LIMNOLOGY and
Limnol. Oceanogr. 00, 2016, 00–00
OCEANOGRAPHY
C 2016 The Authors Limnology and Oceanography published by Wiley Periodicals, Inc. V
on behalf of Association for the Sciences of Limnology and Oceanography doi: 10.1002/lno.10433
Periodic change in coastal microbial community structure associated with submarine groundwater discharge and tidal fluctuation Eunhee Lee,1 Doyun Shin,2,3 Sung Pil Hyun,1,3 Kyung-Seok Ko,1,3 Hee Sun Moon,1,3* Dong-Chan Koh,1,3 Kyoochul Ha,1,3 Byung-Yong Kim4 1
Groundwater Department, Geologic Environment Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon, Korea 2 Urban Mine Department, Mineral Resources Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon, Korea 3 Department of Mineral and Groundwater Resources, University of Science and Technology, Daejeon, Korea 4 ChunLab Inc., Seoul National University, Seoul, Korea
Abstract Coastal areas where submarine groundwater discharge (SGD) occurs are active mixing zones with characteristic biogeochemical and ecological functions. In this study, we investigated the microbial community associated with the changes in groundwater discharge flux at a coastal beach site on Jeju Island, South Korea. We performed water chemistry analyses, 16S rRNA gene-based pyrosequencing, and microbial community statistical analyses on coastal water samples systematically collected as functions of tidal stage and distance from the groundwater discharge point. We also carried out groundwater level monitoring and numerical simulation of the coastal aquifer to reproduce tidally induced variations in the SGD rates of the study site. Pyrosequencing and statistical analyses revealed a periodic shift in the microbial communities in the coastal waters as functions of tidal stage and SGD rates. Interestingly, the community structures in the samples collected at flood and ebb tide were markedly different, despite the similarities in their water chemistry. Groundwater simulation and canonical correspondence analyses suggest that groundwater discharging at higher velocities at ebb tide can detach and transport subsurface bacteria from the aquifer to the coastal water body, resulting in an increase in facultative anaerobes in the ebb tide samples. In addition, release of the sand-attached bacteria as a result of particle resuspension and flushing of shallow subsurface bacteria in the intertidal zone could contribute to shaping the relative abundance of the coastal microbial community. We conclude that SGD rate is an important factor influencing the dynamics of the bacterial community structure at the coastal zone of the study site.
Coastal areas where SGD occurs are biogeochemically active mixing zones. Consequently, the SGD site can provide a unique habitat for certain species adapted to a groundwater– seawater mixing system as well as increased nutrient input, leading to the substantial change in the biogeochemical cycling of coastal ecosystem and its biodiversity (Slomp and Van Cappellen 2004; Santos et al. 2009; Lee et al. 2010). Microbial diversity and community structure in coastal mixing zones have been interesting research topics. Most of the existing microbial studies with regard to SGD have focused on specific nutrient cycles at coastal sandy aquifers in terms of SGD as a nutrient pathway (Lee et al. 2010; Rogers and Casciotti 2010; Garces et al. 2011; Waska and Kim 2011). These studies have demonstrated that the nutrient input along with SGD could alter phytoplankton dynamics of coastal ecosystem, resulting in unexpected environmental
Submarine groundwater discharge (SGD) is a widely recognized phenomenon that carries large volumes of groundwater and chemical species dissolved in it to the ocean (Burnett et al. 2006). SGD serves as important transport pathways for nutrients and contaminants to near-shore ecosystems (Hwang et al. 2005; Burnett et al. 2006; Santos et al. 2008).
*Correspondence:
[email protected] E. Lee and D. Shin contributed equally to this work. Additional Supporting Information may be found in the online version of this article. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
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problems such as red tides and algal blooms (Anderson et al. 2002; Hu et al. 2006; Lee and Kim 2007b). Groundwater-level fluctuations in the coastal aquifer, along with tidal cycles and waves in the ocean, can lead to hydrological changes in SGD rate, making the SGD systems highly dynamic. Many studies have reported that SGD rate displayed spatial and temporal variations depending upon marine and terrestrial factors including tidal stage, groundwater level, currents, waves, storms, etc. (Kim and Hwang 2002; Taniguchi 2002; Michael et al. 2005; Burnett et al. 2006). The strong variations in SGD rate could strongly influence the composition, transport, and dynamics of the near-shore marine microbial communities. These changes in microbial community reflect changes in microbes who play roles in specific biogeochemical processes, which in turn may lead to modifications in biogeochemical functions or modulations in ecological functions in the given coastal system (Fuhrman et al. 1989; Chapin et al. 2000; Azam and Worden 2004; Fuhrman et al. 2006). A few studies have shown that changes in coastal hydrologic conditions such as groundwater levels and tidal fluctuation can affect the microbial composition of the coastal systems (Santoro et al. 2008; Santoro 2010). For example, Santoro et al. (2008) observed a dramatic shift in the abundance of the ammonia-oxidizing archaea and bacteria at a coastal aquifer in Huntington Beach, CA in both winter and summer and concluded that the seasonal movement in the freshwater–saltwater interface due to the groundwater recharge fluctuation was responsible for the community shift between ammonia oxidizing bacterial and archaeal dominance. The study of Santoro (2010) demonstrated that hydrology can have impact on geochemically relevant microbial communities. However, there has been less effort to investigate the effect of periodic variations in the SGD rates driven by tidal cycles or groundwater level changes on the dynamic changes in the microbial community structure and transport of microbes in the coastal systems. The objectives of this study are (1) to investigate the responses of the coastal microbial community structure to tidal cycles and SGD rates at a coastal beach of Jeju Island, Korea, and (2) to suggest possible hydrological mechanisms that are responsible for shaping the microbial community structures observed in the study site. We analyzed the coastal microbial community dynamics subject to tidal cycle and SGD rate variations using 16S rRNA gene-based pyrosequencing and statistical analyses on systematically collected near-shore water samples. Numerical simulation of the coastal aquifer system combined with in-situ measurement was performed to elucidate the relationship between microbial community composition and coastal hydrologic factors including SGD rates. This study will provide valuable information toward better understanding of the relationship between microbial community composition of the near-shore coastal system and coastal hydrological mechanisms associated with SGD and tidal fluctuations on Jeju Island.
Fig. 1. Location of (a) Jeju Island, (b) the study site, and (c) the water sampling and groundwater level monitoring points.
Materials and methods Study site Jeju is a volcanic island located approximately 140 km off the southwestern portion of the Korean peninsula (Fig. 1a). Mt. Halla (EL. 11950 m) rises from the center of the island and creates a steep topography, especially in the North– South direction. The island is mainly composed of basalt, trachytic lava, and surficial sediment, which are underlain by the impermeable sediments (Seogwipo Formation and UFormation) (Kim et al. 2003). Because of this volcanic island’s highly developed fractures and joints, its aquifers are generally permeable with large hydraulic conductivities (Kim et al. 2003). In Jeju, approximately 55% of the annual precipitation (mean annual rainfall; 1,932 mm) occurs during the summer monsoon season (June to September). Due to the island’s high aquifer permeability, the groundwater recharge rate is significantly high (more than 40% of rainfall) (Kim et al. 2003). The high groundwater recharge rate has led to the development of springs and a large amount of fresh SGD to the coast (Kim et al. 2007; Lee and Kim 2007a). The study site, Gongcheonpo Beach, is located on the southern part of the island (Fig. 1b). A large amount of 2
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monitored the groundwater level response to tidal fluctuation from July 2014 to January 2015. In addition, we collected groundwater level data from the 6 coastal monitoring wells (TP1, HH1, WM1, NW1, TH3, and SH1 in Fig. 1c) operated by Jeju Special Self Government within the watershed that include the study site (Groundwater level data accessible at www.jejuwater.go.kr). Data were collected from January 2013 to November 2014 at a monthly interval and used to analyze annual and seasonal variation of groundwater level at a watershed scale. The Seogwipo weather station, the closest rainfall monitoring point to the study site, is located at a distance of 7 km to the western direction. The daily rainfall data of the station from January 2013 to November 2014 were downloaded from the Korea Meteorological Administration (Data accessible at www.kma.go.kr). The groundwater velocity change over the tidal cycle was simulated using a numerical calculation. A two-dimensional vertical domain was constructed using the FEFLOW (Diersch 2005). FEFLOW is a numerical software that is widely used to simulate coastal aquifer processes such as saltwater intrusion and SGD investigation (Michael et al. 2005; Watson et al. 2010; Lee et al. 2013). The numerical domain used for simulation covers the coastal aquifer from the groundwater level monitoring well (LC1, Fig. 1c) to the seabed, extending 100 m from the coastline (Supporting Information, Fig. S1). The groundwater well extends to the upper part of the impermeable layer (Seogwipo Formation), which was used as the bottom of the domain (250 m a.m.s.l). The hydraulic conductivity of the aquifer (529.4 m/day) was referred from the reported average value of the southern area of Jeju Island (Hahn et al. 1997). Specific storage of the aquifer (2.0 3 1024 m21) was obtained based on the observed groundwater level response to tidal fluctuation, assuming confined aquifer condition (Ferris 1952). A specified head boundary condition was assigned along the inland vertical border, and a no-flow boundary was assumed at the bottom of the domain. The model considered three cases, each of which included the three water sampling campaigns (September 2013, March 2014, and July 2014). In each simulation, the boundary conditions of the model were adjusted considering the groundwater level (and sea level) changes at the study site. In the case of the July 2014 simulation, the monitored groundwater level at LC1 (mean: 3.95 m) was assigned as the specified head boundary along the inland vertical border. Since there were no available water level data at LC1 before July 2014, we estimated the groundwater level based on the groundwater level regression analysis results of the study area. Won et al. (2006) observed that the water levels in Jeju Island have highly positive, linear correlations with the topographic elevations, especially in the southern and northern direction. Thus, the regression lines were obtained between the groundwater levels and the topographic elevations of the coastal monitoring wells shown in Fig. 1c. (Regression analysis results are provided in Supporting Information, Fig. S2).
Table 1. Summary of coastal water and black sand samples collected from the study site. Sample type
Station
Coastal
GC1
Sampling date 2013.09
2014.03
Low tide
LT_SEP*
LT_MAR
LT_JUL
Flood tide
FT_SEP
FT_MAR
FT_JUL
High tide Ebb tide
HT_SEP ET_SEP
HT_MAR ET_MAR
HT_JUL ET_JUL
GC2
Low tide
SEA_SEP
SEA_MAR†
SEA_JUL
GC3 GC1
High tide Ebb tide
-
SED_MAR‡
SEA(II)_JUL† -
water
Black
Tidal stage
2014.07
sand * Sample name: Tidal stage (LT: low tide, FT: flood tide, ET: ebb tide, HT: high tide, SED: black sand)-Sampling month. † For the samples taken at GC2 (or GC3) stations, SEA (or SEA(II)) were used for sample name instead of tidal stage. ‡ For the black sand sample, SED was used for sample name instead of tidal stage.
groundwater discharge (>10,000 m3/day) occurs along the coast as mostly spring discharge from the rock fractures (Kim et al. 2007). The site is covered by a thin layer of black sand, which is underlain by fractured trachytic basalts. Sample collection Near-shore water samples were collected at three different locations in September 2013, March 2014, and July 2014 for water chemistry and microbial community analyses. The station GC1 was the spring groundwater discharge point. The GC2 and GC3 were approximately 40 m and 300 m offshore from the GC1, respectively (Fig. 1c). Samples from the GC1 station were taken at four different tidal stages (i.e., high tide, ebb tide, low tide, and flood tide) during a given tidal cycle. Samples from the GC2 and GC3 were obtained during low and high tide, respectively, for comparison with the GC1 sample. A black sand sample was also collected near the GC1 in March 2014 and subjected to microbial analysis for comparison with the water samples. The collected water samples for water chemistry analysis were filtered through 0.45 mm PVDF filters at the site. Two to six liters of water samples were collected and filtered through 0.2 mm cellulose acetate filters in the laboratory as soon as they were transported to obtain enough biomass for microbial analyses. All the samples were stored on ice during transport. The filters with biomass and black sand samples were stored at 2708C until microbial analyses. Table 1 summarizes the water and black sand samples for the water chemistry and microbial analyses. Groundwater level monitoring and numerical calculation of SGD rates We installed TD Diver data logger (D1243, Schlumberger, TX, USA) in the groundwater well (LC1, in Fig. 1c) located 200 m inland from the groundwater discharge point and 3
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sequence 541R was 50 -CCATCTCATCCCTGCGTGTCTCCGACTCAG-X-AC-ATTACCGCGGCTGCTGG-30 , where X represents the unique barcode sequence designed to differentiate the sequencing reads from different samples (http://www.ezbiocloud.net/oklbb/1001). The polymerase chain reaction (PCR) process was performed under the following conditions: the initial denaturation step (5 min at 958C), followed by 30 cycles of denaturation (30 s at 958C for each), primer annealing (30 s at 558C), extension (30 s at 728C), and final elongation (5 min at 728C). The PCR products were purified using the QIAquick PCR purification kit (Qiagen, Valencia, CA, USA) and quantified using a PicoGreen dsDNA Assay kit (Invitrogen, Carlsbad, CA, USA). Equimolar concentrations of each amplicon from different samples were pooled and purified using an AMPure bead kit (Agencourt Bioscience, Beverly, MA, USA) and then were amplified on sequencing beads by emulsion PCR. The recovered beads from emulsion PCR were deposited on a 454 Picotiter Plate and sequenced with a Roche/454 GS Junior System at ChunLab, Inc. (Seoul, Korea). Raw sequence files were processed by (1) demultiplexing, (2) trimming primer sequence, (3) quality filtering, (4) sequencing error correction, (5) taxonomic assignment, and (6) detection of chimeras. Each sample was identified by a unique barcode in the demultiplexing step and low quality reads (average quality score x > 94%), family (94 > x > 90%), order (90 > x > 85%), class (85 > x > 80%), and phylum (80 > x > 75%). If the similarity was below the cutoff of ranks, the read was assigned to an unclassified group. The chimera sequences were removed by UCHIME (Edgar et al. 2011). The final valid reads were randomly normalized based on the minimum valid reads of the samples. The Mothur package (Schloss et al. 2009) was used to calculate abundance-based coverage estimator (ACE), Chao 1 estimator (Chao), interpolated Jackknife richness estimator (Jack), nonparametric Shannon diversity index (NpShannon), Shannon index of diversity (Shannon), Simpson index of diversity (Simpson), and Good’s coverage. Also, OTUs were determined by de novo clustering using CD-HIT program for the sequence assignment. Water samples taken at low tide in September 2013 at GC1 (LT_SEP) and taken at low tide in July 2014 at GC2 (SEA_JUL) were not included in the molecular analyses because LT_SEP did not contain enough biomass for DNA extraction and further analyses, and SEA_JUL was contaminated during the experiments.
The calculated water level at LC1 using the regression line of July 2014 (3.36 m) gave a similar value with the observed one (3.95 m) within the error range of 15%. Based on these observations, we estimated the groundwater levels at LC1 during other sampling campaign using the regression analysis and used the values for the model boundary condition. Finally, a tidal level of seawater was used for a timevarying head boundary condition at the seaside under the following conditions: H5Hs
if
Hs > Zd
H5Zd
if
Hs Zd
(1)
where H is the assigned boundary value along the seaside, H s is the monitored sea level, and Zd is the elevation of the node at the seaside. Equation 1 explains that if the elevation of a groundwater discharge node is below sea level, the hydraulic pressure assigned to the node is equal to the seawater pressure. If the elevation of the node is above sea level and is open to the atmosphere during low tide, the corresponding node becomes a seepage face. Because the actual velocity (V) of groundwater was difficult to determine without reliable porosity data, the relative change in the groundwater discharge velocity (V/V0) was presented instead. The mean groundwater outflux, V0, was obtained by averaging the groundwater outflux over the tidal cycle (0.65 m/day). Water chemistry measurements Water quality parameters including pH, electrical conductivity (EC), temperature, and dissolved oxygen concentration (DO) were measured in the field using a multi-probe water quality meter (HQ40d, HACH, USA). The cations (i.e., Ca, Mg, Na, K, Fe, Si, and Sr) and anions (i.e., Cl and SO4) were analyzed using an inductively coupled plasma-optical emission spectrometer (ICP-OES 7300 DV, PerkinElmer, USA) and an ion chromatograph (ICS-1500, Dionex, USA) equipped with a CD25 conductivity detector and a Dionex IonPac AS14–4 mm column, respectively. The alkalinity was determined using the Gran titration method. The total nitrogen (TN) and total phosphorous (TP) concentrations were obtained using the total Kjeldahl nitrogen and ascorbic acid method, respectively (Korea Ministry of Environment 2002). Chlorophyll-a was extracted with 90% acetone and measured using the spectrophotometry method (Korea Ministry of Environment 2002). Microbial community analyses: DNA extraction, PCR, and pyrosequencing Bulk genomic DNA was extracted from the filters and the black sand sample using a FastDNA SPIN Kit for Soil (MP Biomedicals, USA) following the manufacturer’s instructions. The extracted DNA was amplified using the bar-coded fusion primers targeting the V1 to V3 regions of 16S rRNA gene. The forward sequence of the 9F primer was 50 -CTATCCCCTGTGTGCCTTGGCAGTCTCAG-AC-AGAGTTTGATCMTGGCTCAG-30 and the reverse 4
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Changes in Coastal Microbial Community
Fig. 2. Tidal variation of coastal water chemistry from the groundwater discharge point (GC1): (a) Salinity, (b) Sr, (c) TN, and (d) Si. HT, ET, LT, and FT represent high, ebb, low, and flood tide. the CCA analysis because these samples do not have enough environmental component applicable for the CCA. The CCA was performed both in the absence and presence of the simulated groundwater velocity component to assess the contribution of the groundwater discharge velocity to the microbial community structure of the study site.
Statistical analyses For statistical analysis, paired t-test was performed to assess differences between water chemistry data using the Microsoft Excel 2000. Principal coordinate analyses (PCoA) were used to phlyogenetically cluster the normalized 16S rRNA sequence set among water samples based on the Fast Unifrac distance and the matrix of Fast UniFrac (Hamady et al. 2009) was generated using CLcommunity (ChunLab, Inc., Seoul, Korea). Canonical correspondence analysis (CCA) was applied to elucidate the relationship between the microbial community and the environmental factors based on the Chi-square distances and the weighted linear mapping method (ter Braak and Verdonschot 1995). The environmental parameters used for CCA include alkalinity (Alk), temperature, EC, pH, DO, TP, TN, dissolved iron (Fe) and Chlorophyll-a (Chl) concentrations, and relative groundwater discharge velocity (Vel). The samples taken during September 2013, and the black sand sample were not included in
Results Changes in groundwater chemistry as a function of the tidal phase The chemistry of the water samples from the groundwater discharge point (GC1) was strongly dependent on the tidal phase (Fig. 2a–d). At low tide, the water samples from this station had a chemical composition similar to that of the inland groundwater (LC1), with a constant temperature throughout the year (Table 2). This observation suggests that the water samples collected at low tide are primarily composed of discharging groundwater and the contribution of saline 5
42.7
0.1
16.9
19.6
16.7
FT_JUL
HT_JUL
ET_JUL
SEA(II)_JUL 22.6
LC-1-JUL
16.3
1.0
15.2
LT_JUL
31.1
2.7
0.1
40.9
16.8
0.07
28.96
0.60
21.86
1.68
0.06
33.86
12.32
7.3
8.0
7.1
8.2
7.1
7.1
8.5
8.3
9.3
8.0
9.3
9.9
9.1
8.9
9.7
9.1
0.05
0.04
0.04
0.03
0.04
0.04
0.03
0.05
0.02
0.05
3.7
0.4
4.9
1.3
4.7
4.2
0.8
2.2
0.4
2.3
0.2
0.2
0.2
N.D.
0.2
0.2
0.09
0.19
0.12
0.09
40.7
28.9
107.7
32.1
97.9
30.0
25.2
109.1
58.4
117.3
6.1
256.6
30.7
197.0
21.1
6.8
292.4
58.0
347.2
55.0
5.2
3.3
937.6
48.4
683.7
50.5
3.9
901.0
171.0
1093.5
161.1
4.1
6.2
6853.4
339.9
5467.8
399.0
6.8
7026.7
1350.6
8732.4
1241.9
15.6
-
1.3
386.2
15.9
273.9
19.5
1.3
410.1
63.7
483.7
58.6
2.0
-
0.002
N.D.
N.D.
0.1
0.1
0.2
0.003
N.D.
N.D.
0.003
0.004
-
-
13.2
3.1
15.4
6.5
12.9
12.2
7.4
14.5
5.6
14.8
12.6
-
-
0.05
4.4
0.3
3.4
0.3
0.1
5.1
1.0
5.9
0.90
0.05
-
-
-
7.9
14123.3
721.0
10431.8
744.1
9.9
14952.5
2766.0
17885.7
2574.1
28.3
-
-
-
1.6
1986.6
103.5
1483.1
105.8
1.9
51.5
395.4
2537.3
369.4
5.5
-
-
-
-
14.2
9.4
9.5
35.0
-
-
-
-
15.5
8.2
7.8
0.09
-
-
-
-
SEA_MAR
7.25
42.56
2.7
-
-
-
-
ET_MAR
50.6
10.1
0.07
-
-
-
-
14.6
9.1
-
-
-
-
14.7
7.5
-
-
-
-
HT_MAR
0.12
8.2
-
-
-
FT_MAR
0.2
8.4
-
-
-
14.9
15.80
8.7
-
-
LT_MAR
24.3
7.8
-
-
21.9
5.99
8.4
-
SEA_SEP
9.5
8.1
-
19.8
21.07
9.2
ET_SEP
33.1
7.3
24.2
HT_SEP
1.74
18.5
FT_SEP
2.9
T EC Salinity pH DO TP TN Chl-a ALK Ca Mg Na K Fe Si Sr Cl SO4 (8C) (mS cm21) (mg L21) (mg L21) (mg L21) (mg m23) (mg L21) (mg L21) (mg L21) (mg L21) (mg L21) (mg L21) (mg L21) (mg L21) (mg L21) (mg L21)
Sample
Table 2. Physical and chemical characteristics of coastal water and groundwater samples in the study site.
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Fig. 3. Change in the relative submarine groundwater discharge (SGD) velocity of the study site depending on the tidal stage. Zd indicates the elevation of the spring groundwater discharge point (GC1).
the highest at low tide and the lowest at high tide (Taniguchi 2002; Lee et al. 2013). In case of Gongcheonpo Beach, the spring water discharge point (GC1) is completely open to the atmosphere at ebb tide and it becomes a seepage face. After the groundwater discharge point (GC1) becomes the seepage face (Hs < Zd ), the hydraulic pressure acting on the point does not decrease (solid line in Fig. 3), thus, the SGD would not increase anymore. The maximum groundwater discharge velocity actually occurs immediately after water pressure disturbing groundwater discharge releases. It means that the combining effect of seepage face condition and the strong fresh groundwater discharge rate at GC1 allows the maximum groundwater discharge to occur during ebb tide (Fig. 3). The simulation result clearly reveals that the tidal cycle controls the SGD rate at the study site. The monitored groundwater level at LC1 also showed a clear response to the tidal cycle (Supporting Information, Fig. S3). The time lag and the amplitude ratio (5A/A0 where A is the amplitude of groundwater level fluctuation and A0 is the amplitude of tidal fluctuation) of groundwater level were 2 h and 0.17, respectively. The positive response of groundwater level to tidal cycle implies that the monitoring well (LC1) is hydraulically connected to the ocean through the fracture network of the coastal aquifer. The result is also consistent with the simulation results of groundwater discharge velocity that showed strong dependency on tidal stage. Monthly variations in rainfall and groundwater levels in the coastal groundwater monitoring wells showed seasonal variations in groundwater level throughout the monitoring wells (Supporting Information, Fig. S4). The response of groundwater level to rainfall becomes pronounced
groundwater to total SGD is insignificant in this coastal system. The samples collected at the flood and ebb tide stages had compositions intermediate between groundwater and seawater, indicating active mixing of the discharging groundwater and seawater. There is no significant difference in water chemistry between the flood and ebb tide samples (P value > 0.05). The sample collected at high tide had a seawaterlike composition characterized by high EC, pH, alkalinity, and relatively low-silica, TN, and TP concentrations (Table 2). The water samples from station GC2 collected at low tide and from GC3 at high tide had similar chemistry to the GC1 sample collected at high tide (Table 2). The TN concentration almost doubled in July (Fig. 2c). The water temperature of the samples from GC2 and GC3, and those from GC1 at high tide shows seasonal variation (Table 2). Periodic change in groundwater discharge velocity and groundwater level variations The periodic change in the relative groundwater discharge velocity over the tidal cycle is illustrated in Fig. 3. The simulation results show that the groundwater discharge velocity is inversely proportional to the sea level. The water pressure acting on the discharge outlet increases as the tidal level changes from low to high tide, significantly disturbing the groundwater outflow. The groundwater flow rate increases rapidly again, immediately after the water pressure at the discharge outlet releases during ebb tide. The fluctuation in the groundwater discharge rate periodically repeats following the tidal cycle. Note that the maximum groundwater discharge occurs at ebb tide, not low tide (Fig. 3). This phenomenon is different from many previous studies that reported that SGD rate is 7
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Changes in Coastal Microbial Community
Fig. 4. Principal coordinate analysis (PCoA) plot of the bacterial communities in the coastal waters and black sand of Gongcheonpo Beach, Jeju Island. A–F represent the cluster names. Symbol color indicates sampling time (Orange: September, Gray: March, Blue: July). FT_MAR) or ebb tide (i.e., ET_SEP and ET_MAR), except for samples FT_JUL and ET_JUL, respectively. The low tide (i.e., LT_MAR) and black sand samples (SED_MAR) were separated into the independent Groups D and E, respectively. In addition, seawater-dominant samples had relatively lower richness and evenness values (i.e., low diversity), whereas the discharging groundwater-dominant sample (i.e., LT_MAR) and black sand sample (SED_MAR) showed relatively higher richness and evenness values (i.e., high diversity) (Supporting Information, Table S1). It is interesting that Groups B and C were completely separated from each other, although the flood and ebb tide samples did not show any significant differences in terms of water chemistry (P value > 0.05) (Fig. 2 and Table 2). Group B was located between Groups A and D, indicating that the samples collected at flood tide contained mixtures of microbial components in the discharging groundwater and seawater. On the other hand, Group C was shifted from the A–D mixing line to the PC2-positive direction. The diversity of the ebb tide samples was also lower than that of the flood tide samples (Supporting Information, Table S1).
particularly at the wells with relatively high groundwater levels (TP1 and TH3). In addition, we observed that annual rainfall of Jeju Island in 2013 was much lower than that in 2014 and consequently, the groundwater levels significantly dropped especially during the summer monsoon of 2013. Cluster analysis and microbial diversity of the 16S rRNA gene sequences Overall, 120,854 valid reads were obtained after removing invalid sequences by denoise and chimera checks and 5,538 OTUs (Operational Taxonomic Unit) were produced based on the normalized valid reads (1,478 reads per sample) (Supporting Information, Table S1). The phylogenetic clustering of the normalized 16S rRNA gene sequence set among water samples showed that the bacterial communities of the samples formed six distinct clusters: Groups A, B, C, D, E, and F (Fig. 4). Group A is composed of chemically seawater-dominant samples (i.e., HT_SEP, SEA_SEP, HT_MAR, SEA_MAR, and SEA(II)_JUL), except for sample HT_JUL. Group B or C has sample from groundwater discharge point (GC1) taken at flood tide (i.e., FT_SEP and 8
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Changes in Coastal Microbial Community
Fig. 5. Phylogenetic classification and distribution of the bacterial sequences detected in the costal water and black sand samples at the class level: (a) September sample (2013); (b) March sample (2014); and (c) July sample (2014).
sample (HT_JUL). The high tide sample from GC1 in July 2014 (HT_JUL) was shifted toward Group A and was clustered with Group C, which reflected the mixing effect of seawater.
Most of the July samples from the groundwater discharge point (GC1) formed a new Group F (i.e., LT_JUL, FT_JUL, and ET_JUL), regardless of the tidal stage, except for the high tide 9
Lee et al.
Changes in Coastal Microbial Community
Fig. 6. CCA plot deciphering the relationship between the dominant bacterial classes and the environmental variables of the study site (a) without the groundwater velocity component and (b) including the groundwater velocity component.
ferric iron (Myers and Nealson 1988; Gauthier et al. 1995; Venkateswaran et al. 1999; Chang et al. 2008). Compared with the Group A samples, which are strictly aerobic and halophilic, the Group C samples are also halophilic, but they are associated with a denitrifying or iron reducing condition. The LT_MAR (Group D) and SED_MAR (Group E) samples showed high diversity (Supporting Information, Table S1). The OTU TM7 in the Group D which occupied 2.0% of the total sequence in the sample was detected in chemically and geographically diverse habitats (Hugenholtz et al. 2001) and this OTU does not need NaCl for growth, which means that it likely originated from a freshwater-related environment. Another OTU (DQ123667_g, 5.5%) found in the Group D belongs to the family Sterolibacterium, which was isolated from a sludge reactor (Tarlera and Denner 2003). The OTUs in the Group E sample were closely related to marine sediment bacteria (genus Blastocetella, 7.3%; family Saprospiraceae, 7.7%) (Liu et al. 2011; Piggot et al. 2012). In the Group F samples, a drastic increase in ß-Proteobacteria was observed, which was primarily attributed to the appearance of OTUs belonging to the genus Dechloromonas, the family Oxalobacteraceae, and Rhodocyclaceae. These bacteria can reduce perchlorate, chlorate, nitrate, nitrous oxide, bromate, and ferric iron and some of their OTUs were found in the soil of a rice paddy (Wolterink et al. 2005; Ding et al. 2015).
Taxonomic classification and the abundant OTUs’ response to the tidal cycle The phylogenetic classification distribution of the sequences at the class level clearly showed the periodic response to the tidal stage (Fig. 5). This changing pattern of microbial composition is primarily due to the appearance of a-Proteobacteria, c-Proteobacteria, and ß-Proteobacteria. The abundant OTUs (>5% of sum of each sequences in total samples), their lineages, their abundance values, and accession numbers are shown in Supporting Information, Table S2. The phylogenetic tree of the OTUs and their closely related species are shown in Supporting Information, Fig. S5. The abundant OTUs of Group A were closely related to the genera Loktanella, Candidatus Pelagibacter, Roseovarius, Polaribacter, and the family Cryomorphaceae. These bacterial genera are strictly aerobic and most of them require NaCl or seawater salt as their absolute growth factor (Bowman et al. 1998; Gosink et al. 1998; Labrenz et al. 1999; Rappe et al. 2002; Van Trappen et al. 2004). This is consistent with the fact that Group A consists primarily of seawater-dominant samples. The dominant phylotypes in Group B were similar to those of Group A samples, with a small decrease in a-Proteobacteria and an increase in c-Proteobacteria. The Group C samples showed a comparatively different pattern from the flood tide samples (Group B). A significant increase in c-Proteobacteria (67.0%) was observed, which was attributed to dominant increases in the genera of Shewanella, Pseudomonas, and Flavobacterium. The key phenotypic characteristics of these genera are halophilicity and facultative anaerobes with capability of dissimilatory reduction of manganese oxide and
Relationship between microbial community composition and environmental variables The relationships among the major environmental variables and the dominant bacterial community compositions 10
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Fig. 7. Possible processes controlling the transport of the subsurface bacteria community to the coastal water body at ebb tide: (a) flushing of bacteria off from the subsurface fracture by discharging groundwater; (b) release of bacteria as a result of particle resuspension; and (c) periodic flushing of the coastal sediment depending on tidal stage.
Group B had characteristics intermediate between Groups A and D, indicative of the mixing of the seawater and fresh groundwater. Although the short-term bacterial dynamics in tidal ecosystem has been reported before (Poremba et al. 1999; Grossart et al. 2004; Lunau et al. 2006; Chauhan et al. 2009; Kara and Shade 2009; Olapade 2012), none of the previous studies have observed a drastic change in the microbial structure in response to tidal stage and SGD rate variations. Our results clearly showed that diurnal fluctuation of SGD rate dominates the periodic occurrence of freshwater-related bacteria during low tide and seawater-related bacteria during high tide. The difference in microbial community structure between July 2014 and other sampling periods indicates the seasonal effect of SGD rate variation on coastal microbial community, suggesting a greater groundwater influence on all the samples when groundwater discharge is the highest (in July 2014). In 2013, Jeju Island suffered a severe drought and consequently, groundwater levels dropped significantly (Supporting Information, Fig. S4). The groundwater levels rapidly recovered in a relatively short period during the summer monsoon of the following year. The fast water level recovery resulted in large variations in the groundwater discharge rate over the sampling period in 2014. The significant increases in the groundwater level and subsequent increases in the discharge rate during July 2014 are expected to have promoted flushing of the subsurface bacteria, leading to the observation of the subsurface bacterial biota regardless of tidal stage contrary to other sampling periods, to offset the tidal stage effect on the microbial community composition.
are illustrated in Fig. 6. To elucidate the contribution of the groundwater discharge velocity to the microbial community structure of the study site, we performed CCA both in the absence (Fig. 6a) and presence (Fig. 6b) of the simulated groundwater velocity as an environmental variable and compared the results. In Fig. 6a and b, we observed that samples belonging to group A (Fig. 4) were highly correlated with EC, alkalinity, pH, and DO. TN and Fe were correlated to the group F. Microbial community of ET_MAR sample seemed to be influenced by TP. Group D sample (LT_MAR) showed a positive correlation with temperature (Temp). Comparison between Fig. 6a and b reveals that the inclusion of the relative groundwater velocity improves the CCA results. The percentage of variance explained by the first two CCA axes increases from 49.6% to 60.6% after adding the groundwater velocity component as an environmental variable. In addition, Fig. 6b reveals that the velocity component has a positive influence on Groups C and F (except the sample HT_JUL).
Discussion Short-term dynamics of coastal microbial community subject to SGD We observed that the tidal cycle led to the periodic changes in the coastal microbial community structure by modulating the input of fresh SGD to the coastal system. For instance, the bacterial species of Group A (high tide samples) were halophilic and strictly aerobic, whereas those of Group D (low tide samples) had a highly diverse metabolism and likely originated from a freshwater-related environment. 11
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Release of bacteria as a result of particle resuspension by tidal action (Fig. 7b) is also a possible process to transport the microbes from the coastal aquifer to the ocean. Previous studies support possible release of bacteria from surface sediments in coastal zones. For example, it was reported that bacteria were washed off from the estuary surface in the mixing process of river water with seawater by tidal action in a coastal ocean under an estuarine setting (Crump et al. 1999). Two other studies also reported that tidal turbulence induced sediment resuspension and altered microbial activities in the tidal flats (Grossart et al. 2004; Lunau et al. 2006) in Wadden Sea, German. In a similar manner, increased sediment resuspension induced by tidal currents could have contributed to the release of particle-associated bacteria at our study site. The last mechanism is discharge of the subsurface bacterial biota from the coastal beach during outgoing tide, preceded by washing off of the biota from the beach by the tidal fluctuation promoted by inflowing seawater during high tide (Fig. 7c). It has been well recognized that pore water and groundwater flow can control bacterial transport in subsurface environments (Bales et al. 1995; Johnson and Logan 1996; DeFlaun et al. 1997). Furthermore, several studies have reported periodic flushing of coastal sediments caused by sea level fluctuations can transport organic and inorganic materials from subsurface to the ocean along with discharging groundwater (Moore 1996; Burnett et al. 2006; Kim et al. 2012b). Consequently, it is possible that periodic flushing of coastal sediments at the study site transports the bacteria inhabiting the intertidal zone to the near-shore coastal water body, resulting in periodic changes in the relative abundance of these bacteria. One major limitation, though, of the second and third mechanism is the dissimilarity in the microbial community compositions observed between the water (ET_SEP and ET_MAR) and surficial black sand samples (SED_MAR) collected during ebb tide (Fig. 4). This discrepancy suggests that some of the dominant microbes that occur in the ebb tide water samples might not originate from the surficial sand, suggesting possible combination of the first, second, and third mechanisms. Although limited amount of available data prevents us from drawing a definite conclusion regarding the mechanism(s) responsible for the occurrence of the facultative anaerobes, it is still meaningful to report the occurrence of the subsurface bacteria in the coastal water body as a function of tidal cycle under the coastal setting of the study site.
Possible processes controlling the transport of the coastal subsurface bacterial biota Taxonomic composition analyses showed that varying inputs of fresh SGD cause short-term variations in the microbial community structure at the study site. Interestingly, Groups C (ebb tide samples) and B (flood tide samples) were completely separated from each other in the principal coordinate analysis plot, despite their similarities in water chemistry (Figs. 2 and 4). The dominant phylotypes in Group C are facultative anaerobes originating neither from the groundwater-dominated sample (Group D) taken at the groundwater discharging point, with a chemical composition similar to inland groundwater, nor from the seawaterdominated sample (Group A) (Fig. 4). The length of the tidal cycle was too short (
