Pacic Biosciences Single-molecule Real-time (SMRT) Sequencing Reveals High Diversity of Basal Fungal Lineages and Stochastic Processes Controlled ...
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Paci c Biosciences Single-molecule Real-time (SMRT) Sequencing Reveals
High Diversity of Basal Fungal Lineages and Stochastic Processes Controlled
Fungal Community Assembly in Mangrove Sediments
Zhi-Feng Zhang
Shenzhen University
Yue-Ping Pan
Shenzhen University
Yue Liu
Shenzhen University
Meng Li ( limeng848@szu.edu.cn )
Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen, China https://orcid.org/0000-
0001-8675-0758
Research
Keywords: Fungal community, PacBio SMRT, Neutral community model, Stochastic process, Community assembly, Co-occurrence network, Mangrove
sediment
DOI: https://doi.org/10.21203/rs.3.rs-97364/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
Page 1/14Abstract
Background: Mangrove wetlands are unique ecosystems with speci c environmental characteristics, and are a hotspot of biodiversity. Although they probably
harbor a variety of mangrove-speci c fungi, the compositions of mangrove fungal community has been rarely investigated in detail, except for few published
culture-based studies. In addition, the fungal community assembly and interaction patterns that impact the community composition in mangroves have not
been explored to date.
Results: We used the Paci c Biosciences single-molecule real-time sequencing approach targeting the entire internal transcribed spacer region, to
systematically investigate the composition, biogeographical patterns, assembly processes, co-occurrence patterns and shaping factors of the fungal
communities in sediments of seven representative mangroves across the Southeast China. We recovered 15 phyla, including some early diverging fungal
lineages not previously reported in mangroves. Phylogenetic analysis revealed an incredibly high proportion of Rozellomycota and Chytridiomycota, as
accounting for up to one-third of all fungal abundance. Although the neutral community model described a moderate portion of community variation, the
similarity of fungal communities exhibited strong distance-decay patterns. Furthermore, the mean values and most beta nearest-taxon index fell between -2 to
2, with Bray–Curtis-based Raup–Crick value generally greater than 0.95, suggesting that stochastic processes strongly shape the fungal community
composition. Consistently, nonmetric multidimensional scaling and permutational multivariate analysis of variance con rmed the geographical location as a
crucial factor driving the distribution of both, the dominant and rare taxa of mangrove fungi. The db-RDA analyses indicated a minor role of environmental
selections in shaping the fungal community. Network analyses revealed that the deep sediments harbor more complex fungal networks with highly connected
taxa than surface sediments, and that rare fungal taxa might play important roles in microbial interactions and ecological functions in mangrove sediments.
Conclusions: The investigation revealed high fungal diversity in mangrove sediments, with incredibly high numbers of basal fungal lineages, stochastic
processes driving the assembly of fungal community, and geographic location strongly shaping fungal community composition in mangroves. These
discoveries therefore spur further studies of the utilization and protection of fungal resources and communities in mangrove sediments.
Background
Mangrove trees are unique forest biotypes widely distributed along the coast in tropical and subtropical regions, providing ecosystem services, such as
protection of the coastal area, and provision of food and shelter for sh and shell sh [1, 2]. Despite covering just 0.5–0.7% of the tropical forest area, these
forests account for 11% of the total terrestrial carbon and 10% of the terrestrial dissolved organic carbon exported to the ocean [1, 3]. Therefore, mangroves
have been labeled as the “blue carbon sink” [1, 2, 4]. Speci c environmental conditions, such as high levels of nutrients, high salinity and low oxygen, render
the mangrove ecosystem a hotspot of biodiversity. These conditions support a variety of unique microorganisms with abundant and diverse metabolic
pathways, driving complex nutrient and biogeochemical cycling, such as carbon cycling, ammonia oxidation, sulfate reduction and methane metabolism [2,
4–9].
Fungi are the key components of microbial community in mangrove sediments. They play a major role in the transformation of organic matter and greatly
contribute to the supply of nutrients and energy to the mangrove ecosystem [10]. Early studies of mangrove fungi mostly involved traditional cultivation
techniques. Using these techniques, over 625 fungal species have been obtained from mangroves, most of which are ascomycetes and basidiomycetes [11].
Many of these fungi produce useful enzymes or metabolites, such as extracellular hydrolases for the degradation of lignin and cellulose [4, 12, 13]; peptides
and lactones with antimicrobial activities [14]; and organic acids and phosphatases for the solubilization of phosphate [15]. Fungal communities in the
mangrove sediments are signi cantly affected by the environmental conditions, mainly biotic and abiotic factors. For example, the age and species of
mangrove plant are the two most important biotic factors that shape fungal community compositions [16, 17]. The abiotic factors include, rainfall [18],
sediment depth [16], and physicochemical properties of the sediment, such as salinity, pH, concentration of nutrients, etc. [19, 20].
Considering the limitations of traditional approaches, the mangrove mycobiota have not been extensively studied to date [21]. As an alternative, high-
throughput sequencing (HTS) methods, represented by metabarcoding, metagenomics, and metatranscriptomics, provide ways to effectively explore the
mangrove microbial community, with the potential for recovery of previously undescribed rare populations and biochemical pathways that are otherwise
di cult to elucidate [22, 23]. Nonetheless, studies investigating fungal communities in mangrove sediments are rare. Using 454 pyrosequencing, Ar et al. [24,
25] investigated the fungal diversity and composition in the mangrove sediment in New Caledonia, and revealed a predominance of Ascomycota and
Basidiomycota. By analyzing metagenomics datasets, a similar fungal composition was determined in the gray mangrove sediment in Red Sea, in which
fungal communities are more stable within the rhizosphere than in the bulk soil [26]. Recently, Luis et al. [27] used HTS to investigate the prokaryotic and
fungal community during the wet and dry seasons in New Caledonian mangrove sediments. The data suggest that the prokaryotic communities are mainly
shaped by sediment depth, with the fungal community almost evenly distributed over different depths, vegetation covers and seasons. Since most fungi from
a particular environment are uncultivable [28], a signi cant portion of the mangrove fungal diversity is overlooked using culture-dependent methods [27, 29]. In
addition, only single mangrove and small numbers of samples have been analyzed previously using culture-independent methods, and fail to provide an
overview of the entire fungal community in mangrove sediments.
Second-generation HTS, represented by Illumina, the most widely used sequencing platform, rapidly generates hundreds of thousands of high-quality
sequences per sample, greatly improving the study of microbiome. However, the generated sequence length is too short for accurate microbial identi cation at
species level. For example, while the universal fungal DNA barcode, the internal transcribed spacer (ITS) region, typically spans 500–700 bases [30], the
paired-end approach of MiSeq Illumina sequencing covers amplicons up to 550 bases long, and is only suitable for the ITS1 or ITS2 subregion (typically 250–
400 bases) [30, 31]. The bene ts of targeting the full ITS region include increased taxonomic resolution and suppressed ampli cation of material from dead
organisms [30–32]. Third-generation HTS using Paci c Biosciences (PacBio) and Oxford Nanopore platforms produce long reads averaging 20–25 kb (up to
100 kb), and enable targeting the full ITS region, as well as parts of or even the entire anking rRNA genes [30, 32]. Unfortunately, the high single-pass error
Page 2/14rates (13–15%) limit their application in DNA metabarcoding [31, 33]. However, the circular consensus sequencing (CCS) version of PacBio single-molecule
real-time (SMRT) sequencing greatly reduces the error rate by enabling sequencing of the same reads multiple times, which enables application of PacBio
sequencing in metabarcoding [31, 34].
In the current study, we used PacBio SMRT sequencing to explore the fungal community composition, shaping factors, assembly and co-occurrence patterns
in 110 sediment samples collected from seven representative mangroves in China (Fig. S1, Table S1). We hypothesized that (1) mangrove sediment harbors a
high diversity of fungi, including a number of ancient fungal lineages; (2) stochastic processes control the assembly of fungal community; and (3) geographic
location and sediment depth play signi cant roles in driving the composition of fungal communities in mangrove ecosystems.
Methods
Mangrove locations and sediment sampling
Mangroves cover approximately 17,800 ha in China, and are distributed in the coastal area from Hainan to Zhejiang. Seven representative mangrove nature
reserves from Eastern to Southern China, i.e., Ximendao National Nature Reserve (XMD), Zhangjiangkou Mangrove National Nature Reserve (ZJK), Futian
Mangrove National Nature Reserve (FT), Zhanjiang Mangrove National Nature Reserve (ZJ), Shankou Mangrove National Nature Reserve (SK), Dongzhaigang
Mangrove National Nature Reserve (DZG), and Yalongwan Qingmei Estuary Mangrove Nature reserve (YLW), were selected for the current study (Fig. S1). The
geographical locations of the seven nature reserves are signi cantly different. XMD is an arti cial mangrove, located at the northern boundary at which
mangroves survive. ZJK is the northernmost national nature mangrove reserve. FT is the only national nature reserve located in an urban hinterland in China.
ZJ is the southernmost national mangrove reserve on the mainland of China. SK is located at the northwest of ZJ, separated from ZJ by the Leizhou
Peninsula. DZG is the rst mangrove nature reserve in China and located at northern Hainan Island. YLW, the southernmost mangrove in China, is a provincial
nature reserve and located at southern Hainan Island.
For each mangrove, six (for all but FT) or seven (for FT) sampling sites were selected, with the same distance (> 500 m) between the adjacent sites (Fig. S1).
Every sampling site was at least 1 m away from the nearest tree. At each sampling site, sediment samples were collected using a stainless steel sampler and
separated into three layers, corresponding to the depths of 0–10 cm, 10–20 cm and 20–30 cm. To reduce sampling bias, three replicates at the vertices of an
equilateral triangle were collected and mixed together for each site. Overall, 127 sediment samples were collected. All samples were placed in sterile zip-locked
plastic bags, and transferred on ice to the laboratory, where they were stored at − 40 °C until analysis.
Environmental and spatial variables
The spatial variables included sampling depth, longitude, latitude and the numbers of tree species within 5 m of the sampling sites were documented (Table
S1). The environmental variables included mean annual temperature (MAT), mean annual precipitation (MAP), salinity, pH, gravel proportion, total carbon (TC),
total organic carbon (TOC), total nitrogen (TN), ammonium nitrogen (N/NH4+), nitrate nitrogen (N/NO3−), total phosphorus (TP) and total sulfur (TS) (Table
S1). MAT and MAP data were obtained from the China Meteorological Administration (http://www.cma.gov.cn).
The Salinity, pH, N/NH4+, and N/NO3− were measured in fresh sediments, and other parameters were determined in air-dried sediments. For the latter,
sediments were air-dried for several days until the weights stabilized, and then ground to ne powder using a mortar and a pestle. The powder was
subsequently ltered through a mesh (100 holes per inch) and weighed to calculate the gravel proportion. Coarse gravel was discarded and the ne powder
was left. Salinity was measured by using an automatic compensation salinity refractometer (ATAGO CO., Japan). Sediment pH was determined using a pH
meter (FE20, Mettler Toledo, Switzerland). Further, N/NO3−, N/NH4+, and TP levels were measured using a spectrophotometric method according to the
National Environmental Protection Standards of the People’s Republic of China (http://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/jcffbz/). TC, TOC, and TN were
determined following the method of Zhang et al. [9]. TS was measured using the method of Zhang et al. [35].
Molecular analysis
For each sample, DNA was extracted from 300 mg of sediments using a DNeasy PowerSoil kit (Qiagen, Germany) according to the manufacturer’s
instructions. The quantity and quality of the extracted DNA were examined using NanoDrop ND-2000c UV-Vis spectrophotometer (NanoDrop Technologies,
Wilmington, DE, USA). The DNA samples were stored at -40 °C before ampli cation by polymerase chain reaction (PCR).
After several preliminary trials, a two-step PCR (i.e., nested PCR) approach was selected to amplify the entire ITS region, using the primer pairs
ITS9Munngs/ITS4ngs [30, 36] and ITS1F/ITS4 [37, 38] for the rst and second PCR steps, respectively. For the rst PCR step, the reaction volume was 30 µL;
each reaction tube contained 1.5 U Takara Ex Taq HS (TaKaRa Biotechnology, Japan), 3 µL 10 × PCR Ex Taq buffer, 150 µM dNTPs, 0.12 µM of each primer,
2–10 ng genomic DNA, and water. Triplicate reactions were performed per sample to minimize the stochastic PCR effect of individual reactions. The PCR
parameters were as follows: 94 °C for 5 min; 35 cycles of 94 °C for 30 s, 57 °C for 30 s, and 72 °C for 1 min 20 s; and a nal elongation step at 72 °C for
10 min. The PCR products from triplicate reactions were then pooled equally, and diluted 10–40 folds to approximately the same DNA concentrations. Next,
1.5 µL of the resulting mixture was used as the template for the second PCR step. The ampli cation program was the same as that for the rst PCR step,
except that the annealing stage was performed at 58 °C for 30 s. Meanwhile, unique o cial 16-nt barcodes were added to the 5’-end of the primer pair
ITS1F/ITS4 to enable multiplexing of samples within a single sequencing run.
Amplicon library building and SMRT sequencing were performed by the Annoroad Gene Technology Co. Ltd. (Beijing, China). Brie y, equimolar amounts of
barcoded PCR products were pooled. Then, the hairpin sequencing adapters (SMRTbell template, SMRTbell Express Template Prep Kit 2.0) were ligated with
the pooled amplicon libraries following the blunt-end ligation protocol of PacBio. Sequencing libraries were subsequently puri ed using the Enzyme Clean Up
Page 3/14kit (Paci c Biosciences). The libraries were nalized for sequencing by annealing the sequencing primers to the SMRTbells and binding of the DNA
polymerase to the template complex. The sequences were generated using the PacBio Sequal system.
Bioinformatics and phylogenetic analysis
CCS were extracted from the raw Sequal data using pbccs v4.02 software (https://github.com/Paci cBiosciences/ccs) with default parameters except for -
min-passes = 5, and then converted to fastq format using BAM2fastx tool (https://github.com/paci cbiosciences/bam2fastx/). CCS reads were demultiplexed
based on the barcode-primer sequences, allowing for 0.1 mismatch using exbar [39]. Flexbar was set to trim the barcode sequences from quality reads upon
demultiplexing. To avoid unwanted multi-primer artifacts, reads with a full-length sequencing primer detected within them were removed [32]. The following
quality- lter parameters were implemented in Mothur v1.44.1 [40]: qwindowaverage = 20, qwindowsize = 50, minlength = 100, maxambig = 0, maxhomop = 12.
Full ITS region was extracted from ltered reads by targeting the identi ers of all eukaryotes based on hidden Markov models (HMMs) using ITSx v1.1.2 [41].
Putative chimeric sequences were then detected using uchime [42] de novo and reference-based (with UCHIME reference dataset as a reference,
https://unite.ut.ee/repository.php) and removed using vsearch v2.15 [43]. After chimeras checking, ltered reads ranging from 300 bp to 900 bp in length [44]
were selected using vsearch v2.15. The remaining sequences were de-replicated, sorted and clustered into operational taxonomic units (OTUs) at a 97%
similarity threshold using USEARCH [45, 46]. Representative sequences were chosen using the abundance method [32].
Taxonomy was assigned to the representative OTU sequences by searching the UNITE + INSDC databases of all eukaryotes (version released on 3 February
2020) [47] using BLASTn [48]. The rules for robust taxonomic annotation followed those of Tedersoo et al. [32]. Brie y, the species, genus, family, order, class,
and phylum levels were roughly matched based on 97%, 90%, 85%, 80%, 75% and 70% sequence similarity, respectively. For each OTU, 10 best BLAST matches
were examined; in case of no speci c assignment, 100 best matches were manually checked. However, since some sequences in the UNITE + INSDC
databases for all eukaryotes are denoted as “Eukaryota_kgd_Incertae_sedis”, many OTUs could not be accurately assigned at the kingdom level after
manually checking. To minimize this phenomenon, preliminary phylogenetic identi cation was used. Brie y, a phylogenetic tree was constructed in IQtree [49]
using all representative sequences, and unassigned OTUs that clustered with the assigned fungal OTUs in a single clade were annotated as “Fungi_candidate”
at kingdom level, and “Unidenti ed” at lower taxonomic levels (File S1). Subsequently, a phylogenetic tree comprising all the representative fungal sequences
was constructed for more accurate assignments of fungal OTUs at phylum level. Several distinct clades were generated in the fungal phylogenetic tree
(Fig. 1), representing different phyla, and OTUs clustered in each clade were thus assigned to the corresponding phylum but “Unidenti ed” at lower taxonomic
levels. Representative sequences of all eukaryotic and fungal OTUs were aligned using MUSCLE [50] and trimmed using trimAl v1.2 [51], respectively. The
maximum likelihood (ML) analyses were implemented using IQtree with 1000 replicates under the TIM + F + R10 model for both all eukaryotic and fungal
sequences phylogeny, as selected by ModelFinder in IQtree according to Bayesian information criterion (BIC). The nal consensus tree was visualized using
iTol [52].
In the current study, the relative abundance thresholds were de ned as 0.01% for rare taxa and 1% for dominant taxa, and all OTUs were classi ed into three
categories: dominant taxa (DT), always rare taxa (ART), and conditionally rare taxa (CRT), according to the most recent publications [53]. DT were de ned as
the OTUs with the abundance of ≥ 0.01% in all samples or ≥ 1% in some samples; ART were de ned as taxa with the abundance of < 0.01% in all samples;
and CRT were de ned as taxa with the abundance of < 1% in all samples and < 0.01% in some samples. However, only one OTU was classed as ART. To avoid
confusion, ART and CRT were arti cially combined into the rare taxa (RT) for further analyses.
Statistical analyses
Difference and indicator taxa of fungal communities
Fungal OTUs in the samples of 0–10 cm and other deeper layers were compared using edgeR [54] and limma package [55]. Multipatt command in the
indicspecies package [56] was used to explore the indicator species in different sample depths. IndVal, the indicator value, was calculated to assess the extent
to which a species was an indicator of a given habitat.
Alpha- and beta-diversity analysis
Rarefaction curves of all samples and different groups were calculated using rarecurve command in the vegan package [57]. The relative abundance of an
individual taxon (e.g., phylum) within a sample was determined by comparing the read number of each taxon to the total reads number in that sample. α-
diversity indices, such as OTU richness, Shannon-Wiener, Chao1, and evenness indices were calculated using vegan package [57]. The dataset was rare ed to
the lowest reads number (1833) in the samples before the analysis of α-diversity and β-diversity. One-way analysis of variance (ANOVA) followed by Tukey’s
honest signi cant difference (HSD) test was used to explore the variations of α-diversity across different group strategies. Pearson’s correlation coe cients
and p-value were calculated to explore the associations between α-diversity, main fungal phyla and classes and environmental features. Prior to these
analyses, the OTU richness and environmental feature values were transformed by log (x + 1).
The distance matrix of the fungal community (Hellinger transformation of the OTU abundance data) was constructed by calculating dissimilarity using the
Bray Curtis method [57]. Fungal community compositions of all taxa, DT and RT in all samples and different groups of samples were ordinated using
nonmetric multidimensional scaling (NMDS) using the metaMDS command in the vegan package [57]. Signi cant differences (p-value < 0.05) between groups
were evaluated using the analysis of similarities (ANOSIM).
Fungal community assembly patterns
To determine the role played by stochastic processes in microbial community assembly, neutral community model (NCM) was used to predict the relationship
between OTU detection frequency and relative abundance in the mycobiome [58]. The NCM used herein diverged from the neutral theory, which derives its
name from the de ning assumption of equivalent per-capita growth, death, and dispersal rates of species, and thus assumes that the ecological tness of
Page 4/14species is “neutral” [59, 60]. Brie y, because of the dispersal of dominant taxa at different sampling sites by chance, the model predicts that the dominant taxa
are widespread, and that the rare taxa are more likely to be lost at different sites because of the ecological drift [53]. In the current study, the datasets for all
and individual mangroves were analyzed separately. The sncm. t_function.r code written by Burns et al. [60] was used to explore the relationship between the
OTU detection frequency and relative abundance. Meanwhile, the geographic distance (in km) of samples, i.e., a straight-line distance between the sampling
points, was calculated based on the longitude and latitude coordinates of each sampling site using the package geosphere [61]. Pearson’s correlation
coe cients were calculated to determine the relationships between Bray-Curtis similarity of fungal community and geographic distance of the samples.
To further quantify the relative abundance of stochastic and deterministic processes that drive the fungal community assembly, beta nearest-taxon index
(βNTI) was calculated using phylogenetic distance and OTU abundance [62]. βNTI is the number of standard deviations of the beta mean nearest taxon
distance (βMNTD, i.e., the observed abundance-weighted-mean phylogenetic distance between closest relatives in two different communities) from the mean
of the null distribution [62]. βNTI values between − 2 and 2 indicates a dominance of stochastic processes, whereas βNTI values smaller than − 2 or larger than
2 indicate that deterministic processes play a more important role in community assembly than stochastic processes [9, 62, 63]. In addition, the Bray–Curtis-
based Raup–Crick (RCBray) value was calculated to further partition the pairwise comparisons that were assigned to stochastic processes. The fraction of
pairwise comparisons with |βNTI| < 2 and RCBray < − 0.95 indicates a relative dominant in uence of homogenizing dispersal on the community assembly. The
fraction of pairwise comparisons with |βNTI| < 2 and RCBray > 0.95 suggests a crucial role of dispersal limitation. Finally, the fraction of pairwise comparisons
with |βNTI| < 2 and |RCBray| < 0.95 indicates that“undominated” assembly, including weak selection, weak dispersal, diversi cation, and/or drift, may be the
controlling processes [63, 64]. Rscript bNTI_Local_Machine.r written by Stegen et al. [62] was used to calculate the βNTI and RCBray values.
Fungal community driving factors
To explore the potential factors driving the fungal community composition, spatial and environmental variables were evaluated separately. The env t function
in vegan package was used to generate a preliminary explanation of the effects of spatial variables on the fungal community based on 999 permutations.
Permutational multivariate analysis of variance (PERMANOVA, the Adonis command in the vegan package) was used to test the in uence of spatial variables
on community compositions.
A distance-based redundancy analysis (db-RDA) with Bray-Curtis dissimilarities was performed to explore the variations explained by environmental
parameters [65]. The following steps were used to determine which parameters should be selected to build a db-RDA model. First, the collinearity of the
explanatory variables was resolved by using the vif.cca command to nd the values of variance in ation factors (VIFs) in the full models, including all
variables [57], and the db-rda command was executed using the parameters with VIF values below 10. To identify the important variables associated with the
community structure, forward selection was then performed using an ordiR2step function from a null model containing no explanatory variables to a full
model. The ordiR2step function models forward with a maximum adjusted R2 at every step, stopping when the adjusted R2 starts to decrease, the adjusted R2
of the scope is exceeded, or the selected permutation p-value (0.05) is exceeded [57]. Finally, the variables from step selection with the lowest Akaike’s
Information Criterion (AIC) were used to build the nal model. The vif.cca command was used again to test the collinearity of variables in the nal models to
ensure that the VIF values were below 10. The signi cance of the nal model, axes, and terms was tested using the anova command.
Network patterns and keystone taxa of fungal communities
Network analysis reveals the interactions in a fungal community and keystone taxa within co-occurrence networks that are crucial for the composition and
assembly of the community [66]. The co-occurrence patterns in fungal communities were assessed by performing network analysis using the psych [67] and
igraph packages [68]. To reduce network complexity, only OTUs with a relative abundance of > 0.01% in the whole dataset were used, resulting in 593, 474, 589
and 493 OTUs for all samples, samples in 0–10 cm, 10–20 cm, and 20–30 cm layer samples, respectively. Pairwise correlations based on OTU abundance
were performed using corr.test function in the psych package, and the p-value was adjusted using false discovery rate (FDR) correction. Spearman’s
correlation coe cient between two OTUs with ρ value above 0.7 and p value below 0.01 was considered statically robust. Co-occurrence networks were built
using the igraph package; calculation of network topological properties and visualization of networks were done using the interactive platform Gephi v0.92
[69]. In the networks, a degree represents the number of edges connected to a node. Closeness centrality (CC) is based on the average shortest paths and thus
re ects the central importance of a node in disseminating information [70]. Betweenness centrality (BC) reveals the role of a node as a bridge between network
components. Keystone taxa were therefore OTUs with the highest degree and CC scores, and the lowest BC scores [66]. For the overall network, OTUs with
degree over 6, CC over 0.6, and BC below 0.12 were selected as the keystone taxa. For depth-speci c networks, OTUs with degree over 15, CC over 0.4, and BC
below 0.18 were selected as the keystone taxa.
Results
In the current study, we generated 1,284,478 CCS reads from 110 mangrove sediment samples, with an average Q30 of 98.9%. Pre-processing resulted in
937,168 clean reads were obtained. The number of sequences in each sample ranged from 2186 to 31,416, with an average number of 8520 reads and an
average length of 503 bp (Table S2). Overall, 844,885 sequences (90.2% of all sequences) were clustered into 2389 OTUs based on a 97% similarity threshold,
of which 85.4% (2041 OTUs, accounting for 83.4% of clustered sequences) were assigned to fungi; the other sequences represented OTUs belonging to
Alveolata (41 OTUs), Metazoa (39 OTUs), Protista (39 OTUs), Viridiplantae (20 OTUs), Stramenopila (12 OTUs), and unknown Eukaryota (197 OTUs) (File S1).
Diversity and composition of fungal communities in mangrove sediments
Although the rarefaction curves related to the detected OTUs indicated that most samples did not reach saturation (Fig. S2a), the fungal OTU accumulation
curves for different mangroves were nearly asymptotic (Fig. S2b), indicating that the data were largely representative of the fungal diversity in mangrove
sediments.
Page 5/14Comparative analyses of α-diversity revealed that the fungal diversity in sediments from 0–10 cm and 10–20 cm depth layers was greater than that in the
20–30 cm layer in terms of OTU richness (p < 0.05, Fig. S3a). The lowest OTU richness (83 OTUs) was detected in the 20–30 cm sediment layer in ZJK (ZJK6-
3), and the highest richness (321 OTUs) was detected in the 10–20 cm sediment layer in DZG (DZG4-2). However, no signi cant differences in α-diversity were
observed among the different mangrove locations (Table S3).
For a more accurate identi cation of fungal OTUs, a phylogenetic tree was constructed using representative sequences of all fungal OTUs (Fig. 1). The overall
topology of the constructed phylogenetic tree was generally congruent with previous phylogenetic studies of fungi [71, 72]. In the phylogenetic tree, OTUs were
obviously clustered into four representative clades, namely, Ascomycota, Basidiomycota, Chytridiomycota and Rozellomycota, with additional phyla scattered
throughout (Fig. 1). For instance, OTUs of Blastocladiomycota, Mortierellomycota and Mucoromycota clustered together and located between Basidiomycota
and Chytridiomycota. Aphelidiomycota, together with Kickxellomycota, were closely allied with a clade of unknown fungi within the Rozellomycota clade
(Fig. 1). Further, a number of unidenti ed OTUs clustered into two distinct clades with few known OTUs. The rst of these clades was closely related to the
phyla Kickxellomycota, Aphelidiomycota and Olpidiomycota, and possibly represents a new fungal phylum. The second of these clades was clustered with
Rozellomycota, suggesting that these unidenti ed OTUs might belong to Rozellomycota. Besides these two unknown fungal clades, most of the unidenti ed
OTUs were close to Chytridiomycota and Rozellomycota, and have been assigned accordingly, indicating a high diversity of phyla Chytridiomycota and
Rozellomycota in the mangrove ecosystem.
Based on the results of BLASTn and phylogenetic analyses (Fig. 1), 267 OTUs (13.1% of the fungal OTUs) remained unclassi ed at phylum level; additional
1774 OTUs were a liated to 15 phyla, 43 classes, 88 orders, 187 families and 318 genera (Table S4). Ascomycota, Basidiomycota, Rozellomycota and
Chytridiomycota were highly diverse and predominant (i.e., relative abundance of > 10%) in the fungal communities in mangrove sediments, accounting for
95.2% of all fungal sequences therein. Other phyla, namely Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota,
Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Olpidiomycota, and Zoopagomycota, were recovered in small
proportions, accounting for 3.4% of OTUs and 1.4% of all sequences (Fig. 2a, Fig. S4ab, and Table S4). At different sediment depth, Ascomycota,
Rozellomycota, Basidiomycota, and Chytridiomycota accounted for the largest proportion of OTUs (Fig. S4c). According to the criteria described in Methods,
503 OTUs accounting for 93.7% of all fungal sequences were classi ed as DT and the remaining 1538 OTUs (6.3% of sequences) were classi ed as RT
(Fig. 2a). DT and RT OTUs belonged to nine and 14 phyla, respectively.
Ascomycota (48.8% of sequences, 34.9% of OTUs) were represented by the classes Sordariomycetes (26.3% of sequences, 10.9% of OTUs) and
Dothideomycetes (7.9% of sequences, 12.0% of OTUs). The two other dominant phyla, Basidiomycota (13.1% of sequences, 13.0% of OTUs) and
Chytridiomycota (11.2% of sequences, 9.8% of OTUs) were represented by the classes Agaricomycetes (8.7% of sequences, 6.4% of OTUs) and
Rhizophydiomycetes (1.8% of sequences, 2.5% of OTUs), respectively. However, OTUs from the dominant phylum Rozellomycota (22.1% of sequences, 25.7%
of OTUs) were completely uncategorized at class level (Fig. 2b, Table S4).
Comparison of the fungal OTUs in deep (10–30 cm) and surface (0–10 cm) sediments revealed a signi cant enrichment of 164 OTUs and a signi cant
depletion of 66 OTUs in the deeper layers. Most of the enriched and depleted OTUs belonged to Ascomycota (25 enriched and 56 depleted), Rozellomycota (11
enriched and 42 depleted), Basidiomycota (16 enriched and 21 depleted) and Chytridiomycota (seven enriched and 24 depleted), and 21 OTUs were
unidenti ed at phylum level ( ve enriched and 16 depleted, Fig. 3). Depleted OTU with the highest relative abundance in the deep layers was OTU8, an
unknown species of Psathyrella (Agaricomycetes, Basidiomycota). Enriched OTU with the highest relative abundance in the deep sediments was OTU6, an
unidenti ed species of phylum Rozellomycota.
Based on IndVal and p-values, 327 indicator OTUs were identi ed by indicator species analysis, including 161 OTUs in the 0–10 cm layer; 68 OTUs in the 10–
20 cm layer; and 98 OTUs in the 20–30 cm layer. Most of these belonged to Ascomycota (120 OTUs), Rozellomycota (58 OTUs), Basidiomycota (46 OTUs)
and Chytridiomycota (33 OTUs). The OTUs with the highest IndVal values, which represent the indicator possibility, were OTU3 (unidenti ed fungus), in the 0–
10 cm layer; OTU6 (unidenti ed Rozellomycota), in the 10–20 cm layer; and OTU63 (Malassezia sp.) in the 20–30 cm layer (Table S5).
Fungal community assembly patterns in mangrove ecosystems
The NCM did not t well fungal community assembly in all and individual mangroves (R2 < 0.6) and the estimated immigration rate (m = 0.01 in all mangroves,
m = 0.02–0.05 in individual mangroves) was very low (Fig. 4a, Fig. S5), suggesting the occurrence of only a few dispersal events and low ecological drift in
mangroves. Since the NCM explained the low portion of community assembly in mangroves, βNTI was used to explore the relative roles of stochastic and
deterministic processes in shaping the microbial community assembly (Fig. 4b). The average βNTI values in all (seven) mangroves and the majority of βNTI
(93.8%) in all samples were between − 2 and 2, suggesting that stochastic processes are more important for community assembly than deterministic
processes. Further, the mean values (0.958) and the majority (92.1%) of RCbray in all samples were greater than 0.95, indicating a crucial role of dispersal
limitation on the community assembly (Fig. S6). βNTI values in all samples were signi cantly (p ≤ 0.01) but weakly (R < 0.1) correlated with changes in MAP
and TP (Fig. S7). Further, Bray-Curtis similarity of fungal communities (including all, DT and RT) of all samples and sample depths was signi cantly and
negatively correlated with the geographical distance, indicating a signi cantly strong distance-decay relationship (p < 0.01, Fig. 4c, Fig. S8). Furthermore, Bray-
Curtis similarity of DT was signi cantly higher than that of RT in all samples and sample depths (Fig. S9), suggesting a strong environmental preference or
limitation of ecological dispersal.
Spatial and environmental selection shaping the fungal community composition
NMDS and env t analyses indicated that the composition of fungal community in mangrove sediments is signi cantly related to the geographical location
(R2 = 0.7902, p ≤ 0.001), longitude (R2 = 0.6434, p ≤ 0.001) and latitude (R2 = 0.7325, p ≤ 0.001), which was further supported by the PERMANOVA analysis
(Fig. 5a, Fig. S10a). In addition, geographical location, latitude, and longitude signi cantly affected the fungal communities in different sediment depths (Fig.
Page 6/14S10a and S11). Complementary NMDS, env t and PERMANOVA analyses con rmed a pronounced strong effect of sediment depth on the community
composition in different mangroves (p < 0.05, Fig. S10b and S12). For both DT and RT, the location (p ≤ 0.01), longitude (p ≤ 0.01) and latitude (p ≤ 0.01)
strongly affected the fungal communities in all samples and sample depths (Fig. 5a, Fig. S10a and S11). Further PERMANOVA analysis also con rmed a
signi cant (p ≤ 0.01) effect of depth on the community compositions of DT and RT in all mangroves (Fig. S10b).
For all taxa, pH, N/NO3− and N/NH4+ were positively correlated with OTU richness, and pH was positively correlated with the Shannon and Evenness indices.
For DT, TC, TOC, and TP were positively correlated with OTU richness, and pH showed positive correlations with Shannon and Evenness indices. For RT, gravel
proportion, TN, N/NO3−, and N/NH4+ positively co-varied with OTU richness and Shannon index, while TC, TOC, and TP were only positively correlated with
OTU richness (Table 1, Table S6).
Table 1
Correlation coe cients (Spearman’s rho) between OTU richness, alpha diversity, and the relative abundance of main phyla and classes with environ
Parameter/phylum Correlation coe cient (ρ)
Depth MAT MAP Gravel Salinity pH TC TOC TN N/NH4+ N/NO
OTU richness -0.1474 0.0955 0.0224 0.1247 0.0354 0.2536** 0.0102 0.0217 0.1048 0.2494** 0.198
Alpha diversity -0.0481 -0.0681 -0.077 -0.0464 0.0956 0.246** -0.1657 -0.1443 -0.0628 0.0002 0.028
(Shannon index)
Aphelidiomycota 0.0587 0.0823 -0.0921 0.1393 0.0415 0.0902 -0.1406 -0.1098 -0.1455 -0.0785 0.040
Ascomycota 0.109 0.257** 0.0498 0.0813 -0.1011 -0.2081 0.3649*** 0.386*** 0.2456** 0.0279 -0.051
Basidiomycota 0.1116 0.1376 -0.0348 0.0116 0.1049 -0.0295 -0.0341 0.0187 -0.0156 -0.0155 -0.070
Chytridiomycota -0.1139 -0.7471 -0.2323 -0.4097 0.4211*** 0.0733 -0.4722 -0.5564 -0.472 -0.4457 0.002
Rozellomycota -0.1556 0.0958 0.1273 0.1532 -0.2593 0.132 0.0471 0.015 0.1029 0.3292*** 0.124
Agaricomycetes -0.0985 0.1539 0.1177 -0.0154 -0.1226 -0.1812 0.0914 0.1304 0.1317 0.0529 -0.122
Chytridiomycetes -0.1264 -0.2188 0.0449 -0.1635 0.0365 0.0232 -0.0944 -0.0891 -0.0512 -0.0934 -0.083
Cystobasidiomycetes 0.2844 -0.3682 -0.1744 -0.1824 0.299*** 0.0774 -0.244 -0.3068 -0.2091 -0.3549 0.087
Dothideomycetes 0.1558 -0.0144 0.3157*** 0.1081 -0.1377 0.1505 0.1698* 0.1842* 0.0562 0.1138 -0.196
Eurotiomycetes -0.145 -0.0006 0.0138 -0.0899 0.0778 -0.003 -0.0186 0.0175 -0.0124 0.0208 -0.057
Leotiomycetes 0.1086 -0.0032 0.155 0.0603 -0.1493 0.0984 -0.0284 -0.0125 0.0265 0.087 0.036
Lobulomycetes -0.0965 -0.6081 -0.1945 -0.3081 0.3197 0.0379 -0.3157 -0.3979 -0.3276 -0.3497 0.034
Rhizophydiomycetes -0.1424 -0.0232 -0.0836 -0.0274 0.1622* 0.0228 -0.1819 -0.1543 -0.1455 -0.1107 -0.049
Saccharomycetes -0.0627 0.1004 -0.1228 -0.0007 0.0222 0.083 0.0589 0.0383 0.0129 0.1592* -0.091
Sordariomycetes 0.026 0.3111*** -0.0224 0.0499 -0.0768 -0.3189 0.2612** 0.2893** 0.2374** -0.0251 0.004
Tremellomycetes 0.119 0.0649 -0.1347 0.0518 0.1837* 0.1058 -0.0854 -0.0468 -0.1493 0.119 -0.001
a MAT, mean annual temperature; MAP, mean annual precipitation; Gravel, Gravel proportion; TC, total carbon, TOC, total organic carbon; TN, total nitrogen; TP
total sulfur. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Correlation analyses were further performed to explore the relationship between the environmental variables and the relative abundance of main phyla and
classes (Table 1). The relative abundance of Ascomycota was signi cantly positively correlated with MAT, TC, TOC, TN, and TS. Similarly, the relative
abundance of Sordariomycetes was positively correlated with MAT, TC, TOC, TN, and TS. On the other hand, Eurotiomycetes, another dominant class of
Ascomycota, was signi cantly correlated with MAP, TC, TOC, TP, and TS. Furthermore, a signi cantly positive correlation was observed between the relative
abundance of Chytridiomycota and salinity. The relative abundances of the classes Cystobasidiomycetes, Rhizophydiomycetes and Tremellomycetes were
also positively correlated with salinity. Finally, the relative abundance of Rozellomycota was positively correlated with N/NH4+, TP, and TS.
The environmental variables selected to build the db-RDA models and their effects on the community compositions of different taxa are visualized in Fig. 5b.
All designed models were statistically validated based on ANOVA. For all taxa, a model encompassing 11 signi cant variables (the depth, gravel proportion,
MAT, MAP, N/NH4+, tree number, salinity, TOC, N/NH4+, N/NO3−, and TP) was built; it explained 27.5% of the variations in fungal communities (Fig. 5b). The
explanations of each model build for the seven mangroves ranged from 24.4% (XMD) to 41.0% (SK). Variables in the models for DT communities in all
samples were the same as model for all taxa, but exhibited a slightly higher explanation of variation (28.8%). The models for DT communities in different
mangroves explained 26.0% (SK) to 48.2% (ZJ) of community variation. A 10-variable model was constructed for RT in all samples, but only 19.2% of the
variations. The models for RT communities in different mangroves explained 10.6% (SK) to 23.0% (FT) of the community variations (Fig. 5b).
Co-occurrence patterns of fungal communities in mangrove sediment
Page 7/14Based on the Spearman correlation analysis, a co-occurrence network consisting of 124 nodes (OTUs) and 176 edges was generated for all samples, as
depicted in Fig. 6. This overall network with low complexity and high modularity had a diameter of 5, average degree (AD) of 1.419, modularity of 0.884, and
average path length (APL) of 1.89. The network comprised 24 modules, of which the top eight modules accounted for only 65.32% of the nodes (Fig. 6). The
nodes in the network were assigned to seven phyla, accounting for 75.8% of all nodes, with 24.2% remaining unidenti ed at phylum level. Most nodes were
annotated as DT. Network analysis revealed a 100% positive edge, indicating that the co-occurring relationships accounted for the entire fungal network. In the
overall network, 16 OTUs were recognized as keystones; most of them belonged to modules 1–4 (Table S7). Six keystone taxa belonged to Ascomycota, four
belonged to Basidiomycota, ve belonged to Rozellomycota and Chytridiomycota, and one was unidenti ed fungus. The calculated topological properties are
summarized in Table S8.
To better illustrate the fungal co-occurrence patterns in each sediment depth, fungal networks were then constructed separately for the three sampling depths
(Fig. S13), and their topological properties were calculated correspondingly (Table S8). The network generated for the 0–10 cm layer encompassed 276 nodes
and 610 edges; that for the 10–20 cm layer encompassed 331 nodes and 1136 edges; and that for the 20–30 cm layer encompassed 315 nodes and 1047
edges. The 0–10 cm layer network contained 28 modules, with the top eight modules accounting for 81.9% of all nodes. Five OTUs in the network were
recognized as keystone taxa, with two OTUs belonging to RT (Table S7). The 10–20 cm layer network encompassed 31 modules, and the top eight modules
accounted for 77.7% of nodes. Among these nodes, 11 OTUs were identi ed as keystone taxa, with one belonging to RT (Table S7). The 20–30 cm layer
network comprised of 20 modules, with the top eight in the proportion of 90.2% of all nodes. Further, 32 OTUs in this network were recognized as keystone
taxa, ve of which were RT (Table S7). The numbers of edges, nodes, and average degrees in the deep-sediment fungal network were higher than these in the
surface-sediment network. Further, the APL was lower in the deep-sediment fungal network, indicating a possibly higher frequency of co-occurrence of fungi in
deep sediments, and their relatively close correlation. By contrast, fungi in the surface sediment were less connected and their correlations were relatively
loose.
Discussion
Although mangroves are generally considered as a hotspot of biodiversity and harbor a high diversity of speci c fungi, studies, especially these based on
culture-independent methods, have been poorly performed. The present study constitutes the rst attempt to systematically investigate the fungal
communities in mangrove sediments in China using PacBio SMRT sequencing. The reported discovery of a great number of early diverging fungal lineages,
assembly patterns, and co-occurrence relationships within fungal communities will spur further studies into the utilization and protection of fungal resources
and communities in mangrove sediments.
Mangrove ecosystem encompasses a high number of basal fungal linages
Fungi represent one of the most diverse groups of life on Earth, with an estimated 2.2–5.1 million species [28, 71, 73]. According to the latest taxonomy, the
fungi described to date are a liated with 19 fungal phyla [74]. Remarkably, 15 fungal phyla were identi ed in mangrove sediment samples in the current
study, revealing the presence of highly divergent fungal lineages in mangrove sediments.
In the current study, Rozellomycota and Chytridiomycota, two phyla that have been rarely reported in mangroves to date, were predominant and incredibly
highly diverse. These two zooporic phyla are distributed worldwide and represent the basal fungal clades [71, 75]. Chytridiomycota is highly diverse and widely
distributed in the extreme environments characterized by high salinity, such as ocean sediments [76, 77], hydrothermal vents [78], and methane seeps [79].
Rozellomycota are also remarkably phylogenetically diverse [71] and found in both, aquatic ecosystems and terrestrial habitats [75, 80]. Since these two phyla
have been established based mainly on the molecular data, their members are mostly novel and uncultured [71, 81]. Consistently, OTUs belonging to
Rozellomycota and Chytridiomycota identi ed in the current study remain almost completely unidenti ed at genus and species levels. In addition, several
other basal fungal phyla with low OTU proportion and relative abundance were recovered in the current study, most of which had never been reported in
mangroves.
There are several possible explanations for the high proportion of basal fungal lineages uncovered in the current study. First, mangrove sediments harbor
highly divergent and diverse fungal lineages because of a relatively high speciation but low extinction rate in tropical habitats [71]. However, while mangroves
are transitional ecosystems between the land and sea that have unique characteristics, which may support distinct fungal species, the mangrove fungi are
relatively poorly covered by biodiversity and taxonomic studies. Second, similar to the phyla Ascomycota and Basidiomycota, Rozellomycota and
Chytridiomycota are comprised by a small proportion of DT and a large proportion of RT, which are di cult to detect because of methodological bias and
artifacts [26, 82, 83]. Fortunately, nested PCR is highly sensitive and can detect low amount of DNA [84, 85]. Third, PacBio SMRT can yield homogeneous
results even for GC-rich sequences, and more accurate annotations are supplied in the new version of fungal and eukaryotic UNITE databases.
In the current study, a number of fungal OTUs were found to be speci c to surface or deep sediments; most of these were depleted in deep sediment layers
compared with surface sediments (Fig. 3). That might be because of the different niches or preference for speci c environmental conditions of the identi ed
fungal taxa [27, 86]. Most of such speci c fungal taxa were Ascomycota and Rozellomycota, with some Basidiomycota and Chytridiomycota, which were also
predominant in mangrove ecosystems, indicating that Ascomycota and Basidiomycota might be more sensitive to the vertical variations of environmental
characteristics in mangrove sediments than other fungi. Psathyrella sp. (OTU8) was a depleted fungal taxon with the highest relative abundance; the genus
contains reported as a saprotrophic and mycorrhizal fungi from the forest ecosystem [87]. Similarly, Ganoderma gibbosum, a species of Agaricomycetes, was
the best indicator of the 0–10 cm sediment layer, together with two unknown fungi with a higher IndVal value than G. gibbosum. Interestingly,
Paraconiothyrium cyclothyrioides, previously isolated from a contaminated mangrove in Brazil [88] and reportedly a cause of human disease [89], was the
best indicator of the 10–20 cm sediment layer. Species of Paraconiothyrium are commonly found in the soil or woody plants, as endophytes or agents of
plant disease, or in the clinic as human pathomycetes [89, 90]. In the 20–30 cm sediment layer, Malassezia sp. was the indicator species with highest IndVal
Page 8/14value. Members of Malassezia are likely the most widespread fungi on Earth and have been identi ed in a startling diversity of habitats and locations, from
the human skin, through the polar regions, to deep-sea sediments [91].
Collectively, the presented data indicate an incredibly high divergence and diversity of fungi in mangrove sediments, especially early diverging and unknown
fungal lineages.
Fungal community assembly is mainly controlled by stochastic processes
According to Zhang et al. [9], deterministic processes in the prokaryotic community assembly in mangroves across southeast China to a greater extent than
stochastic processes. By contrast, the data obtained in the current study on the fungal community assembly in mangrove sediments indicate a relatively
greater importance of stochastic processes. NCM accounted for a moderate portion of community variation (R2 = 0.56), with an extremely low estimated
immigration rate (m), suggesting that although stochastic processes are relatively important for the fungal community assembly, the dispersal and ecological
drift are very low in mangrove sediments. The community variation (R2) in individual mangroves determined by NCM was much lower than that in all samples
considered together, as also supported by the ensuing db-RDA analyses, which indicated that the environmental conditions generally in uence the fungal
community composition in an individual mangrove to a greater extent than in overall mangroves (Fig. 5b). These observations indicate that the effect of
stochastic processes on the community assembly in an individual mangrove is weaker than that of overall mangroves, and might be accompanied by other
community assembly mechanisms, including environmental selection and species interactions, which are di cult to evaluate [53, 92].
To explore the relative effects of stochastic and deterministic processes on fungal community assembly, βNTI was then calculated based on the OTU
abundance and their phylogenetic distance. Unlike NCM tting, βNTI analysis supported a crucial role of stochastic processes in the community assembly
(Fig. 4b). Based on the distribution of βNTI scores, 93.8% of all comparisons were consistent with a random phylogenetic turnover; the fungal community
assembly was therefore mainly controlled by stochastic processes in the investigated mangroves [62]. In addition, the RCbray values suggested that dispersal
limitation is more crucial for the community assembly than homogenizing dispersal and “undominated” assembly in mangrove sediments (Fig. S6) [63].
Consistently, only changes in MAP and TP exerted a signi cant (p < 0.01) but weak (-0.1 < R < 0.1) effect on βNTI values (Fig. S7), indicating only a weak effect
of environmental variables on the variation of fungal community composition [54]. The signi cantly strong distance-decay correlation between the similarity
of fungal communities and geographic distance further con rmed the importance of stochastic processes (Fig. 4c, Fig. S8). That is because community
similarity is predicted to decrease along the geographic distance as a result of the dispersal limitation and ecological drift [86, 93]. In summary, the above
observations suggest that stochastic processes, mainly dispersal limitation, strongly shape the fungal community composition and that deterministic
processes play a minor role in community distribution in mangrove sediment.
Spatial and environmental selections on fungal communities in mangrove sediments
Spatial and environmental variables are critical elements determining the microbial diversity and community composition in various environments [9, 94–96].
However, the in uence of spatial and environmental factors on the fungal community in mangrove sediments has rarely been studied, except for the presence
of plants and sediment depth [27]. Further, the knowledge of many ecosystems is mostly based on the dominant and entire communities, while the role of rare
taxa is unaccounted for [53]. Based on previous studies, some rare taxa are in fact metabolically active in the environment and may constitute keystone
species that regulate the functions of aquatic ecosystem; the “rare biosphere” is hence of great importance to the metabolic and ecological functions of
aquatic habitats [53, 83, 97]. Consequently, in the current study, the fungal community was separated into DT and RT, to explore the biogeography and
potential controlling factors of the “rare biosphere” in mangrove sediments.
In the current study, the fungal all taxa, DT and RT communities signi cantly grouped together according to the mangrove location (Fig. 5a), suggesting a
higher similarity of the communities within the same mangrove than between mangroves. This was supported by the distance-decay relationship of
community similarity and geographical distance (Fig. 4c, Fig. S8). These observations suggest a similar biogeography of DT and RT fungi in mangrove
sediments, which is consistent with previous studies of bacterial communities in coastal Antarctic lakes [98] and microeukaryotic communities in Tingjiang
River [53]. In agreement with these observations on the effect of geographic location on fungal community, Zhang et al. [9] reported a similar distribution of
archaea and bacteria in mangrove sediments in China, indicating that microbial communities in mangrove sediments are strongly shaped by the geographical
location, possibly because of the combined effects of climate, niche conservatism, and rates of dispersal, evolutionary radiation and extinction [94, 99]. Depth
is a known and important factor that strongly affects microbial communities in mangrove sediments [8, 16, 27]. Similar to Luis et al. [27], a signi cant but
weak effect of the depth on fungal communities was observed herein for all taxa, DT, and RT fungi, and it impacted DT more so than RT (Fig. 5b, Fig. S10).
Although several signi cant environmental variables were identi ed and used to build the db-RDA model of fungal community composition, the explained
variations indicated that the environmental and spatial factors play only a minor role, as relatively low variations were revealed in all samples (Fig. 5b).
Speci cally for RT, only 19.2% of community variations were explained by ve parameters. Consistently, db-RDA or variance partitioning analysis (VPA) also
revealed a large proportion of unexplained microeukaryotic or prokaryotic community variations in different habitats in several previous studies [53, 100–102].
There are several potential explanations of this phenomenon. First, additional important in uencing factors exist that have not been included in the current
study [53]. Second, while co-occurrence relationships among microbes signi cantly affect the community compositions, they cannot be quanti ed by db-RDA
[22, 103]. Further, as suggested by previous studies, taxa that are rare under one condition may become prevalent once the conditions become suitable. In
other words, a species might be recognized as rare may because of a dearth of suitable microenvironments or microniches [26, 53, 83]. In the current study, the
ensuing db-RDA analyses of DT and RT communities yielded different models and explanations as both these communities were signi cantly impacted by
different environmental parameters. PERMANOVA con rmed the different in uences of spatial variables on DT and RT communities (Fig. S10). That could be
because different taxonomic and functional groups of microbial communities may occupy different ecological niches and be shaped by contrasting
underlying factors [86, 104]. In summary, the above ndings indicate a crucial role of geographic location and a minor role of environmental selections in
driving the fungal community in mangrove sediments.
Page 9/14You can also read
