Fungi, bacteria and oomycota opportunistically isolated from the seagrass, Zostera marina

Fungi in the marine environment are often neglected as a research topic, despite that fungi having critical roles on land as decomposers, pathogens or endophytes. Here we used culture-dependent methods to survey the fungi associated with the seagrass, Zostera marina, also obtaining bacteria and oomycete isolates in the process. A total of 108 fungi, 40 bacteria and 2 oomycetes were isolated. These isolates were then taxonomically identified using a combination of molecular and phylogenetic methods. The majority of the fungal isolates were classified as belonging to the classes Eurotiomycetes, Dothideomycetes, and Sordariomycetes. Most fungal isolates were habitat generalists like Penicillium sp. and Cladosporium sp., but we also cultured a diverse set of rare taxa including possible habitat specialists like Colletotrichum sp. which may preferentially associate with Z. marina leaf tissue. Although the bulk of bacterial isolates were identified as being from known ubiquitous marine lineages, we also obtained several Actinomycetes isolates and a Phyllobacterium sp. We identified two oomycetes, another understudied group of marine microbial eukaryotes, as Halophytophthora sp. which may be opportunistic pathogens or saprophytes of Z. marina. Overall, this study generates a culture collection of fungi which adds to knowledge of Z. marina associated fungi and highlights a need for more investigation into the functional and evolutionary roles of microbial eukaryotes associated with seagrasses.


Introduction
Despite their global importance in terrestrial systems, the diversity, function, evolution, and global importance of fungi in the marine environment remains understudied. There are only *1100 currently accepted species of marine fungi despite estimates that true diversity is much higher, at 10,000 or more species [1,2]. It is well known that fungi play vital roles in land plant health and fitness (e.g. as pathogens or endophytes), and although much less is known about fungi in aquatic ecosystems, it is thought they have important roles in organic matter degradation and food web dynamics [3]. Thus, it is likely that fungi engage in similarly vital functional roles when associated with marine plants, like seagrasses.

PLOS ONE
PLOS ONE | https://doi.org/10.1371/journal.pone.0236135 July 22, 2020 1 / 33 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 Seagrasses are fully submerged marine angiosperms and are foundation species in coastal ecosystems. Seagrass beds perform important ecosystem services and can store carbon over very long timescales in their above and below ground tissues and in surrounding sediments (i.e. "blue carbon") [4]. Unfortunately, seagrass beds are threatened by human-related activities such as pollution, climate change and coastal development, and restoration efforts thus far have been mostly ineffective [5]. In addition to their global ecological importance, seagrasses also have a unique evolutionary history. Sometimes referred to as the "whales of the plant world", seagrasses are a paraphyletic group of multiple lineages that convergently adapted to the marine environment between 70 and 100 million years ago [6,7]. There are only~60 species of seagrass compared to the~250,000 species of terrestrial flowering plants, a testament to the strict selective pressure posed by re-entry to the marine environment. This work focuses on one widespread seagrass species, Zostera marina, which occurs throughout much of the Northern Hemisphere.
In comparison, less is known about the fungal community associated with Z. marina and seagrasses generally. Culture-based studies have found fungi associated with leaves, roots and rhizomes of seagrasses, but there is little agreement between studies about the taxonomic composition of these communities within and between seagrass species [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Recently cultureindependent studies of seagrass-associated fungi have more thoroughly investigated the diversity of these microorganisms and highlighted a need to further understand factors affecting their biogeography and community dynamics [43][44][45][46]. However, these studies were severely hampered by a lack of representation of fungal sequences from the marine environment in public databases and found that taxonomic assignments could not be made for many fungal sequences associated with seagrasses. This suggests both a need to expand molecular knowledge of marine and seagrass-associated fungi in public databases and that seagrasses may harbor diverse and understudied fungal lineages.
Fungi and bacteria are not the only microbes associated with Z. marina and there are many other understudied microorganisms that likely have important roles in the seagrass ecosystem. For example, one culture-independent effort sought to investigate the composition of the entire eukaryote community associated with Z. marina, and found that the bacterial and eukaryotic epibiont communities were highly correlated [9]. Additionally, oomycetes [47,48], protists [49], and viruses [50] have all been cultured in association with Z. marina and seagrass wasting disease is thought to be caused by the heterokont, Labyrinthula zosterae [51].
Here we used a culture-dependent survey followed by molecular and phylogenetic identification to (i) obtain and identify a diverse collection of fungi associated with Z. marina, (ii) place this fungal collection in the phylogenetic context of isolates obtained from other seagrass surveys, and (iii) compare and contrast the composition of this fungal collection to high throughput sequencing results of the composition of the fungal community associated with Z. marina from the same location.

Sample collection and isolation
Zostera marina tissues were collected under California Department of Fish and Wildlife Scientific Collecting Permit # SC 4874 granted to Dr. John J. Stachowicz. Individual Z. marina batches also included the addition of 5 g/L dehydrated crushed Z. marina leaf tissue. For the exact media conditions each isolate was grown on see S1 Table. Plates were wrapped in parafilm to prevent contamination and incubated at room temperature (e.g. as in [27,42]) in the dark (e.g. as in [26,32]) in a cabinet drawer for a minimum of 4 weeks (e.g. as in [26,27,32,33]), up to a maximum of 12 weeks. Plates were observed every 2-3 days for fungal growth. Fungal isolates were then sterilely subcultured onto new plates and the process repeated until we were confident we had a single isolate. We were confident when we had subcultured the organism three times each with consistent morphology and no signs of contamination. During the isolation process, all parent plates and subcultures for an organism were stored at 4˚C for comparative purposes. Plates with contamination were tossed (e.g. with a morphology inconsistent with what had been previously observed or colonies not near tissues or areas that were streaked).

DNA extraction, Polymerase chain reaction (PCR) and Sanger sequencing
DNA was extracted from isolates using the MoBio PowerSoil DNA Isolation kit (MO BIO Laboratories, Inc., Carlsbad, CA, United States) with minor changes to the manufacturer's protocol as follows. To improve fungal lysis, samples were heated at 70˚C for 10 minutes between steps 4 and 5. For step 5, samples were bead beaten on the homogenize setting for 2 minutes using a mini-bead beater (BioSpec Products). For a subset of isolates DNA was instead extracted with either the Qiagen Plant DNeasy (QIAGEN, Hilden, Germany), the Qiagen DNeasy PowerSoil Pro Kit (QIAGEN, Hildren, Germany) or the Zymo Xpedition Fungal/Bacterial DNA Mini Prep (Zymo Research Inc, Irvine, CA, United States) according to the manufacturer's instructions. The reason for the discrepancy between which DNA extraction kit was used is that we initially tried several different DNA extraction kits, before finding that the MoBio PowerSoil DNA Isolation kit provided the best DNA yield and subsequently, extracting isolates only with that kit moving forward. For the DNA extraction kit used for each isolate see S1 Table. Polymerase chain reaction (PCR) was performed using Taq DNA Polymerase (QIAGEN, Hilden, Germany). Initially, PCR was performed on DNA from all isolates to amplify the fungal ITS-28S rRNA gene region. For isolates where PCR was not successful after three attempts, we then attempted to amplify the bacterial 16S rRNA gene. A few samples that had successful amplification for the bacterial 16S rRNA gene had close matches in NCBI GenBank to oomycete mitochondria, so in these cases we then attempted to amplify the oomycete 28S rRNA gene.
The fungal ITS-28S rRNA gene region was obtained using the ITS5 [52] and LR3 [53] primer set, the bacterial 16S rRNA gene was obtained using the 27F [54] and 1391R [55] primer set, and the oomycete 28S rRNA gene was obtained using the LR0R [56] and Un-Lo28S1220 [57] primer set. When amplifying the fungal ITS-28S rRNA gene region, PCR was performed with the following conditions: 95˚C for 5 minutes, 35 cycles at 94˚C for 30 seconds, 52˚C for 15 seconds, 72˚C for 1 minute, and a final extension at 72˚C for 8 minutes [58]. When amplifying the bacterial 16S rRNA gene, PCR was performed with the following protocol: 95˚C for 3 minutes, 40 cycles at 95˚C for 15 seconds, 54˚C for 30 seconds, 72˚C for 1 minute and 30 seconds, and a final extension at 72˚C for 5 minutes (modified from [59]). When amplifying the oomycete 28S rRNA gene, PCR was performed with the following protocol: 94˚C for 4 minutes, 35 cycles at 94˚C for 30 seconds, 57˚C for 30 seconds, 72˚C for 30 sec, and a final extension at 72˚C for 10 minutes (adapted from Bourret et al. [60]).
PCR products were visualized on 2% agarose E-gels (Invitrogen, Carlsbad, CA, United States). PCR products were then purified using the Nucleospin Gel and PCR kit (QIAGEN, Hilden, Germany) and quantified using the Qubit dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, United States). The PCR products were sequenced using the Sanger method by the UC Davis College of Biological Sciences UC DNA Sequencing Facility (http://dnaseq.ucdavis.edu/). The resulting ABI files were visualized and consensus sequences were generated using seqtrace v. 0.9.0 [60] following the Swabs to Genomes workflow [59]. Consensus sequences for the PCR products were deposited at NCBI Genbank under the following accession no. MN543905-MN544012 for the fungal ITS-28S rRNA gene region, MN931878-MN931917 for the bacterial 16S rRNA gene, and MN944508-MN944509 for the oomycete 28S rRNA gene.

Taxonomic analyses
Preliminary taxonomic assignment of sequences from the PCR products generated above were obtained by comparing the best results (or "top match") across three methods to obtain a consensus assignment. The three methods included (1) using NCBI's Standard Nucleotide BLAST's megablast option against the nr/nt database with default settings for all isolates and against the 16S ribosomal RNA sequence database for bacterial isolates, (2) using the Ribosomal Database Project (RDP) classifier with the appropriate respective database (e.g. the 16S rRNA training set for bacteria, the Fungal LSU, WARCUP and UNITE datasets for fungi, the Fungal LSU for oomycetes) and default settings, (3) using the SILVA Alignment, Classification and Tree (ACT) service with the appropriate database (SSU for bacteria, LSU for fungi and oomycetes) and default settings [61][62][63][64][65]. Taxonomic assignments for isolates and associated isolation conditions were then imported into R (v. 3.6.0) for visualization and analysis using the following packages: ggplot2 (v. 3.2.1), dplyr (v. 0.8.4), reshape (v. 0.8.8), patchwork (v. 1.0.0), and tidyverse (v. 1.3.0) [66][67][68][69][70] (S1 File).

Phylogenetic analyses of fungal isolates
Sequences closely related to the fungal ITS-28S rRNA gene PCR products generated above were identified using NCBI's Standard Nucleotide BLAST's megablast option with default settings to further confirm fungal taxonomy through phylogenetic placement (S2 Table). Additionally, we wanted to place the Z. marina associated fungal isolates in the context of the phylogenetic diversity of available other seagrass-associated fungal isolates. To this end, we performed a literature search to obtain, to our knowledge at the time of the search, all available 28S rRNA sequences obtained from seagrass associated fungal isolates for inclusion in phylogenetic analyses (S3 Table) [26,27,42,71,72]. Finally, to provide a further framework for these phylogenies, as well as appropriate outgroup taxa, we downloaded the available 28S rRNA sequences previously used in James et al. [73,74] (S4 Table).
Using the sequences listed in Tables 1 and S2-S4, we generated four different sequence alignments, (1) an alignment to investigate seagrass isolates in the Basidiomycota and Mucoromycota, (2) an alignment to investigate seagrass isolates in the Eurotiomycetes class in the Ascomycota phylum, (3) an alignment to investigate seagrass isolates in the Sordariomycetes class in the Ascomycota phylum, and (4) an alignment to investigate seagrass isolates in the Dothideomycetes class in the Ascomycota phylum.
Each of the four sequence alignments was generated using MAFFT (v. 7.402) [75] with default parameters on the CIPRES Science Gateway web server [76]. The alignments were trimmed using trimAl (v.1.2) with the -gappyout method [77] and then manually inspected with JalView [78]. Sequence alignments were then further trimmed to the D1/D2 regions of the 28S rRNA gene with trimAl using the select option (e.g. Basidiomycota / Mucoromycota alignment {614-2899 }, Eurotiomycetes alignment {0-569 }, Sordariomycetes alignment {501-1224 }, and Dothideomycetes alignment {0-429, 993-1755 }). Spurious sequences (e.g. sequences which contained few or no nucleotides after trimming) were then removed with tri-mAl using -resoverlap .75 -seqoverlap 50. The resulting alignments contained: 80 sequences  JModelTest2 (v. 2.1.10) was run with the number of substitution schemes (-s) set to 3 (JC/ F81, K80/HKY, SYM/GTR) and then otherwise default parameters on the CIPRES Science Gateway web server to select a best-fit model of nucleotide substitution for use in phylogenetic analyses for each alignment [79,80]. The best-fit model based on the Akaike Information Criterion values for all alignments was the GTR + I + G evolutionary model.
Using the CIPRES Science Gateway web server, Bayesian phylogenetic inference for each alignment was performed using MrBayes (v. 3.2.2) with four incrementally heated simultaneous Monte Carlo Markov Chains (MCMC) run over 10,000,000 generations. The analysis stopped early if the optimal number of generations to reach a stop value of 0.01 or less for the convergence diagnostic was achieved [81]. This occurred for the Eurotiomycetes, Sordariomycetes and Dothideomycetes alignments at 2,150,000 generations, 1,375,000 generations and 2,140,000 generations, respectively. The Basidiomycota / Mucoromycota alignment ran for the full 10,000,000 generations, only achieving an average standard deviation of split frequencies of 0.049. The first 25% of trees generated for each alignment were discarded as burn-in and for the remaining trees, a majority rule consensus tree was generated and used to calculate the Bayesian Posterior Probabilities. The resulting phylogenies were then visualized with the ggtree (v. 2.0.1), treeio(v. 1.11.2), ggplot2 (v. 3.2.1), and tidyverse (v. 1.3.0) packages in R (v. Here we report the taxonomic information for each fungal isolate (Class, Order) and the putative taxonomy, provide the GenBank accession number for the ITS-28S rRNA gene sequence for each isolate, and report on the isolation source the isolate was obtained from (e.g. leaf, rhizome, root, seawater or sediment). We also report on whether the genus of each isolate includes marine fungal representatives based on the consensus compiled in Jones et al. [2] and whether the genus of each isolate was detected in the ITS amplicon data in Ettinger & Eisen [43]. Organisms for which a taxonomic identification below the order level was not possible, have a "NA" value for these columns. It is important to note that there is likely significant biological variation within the genera reported here (e.g. among Penicillium), such that finding members of these genera should not be interpreted as meaning that the specific variants isolated here have the same biology as variants found to be member of the same genera in other datasets. https://doi.org/10.1371/journal.pone.0236135.t001 3.6.0) and clade labels were added in Adobe Photoshop CS6 [66,67,[82][83][84][85] (S1 File). Alignments and phylogenies generated here were deposited to Dryad [86].

Comparisons to ITS amplicon data from Ettinger & Eisen [43]
To compare to high throughput sequencing data associated with Z. marina from the same location (Westside Point, Bodega Bay, CA), we utilized an amplicon sequence variant (ASV) dataset previously analysed in Ettinger & Eisen [43]. Specifically, we are using the subset ASV table that was used to investigate differences between bulk sample types. Briefly, this ASV table was previously subset to a depth of 10,000 sequences and included 49 samples from four sample types: leaf epiphytes (n = 13), root epiphytes (n = 14), rhizome epiphytes (n = 7), and sediment (n = 15). We then used this ASV to make comparisons to the fungal taxa isolated in this study.
To investigate whether fungal genera isolated in this study were also detected in the high throughput sequencing data, we generated a list of the unique genera found in the ASV table and compared it to the list of fungal genera isolated here. To investigate whether the fungal genera isolated in this study were detected from the same Z. marina tissues, we collapsed the ASV table to the genus level using the tax_glom function in phyloseq. We then subsampled the ASV table to only include the genera of fungi isolated in this study, transformed this ASV table represent presence / absence and visualized a comparison of the distribution of these genera across sample types (leaf, root, rhizome, sediment) to the distribution of these genera across isolation sources (leaf, root, rhizome, sediment). To investigate the mean relative abundance of the fungal orders isolated in this study in the high throughput sequencing data, we collapsed the ASV table to the order level using the tax_glom function in phyloseq. We then subsampled the ASV table to only include the orders of fungi isolated in this study and visualized the distribution of these orders across sample types (leaf, root, rhizome, sediment). These analyses were performed in R

Isolation efficacy
A total of 160 plates were initially inoculated, 81 with leaves (n whole = 44, n crushed = 22, n washes = 11, n bleached = 2, n surface cleaned = 2), 38 with rhizomes (n whole = 13, n crushed = 8, n washes = 2, n bleached = 2, n surface cleaned = 2), 27 with roots (n whole = 13, n crushed = 8, n washes = 2, n bleached = 2, n surface cleaned = 2), 4 with seawater, and 10 with sediment (S1 and S2 Figs). Microbial growth was observed on 135 plates (84.4% of all inoculated plates). We subcultured 1-5 organisms from all plates with observed microbial growth. However, we only obtained isolates that met our criteria for putatively being single organisms (e.g. which had been subcultured three times each with consistent morphology and no signs of contamination) from 86 of these plates (63.7% of plates with observed growth, 53.8% of all inoculated plates). No isolates were ultimately obtained from bleached or surface cleaned tissues and only one isolate was obtained from Zostera marina agar.
In total 185 putatively anexic microbial isolates were obtained. Of these 185 isolates, we were able to generate PCR products for 176 isolates to send for Sanger sequencing for taxonomic identification. Despite multiple attempts we were unable to generate PCR products for 9 isolates across all primer sets tried here (possibly due to primer mismatch and/or too low concentrations of DNA). Of the 170 isolates where PCR products were sent for sequencing, we received good quality sequencing results for and were able to taxonomically identify 150 isolates. For the 26 isolates where sequencing either failed, was low quality or appeared mixed, 17 appeared to be bacterial in origin, 4 appeared to be fungal and 5 were too poor quality to identify (e.g. comprised of only N's) based on searches using NCBI's Standard Nucleotide BLAST's megablast option with default settings.

Taxonomic diversity of fungi isolated from Z. marina
In an attempt to cultivate a wide diversity of fungal isolates, we used a variety of media types including several which had been used previously to isolate fungi from seagrasses (e.g. PDA [26,27,42], GPYA [33], MEA [32]). A total of 108 fungal isolates were obtained, with the majority cultured from Z. marina leaf tissue (n = 51), resulting in a range of morphological diversity (Fig 1). The rest of isolates were cultured from rhizome tissue (n = 23), root tissue (n = 16), associated sediment (n = 13), and seawater (n = 5) (Figs 2 and S3 and S4).
Eurotiomycetes isolates were further taxonomically classified as Penicillium sp. (n = 59) and We observed a positive relationship between the number of tissue types and number of media types a fungal genus was isolated from (R 2 = 0.86; S5 Fig) which we hypothesize may indicate that some fungal genera are habitat generalists. A similar positive relationship is also observed between the number of tissue types and the number of salt sources (R 2 = 0.92) as well as between the number of media types and number of salt sources (R 2 = 0.87). However, we did not perform any experiments to confirm this pattern. We also did not always attempt to control for effort (e.g. plating the same number of tissue segments on all media types [S1 and S2 Figs]). Therefore, we suggest that these positive relationships be interpreted with caution.

Taxonomic diversity of bacteria and oomycota isolated from Z. marina
Our intent here was to isolate fungi which was why we included broad spectrum antibiotics in our culturing media to help eliminate bacteria which might be associated with Z. marina. However, we still cultivated and identified 40 bacteria and 2 oomycetes. The bacteria are likely naturally resistant to the antibiotics used and the oomycetes, as eukaryotes, are unlikely to be affected by their presence in the media. As with the fungal cultivation results, the majority of bacterial isolates were obtained from Z. marina leaf tissue (n = 17). The rest of the bacterial isolates were cultured from rhizome tissue (n = 9), root tissue (n = 7), associated sediment (n = 5), and seawater (n = 2) (S6 Fig).
Bacterial isolates were taxonomically identified as belonging to the Actinobacteria (n = 4), Firmicutes (n = 2), Bacteroidetes (n = 2), and Proteobacteria (n = 33) ( Table 2). The two Firmicute isolates were further classified as Bacillus sp., the two Bacteroidetes isolates as Joostella sp., and the Actinobacteria isolates as Streptomyces sp. The two oomycete isolates were obtained from Z. marina leaf tissue and were both taxonomically identified as Halophytophthora sp. (Table 3).

Phylogenetic comparison of fungal isolates across seagrass species
To confirm fungal isolate identity and investigate if Z. marina fungal isolates were closely related to fungal isolates obtained from other seagrass species, we built four phylogenetic trees, 1) a phylogeny of seagrass isolates in the Basidiomycota and Mucoromycota (Figs 3 and S7), (2) a phylogeny of seagrass isolates in the Eurotiomycetes class in the Ascomycota phylum (Figs 4 and S8), (3) a phylogeny of seagrass isolates in the Sordariomycetes class in the Ascomycota phylum (Figs 5 and S9), and (4) a phylogeny of seagrass isolates in the Dothideomycetes class in the Ascomycota phylum (Figs 6 and S10). The placements of isolates in these phylogenies were consistent with the taxonomic identities previously determined. Additionally, isolates that were only identified at the order level taxonomically (unidentified Pleosporales sp. and Hypocreales sp.) were not able to be confidently further identified via the phylogenetic methods used here. The closest phylogenetic relatives of unidentified Hypocreales sp. are other unidentified fungi in the order Hypocreales (Fig 5). While the unidentified Pleosporales sp. form an unresolved clade with members in the family Didymellaceae in the order Pleosporales (Fig 6).
We expected to see more shared taxonomic groups and phylogenetic clustering between the fungal isolates of Z. marina and those of other seagrass species than was observed in Figs 3-6. Many of the fungal isolates from Z. marina did not cluster with fungal isolates that had been previously cultivated in association with other seagrass species. One interpretation might be that each seagrass species harbors a unique fungal community. However, this could also be the result of slight differences in collection protocols, media recipes or other methodology involved in isolating fungi in the compared studies.
The fungal taxa that did have close relatives that were associated with other species included Penicillium sp. (Fig 4), Trichoderma sp. (Fig 5), Cladosporium sp. and Ramularia sp. (Fig 6). We note that Penicillium sp., Cladosporium sp. and Ramularia sp. are drivers of the positive relationship observed previously between the number of tissue types and number of media types a fungal genus was isolated from (S5 Fig).

Comparisons to ITS amplicon sequencing data from Ettinger & Eisen
We compared the diversity of the fungi isolated here to high throughput sequencing data associated with Z. marina from the same location (as previously analyzed in Ettinger & Eisen [43]. We found that the fungal genera isolated in this study were generally also detected in the sequencing data (Table 1). Only three genera were not detected in the sequencing data, Pseudozyma sp., Emericellopsis sp. and Absidia cylindrospora. The absence of these genera in the sequencing data may be methodological (e.g. they do not amplify with the primer set used to generate the sequencing data) or biological (e.g. due to seasonal variation).
We then investigated whether the fungal genera isolated in this study were also detected in association with the same types of samples (e.g. leaf, root, rhizome, sediment) in the sequencing data. We observed that many of rare (e.g. not as frequently isolated) genera were not consistently detected on the same sample type with both methods, whereas many of the abundant (e.g. more frequently isolated) genera were detected with both methods (Fig 7). When we looked at the mean relative abundance of the fungal orders isolated in this study in the high throughput sequencing data, we observed that the genera detected using both methods generally were in orders that had higher mean relative abundances in the seagrass ecosystem (S11 Fig). We also observed that some orders such as the Eurotiales (e.g. Penicillium sp.) and Capnodiales (e.g. Cladosporium sp. and Ramularia sp.) had similar mean relative abundances

Discussion
Here, we generated a fungal collection of 108 isolates expanding understanding of the diversity of Z. marina associated fungi, while also underscoring how little we know about these understudied microorganisms. Generally, the taxonomic diversity observed in our cultivation efforts is consistent with that of other culture-dependent studies which found Eurotiomycetes, Dothideomycetes, and Sordariomycetes to be the main classes of fungi associated with seagrasses [26,27]. This is also consistent with what is known of the diversity of fungal associations with terrestrial plants. Members of the Sordariomycetes and Dothideomycetes have been found to be the predominant members of land plant fungal endophyte communities [89], while Eurotiomycetes have been found to be the dominant members of freshwater plant communities [90]. Dark septate endophytes (DSE), particularly members of the Pleosporales within the Dothideomycetes (Fig 6), have been observed to form associations with several seagrass species [33,42,44,71,72,[91][92][93]. DSE are a morphological, not phylogenetic (e.g. not each other's closest relatives) group of plant associated fungi, and are largely uncharacterized. The most well described of these DSE associations is between the Mediteranean seagrass, Posidonia oceanica, and its dominant root-associated fungus, Posidoniomyces atricolor. This Pleosporales member has been found associated with changes in root hair development and can form ecto- Here we report the taxonomic information for each bacterial isolate (Class, Order) and the putative taxonomy, provide the GenBank accession number for the 16S rRNA gene sequence for each isolate, and report on the isolation source the isolate was obtained from (e.g. leaf, rhizome, root, seawater or sediment). We also report the taxonomic identity of the top BLAST match against NCBI's targeted loci 16S ribosomal RNA sequence database, the BLAST % identity to the bacterial isolate and the GenBank accession number for the 16S rRNA gene sequence for the top BLAST match.
https://doi.org/10.1371/journal.pone.0236135.t002 Here we report the taxonomic information for each oomycete isolate (Class, Order) and the putative taxonomy, provide the GenBank accession number for the 28S rRNA gene sequence for each isolate, and report on the isolation source the isolate was obtained from (e.g. leaf, rhizome, root, seawater or sediment). We also report the taxonomic identity of the top BLAST match against NCBI's nr/nt database, the BLAST % identity to the oomycete isolate and the GenBank accession number for the 28S rRNA gene sequence for the top BLAST match. https://doi.org/10.1371/journal.pone.0236135.t003 mycorrhizal-like structures [42,72,92,93]. Here, although we isolated seven members of the Pleosporales, none appeared to be close relatives to Posidoniomyces atricolor. Chytridiomycota were found to be prevalent members of the Z. marina leaf microbiome in Ettinger & Eisen [43], however, no chytrids were cultured in this study. This is likely because A molecular phylogeny of 28S rRNA genes for isolates in the Basidiomycota and Mucoromycota was constructed using Bayesian inference. This alignment was generated using MAFFT (v. 7.402) on the CIPRES Science Gateway web server, trimmed using trimAl (v.1.2) and a phylogenetic tree was inferred on the trimmed alignment with a GTR + I + G model using MrBayes (v. 3.2.2) [75][76][77]81]. Displayed at each node as a circle in the tree are the Bayesian posterior probabilities (e.g. a black circle represents probabilities greater or equal to 90%, a grey circle represents probabilities greater or equal to 70%, a white circle represents probabilities less than 70%). The names of fungi isolated from Z. marina are shown in green, fungi isolated from other seagrass species are shown in black, and all other fungi are shown in grey. For visualization purposes, selected clades have been collapsed and the number of sequences within that clade is indicated. Collapsed clades are shown in green if the majority of sequences in the clade are from isolates associated with Z. marina, black if the majority of isolates are from other seagrass species, and grey otherwise. Clade names that are followed by an asterisk contain sequences from multiple sources. An expanded version of this phylogeny can be found in S7 Fig. The GenBank accession numbers of the sequences used to build this phylogeny can be found in Tables 1 and S2-S4. https://doi.org/10.1371/journal.pone.0236135.g003 the isolation methods used here favor cultivation of Dikarya. Alternative methods and media recipes should be utilized (e.g. baiting) to isolate representatives of these important members of the fungal community. Similarly we note that the methods used here would fail to culture A molecular phylogeny of 28S rRNA genes for isolates in the Eurotiomycetes was constructed using Bayesian inference. This alignment was generated using MAFFT (v. 7.402) on the CIPRES Science Gateway web server, trimmed using trimAl (v.1.2) and a phylogenetic tree was inferred on the trimmed alignment with a GTR + I + G model using MrBayes (v. 3.2.2) [75][76][77]81]. Displayed at each node as a circle in the tree are the Bayesian posterior probabilities (e.g. a black circle represents probabilities greater or equal to 90%, a grey circle represents probabilities greater or equal to 70%, a white circle represents probabilities less than 70%). The names of fungi isolated from Z. marina are shown in green, fungi isolated from other seagrass species are shown in black, and all other fungi are shown in grey. For visualization purposes, selected clades have been collapsed and the number of sequences within that clade is indicated. Collapsed clades are shown in green if the majority of sequences in the clade are from isolates associated with Z. marina, black if the majority of isolates are from other seagrass species, and grey otherwise. Clade names that are followed by an asterisk contain sequences from multiple sources. An expanded version of this phylogeny can be found in S8 Fig. The GenBank accession numbers of the sequences used to build this phylogeny can be found in Tables 1 and S2-S4. https://doi.org/10.1371/journal.pone.0236135.g004

Fig 5. Phylogenetic placement of seagrass fungal isolates in the Sordariomycetes.
A molecular phylogeny of 28S rRNA genes for isolates in the Sordariomycetes was constructed using Bayesian inference. This alignment was generated using MAFFT (v. 7.402) on the CIPRES Science Gateway web server, trimmed using trimAl (v. 1.2) fungi involved in obligate associations with seagrasses. In these cases, a combination of microscopy and/or cell sorting for directed cultivation or sequencing might prove valuable for assessing the functional roles of these organisms to the seagrass ecosystem. Finally, in this study, we only attempted to cultivate aerobic fungi, but there could be anaerobic fungi living in these ecosystems as well.
Previous work on the fungal community associated with the seagrass, Enhalus acoroides, identified a pattern of distance decay, where the fungal community was more similar between seagrass that were closer together geographically than between seagrass that were distant from each other [46]. This suggests that dispersal limitation and/or habitat specialization are playing important roles in structuring the fungal community associated with seagrasses. In this study, we opportunistically sampled fungi associated with a single seagrass species, Z. marina, from a single seagrass patch in Bodega Bay, CA. We did not investigate the fungal community of this seagrass species at other locations and thus, we cannot test for a pattern of distance decay here. However, we do see some examples of habitat specificity/generalism (and/or dispersal efficiency) at a local level in the fungal genera isolated here from Z. marina.
A pattern observed across culture-based studies of seagrass-associated fungi is that ubiquitous fungi are the dominant members of the communities, but that seagrasses also consistently host a diverse set of rare taxa. For example, ubiquitous fungi like Penicillium sp. and Cladosporium sp. have been previously reported as the dominant fungi of leaves in other culturebased studies of Z. marina [36,38,94], other seagrass species [26,34,35,41,95,96] and freshwater aquatic plants [90]. Additionally, Penicillium sp. and Cladosporium sp. were some of the only fungi in this study which were found to have close relatives associated with different seagrass species. We hypothesize that these fungal genera may be habitat generalists (taxa that occur evenly distributed across a wide range of habitats [97,98]) in the seagrass (and potentially larger marine) ecosystem as they were isolated from multiple media types, detected from most sample types (Fig 7) and found to have similar mean relative abundances across sample types (S11 Fig). However, just because these fungi are ubiquitous, does not reflect negatively on their potential importance. These habitat generalists have been shown to be highly adaptable with the innate ability to survive in wide range of extreme conditions (e.g. high salinity), are known to perform ecologically important functions (e.g. degradation of organic matter) and represent sources of biologically interesting and active secondary metabolites [33, 94,99].
We hypothesize that some fungi associated with Z. marina may be habitat specialists (taxa that are more restricted to a specific habitat range [97,98]). For example, some Colletotrichum sp. may be habitat specialists that preferentially associate with Z. marina leaf tissue. A Colletotrichum sp. ASV (SV10) was found to be dominant on leaves in Ettinger & Eisen [43] and a Colletotrichum spp. isolate was previously reported from another seagrass species as a leaf endophyte [95]. However, in this study, we are unable to decouple the contribution of environmental factors (e.g. habitat or niche specialization) from life history strategies (e.g. dispersal, growth rate). For example, although we hypothesize that some genera may be habitat generalists, it is possible that these patterns may also reflect that some genera have more efficient dispersal mechanisms or faster-growth rates and are able to outcompete slower-growing taxa. We realize these ideas and a phylogenetic tree was inferred on the trimmed alignment with a GTR + I + G model using MrBayes (v. 3.2.2) [75][76][77]81]. Displayed at each node as a circle in the tree are the Bayesian posterior probabilities (e.g. a black circle represents probabilities greater or equal to 90%, a grey circle represents probabilities greater or equal to 70%, a white circle represents probabilities less than 70%). The names of fungi isolated from Z. marina are shown in green, fungi isolated from other seagrass species are shown in black, and all other fungi are shown in grey. For visualization purposes, selected clades have been collapsed and the number of sequences within that clade is indicated. Collapsed clades are shown in green if the majority of sequences in the clade are from isolates associated with Z. marina, black if the majority of isolates are from other seagrass species, and grey otherwise. Clade names that are followed by an asterisk contain sequences from multiple sources. An expanded version of this phylogeny can be found in S9 Fig. The GenBank accession numbers of the sequences used to build this phylogeny can be found in Tables 1 and S2-S4. https://doi.org/10.1371/journal.pone.0236135.g005 may not be unconnected and that habitat generalists, by their nature, may be assembled by dispersal related mechanisms, and specialists by species sorting [97]. Regardless, future studies A molecular phylogeny of 28S rRNA genes for isolates in the Dothideomycetes was constructed using Bayesian inference. This alignment was generated using MAFFT (v. 7.402) on the CIPRES Science Gateway web server, trimmed using trimAl (v.1.2) and a phylogenetic tree was inferred on the trimmed alignment with a GTR + I + G model using MrBayes (v. 3.2.2) [75][76][77]81]. Displayed at each node as a circle in the tree are the Bayesian posterior probabilities (e.g. a black circle represents probabilities greater or equal to 90%, a grey circle represents probabilities greater or equal to 70%, a white circle represents probabilities less than 70%). The names of fungi isolated from Z. marina are shown in green, fungi isolated from other seagrass species are shown in black, and all other fungi are shown in grey. For visualization purposes, selected clades have been collapsed and the number of sequences within that clade is indicated. Collapsed clades are shown in green if the majority of sequences in the clade are from isolates associated with Z. marina, black if the majority of isolates are from other seagrass species, and grey otherwise. Clade names that are followed by an asterisk contain sequences from multiple sources. An expanded version of this phylogeny can be found in S10 Fig. The GenBank accession numbers of the sequences used to build this phylogeny can be found in Tables 1 and S2-S4. https://doi.org/10.1371/journal.pone.0236135.g006

Fig 7. Comparison of the detection of a fungal genus across methods.
A heatmap representing a comparison of the detection of the presence / absence of fungal genera isolated in this study (using a culture-dependent method) and fungal genera identified in high throughput sequencing data from Ettinger & Eisen [43] (using a culture-independent method). For each fungal genera, we visualize if it was not detected (light grey), detected using only the culture-dependent method (medium grey), detected using only the culture-independent method (dark grey) or detected by both methods (black) for each sample type / isolation source (leaf, root, rhizome, sediment).
https://doi.org/10.1371/journal.pone.0236135.g007 could use alternative approaches such as adding different combinations and concentrations of fungicides in order to fully survey rare and slow-growing fungi in this system.
Many of the fungal taxa isolated here are known to have complex life history strategies when associated with land plants. For example, the genus Ramularia includes species that are pathogens of a variety of important agricultural plants including barley and sugar beets [100,101] and the genus Colletotrichum includes members that can form endophytic or pathogenic associations with land plants [102]. Additionally there is mounting support for a multi-niche view of fungi, with many phyto-pathogens now being found able to form benign or even beneficial endophytic associations with plants [103]. Thus, future research should endeavour to investigate the true functional roles these fungal genera may have when associated with Z. marina and whether these functional roles shift when Z. marina is stressed or challenged.
Although our goal here was to isolate seagrass-associated fungi, we also identified 40 bacterial isolates associated with Z. marina. Since we were using antibiotics, we do not expect these isolates to be representative of the true diversity of the culturable bacterial community associated with Z. marina, just the subset of the community naturally resistant to the antibiotics used here. Most of the bacterial isolates we obtained are from known ubiquitous marine lineages (Vibrio, Pseudoalteromonas, Pseudomonas, Shewanella, and Bacillus) which are likely habitat generalists and have all been previously cultured from Z. marina from Bodega Bay, CA [18, 20, 22-25]. We also isolated several bacterial isolates that may represent rare or slow-growing taxa with interesting ecological implications for the seagrass ecosystem. This includes several Actinomycetes (Streptomyces sp., Rhodococcus sp. and Isoptericola), representatives of which are known to produce a variety of antibiotics and interesting secondary metabolites [104] and members of the genus, Isoptericola, have been previously isolated as endophytes of mangrove plants [105]. We also isolated a Phyllobacterium sp. and representatives of this genus are slowgrowing N 2 -fixing plant-growth promoting bacteria which have been previously isolated from mangrove rhizosphere and the roots of land plants [106][107][108]. Land plants often overcome nitrogen limitation through beneficial relationships with N 2 -fixing bacteria and similar associations have been observed between N 2 -fixing bacteria and Zostera [14,[109][110][111][112]. Based on their role as established N 2 -fixers in other plant systems, it is possible Phyllobacterium sp. may be involved in fixing nitrogen for seagrasses and this possibility should be further investigated.
Just like marine fungi, oomycetes are neglected in marine systems even though they are often implicated as important pathogens of land plants (e.g. Phytophthora ramorum [113]), Phytophthora infestans [114], Pythium spp. [115]). Historically, oomycetes were thought to be fungi based on their similar morphology, but phylogenetic methods have now shown that these organisms are more closely related to diatoms and brown algae [116][117][118][119]. In the course of this study, we isolated two members of the Halophytophthora. Representatives of Halophytophthora have been previously isolated associated with Z. marina [47] and this genus includes known saprophytes (organisms living on organic matter) and are thought to be important decomposers in mangrove ecosystems [120]. Recently, Govers et al. [121] suggested that Halophytophthora sp. Zostera may be common in Z. marina beds, and that this oomycete may serve as an opportunistic pathogen by decreasing seed germination in Z. marina populations under certain environmental conditions. More work is needed to understand the possible implications of these oomycetes in the seagrass ecosystem.

Conclusion
Overall, this study generated a fungal culture collection which broadens understanding of the diversity of Z. marina associated fungi and highlights a need for further investigation into the functional and evolutionary roles of fungi and microbial eukaryotes (e.g. oomycetes) associating with seagrasses more generally. We placed this fungal collection in the phylogenetic context of isolates obtained from other seagrass surveys and found that only habitat generalists were isolated in association with multiple species. We then compared the composition of this fungal collection to high throughput sequencing results of the fungal community associated with Z. marina from Ettinger & Eisen [43] and found that taxa isolated here were generally present in the sequencing data, but that they were not prevalent, with the exception of the Glomerellales (e.g. Colletotrichum sp.) on the leaves. Although this study adds to general knowledge of the diversity of Z. marina associated fungi, there are still many unanswered questions to be addressed related to the life history strategies, functional roles, and dispersal mechanisms of marine and seagrass-associated fungi. One of the biggest challenges in marine mycology is assessing whether the fungal taxa observed are actively growing in the marine ecosystem [122]. For our study here we could ask-are many of the proposed habitat generalists actively growing in the seagrass ecosystem or merely passing through as spores? Additionally, many of the fungi cultured here in association with Z. marina have close relatives that are also known to be opportunistic pathogens of land plants. Are these fungi Z. marina pathogens or do they serve some other function in the marine environment? Ultimately this work serves as a necessary first step towards experimental and comparative genomic studies investigating the functional roles of these understudied microorganisms and which may lead to important discoveries related to molecular biology, natural product discovery, fungal diversity and evolution, and global importance of marine fungi. A molecular phylogeny of 28S rRNA genes for isolates in the Basidiomycota and Mucoromycota was constructed using Bayesian inference. This alignment was generated using MAFFT (v. 7.402) on the CIPRES Science Gateway web server, trimmed using trimAl (v.1.2) and a phylogenetic tree was inferred on the trimmed alignment with a GTR + I + G model using MrBayes (v. 3.2.2) [75][76][77]81]. Displayed at each node as a circle in the tree are the Bayesian posterior probabilities (e.g. a black circle represents probabilities greater or equal to 90%, a grey circle represents probabilities greater or equal to 70%, a white circle represents probabilities less than 70%). The names of fungi isolated from Z. marina are shown in green, fungi isolated from other seagrass species are shown in black, and all other fungi are shown in grey. For visualization purposes, selected clades have been collapsed and the number of sequences within that clade is indicated. The GenBank accession numbers of the sequences used to build this phylogeny can be found in Tables 1 and S2-S4. (TIF)

S8 Fig. Phylogenetic placement of seagrass fungal isolates in the Eurotiomycetes.
A molecular phylogeny of 28S rRNA genes for isolates in the Eurotiomycetes was constructed using Bayesian inference. This alignment was generated using MAFFT (v. 7.402) on the CIPRES Science Gateway web server, trimmed using trimAl (v.1.2) and a phylogenetic tree was inferred on the trimmed alignment with a GTR + I + G model using MrBayes (v. 3.2.2) [75][76][77]81]. Displayed at each node as a circle in the tree are the Bayesian posterior probabilities (e.g. a black circle represents probabilities greater or equal to 90%, a grey circle represents probabilities greater or equal to 70%, a white circle represents probabilities less than 70%). The names of fungi isolated from Z. marina are shown in green, fungi isolated from other seagrass species are shown in black, and all other fungi are shown in grey. The GenBank accession numbers of the sequences used to build this phylogeny can be found in Tables 1 and S2- A molecular phylogeny of 28S rRNA genes for isolates in the Sordariomycetes was constructed using Bayesian inference. This alignment was generated using MAFFT (v. 7.402) on the CIPRES Science Gateway web server, trimmed using trimAl (v.1.2) and a phylogenetic tree was inferred on the trimmed alignment with a GTR + I + G model using MrBayes (v. 3.2.2) [75][76][77]81]. Displayed at each node as a circle in the tree are the Bayesian posterior probabilities (e.g. a black circle represents probabilities greater or equal to 90%, a grey circle represents probabilities greater or equal to 70%, a white circle represents probabilities less than 70%). The names of fungi isolated from Z. marina are shown in green, fungi isolated from other seagrass species are shown in black, and all other fungi are shown in grey. The GenBank accession numbers of the sequences used to build this phylogeny can be found in Tables 1 and S2- A molecular phylogeny of 28S rRNA genes for isolates in the Dothideomycetes was constructed using Bayesian inference. This alignment was generated using MAFFT (v. 7.402) on the CIPRES Science Gateway web server, trimmed using trimAl (v.1.2) and a phylogenetic tree was inferred on the trimmed alignment with a GTR + I + G model using MrBayes (v. 3.2.2) [75][76][77]81]. Displayed at each node as a circle in the tree are the Bayesian posterior probabilities (e.g. a black circle represents probabilities greater or equal to 90%, a grey circle represents probabilities greater or equal to 70%, a white circle represents probabilities less than 70%). The names of fungi isolated from Z. marina are shown in green, fungi isolated from other seagrass species are shown in black, and all other fungi are shown in grey. The GenBank accession numbers of the sequences used to build this phylogeny can be found in Tables 1 and S2-S4. (TIF) [43]. A histogram representing the mean relative abundance of amplicon sequence variants (ASVs) grouped by order and colored by sample type (leaf, rhizome, root, or sediment). The numbers included on each bar represent the mean relative abundance of the order detected on that particular sample type and only mean relative abundances greater than one percent are shown. (TIF) S1 Table. Collection and media specifications for each microbial isolate associated with Z. marina. Here we report the specifics of the culture media used to initially grow each isolate including the media recipe used, the salt source and amount, and the inclusion of dehydrated crushed seagrass and of various antibiotics. We also report the collection date of the initial inoculum, whether the inoculum if tissue (e.g. roots, rhizome, leaves) came from tissue washes, crushed tissue or whole tissue, whether the plated inoculum was associated with an individual Z. marina plant from a core or multiple Z. marina plants from a bag, and finally the DNA extraction kit used to extract DNA from each isolate. (XLSX) S2 Table. Fungal sequences used in molecular phylogenies found based on top BLAST matches to Zostera marina associated fungal isolates. Here we report the GenBank accession number and taxonomic information (Class, Order, Molecular ID) for each fungal 28S rRNA gene sequence obtained based on top BLAST matches to fungal isolates in Table 1 and used here to generate Figs 3-6 and S7-10. (XLSX) S3 Table. Sequences from fungi isolated from seagrasses used in molecular phylogenies.

S11 Fig. Mean relative abundance of fungal orders isolated in this study across sample types in high throughput sequencing data from Ettinger & Eisen
Here we report information about the fungal 28S rRNA gene sequences used here to generate Figs 3-6 and S7-10 which represent fungal strains previously isolated from seagrasses. We note the seagrass species and tissue material (e.g. leaf, root, matte or rhizomes) the fungus was isolated from, as well as the taxonomic information (Class, Order, Molecular ID, Strain), Gen-Bank accession number and the study of origin for each fungal 28S rRNA gene sequence. (XLSX) S4 Table. Non-seagrass associated fungal isolate sequences from the literature used in molecular phylogenies. Here we report the GenBank accession number and taxonomic information (Phylum, Class, Order, Species, Strain) for each fungal 28S rRNA gene sequence previously used in James et al [73,74] and used here to generate