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Diurnal variations of the microbial community in mesopelagic fish habitats of the northern slope of the south China sea
Deep-Sea Research Part II 167 (2019) 55–61
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Diurnal variations of the microbial community in mesopelagic fish habitats of the northern slope of the south China sea
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Yu Zhanga,b, Ye Lua,b, Jiahua Wanga,b, Lisa Xieb, Lei Xua,b, Ying Heb, Xiang Xiaoa,b, Jun Xua,b,∗ a b
School of Oceanography, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, 200240, Shanghai, People's Republic of China State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, People's Republic of China
A R T I C LE I N FO
A B S T R A C T
Keywords: Microbial community Mesopelagic fish Diurnal variations South China sea
The marine microbial community profoundly influences biogeochemical cycles. On the northern slope of the South China Sea (SCS), a considerable reservoir of mesopelagic fishes that present diurnal vertical movement (DVM) as a diel habit has been discovered. To investigate the microbial community and its potential effects on nutrient conversion in this mesopelagic fish habitat, samples were collected throughout the water column every 6 h to monitor changes over the diel cycle and subjected to high-throughput sequencing of the bacterial and archaeal 16S rRNA genes. The bacterial diversity was high and stratified. Most of the bacteria were R-strategists, which indicates strong dynamics. The dominant bacteria were aerobic or facultatively anaerobic chemo-organotrophs, e.g. Flavobacteriaceae, SAR11, Alteromonadales, and Pseudomonadales, whereas Cyanobacteria was detected throughout the water column in rather low abundances. In deeper layers (≥200 m), Deferribacterales, which are typical nitrate reducers, increased in abundance after fish digestion and defecation, were correlated with the DVM. The archaeal diversity was extremely low in areas where Marine Group I appeared as the absolute dominant group throughout the entire water column, and this group presents a high ammonium oxidizing potential. The microbial community structural profile suggested that bacteria and archaea played separate roles in the nitrogen cycling process, which was correlated with the fish activity. Moreover, the protein digesters became abnormally more abundant in the deep water at night, which was likely stimulated by the fecal matter and detritus from fish. Our results provide insights into the spatial and temporal distribution of the microbial communities in the SCS, particularly in relation to mesopelagic fish, and thus will be of assistance for estimating the ecological impact of these communities on SCS fish and fisheries based on the enhanced carbon export that occurs via the diel vertical migration of mesopelagic fish.
1. Introduction The South China Sea (SCS) is the largest semi-enclosed marginal sea located in the subtropical and tropical region at the western boundary of the Pacific Ocean. The SCS includes deep basins with depths over 5000 m, and the continental shelf that is less than 100 m deep. The central gyre of the SCS is warm, permanently stratified and oligotrophic (Wu et al., 2003). Large amounts of freshwater and nutrients are input from the Pearl River and oceanic water intrudes onto the continental shelf; thus, the northern part of the SCS is characterized by sharp physical and chemical gradients over a small spatial scale. With the multi-scale physical processes mainly driven by monsoon winds, the northern part of the SCS has complex circulation patterns, such as upwelling, coastal currents, and cyclonic eddies (Hu et al., 2000).
Therefore, the biomass therein is relatively rich and divergent (Zhou et al., 2009). The nutrition condition and water dynamics make the SCS an ideal location for massive fishery resources, which play an important role in nutrient transportation, although this role is insufficiently recognized. The fishes and invertebrates that inhabit the mesopelagic depths (200–1000 m) of the SCS are poorly understood. Although of limited economic value, many of these species are important food sources for larger predators in the food web, including many economically important fishes (Kosenok and Naidenko, 2008; Yamamura and Inada, 2001) as well as seabirds (Van Pelt et al., 1997) and marine mammals (Ohizumi et al., 2003). Mesopelagic fishes have a broad geographical distribution and diel vertical migration (DVM) characteristics (Raring and Stevenson, 2010). Throughout the ocean, small mesopelagic fish
∗ Corresponding author. School of Oceanography, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, 200240, Shanghai, People's Republic of China. E-mail address: [email protected] (J. Xu).
https://doi.org/10.1016/j.dsr2.2019.06.018 Received 22 March 2018; Received in revised form 27 June 2019; Accepted 27 June 2019 Available online 28 June 2019 0967-0645/ © 2019 Elsevier Ltd. All rights reserved.
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AYT GGG YDT AAA GNG -3′) and 802R (5′- TAC NNG GGT ATC TAA TCC -3′) (Yan et al., 2017), with a unique 10-bp error correcting barcode at the 5′ end. For archaea, primers were 519F (5′- YMG CCR CGG KAA HAC C -3′) and 802R (5′- GGA CTA CNS GGG TMT CTA AT -3′) (Song et al., 2013). The PCR assays were conducted in triplicate in 50 μL PCR reaction mix that contained 5 μL of 10 × Ex Taq Buffer, 4 μL of 2.5 mM dNTP mixture, 0.25 μL of Ex Taq Polymerase (Takara, Dalian, China), 1 μL of each primer (10 μM), and 100 ng template DNA, and an appropriate amount of Milli-Q water was added up to a final volume of 50 μL. Thermal cycling consisted of initial denaturation at 95 °C for 5 min; followed by 30 cycles of denaturation at 95 °C for 45 s, annealing at 55 °C for 40 s, and extension at 72 °C for 1 min; and a final extension at 72 °C for 10 min. The PCR products were purified with a Gel Extraction Kit (OMEGA). Sequencing was conducted on a Roche Genome Sequencer FLX Titanium platform at Personal Biotechnology Co. Ltd. (Shanghai, China).
swim to depths in the daytime to seek refuge from large predators but return to the surface at night to feed. This DVM can result in the transferal of significant amounts of carbon and nutrients from the surface to depths, in terms of respiratory, defecation and mortality carbon fluxes. For example, this mesopelagic fish mediated carbon export contributes to 15–17% of the total carbon exported in northeast Pacific Ocean (Davison et al., 2013). Meanwhile, the downwards movement of the mesopelagic fishes introduce not only liable carbon but also nitrogen and even phosphorus, e.g. via defection and detritus, into the nutrient pool at the photic zone, which leads to elevated primary production (Robinson et al., 2010). Moreover, as studied in the same sampling area as in this research, the non-migrant and migrant mesopelagic fishes have distinct trophic preferences and their populations alter the biological pump (Wang et al., 2018). Thus, the DVM of mesopelagic fishes can have a significant impact on regional biogeochemical processes. Local microbial communities are easily disturbed by the DVM of mesopelagic fishes because mesopelagic fishes prey upon these communities and transport nutrients. Nonsystematic research on the mesopelagic fishes in the SCS has been reported, and in previous studies on the ecological function of prokaryotes in the SCS, these functions were discussed within the background of various environmental parameters without considering the influence of fish habitat. As part of the project “Dynamics and bio-resources of ecosystems on the northern slope of the SCS”, we had the opportunity to examine the microbial community shift corresponding to the habitat of mesopelagic fish as presented in this paper.
2.3. Real-time quantitative PCR (QPCR) This reaction was performed to measure the total abundance of bacteria in the sample. DNA samples were performed with the primers bac341F and bac519R. Each 20 μL reaction consisted of 10 μL SYBR Premix Ex Taq II (Tli RNaseH Plus) (2 × ), 0.4 μL ROX Reference Dye II (50 × Conc.) (Takara), 1.2 μL of each primer, 1 μL of DNA template and 6.2 μL of Milli-Q water. For the bacteria, the reactions were run at 95 °C for 1 min and then 40 cycles of 95 °C for 30 s, 56 °C for 30 s and 72 °C for 30 s. Data were collected during the extension phase of the reaction, and they included 32 standards (three replicates for building a standard curve with a copy number of 102-109 per reaction) and eight wells without a template. The reactions were conducted in an 8-tube strip with an 8-cap strip (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA). QPCR was performed on an ABI 7500/7500 Fast Real-Time PCR System (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA). The total QPCR counts (16S rRNA bacterial genes mL−1) were estimated based on a standard curve with 100% efficiency that factored in the cycle threshold value of the particular sample obtained and considered the 2 L of sample that was used for DNA extraction. DNA was eluted in 50 μL of Milli-Q water, and 1 μL of this DNA was used in the QPCR reaction. Standards were prepared by inserting a 1467-bp fragment of the E. coli ML-35 16S rRNA gene amplified with primers 27F and 1494R into a Topo Cloning Vector (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA).
2. Materials and methods 2.1. Study sites and sampling Water samples were collected from the Time-Series Station in the northern part of the SCS during the autumn cruise of the RV Nanfeng in October 2014. The Time-Series Station (19.909°N, 115.243°E) is located near the northern slope of the SCS at the DVM path of mesopelagic fishes, where research was conducted to understand the geo-eco-impact of DVM (research articles in this same special issue). Samples were collected from the vertical profile at six depths from the surface to 1350 m every 6 h (at around 3 a.m., 9 a.m., 3 p.m. and 9 p.m., local time) to monitor the diel cycle of fish activities. The six layers are: the surface water at a depth of 5–30 m; the maximum chlorophyll layer at a depth of 65 m; the boundary layer of the euphotic zone at a depth of 200 m, the mesopelagic fish active zone at a depth of 450 m, the oxygen minimum zone at a depth of 800 m, and the near bottom water at a depth of 1000 m. A Seabird SBE 9/17 plus CTD (Seabird Scientific, Bellevue, WA, USA) rosette sampler equipped with Go-Flo bottles was used to measure the temperature, salinity, and dissolved oxygen concentration and collect the water samples. Microbes (11–12 L water) were screened through 0.22 μm pore-size polycarbonate filters (GTTP4700, Millipore, USA) at a pressure of < 0.03 MPa on the ship. The 0.22 μm pore-size filters were considered to contain total archaea and bacteria from the water. All filters were immediately frozen at −20 °C on the ship for transportation and then stored at −80 °C in the laboratory until further analysis.
2.4. Sequence processing and clustering analysis We eliminated sequences that contained more than one ambiguous nucleotide (N), did not have a complete barcode and primer at one end, or were shorter than 100 bp after removal of the barcode and primer sequences. A total of 1,787,142 reads from the DNA libraries passed the pipeline filters (sequences access number: NCBI SRA: SAMN10882726). These remaining sequences were assigned to samples by examining the barcode. Dissimilarities among all samples were calculated, and the resulting beta-diversity matrices were used for two-dimensional nonmetric multidimensional scaling (NMDS) ordinations. After this filtering step, 1,785,073 reads remained for further analysis and were assigned to samples by examining the barcode (split_library.py). All sequences obtained for this study have been deposited in the NCBI Sequence Read Archive. Libraries of sequences and OTUs were analyzed in QIIME (Caporaso et al., 2010). Briefly, chimeras were removed using the Usearch61 algorithm with reference to a pre-aligned SILVA database v123 in QIIME (identify_chimeric_seqs.py). Next, in the OTU picking step, sequences were clustered into OTUs using the Usearch61 algorithm with a cut-off value of 0.03 (pick_otus.py). Then, representative sequences were selected on the basis of these selected OTUs (pick_rep_set.py) and assigned to taxonomic groups by a BLAST search against a reference database (assign_taxonomy.py). A filtered
2.2. DNA extraction and sequencing The filters were cut into pieces using sterile scissors and mixed under sterile conditions. DNA was extracted using the Fast DNA Spin Kit for Soil (MP, USA) according to the manufacturer's instructions. The DNA integrity and size were checked by electrophoresis in 1% agarose gel, and the concentrations were checked by a NanoDrop Spectrophotometer (ND-2000, Thermo Fisher, USA). The DNA was stored at −20 °C for further use. The bacterial hypervariable V4 regions of the 16S rRNA genes were amplified using the primers of 520F (5′56
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version of the SILVA database was used as the reference database, and sequences were clustered by their representative BLAST match (Evalue = 1e-3, SILVA-v123 database ≥ 0.90 similarity). 2.5. Statistical analysis Based on the OTU assignment, library richness and diversity estimates (Chao1 and observed species) were calculated (alpha_diversity.py). Alpha rarefaction was performed using the Chao1 and observed species metrics (alpha_rarefaction.py). Beta diversity was estimated by calculating the weighted and unweighted UniFrac distances between samples using QIIME. A Principal Coordinates Analysis (PCoA) was performed to reduce the dimensionality of the resulting distance matrices similar to the weighted and unweighted UniFrac distances implemented in QIIME. An analysis of molecular variance (AMOVA) was applied to test whether the spatial separation of the defined groups visualized in the PCoA plot was statistically significant. The differences were considered significant at P < 0.05. Dendrograms and a heatmap describing the similarity in community structure were generated using the R platform. The OTU taxonomy tables generated by QIIME were used to produce heat maps with the R package heatmap. Community components with abundance < 0.1% were excluded from the OTU taxonomy tables. The clustering methods were based on Euclidean distances. A network of bacterial communities was constructed by using MENAP (Molecular Ecological Network Analysis Pipeline, http://ieg2. ou.edu/MENA) (Zhang et al., 2014). Community clusters that contained more than 8 samples in the cluster analysis were aggregated in a data set to construct networks. 3. Results and discussion 3.1. Archaeal and bacterial populations and their potential roles The archaeal and bacterial populations were assessed in the different water layers, e.g., 5, 30, 65, 200, 450, 800, and 1000 m, at stations TS07, TS09, TS11 and TS13. The bacterial 16S rRNA gene copy number estimated via QPCR was on the order of 104-107 copies/L, and archaeal 16S rRNA gene copy number was on the order of 103-106 copies/L (Fig. 1). The archaeal cellular numbers at the 30 m, 65 m, and 200 m depths at TS09 were plotted from Xie et al. (2017). As observed for this project, the mesopelagic fishes have a fixed DVM pattern that shows the largest population at ∼450 m of water depth during the day time and at ∼100 m at night (Wang et al., 2019) (Fig. 4). The TS07 samples were collected at 3:34 a.m. when the fish were ascending (water depth ∼100 m) to feed; the TS09 samples were collected at 9:39 a.m. when the fishes had finished feeding and were digesting at the deeper layer (water depth ∼500 m); the TS11 samples were collected at 3:03 p.m. when the fish were still at the ocean depths; and the TS13 samples were collected at 8:58 p.m. when the fish ascended again to feed. In general, the maximal cellular counts were observed at a depth of 65 m at both day and night, which can be explained by the location of this layer of water, which is very close to the estimated chlorophyll maximal layer as well as the weighted mean depth (WMD) of fishes (79 m of water depth) at night (Wang et al., 2019). In addition, peaks of archaeal populations were observed at 200 m at TS09, 450 m at TS07 and 800 m at TS13 (Fig. 1a), and peaks of bacterial populations were observed at 450 m at TS09 and 800 m at TS07 and TS13 (Fig. 1b). The deeper blooms were likely caused by the additional nutrients supplied by fish fecal matter, which was supported by the elevated organic carbon (Zhang et al., 2019) (Fig. 6) and ammonia concentrations.
Fig. 1. Archaeal (a) and bacterial (b) 16S rRNA gene copy numbers of water columns from the four sampling stations.
DNA concentrations and/or unclear technical challenges, library construction failed for the bacteria below a depth of 65 m and the archaea above a depth of 200 m at station TS11; however, these bacterial and archaeal communities clearly showed dissimilar patterns. Based on the 16S rRNA gene sequence analysis, the bacterial diversity was high, especially at the surface layers. The OTU numbers were 271321, 436211, 165791 and 361495 in TS07, TS09, TS11 and TS13, respectively, and 84.9%, 98.7%, 99.7% and 94.8% were assigned to bacterial origins, respectively. Because of the limits in marine microbial cultivation and detection, as well as the limited information provided the 16S rRNA gene short fragment, we were only able to identify the taxonomy of the OTUs on the family level or even on the order level. Among all the OTUs sequenced, the dominant bacterial groups were assigned to Acidimicrobiales, Flavobacteriaceae, Marinimicrobia, Phycisphaeraceae, Rhodospirillaceae, Alteromonadaceae, Pseudoalteromonadaceae, the SAR11 clade, Alcanivoracaceae and Moraxellaceae (Fig. 2b). These observed bacteria are commonly aerobic and highly involved in hydrocarbon degradation (Azam and Malfatti, 2007; Li et al., 2014). Certain groups of Alteromonadaceae have been reported to substantially promote the growth of the toxic dinoflagellate Alexandrium fundyense (Ferrier et al., 2002). A few groups, such as
3.2. Archaeal and bacterial community structures The archaeal and bacterial community compositions were investigated by 16S rRNA gene-based sequencing (Fig. 2). Due to low 57
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Fig. 2. Archaeal (a) and bacterial (b) community compositions from four sampling stations. Figures in brackets indicate different groups at family level according to Supplementary Table S1.
were observed, especially inner-group interactions. The involvement of Bacteroidetes, however, was consistent with negative interactions between the groups as well as the other populations. This network analysis helps identify the intruder organisms and understand the life strategies of different microorganisms. The archaeal populations showed a pattern distinct to the bacterial
Alcanivoracaceae, Pseudomonadales, and Verrucomicrobiales, might be syntrophic or even pathogenic to fish (Azam and Malfatti, 2007). Moreover, the bacterial communities obtained in this study were used for an ecological network analysis. The groups of bacteria formed a complex and intensive network via interactions with each other (Fig. 3). When Proteobacteria are involved, more positive interactions 58
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and ecotype of MG II; thus, the distribution of MG II could be even wider. As suggested by metagenomic studies, MG II species have the capacity for anaerobic respiration within low oxygen microenvironments, such as organic particles (Orsi et al., 2015), and their ability to degrade proteins may represent an important metabolic characteristic of these microbes (Zhang et al., 2015). This protein depredator could be well fed by the SCS fishes and profoundly influenced by their DVM. 3.3. Spatial and temporal heterogeneity in archaeal and bacterial communities The archaeal and bacterial communities in all samples were clustered based on their OTU taxonomy (Fig. 4). The bacterial communities from the shallow layers had approximately 20 OTUs that contributed more than 1% of the total bacterial community, and they appeared to cluster together, especially the communities from TS09, TS11 and TS13, which had high abundances of Acidimicrobiales, Flavobacteria and Marinimicrobia. However, the bacterial communities in the water samples collected at depths of 5 m and 65 m at TS07 did not belong to this cluster, which was mainly because Alteromonadaceae accounted for 17.87% and 18.83% of the two bacterial communities, respectively, but accounted for less than 0.5% of the other samples. Members of the family Alteromonadaceae are generally opportunistic “copiotrophs” capable of seizing labile dissolved organic carbon and rapid growth (Nelson and Wear, 2014). Because Alteromonadaceae blooms have not been observed in other stations except for TS07, the presence of this family could be induced by the fishes’ movement. The bacterial OTUs contributing more than 0.1% of the total communities from deeper mesopelagic water, which is below a depth of 200 m, were distributed among most of the detected branches, and this heterogeneity mainly corresponds to the sampling time rather than the sampling depth. A similar pattern was observed in the archaeal communities, whose clustering was primarily influenced by the sampling depth.
Fig. 3. Calculated bacterial interactions in the water samples. Connections between nodes indicate positive (red) or negative (blue) interactions in terms of population. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
3.4. Microbial community shift influenced by the DVM of mesopelagic fishes
populations, and they were mainly involved in the nitrogen cycling, e.g. ammonium oxidation. Since the total archaeal cellular numbers were one or two orders lower than those of bacteria, only the archaeal communities beneath 65 m were successfully detected. The OTU numbers were 205638, 95404, 55827 and 193386 in TS07, TS09, TS11 and TS13, respectively, and 66.3%, 74.7%, 84.1% and 74.5% were assigned to archaeal origins, respectively. Thaumarchaeota, previously known as Marine Group I (MG I), which could be divided into many orders, was the most dominant archaeal cluster in all samples examined (Fig. 2a). The members of Thaumarchaeota are widely distributed in marine environments, including the SCS (Xie et al., 2014, 2017). These archaea are characterized as chemoautotrophs, which are capable of NH3 oxidation and contain NH3 monooxygenase (amoA) genes, whereas a few species may be mixotrophs and heterotrophs (Mussmann et al., 2011). Other archaeal groups, such as the Soil Crenarchaeotic Group (SCG), were also present in relatively high amounts inside the archaeal communities. The SCG is a monophyletic group commonly thought to be ammonia oxidizers, and it has a close phylogenetic relation to Thaumarchaeota. For example, OTUs affiliated with Thaumarchaeota from Robertson Glacier snow samples were most closely related to the Candidate SCG genus (Hamilton and Havig, 2017). Marine Group II (MG II), was detected in all the samples, with high proportions observed in the samples from TS09 and TS13. Similar to many un-cultivatable microorganisms, our understanding of the ecological and biogeochemical functions of MG II is incomplete. The niches of these species have been suggested as the cause of the separation between MG I and MG II, with MG I presenting more populations in deeper waters and MG II presenting greater abundance in the photic zone rather than the mesopelagic and bathypelagic zones (Massana et al., 2000). However, additional data have confirmed the occurrence of more than one ribotype
Mesopelagic fish provide additional carbon and nitrogen to the local microbial communities along their DVM between depths of ∼100 and ∼500 m, thereby influencing the bacterial and archaeal diversity therein. Since the microbes form a network to utilize and even cycle the nutrients, and a few hours are required before rare groups of microorganisms can be stimulated by nutrient input and grow into larger populations, the oceanic microbial communities often show timelagged correlations with environmental parameters (Fuhrman, 2009). The mesopelagic fish, by exporting organic matter along DVM, have a temporal and subsequent influence on the microbial communities, which was observed via time series sampling. For example, the absolute cellular numbers of MG I and SCG, which are stimulated by the ammonium concentration, were highest at 450 m in TS07 and 200 m in TS09 calculated from the data in Figs. 1a and 2a. These samples were taken a few hours after the fish had passed through the layers. Moreover, the protein digesters became abnormally more abundant in the deep water at night which was likely stimulated by the fecal matter and detritus from fish. As shown in our results, in TS13 the population of archaeal protein digester MG II is high, especially at the water depth of 800 m. And in TS07, the bacterial protein digester Alcanivoracaceae bloomed at the water depth of 450 m. During the past decades, the South China Sea has been intensively investigated from physical, chemical and, of course, biological points of view, with accumulative data showing seasonal variations. Many other model projects, such as Station ALOHA (A Long-term Oligotrophic Habitat Assessment) and BATS (Bermuda Atlantic Time-series Study) project in North Pacific Ocean, all have provided us with deep understanding on the oceanic response in context of global climate change through a significantly long period of time. However, the previous research did not access ecological changes on the time scale of hours, 59
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Fig. 4. Heat maps showing the archaeal (a) and bacterial (b) population from the water samples. Figures in brackets indicate different groups at family level according to Supplementary Table S1 and Table S2 respectively. Color bars indicate the relative percentages. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
which is the same time resolution as the mesopelagic fishes migrate. In this study, we took a time series of sampling along the water column every 6 h at the same location, to capture the microbial communities’ shift as influenced by the fish activities. This is the first attempt to establish the correlation between the microbial community with the DVM of mesopelagic fish and is further revealing the ecological impact of these SCS fish and fisheries.
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Acknowledgments This research is funded by the National Basic Research Program of China (973 Program 2014CB441503). We thank the scientists and crew of the R/V Nanfeng during the cruise in 2014 for providing professional support and kind help in sampling and data collection.
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fundyense growth by bacterial assemblages from the Bay of Fundy. J. Appl. Microbiol. 92, 706–716. Fuhrman, J.A., 2009. Microbial community structure and its functional implications. Nature 459, 193–199. Hamilton, T.L., Havig, J., 2017. Primary productivity of snow algae communities on stratovolcanoes of the Pacific Northwest. Geobiology 15, 280–295. Hu, J., Kawamura, H., Hong, H., Qi, Y., 2000. A review on the currents in the South China sea: seasonal circulation, south China sea warm current and kuroshio intrusion. J. Oceanogr. 56, 607–624. Kosenok, N.S., Naidenko, S.V., 2008. Feeding and daily ration of the chum salmon Oncorhynchus keta in the western Bering Sea in the summer-fall of 2004. Russ. J. Mar. Biol. 34, 17–27. Li, J., Li, N., Li, F., Zou, T., Yu, S., Wang, Y., Qin, S., Wang, G., 2014. Spatial diversity of bacterioplankton communities in surface water of northern South China Sea. PLoS One 9, e113014. Massana, R., DeLong, E.F., Pedros-Alio, C., 2000. A few cosmopolitan phylotypes dominate planktonic archaeal assemblages in widely different oceanic provinces. Appl. Environ. Microbiol. 66, 1777–1787. Mussmann, M., Brito, I., Pitcher, A., Sinninghe Damste, J.S., Hatzenpichler, R., Richter, A., Nielsen, J.L., Nielsen, P.H., Muller, A., Daims, H., Wagner, M., Head, I.M., 2011. Thaumarchaeotes abundant in refinery nitrifying sludges express amoA but are not obligate autotrophic ammonia oxidizers. Proc. Natl. Acad. Sci. U. S. A. 108, 16771–16776. Nelson, C.E., Wear, E.K., 2014. Microbial diversity and the lability of dissolved organic carbon. Proc. Natl. Acad. Sci. U. S. A. 111, 7166–7167. Ohizumi, H., Kuramochi, T., Kubodera, T., Yoshioka, M., Miyazaki, N., 2003. Feeding habits of Dall's porpoises (Phocoenoides dalli) in the subarctic North Pacific and the Bering Sea basin and the impact of predation on mesopelagic micronekton. Deep Sea Res., Part I 50, 593–610. Orsi, W.D., Smith, J.M., Wilcox, H.M., Swalwell, J.E., Carini, P., Worden, A.Z., Santoro, A.E., 2015. Ecophysiology of uncultivated marine euryarchaea is linked to particulate organic matter. ISME J. 9, 1747–1763. Raring, N.W., Stevenson, D.E., 2010. A brief survey of the mesopelagic fishes of the gulf of Alaska. Calif. Fish Game 96, 188–200. Robinson, C., Steinberg, D.K., Anderson, T.R., Arístegui, J., Carlson, C.A., Frost, J.R., Ghiglione, J.-F., Hernández-León, S., Jackson, G.A., Koppelmann, R., 2010. Mesopelagic zone ecology and biogeochemistry–a synthesis. Deep Sea Res., Part II 57, 1504–1518. Song, Z.Q., Wang, F.P., Zhi, X.Y., Chen, J.Q., Zhou, E.M., Liang, F., Xiao, X., Tang, S.K., Jiang, H.C., Zhang, C.L., Dong, H., Li, W.J., 2013. Bacterial and archaeal diversities in
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Diurnal variations of the microbial community in mesopelagic fish habitats of the northern slope of the south China sea
T
Yu Zhanga,b, Ye Lua,b, Jiahua Wanga,b, Lisa Xieb, Lei Xua,b, Ying Heb, Xiang Xiaoa,b, Jun Xua,b,∗ a b
School of Oceanography, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, 200240, Shanghai, People's Republic of China State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, People's Republic of China
A R T I C LE I N FO
A B S T R A C T
Keywords: Microbial community Mesopelagic fish Diurnal variations South China sea
The marine microbial community profoundly influences biogeochemical cycles. On the northern slope of the South China Sea (SCS), a considerable reservoir of mesopelagic fishes that present diurnal vertical movement (DVM) as a diel habit has been discovered. To investigate the microbial community and its potential effects on nutrient conversion in this mesopelagic fish habitat, samples were collected throughout the water column every 6 h to monitor changes over the diel cycle and subjected to high-throughput sequencing of the bacterial and archaeal 16S rRNA genes. The bacterial diversity was high and stratified. Most of the bacteria were R-strategists, which indicates strong dynamics. The dominant bacteria were aerobic or facultatively anaerobic chemo-organotrophs, e.g. Flavobacteriaceae, SAR11, Alteromonadales, and Pseudomonadales, whereas Cyanobacteria was detected throughout the water column in rather low abundances. In deeper layers (≥200 m), Deferribacterales, which are typical nitrate reducers, increased in abundance after fish digestion and defecation, were correlated with the DVM. The archaeal diversity was extremely low in areas where Marine Group I appeared as the absolute dominant group throughout the entire water column, and this group presents a high ammonium oxidizing potential. The microbial community structural profile suggested that bacteria and archaea played separate roles in the nitrogen cycling process, which was correlated with the fish activity. Moreover, the protein digesters became abnormally more abundant in the deep water at night, which was likely stimulated by the fecal matter and detritus from fish. Our results provide insights into the spatial and temporal distribution of the microbial communities in the SCS, particularly in relation to mesopelagic fish, and thus will be of assistance for estimating the ecological impact of these communities on SCS fish and fisheries based on the enhanced carbon export that occurs via the diel vertical migration of mesopelagic fish.
1. Introduction The South China Sea (SCS) is the largest semi-enclosed marginal sea located in the subtropical and tropical region at the western boundary of the Pacific Ocean. The SCS includes deep basins with depths over 5000 m, and the continental shelf that is less than 100 m deep. The central gyre of the SCS is warm, permanently stratified and oligotrophic (Wu et al., 2003). Large amounts of freshwater and nutrients are input from the Pearl River and oceanic water intrudes onto the continental shelf; thus, the northern part of the SCS is characterized by sharp physical and chemical gradients over a small spatial scale. With the multi-scale physical processes mainly driven by monsoon winds, the northern part of the SCS has complex circulation patterns, such as upwelling, coastal currents, and cyclonic eddies (Hu et al., 2000).
Therefore, the biomass therein is relatively rich and divergent (Zhou et al., 2009). The nutrition condition and water dynamics make the SCS an ideal location for massive fishery resources, which play an important role in nutrient transportation, although this role is insufficiently recognized. The fishes and invertebrates that inhabit the mesopelagic depths (200–1000 m) of the SCS are poorly understood. Although of limited economic value, many of these species are important food sources for larger predators in the food web, including many economically important fishes (Kosenok and Naidenko, 2008; Yamamura and Inada, 2001) as well as seabirds (Van Pelt et al., 1997) and marine mammals (Ohizumi et al., 2003). Mesopelagic fishes have a broad geographical distribution and diel vertical migration (DVM) characteristics (Raring and Stevenson, 2010). Throughout the ocean, small mesopelagic fish
∗ Corresponding author. School of Oceanography, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, 200240, Shanghai, People's Republic of China. E-mail address: [email protected] (J. Xu).
https://doi.org/10.1016/j.dsr2.2019.06.018 Received 22 March 2018; Received in revised form 27 June 2019; Accepted 27 June 2019 Available online 28 June 2019 0967-0645/ © 2019 Elsevier Ltd. All rights reserved.
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AYT GGG YDT AAA GNG -3′) and 802R (5′- TAC NNG GGT ATC TAA TCC -3′) (Yan et al., 2017), with a unique 10-bp error correcting barcode at the 5′ end. For archaea, primers were 519F (5′- YMG CCR CGG KAA HAC C -3′) and 802R (5′- GGA CTA CNS GGG TMT CTA AT -3′) (Song et al., 2013). The PCR assays were conducted in triplicate in 50 μL PCR reaction mix that contained 5 μL of 10 × Ex Taq Buffer, 4 μL of 2.5 mM dNTP mixture, 0.25 μL of Ex Taq Polymerase (Takara, Dalian, China), 1 μL of each primer (10 μM), and 100 ng template DNA, and an appropriate amount of Milli-Q water was added up to a final volume of 50 μL. Thermal cycling consisted of initial denaturation at 95 °C for 5 min; followed by 30 cycles of denaturation at 95 °C for 45 s, annealing at 55 °C for 40 s, and extension at 72 °C for 1 min; and a final extension at 72 °C for 10 min. The PCR products were purified with a Gel Extraction Kit (OMEGA). Sequencing was conducted on a Roche Genome Sequencer FLX Titanium platform at Personal Biotechnology Co. Ltd. (Shanghai, China).
swim to depths in the daytime to seek refuge from large predators but return to the surface at night to feed. This DVM can result in the transferal of significant amounts of carbon and nutrients from the surface to depths, in terms of respiratory, defecation and mortality carbon fluxes. For example, this mesopelagic fish mediated carbon export contributes to 15–17% of the total carbon exported in northeast Pacific Ocean (Davison et al., 2013). Meanwhile, the downwards movement of the mesopelagic fishes introduce not only liable carbon but also nitrogen and even phosphorus, e.g. via defection and detritus, into the nutrient pool at the photic zone, which leads to elevated primary production (Robinson et al., 2010). Moreover, as studied in the same sampling area as in this research, the non-migrant and migrant mesopelagic fishes have distinct trophic preferences and their populations alter the biological pump (Wang et al., 2018). Thus, the DVM of mesopelagic fishes can have a significant impact on regional biogeochemical processes. Local microbial communities are easily disturbed by the DVM of mesopelagic fishes because mesopelagic fishes prey upon these communities and transport nutrients. Nonsystematic research on the mesopelagic fishes in the SCS has been reported, and in previous studies on the ecological function of prokaryotes in the SCS, these functions were discussed within the background of various environmental parameters without considering the influence of fish habitat. As part of the project “Dynamics and bio-resources of ecosystems on the northern slope of the SCS”, we had the opportunity to examine the microbial community shift corresponding to the habitat of mesopelagic fish as presented in this paper.
2.3. Real-time quantitative PCR (QPCR) This reaction was performed to measure the total abundance of bacteria in the sample. DNA samples were performed with the primers bac341F and bac519R. Each 20 μL reaction consisted of 10 μL SYBR Premix Ex Taq II (Tli RNaseH Plus) (2 × ), 0.4 μL ROX Reference Dye II (50 × Conc.) (Takara), 1.2 μL of each primer, 1 μL of DNA template and 6.2 μL of Milli-Q water. For the bacteria, the reactions were run at 95 °C for 1 min and then 40 cycles of 95 °C for 30 s, 56 °C for 30 s and 72 °C for 30 s. Data were collected during the extension phase of the reaction, and they included 32 standards (three replicates for building a standard curve with a copy number of 102-109 per reaction) and eight wells without a template. The reactions were conducted in an 8-tube strip with an 8-cap strip (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA). QPCR was performed on an ABI 7500/7500 Fast Real-Time PCR System (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA). The total QPCR counts (16S rRNA bacterial genes mL−1) were estimated based on a standard curve with 100% efficiency that factored in the cycle threshold value of the particular sample obtained and considered the 2 L of sample that was used for DNA extraction. DNA was eluted in 50 μL of Milli-Q water, and 1 μL of this DNA was used in the QPCR reaction. Standards were prepared by inserting a 1467-bp fragment of the E. coli ML-35 16S rRNA gene amplified with primers 27F and 1494R into a Topo Cloning Vector (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA).
2. Materials and methods 2.1. Study sites and sampling Water samples were collected from the Time-Series Station in the northern part of the SCS during the autumn cruise of the RV Nanfeng in October 2014. The Time-Series Station (19.909°N, 115.243°E) is located near the northern slope of the SCS at the DVM path of mesopelagic fishes, where research was conducted to understand the geo-eco-impact of DVM (research articles in this same special issue). Samples were collected from the vertical profile at six depths from the surface to 1350 m every 6 h (at around 3 a.m., 9 a.m., 3 p.m. and 9 p.m., local time) to monitor the diel cycle of fish activities. The six layers are: the surface water at a depth of 5–30 m; the maximum chlorophyll layer at a depth of 65 m; the boundary layer of the euphotic zone at a depth of 200 m, the mesopelagic fish active zone at a depth of 450 m, the oxygen minimum zone at a depth of 800 m, and the near bottom water at a depth of 1000 m. A Seabird SBE 9/17 plus CTD (Seabird Scientific, Bellevue, WA, USA) rosette sampler equipped with Go-Flo bottles was used to measure the temperature, salinity, and dissolved oxygen concentration and collect the water samples. Microbes (11–12 L water) were screened through 0.22 μm pore-size polycarbonate filters (GTTP4700, Millipore, USA) at a pressure of < 0.03 MPa on the ship. The 0.22 μm pore-size filters were considered to contain total archaea and bacteria from the water. All filters were immediately frozen at −20 °C on the ship for transportation and then stored at −80 °C in the laboratory until further analysis.
2.4. Sequence processing and clustering analysis We eliminated sequences that contained more than one ambiguous nucleotide (N), did not have a complete barcode and primer at one end, or were shorter than 100 bp after removal of the barcode and primer sequences. A total of 1,787,142 reads from the DNA libraries passed the pipeline filters (sequences access number: NCBI SRA: SAMN10882726). These remaining sequences were assigned to samples by examining the barcode. Dissimilarities among all samples were calculated, and the resulting beta-diversity matrices were used for two-dimensional nonmetric multidimensional scaling (NMDS) ordinations. After this filtering step, 1,785,073 reads remained for further analysis and were assigned to samples by examining the barcode (split_library.py). All sequences obtained for this study have been deposited in the NCBI Sequence Read Archive. Libraries of sequences and OTUs were analyzed in QIIME (Caporaso et al., 2010). Briefly, chimeras were removed using the Usearch61 algorithm with reference to a pre-aligned SILVA database v123 in QIIME (identify_chimeric_seqs.py). Next, in the OTU picking step, sequences were clustered into OTUs using the Usearch61 algorithm with a cut-off value of 0.03 (pick_otus.py). Then, representative sequences were selected on the basis of these selected OTUs (pick_rep_set.py) and assigned to taxonomic groups by a BLAST search against a reference database (assign_taxonomy.py). A filtered
2.2. DNA extraction and sequencing The filters were cut into pieces using sterile scissors and mixed under sterile conditions. DNA was extracted using the Fast DNA Spin Kit for Soil (MP, USA) according to the manufacturer's instructions. The DNA integrity and size were checked by electrophoresis in 1% agarose gel, and the concentrations were checked by a NanoDrop Spectrophotometer (ND-2000, Thermo Fisher, USA). The DNA was stored at −20 °C for further use. The bacterial hypervariable V4 regions of the 16S rRNA genes were amplified using the primers of 520F (5′56
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version of the SILVA database was used as the reference database, and sequences were clustered by their representative BLAST match (Evalue = 1e-3, SILVA-v123 database ≥ 0.90 similarity). 2.5. Statistical analysis Based on the OTU assignment, library richness and diversity estimates (Chao1 and observed species) were calculated (alpha_diversity.py). Alpha rarefaction was performed using the Chao1 and observed species metrics (alpha_rarefaction.py). Beta diversity was estimated by calculating the weighted and unweighted UniFrac distances between samples using QIIME. A Principal Coordinates Analysis (PCoA) was performed to reduce the dimensionality of the resulting distance matrices similar to the weighted and unweighted UniFrac distances implemented in QIIME. An analysis of molecular variance (AMOVA) was applied to test whether the spatial separation of the defined groups visualized in the PCoA plot was statistically significant. The differences were considered significant at P < 0.05. Dendrograms and a heatmap describing the similarity in community structure were generated using the R platform. The OTU taxonomy tables generated by QIIME were used to produce heat maps with the R package heatmap. Community components with abundance < 0.1% were excluded from the OTU taxonomy tables. The clustering methods were based on Euclidean distances. A network of bacterial communities was constructed by using MENAP (Molecular Ecological Network Analysis Pipeline, http://ieg2. ou.edu/MENA) (Zhang et al., 2014). Community clusters that contained more than 8 samples in the cluster analysis were aggregated in a data set to construct networks. 3. Results and discussion 3.1. Archaeal and bacterial populations and their potential roles The archaeal and bacterial populations were assessed in the different water layers, e.g., 5, 30, 65, 200, 450, 800, and 1000 m, at stations TS07, TS09, TS11 and TS13. The bacterial 16S rRNA gene copy number estimated via QPCR was on the order of 104-107 copies/L, and archaeal 16S rRNA gene copy number was on the order of 103-106 copies/L (Fig. 1). The archaeal cellular numbers at the 30 m, 65 m, and 200 m depths at TS09 were plotted from Xie et al. (2017). As observed for this project, the mesopelagic fishes have a fixed DVM pattern that shows the largest population at ∼450 m of water depth during the day time and at ∼100 m at night (Wang et al., 2019) (Fig. 4). The TS07 samples were collected at 3:34 a.m. when the fish were ascending (water depth ∼100 m) to feed; the TS09 samples were collected at 9:39 a.m. when the fishes had finished feeding and were digesting at the deeper layer (water depth ∼500 m); the TS11 samples were collected at 3:03 p.m. when the fish were still at the ocean depths; and the TS13 samples were collected at 8:58 p.m. when the fish ascended again to feed. In general, the maximal cellular counts were observed at a depth of 65 m at both day and night, which can be explained by the location of this layer of water, which is very close to the estimated chlorophyll maximal layer as well as the weighted mean depth (WMD) of fishes (79 m of water depth) at night (Wang et al., 2019). In addition, peaks of archaeal populations were observed at 200 m at TS09, 450 m at TS07 and 800 m at TS13 (Fig. 1a), and peaks of bacterial populations were observed at 450 m at TS09 and 800 m at TS07 and TS13 (Fig. 1b). The deeper blooms were likely caused by the additional nutrients supplied by fish fecal matter, which was supported by the elevated organic carbon (Zhang et al., 2019) (Fig. 6) and ammonia concentrations.
Fig. 1. Archaeal (a) and bacterial (b) 16S rRNA gene copy numbers of water columns from the four sampling stations.
DNA concentrations and/or unclear technical challenges, library construction failed for the bacteria below a depth of 65 m and the archaea above a depth of 200 m at station TS11; however, these bacterial and archaeal communities clearly showed dissimilar patterns. Based on the 16S rRNA gene sequence analysis, the bacterial diversity was high, especially at the surface layers. The OTU numbers were 271321, 436211, 165791 and 361495 in TS07, TS09, TS11 and TS13, respectively, and 84.9%, 98.7%, 99.7% and 94.8% were assigned to bacterial origins, respectively. Because of the limits in marine microbial cultivation and detection, as well as the limited information provided the 16S rRNA gene short fragment, we were only able to identify the taxonomy of the OTUs on the family level or even on the order level. Among all the OTUs sequenced, the dominant bacterial groups were assigned to Acidimicrobiales, Flavobacteriaceae, Marinimicrobia, Phycisphaeraceae, Rhodospirillaceae, Alteromonadaceae, Pseudoalteromonadaceae, the SAR11 clade, Alcanivoracaceae and Moraxellaceae (Fig. 2b). These observed bacteria are commonly aerobic and highly involved in hydrocarbon degradation (Azam and Malfatti, 2007; Li et al., 2014). Certain groups of Alteromonadaceae have been reported to substantially promote the growth of the toxic dinoflagellate Alexandrium fundyense (Ferrier et al., 2002). A few groups, such as
3.2. Archaeal and bacterial community structures The archaeal and bacterial community compositions were investigated by 16S rRNA gene-based sequencing (Fig. 2). Due to low 57
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Fig. 2. Archaeal (a) and bacterial (b) community compositions from four sampling stations. Figures in brackets indicate different groups at family level according to Supplementary Table S1.
were observed, especially inner-group interactions. The involvement of Bacteroidetes, however, was consistent with negative interactions between the groups as well as the other populations. This network analysis helps identify the intruder organisms and understand the life strategies of different microorganisms. The archaeal populations showed a pattern distinct to the bacterial
Alcanivoracaceae, Pseudomonadales, and Verrucomicrobiales, might be syntrophic or even pathogenic to fish (Azam and Malfatti, 2007). Moreover, the bacterial communities obtained in this study were used for an ecological network analysis. The groups of bacteria formed a complex and intensive network via interactions with each other (Fig. 3). When Proteobacteria are involved, more positive interactions 58
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and ecotype of MG II; thus, the distribution of MG II could be even wider. As suggested by metagenomic studies, MG II species have the capacity for anaerobic respiration within low oxygen microenvironments, such as organic particles (Orsi et al., 2015), and their ability to degrade proteins may represent an important metabolic characteristic of these microbes (Zhang et al., 2015). This protein depredator could be well fed by the SCS fishes and profoundly influenced by their DVM. 3.3. Spatial and temporal heterogeneity in archaeal and bacterial communities The archaeal and bacterial communities in all samples were clustered based on their OTU taxonomy (Fig. 4). The bacterial communities from the shallow layers had approximately 20 OTUs that contributed more than 1% of the total bacterial community, and they appeared to cluster together, especially the communities from TS09, TS11 and TS13, which had high abundances of Acidimicrobiales, Flavobacteria and Marinimicrobia. However, the bacterial communities in the water samples collected at depths of 5 m and 65 m at TS07 did not belong to this cluster, which was mainly because Alteromonadaceae accounted for 17.87% and 18.83% of the two bacterial communities, respectively, but accounted for less than 0.5% of the other samples. Members of the family Alteromonadaceae are generally opportunistic “copiotrophs” capable of seizing labile dissolved organic carbon and rapid growth (Nelson and Wear, 2014). Because Alteromonadaceae blooms have not been observed in other stations except for TS07, the presence of this family could be induced by the fishes’ movement. The bacterial OTUs contributing more than 0.1% of the total communities from deeper mesopelagic water, which is below a depth of 200 m, were distributed among most of the detected branches, and this heterogeneity mainly corresponds to the sampling time rather than the sampling depth. A similar pattern was observed in the archaeal communities, whose clustering was primarily influenced by the sampling depth.
Fig. 3. Calculated bacterial interactions in the water samples. Connections between nodes indicate positive (red) or negative (blue) interactions in terms of population. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
3.4. Microbial community shift influenced by the DVM of mesopelagic fishes
populations, and they were mainly involved in the nitrogen cycling, e.g. ammonium oxidation. Since the total archaeal cellular numbers were one or two orders lower than those of bacteria, only the archaeal communities beneath 65 m were successfully detected. The OTU numbers were 205638, 95404, 55827 and 193386 in TS07, TS09, TS11 and TS13, respectively, and 66.3%, 74.7%, 84.1% and 74.5% were assigned to archaeal origins, respectively. Thaumarchaeota, previously known as Marine Group I (MG I), which could be divided into many orders, was the most dominant archaeal cluster in all samples examined (Fig. 2a). The members of Thaumarchaeota are widely distributed in marine environments, including the SCS (Xie et al., 2014, 2017). These archaea are characterized as chemoautotrophs, which are capable of NH3 oxidation and contain NH3 monooxygenase (amoA) genes, whereas a few species may be mixotrophs and heterotrophs (Mussmann et al., 2011). Other archaeal groups, such as the Soil Crenarchaeotic Group (SCG), were also present in relatively high amounts inside the archaeal communities. The SCG is a monophyletic group commonly thought to be ammonia oxidizers, and it has a close phylogenetic relation to Thaumarchaeota. For example, OTUs affiliated with Thaumarchaeota from Robertson Glacier snow samples were most closely related to the Candidate SCG genus (Hamilton and Havig, 2017). Marine Group II (MG II), was detected in all the samples, with high proportions observed in the samples from TS09 and TS13. Similar to many un-cultivatable microorganisms, our understanding of the ecological and biogeochemical functions of MG II is incomplete. The niches of these species have been suggested as the cause of the separation between MG I and MG II, with MG I presenting more populations in deeper waters and MG II presenting greater abundance in the photic zone rather than the mesopelagic and bathypelagic zones (Massana et al., 2000). However, additional data have confirmed the occurrence of more than one ribotype
Mesopelagic fish provide additional carbon and nitrogen to the local microbial communities along their DVM between depths of ∼100 and ∼500 m, thereby influencing the bacterial and archaeal diversity therein. Since the microbes form a network to utilize and even cycle the nutrients, and a few hours are required before rare groups of microorganisms can be stimulated by nutrient input and grow into larger populations, the oceanic microbial communities often show timelagged correlations with environmental parameters (Fuhrman, 2009). The mesopelagic fish, by exporting organic matter along DVM, have a temporal and subsequent influence on the microbial communities, which was observed via time series sampling. For example, the absolute cellular numbers of MG I and SCG, which are stimulated by the ammonium concentration, were highest at 450 m in TS07 and 200 m in TS09 calculated from the data in Figs. 1a and 2a. These samples were taken a few hours after the fish had passed through the layers. Moreover, the protein digesters became abnormally more abundant in the deep water at night which was likely stimulated by the fecal matter and detritus from fish. As shown in our results, in TS13 the population of archaeal protein digester MG II is high, especially at the water depth of 800 m. And in TS07, the bacterial protein digester Alcanivoracaceae bloomed at the water depth of 450 m. During the past decades, the South China Sea has been intensively investigated from physical, chemical and, of course, biological points of view, with accumulative data showing seasonal variations. Many other model projects, such as Station ALOHA (A Long-term Oligotrophic Habitat Assessment) and BATS (Bermuda Atlantic Time-series Study) project in North Pacific Ocean, all have provided us with deep understanding on the oceanic response in context of global climate change through a significantly long period of time. However, the previous research did not access ecological changes on the time scale of hours, 59
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Fig. 4. Heat maps showing the archaeal (a) and bacterial (b) population from the water samples. Figures in brackets indicate different groups at family level according to Supplementary Table S1 and Table S2 respectively. Color bars indicate the relative percentages. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
which is the same time resolution as the mesopelagic fishes migrate. In this study, we took a time series of sampling along the water column every 6 h at the same location, to capture the microbial communities’ shift as influenced by the fish activities. This is the first attempt to establish the correlation between the microbial community with the DVM of mesopelagic fish and is further revealing the ecological impact of these SCS fish and fisheries.
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Acknowledgments This research is funded by the National Basic Research Program of China (973 Program 2014CB441503). We thank the scientists and crew of the R/V Nanfeng during the cruise in 2014 for providing professional support and kind help in sampling and data collection.
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