Fungal community dynamics during a marine dinoflagellate (Noctiluca scintillans) bloom

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Fungal community dynamics during a marine dinoflagellate (Noctiluca scintillans) bloom Jing-Yun Suna,b, Yu Songa, Zhi-Ping Maa, Huai-Jing Zhanga, Zhong-Duo Yangb,c, Zhong-Hua Caia,∗∗, Jin Zhoua,∗ a Shenzhen Public Platform for Screening and Application of Marine Microbial Resources, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, Guangdong Province, PR China b School of Life Science and Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu Province, PR China c The Provincial Education Key Laboratory of Screening, Evaluation and Advanced Processing of Traditional Chinese Medicine and Tibetan Medicine, School of Life Science and Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu Province, PR China

A R T I C L E I N F O

A B S T R A C T

Keywords: Fungal community Dinoflagellate Algal bloom Noctiluca scintillans Algal-microbes interaction

Contamination and eutrophication have caused serious ecological events (such as algal bloom) in coastal area. During this ecological process, microbial community structure is critical for algal bloom succession. The diversity and composition of bacteria and archaea communities in algal blooms have been widely investigated; however, those of fungi are poorly understood. To fill this gap, we used pyrosequencing and correlation approaches to assess fungal patterns and associations during a dinoflagellate (Noctiluca scintillans) bloom. Phylum level fungal types were predominated by Ascomycota, Chytridiomycota, Mucoromycotina, and Basidiomycota. At the genus level drastic changes were observed with Hysteropatella, Malassezia and Saitoella dominating during the initial bloom stage, while Malassezia was most abundant (> 50%) during onset and peak-bloom stages. Saitoella and Lipomyces gradually became more abundant and, in the decline stage, contributed almost 70% of sequences. In the terminal stage of the bloom, Rozella increased rapidly to a maximum of 50–60%. Fungal population structure was significantly influenced by temperature and substrate (N and P) availability (P < 0.05). Inter-specific network analyses demonstrated that Rozella and Saitoella fungi strongly impacted the ecological trajectory of N. scintillans. The functional prediction show that symbiotrophic fungi was dominated in the onset stage; saprotroph type was the primary member present during the exponential growth period; whereas pathogentroph type fungi enriched in decline phase. Overall, fungal communities and functions correlated significantly with N. scintillans processes, suggesting that they may regulate dinoflagellate bloom fates. Our results will facilitate deeper understanding of the ecological importance of marine fungi and their roles in algal bloom formation and collapse.

1. Introduction Harmful algal blooms (HABs) are common ecological disasters with serious global consequences, including effects on the trophic structures of ecosystems, alteration of biogeochemical cycles (Zhang et al., 2016), and release of toxins, that can lead to loss of wild and cultured seafood resources and threaten to human health through bioconcentration (Anderson et al., 2012). Much effort has been expended to investigate the triggers and formation mechanisms of HABs, as understanding the factors regulating their dynamics is crucial to their effective management and control. In recent years, the formation, development,

maintenance, and termination of HABs have been explained by a combination of abiotic and/or biotic factors; the former include environmental conditions and nutrient availability, while the latter consist of grazing, pathogenicity, and parasitism (Bouchouicha et al., 2012; Carnicer et al., 2015). Among biotic drivers, microbes are increasingly cited as influencing HAB development (Teeling et al., 2012; Klindworth et al., 2014; Needham and Fuhrman, 2016), because of their roles in biogeochemical cycling, micro-food web structure, and production of essential elements which stimulate algal growth (Ferrier et al., 2002; Harvey et al., 2016). Microbes can also promote algal cyst formation, absorb essential elements, exhibit algicidal activity, inhibit sexual

∗ Corresponding author. Room 902, Marine Building, Graduate School at Shenzhen, Tsinghua University, Shenzhen University Town, Xili Town, Shenzhen, 518055, Guangdong Province, PR China. ∗∗ Corresponding author. E-mail addresses: [email protected] (Z.-H. Cai), [email protected] (J. Zhou).

http://dx.doi.org/10.1016/j.marenvres.2017.10.002 Received 23 August 2017; Received in revised form 27 September 2017; Accepted 2 October 2017 0141-1136/ © 2017 Elsevier Ltd. All rights reserved.

Please cite this article as: Sun, J.-Y., Marine Environmental Research (2017), http://dx.doi.org/10.1016/j.marenvres.2017.10.002

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during the bloom period over a two weeks period in this season. Dates of collection were as follow: Feb. 03, 04, 05, 06, till to 16. There are total 14 time-point samples in the bloom cycle. The related names were DC1, DC2, DC3, DC4, DC5, DC6, DC7, DC8, DC9, DC10, DC11, DC12, DC13 and DC14, respectively. Three parallel samples were collected at each time-point. Each of the samples consisted of 5 L of seawater with a niskin bottle. Water samples were then transferred to a polyethylene container and kept at 4 °C during delivery to the laboratory. Samples were successively filtered through a 300-mesh plankton net to remove large zooplankton and then through a 0.22-μm-composite fiber membrane (diameter 47 mm, Millipore) to collect microbes. Filters were stored immediately at −80 °C before DNA extraction. In addition, part of each water sample was preserved with 1% Lugol's iodine solution for phytoplankton identification and enumeration. N. scintillans were observed and counted under an optical microscope (magnification, ×100–400) (ZEISS, Germany).

reproduction, and regulate algae-bacteria signaling (e.g., quorum sensing) (Adachi et al., 2003; Sanders, 2014; Brosnahan et al., 2015; Demuez et al., 2015; Bloh et al., 2016; Zhou et al., 2016). Microbial community structures during bloom events are complex and change depending on the algal species, physiological status, environmental conditions, and bloom stage (Zheng et al., 2011). Various examples of microbial community structures during natural or mesocosm phytoplankton blooms have been reported, and certain dominant organisms (including bacteria and archaea) were commonly associated with phytoplankton blooms (Buchan et al., 2014). There is strong evidence for a close association between HAB emergence and symbiotic bacteria at the microcosm and mesocosm scales (Lamy et al., 2009; Tada et al., 2012; Chen et al., 2016). The structure and metabolic properties of these communities influence their ecological function, which can include provision of nutrients, release of organic compounds, and competition with algae for particular ecological niches (Amin et al., 2015). Field survey data demonstrate that phytoplankton blooms, as recurring seasonal phenomena, could lead to predictable patterns of bacterial succession (Teeling et al., 2016). Hence, it is possible to investigate the interactions between rhythmic biogeochemical variables, HABs, and bacterial behavior. Together, these microecological behaviors of bacteria and archaea create regulatory networks that operate throughout bloom formation, duration, and collapse (Tan et al., 2015). Compared with prokaryotic bacteria and archaea, relatively little is known about the composition and dynamics of eukaryotic fungal communities during HABs. Fungi have multiple roles in marine ecosystems, including decomposition of organic matter, denitrification, mediation of energy flow, and interfacing symbiotic process such as parasitism and mutualism (Richards et al., 2012; Gibbons and Rokas, 2013). In aquatic ecosystems, fungi can influence the initiation and development of blooms of various phytoplankton species, including dinoflagellates and diatoms (Kagami et al., 2007). Some fungi affect the photobiological properties (Fv/Fm) or killing of phytoplankton by mechanical division or secretion of secondary metabolites (Rasconi et al., 2012; Gerphagnon et al., 2013, 2015; Furbino et al., 2014; Hayashi et al., 2016), and they can cause the collapse and increase the mortality of cultured algae (Rasconi et al., 2012). Both of these features have central, yet largely overlooked, roles in algal blooms (Cooper et al., 2014). Despite recent advances, a holistic understanding of the relationships between algae and fungi and their population dynamics and ecological roles is still lacking, particularly in marine environments. In addition, previous studies have mainly focused on laboratory scale, or incomplete phycosphere samples from field environments; however, few studies have focused on the successional trajectories of fungal communities over entire phytoplankton bloom cycles. In this study, comprehensive algal-bloom cycle samples were collected from Dongchong Bay (a semi-opened basin in coastal of Shenzhen, China). Annual phytoplankton blooms occur in this area, making it an ideal “natural laboratory” for the study of algal-fungal dynamics. Fungal-algal interactions are among the critical relationships influencing both bloom initiation and termination (Zai, 2013). Here, we used an N. scintillans bloom as an example to investigate the successional pattern of fungi and its correlation with environmental factors over a natural bloom-induced perturbation. The final goal is better understand the importance of fungal roles in the fate of HABs.

2.2. Environmental parameters For each sampling time, the physicochemical parameters (temperature, salinity, and pH) of the seawater were recorded using a MultiParameter Water Quality Sonde 6600 V2 (YSI, USA). Chlorophyll a (Chll a) concentration and photosynthetic activity (Fv/Fm) were measured using a Chlorophyll Fluorescence System (PHYTO-PAM, Germany). Other environmental factors, including nitrate nitrogen (NO3−), nitrite nitrogen (NO2−), ammonium nitrogen (NH4+), and phosphate phosphorus (PO43−) were performed on a Discrete Chemistry Analyzer (CleverChem Anna, Germany), according to the operating instruction. NH4+ was measured according to the indophenol blue method using a spectrophotometer (DR/2800, Hach) (Gordon et al., 1992). The phosphate (PO43−) is measured by spectrophotometry following the formation of phosphomolybdic acid according to Murphy and Riley (1962). The NO3− and NO2− analyses were referenced from the Center for Microbial Oceanography methods at the University of Hawaii. The complete protocols were available online at http://cmore. soest.hawaii.edu. The standards for NO3−, NO2−, and PO43− were obtained from Shanghai Macklin Biochemical Co., Ltd (China), and the purity was > 99%.

2.3. DNA extraction and PCR amplification Total DNA was extracted from filters using a Fast DNA Spin Kit (mBio, USA) according to the manufacturer's instructions. The extracted DNA was dissolved in 100 μL TE buffer, quantified by spectrophotometer (Nanodrop™, 2000) (OD260/OD230 > 1.8), and stored at −20 °C until further use. Fungal DNA amplification of the ITS1 region was performed using the ITS1F primer (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and the ITS2 primer (5′-GCTGCGTTCTTCATCGATGC-3′) (Wainwright et al., 2017). The PCR reaction mixture (20 μl) consisted of 2 μl 10 × buffer (500 mM KCl, 100 mM Tris-HCl, 15 mM MgCl2, and 1% Triton X-100), 1.6 μl of 2.5 mM dNTPs, 0.8 μl of each primer, 0.2 μl (5 U) of Taq DNA polymerase (Takara Biotechnology Co., Ltd., Dalian, China), 13.6 μl of water, and 1.0 μl of template DNA (10–50 ng). PCR reactions were conducted using an Eppendorf Mastercycler (Eppendorf Co., Ltd., Hamburg, German) and the following program: denaturation at 95 °C for 5 min; followed by 25 cycles of 30 s at 95 °C, 30 s at 55 °C, and 90 s at 72 °C; then a final extension at 72 °C for 10 min. Reaction mixtures containing no template DNA were used as negative controls. PCR products were pooled, purified, and concentrated using a commercial kit (QIAquick, Qiagen, USA). A single composite sample was prepared for pyrosequencing by combining approximately equimolar amounts of PCR products from each sample. Sequencing work was carried out at MeiGe Biotech. Co. Ltd. (Guangzhou, China).

2. Materials and methods 2.1. Sampling collection The samples were collected from a coastal region of Dongchong (abbreviation DC, 117°.03′24 E, 23°.44′.19 N) in Shenzhen, China, where a N. scintillans bloom regularly occurs. In order to obtain a complete algal-blooming samples, we monitored phytoplankton concentration twice a week by track-sampling. Fortunately, an algal bloom has occurred in Feb. 03. 2016. Surface seawater samples were collected 2

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ratio of the variance of the total numbers of species in the samples to the sum of their individual species variances. VR was used to examine species abundance associations, with VR > 1 or < 1 indicating that species covaried positively or negatively, respectively. W is a statistic with chi-squared distribution for testing the significance of VR using a two-tailed test. The significance of associations was evaluated using profile data converted to binary matrices (Schluter, 1984). Further analyses focused on pair-wise relationships among OTUs applying Spearman rank correlation (rs) to OTU abundances. To visualize the correlations generated by the analysis, a network was create to identify interspecific associations and correlations among species.

2.4. Processing of sequence data Raw sequences were processed and checked using pipelines built using mothur and QIIMe tool, and were trimmed and filtered according to previously described methods (Huse et al., 2007). After low-quality reads were removed, representative sequences were annotated using an alignment tool (BLAST) by searching against the Ribosomal Database Project and the Silva database augmented with sequences from major marine fungal taxa (for 18S rRNA sequences) (Quast et al., 2013). We assigned OTUs at a 3% sequence clustering level using the UPARSE (version 7.1; http://drive5.com/uparse/) pipeline (Edgar et al., 2011). To prevent artificial diversity inflation, singletons were removed as putative sequencing errors or PCR amplification artifacts (Kunin et al., 2010). The sequence data reported here have been deposited in the NCBI GenBank database under the accession number SUB3073913.

2.8. Functional prediction The functional prediction of fungi were carried out using FUNGuild software (https://omictools.com/fungi-functional-guild-tool) and the dataset of (Tedersoo et al., 2014; Nguyen et al., 2016), resulting in 2238 OTUs with identified functional guilds. Only sequence taxonomy identity above 93% and the guild confidence ranking assigned to “highly probable” and “probable” was accepted. Guilds were combined as follows: symbiotroph is equivalent to mycoparasite; pathogen include plant pathogen and animal pathogen; saprotroph include endophytic saprotroph, parasitic saprotroph and undefined saprotroph; other major species include ericoid mycorrhizal, lichenized and arbuscular mycorrhizal. The taxa could not reliably be placed into a single functional group and were listed as unknown. These combinations were made to reflect the key questions about fungal functional change resulting from HAB events. Differences in proportions of symbiotrophs, saprotrophs and pathogens were calculated among the different blooming stages. The differences were tested against a null hypothesis of no difference between pairs using a Wilcoxon signed-rank test with a 95% confidence interval, p < 0.05 (Wilcoxon and Wilcox, 1964).

2.5. Community structure analysis Heatmaps of eukaryote fungi communities were created using the PHYLOTEMP tool (http://www.phylotemp.microeco.org) developed by Polson (2007), whereby relative abundance data from sample libraries were clustered based on the Bray-Curtis similarity algorithm. For higher resolution of relationships at the genus level, the 18S rRNA gene sequences generated in this study were compared with known sequences using BLAST. Sequences were aligned with the Clustal W program (Thompson et al., 1994), and phylogenetic analyses performed with the MEGA 4.0 software package using the neighbor-joining method (Tamura et al., 2007). Clustering analysis was implemented using Online Bayesian Software (http://openscholarship.wustl.edu/cse_ research/15). 2.6. Statistical analyses Alpha-diversity and beta-diversity indices for each sample were calculated using the Mothur software package (Schloss et al., 2009). Statistical differences in taxa abundance were calculated with a Welch's t-test in the STAMP program (Welch, 1947). The structure of the fungal community at each sampling time was compared by nonmetric multidimensional scaling. Statistical tools [canonical correspondence analysis (CCA) or principal component analysis (PCA)] were applied to evaluate relationships between fungal community composition and environmental parameters. Variation partitioning analysis (VPA) was conducted to examine the contribution of environmental factors in influencing microbial communities according to the CCA analysis. All significant differences were defined as a p-value of < 0.05. Groups were compared using the Statistical Analysis of Metagenomic Profile package, STAMP (http://kiwi.cs.dal.ca/Software/STAMP) (Parks and Beiko, 2010). The tested environmental variables (NH4+, NO2−, NO3−, PO43−, pH, salinity, and temperature) were calculated using SPSS 10.0 software (SPSS, Chicago, USA).

3. Results 3.1. Environmental parameters and bloom features Fourteen time-point samples (DC1-DC14) were obtained at different temporal stages of the bloom: I, pre-bloom stage; II, onset stage; III, logarithmic stage; IV, decline stage; and V, terminal stage. Over the duration of the sampling period, the temperature, salinity, and pH of sea waters ranged from 21.2 to 23.5 °C, 29.8‰–33.1‰, and 8.06 to 8.36, respectively (Table 1). N. scintillans cell densities ranged from 1.05 × 102 to 2.53 × 103 cells/mL, and the highest biomass value appeared at the bloom's peak (DC4, Fig. 1A). Maximum chlorophyll a concentration and algal biomass accompanied the occurrence of the N. scintillans bloom (Fig. 1B). Fv/Fm values ranged from 0.35 to 0.67, and were relatively elevated during the bloom period, then decreased gradually, reaching a minimum value at DC8 (decline stage), rising slightly toward the end of the bloom (Fig. 1B). Nutrient concentrations at each time point are presented in Fig. 1C. NH4+ and NO3− were the main inorganic N substances; concentrations of NO3− ranged from 0.015 to 0.27 mg/L, with higher concentrations detected during bloom onset stage (DC2-DC4), and relatively lower concentrations at the plateau stage (DC5). Similarly, NH4+ concentration increased gradually, reaching a maximum during the logarithmic

2.7. Association analyses Microbial interspecific associations constitute the basis of microbial ecology (Fuhrman, 2009). Interspecific associations between fungi and target species (N. scintillans) were examined using a variance ratio (VR) test (Schluter, 1984). Species associations were determined from the Table 1 The environmental parameters during the sampling period.

Temperature (°C) Salinity (‰) pH

DC1

DC2

DC3

DC4

DC5

DC6

DC7

DC8

DC9

DC10

DC11

DC12

DC13

DC14

21.0 30.4 8.10

22.2 29.8 8.08

22.8 30.5 8.10

21.4 30.6 8.06

23.3 29.9 8.10

23.5 30.6 8.20

23.3 31.5 8.12

22.8 31.4 8.09

22.7 33.1 8.08

21.9 30.8 8.10

21.5 32.4 8.06

22.6 31.5 8.10

21.9 31.6 8.12

23.2 30.8 8.36

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Fig. 1. Biological and environmental parameters during the N. scintillans bloom in Dongchong Bay, Shenzhen, China. A: target species N. scintillans biomass and morphology (the insert picture); B, the chlorophyll a concentration and Fv/Fm value in the whole algal bloom cycle. C: nutrients concentrations, including NO3−, NO2−, NH4+ and PO43−.

fungal organisms in the marine environment. Clear differences in alphadiversity indices (Chao1) were observed among different bloom stages (Fig. 2A); values gradually increased from DC1 to DC3, reached a peak at DC3 (logarithmic phase), and subsequently remained stable until DC8. The alpha-diversity index reached a minimum when the bloom reached the second decline time-point (DC9), then rose somewhat until the bloom disappeared. The Shannon index decreased slowly from DC1 to DC4, then gradually increased, with two peak values at DC7 and DC13. Notably, there was an inflection point at DC9 (between DC7 and DC13). The change trend of the Simpson index was similar to that of the Shannon index. Beta-diversity patterns of the fungal communities (among-sample differences in OTU composition) were examined by heatmap (Fig. 2B),

phase (DC3), followed by a decrease in concentration. When the bloom entered the decline stage, NH4+ levels increased to 0.15 mg/L. In contrast, PO43− concentrations followed a shallow bell-shaped curve with relatively low PO43− (< 0.01 mg/L) at plateau and post-bloom stages, compared with higher concentrations (0.019–0.028 mg/L) at pre-bloom period. 3.2. Diversity A total of 3468 operational taxonomic units (OTUs) were assigned at the 97% similarity threshold. Relatively consistent coverage values were observed at a 3% dissimilarity threshold (79.6%–91.8% among all samples), indicating that the results likely represented the majority of

Fig. 2. The original and rarefied OTUs, biodiversity calculated for the samples collected during the bloom, at a 0.03 distance level. A is the α-diversity, including Chao1, Shannon, and Simpson indices; and B is the β-diversity.

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Fig. 3. Fungal community composition at genus levels (top 30) in samples during the N. scintillans bloom.

strongly associated with N. scintillans. At the bloom peak, the majority of associated 18S OTUs consisted of Aspergillum, Isaria, and Schizosaccharomyces. During bloom decline, diversity was higher than in the previous two stages; Galactomyces, Ustilago, Saitoella, Funneliformis, Paulia, Lipomyces and some low-abundance species, co-existed during this phase. Interestingly, when the bloom entered the final stages (DC12-DC14), the number of associated genera increased significantly, and Mortierella, Myxozyma, and Dipodascus became the top three positively-associated species in the fungal biosphere (Fig. 4B). To gain further insight into the differences among fungal communities at the five different bloom stages, we applied Venn map analyses of the main OTUs to illuminate their distributions (Fig. 5A). The results demonstrated that fungal OTUs exhibited temporal heterogeneity across the entire algal bloom cycle. The number of overlapping OTUs was 88. There were more taxa specific to early and late phases (stages I and V) than in middle phases (stages II, III, and IV) of the bloom. In particular, sampling period V had the highest number of specific OTUs, with 323 tags detected. The cluster picture (Fig. 5B) further demonstrated approximately five clusters according to bloom stage. Samples exhibited distinct phylogenetic compositions along the temporal axis. For example, fungi collected from DC1 to DC3 formed one cluster (preand onset-stage), while those collected between DC4 and DC7 formed a second cluster (plateau stage), and the final samples (DC8-DC14) formed the third to fifth clusters (decline- and collapse stages). Notably, as illustrated in Fig. 5B, the taxa clusters exhibited clear differences during the algal-bloom process; however, some overlaps were observed among the bloom sampling times, indicating that the clusters were not rigidly conserved.

where the color intensity shows the similarity between the species present. We saw three clear distributions according to time scale in spite of some overlap. The general trend in dissimilarity of species was group I > group II > group III. There were some differences in betadiversity among the collected samples; from the before-, during-, to after-bloom stages. The biggest difference was exhibited at terminal stage (DC13); partly differences appeared in decline stage (DC7 and DC12); and relatively low dissimilarity was observed in the rest of samples (onset- and during stages). Interestingly, when the blooming entry the last time-point (DC14), the beta-diversity index returned to the original decline stage (DC7) level (Fig. 2B). 3.3. Fungal community composition Among the OTU tags, Ascomycota, Chytridiomycota, Mucoromycotina, Basidiomycota, and Cryptomycota were the most prevalent phyla throughout the sampling period. Detailed profiles of fungal community structures (genus level) during N. scintillans blooms are shown in Fig. 3. Pre-bloom, Hysteropatella, Malassezia, Saitoella, and Mycosphaerella species comprised the majority of the fungal community, then the proportion of Malassezia significantly increased, reaching peak abundance (> 60%) at the height of the bloom (DC4). During later stages, Malassezia and Rozella were gradually replaced by other fungal taxa, including Saitoella, Lipomyces, Dicellomyces, and some low-abundance genera (Funneliformis, Penicillium, Capronia, and Allomyces). Interestingly, when the algal-bloom was over (DC12-DC14), Rozella became predominant, making up 40%–65% of all fungi, with the remainder made up of 29 other genera (Fig. 3). To better elucidate the distribution of fungal OTUs at different bloom stages and identify OTUs unique to certain phases, heatmap analysis was performed (Fig. 4). The distribution of fungal orders differed among the 14 sampling time-points (Fig. 4A). Of the nine main orders, Basidiomycota was strongly associated with the pre-bloom stage, Ascomycota with onset, and Neocallimastigomycota primarily with the peak-bloom stage, whereas the orders strongly associated with the decline and terminal stages made up two groups: group 1 (Mucoromycotina, Glomeromycota, and Blastocladiomycota), and group 2 (Mortierellomycotina, Chytridiomycota, and Cryptomycota). Additionally, the predominant genera (> 100 reads) exhibited distinct dynamics during the bloom (Fig. 4B). Pre-bloom, Malassezia, Allomyces, and Tuber were

3.4. Associations between fungal communities and environmental variables Associations between fungal communities and environmental factors are presented in Fig. 6A. Among the physical parameters, the biggest determinant of community structure was temperature; with salinity the second most influential factor, exhibiting an opposite correlation relative to temperature. There was no obvious negative or positive correlation between population dynamics and pH value. Among the chemical variables, NO3−, NO2−, NH4+, and PO43− cocontribution to the variability of fungal communities. Moreover, the data suggest that NH4+ was likely a factor in controlling population 5

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Fig. 4. Correlation heatmap diagrams visualizing (log2 fold change of the abundances) the correlation of the samples at different taxonomic levels. (A) Relative abundances of the dominant fungal order; (B) relative abundances at deeper classifications (genus level). Blue color represents a lower log2 fold change while red illustrates a higher log2 fold change. The scale on the right matches colours to log2 fold change values. Cluster results of the dominant genera in the different samples are shown in the top heatmap of B (based on Bray-Curtis analysis). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

correlated with PO43− and NO2− to some extent, whereas NH4+ had a negative influence; and in group 3 (the biggest branch), most genera exhibited negative associations with nutrients, while a few exhibited positive correlations, including Schizosaccharomyces with NO3− and PO43− (p < 0.05), Ustilago and Saitoella with NH4+ (P < 0.05) and Ustilago with NO2− (P < 0.01). To partition the contributions of environmental attributes, variation

variation, particularly in later bloom stages. To more closely examine the relationship between fungal genera and nutrient parameters, Pearson correlation analysis of the dominant genera was carried out. Fungal family group 1 (Penicillium, Aspergilius, and Isaria) was positively correlated with phosphate, nitrate, and nitrite nutrients, and negatively correlated with NH4+ (Fig. 6B); group 2 (Protomyces, Dicellomyces, and Mycosphaerella, etc.) was positively

Fig. 5. The Venn diagram (A) showing the degree of overlap of fungal OTUs among the five different algal bloom stages, i.e. I: pre-bloom stage, II: onset stage, III: logarithmic stage, IV: decline-stage, and V: terminal stage. (B): cluster analysis of the fungal samples during the algal bloom period.

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Fig. 6. (A) Correspondence canonical analysis (CCA) of the relationship between the environmental factors and fungal community composition. The VR test result showed in the insert picture in upper right hand corner. (B) Correlation analysis between relative abundances of the abundant fungal OTUs and environmental variables based on Pearson correlations. Correlation values depict r-values of Pearson correlations. Statistical significance levels: ∗p < 0.05 and +p < 0.01.

partitioning analysis was carried out. The results showed that physical factors (temperature, salinity, and pH value) and chemical factors (nutrients) accounted for 28.3% and 32.5% of fungal community variations in the OTU data, respectively (insert picture, Fig. 6A). In addition, interaction between physical and chemical parameters could explain 6.9% of the variation. About 39.2% of the fungal community variation from OTU data could not be explained by these components, indicating that other biotic and/or abiotic parameters exist in this process.

Endophytes and saprotrophs were the primary gulids present during the exponential growth phase. Intriguingly, class related to various types of gulids were depleted in the initial phase of bloom termination, whereas ectomycorrhizal fungi was slightly enriched during this phase. When the bloom entered the decline stage, the most abundant changes were primarily those involved in saprotrophs and pathogens, suggesting that the fungal community in the bloom in decline phase may have more degradation-related and stress response members than those at other stages.

3.5. Association network

4. Discussion

To visualize correlations, a network was constructed based correlations between the predominant taxa and the target species, N. scintillans (Fig. 7). In order to reduce network complexity, some low abundance OTUs were filtered by Excel software. There were 36 correlations between fungal phylotypes and N. scintillans at the genus level, of which 20 were negative and 16 were positive. N. scintillans was strongly positively correlated with Malassezia, Rozella, and Candida in the network (P < 0.01), and positively correlated with Tuber, Allomyces, and Isaria, etc. (P < 0.05); however, the target species was significantly negatively correlated with Saitoella (P < 0.01) and less strongly negatively correlated with the remaining 19 genera (including Rhizophydium, Mycosphaerella, etc.) (P < 0.05).

4.1. OTU characteristic Fungi occupy an important ecological niche in marine habitats and have critical roles in biogeochemical cycling and trophodynamics (Richards et al., 2012); however, relative to terrestrial environments, our understanding of the diversity and spatiotemporal dynamics of fungi inhabiting the ocean remains limited. In the present study, approximately 10%–25% of sequences could not be matched to any known phylogenetic group in the NCBI database using BLASTO (Zhou and Landweber, 2007). This is not surprising, given the poor representation of marine fungi in public databases, which predominantly comprise sequences derived from soil environments (Kõljalg et al., 2005). In this work, only five phyla were identified in the OTU databases; Ascomycota was the most abundant, followed by Chytridiomycota, Basidiomycota, Mucoromycotina, and Cryptomycota. These community characteristics may in part explain why fungi have been considered both non-diverse and of low abundance in many coastal water samples (Massana and Pedrós-Alió, 2008). In addition, the fungal diversity observed in this investigation was relatively low compared with that found in ocean water (Diao, 2009). We attribute this observation to human activities (such as input of nutrients), because coastal anthropogenic activity profoundly disturbs marine fungal communities and diversity differences were likely masked by this influential external factor (Wang and Zhang, 2003).

3.6. Functional prediction Of the OTUs run through Funguild, sequences belonging to the symbiotroph, pathogen, and saprotroph guild accounted for an average of 35.5%, 18.9%, and 29.4% of assigned reads, respectively. In general, mycoparasite guild members were more abundant in pre-blooming stage; the dominant guild in during-stage was the saprotroph guild; whereas in the collapsed stage, the dominant guild was generally cooccupied by pathogen and saprotrophic fungi. Sequences belonging to the “other” guild class accounted for an average of 16.2% of total reads. After Wilcoxon signed-rank test, a significant change in guild percent abundance among the various stages of the bloom (Fig. 8). Three groups representing major functions (mycoparasites, lichenicolous fungi, and other functional fungi) exhibited higher percentage during the onset stage, compared with the pre-bloom period (P < 0.05).

4.2. Fungal community structure in phycosphere environment In the fungal biosphere, obvious variations were associated with 7

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Fig. 7. Spearman analysis of the relationship between the main fungi and the bloom-forming species N. scintillans. There are total 36 fungal genera were analyzed based on significant Spearman rank correlation coefficients. Solid lines indicate positive correlations and dotted lines indicate negative associations; bold solid lines indicate strong negative correlations, bold dotted lines indicate strong negative associations. The single star indicates P < 0.05 and the double star indicates P < 0.01.

primarily of Malassezia (maximum > 60%) (DC3-DC7). Following the algal bloom process (DC8-DC10), there was co-occurrence of multiple fungi groups, ranging from nutrient specialists to nutrient opportunists (Christieoleza et al., 2011), which were gradually replaced by Saitoella

bloom dynamics. At the beginning of the bloom (DC1-DC2), fungal communities were dominated by Hysteropatella, with various other minor contributors (Fig. 3). Several days later, the number of target dinoflagellate cells increased, and the fungal community was comprised

Fig. 8. The predicted fungal function associated with algal bloom status after fungulid analysis. Relative signal intensity was normalized by the number of the genes for each indicated group. The main gulids are showed in the radar graph.

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their ability to destroy their hosts and provide energy sources for higher trophic levels (Grami et al., 2011). Fungi are important parasites of primary producers and nutrient cyclers in aquatic ecosystems. Fungal parasitism is linked to light intensities and algal stress that can elevate disease incidence on algae and reduce diatom concentrations (Hassett et al., 2017). In this study, we provide further evidence that fungal parasites can regulate marine dinoflagellate populations, and act as topdown regulators of algal-bloom dynamics.

and Lipomyces. An obvious variation occurred during the terminal phase of the bloom, where Rozella became the most abundant genus. Fungi community composition varied greatly at different bloom stages, indicating ecological specialization and adaptation within this diverse phylum. Some fungi (such as Aspergillus and Schizosaccharomyces) also exhibited increases at the bloom collapse stage and thereafter, possibly because species of these genera are generally opportunistic and rapidly growing, conferring strong competitive advantages in the phycosphere environment (Bernhard et al., 2014). During the bloom development and plateau periods, Ascomycota and Chytridiomycota were the dominant actors. Some members of these groups are saprotrophic (e.g., Aspergillus and Schizosaccharomyces). In soil environments, saprotrophic organisms are generally associated with nutrient concentrations and decomposition of organic substances (Kagami et al., 2012). In this study, we observed an increase in the abundance of Aspergillus and Schizosaccharomyces and speculate that their role is decomposition of N. scintillans. Similar to terrestrial ecosystems, saprotrophic fungi are major players in the C cycle, through their influence on C sequestration (Heinemeyer et al., 2007), and mediation of C allocation (Heimann and Reichstein, 2008). Saprotrophic fungi can serve as effective decomposers that boost C mineralization processes in marine environments, which is important for long-term C storage (Treseder and Lennon, 2015). These pathways, through which organic matter re-enters the food web, are vital for the survival of phytoplankton, which are unable to synthesize such compounds themselves (Raghukumar et al., 2004). Aspergillus and Schizosaccharomyces may participate in this process and provide essential nutrients (including amino acids, vitamins, polyunsaturated fatty acids and sterols) to their host (Mayor et al., 2009), which contribute to the bottom-up process of N. scintillans maintenance. When the bloom entered the decline stage, Chytridiomycetes was the dominant taxa at class level. The substantial representation of this group in the late-stage of the bloom identified here supports an emerging view of the ecological significance of chytrids in algal ecology (Gleason et al., 2017). These organisms perform vital functions as decomposers, driving nutrient cycles in detritus environments, and as parasites and symbionts (Kagami et al., 2014). Previously, Chytridiomycetes were considered primarily parasitic fungi and often thought to contribute to termination of algal blooms. For example, the spring diatoms, Asterionella and Synedra, are inhibited by the chytrid Rhizophydium planktonicum in the oligotrophic Lake Pavin (France) (Carrias et al., 1996). Another diatom, Fragilaria, became abundant; however, the proliferation of their parasite, Rhizophydium fragilaria, interrupted their development (Rasconi et al., 2012). Recent studies have confirmed the parasitic role of chytrids in destroying large filamentous phytoplankton, which are considered important for seasonal pelagic succession (Gerphagnon et al., 2013). Among the dinoflagellates, chytrids have been linked to mass mortalities of host organisms, suppression or retardation of phytoplankton blooms, and selective effects on species composition, leading to successional changes in plankton communities. For example, Sommer et al. linked fungal parasitism to changes in Ceratium hirundinella population density in Lake Constance (Sommer et al., 1984), while Heaney et al. demonstrated that the biflagellate fungus, Aphanomycopsis cryptica, can facilitate reduction of Ceratium populations in the English Lake District (Heaney et al., 1998). Subsequently, Kudoh and Takahashi showed that fungal infection can control the population size of Asterionella formosa in a shallow eutrophic lake (Kudoh and Tokahashi, 2010). In the marine environment, few studies have addressed such infections in dinoflagellates. Whether or not marine fungal parasites exert control over dinoflagellate populations similar to their freshwater counterparts, remains unclear. To date, only Hallegraeff et al. have reported that an Australian fungus caused a minor reduction in the motility of Alexandrium, or altered the morphology of Chattonella, algal cultures (Hallegraeff et al., 2014). These findings raise the possibility that parasites may have important roles during monospecific blooms, due to

4.3. Environmental factors regulating fungal structure Among analyzed environmental factors, temperature was the most important driver governing the distribution patterns of fungal communities (Fig. 6A), indicating that temperature is a major abiotic force shaping fungal community structure dynamics. Several previous studies have reported that temperature has a pronounced impact on fungal composition (Xie et al., 2013; Damialis et al., 2015). Recently, Diao demonstrated that temperature is the strongest environmental factor shaping fungal composition, particularly lignicolous sp., in marine water bodies (Diao, 2009). In German Bight, short-term bacterioplankton (including fungi) succession in response to algal blooms was also indirectly influenced by temperature (Lucas et al., 2015). Our results are consistent with those of previous reports, and demonstrate that temperature-mediated succession of the plankton community contributed to variation in patterns of microbial composition (Deng et al., 2014). Similar with the temperature, salinity is the second factor to regulate fungal composition. Previous study has demonstrated that fungi are somewhat sensitive to osmotic pressure and salinity as a candidate factor driving fungal community composition (Guo and Gong, 2014). In this investigation, we also identified a role of salinity in regulation of fungal homeostasis and control of their community distributions. However, compared with temperature, the correlation observed with salinity was not very strong; this is due to the robust chitin-rich cell walls of fungi, which can withstand osmotic shock to some degree. Similar to the physical factors, N and P are key chemical factors that influence fungal communities through their effects on substance utilization and growth (Dini-Andreote et al., 2016). Here, we observed similar relationships between NO3− and PO43− concentrations and fungal abundance during the bloom. The concentration of NO3− was generally negatively correlated with the abundance of fungi and their symbiosis during the bloom (Fig. 1); an abrupt initial decrease in NO3− (pre- and onset stages) was observed, followed by an increase in the post-bloom stage (when algal cells begin to lyse). Teeling et al. (2012) demonstrated that, during algal blooms, N substrate availability triggers corresponding microbial populations; therefore, our results support prior findings that algal bloom community succession is regulated by substrate availability, and that nutrients exert bottom-up control of fungi. Regarding P source, our observed relatively low P concentration. Some fungi (such as Rozella) can be strong competitors with algal cells for P, which may help explain why their abundance increased in the bloom collapse stage (Pollet et al., 2014). In the terminal phase, P contents were at an extremely low level, indicating that phosphorous deficiency can promote algal-bloom decline. Based on the variation partitioning analysis (VPA), the selected physical factors (temperature, pH, and salinity) and chemical factors (nutrient concentrations) explained 28.3% and 32.5% of the observed variation in the phycosphere fungal community, respectively (Fig. 6A, insert picture). However, still 39.2% of the fungal community variation could not be explained, suggesting that there are other factors, such as flow, conductivity, or oxygen conditions, as well as biological interactions, which may co-affect fungal distribution in the phycosphere environment. Further experiments should be performed in the future to confirm the influence of unmeasured parameters and relationships in algal blooms.

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suggests that mycoplankton (such as saprotrophic fungi) is a component of the marine carbon cycle that cannot be ignored. At present, it is difficult to estimate carbon biomass from the obtained data. However, based on the existed results, it let us to postulate that fungal communities exhibit taxonomic and functional diversity, and exhibit part potential to influence biogeochemical cycles in phycosphere environment (McGenity et al., 2012).

4.4. Association network correlations Correlations between fungal and phytoplankton community interactions and dynamics are extremely complex in the phycosphere environment (Buck et al., 2015). In previous studies (laboratory or mesocosm level), specific fungal associations with related algae taxonomic groups were reported (Sime-Ngando, 2012; Paver et al., 2015); however, few studies have attempted to determine the correlations between fungi and algae at the field level. In this study, a multiplicity of interspecific associations within the network were analyzed. N. scintillans was positively correlated with Malassezia, Rozella, Protomyces, and Physcia, among other fungi (P < 0.05 or 0.01). These species are characterized as opportunistic, and have multiple roles, including that of a nutrient “cooker” (producing vitamins, Fe, and dissolved organic matter), degradation of organic matter, and metabolism of dimethylsulfoniopropionate (Bacic et al., 1998). This positive correlation offered the opportunity for species to adapt to the surrounding environment and is also a likely explanation for the duration of the algal bloom (Tan et al., 2015). In contrast, negative correlations were also observed between fungi and N. scintillans, reflecting the appearance of nutrient competitors (e.g., Saitoella and Myxozyma) or pathogenic fungi (e.g., Aspergillus and Penicillium) during bloom termination, since these two types of genera were more abundant at the bloom decline stage. Nutrient competitor species are more efficient than phytoplankton at acquiring carbon and nitrogen from organic compounds (Lang et al., 2005), while the pathogenic fungi can release molecules that kill or lyse algal cells (Redhead and Wright, 1978; Carrias et al., 1996). Taken together, the current results show that existence of promoter or competitor species among the fungal taxa in the phycosphere environment. Bloom formation and termination were influenced by positive and negative associations, which may help to explain the occurrence and extent of duration of algal blooms.

5. Conclusion This study comprised a comprehensive survey of fungal communities from a naturally occurring N. scintillans bloom, and found that fungi exhibited remarkable heterogeneity in response to this ecological event. Fungal community structures observed during the bloom were co-shaped by physiochemical factors and substance availability. The observed correlations suggest that a multitude of fungal interactions (both positive and negative) are likely involved in determining the fate of a HABs at a specific times and locations. Functional predictions demonstrated that fungi exhibited some functional plasticity to regulate algal dynamics, facilitating understanding of the microbial loop and ecological interactions (competition, symbiosis, and predation) between algae and fungi. In the future, based on the significant roles of fungi in the processes of generating and removing HABs, detailed examination of the functional relationships between phytoplankton and fungal communities and metabolic characters are required to better understand the mechanisms involved in HAB formation. Conflict of interest The authors declare no conflict of interest. Acknowledgements This study was supported by NSFC (41476092), the S & T Projects of Shenzhen Science and Technology Innovation Committee (JCYJ20150529164918736, JCYJ20170412171959157, and JCYJ20170412171947159), and the Key Research and Development Plan of Ministry of Science and Technology of China (2017YFC1403600).

4.5. Fungal potential functions in algal bloom The results of functional prediction analyses demonstrated that the most significant differences among functional categories among the different stages were in symbiotroph, pathotroph, and saprotroph (Fig. 8). At the beginning of bloom (pre- and onset-stages), mycoparasites and lichenized fungi were dominated, which are in agreement with previously result (Chen et al., 2014). Mycoparasites are common symbiotic species in plant, soil and water-body, they were anticipated to benefit from the expansion of their host. It was not surprised to see relatively high portion in the early-bloom stage. When the algal-bloom entry the platform phase, the majority of observed change is the increased saprotrophic fungi (a key regulator of substance and nutrient cycling), suggest it is an important contributor to assistant HAB development. In decline stage, some obviously alteration were observed in fungal guild community, particularly saprotrophic and pathotrophic fungi. The major functions of pathotrophic is infect host and limit the establishment and/or recovery of host species (Richards et al., 2012). In this stage, we predict relationship between the algae and fungi was changed from symbiotroph to competition. The two-type fungi taxa are important contributors to accelerate HAB decline. It was interesting that we observed that gulids related to saprotroph were enriched among the fungi present in both of platform stage and collapse stages. The possible reasons are their board-spectrum adaptation and ecological roles in matter cycle (Raffaello et al., 2014). The major functions of saprotroph include the degradation and transport of organic matters, which maintain microbial loop cycles (particularly the carbon pump) in water bodies (Jiao and Zheng, 2011). Dini-Andreote et al. (2016) proposed that saprotroph can assist in quantification of the roles of fungi in aquatic ecosystem C budgets. Hassett and Gradinger (2016) also demonstrated that Chytridiomycota have the potential to rapidly change primary production patterns and modulate the flow of carbon through food webs with increased light penetration. This

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