generated
using the “gplots” package (Warnes et al., 2009).
Non-metric multidimensional scaling (NMDS) analysis
was performed on the Bray–Curtis dissimilarity matrix
to visualize patterns of community composition, and the
significant differences between sample types were tested by
running a permutational multivariate analysis of variance
(ADONIS) (Anderson, 2001;Clarke et al., 2008;Yoshioka,
2008). Redundancy analysis (RDA) was performed to explore the
relationships between microeukaryotic communities of sample
types and water environmental factors. This method was chosen
because preliminary detrended correspondence analysis on
microeukaryotic communities revealed that the longest gradient
lengths were shorter than 3.0, indicating that the majority
of species exhibited a linear response to the environmental
variation (Lepš and Šmilauer, 2003). The significance of the
axes obtained by the RDA was determined based on the Monte
Carlo permutation test (Manly, 2006). A Kruskal–Wallis test
FIGURE 4 | Richness and Shannon–Wiener index of microeukaryotic communities from glass slides (G, red), PFUs (P, green), and water columns (W, blue) in lake (A)
across sampling time (1st, 3rd, 7th, 11th, 15th, 21th, 28th days). Richness and Shannon–Wiener index of glass slides (GR, red), PFUs (PR, green), water columns
(WH, blue) at high tide, and water columns at low tide (WL, black) in river (B) across sampling time (1st, 3rd, 7th, 11th, 15th days).
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(Breslow, 1970) was performed to test significant difference for
α-diversities and environmental factors between lake and river,
water at high tide and low tide, and α-diversities among different
sample types within the same environment. A neutral community
model (NCM) was used to determine the potential importance of
neutral processes on community assembly of different biofilms
in different environment (Sloan et al., 2006). The NCM is an
adaptation of Hubbell’s NCM (Hubbell, 2001) adjusted to large
microbial populations analyzed with molecular tools (Sloan et al.,
2006). NCM is used to determine the potential contribution
of neutral processes (such as dispersal and ecological drift) to
community assembly by predicting the relationship between
OTU occurrence frequency and their relative abundance (Sloan
et al., 2006). In this model, R2represents the overall fit to the
neutral model; the Nm indicates the metacommunity size (N)
times immigration (m). The confidence is 95%, based on 1,000
bootstrap replicates.
RESULTS
Comparison of Diversity and Community
Composition
In total, 2,585 OTUs were detected across all samples. There
were 1,259 and 2,299 OTUs detected in lake and river samples,
respectively (Figures 2A,B). Among them, 620 (49.25%) and 813
(35.36%) were shared among three sample types (water columns,
glass slides, and PFUs) in lake and river samples, respectively
(Figures 2A,B). For the substrate sample types, totals of 1,918
OTUs and 1,990 OTUs were detected in glass slide (Figure 2C)
and PFU samples (Figure 2D), respectively. Of these, 406 OTUs
(21.17%) were shared between glass slide samples from the lake
and river, and 479 (24.07%) were shared between PFU samples
from the lake and river. Both richness and Shannon–Wiener
indices among three sample types were significantly different
overall (P<0.05) in the lake samples, but not in the river
samples (Figure 3). Comparing samples from the lake and
river (Supplementary Figure S1A), both richness and Shannon–
Wiener indexes of all samples in the river were significantly
higher than those of the lake. However, there were no significant
α-diversity differences between all water column samples in river
at high tide and low tide (Supplementary Figure S1B).
Of the 21 lake samples, the highest richness and the highest
Shannon–Wiener index occurred in the water sample on the 11th
day (W4). The richness of glass slide and PFU richness levels
were highest on day 1 (G1) and day 3 (P2), respectively. The
Shannon–Wiener diversity levels of both glass slides and PFUs
were highest on day 1 (G1, P1), but always lower than water.
The richness and diversity of PFUs were consistently higher
than those on glass slides (Figure 4A). Of the 20 river samples,
richness and Shannon–Wiener diversity indices of PFUs and glass
slides showed much greater variation than in the lake, sometimes
exceeding that of the water samples (Figure 4B). The richness
of samples from glass slides and PFUs were highest on the 1st
day (GR1, PR1). The highest Shannon–Wiener index of samples
from glass slides and PFUs occurred on the 3rd day (GR2) and
the 1st day (PR1), respectively. The variability of microeukaryotic
assemblages was greater across the sampling period in the river
samples than in the lake (Figure 5). For example, the changes
in relative abundance of Metazoa on GR (SD = 0.17) and in
FIGURE 5 | Taxonomic profile of microeukaryotic OTUs of every sample type from the lake (A) and river (B) over time. Samples are named as follows: G, glass slides
in Ming Lake; P, PFUs in Ming Lake; W, water columns in Ming Lake; GR, glass slides in Pearl River; PR, PFUs in Pearl River; WH, water columns at high tide in Pearl
River; WL, water columns at low tide in Pearl River. The relative abundance of each group >1% is shown.
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water (0.25) were greater than that of lake (SD = 0.10 and
0.09, respectively).
A heatmap showing the occurrence of the top 100 most
frequently detected OTUs showed a strong distinction between
river and lake samples (Figure 6). The 41 samples were divided
into two clades: clade “Lake” containing all 21 samples from
Ming Lake and clade “River” containing all 20 samples from
Pearl River. Within the lake clade, microeukaryotic communities
clustered according to sample types: the water and PFU
clusters being more closely related to each other than either to
the glass slides. Chlorophyta (Archaeplastida) and Ochrophyta
(Stramenopiles) were relatively abundant in water (41 and 20%,
respectively) and PFU samples (40 and 21%, respectively) but
rare on glass slides (<10%) (Figure 7). In contrast, OTUs
annotated as peritrich and suctorian ciliates (Alveolata), with a
primarily sessile lifestyle, were abundant in biofilm samples of
glass slides (25 and 11%, respectively) but rare in water and PFUs
(lower than 1%). The river clade comprised two subclades, one
containing two clusters of water samples, one mostly (4/5) high
tide, the other mostly (4/5) low tide. The other subclade also
comprised two clusters, one mostly (4/5) from PFU samples,
and the other mostly (4/5) from glass slides. OTUs assigned to
Metazoa (Opisthokonta) were abundant in biofilm samples of
PFUs (34%) and glass slides (49%), whereas they were moderate
in water columns (10% for high tide and 14% for low tide,
respectively). In contrast, Perkinsidae (Alveolata) was abundant
in samples of water columns compared to those from glass
slides and PFUs.
Dissimilarity Among Communities
Non-metric multidimensional scaling ordinations showed strong
separation of lake and river communities (Figure 8), consistent
with the heatmap (Figure 6); all sample types clustered
significantly separately from each other (ADONIS; P<0.05;
FIGURE 6 | Relative abundance of top 100 microeukaryotic OTUs with most abundant read counts. Samples are labeled in lateral axis, and abundance of each OTU
is labeled in vertical axis. The phylogenetic tree was calculated using neighbor-joining method, and the relationship among samples was determined by Bray
distance. The relative abundance for each OTU was depicted by color intensity with the legend indicated at the left top of the figure. Samples are named as follows:
G, glass slides in Ming Lake; P, PFUs in Ming Lake; W, water columns in Ming Lake; GR, glass slides in Pearl River; PR, PFUs in Pearl River; WH, water columns at
high tide in Pearl River; WL, water columns at low tide in Pearl River.
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Zhu et al. Microeukaryotic Colonization on Substrates
FIGURE 7 | Comparison of taxonomic profile of microeukaryotic OTUs among different sample types from the lake and river. Samples are named as follows: G,
glass slides in Ming Lake; P, PFUs in Ming Lake; W, water columns in Ming Lake; GR, glass slides in Pearl River; PR, PFUs in Pearl River; WH, water columns at high
tide in Pearl River; WL, water columns at low tide in Pearl River. The relative abundance of each group >5% is shown. Abundant groups represented by >10% of
each taxonomic unit were labeled with group name, and the relative abundance of each group >5% was labeled with percentage in pie charts.
Figure 8). And the distance/community dissimilarity between
biofilms and water columns were shorter/lower in lake than in
river, indicating that the habitat type played an important role in
dissimilarities between biofilms and water columns. Additionally,
in the river, the state of the tide also played an important role in
modifying the microeukaryotic assemblages.
The Effect of Environmental Factors on
Microeukaryotic Community Structures
Temperature, pH, and DO in the lake were significantly higher
than those in river, whereas the TP, TN, and AN in river were
significantly higher than those in lake (P<0.05, Table 1). The
mean value of AN in river (10.01) was approximately 33 times of
that in lake (0.30). Further, environmental factors of river water
at high tide were significantly different from low tide except for
temperature. COD was the most different factor between river at
high tide and river at low tide (three times).
Redundancy analysis revealed significant relationships
between variation in environmental factors and microeukaryotic
communities (Supplementary Figure S2). The variance between
microeukaryotic communities explained by the first two axes of
RDA was 56.7% for water column samples, 60.6% for biofilm
samples of glass slides, and 63.8% for biofilm samples of PFUs.
A Monte Carlo permutation test (Table 2) also a revealed a
significant (P<0.05) relationship between the environmental
factors and community structures. For the glass slide samples,
the correlation coefficient between AN and microeukaryotic
community structures was highest of all sample types (0.97).
For PFU samples, the highest correlation coefficient was 0.97,
which was contributed by TN. For water column samples, AP
showed the strongest correlation (0.92) with microeukaryotic
community structures. Although the environmental factors
with the highest correlation coefficient were different among
three sample types, nutrients (TP, TN, and AN) have a strong
correlation with microeukaryotic community structures of glass
slides (0.95 on average), PFUs (0.96 on average), and water
columns (0.85 on average).
The NCM Partly Explains Community
Variation
The NCM was used to determine the potential importance of
neutral processes on community assembly of different biofilms in
different environment. The NCM explained nearly 50% of taxon
detection frequency in both lake and river (Figure 9), indicating
that both species sorting and neutral process played an important
role in microeukaryotic biofilm assembly. Furthermore, the NCM
explained >50% of taxon detection frequency from the lake
(51% for glass slides and 60% for PFUs) (Figures 9A,B), but
<50% in the river (41% for glass slides and 48% for PFUs)
(Figures 9C,D). Thus, the neutral process exerted slightly more
effect on the lake than the river. The NCM also explained more
variation in PFU samples (60% for lake and 48% for river)
(Figures 9B,D) than glass slides counterparts (51% for lake and
41% for river) (Figures 9A,C).
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FIGURE 8 | NMDS ordination of 21 samples in Ming Lake and 20 samples in
Pearl River, showing clustering into groups according to environments (Ming
Lake or Pearl River) and sample types (glass slides, PFUs and water columns).
G, glass slides samples from lake; P, PFU samples from lake; W, water
column samples from lake; GR, glass slides samples from river; PR, PFU
samples from river; WH, water column samples at high tide from river; WL,
water column samples at low tide from river. The ellipses represent 95%
confidence intervals. Prepresents significance between every two sample
types based on ADONIS analysis.
DISCUSSION
Comparing Microeukaryotic Diversity
Between Habitats and Sample Types
The microeukaryotic diversity (water and biofilms) of
river samples was higher overall than that of lake samples
(Figure 3 and Supplementary Figure S1). High abundance
of microeukaryotes in river samples can be attributed to a
fluctuating environment, providing a wider range of ecological
conditions and greater opportunities for microorganisms
to successfully establish on substrates than the lake, as also
indicated by a previous study (Besemer et al., 2007). In addition,
flow velocity can also promote the flux of microorganisms
from the bulk liquid to biofilms (Besemer et al., 2007). The
Pearl River is composed of many tributaries from different
ecological conditions (Huang et al., 2003), providing a diverse
set of species from many niches to potentially colonize
substrates, resulting in higher microeukaryotic diversity both
in water columns and biofilms from the river than in the lake.
Another reason for elevated diversity may be the high load
of anthropogenic nutrients into Pearl River from increased
agricultural activities, fish dike farming, and wastewater runoff
due to the increase in population and economic development
along the river (Huang et al., 2003), considering that nutrients
(TP, TN, and AN) have strong correlation with microeukaryotic
community structures (Table 2). It has been demonstrated that
nutrients could directly affect photosynthesis by autotrophic
microeukaryotes in water columns, and then phytoplankton
growth impacts abundance of heterotrophic ones (Wang et al.,
2014). A previous investigation also revealed that bacterial
diversity in tropical stream biofilms increased with nitrate
concentrations (Burgos-Caraballo et al., 2014).
Operational taxonomic units affiliated to Metazoa and
Perkinsidae were more abundant in river samples than in lake
(Figure 7), raising the possibility of a parasitic relationship
between them in the river. Perkinsidae is group of parasitic
microeukaryotes (Adl et al., 2019), which is hosted by metazoans
such as bivalves, frogs, and fish (Chambouvet et al., 2015;
Freeman et al., 2017).
In the present study, a large algal diversity in different
sample types was detected. This is consistent with previous
studies showing that algae were the most important primary
producers in freshwater (Besemer et al., 2007). Among the
three sample types, biofilms on glass slides contained a high
proportion of sessile ciliates (Peritrichia) (Figure 7). This is
concordant with previous studies showing that glass slides as
artificial substrates allow microorganisms to form a periphyton
or biofilm, in which periphytic ciliates (especially peritrich
ciliates) were usually in high abundance and richness (Cairns and
Yongue, 1968). The glass slides also hosted a high proportion
of metazoans (Figure 7), in agreement with a previous study
showing that Metazoa was the most abundant phylum on glass
slides from Jauron River (Bricheux et al., 2013). However,
our PFUs hosted a high portion of algae (Chlorophyta and
Ochrophyta), which was inconsistent with previous studies
showing that flagellates and ciliates were abundant on this
substrate (Xu et al., 2005;Jiang et al., 2007). One explanation is
that the high nutrient concentration in our sampling region leads
to the high proportion of algae, which was also demonstrated in
previous investigations (Biggs, 2000).
Factors and Potential Mechanisms
Influencing Microeukaryotic Community
Structures
Both the heatmap (Figure 6) and NMDS (Figure 8) revealed
that environmental parameters exerted stronger effects on
microeukaryotic communities than sample type in our study. The
Ming Lake is a small closed lake (Figure 1 and Supplementary
Table S1), representing a stable aquatic environment, while the
Pearl River represents a typical fluctuating river environment
about 66 km far away from the river mouth (Figure 1 and
Supplementary Table S1), subject to semidiurnal tides. Variation
in microeukaryotic α-diversities and community compositions
were greater in the river than the lake (Figures 4,5). There
could be several explanations for this. In the river, environmental
factors and water flow direction differed daily at low and high
tide (Table 2), resulting in diverse environmental stresses on
the microbial communities (Böer et al., 2009;Zhang et al.,
2020). Changing tides can affect biotic factors such as source
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Zhu et al. Microeukaryotic Colonization on Substrates
TABLE 1 | The comparisons for difference of environmental factors among lake, river, river at high tide, and river at low tide.
Comparison Lake vs. river Lake vs. river at low tide Lake vs. river at high tide High tide vs. low tide
Factor/mean value Lake River Lake River at low tide Lake River at high tide River at high tide River at low tide
Temperature 25.66*18.77*25.66*18.7*25.66*18.84*18.84 18.7
DO 5.23*0.98*5.23*0.45*5.23*1.50*1.50*0.45*
pH 7.27*6.91*7.27*6.96*7.27*6.86*6.86 6.96
COD 31.39 52.73 31.39*81.98*31.39 23.48 23.48*81.98*
TP 0.28*0.95*0.28*1.23*0.28*0.68*0.68*1.23*
TN 1.53*12.15*1.53*15.30*1.53*9.00* 9.00* 15.30*
AN 0.30*10.01*0.30*14.02*0.30*6.00* 6.00* 14.02*
Numbers represent mean value of environmental factors. *P <0.05 is considered as significant effect.
communities and species interaction. This may result from
“seed” planktonic microeukaryotic species from upstream and
downstream being different and possible increased competition
between microbes due to tidal fluctuation (Lee et al., 2011,
2014). Tidal activity is also likely to influence the structure and
function of bacterial community in tidal habitats (Lv et al.,
2016). Both abiotic (environmental factors) and biotic factors
(source community and species interaction) play important roles
in shaping microeukaryotic communities.
Within a given habitat, substrate types shaped
microeukaryotic community assembles. The heatmap (Figure 6)
and NMDS analyses (Figure 8) showed that within both lake
and river environments, community compositions of biofilms
generally grouped according to type of substrates (PFUs, slides),
as well as sampling time. Some previous investigations studying
bacterial and archaeal communities also recognized substrate
type as a key factor affecting microbial community compositions
(Sundberg et al., 2013;Ziganshin et al., 2013;Kalenitchenko
et al., 2015). Obviously, properties of the substrates influenced
microeukaryotic adhesion as has been shown for bacterial
adhesion (Bernardes et al., 2012). In our study, those species with
strong adhesion (sessile type) were more likely to be enriched
on smooth surfaces of glass slides (Figure 7), on which other
species are less able to live (Gong et al., 2005). By contrast, both
TABLE 2 | The correlation coefficient between the microeukaryotic community
structure and environmental factors and result of Monte Carlo permutation test in
different sample types.
Sample Glass slides PFUs Water columns
types/
factors r2P r2P r2P
Temperature 0.77** 0.002 0.89*** 0.001 0.82*** 0.001
DO 0.77** 0.002 0.81** 0.002 0.83*** 0.001
pH 0.58*0.018 0.65** 0.007 0.73** 0.002
COD 0.76** 0.002 0.74** 0.008 0.88*** 0.001
TP 0.92** 0.002 0.96** 0.002 0.92*** 0.001
TN 0.97** 0.002 0.97** 0.002 0.80*** 0.001
AN 0.97*** 0.001 0.96** 0.002 0.82*** 0.001
Significant codes: ***P ≤0.001; **P ≤0.01; *P ≤0.05. Permutation: free. Number
of permutations: 1,000.
free-swimming and attached organisms can easily colonize PFUs
(Figures 5,7) due to the holes and increased surface area inside
the PFU, through which water can flow, but more slowly than
across glass slides. In general, the smooth surfaces of glass slides
more strongly selected specific groups of microeukaryotes than
PFUs (Gong et al., 2005).
The community structures of biofilms from the lake and
river were driven by both species sorting and neutral processes
(Figure 9). Previous studies also showed that the relative
importance of both species sorting and neutral processes changes
not only across scales, but also during biofilm succession
(Gong et al., 2005;Ragon et al., 2012). In our study, species
sorting can be characterized by the effects of environmental
parameters (environmental factors and tide) and substrates.
Neutral process can be characterized by immigration and
emigration of microeukaryotes on substrates. Selective pressures
of environmental parameters and substrate have effects on
microeukaryotic biofilms, but no overwhelming effect was
detected, in agreement with a previous study that showed
substrate characteristics impact the abundance/biomass but not
microeukaryotic diversity (Cutler et al., 2013).
Interestingly, the effect of species sorting was slightly higher
than the neutral process in river biofilms, whereas the opposite
applied to the lake samples (Figure 9). We suggest this is due to
stronger environmental filtering in the river, with its constantly
changing conditions (tide and environmental factors). On the
other hand, water flow in the river likely increases dispersal,
increasing neutral processes relative to species sorting. In the
relatively stable lake, environmental fluctuations were lower, and
therefore neutral processes more strongly influenced community
assembly. Although dispersal effects in the lake were likely lower
than in the river, local alpha diversity of microeukaryotes is
known to be very high relative to spatially more expansive levels
of diversity (beta, gamma), and therefore the local “pool” of
available lineages to colonize the different substrates is very large,
so that the differences between substrate type are likely to be
the strongest determinant of community assembly in the lake.
Concordantly, the overall effect of species sorting was slightly
lower for PFUs than glass slides, as PFUs provide a greater
diversity of niches for microeukaryotes. In contrast to glass
slides, which are generally only suitable for sessile taxa (Gong
et al., 2005), PFUs can host planktonic, periphytic, and benthic
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Zhu et al. Microeukaryotic Colonization on Substrates
FIGURE 9 | Frequency of occurrence of OTUs as a function of mean relative abundance (logarithmic transformed) for lake glass (A), lake PFUs (B), river glass (C),
and river PFUs (D). The solid blue line indicates the best fit to the neutral model as in Sloan et al. (2006), and the dashed blue line represents 95% confidence
intervals around the model prediction. Different colors represent OTUs with more or less frequency than predicted. Nm indicates metacommunity size times
immigration. R2indicates the fit to the neutral model, and negative R2values indicate no fit to this model.
protozoan assemblages and have relative large surface areas for
colonization of sessile eukaryotes and lacunae, which can host
planktonic forms (Lugo et al., 1998).
CONCLUSION
As the first investigation focusing on influencing factors and
potential assembly mechanisms for microeukaryotic community
on biofilms in subtropical eutrophic freshwaters, we revealed
the relative importance of species sorting and neutral processes
for microeukaryotic community assembly on different substrates,
and how this differed between a stable (lake) and fluctuating
(tidal river) environment. The effect of species sorting was
slightly higher than neutral processes in river biofilms due
to stronger environmental filtering. Sessile species were more
likely to be enriched on smooth surfaces of glass slides, while
both free-swimming and attached organisms occurred within
holes inside PFUs. Species sorting was enhanced on glass slides
relative to PFUs, which have a greater diversity of niches.
Environmental parameters such as environmental factors and
tidal effects were more important than substrate types in
structuring microeukaryotic community of biofilms in both lake
and river. To test these preliminary results further, a more highly
replicated study is now justified, both at individual time points
and across a longer period of time.
DATA AVAILABILITY STATEMENT
All Illumina sequencing datasets are deposited in the NCBI under
the accession number PRJNA623677.
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Zhu et al. Microeukaryotic Colonization on Substrates
AUTHOR CONTRIBUTIONS
ZY conceived the study. CZ carried out the experiments
and performed the data analyses. CZ, ZY, and DB primarily
wrote this manuscript. YW, ZS, and WS prepared the figures
and participated in discussion. All authors agreed to be held
accountable for the work performed therein.
FUNDING
This work was supported by the Marine S&T Fund of Shandong
Province for Pilot National Laboratory for Marine Science
and Technology (Qingdao) (2018SDKJ0406-1), the National
Natural Science Foundation of China (grant no. 31772440),
Guangdong MEPP Fund [No. GDOE(2019)A23], the Pearl
River S&T Nova Program of Guangzhou (201806010186),
the YangFan Innovative and Entrepreneurial Research Team
Project (2015YT02H032), and Research Fund for Water
Science and Technology of Guangzhou (Guangzhou Water
Bureau, grant no. GZCPJHB-2016-07). The BBSRC China
Partnering Award Scheme provided support for training in
data analysis.
ACKNOWLEDGMENTS
We thank Miss Shuqing Ou, Mr. Jun Huang, and Mr. Wenjun
Shi, former undergraduates of South China Normal University,
for their help on sampling.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmicb.
2020.555795/full#supplementary-material
FIGURE S1 | The boxplots for richness and Shannon-Wiener index of between
different environment (Lake vs. River) and different water columns in river (WH
vs. WL).
FIGURE S2 | RDA ordination showing the microbial eukaryotic community
structures of glass slides (A), PFUs (B) and water columns (C) in lake and river in
relation to environmental factors. Tem, temperature; pH; DO, dissolved oxygen;
TN, total nitrogen; TP, total phosphorus; AN, ammonia nitrogen; COD, chemical
oxygen demand. The black characters represent samples, blue arrows represent
measured environmental factors.
TABLE S1 | The information of samples (sample ID, Sample Type, Date Location,
Latitude and longitude and environmental Factors).
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Conflict of Interest: YW was employed by Dongli Planting and Farming Industrial
Co., Ltd.
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
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