Water mass-driven multiple ecological effects determine the biodiversity
and assembly processes of microbial flagellates’ communities in
subtropic-tropic marginal seas of China
Running head: Microbial flagellate community assembly
Xin Guo1, Qiang Liu1, Xiaoqing
Lin1, Xinyi Zheng1,
Cheng
Huang1, Mengwen Pang2, Lingfeng
Huang1 *
1Key Laboratory of the Ministry of Education for
Coastal and Wetland Ecosystems, College of the Environment and Ecology,
Xiamen University, Xiamen, Fujian, China
2Department of Ocean Science, Hong Kong University of
Science and Technology, Hong Kong, China
*Corresponding Author:Lingfeng Huang: Phone
(Office): +86 592-2188455, Fax (Office): +86 592-2188455, Phone (Home):
+8613906053195, iD: 0000-0002-2428-8147 , E-mail:huanglf@xmu.edu.cn
Abstract:
Marine microbial flagellates form
an important part of marine ecosystems, and play an essential role in
maintaining ecosystem functions. However, the underlying biogeographic
processes and ecological effects that shape marine microbial flagellate
communities (MFCs) on the geographical scale (~ 2,000
km) remain unclear, especially how their composition is related to
movements of water masses. In this study, high-throughput sequencing of
18S rRNA genes was conducted to survey two size-fractioned groups
(0.8–2.0 μm for pico-sized and 2.0–20 μm for nano-sized groups) of
MFCs in three subtropic-tropic marginal seas of China.
Furthermore, the impacts of
environmental factors, spatial factors, and water masses on MFCs were
explored and compared across different spatiotemporal conditions. The
results demonstrate non-random biogeographic distributions of MFCs in
the studied area. These distributions were affected by several
ecological processes, such as
environmental selection, dispersal
limitation, neutral process, and interactions within communities.
These processes were driven by
complex water masses that formed on a geographical scale. Notably,
environmental heterogeneity was identified as the principal determinant
of MFCs in each sea area. However, the importance of spatial factors
increased with the spatial scale, which weakened biotic interactions
within the community on a geographical scale. This effect was more
apparent in nano-sized MFCs, indicating stronger dispersal limitation
because of their larger cells and weaker dispersal ability. In summary,
this study expands the available knowledge on the dynamic biogeographic
patterns of MFCs associated with water masses on a geographical scale
where strong spatial and environmental gradients exist.
Keywords: Microbial
flagellate, community assembly, water mass, biodiversity, co-occurrence
network, geographical scale
Introduction
The marine microbiol food web plays a
key role in maintaining both the structure and function of the marine
ecosystem and is as important as the classic food chain (Sherr & Sherr,
2009). Microbial flagellates, with a cell size less than 20 μm, have
become a crucial part of microbial food web due to their large biomass,
high diversity, multiple trophic modes, and broad ecological niches in
the global marine ecosystem (Throndsen,
1997).
With differing trophic modes,
e.g., autotrophy (Haeder & Lebert, 2009), mixotrophy (Caron, 2016),
heterotrophy (Jeuck et al., 2017), and parasitic lifestyle (Lukes,
Skalicky, Tyc, Votypka, & Yurchenko, 2014), microbial flagellates
greatly contribute to biogeochemical cycling.
They
conduct photosynthesis, phagotrophy, osmotrophy, and parasitism, thus
maintaining carbon balance and forming an essential trophic link for the
transfer of energy from the microbial food web to the classic food web
(Worden et al., 2015). The term flagellates is not a taxonomic
definition, but rather represents an assembly of single-celled protists
with similar ecological characteristics—they share one or more
flagella, which they use for locomotion or feeding (Throndsen, 1997).
In
recent years, the rapid development of DNA sequencing technology and in
particular, the application of high-throughput sequencing (HTS) combined
with metabarcoding approaches, has revealed incredible diversity of
marine microbial flagellates, far exceeding the recognition from
traditional microscopic identification methods (Stoeck et al., 2010).
However, few studies distinguished flagellates from other protists or
microbial eukaryotes, but rather pooled them into a community (Guo, Wu,
& Huang, 2020; Z. M. Xu et al., 2020).
The
reason for this approach was likely associated with a lack of ecological
knowledge on most eukaryotic lineages, especially those mainly known by
environmental sequences, which makes their division into flagellates and
not-flagellates challenging. However, it is necessary to study microbial
flagellates separately from other protists, such as ciliates (the main
predators of microbial flagellates mostly with a size greater than 20 μm
(Sherr & Sherr, 2009)) and fungi (which usually attach to substrates
via hyphae or rhizoids and obtain nutrients exclusively by feeding
osmotrophically (Richards, Jones, Leonard, & Bass, 2012)). Thus, the
unique ecological status and adaption mechanisms to the environment of
microbial flagellates in the microbial food web must be explored.
For more than one century,
ecological research has predominantly focused on the key scientific
issue of the formation, distribution, and maintenance of biodiversity,
i.e., biological community assembly (Konopka, 2009).
Community assembly embodies the response
and adaptation of organisms to short-term environmental disturbance
(Logue, Findlay, & Comte, 2015) and long-term climatic changes (Caron
& Hutchins, 2012). Therefore, understanding community composition and
assemblage will help to understand
their relationship with the
environment, and further explore how the community structure affects
microbially-mediated ecological functions (Logue et al., 2015).
Niche
theory (Keddy, 1992) and neutral theory (Hubbell, 2006) are two major
classic ecological theories that
focus on explaining the community assembly process. Niche theory
emphasizes niche differentiation of species under the action of
environmental filtering, leading to dynamic community distributions in
response to environment gradients (Caron & Hutchins, 2012). In
contrast, neutral theory suggests that community dynamics are stochastic
processes subject to the random effects of dispersal, ecological drift,
and historical events (Martiny et al., 2006). An increasing number of
studies have suggested that both processes play essential roles in the
assembly of microbial communities (Xiong et al., 2017; Zhang, Zhao, Dai,
Jiao, & Herndl, 2014). However, many factors may affect the relative
importance of these two processes, examples for which are environmental
changes, trophic interactions, interference events, dispersal effects,
community connectivity, spatial scales, and various biological
attributes such as niche width and species abundance (Hanson, Fuhrman,
Horner-Devine, & Martiny, 2012; Langenheder & Lindström, 2019; Q. Xu
et al., 2022). Exploring the relative contributions of both processes on
microbial community assembly is the key for understanding the ecology
and biogeography of microbial communities (Martiny et al., 2006).
The subtropic-tropic marginal seas of
China have sloping topography and clear monsoon climate characteristics,
and form complex seasonally changing water masses. Mixing occurs between
these water masses, which results in mixtures of biological communities
with different properties at various distances from their formation
areas (Liu & Tanhua, 2021). Therefore,
seasonal changes of microbial communities not only reflect community
internal dynamics, but also biological flow between different
communities in adjacent waters. This should be considered with the
movement and mixing of water masses to better understand temporal
dynamics and spatial variations (Fuhrman, Cram, & Needham, 2015).
Studying the biogeography of microbial eukaryotic communities on a
geographical scale is vital because it is the scale at which different
water masses create contrasting hydrological conditions and spatial
heterogeneity (Hong et al., 2011; Lee & Chao, 2003) that shapes
spatiotemporal distribution patterns of microbial communities (W. Li et
al., 2018). The transformations of these influences over time and space
can be observed in the framework of water masses (Liu & Tanhua,
2021).
However, previous studies on the biogeographic distribution of microbial
eukaryotic communities were mostly restricted to regional sea areas at
small spatial scales. For example, for the Xiamen coast, it has been
shown that the distribution of microbial eukaryotic communities was
affected by complex mechanisms such as environmental factors, dispersal
limitation, neutral process, and biotic interactions (W. D. Chen, Pan,
Yu, Yang, & Zhang, 2017). The same was shown for the Yangtze River
plume and its adjacent waters (Guo et al., 2020).
Recently, the geographical or
global distributions of various microbial eukaryotes were explored for
the Tara Oceans (De Vargas et al., 2015; Sommeria-Klein et al., 2021),
Baltic Sea (Hu, Karlson, Charvet, & Andersson, 2016), and Mediterranean
Sea (Zhou et al., 2018). Multiple ecological factors that shape
spatiotemporal microbial patterns were identified. However, the effects
of water masses on microbial eukaryotic communities at a geographical
scale (~2,000 km) have not been explored, especially for
cases where these effects are associated with strong environmental
gradients and spatial factors. Moreover, such research has rarely been
reported along a large scale of marginal seas in China and more
attention is warranted (Z. M. Xu et al., 2020). As a result, notable
knowledge gaps exist regarding how environmental conditions, spatial
factors, and movement of water masses jointly determine the assembly of
microbial eukaryotic communities on a geographical scale.
By
using high-throughput sequencing of the 18S rRNA gene, this study
described the spatiotemporal distribution of microbial flagellate
communities (MFCs) in subtropic-tropic marginal seas of China. These
data were used to explore how their distributions were related to
different ecological processes driven by multiple water masses. For this
study, MFCs were divided into two size-fractioned groups: pico-sized
(0.8–2.0 μm) and nano-sized (2–20 μm)
MFCs.
This
size fractionation was employed to assess the potential impact of cell
size on diversity and ecological processes because cell size is a prime
factor in many physiological and metabolic processes (e.g.,
picoeukaryotes have a larger cell surface to volume ratio to grow and
disperse easier compared to nanoplankton) (Elferink et al., 2017;
Martiny et al., 2006; Worden et al., 2015). For both size groups, the
diversity, community composition, and biogeographic distribution were
studied and compared. The effects of multiple ecological processes
(including environmental selection, dispersal limitation, neutral
process, and biological interactions) on MFCs were also assessed.
It was hypothesized that the
biogeographic distribution of MFCs depends on a variety of ecological
processes, the degree of importance of which changes under different
spatial scales, for different ecological MFC groups, and in different
seasons. Testing the validity of these hypotheses helped to assess the
diversity, distribution patterns, and assembly mechanisms of marine
MFCs.
Materials and methods
Study area and sampling
Environmental and biotic samples
were collected from three subtropic-tropic marginal seas of China,
spanning 15.16–32.34°N and 114.78–126.6°E (Fig. 1a, Fig. 2a). These
seas include the East China Sea (ECS), the Taiwan Strait (TS), and the
northeastern South China Sea (SCS). Sampling in the ECS was conducted in
spring (13–29 May, 2019) and autumn (9–28 September, 2019), sampling
in the TS was conducted in spring (4–15 April, 2019) and summer (21–30
July, 2019), and sampling in the
SCS was conducted in summer (11 July to 11 August, 2017).
All summer and autumn samples
were considered as in transitional
phases from summer to autumn, as these phases showed similar
environmental variations and
community compositions (Fig. S1 and Supplementary Methods A). Thus, to
better compare spatiotemporal differences among samples, the sampling
seasons were simplified to spring and summer-autumn (Supplementary
Methods A). The spatial scales of
each sea area (i.e., ECS, TS, and SCS) were referred to as “regional
scales”, whereas the spatial scales of two sea areas in spring (ECS and
TS) or three in summer-autumn (ECS, TS, and SCS) connected by water
masses were referred to as “geographical scales”.
Samples
from ECS and TS were collected at two water depths (2 m beneath the sea
surface representing the surface layer and 3–4 m above the seabed
representing the bottom layer). Samples from the SCS were collected at
three layers (surface, deep chlorophyll maximum (DCM), and the bottom of
the photic zone, referred to as Bottom, at a depth of
~200 m) (Fig. 1a, Fig. 2a). Maps of sampling stations
were made by Ocean Data View v.5.2.1.
Totals of 5–10 L of seawater were pre-filtered sequentially through
200-μm and 20-μm nylon meshes to remove meso- and micro-plankton. The
water was then sequentially filtered through 2-μm and a 0.8-μm
polycarbonate membranes (47 mm in diameter, Millipore, USA) to obtain
nano-sized (2.0–20 μm) and pico-sized (0.8–2.0 μm) microbial plankton
samples. In total, 330 samples (167 pico-sized and 163 nano-sized
samples) were collected (Table S1). Filters were immediately frozen in
liquid nitrogen in the field and stored at -20 ℃ in the laboratory.
DNA was extracted within one month
after sampling. Details of the sampling of abiotic and biotic
environmental factors are presented in Supplementary Methods B,
including temperature, salinity, fluorescence, dissolved oxygen (DO),
dissolved inorganic nutrients, chlorophyll a (Chl a ), and
abundance of heterotrophic bacteria (HB), cyanobacteria (Cyan),
pico-sized eukaryotic algae (pico-Euk), and nano-sized flagellate (NF).
DNA extraction, high-throughput
sequencing, and sequence analyses
DNA
was extracted using the
PowerWater® DNA Isolation Kit (Qiagen, USA) following
the manufacturer’s instructions. PCR amplification was conducted using
primers with barcodes to get the
V4 region of eukaryotic 18S rRNA gene: 3NDF (Cavalier-Smith, Lewis,
Chao, Oates, & Bass, 2009) with the reverse primer V4_euk_R2 (Brate
et al., 2010). Purified PCR
products were sequenced using V3 chemistry with a paired-end 2 × 250 bp
run on an Illumina Hiseq 2500 platform (Illumina, Inc., San Diego, CA,
USA). Sequencing data were processed on Mothur v.1.40.5 (Schloss et al.,
2009) following data quality filtering, operational taxonomic unit (OTU)
clustering, and taxonomic classification (Guo et al., 2020).
Sequences
were clustered into OTUs applying a 97% similarity threshold and were
taxonomically classified based on the PR2 protist
database (Guillou et al., 2013) at an 80% confidence level.
Normalization was conducted to
enable comparison between samples in different sequencing coverage
depths (Fig. S2a (i, ii)), based on the
lowest sequence count (1,083
sequences for pico-sized samples and 1,107 sequences for nano-sized
samples) from each dataset (Table S2, Fig. S2a (iii, iv)). To focus on
our target organism, we then only selected OTUs affiliated to the
microbial flagellates according to the
literature (Table S3) (Adl et al.,
2019; Archibald et al.; Throndsen, 1997). Taxa that are not affiliated
with flagellates were removed from the datasets. Finally, two OTU tables
of pico-sized and nano-sized MFCs were formed and used for further
analyses and comparisons (see Supplementary Methods part C for details
on this method).
Statistical analyses
Determination
of water masses and environmental properties
The typical water masses in spring
and summer-autumn were determined based on temperature and salinity data
using fuzzy cluster and temperature-salinity water similarity methods as
proposed by Zhu et al. (2019) (see details under Supplementary methods
D). Temperature and salinity data used for water mass classification
were collected in situ with
the SeaBird conductivity-temperature-depth (CTD) profiler (SBE917plus,
SeaBird Corp., WA, USA), covering all stations throughout the entire
regions of cruises to better identify the distribution of water masses
(Figs. S3, S4, and S5). After classifying the water masses in the whole
study area in spring and summer-autumn, only the stations with the
environmental and biotic samples were chosen for the further analysis
and matched with corresponding water masses (Fig. 1a, Fig. 2a). All
environmental factors in the selected stations were log(x+1) transformed
before analyses. Principal component analysis (PCA) was conducted to
test the explanatory power of significant environmental variables
(p < 0.001) on the variation of samples. The
significance of environmental differences among groups was further
calculated using permutational multivariate analysis of variance
(Adonis).
Diversity analysis and community compositions
Rarefaction
curves, species accumulation curves, and Goods’ coverage index were
calculated to test the sequencing depth. The two alpha-diversity indices
Chao1 and Shannon were calculated to evaluate the richness and diversity
of MFCs. OTU data were Hellinger-transformed prior to analyses to
provide unbiased estimates (Legendre, 2008). Analyses of similarities
(ANOSIM) were conducted to assess the difference in MFCs among groups
based on Bray-Curtis similarity matrixes. Community dissimilarity was
visualized by principal coordinate analysis (PCoA). Relative richness
(i.e., number of OTUs) and relative abundance (i.e., number of reads) of
lineages at different taxonomic levels were plotted using OriginPro
2019.
The effects of water masses,
environmental factors, and spatial factors on MFC assembly
Correlation analyses and Mantel tests based on Spearman’s rank
correlation coefficients were conducted to assess the relationships
among MFCs’ alpha diversity and compositions with environmental
parameters. Water masses were represented by temperature and salinity,
whereas environmental factors were represented by all biotic and abiotic
factors except for temperature and salinity. A set of spatial factors
was generated using the principal coordinates of neighbor matrices
(PCNMs) approach based on the longitude and latitude coordinates of each
sampling site (Dray, Legendre, & Peres-Neto, 2006). Variations of water
masses, environmental factors, and spatial factors among samples were
analyzed by calculating the Euclidean distances between sampling points.
The relative contributions of these three factors to MFC variances were
explored using variance partitioning analysis (VPA) with adjusted
R2 coefficients based on redundancy analysis (RDA)
(Legendre, 2008). Simple and partial Mantel tests was conducted to
assess the effects of three
factors on alpha diversity (i.e., Euclidean distance of samples based on
Chao1 and Shannon indices) and beta diversity (Bray-Curtis distance) of
MFCs (Sun, Wang, Laws, & Huang, 2020).
Community
neutral model
A community neutral model (NM) was used
to assess the potential importance of neutral processes on MFC assembly
by fitting the relationship between OTU occurrence frequency and
relative abundance (Sloan et al., 2006). In this model,R2 indicates the fitness to the NM andNm values represent the metacommunity size (N) times immigration
rate (m).
Co-occurrence network analyses
To assess the biotic interactions in MFCs, Spearman’s rank correlations
(r ) between pairs of OTUs were calculated using the WGCNA package
(Pei, Chen, & Zhang, 2017).P -values were adjusted by
“q-values” using a modified version of the false discovery rate (FDR)
test(Storey, 2003). Only robust (|r |
> 0.6) and statistically significant (p <
0.05) correlations were incorporated into the network and visualized
using the Gephi 0.9.2 interactive platform
(https://www.gephi.org/, 6th Sept.,
2020 ). Several network-level
topological properties were computed in the igraph R package to
explore the complexity and
stability of the network structure in different spatiotemporal
conditions (Deng et al., 2012).
All statistics analyses were performed in R (v.3.6.1) using the named
packages
(https://www.r-project.org/,
30th Aug., 2020) unless otherwise
stated.
Results
Water masses in the study area and
their environmental heterogeneity
There were 5 and 10 water masses
recognized in spring (Fig. 1a, b) and summer-autumn (Fig. 2a, b),
respectively, across the area of the selected stations. Differences of
environmental characteristics among these water masses were assessed
with PCA (Fig. 1c, Fig. 2c).
The
Minzhe coastal water (Mc) along the ECS had low salinity and
temperature, and high Chl a concentration, nutrients, and
photosynthetic plankton in spring (Fig. 1b, c).
In
summer-autumn, it had low salinity and higher water temperature (Fig.
2b, c). The ECS was largely affected by the East China Sea surface water
(Es) in both seasons (Fig. 1a, Fig. 2a). The branch of the Kuroshio
surface water (Ks) intruded the ECS from the bottom in spring (Fig. 1a),
while Kuroshio subsurface water
(Ku) rose along the continental slope of the ECS in summer-autumn and
mixed with the Ks, thus forming Kuroshio surface-subsurface mixed water
(Km) (Fig. S4b, Fig. 2a). Both water masses had high salinity (Fig. 1b,
c; Fig. 2b, c). The northern TS was mainly affected by the Coastal mixed
water (Cm) in both seasons, and had similar environmental conditions
with the Es (Fig. 1, Fig. 2). However, the southern TS had different
distribution of water masses in spring and summer-autumn. In spring, the
surface layer of the southern TS was mainly affected by the South China
Sea surface water (Ss) (Fig. 1a), which might have originated from the
branch of the Ks that crossed into the SCS from the Luzon Strait, thus
the environmental conditions of the Ss and the Ks were similar (Fig. 1b,
c). In summer-autumn, the Cm and the Zhujiang River diluted water (Zd)
affected the surface layer of the southern TS, while the bottom layer
was affected by the South China Sea surface-subsurface mixed water (Sm)
(Fig. 2a). Samples collected at three layers in the SCS in summer-autumn
in 2017 were affected by South China Sea surface water (Ss-2017), South
China Sea subsurface water (Su-2017), and Pacific Ocean subsurface water
(Pu-2017) (Fig. 2a). In these abbreviations, the year 2017 was added
after the name as a distinction from the SCS water mass in 2019
(Supplementary methods D). Furthermore, water masses were sorted into
three types according to their
salinity, source, and distance from the shore. Specifically, the Mc and
Zd were considered coastal waters, the Cm, Es, and Ss were considered
mixed waters, while the Ks, Km, Sm, Ss-2017, Su-2017, and Pu-2017 were
considered oceanic waters (Fig. 1, Fig. 2). The general environmental
parameters of the above-mentioned water masses are listed in Table S5.
Throughout the studied spatiotemporal scale, the environmental
properties presented differences in several dimensions: seasonal, water
masses, latitudinally from north to south area, longitudinally from
coastal to oceanic waters, and vertically from surface to bottom layers
(Table S6). Overall, the greatest heterogeneity of environmental factors
was associated with water masses, followed by latitudinal and vertical,
seasonal, and longitudinal comparisons (Adonis, Table S6).
This
indicated a significant influence of water masses on environmental
conditions on a geographical scale, which shaped the biogeographic
patterns of MFCs (Table S7).
Community diversity, composition, and
non-random biogeographic distribution at the geographical scale
After subsampling 1,083 and 1,107
sequences per sample for the datasets of pico- and nano-sized protists,
totals of 180,861 sequences (3,227
OTUs) and 180,441 sequences (3,019 OTUs) were retained, respectively
(Table S4).
The
slightly increasing trends of
rarefaction curves (Fig. S2a), the species accumulation curves for OTUs
(Fig. S2b), and the high values of Good’s Coverage index
(82.48–99.82%, Fig. S2c) indicated good recovery of sequenced protist
taxa. The relative richness and abundance of microbial flagellates
accounted for about 70% of protists (Table S4), indicating the dominant
role of microbial flagellates in protist communities.
Alpha diversity of MFCs displayed
distinct spatiotemporal distribution patterns. Summer-autumn samples
possessed higher alpha diversity than spring samples, although the
difference was not significant (Fig. 3). Alpha diversity followed
significant increasing trends with distance of water masses from the
shore (Fig. 3). The bottom layers of the ECS and the TS had
significantly higher alpha diversity than surface layers (Fig.
S7). Correlation analyses
demonstrated that alpha diversity was significantly associated with
water chemistry (i.e., salinity and dissolved oxygen), abundance of food
sources (i.e., heterotrophic bacteria and cyanobacteria), and abundance
of nano-sized flagellates (Fig. S8).
For taxonomic compositions of pico-sized and nano-sized MFCs, the
assigned 1,489 and 1,340 OTUs were classified into 32 and 34
deep-branching lineages among seven supergroups, respectively Fig. S9).
Alveolata was the most dominant and diverse supergroup in both
size-fractioned communities, accounting for about 60% of OTUs and more
than 80% of DNA reads. It was followed by Archaeplastida, which
contributed 2.66% and 3.70% of the total OTUs number and 10.11% and
6.25% of the total reads in pico- and nano-sized MFCs, respectively
(Fig. S9). Stramenopiles, Rhizaria, and Hacrobia had high relative
richness values, but low relative abundance. The sums of the relative
richness of these three supergroups were 32.05% and 25.67% in pico-
and nano-sized MFCs, respectively, while the sums of the relative
abundance only accounted for 7.56% and 5.52%, respectively (Fig. S9).
The biogeographic distribution of MFCs was reflected by the results of
PCoA (Fig. S10) and ANOSIM (Table S7). Overall, the strongest
differences in community composition were found among water masses,
followed by latitudinal comparisons of different sea areas, while
seasonal, longitudinal, and vertical differences were relatively weaker
(Table S7).
Mantel tests showed strong
associations of MFCs composition with water temperature, nutrients, and
microbial abundance (Fig. S8). In
addition, the dominant taxa responded differently to environmental
conditions (Fig. S11c, d), leading to non-random spatiotemporal
distributions of MFCs in the water masses across the studied area (Fig.
4). Dinophyceae and the marine alveolates (MALVs) were the two most
abundant groups in Alveolata, dominating in all water masses (Fig. 4).
However, MALVs displayed lower relative abundance in spring (P< 0.05), while Dinophyceae had high abundance in both seasons
(Fig. 4).
Trebouxiophyceae,
a major class of Archaeplastida, was found to be widely distributed from
coastal to oceanic waters in two seasons (Fig. 4). Another two key
Archaeplastida lineages were Micromonas and Prasino-Clade, with
an average relative abundance up to 15.04% in Mc and 8.48% in Sm in
summer-autumn, respectively (Fig. 4b). Cryptophyta and Haptophyta, the
two most abundant phyla of Hacrobia, were widely distributed in all
water masses (Fig. 4), despite their average relative abundances of only
~2% (Fig. S9). Cercozoa, a major class of Rhizaria,
with the average relative abundance of only ~1%, was
more distributed in coastal and mixed currents (Fig. 4b, d). We also
found only about 1% relative abundance of Labyrinthulea, a class in the
Stramenopiles, occurred in the Km in summer-autumn, whereas acccounted
for less than 0.1% on average in other water masses (Fig. 4b, d).
Apusozoa was discovered with relative abundances up to 5.08% and
13.80% in the Ss-2017 and Pu-2017, respectively, which was less than
0.01% on average in other water masses (Fig. 4b).
Water mass-driven spatial effects and environmental heterogeneity on
microbial flagellates’ communities
Simple and partial Mantel tests showed that MFCs covaried significantly
with water mass, environmental heterogeneity, and spatial variability
(Table 1). Among these factors, water mass was most important in most
cases (Table 1). However, the contributions of the environmental and
spatial factors to the MFCs’ variation differed under different
spatiotemporal conditions. In terms of the spatial scale, the impacts of
environmental factors were
dominant in MFC assembly in each sea area (i.e., ECS, TS, and SCS),
where the impact of spatial factors was weak and non-significant (Table
1). These sea areas were
considered as regional scale in this study. However, at the geographical
scale of two or three sea areas, the importance of spatial factors was
significantly enhanced, especially for nano-sized communities (Table 1).
This effect was more apparent when samples from the SCS in summer were
added to analyses. Moreover, alpha diversity variation also showed
stronger correlations with spatial variations on the geographical scale
(Table S8). To further clarify the potential impact of spatial factors
on structuring MFCs’ distributions at the geographical scale, we applied
a neutral model. The NM indicated that neutral process explained more
than 50% (R2 > 0.50) of the
variation in the occurrence frequency of OTUs in MFCs (Fig. 5). In
addition,
seasonal
variations of the importance of environmental and spatial factors on
MFCs were also observed (Table 1). To eliminate the influence of the
spatial scale, samples from the SCS in summer were excluded from
comparisons. The results
demonstrated greater spatial effects and weaker environmental effects on
both sizes of MFCs in spring than in summer-autumn (Table
1).
MFCs co-occurrence networks
The present study explored the co-occurrence network that generated from
strongly significant pairwise correlations (|r |
> 0.6, p < 0.05) among OTUs, to search for
how biotic interactions could be important to structure the MFCs.
The community on a geographical
scale had more than eight sub-modules, composed of multiple taxonomic
lineages in two size-fractions with different species abundance (Fig.
S12). According to various topological properties, networks on the
geographical scale were loosely and weakly connected compared with the
three regional networks (Table 2). Specifically, the network at the
geographical scale held lower indices of average degree (19.499),
betweenness centralization (0.014), degree centralization (0.020), and
connectedness (0.007), while the values of average clustering
coefficient (0.642), average path length (5.034), diameter (12),
modularity (0.766), and number of sub-modules (276) were larger than
those of regional networks (Table 2).
Correspondingly, we also observed
a clear seasonal pattern of co-occurrence networks. On the geographical
scale, community displayed more closely connected networks in spring and
relatively looser networks in summer-autumn according to corresponding
topological features (Table 2). This is particularly pronounced
considering the samples from the SCS (Table 2).
Discussion
In this study, the spatial scale
ranged between 100–2,000 km with a substantial scope that led to the
clear spatial distributions of different water masses. On the one hand,
the distribution of water masses forms certain connections that drive
marine physicochemical conditions and microbial communities to display
gradient distributions on the geographical scale. On the other hand,
water masses from different directions and with differences in speed and
hydrological characteristics may impose a certain dispersal limitation
on microbial communities and therefore cause complex distribution
patterns.
Geographical alpha diversity
The spatiotemporal patterns of alpha diversity of MFCs were largely
influenced by environmental conditions. The present study revealed
associations of alpha diversity with water chemistry and microbial
abundance (Fig. S8). Previous studies found an increasing trend of
microeukaryote diversity with salinity (Z. M. Xu et al., 2020), which is
consistent with the results of the present research (Fig. S8),
explaining the increase of diversity from coastal waters to oceanic
waters (Fig. 1b; Fig. 2b; Fig. 3; Table
S5). It has been predicted that
the expansion of hypoxic areas in the oceans could favor trophic
relationships dominated by protistan micro-grazers with the ability to
tolerate low-oxygen environments (Caron & Hutchins, 2012). In this
study, alpha diversity showed negative relationships with the dissolved
oxygen and abundance of food sources (Fig. S8).
This is one of the possible
explanations of the higher
diversity of MFCs in the bottom layer of the ECS and TS (Fig. S7), where
an annual hypoxia cycle was observed because of decomposition and
stratification of organic matter (Z.-Y. Zhu et al., 2011).
As the main food sources of
flagellates, higher abundance of heterotrophic bacteria and
cyanobacteria (or other suitable environmental conditions) may
facilitate the rapid growth and reproduction of certain species that
thus become abundant and suppress the growth of others in the community;
consequently, the diversity decreases (Caron & Hutchins, 2012). In this
study, a higher abundance of nano-sized flagellates was observed in
coastal waters with sufficient food abundance (Table S5), which may be
the cause for their lower diversity compared with oceanic waters (Fig.
3).
In addition to the presented
environmental factors, several other unmeasured environmental factors
may affect alpha diversity. For example, the lower diversity of surface
layer waters may be related to the strong ultraviolet radiation the
surface layer is subjected to, which restricts the growth and behavior
of certain UV sensitive protists (Caron & Hutchins, 2012;
Rodríguez‐Martínez et al., 2009). pH is also an important factor that
impacts calcification and motility in some protists (Caron and Hutchins
2012). The higher diversity of the bottom layer of the ECS and the TS
could also be a result of environmental heterogeneity because of poor
patch connectivity at the bottom (Yeh et al., 2015) causing competition
and niche differentiation.
Geographical assemblages
In this study, the geographical assemblages of MFCs were strongly
characterized by water masses, while the latitudinal, longitudinal,
vertical, and seasonal differences were also displayed (Table S7). These
were related to the environmental heterogeneity such as temperature,
nutrients, and microbial abundance according to the Mantel tests (Fig.
S8). Temperature and nutrients are the main driving factors that
facilitate the growth of phytoplankton and bacteria (Chang et al., 2022)
and increase grazing pressure of nanoflagellates (Choi et al., 2012),
resulting in “bottom-up” controls on the MFCs (Caron & Hutchins,
2012). The influence of temperature on MFCs may reflect the community
response to the variation of water masses, because temperature is
considered one of the two parameters to identify the water mass in this
study. It was reported that the plankton community composition from
oligotrophic to productive areas in the tropical and subtropical regions
were highly variable (Armengol, Calbet, Franchy, Rodriguez-Santos, &
Hernandez-Leon, 2019). The nutrients could reflect the anthropogenic
impacts on MFCs’ variation from the coastal to oceanic waters due to the
human activities such as fishing, shipping, etc.
More specifically, our discovery of non-random spatiotemporal
distributions of MFCs among water masses revealed strong niche
differentiation in the community, especially for the dominant taxa,
which played important role in community assembly (Fig. 4, Fig. S11c,
d). For example, two dominant groups Dinophyceae and MALVs were
prevalent in all the water masses, but their ecological niches were
different. The sizes of both groups were clearly different, with larger
cells of Dinophyceae and smaller MALV sizes (Fig. S9). The size
difference reveals one of the intrinsic characteristics of two groups
that may cause nich seperation through many physiological and metabolic
processes such as nutrient uptake, growth and reproduction, dispersal,
etc (Martiny et al., 2006). A recent study of the seasonality of
microbial eukaryotes has reported that spring blooms of specific taxa
could reduce the advantages of MALV hosts or affect the cell viability
of MALVs themselves, thereby decreasing the abundance of MALVs
(Marquardt, Vader, Stubner, Reigstad, & Gabrielsen, 2016).It also
reported the predominance of Dinophyceae throughout the year (Marquardt
et al., 2016). This was probably due to their mixotrophic nutrient mode
that could either feed on bacteria, cyanobacteria or other smaller
protists or compete for nutrients with phytoplankton (Stoecker, 1999),
thus showing the relationships and potential interactions with other
organisms (Fig. S11c, d). Dinophyceae is also one of the common
parasitic hosts of MALVs (Guillou et al., 2008), so their distribution
was correlated to a certain extent (Fig. S11c, d). In addition, MALV-II
have been found to have a higher richness and diversity than MALV-I
(Guillou et al., 2008), which was consistent with the findings on the
present study (Fig. S9).
Archaeplastida was the second dominant supergroups in this study.Micromonas and Prasino-Clade were mainly found in Mc and Sm in
summer-autumn respectively, displaying very different distributions in
water masses (Fig. 4b). Another study also observed the highest
abundances of Micromonas and Prasino-Clade in the west coast of
the English Channel in summer (Not et al., 2004). Both studies reflected
the dominance of photosynthetic picoeukaryotes in the euphotic zone of
coastal areas (Not et al., 2004). The prevalence of Micromonaswas also related to the highest abundance of bacteria in Mc in
summer-autumn (3.36 ×106 mL-1, Table
S5), because it is a well-known bacterivorous protist (Marquardt et al.,
2016) which showed positive correlations with bacteria (Fig. S11c).
Another dominant taxa Trebouxiophyceae has been found to distributed
widely in the studied area, especially in coastal waters in spring (Fig.
4a, c). The most abundant taxon (OTU2) of Trebouxiophyceae belonged to
the genus Picochlorum , according to the BLAST results in the NCBI
database
(https://blast.ncbi.nlm.nih.gov/Blast.cgi,
30th Apr., 2020). Picochlorum is a microbial
green alga with wide ecological niche and a diameter of 2–3 μm. It is
considered an extremophile with tolerance to highly variable
environments such as largely fluctuating salinity and temperature, which
includes water bodies from fresh water to 3-fold salinity of seawater
and within a temperature range of 16–33 ℃ (Wang, Lambert, Giang,
Goericke, & Palenik, 2014). Picochlorum was also reported to
grow better under low temperature and low illuminance in winter and
spring, thus were prone to algal blooms (Somogyi et al., 2009).
Lineages in Hacrobia, Rhizaria, and Stramenopiles were highly diverse
with very low relative abundance for each taxon (Fig. S9).
Taxa belonging to Cryptophyta and
Haptophyta are usually mixotrophic types, which offer survival
advantages over autotrophic phytoplankton or heterotrophic protists
(Lepere, Masquelier, Mangot, Debroas, & Domaizon, 2010). This
facilitated them to distribute widely in all water masses (Fig. 4) and
played active roles in network relationships (Fig. S12b).
Cercozoa was mainly found in
coastal and mixed water masses, which may be affected by high
productivity and high microbial activities of the microbial food loop in
these water masses (Huang et al., 2011). Cercozoa feed on bacteria,Synechococcus , small algea, and even other protists (Lepere et
al., 2010), which yielded positive correlations with these biotic
factors (Fig. S11c, d). Similarly, Cercozoa was also a key member in
networks with low relative abundance (Fig. S12b). Labyrinthulea was
mostly discovered in the Km in summer-autumn in this study (Fig. 4b, d).
A study on the seasonal distribution of Labyrinthulea in estuaries and
coastal waters during six years found that Labyrinthulea was generally
more abundant in summer than in other seasons (Ueda, Nomura, Doi,
Nakajima, & Honda, 2015). This was related closely with the warmer
water and higher concentration of dissolved organic matter in summer
(Ueda et al., 2015). The input of the Kuroshio water mass brings a lot
of nutrients and organic matters to the shelf area of the ECS (H. M. Li,
Shi, Wang, & Han, 2014), which promotes microbial activities and the
decomposition effect of Labyrinthulea (Ueda et al., 2015).
Here we also observed a high relative abundance of Apusozoa in the SCS
in summer-autumn compared with other water masses (Fig. 4b). A previous
study by Xu et al. (2017) at a similar location in the SCS basin showed
that Apusozoa were mainly distributed in deep waters below 1000 m, where
they had higher metabolic activity than in shallow waters (D. P. Xu et
al., 2017). It has also been previously reported that Apusozoa were
commonly detected in freshwater or sediments, with a high concentration
of organic matter. They were identified as euryhaline organisms, which
distributed across a wide range of salinity levels from freshwater to
seawater (Torruella, Moreira, & Lopez-Garcia, 2017). The above relevant
research showed that Apusozoa may inhabit a very wide ecological niche
and should be further explored.
These results indicated that there
was a non-random spatiotemporal distribution of microbial flagellates in
the water masses across the studied area. This was related to the
specific environmental conditions and the connected or isolated spatial
distributions of different water masses. As the carrier of the
biological community, water mass had environmental heterogeneity and
spatial distribution characteristics, both of which had certain
correlations (Fig. S11). This indicated that the role of water mass on
the MFCs may be reflected in the regulation of environmental factors and
spatial factors. Thus, multiple interactive factors jointly determined
the MFCs’ assembly process in different water masses or areas.
Controlling mechanisms shaping MFCs diversity and assembly process
Effect of spatial scale
In this study, we studied microbial flagellates’ communities across the
subtropic-tropic marginal seas in China on the geographical scale., of
which the assembly process was shaped by environmental gradients and
spatial distances under complex movements of water masses (Table 1).
However, their contributions to
the MFCs variation differed when the spatial scales, target organism
groups, and seasons were different. The most apparent effect was related
to the spatial scale. The correlations between spatial factors and MFCs
can be equivalent to the distance-based dispersal limitation effect on
biological communities (Hanson et al., 2012). It has been reported that
the spatial scale of research areas determines which assembly process
will be more important (Langenheder & Lindström, 2019). Specifically,
at the smallest spatial scale, taxa can spread quickly among fully
connected communities, leading to a high degree of similarity (Leibold
et al., 2004). In this case, the interaction between organisms is strong
(Genitsaris, Monchy, Breton, Lecuyer, & Christaki, 2016). With
increasing spatial scale, species slowly and sufficiently diffuse into
their respective habitats through the environmental filtering effect
(Moreno-Pino et al., 2016). This effect is the dominant factor of
community assembly for three regional sea areas in this work. Finally,
on a larger spatial scale, for example the geographical scale from
subtropic to tropic seas as addressed in the present study, dispersal
limitation plays the leading role in determining community assembly
(Langenheder & Lindström, 2019). In addition, spatial effects also
provide evidence of the importance of historical processes for microbial
composition, as certain historical environmental conditions have been
preserved until today because of limitations imposed on dispersal
(Hanson et al., 2012).
However, the variation caused by geographic distance may also originate
from unobserved environmental
parameters, such as irradiance or
pH, which could lead to overestimating the importance of spatial factors
(Sun et al., 2020). Another factor that may cause an overestimation of
role of the spatial factors in community assembly could originate from
the repeated calculation of the spatial variables because of repeated
station settings in different seasons or water layers. However, when the
seasons and layers were calculated separately, the spatial factors still
played leading roles in most circumstances (Table S9). Thus, we
speculated that the importance of spatial factors increased and exceeded
that of environmental factors in determining MFC composition from the
regional to the geographical scale.
The importance of spatial factors on the geographical scale was further
corroborated through NM. According to neutral theory, the effect of
drift interacting with dispersal could create a distance-decay pattern,
reflecting purely spatial effect and unexplained variation even in the
absence of environmental selection (Hanson et al.,
2012).
In this study, more than 50%
explanation of neutral process was a relatively intermediate value of
fitness to the NM compared to other studies (Fig. 5). For example,
neutral model fitted 25% to the biogeographic pattern of microbial
plankton in Xiamen Bay (W. D. Chen et al., 2017), a smaller area
compared to the current study. Xu et al. (2020) found around 74%
explanation of neutral process for the variation in microbial eukaryotic
communities along the southern coastal areas of China. They attributed
this to the high and random dispersal rate driven by the Zhejiang-Fujian
(Zhe-Min) Coastal Current (Z. M. Xu et al.,
2020).
In this study, multiple water
masses existed and their movement may facilitate high dispersal rates of
microorganisms that inhabited the same water mass, but also limited
exchanges of MFCs across water masses, thus resulting in a relatively
intermediate explanation level of neutral process on communities.
Therefore, water masses had the
greatest relationships with MFCs in most cases independent of whether
the spatial scale was regional or geographical (Table 1). Accordingly,
this suggested that water mass was a key factor that influence MFC
assembly through environmental conditions and spatial variability (Sun
et al., 2020).
Mechanisms in shaping different size groups of MFCs
According
to Mantel test results, the impact of environmental factors on
pico-sized MFCs exceeded spatial factors for both regional or
geographical scales (Table 1). However, for nano-sized MFCs, spatial
factors exerted stronger effects on the geographical scale (Table 1).
This result is mirrored by
VPA, which showed greatest
explanations of environmental factors (8.3%) on pico-sized MFCs and
spatial factors (7.9%) on nano-sized MFCs (Fig. S11a, b).
This
was further supported by the NM that showed larger Nm values for
pico-sized MFCs, reflecting their strong dispersal ability (Fig. 5).
Studies on west Greenland (Elferink et al., 2017) and the Southern Ocean
(Clarke & Deagle, 2020) reported the increase of dispersal limitation
with the cell size of microbial plankton. This may be related to
intrinsic characteristics such as cell size, population size, and
diffusion mode (i.e., active or passive) (Martiny et al., 2006). With
passive diffusion modes of both size fractions, the larger cells of
nano-sized MFCs are more susceptible to dispersal limitation because of
their weaker dispersal ability (Hanson et al., 2012; Martiny et al.,
2006). Moreover, the smaller
pico-sized MFCs require less resources from the environment to grow and
reproduce, and are thus more likely to reach higher population density,
making long-distance dispersal easier to achieve (Martiny et al., 2006).
Seasonal variations of the MFCs assembly mechanisms
Mantel tests demonstrated that the spatial factors had greater impacts
on MFCs than environmental factors in spring, while the situation was
reverse in summer-autumn (Table 1). This may be related to the monsoon
and currents exchange in different seasons (Fig. S6). In spring,
monsoons exchange frequently, alternating from the northeast to the
southwest. The movement of water masses
is also complex and multi-directed, with the Yangtze River diluting
water from the west, the warming-up of Jiangsu-Zhejiang-Fujian
(Su-Zhe-Min) Coastal Currents, Kuroshio branch waters entering the SCS
across the Luzon Strait and extending northward (D. Y. Chen et al.,
2020), and the branch of the Kuroshio surface water extending downwards
and wedge-shaping into the ECS shelf area (Qi, Yin, Zhang, Yang, & Xu,
2014) (Fig. S6a). In summer-autumn, because of the prevalence of the
southwesterly monsoon, the Zhe-Min Coastal Currents weaken (Hong et al.,
2011), while the Yangtze River mediated water dilution strengthens (Lee
& Chao, 2003). The SCS surface
currents run through the TS and
extend to the southern ECS (Lee & Chao, 2003; Yeh et al., 2015), while
the Kuroshio branch waters retreats southward without entering the SCS
from the northern Luzon Strait (Hong et al., 2011; J. Zhu et al., 2019).
Instead, the branch of the Kuroshio subsurface water rises along the
continental slope of the ECS in summer and retreats in autumn (Qi et
al., 2014) (Fig. S6b). The Zhujiang River diluted water considerably
extends northeastward in summer and autumn (J. Zhu et al., 2019).
Overall, the water masses in spring exhibit complex and diverse
characteristics with each type being relatively weak, and hydrographic
boundaries may impose strong dispersal limitation on MFCs (Yeh et al.,
2015). This may also explain the weak correlations between MFCs and
environmental factors in spring (Table 1), as highly selective
environmental conditions may weaken the response of biological
communities to the environment and produce a lag-back effect (Guo et
al., 2020). In contrast, the water masses in summer-autumn are
relatively simple but have higher speed and flux than those in spring
(Lee & Chao, 2003). This led to
weaker dispersal limitation on MFCs, as well as relatively stable and
homogenized environmental conditions (Table S6), showing strong
correlations with MFCs (Table 1) which can quickly respond and adapt to
the environment. This was further reflected in the NM with a higher
degree of fitness R2 in spring than in
summer-autumn for both sizes, although the difference was weak (Fig. 5).
This indicated a relatively stronger spatial effect of dispersal
interacting with drift or other neutral processes on MFCs in
spring(Hanson et al., 2012).
Other possible factors in shaping MFCs diversity and assemblages
Notably, the tested variables
explained only a little of the variability in MFCs, both in Mantel tests
and VPA (Table 1, Fig. S11). This indicated more complex mechanisms
underlying the determination of MFCs (e.g., other unmeasured
environmental components or undetected neutral process such as
ecological or genetic drift, or accidental or historical events) because
of the limited sampling efforts. Furthermore, many studies have shown
that the co-occurrence network is a supplement to the unexplainable part
of environmental factors and spatial factors in the determination of MFC
assemblage, reflecting the importance of inter-species relationships in
shaping the community structure (Genitsaris et al., 2016; Guo et al.,
2020). In this study, the complex and strong associations in
co-occurrence networks showed putative non-random coexistence patterns
of MFCs under different spatiotemporal conditions, indicating multiple
biological interactions such as mutualisms through cross-feeding or
metabolic exchange, antagonisms through competition, predation or
parasitism, and functional redundancy (Hunt & Ward, 2015).
The large number of sub-modules in the network at the geographical scale
reflected numerous micro-habitats that provide a wide range of niches
for organisms to maintain a relatively stable community structure under
environmental heterogeneity (Deng et al., 2012). Organisms having high
co-occurrence in the same sub-modules indicate similar or overlayed
ecological niches with highly correlated relationships, while organisms
with different sub-modules display different niches and poor
correlations with each other (Deng et al., 2012). However, an increasing
spatial scale increase the number of sub-modules and decreases the
degree of network connectivity (Table 2), leading to weaker
inter-species correlations within the community. This may be related to
the distance-decay effect of community similarity suggesting a positive
relationship between spatial distance and community distance (Nekola &
White,
1999).
In terms of seasonal patterns, the co-occurrence networks were more
closely connected in spring (Table 2). As stated above, complex,
multi-directional but weak movement of multiple water masses in spring
could cause considerable differences in environmental conditions in
different water masses (Table S6), i.e., high habitat heterogeneity.
Complex connections between species usually form under high
environmental pressure (Genitsaris et al., 2016). However, communities
with closer connections between species tend to exhibit poor stability
because of their susceptibility and vulnerability to environment
disturbance, which in turn leads to lower biodiversity (Konopka, 2009).
Overall, these results showed a more closely connected network (Table 2)
and lower diversity (Fig. 3) in spring MFCs. Correspondingly, under the
action of simple but strong water masses in summer-autumn, the
connections within MFCs were relatively looser (Table 2), leading to
better stability and higher diversity of MFCs in summer-autumn (Fig. 3).
Conclusion
The results of this study
demonstrate that the non-random biogeographic distribution of MFCs in
the subtropic-tropic marginal seas of China depends on the balance of
multiple ecological processes. These are
environmental selection,
dispersal limitation, neutral process, and interaction within MFC
communities. These effects are associated with water masses on the
geographical scale and the relative importance of each effect changed
with spatial scales, target organism groups, and seasons. In this study,
the importance of spatial factors increased with the spatial scale,
especially for nano-sized MFCs. This mainly represented the dispersal
limitation effect, which also weakened interactions within the community
at the geographical scale.
Community network interactions
were more closely connected in spring because of the complexity and
multi-directionality of water masses, which enhanced environmental
heterogeneity. Overall, to obtain a comprehensive understanding of MFC
biogeography, future studies must determine the relative importance of
different ecological processes and how they interact to influence MFC
assembly under specific spatiotemporal conditions.
Acknowledgments
We sincerely thank Dr. Yonggang Huang for providing CTD data, nutrient
data, and Chl a data of the ECS (2019) cruises. We thank Dr.
Bangqin Huang, Dr. Xin Liu, and Fujian Institute of Oceanography to
provide CTD, Chl a , and nutrient data of the TS (2019) cruises
respectively. We also thank Dr. Wei Zhao and Minhan Dai for providing
CTD and nutrient data of the SCS (2017) cruise. We are also grateful to
the captains and crews of R/V “Xiangyanghong 18”, “Yan Ping II” and
R/V “Dongfanghong 2” for their helps in the field sampling from the
ECS, TS, and SCS, respectively. We also appreciate Dr. Jia Zhu and Dr.
Jianyu Hu for the guidance on the water mass classification and
constructive comments on the article. This work was supported by the
National Natural Science Foundation of China (no. 41676131 and no.
41876155) and the National Basic Research Program of China (973 Program,
no. 2011CB409804).
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Data Accessibility and Benefit-Sharing
The raw sequence data have been deposited in the NCBI Sequence Read
Archive (SRA) under the BioProject accession number PRJNA673365
(https://www.ncbi.nlm.nih.gov/bioproject/PRJNA673365).
Benefits Generated: Benefits from this research accrue from the sharing
of our data and results on public databases as described above.
Author Contributions
The contributions of each author are listed as follows. Xin Guo: Study
design, Investigation, Samples processing, Data analysis, Writing the
manuscript, Review and editing. Qiang Liu, Xiaoqing Lin, Xinyi Zheng,
Cheng Huang, and Mengwen Pang: Investigation, Samples processing, Review
and editing. Lingfeng Huang: Conceptualization, Review and editing,
Funding support, Project administration, Supervision.
Tables and Figures
Table 1 Simple and partial Mantel tests were performed to show
the effects of water mass (WM), environmental factors (E), and spatial
factors (S) on the beta diversity of pico- and nano-sized microbial
flagellate communities based on Bray-Curtis distance.