Metabarcoding reveals differences in fungal communities between unflooded versus tidal flat soil in coastal saline ecosystem

Science of the Total Environment 690 (2019) 911–922

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Metabarcoding reveals differences in fungal communities between unflooded versus tidal flat soil in coastal saline ecosystem Pu-Dong Li a, Rajesh Jeewon b, Basiboyana Aruna a, Hong-Ye Li a, Fu-Cheng Lin a, Hong-Kai Wang a,⁎ a b

State Key Laboratory of Rice Biology, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China Faculty of Science, University of Mauritius, Reduit, Mauritius

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• This is the first report on differentiation of fungal niches in two different types of coastal saline ecosystem. • The variation of community evenness in coastal saline ecosystem exhibits significant differences. • ITS based sequencing reveals that both unflooded and tidal flat soil are mainly composed of Ascomycota fungal species. • Tidal flats soil are colonized with more fungal saprotrophs than unflooded soils.

a r t i c l e

i n f o

Article history: Received 3 June 2019 Received in revised form 27 June 2019 Accepted 27 June 2019 Available online 28 June 2019 Editor: Frederic Coulon Keywords: Amplicon sequencing Fungal microbiome Coastal saline-affected ecosystem Reclamation

a b s t r a c t In the saline-affected ecosystem, fungi have huge potential to promote growth, induce disease resistance and enhance tolerance against salt-stress of host plants. Since areas of plowland are gradually decreasing, the reclamation of coastal saline lands could play a crucial role in maintaining agricultural productivity and crop security globally. Therefore, it is of great significance to explore the fungal diversity in the coastal saline ecosystem. Here, we collected saline soil samples from unflooded areas and tidal flat areas, the two typical distinct landforms in coastal saline ecosystems, and used ITS metabarcoding to depict the diversity of fungal communities. We found that fungal species evenness had a remarkably higher variation from the tidal flat compared to unflooded soil samples. Furthermore, we also confirmed that the fungal niches differentiation reports in the coastal saline ecosystem. Our ITS based DNA sequencing revealed that both unflooded and tidal flat soil were mainly composed of amplicon sequence variants (ASVs) belonging to Ascomycota (93.43% and 86.91% respectively). Based on our findings, understanding the associations and distinctions of fungal microbiome between unflooded soil and tidal flat could provide the basis for the development of reclamation in coastal saline lands. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Soils harbor some of the most diverse microbiomes and are critical to carbon storage and nutrient cycling on Earth (Bahram et al., 2018). ⁎ Corresponding author. E-mail address: [email protected] (H.-K. Wang).

https://doi.org/10.1016/j.scitotenv.2019.06.473 0048-9697/© 2019 Elsevier B.V. All rights reserved.

Furthermore, microorganisms also dominate terrestrial soil habitats and contribute to biodiversity, biomass and impact on essential soil processes (Bardgett and Van Der Putten, 2014). Inside the plant-microbiome assemblage (i.e. plant holobiont), the plant microbiome plays crucial roles for plant growth and health, and roots are the primary site for plant-microbiome interactions (Hassani et al., 2018). Since microbial interactions with host plants are a

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samples) (Fig. 2, Table S1). Soil samples were collected from topsoil (10–25 cm), and the remains of the plant were removed as much as possible. In each area, samples were collected N500 m apart, and each sample contained five subsamples that were collected from one sampled site of the coastal saline lands. Each sampled site was an approximately square area of about 1 m by 1 m, where five subsamples were collected (center and corners). Then the five subsamples were mixed thoroughly as one sample. All the soil samples were stored in 50 mL sterile centrifuge tube at −80 °C pending further processing.

component of the living ecosystem, they are considered to be the natural partners in the regulation of local and systemic mechanisms in plants, providing biotic and abiotic stress tolerance to its host plant (Meena et al., 2017). In saline lands, the growth of crops is affected mainly by high soil salinity (Rengasamy, 2010). Moreover, excessive salinity stress often leads to changes in microbiome structure and function and alterations in soil physicochemical properties (respiration, photosynthesis, and protein synthesis), and these are associated with significant reductions in agricultural production and quality of most crops (Hussain et al., 2013; Munns and Tester, 2008; Mustafa et al., 2014; Parida and Das, 2005; Tuteja, 2007). It has been estimated that saline soils account for approximately 45 million ha (20%) of arable land, producing 1/3 of grain production around the world (Arzani, 2008). In China, N7.6 million ha (at least 20%) of the agricultural land is threatened by salinization (Huang and Rozelle, 1995). The tidal flat is also an important ecosystem in saline land, where marine sediments are regularly exposed and submerged by the tides, and gently slope towards the sea (Reise, 2012). According to the latest report, N12.7 million ha of the Earth's surface consists of tidal flat ecosystems (Murray et al., 2019). China has the second greatest extent of tidal flats (1.2 million ha) globally (Murray et al., 2019). The scarcity of cultivated land has a significant adverse effect on human livelihoods and the reclamation of tidal flats has been an effective method in replenishing agricultural lands in coastal areas all over the world (Li et al., 2012). The fungal association is of great significance to salt-stress plants and studies have demonstrated that their colonization, as endophytes, they can improve growth, induce resistance of disease and increase salinity tolerance in host plants (Brotman et al., 2013; Hajiboland et al., 2010; Kumar and Verma, 2018; Waller et al., 2005). Previous studies have reported that long-term reclamation significantly affected soil quality, microbial activity and community diversity (Hua et al., 2017; Liu et al., 2013). Therefore, restoring saline lands and its microbiota might be critical to maintaining agricultural productivity and food security globally (Cheng et al., 2018; Hua et al., 2017; Liu et al., 2013; Shi et al., 2019). Interestingly, we have noticed that the tidal flat revealed a significant difference in geomorphic features which are regularly exposed and submerged by the tides compared to unflooded soil (not covered by seawater) in coastal saline lands (Reise, 2012). Although some studies have focused on the environmental impacts of river ecosystem (Kuriqi and Ardiçlioǧlu, 2018; Kuriqi et al., 2017; Kuriqi et al., 2019), including microorganisms, soil microbiota in many marine environments are still unknown, and there is little information on the fungal community of the two typical landforms in the coastal saline ecosystem (Marzinelli et al., 2018; Simon et al., 2014). Therefore, it is essential to explore the functional roles and the differences in fungal diversity between tidal flat and unflooded soil in coastal saline lands. Here we sampled 69 soil samples in the coastal saline ecosystem (tidal flat and unflooded soil), covering the coastline of Zhejiang province in eastern China and used ITS (internal transcribed spacers) metabarcoding (Schoch et al., 2012) and other bioinformatics tools to compare fungal diversity (Fig. 1). The specific objectives are to (1) compare the composition and diversity of the fungal community in two different coastal saline ecosystems, (2) explore potential ecological functions of soil microbiome.

All sequence analyses were conducted in QIIME2 (Quantitative Insights Into Microbial Ecology 2, version 2019.1) and its plugins (Caporaso et al., 2010). Non-biological sequences (adapter and barcode) were removed and trimmed using Cutadapt plugin (qiime cutadapt trim-paired). To construct ASVs (amplicon sequence variants), denoise and quality control (including removal of chimeras) were performed with the DADA2 (Callahan et al., 2016) (deficiency of adenosine deaminase type 2) plugin (qiime dada2 denoise-paired) and reads were truncated to avoid low-quality scores (N235 bp for forward, N142 bp for reverse reads) (truncQ = 2, maxEE = 2). Besides, taxonomy was assigned to ASVs with UNITE dynamic database (Abarenkov et al., 2010) (97%– 99% similarity, version 2019.2) (qiime feature-classifier classify-sklearn). To generate a tree for phylogenetic diversity analyses (including Faith's Phylogenetic Diversity, Weighted UniFrac, Unweighted UniFrac, and phylogenetic tree), we used phylogeny plugin from QIIME2 (qiime phylogeny align-to-tree-mafft-fasttree). A phylogenetic tree was constructed based on the maximum-likelihood method by FastTree using default settings (aligned by MAFFT) (Price et al., 2010). To the following diversity analysis, we calculated alpha diversity indices (Richness, Pielou's Evenness and Faith's Phylogenetic Diversity) and beta diversity metrics (Bray-Curtis, Jaccard, Weighted UniFrac and Unweighted UniFrac) through the diversity plugin (qiime diversity core-metrics-phylogenetic).

2. Materials and methods

2.4. Statistical analysis

2.1. Soil sampling

All statistical analyses were performed using specific packages in R version 3.5.1 (The R Foundation for Statistical Computing, Vienna, Austria) unless otherwise noted. Rarefaction curves based on the ASVs tables were calculated by USEARCH (Edgar, 2010) (version 10.0.240). We also used boxplots to compare the alpha diversities through Richness, Pielou's Evenness and Faith's Phylogenetic indices. While controlling for the sampling effort, each sample was rarefied to the same number of reads (4128 sequences, the minimum number of sequences). To represent the beta diversity,

In order to explore the fungal diversity of coastal saline ecosystem, we collected two groups of soil samples with different geomorphic features, that is, the group of tidal flat (TF, regularly exposed and submerged by the tides) and the group of unflooded soil (US, near the beach but not covered by the seawater). 69 soil samples of 9 areas were collected from Zhejiang province in China (all sites chosen for study were public sites), which include TF (32 samples) and US (37

2.2. DNA extraction, PCR, and ITS2 rRNA gene sequencing The extraction of genomic DNA from 500 mg samples was performed using FastDNA® Spin kit for soil (MP Biomedicals, Solon, OH, USA) according to the manufacturer's instructions. The amplification of ITS2 (internal transcribed spacer 2) region (353 bp fragment) was performed using fITS7 (5′-GTGARTCATCGAAT CTTTG-3′) and ITS4 (5′-TCCTCCGCTTATTGATATGC-3′) primers (Jarvis et al., 2015). PCR reaction was done for each sample under the following conditions: 30 s at 98 °C; 35 cycles of denaturation at 98 °C for 10 s, annealing at 54 °C for 30 s, and extension at 72 °C for 45 s; final step 10 min at 72 °C. The 25 μL PCR mixture contained 12.5 μL of Phusion® Hot Start Flex 2× Master Mix (New England Biolabs Inc., Beverly, MA), 2.5 μL of each primer (1 μM) and 50 ng of template DNA. PCR products were collected and purified using the Agarose Gel DNA purification kit (TaKaRa Bio Inc., Shiga, Japan), and then sequencing was conducted using the Illumina MiSeq PE300 Sequencer (Illumina, Inc., CA, USA) at LC-Bio Technologies (Hangzhou, P. R. China) Co., Ltd. 2.3. Sequence analysis

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100%

75%

50%

25%

0%

TF

US

Ascomycota

Chytridiomycota

Mortierellomycota

Basidiomycota

Glomeromycota

Mucoromycota

TF

US

Dothideomycetes

Pezizomycetes

Agaricomycetes

Eurotiomycetes

Saccharomycetes

Tremellomycetes

Leotiomycetes

Sordariomycetes

Glomeromycetes

913

Sordariales Savoryellales Ophiostomatales Myrmecridiales Microascales Magnaporthales Lulworthiales Hypocreales Glomerellales Diaporthales Coniochaetales Chaetosphaeriales Calosphaeriales Branch06 Saccharomycetales Olpidiomycota Pezizales Rozellomycota Orbiliales Thelebolales Helotiales Erysiphales Teloschistales Phaeomoniel ales Onygenales Eurotiales Chaetothyriales Venturiales Valsariales Tubeufiales Pleosporales Mytilinidales Minutisphaerales Hysteriales Dothideomycetes_ord_Incertae_sedis Dothideales Mortierellomycetes Capnodiales Botryosphaeriales Low abundance

TF

US

Family 100%

TF

Hypocreales

US

Glomerellales Eurotiales

Unidentified 24

P 75%

82

68

Pathotroph

Onygenales

P

Sordariales

P

Helotiales Spizellomycetales

50%

P

Genus

Polyporales

51

25%

92

175 71

Symbiotroph

Lulworthiales

Co-occurrence network

Species

Saprotroph

Torpedosporales

58

242

Magnaporthales Saccharomycetales -4.8

-3.6

-2.4

-1.2

0

1.2

2.4

TF

0%

3.6

4.8

TF

US

US

LDA SCORE (log 10)

ITS2 rDNA ASVs, community composition and diversity analyses

Soil Samples AGCTAC

DNA Extraction

Illumina Sequencing ITS2 Region Amplicon

Phylogeny analyses Fig. 1. A workflow for comparing the fungal communities in coastal saline ecosystem using metabarcoding and bioinformatics analyses. In this study, we provided insights into the differences of coastal saline ecosystem from the perspective of microbial communities. First, we collected soil samples from coastal saline lands, extracted DNA, and PCR by ITS2 rDNA gene. Second, metabarcoding sequencing was conducted using the Illumina MiSeq PE300 Sequencer. Finally, we further performed the bioinformatics analyses to compare the community differences in coastal saline ecosystem. Diversity analyses explain the differences of microbial communities in coastal saline ecosystems from the perspective of the taxonomic composition. Co-occurrence network analysis reveals the network relationships among microbial communities and might explain the functional relationships of the environment. Phylogeny analysis provides an evolutionary relationship between microbes that provides a deeper understanding of the community structure of different habitats.

principal coordinates analysis (PCoA) of the different groups (TF and US) in coastal saline lands were used to analyze the dissimilarity matrices generated through Bray-Curtis, Jaccard, Weighted UniFrac and Unweighted UniFrac of the ASVs. In order to predict fungal functional properties, the ecological guild (e.g., parasite fungi, pathogenic fungi and saprotrophic fungi) was parsed using FUNGuild database (Nguyen

et al., 2016). We used PERMANOVA (Anderson, 2001) (permutational multivariate analysis of variance) to test whether the sample groups harbors difference of microbial community structures in PCoA significantly. EdgeR (Robinson et al., 2010) (P b 0.05, FDR b 0.2) was also used to identify the significant differences of the relative abundance at different taxa between the groups. Moreover, to identify the biomarker

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A

C

B

Zhejiang province

Fig. 2. The two habitats in coastal saline ecosystem, (A) tidal flat; (B) unflooded soil. Location of 9 sampling areas covering the coastline of Zhejiang province in eastern China (C).

between TF and US, we performed the linear discriminant analysis (LDA) effect size (LEfSe) method (http://huttenhower.sph.harvard. edu/lefse/) based on the ASVs (the P-value of Kruskal-Wallis = 0.05, LDA score = 4.0) (Segata et al., 2011). To compare the proportion of shared and exclusive at different taxa (family, genus and species) between the groups, Venn diagrams were also generated using the VennDiagram package (Chen and Boutros, 2011). A co-occurrence network of the soil samples were performed using the Spearman correlation matrix (Junker and Schreiber, 2011) constructed with the igraph package (Csardi and Nepusz, 2006) to assess the complexity of the

interactions in microbiota. The high relative abundances (N0.03%) and statistically significant correlations (P b 0.01, Spearman's coefficient N 0.8 or b−0.8) among fungal genera were included into the network analyses. To analyze the topology of the co-occurrence networks, the network graphs were conducted based on a set of measures (number of edges and nodes, clustering coefficient, modularity, connectance, centralization degree, centralization closeness, average degree and average path length). Moreover, the phylogenetic tree (generated by FastTree) was uploaded to iTOL (Letunic and Bork, 2016) (Interactive Tree Of Life) online.

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915

metrics were significantly dissimilar (P values b 0.01) between the groups at the ASVs level (Table S2).

3. Results 3.1. Sequencing analysis and data processing Of the 1,808,088 raw reads, 1,249,294 high-quality reads were retained after denoising, removing low-quality sequences and chimeric with DADA2, and subsequently generated 2993 ASVs. Among them, 1235 ASVs failed to be identified to any known phylum based on the UNITE database (97%–99% similarity, version 2019.2), accounting for 10.59% (132,277 sequences) of the total sequences. In TF, a large fraction of reads could not be classified with certainty at the phylum level (29.81% of sequences; 66.09% of ASVs). In US, we were able to identify the majority of reads at the phylum level and only a small fraction of reads remained unclassified (2.22% of sequences; 20.25% of ASVs) (Tables 1, S3). 3.2. Fungal rarefaction curves and alpha diversity of coastal saline ecosystem To construct alpha rarefaction curves, we rarefied each sample to the minimum number of sequences (4128 reads). Both TF and US displayed a high degree of variation in the shape of their rarefaction curves. The rarefaction curves of ASVs abundance of each sample gradually approached saturation, which reflects the species richness. Specifically, the majority of TF were saturated around 18-135 ASVs, compared to the US around 37-215 ASVs (Fig. 3A, B). To calculate alpha diversity, each sample was normalized to the same number of reads (4128 sequences, the minimum number of sequences). For a more comprehensive analysis, we calculated the different indices (Richness, Pielou's Evenness and Faith's Phylogenetic Diversity) to analyze fungal alpha diversity. For Richness index, US (98.03) presented significantly higher fungal community diversity when compared with TF (64.53) (Kruskal-Wallis; P b 0.001). Considering the Faith's Phylogenetic Diversity (TF: 21.04; US: 22.02) and Pielou's Evenness (TF: 0.63; US: 0.61), two groups of samples revealed the highly comparable estimates (Fig. 3C–E, Tables S1, S2). 3.3. Fungal beta diversity of coastal saline ecosystem To compare the composition of fungal diversity within different groups in coastal saline lands, we conducted PCoA (principal coordinates analysis) to exhibit the beta diversity at the level of ASVs base on the four different dissimilarity metrics after normalized. PCoA exhibited different degrees of clustering and different explanation (5%–29%) of the total variation in fungal communities between TF and US. Base on the four different dissimilarity metrics (Bray-Curtis, Jaccard, Weighted UniFrac and Unweighted UniFrac), PC1 explained 9%, 7%, 29%, 16%, and PC2 explained 8%, 5%, 19%, 6% of the total variation, respectively. The communities of TF were distinguished from communities obviously of US with 90% confidence ellipses (Fig. 4). Moreover, to statistically support the clustering of the fungal communities between TF and US in the PCoA, P-value evaluated via PERMANOVA method. All the fungal microbiota conducted by different

3.4. Community composition of coastal saline ecosystem Eight phyla (Ascomycota, Basidiomycota, Chytridiomycota, Glomeromycota, Mortierellomycota, Mucoromycota, Rozellomycota and Olpidiomycota) were classified with UNITE reference database for all the samples (the reads which were unclassified at the fungal phylum were removed from the sequencing data). As Fig. 5A shows, the US are mainly composed of Ascomycota (93.43%), while TF are mainly composed of Ascomycota (86.91%) and Basidiomycota (8.21%) at the phylum level (Table S3). The two groups of samples presented differences in taxonomic compositions at the class level. Eurotiomycetes was widely present in US (18.03%), in contrast, abundance from TF (3.37%) was low. Furthermore, in TF the percentages of Agaricomycetes was 10.5% and Saccharomycetes 13.05%, while in the US the percentages were minimal (1.06% and 0.16%, respectively) (Fig. 5B, Table S4). At the order level, we performed the EdgeR (Fig. 5C) (P b 0.05, FDR b 0.2) and LEfSe (Fig. 5D) (Kruskal-Wallis; P b 0.05, LDA score N 4.0) methods to compare the significant differences in relative abundance between the groups. Amplicon sequence variants belong mostly to nine orders: Lulworthiales, Magnaporthales and Saccharomycetales were enriched in TF; Hypocreales, Glomerellales, Eurotiales, Onygenales, Sordariales and Helotiales were enriched in US (more information on Figs. S1, S2 and Table S5). Unique and shared families, genera and species across all soil samples were displayed in Venn diagrams. TF and US shared 82 families, 92 genera and 58 species. In the shared families, the abundant top five were Nectriaceae, Aspergillaceae, Plectosphaerellaceae, Didymosphaeriaceae and Pleosporaceae. In the shared genera, the abundant top five were Fusarium, Plectosphaerella, Alternaria, Pyricularia and Aspergillus. In the shared species, the abundant top five were Plectosphaerella cucumerina, Alternaria alternata, Chrysosporium pseudomerdarium, Phaeosphaeria halima and Hortaea werneckii (Fig. 5F, Tables S6, S7 and S8). Furthermore, to investigate the function of fungal community, the FUNGuild was used to identify the ecological functions (trophic modes and guilds) in the groups (TF and US). 94.7% (TF) and 99.9% (US) of ASVs (accurate classification to genus) were identified as trophic modes. The majority of trophic modes fell into Pathotroph (18.3%), Saprotroph (53.1%), Pathotroph−Saprotroph−Symbiotroph (10.3%) and Pathotroph−Symbiotroph (6.9%) in TF. By contrast, US was mainly composed of Pathotroph (28.9%), Saprotroph (42.3%) and Pathotroph −Saprotroph−Symbiotroph (20.0%) (Fig. 5E, Table S9). At the ASVs level, we defined the core fungal communities as the shared ASVs (accurate classification to genus) of each of the groups (ASVs abundance N 0.2%) conducted in the phylogenetic tree (28 ASVs altogether) (Fig. 6). The percentages of total ASVs by the core ASVs was 26.53%. The core fungal communities consisted of eight orders, Eurotiales (8.89%), Capnodiales (6.63%), Pleosporales (29.65%), Diaporthales (0.26%), Glomerellales (4.42%), Sordariales (1.70%), Hypocreales (32.61%) and Magnaporthales (15.83%) (Table S10).

Table 1 Overview of Illumina sequencing data. Raw readsa Total TF US

637,383 1,170,705

Clean readsb

ASVsc

Minimum

Maximum

Reads after quality control and denoising

Merged reads

Non-chimeric reads

Total

The number of assigned ASVs

11,245 14,365

37,881 58,379

494,856 946,501

385,353 895,974

378,755 870,539

1336 1778

453 1418

TF: the group of tidal flat; US: the group of unflooded soil. a, b: The number of reads before or after DADA2 based on 69 samples (32 samples of TF and 37 samples of US) in coastal saline ecosystem. c: The number of ASVs (amplicon sequence variants), and the number of ASVs that could be assigned at the phylum level with UNITE database. d: Reads that could not be classified at any fungal phylum with UNITE database.

Unclassified readsd (%) 29.81% 2.22%

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The number of ASVs

A

B

250

250

200

200

150

150

100

100

50

50

TF 0

US

0 10

20

30

40

50

60

70

80

90

100

Rarefraction Percentage

C

10

20

30

40

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60

70

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Rarefraction Percentage

D

E

200

0.8 40

150 0.6

30

100 20

0.4

TF US

50 10

0.2

Richness

Faith’s Phylogenetic

Pielou’s Evenness

Fig. 3. Rarefaction curves for each samples in different groups (A: TF, the group of tidal flat, B: US, the group of unflooded soil). Alpha diversity estimates of the fungal communities (C ASVs Richness, D Faith's Phylogenetic, E Pielou's Evenness).

3.5. Co-occurrence network of coastal saline ecosystem To explore the complexity of connections within the soil microbiomes of different groups (TF and US), we conducted cooccurrence network analysis. Following this, we calculated the topological properties of the co-occurrence networks to identify differences between the groups, which were analyzed by Spearman correlations at genus level. Specifically, the TF microbial network consisted of 89 nodes and 170 edges (all positive, average degree 3.820), compared to the US microbial network consisted of 147 nodes and 446 edges (all positive, average degree 6.068). The clustering coefficient of communities was 0.9986 (TF), 0.9334 (US), and the modularity was 0.894 (TF), 0.853 (US) (values N 0.4 indicated that the networks had a modular structure (Newman,

2006)). Moreover, the values of connectance, centralization degree, and centralization closeness were 0.0434, 0.0361 and 0.0009 of TF, and 0.0416, 0.0680 and 0.0021 of US, respectively (Table 2). After Spearman correlations, the nodes in the network of US were assigned to five fungal phyla, Ascomycota, Basidiomycota, Chytridiomycota, Glomeromycota and Mortierellomycota (Fig. 7B). In contrast, the TF network consists of four phyla, Ascomycota, Basidiomycota, Chytridiomycota and Mortierellomycota (Fig. 7A). 4. Discussion Fungi play important roles in salt-tolerant plants and can promote crop growth and enhance crop stress resistance (Evelin et al., 2009; Waller et al., 2005). Since reclamation of salt-affected lands is one of

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A

B

R2= 0.071 P= 0.001

C

R2= 0.063 P= 0.001

917

TF

D

R2= 0.167 P= 0.001

US

R2= 0.139 P= 0.001

0.4 0.50

0.5 0.2

0.25

0.00

PC2: 6 %

0.00

PC2: 19 %

PC2: 5 %

PC2: 8 %

0.25

0.0

−0.5

0.0

−0.2

−0.25 −0.25

−0.4

−1.0 −0.50 −0.4

−0.2

0.0

PC1: 9 %

Bray-Curtis

0.2

−0.2

0.0

0.2

PC1: 7 %

Jaccard

−0.5

0.0

0.5

1.0

−0.4

−0.2

0.0

0.2

0.4

PC1: 29 %

PC1: 16 %

Weighted UniFrac

Unweighted UniFrac

Fig. 4. PCoA (principal coordinates analysis) performed at the ASVs level for ITS2 rRNA data set between the groups (A Bray-Curtis, B Jaccard, C Weighted UniFrac, D Unweighted UniFrac). TF: the group of tidal flat; US: the group of unflooded soil.

the effective solution to relieving food crisis caused by the gradual increase of population, research of fungal communities in saline lands has become crucial (Liu et al., 2013). Our study compared fungal communities of two different habitats in coastal saline ecosystems, which might provide the basis for the development of reclamation in saltaffected coastal lands. Microbial diversity is considered to be crucial to the integrity, ecological function and long-term sustainability of soil ecosystems (Cheng et al., 2018). In our studies we used three different alpha diversity measures (Richness, Pielou's Evenness and Faith's Phylogenetic Diversity) to compare the fungal diversity of different habitats (TF and US) at the ASVs level in coastal saline ecosystem (Fig. 3). We found that the fungal alpha diversity exhibited high variation, which is in accordance with the findings of some previous studies (Li et al., 2016). Richness index, which considers only the number of species (ASVs) to describe the alpha diversity. Based on this, we observed the higher variations (Richness index) of US in the shape of the rarefaction curves and the boxplots when comparing TF (Fig. 3A–C). Interestingly, remarkably higher variation was revealed within the TF when analyzed by Pielou's Evenness index, which measures not only the number of species but also the evenness or equitability of the abundances of communities (Fig. 3E) (Smith et al., 2006). Results are indicative that fungal species evenness has a remarkably higher variation from the tidal flat compared to the unflooded soil in coastal saline ecosystem. Moreover, previous studies using microbial microcosms revealed that the evenness of community is a critical factor in preserving the functional stability in an ecosystem (Wittebolle et al., 2009). Understanding soil microorganisms before reclamation is crucial for guiding crop cultivation in saline-affected lands (Liu et al., 2013). In the event of salinity stress, the stability of the ecological function was strongly influenced by the evenness of the community comparing richness (Wittebolle et al., 2009). When communities are highly uneven, or one or a few species are extremely dominant, the ecological function is less resistant to environmental stress (Wittebolle et al., 2009). During the reclamation, it seems to indicate that the assessment of community evenness is more important than richness in adverse habitats. In tidal flats, the impact of tides probably affects the affinity of fungal communities (Li et al., 2016). Furthermore, the coastal ecosystem is seriously disturbed by human activities, for instance with exogenous microbiome, input of nutrients, sewage, pesticides and industrial wastes (González and Hanlin, 2010; He et al., 2014; Tsui et al., 1998). Therefore, the difference in physicochemical properties of the soils caused by these human activities collected from different tidal flats might mask the effect of habitat types (Li et al., 2016). Furthermore, the tidal flat is physically dynamic with extreme shifts in temperature, salinity, sunlight radiation, water content and intermittent nutritional substrate availability (González and Hanlin, 2010). Some important abiotic factors, such as inundation frequency, tidal height, tidal exchange water quality

(e.g., nutrients and heavy metals), might also influence the evenness of the community (Van Ryckegem and Verbeken, 2005). For these reasons, in coastal saline ecosystem it is not surprising that the evenness of fungal diversity in tidal flats presents higher variation at different sampling sites when compared with unflooded soil. However, in this study, it remains unclear whether the higher evenness variation of tidal flats is local or global. In the future, further exploration of the regularity of fungal evenness in coastal saline ecosystems might offer more insights into their importance in reclamation. To compare the fungal community structures present in TF and US, we clustered all samples using PCoA based on four different metrics (Bray-Curtis, Jaccard, Weighted UniFrac and Unweighted UniFrac) (Fig. 4). Our study reports that the composition of the fungal community between TF and US is significantly different. Samples are segregated in different groups (TF and US) in coastal saline lands and presented microbiota that are significantly dissimilar from each other at the ASVs level (PERMANOVA; P b 0.001) (Table S2). The different habitats might lead to the separation of community composition, as reported in some previous studies (Cantrell et al., 2011). This difference might be caused by the distinct geomorphic features in tidal flats, which are regularly exposed and submerged by the tides, as mentioned above (Li et al., 2016). Previous research has suggested that salinity has adverse effects on fungi, might act as a toxic component interfering with enzymatic and membrane activities (Frankland et al., 1996). In this study, we have not investigated these environmental factors, however, some previous studies have found that salinity does not significantly affect niche differentiation of the microbial community in salt-affected soil (Mohamed and Martiny, 2011; Wichern et al., 2006). Tidal dynamics affect fungal structures mechanically and introduce periodic osmotic stress by periodical wetting with tidewater in tidal flats, as compared to the water regime in unflooded soil which fluctuates less drastically (Van Ryckegem and Verbeken, 2005). A previous study found that changes in soil osmotic pressure could change the structure of the fungal community (Chowdhury et al., 2011). Therefore, we can consider that the periodic changes of osmotic pressure based on the distinct geomorphic feature of the tidal flat lead to the differences of the fungal niches between TF and US in the coastal saline ecosystem. The significant differences between tidal flats and unflooded soil in fungal niches suggest that micro-environments might present differences even in the geographical proximity of habitats. It reminds us that habitats should be treated differently when plans are made for reclamation. In saline lands, crops are closely related to the function of soil microbiome (Brotman et al., 2013; Hajiboland et al., 2010). Therefore, the significant differences in the communities of coastal saline ecosystems could provide more specific requirements for the selection of reclaimed crops. In this study, we also depict different fungal community structures through co-occurrence networks. US exhibited a higher level of

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A

C

Relative abundance (%)

100%

75%

50%

25%

0%

TF Ascomycota Basidiomycota

US Chytridiomycota Glomeromycota

Mortierellomycota Mucoromycota

Relative abundance (%)

B100% 75%

50%

25%

0%

TF

US

Dothideomycetes Eurotiomycetes

Pezizomycetes Saccharomycetes

Agaricomycetes Tremellomycetes

Leotiomycetes

Sordariomycetes

Glomeromycetes

D

E

Hypocreales

TF US

TF Unidentified P Pathotroph P P P Saprotroph

Eurotiales 75%

Sordariales Helotiales Spizellomycetales

50%

Torpedosporales Polyporales

Symbiotroph

25%

Lulworthiales

2

4

6

8

Sordariales Savoryellales Ophiostomatales Myrmecridiales Microascales Magnaporthales Lulworthiales Hypocreales Glomerellales Diaporthales Coniochaetales Chaetosphaeriales Calosphaeriales Branch06 Saccharomycetales Olpidiomycota Pezizales Rozellomycota Orbiliales Thelebolales Helotiales Erysiphales Teloschistales Phaeomoniellales Onygenales Eurotiales Chaetothyriales Venturiales Valsariales Tubeufiales Pleosporales Mytilinidales Minutisphaerales Hysteriales Dothideomycetes_ord_Incertae_sedis Dothideales Mortierellomycetes Capnodiales Botryosphaeriales Low abundance

100%

Glomerellales Onygenales

0

US

Family

F

24

82

68

Genus Species 51

92

175 71

58

242

Magnaporthales Saccharomycetales -4.8

-3.6

-2.4

-1.2

0

1.2

LDA SCORE (log 10)

2.4

3.6

TF 4.8

US

0%

TF

US

Fig. 5. Relative sequence abundance of fungal (A) phylum and (B) class associated with TF (the group of tidal flat) and US (the group of unflooded soil). Heat map indicating differences in relative abundances at the fungal (C) order between the groups using EdgeR method (P b 0.05, FDR b 0.2). (D) LEfSe analysis identified the differentially abundant orders between TF and US (LDA significance threshold N 4.0). (E) Relative abundance of FUNGuild trophic modes between the groups. (F) Venn plot depicting the proportion of shared and exclusive at different taxa (family, genus or species) between the groups.

complexity and connected structure, whereas TF presented a less complicated and less connected network (Fig. 7). Co-occurrence networks were explored to offer insights into the interactions and potential ecological functions of the microbiome (Jiao et al., 2016). The nodes in different modules perform different ecological functions (Fig. 7C, D) (Newman, 2006). Moreover, the stronger ecological linkages which are displayed as the clusters of co-occurrence correlations were observed in US compared to TF (Fig. 7). The network of US exhibited a higher average degree (6.068), degree centrality (0.068) and closeness centrality (0.002) in comparison with TF, indicating a highly complicated community (Table 2). Diverse microbiota present higher interactions of species and intensified competition for microbiome niche (Kennedy et al., 2002; van Elsas et al., 2012). A highly connected microbiome network of US might decrease pathogen invasion success, as reported by some previous studies (van der Heijden and Hartmann, 2016). Furthermore, in our study the network of TF exhibited a higher

modularity (0.894) and shorter average path length (1.006) (Table 2), which might indicate a more rapid response of the microbial community to environmental disturbances (Faust and Raes, 2012). In coastal saline lands, resistance to diseases and environmental stresses are two important ecological functions for the reclamation of crops (Kumar and Verma, 2018). It seems to imply that the two habitats perform different ecological functions in terms of plant disease resistance and stress tolerance, and this might have important significance for the reclamation of coastal saline ecosystem. We also found that the Filamentous fungi (such as Sordariomycetes and Dothideomycetes) dominated the fungal communities in coastal saline ecosystem (Fig. 5B) similar as previously reported (Li et al., 2016). However, this differs from other findings of fungal composition in the deep ocean (Bass et al., 2007). One previous study revealed that deep ocean environments are dominated by ascomycetes and basidiomycetes closely related to phylotypes of yeasts (Bass et al., 2007). A

pe

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illu

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s

As

illu

As

m

myce s Cla

lus

rg

US

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do

illus

rgil

pe

Tala ro

Asperg

e Asp

As

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Penicillium

P.-D. Li et al. / Science of the Total Environment 690 (2019) 911–922

s

ad Cl

os

po

r iu

lus Cla

m

dos

por

Colored ranges Didym

Eurotiales

ium

ella

Fusarium Paraconiothyrium

Capnodiales

Fusarium

Pleosporales

Altern

Fus

Diaporthales

ariu

Ste m

ric

ula

ria Al

m

lium

ium

m Ch a

Chaetom

Le

ia

aria

orthe Diap

Gibellulopsis

Magnaporthales

ia

ar

um

liu

miu

cil

eto

eo

ca

ur

e rn Alt

P

p ur

rn ar

rn te Al

Sordariales Hypocreales

phy

te

i l li

Py

nic

Glomerellales

aria

m

Fig. 6. The core fungal communities associated with the coastal saline ecosystem. Color ranges identify fungal order within the tree. The phylogenetic tree was generated with the maximum-likelihood method by FastTree and uploaded to iTOL (Interactive Tree Of Life). Colored bars represent the relative abundance of each ASVs between the groups (TF: the group of tidal flat; US: the group of unflooded soil).

possible explanation is that the soils and sediments which are richer in organic matter can harbor more filamentous fungi (Richards et al., 2012). Comparing with US (2.22%), a large fraction of reads in TF (29.81%) recovered could not be classified with certainty to any known phylum (Table 1), and this can probably be attributed to the paucity of reference sequences currently available in the database (Kõljalg et al., 2013; Nilsson et al., 2015). It might also indicate that a significant number of undescribed fungal populations have not yet been identified (Jeewon et al., 2018). A large number of unidentified communities might mask their real ecological functions in coastal saline ecosystem. We speculate that there might be a myriad of more cryptic taxa in tidal flats. To further provide insights into the ecological function of the microbial community in tidal flat, future work should also target into possible ways to cultivate those taxa, assess their physiological characteristics, and explore their physiological functions. To explore the drivers of fungal niche differentiation in coastal saline ecosystem, we compared the significantly different abundance (EdgeR: P b 0.05, FDR b 0.2; LEfSe: the P-value of Kruskal-Wallis b 0.05, LDA score N 4.0) of microbiome between the two habitats (TF and US), and the results revealed that one phylum

(Mortierellomycota) and six orders (Sordariales, Hypocreales, Glomerellales, Eurotiales, Onygenales and Helotiales) which were enriched in US (Fig. 5C, D). Among the phyla uncovered in this study, Mortierellomycota (all is Mortierella genus) is widely present in US, compared with virtually none in TF. Some studies suggested that Mortierella sp. could defend against soil degradation, improve soil health, and stimulate the production of plants. Genome characteristics indicated that Mortierella sp. possessed the functional ability to degrade a range of toxic organics compounds (Li et al., 2018). Furthermore Mortierella sp. not only stimulate plant growth and biomass production (Johnson et al., 2019), but also significantly improve poor habitats (Cui et al., 2017). In this study, we observed that Hypocreales were widespread in US, however, rarely in TF (EdgeR: P b 0.001, FDR b 0.2; LDA score N 4.5). In US, the highest abundance of Hypocreales was that of Fusarium sp. (29.50%), one of the most important genera able to develop diseases in cereals (María and Eliana, 2017). Among them, Fusarium solani (29.37% of Fusarium sp. in US) can cause a variety of crops to wither (Ploetz, 2006). It might adversely affect crops after reclamation in coastal saline lands. In contrast, another three orders (Saccharomycetales,

Table 2 Topological properties of fungal co-occurrence networks of coastal saline ecosystem. Number of edgesa TF US

170 446

Number of nodesb 89 147

Clustering coefficientc

Modularityd

Connectancee

Degree centralityf

Closeness centralityg

Average degreeh

0.99861 0.93335

0.89356 0.85291

0.04341 0.04156

0.03613 0.06803

0.00090 0.00206

3.82022 6.06803

TF: the group of tidal flat; US: the group of unflooded soil. a The number of connections obtained by Spearman correlations. b Fungal genera with significant (P b 0.01) and strong (Spearman N 0.8) correlation. c The degree that the nodes tend to cluster together, and how nodes are embedded in the community. d The ability of the nodes to form highly associated communities, that is, the structure with more complicated between nodes connections. e The proportion of possible connections between fungal genera. f The number of direct connections between nodes indicates the importance of nodes in the network. g The closeness centrality is conducted by the average distance of this node to any other node. h The node connectivity, that is, the average number of connections per node in the network. i Average network distance between all pair of nodes or the average length off all edges in the network.

Average path lengthi 1.00585 2.75694

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A

B

Ascomycota

Chytridiomycota

Basidiomycota

Glomeromycota

C

Mortierellomycota

D

Module 1

Module 4

Module 1

Module 4

Module 2

Module 5

Module 2

Module 5

Module 3

Module 3

Fig. 7. Co-occurrence networks of fungal communities using the Spearman correlation matrix (P b 0.01, Spearman's coefficient N 0.8) of (A, C) tidal flat and (B, D) unflooded soil. Each node represents taxa affiliated at genus level (based on ITS2 rRNA), and the size of node is proportional to the relative abundance of genus. The lines between the nodes indicate positive connections among the genera. Each node was labeled at (A, B) phylum or (C, D) module level.

Lulworthiales and Magnaporthales) were enriched in TF. It is commonly known that yeasts (including ascomycetous and basidiomycetous yeasts) occur in a wide range of soil types (Botha, 2006). Although many species of order Saccharomycetales (ascomycetous yeasts, the only order in the class Saccharomycetes) had been reported in hypersaline environments including Candida sp. (the yeast of most abundance in our study) (Butinar et al., 2005; Gehlot and Singh, 2018), interestingly, few of them were found in US based on our study. Previous also suggest that the diversity of yeast species are more supported by moist soil with rich organic carbon than arid soil with poor organic carbon (Moawad et al., 1986; Spencer and Spencer, 1997). Lulworthiales exhibits significant differences (EdgeR: P b 0.001, FDR b 0.2; LDA score N 4.5) between the two habitats in coastal saline ecosystem. It is a common marine order and has been reported in many previous studies (Maharachchikumbura et al., 2016; Rédou et al., 2016; Vohník et al., 2016), however, was not found in US based in this study. The potential reasons for such a significant abundance difference in coastal saline ecosystem are unknown. Future studies of its physiological functions might provide more insight into this difference. FUNGuild provides a way to comprehensively analyze the differentiation of fungal niche and functions from an ecological perspective (Nguyen et al., 2016). Based on our study, the compositions of the coastal saline lands between TF and US were obviously different in ecological group (PERMANOVA based Bray-Curtis; P b 0.05) at the trophic mode level (Table S2), although both TF and US were dominated by Pathotroph, Saprotroph and Pathotroph−Saprotroph−Symbiotroph. However, we found that Saprotroph were more enriched in TF, comparing Pathotroph were more enriched in US (Fig. 5E). As reported in previous studies, the saprotrophic fungi received nutrients by breaking down dead host cells (Nguyen et al., 2016). In contrast, pathotrophic fungi received nutrients by harming host cells including phagotrophs (Nguyen et al., 2016). In the process of reclamation, “Saprotroph” Ascomycetes in gradually become dominant, and this corroborates with previous studies (Zhang et al., 2019). In this regard, TF benefits from a higher proportion of saprotrophic fungi before reclamation, which is more conducive to improving soil fertility (Zhang et al., 2019). Therefore, it seems to imply that these two habitats (TF and US) in the coastal saline ecosystem have different ecological functions, constituting the two different fungal niches. This also corresponds to the results of cooccurrence networks mentioned above. Reclamation is an effective method to solve the gradual decrease of cultivated land and alleviate the food crisis (Li et al., 2012). However, previous studies have focused less on pre-reclamation soil communities and more on post-reclamation communities (Cheng et al., 2018; Hua et al., 2017; Liu et al., 2013). In our study, it provided a basis for tracing

the dynamic changes of soil microorganisms before and after reclamation. From the perspective of microbiome, we first report the separation of fungal niches in the coastal saline ecosystem. It indicated that the coastal saline ecosystem are characterised by habitats with different ecological functions. This means that we should pay attention to the different microbiome in coastal saline habitats and choose appropriate reclamation plans in the future. The adaptability of crops to habitats with different microbiome and changes in the ecology of the environment during reclamation will be the focus of future research.

5. Conclusion Reclamation plays an important role in the coastal saline ecosystem. However, the associations and distinctions of fungal microbiome in coastal saline lands are not well documented. Here, we demonstrated that the evenness variability of the tidal flat was remarkably higher than that of the unflooded soil in fungal communities and also confirmed that the differentiation of fungal niche in coastal saline lands. We also showed that the potential functional networks through co-occurrence analyses, that the unflooded soil exhibited a more complicated community structure, while tidal flats exhibited a higher modularity. We speculate that the distinct geomorphologic features of the tidal flat lead to the higher variation of fungal evenness, the separation of fungal niches and differentiations of potential ecological functions. Future research including analysis of metagenome and metatranscriptome could provide more insights into the functions of the soil communities in the coastal saline ecosystem. Finally, understanding the differentiation of fungal microbiome between tidal flats and unflooded soils in the coastal saline ecosystem could provide the basis for the exploitation of reclamation to maintain agricultural productivity and crop security globally.

Acknowledgements This study was supported by Natural Science Foundation of Zhejiang Province [No. LY15C140001]. The authors also wish to thank Yun-Zeng Zhang (Yangzhou University) for his helpful with reviewing the manuscript. Rajesh Jeewon thanks the University of Mauritius for support. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.06.473.

P.-D. Li et al. / Science of the Total Environment 690 (2019) 911–922

References Abarenkov, K., Nilsson, R.H., Larsson, K.-H., Alexander, I.J., Eberhardt, U., Erland, S., et al., 2010. The UNITE database for molecular identification of fungi-recent updates and future perspectives. New Phytol. 186, 281–285. https://doi.org/10.1111/j.14698137.2009.03160.x. Anderson, M.J., 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x. Arzani, A., 2008. Improving salinity tolerance in crop plants: a biotechnological view. In Vitro Cell. Dev. Biol.-Plant. 44, 373–383. https://doi.org/10.1007/s11627-008-9157-7. Bahram, M., Hildebrand, F., Forslund, S.K., Anderson, J.L., Soudzilovskaia, N.A., Bodegom, P.M., et al., 2018. Structure and function of the global topsoil microbiome. Nature 560, 233. https://doi.org/10.1038/s41586-018-0386-6. Bardgett, R.D., Van Der Putten, W.H., 2014. Belowground biodiversity and ecosystem functioning. Nature 515, 505. https://doi.org/10.1038/nature13855. Bass, D., Howe, A., Brown, N., Barton, H., Demidova, M., Michelle, H., et al., 2007. Yeast forms dominate fungal diversity in the deep oceans. Proc. R. Soc. B Biol. Sci. 274, 3069–3077. https://doi.org/10.1098/rspb.2007.1067. Botha, A., 2006. Yeasts in soil. In: Rosa, C., Peter, G. (Eds.), Biodiversity and Ecophysiology of Yeasts. Springer, Berlin, Germany, pp. 221–240 https://doi.org/10.1007/3-54030985-3_11. Brotman, Y., Landau, U., Cuadros-Inostroza, Á., Takayuki, T., Fernie, A.R., Chet, I., et al., 2013. Trichoderma-plant root colonization: escaping early plant defense responses and activation of the antioxidant machinery for saline stress tolerance. PLoS Pathog. 9, e1003221. https://doi.org/10.1371/journal.ppat.1003221. Butinar, L., Sonjak, S., Zalar, P., Plemenitaš, A., Gunde-Cimerman, N., 2005. Melanized halophilic fungi are eukaryotic members of microbial communities in hypersaline waters of solar salterns. Bot. Mar. 48, 73–79. https://doi.org/10.1515/bot.2005.007. Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581. https://doi.org/10.1038/nmeth.3869. Cantrell, S., Dianese, J., Fell, J., Gunde-Cimerman, N., Zalar, P., 2011. Unusual fungal niches. Mycologia 103, 1161–1174. https://doi.org/10.3852/11-108. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., et al., 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303. Chen, H., Boutros, P.C., 2011. VennDiagram: a package for the generation of highlycustomizable Venn and Euler diagrams in R. BMC Bioinf 12, 35. https://doi.org/ 10.1186/1471-2105-12-35. Cheng, Z., Chen, Y., Zhang, F., 2018. Effect of reclamation of abandoned salinized farmland on soil bacterial communities in arid northwest China. Sci. Total Environ. 630, 799–808. https://doi.org/10.1016/j.scitotenv.2018.02.259. Chowdhury, N., Marschner, P., Burns, R., 2011. Response of microbial activity and community structure to decreasing soil osmotic and matric potential. Plant Soil 344, 241–254. https://doi.org/10.1007/s11104-011-0743-9. Csardi, G., Nepusz, T., 2006. The igraph software package for complex network research. Inter J, Comp Syst 1695, 1–9. Cui, Z., Zhang, X., Yang, H., Sun, L., 2017. Bioremediation of heavy metal pollution utilizing composite microbial agent of Mucor circinelloides, Actinomucor sp. and Mortierella sp. J. Environ. Chem. Eng. 5, 3616–3621. https://doi.org/10.1016/j.jece.2017.07.021. Edgar, R.C., 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461. Evelin, H., Kapoor, R., Giri, B., 2009. Arbuscular mycorrhizal fungi in alleviation of salt stress: a review. Ann. Bot. 104, 1263–1280. https://doi.org/10.1093/aob/mcp251. Faust, K., Raes, J., 2012. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538. https://doi.org/10.1038/nrmicro2832. Frankland, J.C., Magan, N., Gadd, G.M., 1996. Fungi and Environmental Change. Cambridge University Press, Cambridge, UK https://doi.org/10.1017/cbo9780511753190. Gehlot, P., Singh, J., 2018. Fungi and Their Role in Sustainable Development: Current Perspectives. Springer, Berlin, Germany https://doi.org/10.1007/978-981-13-0393-7. González, M.C., Hanlin, R.T., 2010. Potential use of marine arenicolous ascomycetes as bioindicators of ecosystem disturbance on sandy Cancun beaches: Corollospora maritima as a candidate species. Bot. Mar. 53, 577–580. https://doi.org/10.1515/ bot.2010.073. Hajiboland, R., Aliasgharzadeh, N., Laiegh, S.F., Poschenrieder, C., 2010. Colonization with arbuscular mycorrhizal fungi improves salinity tolerance of tomato (Solanum lycopersicum L.) plants. Plant Soil 331, 313–327. https://doi.org/10.1007/s11104009-0255-z. Hassani, M.A., Durán, P., Hacquard, S., 2018. Microbial interactions within the plant holobiont. Microbiome 6, 58. https://doi.org/10.1186/s40168-018-0445-0. He, Q., Bertness, M.D., Bruno, J.F., Li, B., Chen, G.Q., Coverdale, T.C., et al., 2014. Economic development and coastal ecosystem change in China. Sci. Rep. 4. https://doi.org/ 10.1038/srep05995. Hua, J., Feng, Y., Jiang, Q., Bao, X., Yin, Y., 2017. Shift of bacterial community structure along different coastal reclamation histories in Jiangsu, Eastern China. Sci. Rep. 7, 10096. https://doi.org/10.1038/s41598-017-10608-3. Huang, J., Rozelle, S., 1995. Environmental stress and grain yields in China. Am. J. Agric. Econ. 77, 853–864. https://doi.org/10.2307/1243808. Hussain, S., Khaliq, A., Matloob, A., Wahid, M.A., Afzal, I., 2013. Germination and growth response of three wheat cultivars to NaCl salinity. Soil Environ 32, 36–43. https:// doi.org/10.3923/pjbs.1999.1594.1597. Jarvis, S.G., Woodward, S., Taylor, A.F., 2015. Strong altitudinal partitioning in the distributions of ectomycorrhizal fungi along a short (300 m) elevation gradient. New Phytol. 206, 1145–1155. https://doi.org/10.1111/nph.13315. Jeewon, R., Yeung, Q.S., Wannasinghe, D.N., Rampadarath, S., Puchooa, D., Wang, H.-K., et al., 2018. Hidden mycota of pine needles: molecular signatures from PCR-DGGE and

921

Ribosomal DNA phylogenetic characterization of novel phylotypes. Sci. Rep. 8, 18053. https://doi.org/10.1038/s41598-018-36573-z. Jiao, S., Liu, Z., Lin, Y., Yang, J., Chen, W., Wei, G., 2016. Bacterial communities in oil contaminated soils: biogeography and co-occurrence patterns. Soil Biol. Biochem. 98, 64–73. https://doi.org/10.1016/j.soilbio.2016.04.005. Johnson, J.M., Ludwig, A., Furch, A.C., Mithöfer, A., Scholz, S., Reichelt, M., et al., 2019. The beneficial root-colonizing fungus Mortierella hyalina promotes the aerial growth of Arabidopsis and activates calcium-dependent responses that restrict Alternaria brassicae–induced disease development in roots. Mol. Plant-Microbe Interact. https://doi.org/10.1094/mpmi-05-18-0115-r MPMI-05-18-0115-R. Junker, B.H., Schreiber, F., 2011. Analysis of Biological Networks. vol 2. John Wiley & Sons, Hoboken, New Jersey. https://doi.org/10.1002/9781119165057.ch10. Kennedy, T.A., Naeem, S., Howe, K.M., Knops, J.M., Tilman, D., Reich, P., 2002. Biodiversity as a barrier to ecological invasion. Nature 417, 636. https://doi.org/10.1038/ nature00776. Kõljalg, U., Nilsson, R.H., Abarenkov, K., Tedersoo, L., Taylor, A.F., Bahram, M., et al., 2013. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277. https://doi.org/10.5200/ksu.02. Kumar, A., Verma, J.P., 2018. Does plant—microbe interaction confer stress tolerance in plants: a review? Microbiol. Res. 207, 41–52. https://doi.org/10.1016/j. micres.2017.11.004. Kuriqi, A., Ardiçlioǧlu, M., 2018. Investigation of hydraulic regime at middle part of the Loire River in context of floods and low flow events. Pollack Periodica 13, 145–156. https://doi.org/10.1556/606.2018.13.1.13. Kuriqi, A., Pinheiro, A., Sordo-Ward, A., Garrote, L., 2017. Trade-off between environmental flow policy and run-of-river hydropower generation in Mediterranean climate. Eur. Water. 60, 123–130. https://doi.org/10.13140/RG.2.2.23257.21608. Kuriqi, A., Pinheiro, A.N., Sordo-Ward, A., Garrote, L., 2019. Influence of hydrologically based environmental flow methods on flow alteration and energy production in a run-of-river hydropower plant. J. Clean. Prod. https://doi.org/10.1016/j. jclepro.2019.05.358. Letunic, I., Bork, P., 2016. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245. https://doi.org/10.1093/nar/gkw290. Li, Q., Chen, Y., Fu, H., Cui, Z., Shi, L., Wang, L., et al., 2012. Health risk of heavy metals in food crops grown on reclaimed tidal flat soil in the Pearl River Estuary, China. J. Hazard. Mater. 227, 148–154. https://doi.org/10.1016/j.jhazmat.2012.05.023. Li, W., Wang, M.M., Bian, X.M., Guo, J.J., Cai, L., 2016. A high-level fungal diversity in the intertidal sediment of chinese seas presents the spatial variation of community composition. Front. Microbiol. 7. https://doi.org/10.3389/fmicb.2016.02098. Li, F., Chen, L., Redmile-Gordon, M., Zhang, J., Zhang, C., Ning, Q., et al., 2018. Mortierella elongata's roles in organic agriculture and crop growth promotion in a mineral soil. Land Degrad. Dev. 29, 1642–1651. https://doi.org/10.1002/ldr.2965. Liu, C., Xu, J., Ding, N., Fu, Q., Guo, B., Lin, Y., et al., 2013. The effect of long-term reclamation on enzyme activities and microbial community structure of saline soil at Shangyu, China. Environ. Earth Sci. 69, 151–159. https://doi.org/10.1007/s12665012-1943-1. Maharachchikumbura, S.S., Hyde, K.D., Jones, E.G., McKenzie, E., Bhat, J.D., Dayarathne, M.C., et al., 2016. Families of sordariomycetes. Fungal Divers. 79, 1–317. https://doi. org/10.1007/s13225-016-0369-6. María, D., Eliana, C., 2017. Fusarium–plant interaction: state of the art–a review. Plant Prot. Sci. 53, 61–70. https://doi.org/10.17221/182/2015-pps. Marzinelli, E.M., Qiu, Z., Dafforn, K.A., Johnston, E.L., Steinberg, P.D., Mayer-Pinto, M., 2018. Coastal urbanisation affects microbial communities on a dominant marine holobiont. npj Biofilms Microbiomes 4 (1). https://doi.org/10.1038/s41522-017-0044-z. Meena, K.K., Sorty, A.M., Bitla, U.M., Choudhary, K., Gupta, P., Pareek, A., et al., 2017. Abiotic stress responses and microbe-mediated mitigation in plants: the omics strategies. Front. Plant Sci. 8 (172). https://doi.org/10.3389/fpls.2017.00172. Moawad, H., Salem, S., El-Din, S.B., Khater, T., Iskandar, M., 1986. Yeasts in soils of Egypt. Zentralbl. Mikrobiol. 141, 431–435. https://doi.org/10.1016/s0232-4393(86)80033-6. Mohamed, D.J., Martiny, J.B., 2011. Patterns of fungal diversity and composition along a salinity gradient. ISME J 5, 379. https://doi.org/10.1038/ismej.2010.137. Munns, R., Tester, M., 2008. Mechanisms of salinity tolerance. Annu. Rev. Plant Biol. 59, 651–681. https://doi.org/10.1146/annurev.arplant.59.032607.092911. Murray, N.J., Phinn, S.R., DeWitt, M., Ferrari, R., Johnston, R., Lyons, M.B., et al., 2019. The global distribution and trajectory of tidal flats. Nature 565, 222. https://doi.org/ 10.1038/s41586-018-0805-8. Mustafa, Z., Pervez, M.A., Ayyub, C.M., Matloob, A., Khaliq, A., Hussain, S., et al., 2014. Morpho-physiological characterization of chilli genotypes under NaCl salinity. Soil & Environment 33. Newman, M.E., 2006. Modularity and community structure in networks. PNAS 103, 8577–8582. https://doi.org/10.1073/pnas.0601602103. Nguyen, N.H., Song, Z., Bates, S.T., Branco, S., Tedersoo, L., Menke, J., et al., 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248. https://doi.org/10.1016/j.funeco.2015.06.006. Nilsson, R.H., Tedersoo, L., Ryberg, M., Kristiansson, E., Hartmann, M., Unterseher, M., et al., 2015. A comprehensive, automatically updated fungal ITS sequence dataset for reference-based chimera control in environmental sequencing efforts. Microbes Environ., ME14121 https://doi.org/10.1264/jsme2.me14121. Parida, A.K., Das, A.B., 2005. Salt tolerance and salinity effects on plants: a review. Ecotoxicol. Environ. Saf. 60, 324–349. https://doi.org/10.1016/j.ecoenv.2004.06.010. Ploetz, R.C., 2006. Fusarium-induced diseases of tropical, perennial crops. Phytopathology 96, 648–652. https://doi.org/10.1094/phyto-96-0648. Price, M.N., Dehal, P.S., Arkin, A.P., 2010. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490. https://doi.org/10.1371/journal. pone.0009490.

922

P.-D. Li et al. / Science of the Total Environment 690 (2019) 911–922

Rédou, V., Vallet, M., Meslet-Cladière, L., Kumar, A., Pang, K.-L., Pouchus, Y.-F., et al., 2016. Marine fungi. The Marine Microbiome. Springer, pp. 99–153 https://doi.org/10.1007/ 978-3-319-33000-6_4. Reise, K., 2012. Tidal Flat Ecology: An Experimental Approach to Species Interactions. vol. 54. Springer, Berlin, Germany. https://doi.org/10.1007/978-3-642-70495-6. Rengasamy, P., 2010. Soil processes affecting crop production in salt-affected soils. Funct. Plant Biol. 37, 613–620. https://doi.org/10.1071/fp09249. Richards, T.A., Jones, M.D., Leonard, G., Bass, D., 2012. Marine fungi: their ecology and molecular diversity. Annu. Rev. Mar. Sci. 4, 495–522. https://doi.org/10.1146/annurevmarine-120710-100802. Robinson, M.D., McCarthy, D.J., Smyth, G.K., 2010. EdgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. https://doi.org/10.1093/bioinformatics/btp616. Schoch, C.L., Seifert, K.A., Huhndorf, S., Robert, V., Spouge, J.L., Levesque, C.A., et al., 2012. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. PNAS 109, 6241–6246. https://doi.org/10.1073/ pnas.1117018109. Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W.S., et al., 2011. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60. https:// doi.org/10.1186/gb-2011-12-6-r60. Shi, S., Tian, L., Nasir, F., Bahadur, A., Batool, A., Luo, S., et al., 2019. Response of microbial communities and enzyme activities to amendments in saline-alkaline soils. Appl. Soil Ecol. 135, 16–24. https://doi.org/10.1016/j.apsoil.2018.11.003. Simon, H.M., Smith, M.W., Herfort, L., 2014. Metagenomic insights into particles and their associated microbiota in a coastal margin ecosystem. Front. Microbiol. 5 (466). https://doi.org/10.3389/fmicb.2014.00466. Smith, J.R., Fong, P., Ambrose, R.F., 2006. Dramatic declines in mussel bed community diversity: response to climate change? Ecology. 87, 1153–1161. https://doi.org/ 10.1890/0012-9658(2006)87[1153:ddimbc]2.0.co;2. Spencer, J., Spencer, D., 1997. Ecology: where yeasts live. In: Spencer, J., Spencer, D. (Eds.), Yeasts in Natural and Artificial Habitats. Springer, Berlin, Germany, pp. 33–58 https:// doi.org/10.1007/978-3-662-03370-8_4.

Tsui, K.-M., Fryar, S.C., Hodgkiss, I.J., Hyde, K.D., Poonyth, A.D., Taylor, J.E., 1998. The effect of human disturbance on fungal diversity in the tropics. Fungal Divers. 1, 19–26. Tuteja, N., 2007. Mechanisms of high salinity tolerance in plants. In: Anna, M.P., David, W.C. (Eds.), Methods in Enzymology. Elsevier, Amsterdam, Netherlands, pp. 419–438 https://doi.org/10.1016/s0076-6879(07)28024-3. van der Heijden, M.G., Hartmann, M., 2016. Networking in the plant microbiome. PLoS Biol. 14, e1002378. https://doi.org/10.1371/journal.pbio.1002378. van Elsas, J.D., Chiurazzi, M., Mallon, C.A., Elhottovā, D., Krištůfek, V., Salles, J.F., 2012. Microbial diversity determines the invasion of soil by a bacterial pathogen. PNAS 109, 1159–1164. https://doi.org/10.1073/pnas.1109326109. Van Ryckegem, G., Verbeken, A., 2005. Fungal diversity and community structure on Phragmites australis (Poaceae) along a salinity gradient in the Scheldt estuary (Belgium). Nova Hedwigia 80, 173–197. https://doi.org/10.1127/0029-5035/2005/ 0080-0173. Vohník, M., Borovec, O., Kolařík, M., 2016. Communities of cultivable root mycobionts of the seagrass Posidonia oceanica in the northwest Mediterranean Sea are dominated by a hitherto undescribed pleosporalean dark septate endophyte. Microb. Ecol. 71, 442–451. https://doi.org/10.1007/s00248-015-0640-5. Waller, F., Achatz, B., Baltruschat, H., Fodor, J., Becker, K., Fischer, M., et al., 2005. The endophytic fungus Piriformospora indica reprograms barley to salt-stress tolerance, disease resistance, and higher yield. PNAS 102, 13386–13391. https://doi.org/10.1073/ pnas.0504423102. Wichern, J., Wichern, F., Joergensen, R.G., 2006. Impact of salinity on soil microbial communities and the decomposition of maize in acidic soils. Geoderma 137, 100–108. https://doi.org/10.1016/j.geoderma.2006.08.001. Wittebolle, L., Marzorati, M., Clement, L., Balloi, A., Daffonchio, D., Heylen, K., et al., 2009. Initial community evenness favours functionality under selective stress. Nature 458, 623. https://doi.org/10.1038/nature07840. Zhang, Y., Li, Q., Chen, Y., Dai, Q., Hu, J., 2019. Mudflat reclamation causes change in the composition of fungal communities under long-term rice cultivation. Can. J. Microbiol. https://doi.org/10.1139/cjm-2019-0005.