Diversity and ecology of fungi in the sediments and surface water of brackish and salt marshes

doi:10.21203/rs.3.rs-5263393/v1

Download PDF

Research Article

Diversity and ecology of fungi in the sediments and surface water of brackish and salt marshes

https://doi.org/10.21203/rs.3.rs-5263393/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Fungi are key drivers of biogeochemical processes, yet marine fungi remain understudied. While various regions of fungal ribosomal RNA have been targeted to study fungal diversity, the ITS region has been the most prevalent region in the literature since 2012. However, ITS metabarcoding has limitations in marine environments, partly due to database biases. We conducted a metabarcoding survey targeting the small and large subunit rRNA genes and the internal transcribed spacer region of fungi (18S, 28S, and ITS2) in the sediment and surface water of salt and brackish marshes in South Carolina, USA. The 28S primer set (LR0R and LF402) excelled at identifying early diverging fungal lineages, including Chytridiomycota, Mucoromycota, Zoopagomycota, and Blastocladiomycota; however, only the ITS2 primer set amplified Cryptomycota and Olpidiomycota. The universal 18S/16S primer set (515F-Y and 926R) identified few fungal taxa because most reads were prokaryotic. The results based on 28S rRNA amplicons revealed that Dikarya fungi dominated salt marshes, whereas early diverging fungi dominated brackish marshes, suggesting Dikarya are more salt-tolerant. Over half of the fungal OTUs identified by the 28S primer set were from early diverging lineages. A FUNGuild analysis found that saprotrophic fungi are the function of most lineages, but in the brackish marsh, saprotrophic fungi from Zoopagomycota, Blastocladiomycota, and Chytridiomycota were more prevalent. Differential abundance analysis revealed that early diverging fungi were key drivers of community composition between the various marsh types. This study advances our understanding of marine fungal diversity by identifying early diverging lineages that were previously overlooked in marine environments. Our study highlights the vast, unexplored fungal diversity in marine environments.

Marine fungi

Metabarcoding

Salt marsh

Brackish marsh

Early diverging fungi

Dikarya

Fungi play crucial roles in biogeochemical processes in marshes, contributing to carbon remineralization (Tobias & Neubauer, 2019; Torzilli & Andrykovitch, 1986) and phosphorus and nitrogen cycling (Amend et al., 2019; Otte & Landy, 2006; Peng et al., 2024). In aquatic ecosystems, most fungi function as decomposers, breaking down complex organic materials such as macrophyte litter, dead algae, and other organic debris (Bärlocher & Boddy, 2016). Like terrestrial fungi, many marine fungi produce extracellular enzymes that degrade complex organic matter such as lignin and cellulose while also assimilating nitrogen in forms such as ammonium or nitrate, which are common in plants, phytoplankton, and microbes (d’Entremont et al., 2021). Additionally, some fungi play a key role in denitrification, removing nitrate from the environment and producing nitrous oxide, a potent greenhouse gas (Jones, 2011; Lazo-Murphy et al., 2022; Maeda et al., 2015).

While most marine fungi identified belong to Ascomycota and Basidiomycota (Amend et al., 2019), recent environmental sequencing data have revealed early diverging fungal lineages, such as Blastocladiomycota, Zoopagomycota, and Chytridiomycota, in coastal environments. However, despite these findings, the functions and contributions of these early lineages to nutrient cycling in estuarine ecosystems remain poorly understood (Comeau et al., 2016; Duan et al., 2018; Jones et al., 2015; Pham et al., 2021; Picard, 2017). This study aims to address this critical knowledge gap by focusing on the diversity and ecological roles of these rarely described early diverging fungi in marsh ecosystems (d’Entremont et al., 2021; Picard, 2017). By investigating taxa that are often underrepresented in traditional surveys, we enhance our understanding of their biodiversity and functions within these habitats. Our findings suggest that early diverging fungi may play significant roles in processes such as organic matter degradation and nutrient turnover, ultimately contributing to biogeochemical cycling in marsh environments. Furthermore, some of these lineages, particularly those in Blastocladiomycota and Chytridiomycota, are known to cause infectious diseases in various hosts, including phytoplankton, amphibians, and saltmarsh plants (d’Entremont et al., 2021; Frenken et al., 2018; Gleason et al., 2014). Thus, our research not only expands the taxonomic understanding of marine fungi but also highlights their potential ecological significance and impacts on ecosystem health.

Despite the growing interest in marine fungi, our understanding of their diversity in marine environments remains limited. One reason for this is the infrequent use of shotgun metagenomics, as fungal reads often represent a low percentage of community DNA (Peng & Valentine, 2021). To address this gap, amplicon-based metabarcoding surveys continue to constitute a key method for expanding our knowledge of marine fungal diversity. Most studies conducted in marine and coastal ecosystems have focused on sequencing the ITS region—specifically, ITS1, ITS2, or the entire ITS region (ITS1, 5.8S, and ITS2)—which has been recognized as the recommended DNA barcode marker for fungi by the Consortium for the Barcode of Life since 2011 (Schoch et al., 2012). The ITS region generally provides high taxonomic resolution and has the highest probability of correct identification for broadly sampled fungi (Schoch et al., 2012). Although the ITS region provides high taxonomic resolution, challenges such as high variability, primer biases that exclude some groups, and the lack of early diverging fungal sequences in ITS databases can complicate alignment and classification (Heeger et al., 2018; Raja et al., 2017; Reynolds et al., 2022). In comparison, the large subunit (LSU) ribosomal RNA (rRNA) (28S) region has received increasing interest because it offers greater taxonomic resolution than the 18S small subunit (SSU) rRNA region does (Xu et al., 2016). The 28S region also enables phylogenetic reconstruction, which could improve the classification of novel fungal lineages. Projects such as the Fungal 28S Ribosomal RNA (LSU) RefSeq Targeted Loci Project have facilitated the use of 28S for taxonomic classification (Robbertse, 2024). Additionally, testing universal primer sets, such as the one developed by Parada et al. (2016), offers the potential to quantify the relative abundance of both prokaryotes and eukaryotes, although this approach has rarely been applied to fungi and prokaryotes simultaneously (Moccia et al., 2020).

Coastal salt marshes, which are highly productive habitats, provide essential ecosystem services, such as acting as nurseries for fisheries, protecting shorelines from erosion, and reducing nutrient loading in coastal waters (Simas et al., 2001). As of 2017, 29% (94.7 million) of the U.S. population lived along the coastline, with 13.6% residing on the Atlantic coast (United States Cenus Bureau, 2019). Salt marshes, which serve as sinks for anthropogenic contamination, are significantly influenced by nutrient inputs (Crain, 2007; Czapla et al., 2020). These ecosystems store an estimated 4.8–87.2 Tg of carbon per year through the burial of organic carbon by plants and phytoplankton (Mcleod et al., 2011). Microbes, including fungi, utilize buried carbon for aerobic and anaerobic metabolism, which releases sequestered carbon (Mcleod et al., 2011), making salt marshes critical sites for carbon sequestration (Drake et al., 2015; Mcleod et al., 2011) and biogeochemical cycling (Tobias & Neubauer, 2019).

This study aimed to determine which rRNA region is most suitable for studying marine fungal diversity. We conducted a metabarcoding survey targeting the 18S SSU, 28S LSU, and ITS2 rRNA regions to analyze fungal community composition in the North Inlet–Winyah Bay (NI-WB) estuary in South Carolina, USA, during the summer and winter of 2020 and 2021. Our results revealed significant variation in fungal identification and diversity between primer sets in both salt and brackish marsh surface water and sediment samples. We also identified key parameters influencing community composition. We hypothesized that fungal diversity would vary significantly between primers and that salinity and seasonality would have substantial effects on community composition and diversity.

Study Area

North Inlet-Winyah Bay (NI-WB) is located in Georgetown, SC, and hosts a National Oceanic and Atmospheric Administration (NOAA) National Estuarine Research Reserve (NERR) with four long-term monitoring sites: Oyster Landing, Clambank, Debidue Creek, and Thousand Acre (Figure 1; Allen et al., 2014). The NI-WB NERR consists of approximately 19,000 acres of tidal marshes of varying salinity. Winyah Bay is a brackish, river-dominated estuary controlled by the Waccamaw, Sampit, Black, and Pee Dee Rivers (7.5 ± 10.4 psu) and receives approximately 557 m³/s freshwater input annually (Patchineelam et al., 1999). At the Thousand Acre monitoring station, which is located alongside Winyah Bay, the vegetation in the low marsh is dominated by Spartina alterniflora, whereas the high marsh is home to Spartina cynosuroides, Juncus roemerianus, Typha angustifolia, Scirpus americanus, and Eleocharis engelmannii (Baden III et al., 1975; H. Li et al., 2022; Stalter et al., 2021; Stalter & Baden, 1994). The other three sampling sites—Clambank, Oyster Landing, and Debidue Creek—are located in different reaches of the North Inlet estuarine system, a relatively pristine, high-salinity, ocean-dominated salt marsh (29.6 ± 5.9 psu) also dominated by Spartina alterniflora (H. Li et al., 2022).

Sample collection

Seawater and sediment samples (48 of each) were collected from the NI-WB in June 2020, August 2020, February 2021, and November 2021 to study seasonal fungal diversity across the salt marshes at Oyster Landing, Clambank, Debidue Creek, and Thousand Acre (Figure 1). The physical and chemical data (temperature, salinity, pH, and chlorophyll a content) at each site were monitored via NERR. At each sampling location, surface sediment samples were taken in 1.5 mL tubes, and approximately 50 mL of surface water was filtered through a 0.22 μm Sterivex filter (EMD Millipore #SVGP01050, Burlington, MA). Three biological replicates were collected at each site during each sampling campaign. The samples were placed in a cooler, transported to the laboratory, and stored in a -80°C freezer until extraction.

DNA Extraction

In the laboratory, DNA was extracted from the sediment via the DNeasy PowerSoil Pro Kit, following the manufacturer’s protocol except for the first step (cell lysis). The sediment (0.50 ± 0.01 g) was weighed into MN bead type A tubes (Macherey-Nagel #740786.50), which were bead-beaten in a Biospec Mini-BeadBeater-16 for 60 seconds. The remaining extraction steps followed the DNeasy PowerSoil Pro kit protocol without further modifications.

DNA was extracted from the Sterivex filters via the DNeasy PowerWater extraction kit, following the kit’s protocol except for the first step (cell lysis). Using sterilized scissors, half of each Sterivex filter paper was cut into 2 mm squares. The filters were placed into MN bead tubes type A (Macherey-Nagel #740786.50) and bead-beaten in a Biospec Mini-BeadBeater-16 for 60 seconds. The concentration of the isolated DNA was determined via a Qubit fluorometer, and the DNA samples were stored at -20°C until amplicon sequencing. The remaining extraction steps followed the DNeasy PowerWater kit protocol without further modifications.

Amplicon library construction and sequencing

Three PCR primers were used to target different fungal regions within the samples. Two sediment negative controls and two surface water negative controls for each primer set were processed through DNA extraction and amplicon library construction. The ZymoBIOMICS Microbial Community Standard and Standard II (log distribution) (ZymoBIOMICS #D6300) were processed through DNA extraction and amplicon library construction, and the ZymoBIOMICS Microbial Community DNA Standard and Standard II (log distribution) (ZymoBIOMICS #D6311) were included in the amplicon library (Supplementary Figure S1). We followed Illumina’s 16S library protocol (Illumina #15044223 Rev.B; Peng and Valentine, 2021), which is a two-step process: the first step involves amplicon PCR, and the second step involves index PCR.

To sequence the V4-V5 regions of the 16S and 18S rRNA genes, the universal primers 515F-Y (5’-GTGYCAGCMGCCGCGGTAA-3’) and 926R (5’-CCGYCAATTYMTTTRAGTTT-3’) were used. The primers ITS3tagmix (Supplementary Table S1) (Tedersoo et al., 2015) and ITS4tag001 (5’-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGACGAGTGCGTTCCTSCGCTTATTGATATGC-3’) (Tedersoo et al., 2014) were used to sequence the ITS2 region. The LR0R (5’-ACSCGCTGAACTTAAGC-3’) and LF402 (5’-TTCCCTTTYARCAATTTCAC-3’) primers were used to sequence the 28S LSU region of fungi (Tedersoo et al., 2015).

A 10 μL PCR was performed for each sample in triplicate, with the following mixture: 0.1 μL of forward primer (10 μM), 0.1 μL of reverse primer (10 μM), 2 μL of 5x GC Buffer (Thermo Scientific #F530S, Vilnius, LT), 0.2 μL of 10 mM dNTPs (Thermo Scientific #R0181, Vilnius, LT), and 0.1 μL of Phusion High-Fidelity DNA Polymerase (Thermo Scientific #F530S, Vilnius, LT). The thermal cycling settings for the universal primer set were as follows: initial denaturation at 98°C for 30 seconds, followed by 25 cycles of 98°C for 10 seconds, 50°C for 30 seconds, and 72°C for 45 seconds, with a final elongation at 72°C for 10 minutes. For the 28S primer, the settings were as follows: initial denaturation at 98°C for 30 seconds, followed by 30 cycles of 98°C for 10 seconds, 53°C for 30 seconds, and 72°C for 45 seconds, with a final elongation at 72°C for 10 minutes. For the ITS2 primers, the settings were as follows: initial denaturation at 98°C for 30 seconds, followed by 30 cycles of 98°C for 10 seconds, 55°C for 30 seconds, and 72°C for 45 seconds, with a final elongation at 72°C for 10 minutes.

The triplicates were pooled and cleaned using sparQ PureMag beads at a 1:1 ratio of beads to PCR products following the sparQ PureMag bead clean-up protocol (Quantabio #95196/IFU-124.1 REV 03). The cleaned PCR products were quantified via the high-sensitivity 1x DNA Qubit assay. Different indices were used to identify each sample after sequencing with the Nextera XT DNA Library Preparation Kit (Illumina #FC-131-1096). The indexed amplicon libraries were cleaned again via the same bead clean-up protocol described above. A total of 293 amplicon libraries were pooled to reach a concentration of 2 nM and sent to the Duke Genome Center for sequencing in one lane of an S-Prime flow cell (250 bp PE) on the Illumina NovaSeq 6000 platform, which generated a total of 468,658,500 raw reads.

Sequence analysis

A modified protocol from Fuhrman & Needham (2019) was used to analyze the 18S rRNA sequencing results (Needham et al., 2018; Parada et al., 2016). The raw reads were quality-filtered via bbduk.sh (ktrim=r ordered minlen=51 minlenfraction=0.33 mink=11 tbo tpe rcomp=f k=23 hdist=1 hdist2=1 ftm=5 pigz=t unpigz=t zl=4 ow=true) and Trimmomatic (ILLUMINACLIP:$adapters:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:200). Using USEARCH v11.0.667, the filtered forward and reverse reads were merged with the “-fastq_mergepairs” function. The unmerged reads represented the 18S reads, and the merged reads represented the 16S reads. All nonmerged reads were trimmed to 190 bp via Trimmomatic (CROP:190 MINLEN:190). The nonmerged reverse reads were converted to their reverse complements with the “seqtk seq -r” function in seqtk. An “N” was added to the end of the forward nonmerged sequences as a placeholder for the missing base pairs between the nonmerged forward and reverse complement reads, and an “F” was added to the end of the nonmerged forward quality scores. Forward and reverse complement reads were merged via the “-fastq_mergepairs” function and filtered with maxEE set to 1 via the “-fastq_filter” function in USEARCH v11.0.667. The reads were dereplicated via the “-fastx_unique” function and clustered into 3,762 97% operational taxonomic units (OTUs, “-cluster_otus” function) via USEARCH v11.0.667 (Supplementary Table S2). The nucleotide sequences of the 18S primer set were classified via the SILVA SSU 138--99 naïve Bayesian classifier (Quast et al., 2012) within QIIME 2 (Caporaso et al., 2010).

For the ITS2 sequencing results, the raw reads were merged and trimmed to the ITS2 region via ITSxpress. With USEARCH v11.0.667, the merged reads were filtered with maxEE set to 1 via the “-fastq_filter” function. The reads were dereplicated via the “-fastx_unique” function and clustered into 18,389 97% operational taxonomic units (OTUs, “-cluster_otus” function) via USEARCH v11.0.667 (Supplementary Table S2). The nucleotide sequences of the ITS2 primer set OTUs were imported into QIIME2 v2022-11 (Caporaso et al., 2010) and classified by a naïve Bayesian classifier via the UNITE 9.0 database for eukaryotes (Nilsson et al., 2019).

For 28S sequencing reads, raw reads were quality-filtered via bbduk.sh (ktrim=r ordered minlen=51 minlenfraction=0.33 mink=11 tbo tpe rcomp=f k=23 hdist=1 hdist2=1 ftm=5 pigz=t unpigz=t zl=4 ow=true) and Trimmomatic (ILLUMINACLIP:$adapters:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:200). With USEARCH v11.0.667, the filtered forward and reverse reads were merged with the “-fastq_mergepairs” function and filtered with maxEE set to 1 via the “-fastq_filter” function. The reads were dereplicated via the “-fastx_unique” function and clustered into 8,461 97% operational taxonomic units (OTUs, “-cluster_otus” function) via USEARCH v11.0.667 (Supplementary Table S2). The nucleotide sequences of the 28S primer set OTUs were classified via the RDP classifier 2.14 (Q. Wang et al., 2007; Q. Wang & Cole, 2024).

Statistical Analyses

The relative abundance of each OTU was calculated via the phyloseq package in R (McMurdie & Holmes, 2013). The full OTU abundance table was rarefied to the same sequencing depth (threshold of 21,362) for each sample (Cameron et al., 2021). These rarefied OTUs were used to calculate Shannon diversity (using the diversity function in the vegan package in R), Simpson diversity (using the diversity function in the vegan package in R), richness (using the specnumber function in the vegan package in R), and evenness (Shannon diversity divided by the log of richness in R) for each sample. Hutcheson t tests (using the Hutcheson_t_test function in the ecolTest package in R) were performed to determine the significance of the differences in Shannon diversity, Simpson diversity, and evenness among the different primers. The environmental parameters of the surface water, including salinity, temperature, pH, and chlorophyll a content at each station, were investigated via nonmetric multidimensional scaling (NMDS) to determine their roles in shaping the microbial community composition in the surface water via the vegan package in R. A multiple regression on distance matrices for NMDS was conducted to determine the significance of the environmental variables.

The raw fungal OTU counts from the 28S region were normalized via the transfer matrix method (EdgeR package in R; Robinson et al., 2010). Differential abundance analysis was performed on these normalized OTU counts based on three salinity ranges (low: <10 ppt, moderate: 10–25 ppt, and high: >25 ppt). The normalized counts per million of the top 32 differentially regulated OTUs were visualized and grouped by sample type (sediment or surface water) and sampling location (Clambank, Oyster Landing, Debidue Creek, or Thousand Acre) via ggplot in R. FUNGuild (Nguyen et al., 2016; Põlme et al., 2020) was used to hypothesize the function of the fungi that were differentially abundant in the salt and brackish marhses identified by the 28S primer set.

All fungal sequences from the 28S region and two outgroup sequences from the SAR supergroup (Frustulia sp. and Amphora commutata; sequences from NCBI) were aligned (using MUSCLE; Edgar, 2004) and manually trimmed (using Jalview). A maximum-likelihood tree was generated via IQ-TREE2 with the settings “-m MFP+MERGE -B 1000 -alrt 1000 –mem 164GB -T 16” (Minh et al., 2020). The tree was visualized via the interactive tree of life (Letunic & Bork, 2021), with the respective phylum of each OTU shown in various colors via a color strip dataset.

Environmental context

The February and November samples were collected at nearly the lowest temperatures, whereas the June and August samples were collected near the highest temperatures (Figure 2). At Clambank (North Inlet), the water temperature ranged from 6.8°C to 34.2°C, with averages of 21.2°C ± 6.3°C in 2020 and 20.7°C ± 6.9°C in 2021 (Figure 2A). Salinity ranged from 6.5 ppt to 34.5 ppt in 2020 and 9.2 ppt to 41.6 ppt in 2021 (Figure 2E). The average pH was 7.7 ± 0.2 in 2020 and 7.6 ± 0.2 in 2021 (Figure 2I). The chlorophyll a levels peaked in May 2020 (77.1 µg/L) and July 2021 (22.2 µg/L) (Figure 2DM). Similar trends were observed at Debidue Creek and Oyster Landing (both are situated in the North Inlet; Figure 2B, C, F, G, J, K), with chlorophyll a maxima occurring in August 2020 and 2021 (Figure 2N & O).

Thousands of Acre (Winyah Bay) exhibited seasonal variations, with average temperatures of 20.3°C ± 6.6°C in 2020 and 20.7°C ± 6.9°C in 2021 (Figure 2D). Salinity ranged from 0 to 26.7 ppt (Figure 2H), whereas pH values averaged 6.7 ± 0.3 in 2020 and 7.1 ± 0.4 in 2021 (Figure 2L). The chlorophyll a maxima were observed in August 2020 (172.1 µg/L) and July 2021 (96.9 µg/L) (Figure 2P). The August time point was taken shortly after the August 2020 phytoplankton bloom (Figure 2).

Fungal community composition identified by the universal SSU primer set (18S and 16S rRNA)

In this study, 82.1% of the reads generated by the universal primer set (9,767,053 in total) were aligned with 16S OTUs. The remaining reads (1,746,250) mapped to 18S OTUs, of which only three (one Pleosporaceae, one Saccharomycetaceae, and one Malasseziaceae) were identified as fungi (Supplementary Table S3). No early diverging fungi were detected. The percentage of these fungal OTUs in the 18S universal primer set library ranged from 0.003% to 0.03%. These findings demonstrate that this universal primer set is not suitable for assessing fungal diversity, even though it has the potential to assess both prokaryotic and eukaryotic microbial communities simultaneously (McNichol et al., 2021). This is because the amount of fungal DNA is much lower than that of bacterial DNA, so the vast majority of the sequencing reads are bacterial. Fungi-specific 18S primers are likely better suited for studying marine fungal diversity by targeting the small subunit (SSU) rRNA (Banos et al., 2018).

Fungal community composition identified by the ITS2 region primer set

A total of 8,751 fungal OTUs (clustered at 97% similarity level) were identified from our metabarcoding survey targeting the ITS2 region. Ascomycota and Basidiomycota dominated all the samples and constituted, on average, 81.6% and 4.4% of the fungal community, respectively. Unclassified fungi made up an average of 11.0% (ranging between 0.0% and 80.4%) of all fungal reads. The composition and diversity of the fungal community assessed by ITS2 sequencing varied between sample types (sediment and surface water), stations, and sampling dates (Figure 3). Ascomycota dominated most samples from both the sediment and surface water, whereas Basidiomycota dominated in only one Thousand Acre sediment sample in August 2021 (Figure 3). The relative abundance of early diverging fungi was low (< 2.9% on average). Cryptomycota were more prevalent in the sediment than in the surface water, and their relative abundance reached 15.6% in November 2021 in Thousand Acre sediments. Chytridiomycota were present in all the samples, with an average relative abundance of 1.0% in the sediment and 1.7% in the surface water (Figure 3). Other early diverging phyla (Mucoromycota, Zoopagomycota, Olpidiomycota, and Neocallimastigomycota) were detected, but all were present at extremely low levels (< 0.07% relative abundance on average).

Most marine studies using primers targeting the ITS2 region (Table 1) consistently detected Ascomycota, Basidiomycota, Chytridiomycota, and a notable proportion of unclassified fungi, each representing over 1% relative abundance (Da Silva et al., 2022; Duan et al., 2018; Duan et al., 2021; Kearns et al., 2019; W. Li et al., 2019; Peng & Valentine, 2021; Polinski et al., 2019; Retter et al., 2019; M. Wang et al., 2023; Zhang et al., 2015). These taxa dominate marine fungal communities, reflecting the high prevalence of Dikarya (Ascomycota and Basidiomycota) across a range of ecosystems, whereas Chytridiomycota typically represents a smaller but consistent component. The substantial proportion of unclassified fungi underscores the complexity and novelty within marine fungal communities, revealing limitations in current databases to fully resolve this diversity (Amend et al., 2019). Studies targeting the ITS1 region (Table 1) have identified more Zoopagomycota than those using the ITS2 primer set (Banchi et al., 2024; Chrismas et al., 2023; M. Li et al., 2023; W. Li et al., 2016; Sun Shan et al., 2017), suggesting that the choice of the ITS primer set can significantly influence the detection of certain fungal groups. However, similar to the ITS2 primer set, the majority of the fungal community identified by the ITS1 primers included Dikarya, Chytridiomycota, and unclassified fungi (Table 1) (Banchi et al., 2024; Cheung et al., 2018; Chrismas et al., 2023; M. Li et al., 2023; W. Li et al., 2016; Orsi et al., 2022; Posadas et al., 2024; Su et al., 2021; Sun Shan et al., 2017; Vargas-Gastélum et al., 2019; Y. Wang et al., 2018; Z. Wang et al., 2019). This highlights the dominance of these groups across different ITS markers, although subtle differences in community composition may emerge depending on the primer region targeted. A previous study using the ITS2 primer set in salt marsh sediment reported a similar distribution of Ascomycota, Basidiomycota, Chytridiomycota, and unclassified fungi compared with our findings (Kearns et al., 2019), but this study did not detect Cryptomycota or other early diverging fungal lineages, which are increasingly recognized as important yet often overlooked members of marine ecosystems. The absence of these taxa could be attributed to primer bias or environmental variation, raising important considerations regarding the completeness of fungal community assessments via specific primer sets such as ITS2.

Table 1: The presence of marine fungal phyla in studies from coastal and open oceans (water and sediment) using different primer sets. Three asterisks indicate that the relative abundance of the respective fungal phylum is greater than 10% on average across all samples. Two asterisks indicate that the relative abundance of the respective fungal phylum is less than 10% and greater than 1% on average across all samples. One asterisk indicates that the relative abundance of the respective fungal phylum is less than 1% and greater than 0% on average across all samples. The Picard 2017 study relative abundance was based on the analyses we performed (Supplementary Figure S5).

Study	Primer	Dikarya	Blastocladio- mycota	Chytridio- mycota	Crypto- mycota	Mucoro- mycota	Olpidio- mycota	Neocallimastigo- mycota	Zoopago- mycota	Unclassified Fungi
This Study	28S	***	**	***		**		**	***	*
	ITS2	***		**	**	*	*	*	*	***
	18S	***
Abdel-Wahab et al., 2021	28S	***		**		***		***	***
Picard, 2017	28S	***	**	***		**		**	***
French et al., 2024	ITS2	***		***	*		*			***
French et al., 2024	28S	***	*	**	*	**			*	**
Banchi et al., 2024	ITS1	***		**					*	**
Cheung et al., 2018	ITS1	***		*
Chrismas et al., 2023	ITS1	***								***
W. Li et al., 2016	ITS1	***		*		*			**	***
M. Li et al., 2023	ITS1	***		**				*	***	***
Orsi et al., 2022	ITS1	***
Posadas et al., 2024	ITS1	***								***
Su et al., 2021	ITS1	***		*			*			***
Sun Shan et al., 2017	ITS1	***		*					**	***
Vargas-Gastélum et al., 2019	ITS1	***		*		*				***
Y. Wang et al., 2018	ITS1	***		**	*	*				***
Z. Wang et al., 2019	ITS1	***						*		***
Da Silva et al., 2022	ITS2	***								***
Kearns et al., 2019	ITS2	***		**						**
W. Li et al., 2019	ITS2	***		*		*				***
Peng & Valentine, 2021	ITS2	***		**						***
Polinski et al., 2019	ITS2	***		*						***
Retter et al., 2019	ITS2	***		**	*	*			*	***
M. Wang et al., 2023	ITS2	***		***						***
Zhang et al., 2015	ITS	***		*					*	***
Duan et al., 2018	ITS1 & ITS2	***		***		*
Duan et al., 2021	ITS1 & ITS2	***								***
Sen et al., 2022	18S	***		*	**					*
	ITS1	***		*	*					***
	ITS2	***		*		**				*
Banos et al., 2018	18S	***		***	**	*			**	*
Hassett et al., 2017	18S	***		***						**
Hassett et al., 2020	18S	**		**						***
Priest et al., 2021	18S	***		**	***					**
Rojas-Jimenez et al., 2019	18S	***	*	**	**	*			*

Fungal community composition identified by the 28S LSU region primer set

A total of 3,821 fungal OTUs (clustered at 97% similarity level) were identified from our metabarcoding survey targeting the D1 region of the large subunit rRNA. Across nearly all seasons and sample types (water and sediment), the 28S rRNA gene revealed pronounced differences in fungal community composition between salt and brackish marshes. In the salt marsh sediment samples, Ascomycota was consistently predominant and consisted of, on average, 80.0% of the salt marsh fungal community (Figure 4). Various Sordariomycetes dominated the sediment samples (Supplementary Figures S2 & S3), whereas Capnodiales and Pleosporales (two Dothideomycetes) dominated the surface water samples (Supplementary Figures S2 & S4). In the brackish marsh, while Ascomycota was still the dominant phylum (37.6% on average), Zoopagomycota and Chytridiomycota accounted for 23.8% and 20.5% of the fungal community, respectively (Figure 4). Unclassified fungi made up an average of 0.4% (ranging between 0.0% and 3.1%) of all fungal reads. Zoopagomycota (mostly Entomophthorales, see Supplementary Figure S11) were more prevalent in the sediment and surface water in the summer of 2020 than in the winter of 2021, and their relative abundance reached 52.3% in August 2020 in Thousand Acre surface water. Chytridiomycota were present in most samples, with an average relative abundance of 12.1% in the sediment and 8.5% in the surface water (Figure 4). Blastocladiomycota (mostly Catenariaceae, see Supplementary Figure S13) was predominant in the brackish marsh water column, with a relative abundance of 10.4%, and was present in most samples, with an average relative abundance of 3.1%. Other early diverging phyla (Mucoromycota and Neocallimastigomycota) were detected, but all were present at low levels (2.5% relative abundance on average).

A very small number of studies investigating marine fungal communities have utilized the 28S primer set, highlighting a gap in the literature (Abdel-Wahab et al., 2021; French et al., 2024; Picard, 2017) (Table 1). A previous survey of fungal diversity targeting 28S rRNA in coastal sediments approximately three hundred kilometers away in North Carolina revealed similar fungal community compositions (Picard, 2017). Given the similar physical environments between the sampling sites in our study and those in Picard (2017), the similar results in terms of fungal diversity suggest that surveys targeting 28S rRNA are likely reproducible. By applying our 28S analysis pipeline to a previous survey of fungal diversity in coastal sediments from North Carolina (Picard, 2017) via the updated RDP 2.14 classifier (Wang & Cole, 2024), we successfully classified all fungal OTUs at the phylum level (Supplementary Figure S5). Among the studies that have employed this marker, taxa from a range of fungal phyla, including Chytridiomycota, Mucoromycota, Zoopagomycota, and Dikarya, were consistently identified, indicating that the 28S region can capture broad phylogenetic diversity. Interestingly, Blastocladiomycota, a lesser-known group of early diverging fungi, was detected in two (French et al., 2024; Picard, 2017 results from our pipeline) of the three studies focused on marine environments. These findings suggest that the 28S primer set has the potential to detect not only common marine fungal taxa but also more rare and ecologically significant groups. Additionally, these studies reported that fewer than 10% of fungal reads were unclassified (Abdel-Wahab et al., 2021; French et al., 2024; Picard, 2017), which contrasts with the often higher proportion of unclassified reads observed with other ribosomal markers in marine fungal surveys. This relatively low percentage of unclassified reads highlights the robustness of the 28S primer set in accurately assigning taxonomic identities, further supporting its utility in marine fungal research.

Variation in Fungal Diversity across Primer Sets

The fungal diversity measured with the 28S primer set was significantly greater in both sediment and surface water than that measured with the ITS2 primer set, as indicated by Hutcheson t tests (p < 0.01; Figure 5A & B, Supplementary Table S4). In the sediment samples, the mean Shannon diversity for the 28S primer set was 0.9 ± 0.9, whereas the ITS2 primer set yielded a mean of 0.6 ± 0.7. In surface water, the mean Shannon diversity for the 28S primer set was 0.8 ± 0.8, whereas it was 0.2 ± 0.5 for the ITS2 primer set. Additionally, Simpson diversity was significantly greater with the 28S primer set in sediment (Hutcheson t tests, p < 0.01; Figure 5C), whereas the ITS2 primer set exhibited greater Simpson diversity in surface water (p < 0.01; Figure 5D). Evenness in sediment was similar for both primer sets (Figure 5E), but in surface water, the 28S primer set demonstrated greater evenness than the ITS2 primer set did (Figure 5F).

The ITS2 primer set failed to detect Blastocladiomycota or Zoopagomycota, which were present in the brackish marsh at average abundances of 7.1% and 23.8%, respectively, as revealed by the 28S primer set (Supplementary Table S5). Furthermore, the 28S primer set identified a greater proportion of Ascomycota in both the brackish (37.6%) and salt marshes (79.9%) than did the ITS2 set (26.3% in the brackish and 50.6% in the salt marshes). Compared with the ITS2 primer set, the 28S primer set also consistently detected a greater number of early diverging fungi and fewer unclassified fungi (Figure 4, Supplementary Table S6). Although early diverging fungi often constitute less than half of the total fungal reads in marine environments, their ecological importance is substantial, contributing to nutrient cycling, decomposition, and symbiotic interactions with marine organisms such as algae and invertebrates (Picard, 2017). The underestimation of these lineages in previous studies is frequently attributed to the limitations of culture-based methods and biases in molecular techniques, particularly when targeting ribosomal RNA regions that do not fully capture the diversity of early diverging fungi (Picard, 2017). While the ITS2 primer set identified 8,751 fungal OTUs—more than twice as many as did the 28S primer set—it predominantly detected Ascomycota and Basidiomycota, with an average of 11.0% of all fungal reads and 19.8% of ITS2 OTUs unclassified (Supplementary Table S6). Previous studies utilizing long 18S-ITS-28S sequences suggest that many undescribed taxa belong to early diverging fungal lineages (Tedersoo et al., 2017, 2020). Additionally, marine fungal studies employing ITS primer sets have reported that 20% to 80% of fungal OTUs are unclassified at the phylum level (Da Silva et al., 2022; W. Li et al., 2016, 2019; Retter et al., 2019). These discrepancies may stem from limitations in ITS-region databases (Raja et al., 2017), as ITS primer sets were developed primarily from terrestrial sequences, resulting in a bias toward terrestrial Dikarya (Amend et al., 2019).

The 28S primer set provides several advantages, including broader taxonomic coverage (Supplementary Table S6). It consistently detects not only dominant fungal groups, such as Ascomycota, Basidiomycota, and Chytridiomycota but also early diverging taxa, such as Zoopagomycota and Blastocladiomycota, which are often overlooked by ITS2 primer sets (Table 1). This broader phylogenetic scope is critical for capturing the full extent of fungal diversity in marine ecosystems, where early diverging lineages play essential ecological roles. Additionally, studies utilizing the 28S primer set report a lower proportion of unclassified reads—less than 10%—than the higher percentages observed with ITS primer sets, likely due to biases in databases that are more focused on terrestrial fungi (Abdel-Wahab et al., 2021; French et al., 2024; Picard, 2017). Moreover, compared with the ITS2 primer set, the 28S primer set consistently yielded higher diversity indices in the sediment and water samples, demonstrating significantly greater Shannon and Simpson diversity metrics (Figure 5). These findings indicate that the 28S region is more effective at detecting fungal taxa across various habitats and environmental conditions, including salinity gradients and seasonal shifts.

The 28S primer set in our study had limitations, including its inability to detect members of Cryptomycota, a fungal group of increasing interest in marine ecosystems (Y. Wang et al., 2018). In contrast, the ITS2 primer set successfully identified Cryptomycota, with relative abundances of 3.1% in brackish samples and 0.9% in salt marsh samples (Supplementary Table S5). These findings suggest that the 28S region may not be optimal for capturing the full diversity of Cryptomycota. Consequently, when Cryptomycota is a specific target, the ITS2 region should be prioritized for fungal community assessments. Similarly, the 28S primer set did not detect Olpidiomycota, another early diverging fungal lineage; however, the ITS2 primer set identified Olpidiomycota in low relative abundance (0.0001%), demonstrating its potential to capture even rare fungal taxa. These findings highlight the importance of considering the limitations of each primer set when specific fungal groups are studied. Despite the more comprehensive assessment of marine fungal diversity offered by the 28S primer set, the detection of specific groups such as Cryptomycota and Olpidiomycota remains limited.

The fractions of fungal reads generated by the ITS2 and 28S primer sets were comparable (nearly half, Supplementary Figure S6), as were the proportions of the total number of OTUs (Supplementary Table S2). Notably, despite the 28S primer set being designed for fungal specificity (Tedersoo et al., 2015), approximately half of the reads were nonfungal. This observation underscores a critical consideration for experimental design when targeting fungal communities. Specifically, experimental designs including sequencing depth per sample should consider allocating resources to generate twice the number of reads necessary for fungal-specific analysis to adequately account for nontarget amplification.

Comparison of the ITS2 and 28S Primer Sets Using ZymoBIOMICS Standards

When the ZymoBIOMICS DNA standard community is evaluated via the ITS2 and 28S primer sets, the expected relative abundance ratio of Saccharomyces cerevisiae to Cryptococcus neoformans should be 1:1. This ratio was closely reflected in the community composition obtained via the 28S primer set (Supplementary Figure S1A). However, when the community was analyzed with the ITS2 primer set, the relative abundance of C. neoformans was 64.4% (Supplementary Figure S1B). Although the analysis was based on a single replicate, this discrepancy suggests that the ITS2 primer may not perform as effectively as the 28S primer in accurately representing the relative abundances of fungal taxa.

For the ZymoBIOMICS Community (cells) Standard, where the same expected 1:1 ratio of S. cerevisiae to C. neoformans should apply, neither the 28S nor ITS2 primer sets resulted in C. neoformans reaching 50% relative abundance (Supplementary Figure S1). This deviation may be caused by a lower rRNA copy number in C. neoformans than in S. cerevisiae within the cell mixture provided by ZymoBIOMICS. Alternatively, the cell wall of C. neoformans may present greater resistance to lysis due to the presence of a large polysaccharide capsule, which has been reported for this species (Bolano et al., 2001). These findings suggest that our study, along with other metabarcoding surveys of environmental samples, could exhibit a community compositional bias against microorganisms recalcitrant to cell lysis (Feinstein et al., 2009; Frostegård et al., 1999).

A deeper look into the fungal community composition identified by the 28S LSU primer set

To investigate the phylogenetic diversity of fungi in the surface water and sediments of North Inlet–Winyah Bay, a maximum-likelihood tree was constructed using the 3,821 fungal OTUs identified by the 28S primer set, along with two outgroup sequences from the SAR supergroup (Frustulia sp. and Amphora commutata; Figure 6). Over half (51.0%) of the fungal OTUs were classified as early diverging fungi via the RDP naïve Bayesian classifier (Wang & Cole, 2024). Among these early diverging lineages, 22.7% were identified as Chytridiomycota, 15% as Zoopagomycota, 6.9% as Blastocladiomycota, 3.7% as Neocallimastigomycota, and 2.7% as Mucoromycota (Figure 6). The remaining 48.8% of the OTUs were classified as Dikarya fungi, with Ascomycota accounting for 37.5% and Basidiomycota accounting for 11.3% (Figure 6). Most Ascomycota OTUs were from the classes Dothideomycetes (13.7%) and Sordariomycetes (11.7%). Additionally, seven fungal OTUs that could not be classified via the RDP classifier clustered with Ascomycota (Figure 6).

The maximum-likelihood phylogenetic tree supported the monophyly of Ascomycota and Basidiomycota, confirming their shared descent from a common ancestor. These results align with those of previous studies (Hibbett et al., 2018; James et al., 2006; Spatafora et al., 1998), which revealed that Ascomycota and Basidiomycota form a clade of Dikarya fungi, diverging from other early diverging fungal lineages (Hibbett et al., 2018). In line with earlier findings (James et al., 2006; O’Donnell et al., 2001; Porter et al., 2011; White et al., 2006), over half of the sequences identified in this study were from early diverging fungi, many of which are polyphyletic. Research has shown that early diverging fungi in marine environments can represent between 1.7% and 72% of fungal reads, primarily from Chytridiomycota or Mucoromycota (Chrismas et al., 2023; Picard, 2017; Richards et al., 2015).

Fungal lineages drive community composition differences across salinity gradients

There were significant differences in fungal community composition between salinity ranges in both sediment and surface water (Figure 7). These differences were observed in the sediment between high (>25 ppt) and low (<10 ppt) salinities, as well as between high and middle (10–25 ppt) salinities. In surface water, differences were found between middle and low salinities and between high and moderate salinities (Supplementary Table S7 & S8, Supplementary Figure S7). Fungal community composition also varied significantly between sampling stations and sample types (sediment vs. surface water; Supplementary Table S7) for the 28S primer sets (Figure 7).

The 28S primer set revealed that Ascomycota was the dominant phylum in most of the salt marsh samples, whereas early diverging fungi were more abundant in the brackish marsh samples. Salinity was clearly a key factor driving variations in fungal community composition (Figure 7), which is consistent with previous studies showing the impact of salinity on fungal diversity and community structure in marine and estuarine environments (Mohamed & Martiny, 2011; Rojas-Jimenez et al., 2019; Velez et al., 2013). Salinity acts as a selective environmental filter, favoring fungi with specific physiological adaptations (Mohamed & Martiny, 2011), thereby reducing overall fungal diversity while promoting the dominance of salt-tolerant species. This results in a fungal community that is often less diverse but highly specialized, reflecting the challenging conditions of high-salinity environments. Significant differences in diversity were identified between high salinity levels (>25 ppt) and low salinity levels (<10 ppt) in sediment, as well as between mid-range salinity levels (10–25 ppt) and low salinity levels in surface water (Supplementary Figure S7, Supplementary Table S9).

Early diverging fungi play crucial roles in driving differences across salinity levels (Figure 8). OTUs from early diverging fungi, such as Zoopagomycota (Basidiobolus), Chytridiomycota (Spizellomyces, Entophlyctis, Olpidium, and Oedogoniomyces), Mucoromycota (Diversispora), and Blastocladiomycota (Catenomyces), along with Basidiomycota, were more differentially abundant in the brackish marsh sites than were Dikarya fungi (Supplementary Table S8; Figure 8). Moreover, Sordariomycetes and Dothideomycetes (Ascomycota) were more differentially abundant in the salt marsh sites (Figure 8; Supplementary Figure S2). OTUs, such as Basidiobolus, Oedogoniomyces, Cryptococcus, and Magnaporthe, which are abundant in both environments, were differentially abundant in both surface water and sediment. These findings suggest that salinity is a critical factor influencing the ecological dynamics of fungal communities. Below, we discuss the ecological implications of this salinity divide for fungal communities.

Dikarya

Ascomycota is the largest fungal phylum, with over 64,000 known species (Wijayawardene et al., 2017). It dominates most marsh ecosystems, contributing up to 95% of the fungal community composition (Calabon et al., 2021; J. Li et al., 2022; Mohamed & Martiny, 2011; Walker & Campbell, 2010), and is a predictor of carbon, nitrogen, and phosphorus cycling processes in salt marshes (J. Li et al., 2022). Most Ascomycota fungi are metabolically versatile saprotrophs that secrete extracellular enzymes to break down complex organic matter (Várnai et al., 2014). Mycosphaerella sp., Aniptodera sp., Ceriosporopsis sp., Nectria sp., Kirschsteiniothelia sp., Pyrenochaeta sp., and Mycosphaerella sp., which are more abundant in brackish marshes, have been linked to the early stages of Spartina leaf decay (Al-Nasrawi & Hughes, 2012; Buchan et al., 2003; Lyons et al., 2010). On the other hand, Ceriosporopsis sp., Lulworthia sp., Ophiosphaerella sp., and Ramulispora sp., which are also saprotrophic, were more abundant in the salt marsh, indicating their ability to adapt to saline conditions.

Plant diversity plays a key role in shaping the abundance and richness of pathotrophic fungi. In our study, we detected a greater abundance of pathotrophic fungi, such as Magnaporthe sp., Phaeoacremonium sp., Pithomyces sp., and Ramulispora sp., in brackish marshes than in salt marshes. This difference is likely due to the greater plant diversity in brackish marshes, which offers a wider range of potential hosts for these fungi. In contrast, the monoculture of Spartina alterniflora in salt marshes limits fungal host availability, resulting in lower pathotrophic fungal abundance (Benítez et al., 2013; Chaudhary et al., 2018; Mitchell & Power, 2003; Zogg et al., 2018). Previous studies have consistently shown that diverse plant communities create more opportunities for pathogen‒host interactions, increasing the diversity and richness of pathogens, including fungi, across ecosystems (Benítez et al., 2013; Mitchell & Power, 2003). This highlights the strong link between plant diversity and microbial community structure, particularly in estuarine environments.

Basidiomycota, the second-largest fungal phylum, includes approximately 40,000 species, including mushrooms, plant pathogens, symbiotic fungi, and saprotrophic fungi (He & Zhao, 2021). In our study, Basidiomycota were present in all the salt and brackish marsh samples, particularly in the sediments from June 2020 to November 2021. Among the Basidiomycota, Agaricomycetes was the dominant class across both the sediment and surface water samples during most seasons (Supplementary Figure S8). Agaricomycetes are well known for their ability to degrade plant material, particularly lignin and cellulose, which are the toughest components of plant cell walls (Floudas et al., 2012). This class includes a wide range of saprotrophic fungi, such as mushrooms and polypores, that play crucial roles in nutrient cycling by breaking down organic matter (Floudas et al., 2012). In salt and brackish marshes, where plant detritus is abundant, Agaricomycetes likely contribute significantly to the decomposition of plant material, thus driving carbon cycling and organic matter turnover. All the differentially abundant genera within the Basidiomycota phylum were from the Agaricomycetes class (Figure 8). However, in November 2021, Tremellomycetes became the dominant class in sediments at Clambank and Oyster Landings, indicating possible seasonal shifts in fungal community composition (Supplementary Figure S8). Despite this, the consistent presence of Agaricomycetes highlights their essential ecological function as decomposers in these marsh ecosystems.

Cryptococcus sp. was more abundant in salt marsh sediments, whereas Datronia sp. and Rhizopogon sp. were more abundant in brackish marsh sediments (Figure 8). Cryptococcus sp. and Datronia sp. are saprotrophic fungi that degrade leaf and woody material (Nguyen et al., 2016; Põlme et al., 2020). Rhizopogon sp., a symbiotrophic fungus associated with Pinaceae species, likely originates from nearby coastal pine forests (dominated by Pinus taeda) (Nguyen et al., 2016; Põlme et al., 2020; Allen et al., 2014). The presence of Rhizopogon and Datronia DNA in brackish marsh sediments may represent spores from nearby pine trees. Additionally, Malasseziaceae, commonly found in marine environments (Amend et al., 2019), was detected at low abundance across all primers used in this study (average relative abundance: 0.0004% from ITS, 0.32% from 28S, and 0.33% from 18S; Supplementary Figures S9 & S10).

Early diverging fungi

Zoopagomycota was the dominant early diverging fungal phylum in many brackish marsh samples, particularly in June and August 2020, in both sediment and surface water (Figure 4). Entomophthorales was the dominant Zoopagomycota order in North Inlet–Winyah Bay (Supplementary Figure S11). Zoopagomycota are nonzoosporic fungi (i.e., they do not produce free-swimming spores) (Naranjo‐Ortiz & Gabaldón, 2019), and most are animal parasites, although some are saprotrophs (Põlme et al., 2020). We found five Basidiobolus OTUs that were more differentially abundant in brackish marsh sediment than in salt marsh sediment (Figure 8). Basidiobolus sp. are saprotrophic fungi known to degrade plant material (Nguyen et al., 2016; Põlme et al., 2020), and Spartina alterniflora and Juncus roemerianus, which dominate brackish marshes (H. Li et al., 2022), likely provide organic matter for these fungi. Previous studies have also revealed Basidiobolus sp. to be dominant in North Carolina coastal sediments (Picard, 2017).

Chytridiomycota were present in all samples collected from NI-WB but were particularly prominent in the sediment of the brackish marsh in August 2020 and in the surface water of the brackish marsh in June 2020 and February 2021 (Figure 4; Supplementary Figure S12). Chytridiomycota are abundant in many coastal ecosystems (Debeljak & Baltar, 2023) and are commonly recognized as algal parasites, particularly diatoms and Cyanobacteriota (Rasconi et al., 2009; Sønstebø & Rohrlack, 2011). The samples taken in June and August 2020, shortly after algal blooms (Figure 2), likely provided a host for these Chytridiomycota. Entophlyctis sp. and Olpidium sp. are two genera known to parasitize algae (Nguyen et al., 2016; Põlme et al., 2020). Two Entophlyctis sp. OTUs were more differentially abundant in the brackish marsh, whereas one Olpidium sp. OTU was more abundant in the salt marsh (Figure 8). Spizellomyces sp. is a saprotrophic fungus known for degrading pollen (Nguyen et al., 2016; Põlme et al., 2020), with approximately 29% of known Chytridiomycota being pollen saprotrophs (Põlme et al., 2020). One Spizellomyces sp. OTU was more differentially abundant in the brackish marsh sediment. Oedogoniomyces sp., another saprotrophic fungus known for degrading plant and algal material (Nguyen et al., 2016; Põlme et al., 2020), was also more differentially abundant in the brackish marsh than in the salt marsh (Figure 8). Brackish marshes support more diverse plant ecosystems than salt marshes do (Allen et al., 2014; Baden III et al., 1975; H. Li et al., 2022; Stalter et al., 2021; Stalter & Baden, 1994), which results in a wider variety of organic matter inputs (e.g., decaying leaves, roots, pollen, and other plant debris). Saprotrophic fungi, such as Spizellomyces sp. and Oedogoniomyces sp., thrive on organic matter, including plant and algal material. A more diverse plant community produces a richer and more varied substrate for these fungi to degrade, leading to their increased abundance in the brackish marsh.

Blastocladiomycota were present in most of the sediment and surface water samples from NI-WB, with a particularly high relative abundance in the brackish marsh surface water in August 2020 and February 2021 (Figure 4). The family Catenariaceae was the dominant Blastocladiomycota group identified in NI-WB (Supplementary Figure S13). Blastocladiomycota are primarily found in soil and freshwater environments as saprotrophs and parasites of invertebrates, plants, algae, oomycetes, and other blastoclads (Powell, 2016). Fewer than 200 species of Blastocladiomycota have been documented (Money, 2016). Three Catenomyces sp. OTUs were more differentially abundant in the sediment and surface water of the brackish marsh than in those of the salt marsh. Catenomyces sp. are saprotrophs that degrade plant material in soil ecosystems (Nguyen et al., 2016; Põlme et al., 2020). Since Blastocladiomycota are found primarily in freshwater systems, we hypothesize that they may outcompete Ascomycota for organic matter in low-salinity environments.

Mucoromycota were highly abundant in brackish marsh sediment in February 2021 (Figure 4). Diversisporaceae was the dominant Mucoromycota family in the brackish sediment in February 2021 (Supplementary Figure S14), whereas Mucoraceae and Endogonaceae dominated the remaining samples (Supplementary Figure S14). One Diversispora sp. OTU was more differentially abundant in the brackish marsh sediment in February 2021 (Figure 8). Diversispora sp. are endosymbiotrophs that form symbiotic relationships with plant roots (Nguyen et al., 2016; Põlme et al., 2020). Mucoromycota are primarily soil saprotrophs, comprising approximately 80% of the group (Põlme et al., 2020), although 1.5% are known to be endophytes (Põlme et al., 2020).

This study revealed that the primer set targeting the D1 region of the 28S large subunit rRNA was able to identify a more diverse fungal community, including many early diverging fungi, in salt and brackish marsh surface water and sediment samples compared with the diversity detected via the commonly employed ITS2 primer set. This difference is likely due to the lack of early diverging fungal reference sequences in the ITS2 database (Banos et al., 2018) and the bias of the ITS2 primer set toward terrestrial Dikarya (Amend et al., 2019). This study represents a significant advancement in our understanding of marine fungal diversity by demonstrating that the 28S large-subunit rRNA primer set can reveal a richer community of fungi, particularly early diverging lineages, in salt and brackish marshes. By revealing that these early diverging fungi play crucial roles in the ecological dynamics of these habitats, our research underscores the necessity of integrating diverse molecular markers in fungal biodiversity assessments. Using the 28S primer region, we determined that early diverging fungi were key contributors to the differences observed between the brackish and salt marshes. Our findings also highlight the previously overlooked saprotrophic potential of early diverging fungi in marine environments. Therefore, the 28S primer set is likely preferable for single-marker metabarcoding studies aimed at revealing the diversity of early diverging fungi in marine environments. Thus, the use of the 28S primer set could improve the accuracy of future metabarcoding studies focused on early diverging fungi while still considering the importance of other regions, such as ITS2, for a more comprehensive understanding of marine fungal communities. Nevertheless, sequencing both the 28S and ITS2 regions is recommended for studies that also consider Cryptomycota and Olpidiomycota, which were not detected by the 28S primer set in our study.

Data availability: Raw reads generated in this study are available at the National Center for Biotechnology Information (NCBI) under BioProject PRJNA1158776. Below is the reviewer-only link: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1158776

Competing interests: The authors declare that they have no competing interests.

Funding: This work was supported by the U.S. National Science Foundation (DEB-2303089) and a University of South Carolina Advanced Support for Innovative Research Excellence (ASPIRE) grant awarded to Xuefeng Peng.

Author Contributions: Madeleine A. Thompson: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing; Bruce W. Pfirrmann: Methodology, Project administration, Resources, Writing – review & editing; William H.J. Strosnider: Methodology, Project administration, Resources, Writing – review & editing; James L. Pinckney: Methodology, Resources, Writing – review & editing; Xuefeng Peng: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing.

Acknowledgments: We thank Birch Lazo-Murphy for assisting with sample collection. This is contribution number XXXX of the Belle W. Baruch Institute for Marine and Coastal Sciences of the University of South Carolina.

Abdel-Wahab, M. A., Bahkali, A. H., Elgorban, A. M., & Jones, E. B. G. (2021). High-throughput amplicon sequencing of fungi and microbial eukaryotes associated with the seagrass Halophila stipulacea (Forssk.) Asch. From Al-Leith mangroves, Saudi Arabia. Mycological Progress, 20(10), 1365–1381. https://doi.org/10.1007/s11557-021-01744-2
Allen, D., Allen, W., Feller, R., & Plunket, J. (2014). Site profile of the North Inlet–Winyah Bay National Estuarine Research Reserve. North Inlet–Winyah Bay National Estuarine Research Reserve. Georgetown, SC, 432.
Al-Nasrawi, H. G., & Hughes, A. R. (2012). Fungal diversity associated with salt marsh plants Spartina alterniflora and Juncus roemerianus in Florida. Jordan Journal of Biological Sciences, 5(4).
Amend, A., Burgaud, G., Cunliffe, M., Edgcomb, V. P., Ettinger, C. L., Gutiérrez, M. H., Heitman, J., Hom, E. F. Y., Ianiri, G., Jones, A. C., Kagami, M., Picard, K. T., Quandt, C. A., Raghukumar, S., Riquelme, M., Stajich, J., Vargas-Muñiz, J., Walker, A. K., Yarden, O., & Gladfelter, A. S. (2019). Fungi in the Marine Environment: Open Questions and Unsolved Problems. mBio, 10(2), e01189-18. https://doi.org/10.1128/mBio.01189-18
Baden III, Batson, W. T., & Stalter, R. (1975). Factors Affecting the Distribution of Vegetation of Abandoned Rice Fields, Georgetown Co., South Carolina. Castanea, 40(3), 171–184. JSTOR.
Banchi, E., Manna, V., Muggia, L., & Celussi, M. (2024). Marine Fungal Diversity and Dynamics in the Gulf of Trieste (Northern Adriatic Sea). Microbial Ecology, 87(1), 78. https://doi.org/10.1007/s00248-024-02394-z
Banos, S., Lentendu, G., Kopf, A., Wubet, T., Glöckner, F. O., & Reich, M. (2018). A comprehensive fungi-specific 18S rRNA gene sequence primer toolkit suited for diverse research issues and sequencing platforms. BMC Microbiology, 18(1), 190. https://doi.org/10.1186/s12866-018-1331-4
Bärlocher, F., & Boddy, L. (2016). Aquatic fungal ecology – How does it differ from terrestrial? Aquatic Fungi, 19, 5–13. https://doi.org/10.1016/j.funeco.2015.09.001
Benítez, M.-S., Hersh, M. H., Vilgalys, R., & Clark, J. S. (2013). Pathogen regulation of plant diversity via effective specialization. Trends in Ecology & Evolution, 28(12), 705–711. https://doi.org/10.1016/j.tree.2013.09.005
Bolano, A., Stinchi, S., Preziosi, R., Bistoni, F., Allegrucci, M., Baldelli, F., Martini, A., & Cardinali, G. (2001). Rapid methods to extract DNA and RNA from Cryptococcus neoformans. FEMS Yeast Research, 1(3), 221–224. https://doi.org/10.1111/j.1567-1364.2001.tb00037.x
Buchan, A., Newell, S. Y., Butler, M., Biers, E. J., Hollibaugh, J. T., & Moran, M. A. (2003). Dynamics of bacterial and fungal communities on decaying salt marsh grass. Applied and Environmental Microbiology, 69(11), 6676–6687.
Calabon, M. S., Jones, E. G., Promputtha, I., & Hyde, K. D. (2021). Fungal biodiversity in salt marsh ecosystems. Journal of Fungi, 7(8), 648.
Cameron, E. S., Schmidt, P. J., Tremblay, B. J.-M., Emelko, M. B., & Müller, K. M. (2021). Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities. Scientific Reports, 11(1), 22302. https://doi.org/10.1038/s41598-021-01636-1
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., Huttley, G. A., Kelley, S. T., Knights, D., Koenig, J. E., Ley, R. E., Lozupone, C. A., McDonald, D., Muegge, B. D., Pirrung, M., … Knight, R. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7(5), 335–336. https://doi.org/10.1038/nmeth.f.303
Chaudhary, D. R., Kim, J., & Kang, H. (2018). Influences of Different Halophyte Vegetation on Soil Microbial Community at Temperate Salt Marsh. Microbial Ecology, 75(3), 729–738. https://doi.org/10.1007/s00248-017-1083-y
Cheung, M. K., Wong, C. K., Chu, K. H., & Kwan, H. S. (2018). Community Structure, Dynamics and Interactions of Bacteria, Archaea and Fungi in Subtropical Coastal Wetland Sediments. Scientific Reports, 8(1), 14397. https://doi.org/10.1038/s41598-018-32529-5
Chrismas, N., Allen, R., Allen, M. J., Bird, K., & Cunliffe, M. (2023). A 17-year time-series of fungal environmental DNA from a coastal marine ecosystem reveals long-term seasonal-scale and inter-annual diversity patterns. Proceedings of the Royal Society B: Biological Sciences, 290(1992), 20222129. https://doi.org/10.1098/rspb.2022.2129
Comeau, A. M., Vincent, W. F., Bernier, L., & Lovejoy, C. (2016). Novel chytrid lineages dominate fungal sequences in diverse marine and freshwater habitats. Scientific Reports, 6(1), 30120.
Crain, C. M. (2007). Shifting nutrient limitation and eutrophication effects in marsh vegetation across estuarine salinity gradients. Estuaries and Coasts, 30(1), 26–34. https://doi.org/10.1007/BF02782964
Czapla, K. M., Anderson, I. C., & Currin, C. A. (2020). Net Ecosystem Carbon Balance in a North Carolina, USA, Salt Marsh. Journal of Geophysical Research: Biogeosciences, 125(10), e2019JG005509. https://doi.org/10.1029/2019JG005509
d’Entremont, T. W., Migicovsky, Z., López-Gutiérrez, J. C., & Walker, A. K. (2021). Saltmarsh rhizosphere fungal communities vary by sediment type and dominant plant species cover in Nova Scotia, Canada. Environmental Microbiology Reports, 13(4), 458–463. https://doi.org/10.1111/1758-2229.12904
Da Silva, M. K., De Souza, L. M. D., Vieira, R., Neto, A. A., Lopes, F. A. C., De Oliveira, F. S., Convey, P., Carvalho-Silva, M., Duarte, A. W. F., Câmara, P. E. A. S., & Rosa, L. H. (2022). Fungal and fungal-like diversity in marine sediments from the maritime Antarctic assessed using DNA metabarcoding. Scientific Reports, 12(1), 21044. https://doi.org/10.1038/s41598-022-25310-2
Debeljak, P., & Baltar, F. (2023). Fungal Diversity and Community Composition across Ecosystems. Journal of Fungi, 9(5), 510. https://doi.org/10.3390/jof9050510
Drake, K., Halifax, H., Adamowicz, S. C., & Craft, C. (2015). Carbon Sequestration in Tidal Salt Marshes of the Northeast United States. Environmental Management, 56(4), 998–1008. https://doi.org/10.1007/s00267-015-0568-z
Duan, Y., Xie, N., Song, Z., Ward, C. S., Yung, C.-M., Hunt, D. E., Johnson, Z. I., & Wang, G. (2018). A high-resolution time series reveals distinct seasonal patterns of planktonic fungi at a temperate coastal ocean site (Beaufort, North Carolina, USA). Applied and Environmental Microbiology, 84(21), e00967-18.
Duan Yingbo, Xie Ningdong, Wang Zhao, Johnson Zackary I., Hunt Dana E., & Wang Guangyi. (2021). Patchy Distributions and Distinct Niche Partitioning of Mycoplankton Populations across a Nearshore to Open Ocean Gradient. Microbiology Spectrum, 9(3), e01470-21. https://doi.org/10.1128/Spectrum.01470-21
Edgar, R. C. (2004). MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research, 32(5), 1792–1797. https://doi.org/10.1093/nar/gkh340
Feinstein Larry M., Sul Woo Jun, & Blackwood Christopher B. (2009). Assessment of Bias Associated with Incomplete Extraction of Microbial DNA from Soil. Applied and Environmental Microbiology, 75(16), 5428–5433. https://doi.org/10.1128/AEM.00120-09
Floudas, D., Binder, M., Riley, R., Barry, K., Blanchette, R. A., Henrissat, B., Martínez, A. T., Otillar, R., Spatafora, J. W., Yadav, J. S., Aerts, A., Benoit, I., Boyd, A., Carlson, A., Copeland, A., Coutinho, P. M., de Vries, R. P., Ferreira, P., Findley, K., … Hibbett, D. S. (2012). The Paleozoic Origin of Enzymatic Lignin Decomposition Reconstructed from 31 Fungal Genomes. Science, 336(6089), 1715–1719. https://doi.org/10.1126/science.1221748
French, L. C., Jusino, M. A., Chambers, R. M., & Skelton, J. (2024). Community structure and functional diversity of aquatic fungi are correlated with water quality: Insights from multi-marker analysis of environmental DNA in a coastal watershed. Environmental DNA, 6(3), e576. https://doi.org/10.1002/edn3.576
Frenken, T., Wierenga, J., van Donk, E., Declerck, S. A. J., de Senerpont Domis, L. N., Rohrlack, T., & Van de Waal, D. B. (2018). Fungal parasites of a toxic inedible cyanobacterium provide food to zooplankton. Limnology and Oceanography, 63(6), 2384–2393. https://doi.org/10.1002/lno.10945
Frostegård Åsa, Courtois Sophie, Ramisse Vincent, Clerc Sylvie, Bernillon Dominique, Le Gall Francoise, Jeannin Pascale, Nesme Xavier, & Simonet Pascal. (1999). Quantification of Bias Related to the Extraction of DNA Directly from Soils. Applied and Environmental Microbiology, 65(12), 5409–5420. https://doi.org/10.1128/AEM.65.12.5409-5420.1999
Fuhrman, J., & David Needham, J. F., Erin Fichot, Alma Parada, Yi-Chun Yeh. (2019). Fuhrman Lab 515F-926R 16S and 18S rRNA Gene Sequencing Protocol. Protocols.Io.
Gleason, F. H., Chambouvet, A., Sullivan, B. K., Lilje, O., & Rowley, J. J. L. (2014). Multiple zoosporic parasites pose a significant threat to amphibian populations. Fungal Ecology, 11, 181–192. https://doi.org/10.1016/j.funeco.2014.04.001
Hassett, B. T., Ducluzeau, A.-L. L., Collins, R. E., & Gradinger, R. (2017). Spatial distribution of aquatic marine fungi across the western Arctic and sub-arctic. Environmental Microbiology, 19(2), 475–484. https://doi.org/10.1111/1462-2920.13371
Hassett, B. T., Vonnahme, T. R., Peng, X., Jones, E. B. G., & Heuzé, C. (2020). Global diversity and geography of planktonic marine fungi. 63(2), 121–139. https://doi.org/10.1515/bot-2018-0113
He, M.-Q., & Zhao, R.-L. (2021). Outline of Basidiomycota. In Encyclopedia of Mycology (pp. 310–319). Elsevier. https://doi.org/10.1016/B978-0-12-819990-9.00065-2
Heeger, F., Bourne, E. C., Baschien, C., Yurkov, A., Bunk, B., Spröer, C., Overmann, J., Mazzoni, C. J., & Monaghan, M. T. (2018). Long‐read DNA metabarcoding of ribosomal RNA in the analysis of fungi from aquatic environments. Molecular Ecology Resources, 18(6), 1500–1514.
Hibbett, D. S., Blackwell, M., James, T. Y., Spatafora, J. W., Taylor, J. W., & Vilgalys, R. (2018). Phylogenetic taxon definitions for Fungi, Dikarya, Ascomycota and Basidiomycota. IMA Fungus, 9(2), 291–298. https://doi.org/10.5598/imafungus.2018.09.02.05
James, T. Y., Kauff, F., Schoch, C. L., Matheny, P. B., Hofstetter, V., Cox, C. J., Celio, G., Gueidan, C., Fraker, E., Miadlikowska, J., Lumbsch, H. T., Rauhut, A., Reeb, V., Arnold, A. E., Amtoft, A., Stajich, J. E., Hosaka, K., Sung, G.-H., Johnson, D., … Vilgalys, R. (2006). Reconstructing the early evolution of Fungi using a six-gene phylogeny. Nature, 443(7113), 818–822. https://doi.org/10.1038/nature05110
Jones, E. G. (2011). Fifty years of marine mycology. Fungal Diversity, 50(1), 73–112.
Jones, E. G., Suetrong, S., Sakayaroj, J., Bahkali, A. H., Abdel-Wahab, M. A., Boekhout, T., & Pang, K.-L. (2015). Classification of marine ascomycota, basidiomycota, blastocladiomycota and chytridiomycota. Fungal Diversity, 73, 1–72.
Kearns, P. J., Bulseco-McKim, A. N., Hoyt, H., Angell, J. H., & Bowen, J. L. (2019). Nutrient Enrichment Alters Salt Marsh Fungal Communities and Promotes Putative Fungal Denitrifiers. Microbial Ecology, 77(2), 358–369. https://doi.org/10.1007/s00248-018-1223-z
Lazo-Murphy, B. M., Larson, S., Staines, S., Bruck, H., McHenry, J., Bourbonnais, A., & Peng, X. (2022). Nitrous oxide production and isotopomer composition by fungi isolated from salt marsh sediments. Frontiers in Marine Science, 9, 1098508. https://doi.org/10.3389/fmars.2022.1098508
Letunic, I., & Bork, P. (2021). Interactive Tree Of Life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Research, 49(W1), W293–W296. https://doi.org/10.1093/nar/gkab301
Li, H., Wang, C., Yu, Q., & Smith, E. (2022). Spatiotemporal assessment of potential drivers of salt marsh dieback in the North Inlet-Winyah Bay estuary, South Carolina (1990–2019). Journal of Environmental Management, 313, 114907. https://doi.org/10.1016/j.jenvman.2022.114907
Li, J., Cui, L., Delgado-Baquerizo, M., Wang, J., Zhu, Y., Wang, R., Li, W., Lei, Y., Zhai, X., & Zhao, X. (2022). Fungi drive soil multifunctionality in the coastal salt marsh ecosystem. Science of The Total Environment, 818, 151673.
Li, M., Zhao, T., Liang, D., Dong, D., Guo, Z., Hua, X., & Zhong, S. (2023). Diversity characterization of bacteria and fungi in water, sediments and biofilms from Songhua River in Northeast China. Chemosphere, 338, 139524. https://doi.org/10.1016/j.chemosphere.2023.139524
Li, W., Wang, M., Bian, X., Guo, J., & Cai, L. (2016). A High-Level Fungal Diversity in the Intertidal Sediment of Chinese Seas Presents the Spatial Variation of Community Composition. Frontiers in Microbiology, 7. https://doi.org/10.3389/fmicb.2016.02098
Li, W., Wang, M., Burgaud, G., Yu, H., & Cai, L. (2019). Fungal Community Composition and Potential Depth-Related Driving Factors Impacting Distribution Pattern and Trophic Modes from Epi- to Abyssopelagic Zones of the Western Pacific Ocean. Microbial Ecology, 78(4), 820–831. https://doi.org/10.1007/s00248-019-01374-y
Lyons, J. I., Alber, M., & Hollibaugh, J. T. (2010). Ascomycete fungal communities associated with early decaying leaves of Spartina spp. From central California estuaries. Oecologia, 162, 435–442.
Maeda, K., Spor, A., Edel-Hermann, V., Heraud, C., Breuil, M.-C., Bizouard, F., Toyoda, S., Yoshida, N., Steinberg, C., & Philippot, L. (2015). N2O production, a widespread trait in fungi. Scientific Reports, 5(1), 9697.
Mcleod, E., Chmura, G. L., Bouillon, S., Salm, R., Björk, M., Duarte, C. M., Lovelock, C. E., Schlesinger, W. H., & Silliman, B. R. (2011). A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO ₂. Frontiers in Ecology and the Environment, 9(10), 552–560. https://doi.org/10.1890/110004
McMurdie, P. J., & Holmes, S. (2013). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE, 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217
McNichol, J., Berube, P. M., Biller, S. J., & Fuhrman, J. A. (2021). Evaluating and Improving Small Subunit rRNA PCR Primer Coverage for Bacteria, Archaea, and Eukaryotes Using Metagenomes from Global Ocean Surveys. mSystems, 6(3), e00565-21. https://doi.org/10.1128/mSystems.00565-21
Minh, B. Q., Schmidt, H. A., Chernomor, O., Schrempf, D., Woodhams, M. D., Von Haeseler, A., & Lanfear, R. (2020). IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Molecular Biology and Evolution, 37(5), 1530–1534. https://doi.org/10.1093/molbev/msaa015
Mitchell, C. E., & Power, A. G. (2003). Release of invasive plants from fungal and viral pathogens. Nature, 421(6923), 625–627. https://doi.org/10.1038/nature01317
Moccia, K., Papoulis, S., Willems, A., Marion, Z., Fordyce, J. A., & Lebeis, S. L. (2020). Using the Microbiome Amplification Preference Tool (MAPT) to Reveal Medicago sativa-Associated Eukaryotic Microbes. Phytobiomes Journal, 4(4), 340–350. https://doi.org/10.1094/PBIOMES-02-20-0022-R
Mohamed, D. J., & Martiny, J. B. H. (2011). Patterns of fungal diversity and composition along a salinity gradient. The ISME Journal, 5(3), 379–388. https://doi.org/10.1038/ismej.2010.137
Money, N. P. (2016). Fungal Diversity. In The Fungi (pp. 1–36). Elsevier. https://doi.org/10.1016/B978-0-12-382034-1.00001-3
Naranjo‐Ortiz, M. A., & Gabaldón, T. (2019). Fungal evolution: Diversity, taxonomy and phylogeny of the Fungi. Biological Reviews, 94(6), 2101–2137. https://doi.org/10.1111/brv.12550
Needham, D. M., Fichot, E. B., Wang, E., Berdjeb, L., Cram, J. A., Fichot, C. G., & Fuhrman, J. A. (2018). Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling. The ISME Journal, 12(10), 2417–2432. https://doi.org/10.1038/s41396-018-0169-y
Nguyen, N. H., Song, Z., Bates, S. T., Branco, S., Tedersoo, L., Menke, J., Schilling, J. S., & Kennedy, P. G. (2016). FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology, 20, 241–248. https://doi.org/10.1016/j.funeco.2015.06.006
Nilsson, R. H., Larsson, K.-H., Taylor, A. F. S., Bengtsson-Palme, J., Jeppesen, T. S., Schigel, D., Kennedy, P., Picard, K., Glöckner, F. O., Tedersoo, L., Saar, I., Kõljalg, U., & Abarenkov, K. (2019). The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research, 47(D1), D259–D264. https://doi.org/10.1093/nar/gky1022
O’Donnell, K., Lutzoni, F. M., Ward, T. J., & Benny, G. L. (2001). Evolutionary relationships among mucoralean fungi (Zygomycota): Evidence for family polyphyly on a large scale. Mycologia, 93(2), 286–297. https://doi.org/10.1080/00275514.2001.12063160
Orsi, W. D., Vuillemin, A., Coskun, Ö. K., Rodriguez, P., Oertel, Y., Niggemann, J., Mohrholz, V., & Gomez-Saez, G. V. (2022). Carbon assimilating fungi from surface ocean to subseafloor revealed by coupled phylogenetic and stable isotope analysis. The ISME Journal, 16(5), 1245–1261. https://doi.org/10.1038/s41396-021-01169-5
Otte, N. C. M., & Landy, E. (2006). Biogeochemical roles of fungi in marine and estuarine habitats. Fungi in Biogeochemical Cycles, 24, 436.
Parada, A. E., Needham, D. M., & Fuhrman, J. A. (2016). Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environmental Microbiology, 18(5), 1403–1414. https://doi.org/10.1111/1462-2920.13023
Patchineelam, S. M., Kjerfve, B., & Gardner, L. R. (1999). A preliminary sediment budget for the Winyah Bay estuary, South Carolina, USA. Marine Geology, 162(1), 133–144. https://doi.org/10.1016/S0025-3227(99)00059-6
Peng, X., Amend, A. S., Baltar, F., Blanco‐Bercial, L., Breyer, E., Burgaud, G., Cunliffe, M., Edgcomb, V. P., Grossart, H., Mara, P., Masigol, H., Pang, K., Retter, A., Roberts, C., Van Bleijswijk, J., Walker, A. K., & Whitner, S. (2024). Planktonic Marine Fungi: A Review. Journal of Geophysical Research: Biogeosciences, 129(3), e2023JG007887. https://doi.org/10.1029/2023JG007887
Peng, X., & Valentine, D. L. (2021). Diversity and N2O Production Potential of Fungi in an Oceanic Oxygen Minimum Zone. Journal of Fungi, 7(3), 218. https://doi.org/10.3390/jof7030218
Pham, T. T., Dinh, K. V., & Nguyen, V. D. (2021). Biodiversity and enzyme activity of marine fungi with 28 new records from the tropical coastal ecosystems in Vietnam. Mycobiology, 49(6), 559–581.
Picard, K. T. (2017). Coastal marine habitats harbor novel early-diverging fungal diversity. Fungal Ecology, 25, 1–13. https://doi.org/10.1016/j.funeco.2016.10.006
Polinski, J. M., Bucci, J. P., Gasser, M., & Bodnar, A. G. (2019). Metabarcoding assessment of prokaryotic and eukaryotic taxa in sediments from Stellwagen Bank National Marine Sanctuary. Scientific Reports, 9(1), 14820. https://doi.org/10.1038/s41598-019-51341-3
Põlme, S., Abarenkov, K., Henrik Nilsson, R., Lindahl, B. D., Clemmensen, K. E., Kauserud, H., Nguyen, N., Kjøller, R., Bates, S. T., Baldrian, P., Frøslev, T. G., Adojaan, K., Vizzini, A., Suija, A., Pfister, D., Baral, H.-O., Järv, H., Madrid, H., Nordén, J., … Tedersoo, L. (2020). FungalTraits: A user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Diversity, 105(1), 1–16. https://doi.org/10.1007/s13225-020-00466-2
Porter, T. M., Martin, W., James, T. Y., Longcore, J. E., Gleason, F. H., Adler, P. H., Letcher, P. M., & Vilgalys, R. (2011). Molecular phylogeny of the Blastocladiomycota (Fungi) based on nuclear ribosomal DNA. Fungal Biology, 115(4), 381–392. https://doi.org/10.1016/j.funbio.2011.02.004
Posadas, J., Velez, P., Pajares, S., Gasca-Pineda, J., & Espinosa-Asuar, L. (2024). Fungal diversity in sediments of the eastern tropical Pacific oxygen minimum zone revealed by metabarcoding. PLOS ONE, 19(5), e0301605. https://doi.org/10.1371/journal.pone.0301605
Powell, M. J. (2016). Blastocladiomycota. In J. M. Archibald, A. G. B. Simpson, C. H. Slamovits, L. Margulis, M. Melkonian, D. J. Chapman, & J. O. Corliss (Eds.), Handbook of the Protists (pp. 1–25). Springer International Publishing. https://doi.org/10.1007/978-3-319-32669-6_17-1
Priest, T., Fuchs, B., Amann, R., & Reich, M. (2021). Diversity and biomass dynamics of unicellular marine fungi during a spring phytoplankton bloom. Environmental Microbiology, 23(1), 448–463. https://doi.org/10.1111/1462-2920.15331
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2012). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research, 41(D1), D590–D596. https://doi.org/10.1093/nar/gks1219
Raja, H. A., Miller, A. N., Pearce, C. J., & Oberlies, N. H. (2017). Fungal identification using molecular tools: A primer for the natural products research community. Journal of Natural Products, 80(3), 756–770.
Rasconi, S., Jobard, M., Jouve, L., & Sime-Ngando, T. (2009). Use of Calcofluor White for Detection, Identification, and Quantification of Phytoplanktonic Fungal Parasites. Applied and Environmental Microbiology, 75(8), 2545–2553. https://doi.org/10.1128/AEM.02211-08
Retter, A., Nilsson, R. H., & Bourlat, S. J. (2019). Exploring the taxonomic composition of two fungal communities on the Swedish west coast through metabarcoding. Biodiversity Data Journal, 7, e35332. https://doi.org/10.3897/BDJ.7.e35332
Reynolds, N. K., Jusino, M. A., Stajich, J. E., & Smith, M. E. (2022). Understudied, underrepresented, and unknown: Methodological biases that limit detection of early diverging fungi from environmental samples. Molecular Ecology Resources, 22(3), 1065–1085. https://doi.org/10.1111/1755-0998.13540
Richards, T. A., Leonard, G., Mahé, F., Del Campo, J., Romac, S., Jones, M. D. M., Maguire, F., Dunthorn, M., De Vargas, C., Massana, R., & Chambouvet, A. (2015). Molecular diversity and distribution of marine fungi across 130 European environmental samples. Proceedings of the Royal Society B: Biological Sciences, 282(1819), 20152243. https://doi.org/10.1098/rspb.2015.2243
Robbertse, B. (2024). Fungal 28S Ribosomal RNA (LSU) RefSeq Targeted Loci Project. [Dataset]. National Center for Biotechnology Information (NCBI). https://doi.org/10.15468/JZFDEW
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(1), 139–140. https://doi.org/10.1093/bioinformatics/btp616
Rojas-Jimenez, K., Rieck, A., Wurzbacher, C., Jürgens, K., Labrenz, M., & Grossart, H.-P. (2019). A Salinity Threshold Separating Fungal Communities in the Baltic Sea. Frontiers in Microbiology, 10, 680. https://doi.org/10.3389/fmicb.2019.00680
Schoch, C. L., Seifert, K. A., Huhndorf, S., Robert, V., Spouge, J. L., Levesque, C. A., Chen, W., Fungal Barcoding Consortium, Fungal Barcoding Consortium Author List, Bolchacova, E., Voigt, K., Crous, P. W., Miller, A. N., Wingfield, M. J., Aime, M. C., An, K.-D., Bai, F.-Y., Barreto, R. W., Begerow, D., … Schindel, D. (2012). Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proceedings of the National Academy of Sciences, 109(16), 6241–6246. https://doi.org/10.1073/pnas.1117018109
Sen, K., Sen, B., & Wang, G. (2022). Diversity, Abundance, and Ecological Roles of Planktonic Fungi in Marine Environments. Journal of Fungi, 8(5). https://doi.org/10.3390/jof8050491
Sønstebø, J. H., & Rohrlack, T. (2011). Possible Implications of Chytrid Parasitism for Population Subdivision in Freshwater Cyanobacteria of the Genus Planktothrix. Applied and Environmental Microbiology, 77(4), 1344–1351. https://doi.org/10.1128/AEM.02153-10
Spatafora, J. W., Volkmann‐Kohlmeyer, B., & Kohlmeyer, J. (1998). Independent terrestrial origins of the Halosphaeriales (marine Ascomycota). American Journal of Botany, 85(11), 1569–1580. https://doi.org/10.2307/2446483
Stalter, R., & Baden, J. (1994). A Twenty Year Comparison of Vegetation of Three Abandoned Rice Fields, Georgetown County, South Carolina. Castanea, 59(1), 69–77. JSTOR.
Stalter, R., Rachlin, J., & Baden, J. (2021). A FORTY-SEVEN YEAR COMPARISON OF THE VASCULAR FLORA AT THREE ABANDONED RICE FIELDS, GEORGETOWN, SOUTH CAROLINA, U.S.A. Journal of the Botanical Research Institute of Texas, 15(1), 271–282. JSTOR.
Su, X., Yang, X., Li, H., Wang, H., Wang, Y., Xu, J., Ding, K., & Zhu, Y. (2021). Bacterial communities are more sensitive to ocean acidification than fungal communities in estuarine sediments. FEMS Microbiology Ecology, 97(5), fiab058. https://doi.org/10.1093/femsec/fiab058
Sun Shan, Li Song, Avera Bethany N., Strahm Brian D., & Badgley Brian D. (2017). Soil Bacterial and Fungal Communities Show Distinct Recovery Patterns during Forest Ecosystem Restoration. Applied and Environmental Microbiology, 83(14), e00966-17. https://doi.org/10.1128/AEM.00966-17
Tedersoo, L., Anslan, S., Bahram, M., Kõljalg, U., & Abarenkov, K. (2020). Identifying the ‘unidentified’ fungi: A global-scale long-read third-generation sequencing approach. Fungal Diversity, 103(1), 273–293. https://doi.org/10.1007/s13225-020-00456-4
Tedersoo, L., Anslan, S., Bahram, M., Põlme, S., Riit, T., Liiv, I., Kõljalg, U., Kisand, V., Nilsson, H., Hildebrand, F., Bork, P., & Abarenkov, K. (2015). Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys, 10, 1–43. https://doi.org/10.3897/mycokeys.10.4852
Tedersoo, L., Bahram, M., Põlme, S., Kõljalg, U., Yorou, N. S., Wijesundera, R., Ruiz, L. V., Vasco-Palacios, A. M., Thu, P. Q., Suija, A., Smith, M. E., Sharp, C., Saluveer, E., Saitta, A., Rosas, M., Riit, T., Ratkowsky, D., Pritsch, K., Põldmaa, K., … Abarenkov, K. (2014). Global diversity and geography of soil fungi. Science, 346(6213), 1256688. https://doi.org/10.1126/science.1256688
Tedersoo, L., Bahram, M., Puusepp, R., Nilsson, R. H., & James, T. Y. (2017). Novel soil-inhabiting clades fill gaps in the fungal tree of life. Microbiome, 5(1), 42. https://doi.org/10.1186/s40168-017-0259-5
Tobias, C., & Neubauer, S. C. (2019). Salt Marsh Biogeochemistry—An Overview. In Coastal Wetlands (pp. 539–596). Elsevier. https://doi.org/10.1016/B978-0-444-63893-9.00016-2
Torzilli, A. P., & Andrykovitch, G. (1986). Degradation of Spartina lignocellulose by individual and mixed cultures of salt-marsh fungi. Canadian Journal of Botany, 64(10), 2211–2215. https://doi.org/10.1139/b86-295
United States Cenus Bureau. (2019, June). Coastline America. https://www.census.gov/content/dam/Census/library/visualizations/2019/demo/coastline-america.pdf
Vargas-Gastélum, L., Chong-Robles, J., Lago-Lestón, A., Darcy, J. L., Amend, A. S., & Riquelme, M. (2019). Targeted ITS1 sequencing unravels the mycodiversity of deep-sea sediments from the Gulf of Mexico. Environmental Microbiology, 21(11), 4046–4061. https://doi.org/10.1111/1462-2920.14754
Várnai, A., Mäkelä, M. R., Djajadi, D. T., Rahikainen, J., Hatakka, A., & Viikari, L. (2014). Carbohydrate-Binding Modules of Fungal Cellulases. In Advances in Applied Microbiology (Vol. 88, pp. 103–165). Elsevier. https://doi.org/10.1016/B978-0-12-800260-5.00004-8
Velez, P., González, M. C., Rosique-Gil, E., Cifuentes, J., Reyes-Montes, M. D. R., Capello-García, S., & Hanlin, R. T. (2013). Community structure and diversity of marine ascomycetes from coastal beaches of the southern Gulf of Mexico. Fungal Ecology, 6(6), 513–521. https://doi.org/10.1016/j.funeco.2013.10.002
Walker, A. K., & Campbell, J. (2010). Marine fungal diversity: A comparison of natural and created salt marshes of the north-central Gulf of Mexico. Mycologia, 102(3), 513–521.
Wang, M., Mara, P., Burgaud, G., Edgcomb, V., Long, X., Yang, H., Cai, L., & Li, W. (2023). Metatranscriptomics and metabarcoding reveal spatiotemporal shifts in fungal communities and their activities in Chinese coastal waters. Molecular Ecology, 32(11), 2750–2765. https://doi.org/10.1111/mec.16905
Wang, Q., & Cole, J. R. (2024). Updated RDP taxonomy and RDP Classifier for more accurate taxonomic classification. Microbiology Resource Announcements, 13(4), e01063-23. https://doi.org/10.1128/mra.01063-23
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. https://doi.org/10.1128/AEM.00062-07
Wang, Y., Sen, B., He, Y., Xie, N., & Wang, G. (2018). Spatiotemporal Distribution and Assemblages of Planktonic Fungi in the Coastal Waters of the Bohai Sea. Frontiers in Microbiology, 9. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2018.00584
Wang, Z., Juarez, D. L., Pan, J., Blinebry, S. K., Gronniger, J., Clark, J. S., Johnson, Z. I., & Hunt, D. E. (2019). Microbial communities across nearshore to offshore coastal transects are primarily shaped by distance and temperature. Environmental Microbiology, 21(10), 3862–3872. https://doi.org/10.1111/1462-2920.14734
White, M. M., James, T. Y., O’Donnell, K., Cafaro, M. J., Tanabe, Y., & Sugiyama, J. (2006). Phylogeny of the Zygomycota based on nuclear ribosomal sequence data. Mycologia, 98(6), 872–884. https://doi.org/10.1080/15572536.2006.11832617
Wijayawardene, N. N., Hyde, K. D., Rajeshkumar, K. C., Hawksworth, D. L., Madrid, H., Kirk, P. M., Braun, U., Singh, R. V., Crous, P. W., Kukwa, M., Lücking, R., Kurtzman, C. P., Yurkov, A., Haelewaters, D., Aptroot, A., Lumbsch, H. T., Timdal, E., Ertz, D., Etayo, J., … Karunarathna, S. C. (2017). Notes for genera: Ascomycota. Fungal Diversity, 86(1), 1–594. https://doi.org/10.1007/s13225-017-0386-0
Xu, W., Luo, Z.-H., Guo, S., & Pang, K.-L. (2016). Fungal community analysis in the deep-sea sediments of the Pacific Ocean assessed by comparison of ITS, 18S and 28S ribosomal DNA regions. Deep Sea Research Part I: Oceanographic Research Papers, 109, 51–60. https://doi.org/10.1016/j.dsr.2016.01.001
Zhang, T., Fei Wang, N., Qin Zhang, Y., Yu Liu, H., & Yan Yu, L. (2015). Diversity and distribution of fungal communities in the marine sediments of Kongsfjorden, Svalbard (High Arctic). Scientific Reports, 5(1), 14524. https://doi.org/10.1038/srep14524
Zogg, G. P., Travis, S. E., & Brazeau, D. A. (2018). Strong associations between plant genotypes and bacterial communities in a natural salt marsh. Ecology and Evolution, 8(9), 4721–4730. https://doi.org/10.1002/ece3.4105

No competing interests reported.

SuppInfoPrimerpaper2024.10.7.docx

Download PDF

Editorial decision: Revision requested
30 Oct, 2024
Editor assigned by journal
30 Oct, 2024
Submission checks completed at journal
17 Oct, 2024
First submitted to journal
14 Oct, 2024

You are reading this latest preprint version

Diversity and ecology of fungi in the sediments and surface water of brackish and salt marshes

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Sample collection

DNA Extraction

Sequence analysis

Statistical Analyses

Results and Discussion

Environmental context

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1