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Studies on temporal changes in the sediment oxygen consumption and bacterial community structure in a seasonally
hypoxic enclosed bay, Omura Bay
July, 2018
Graduate School of Fisheries and Environmental Sciences, Nagasaki University
Fumiaki Mori
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Studies on temporal changes in the sediment oxygen consumption and bacterial
community structure in a seasonally hypoxic enclosed bay, Omura Bay ... 1
Abstract ... 1
I. General Introduction ... 6
Coastal hypoxia ... 6
Sediment oxygen consumption in coastal bottom and its relationship with hypoxia ... 7
Methodology of sediment oxygen consumption ... 8
Dynamics of sediment bacterial community composition in seasonal hypoxia 10 Sulfate reducing bacteria diversity and community structure in seasonal hypoxia ... 11
Objective of the study ... 12
II. Application of INT reduction assay to estimate sediment oxygen consumption rate in coastal area ... 15
Introduction ... 15
Materials and methods ... 21
Measurement of whole INT reduction rate ... 21
Measurement of whole sediment oxygen consumption rate ... 24
Measurement of chemical INT reduction rate ... 24
Measurement of chemical oxygen consumption rate and acid volatile sulfides ... 26
Time-course experiments... 26
Results ... 28
Relationship between WSOC and whole INT reduction ... 28
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Relationship between COC and chemical INT reduction ... 28
Relationship between whole INT reduction rate and period of incubation ... 29
Discussion ... 29
III. Dynamics of microbial community respiration in sediment at Omura Bay in response to seasonal hypoxia ... 39
Introduction ... 39
Material and methods ... 42
Study site and sampling ... 42
The INT reduction method with sediment core ... 43
Sediment organic carbon and acid-volatile sulfides analysis ... 45
Bacteria counting ... 46
Statistical analysis ... 46
Results ... 48
Environmental variables in overlying water and sediment ... 48
SOC measured by INT reduction assay ... 49
Discussion ... 51
IV. Effects of bottom-water hypoxia on sediment bacterial community composition ... 60
Introduction ... 60
Materials and methods ... 64
Study site and sampling ... 64
Bacterial community analysis using ARISA ... 66
Linking ARISA fragments to phylogenetic affiliations ... 68
Nucleotide sequence accession numbers ... 69
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Statistical analysis and diversity ... 69 Illumina MiSeq 16S rRNA Gene Amplicon Sequencing ... 71 Results ... 73 Environmental variables and bacterial abundance in overlying water and sediment ... 73
Diversity of the bacterial community and its correlation with DO and other environmental variables ... 74
Changes in relative abundance of the individual OTUs in response to DO availability ... 77
Relative abundance of the individual ASVs ... 80 Discussion ... 80 Effect of bottom-water hypoxia on organic matter preservation and bacterial abundance ... 82
Effect of bottom-water hypoxia on bacterial community structure and diversity ... 83 V. Community structure of sulfate-reducing bacteria as revealed by dsrA T-RFLP
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Introduction ... 103 Materials and Methods ... 107 Site description and sampling ... 107 Terminal restriction fragment length polymorphism analysis of dsrA genes . 107 Statistical analysis ... 109 Pyrosequencing analysis ... 110 Results and Discussion ... 112
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VI. General Discussion ... 126
Does INT reduction method give realistic estimates for SOC in hypoxic condition? ... 127
How does SOC change in seasonally hypoxic coastal area? ... 128
How do BCC and diversity change in seasonally hypoxic coastal sediment? 129 How do SRB change in response to temporal variation of DO in bottom water? ... 130
VII. Acknowledgments ... 133
VIII. Reference ... 135
IX. Appendix ... 165
1 Abstract
Seasonal formation of oxygen-depleted water masses in bottom environments is a widespread phenomenon in coastal areas around the world. Dissolved oxygen (DO) depletion in bottom water is lethal to macrobenthic animals and eliminates sensitive species, while hypoxic conditions would enhance microbial heterotrophic activity and hence diversion of energy-flow into the microbial food web. In general, microbial respiration is responsible for the depletion of DO, and its availability in turn exerts fundamental changes in the respiratory metabolism, thereby drives shift in microbial community structure in aquatic ecosystem. Omura Bay, the study area of this investigation, is a shallow enclosed bay that experiences severe bottom water hypoxia (less than <3 mg O2/L) every summer from mid-June through September. Strong wind force associated with a typhoon or low-pressure system occasionally enhances vertical mixing and thus transiently increases DO level in the bottom water to a normoxic condition in the middle of hypoxia. However, the DO level would be often brought back to hypoxic condition fairly rapidly. Sediment oxygen consumption (SOC) mediated by sediment microbial community in the center region of the bay has been believed to play a fundamental role in the formation of basin-wide hypoxia (Wada et al. 2012) and is also likely to contribute to buffering such a transient increase in DO level during hypoxia
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(Mori et al. 2015). However, little has been known about temporal dynamics of SOC and the responsible microbial community. In order to gain quantitative insights into how sediment microbial community respiration and hypoxia interact with each other, and how seasonal bottom hypoxia would affect the sediment microbial (bacterial) community richness and structure, a series of sediment sampling and monitoring of bottom-water hypoxia were conducted in Omura Bay for three consecutive years (2011 – 2013).
An INT reduction method (Wada et al. 2012) was used to demonstrate potential SOC in the samples. This method is based on the measurement of reduction rate of a tetrazolium compound (INT) in samples in either the presence or the absence of a fixative (formalin) to infer the extent to which whole sediment oxygen consumption (WSOC) was mediated by reduced chemical compounds or living microorganisms (COC and BOC, respectively). Direct measurement of DO was first made in some selected samples with a fiber optic oxygen sensor to confirm oxygen consumption in conjunction with the INT reduction method. A significant positive correlation was found between WSOC and whole INT reduction (WIR) with a value of 29.1 for WSOC/WIR (R-w/INT-F-w) ratio. WIR remained stable within 24 hours under a laboratory condition. These results provide empirical evidence that (1) WIR rate can be used to obtain realistic estimate of WSOC in the sediment samples, and (2) that the relative contribution of COC and BOC to WSOC
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can be inferred by subtracting chemical INT reduction (CIR) from WIR (Chapter II). In the following chapters, WSOC, COC and BOC estimated from INT reduction are shown as “WSOCINT”, “COCINT” and “BOCINT”, respectively.
In order to clarify seasonal dynamics of SOC and bottom-water hypoxia in Omura Bay, temporal changes in WSOCINT, COCINT and BOCINT were examined. Not only COCINT but BOCINT increased noticeably during hypoxia. Both WSOCINT and COCINT
correlated with DO and temperature of bottom-water. This suggests oxygen and temperature dependence of sulfate reducing bacterial (SRB) activity. On the other hand, BOCINT was correlated only with DO. Preservation of labile organic matter in the sediment and/or increase in sediment bacterial abundance during hypoxia may have contributed to the increase in BOCINT during hypoxia (Chapter III).
In order to clarify how bacterial community composition at surface sediment would change during hypoxia in Omura Bay, diversity, richness and structure of the bacterial population were examined in the uppermost (0-5 or 0-7 mm depth) and the subsurface layers (5-10 or 7-14 mm depth). Automated ribosomal intergenic spacer analysis (ARISA) revealed a unimodal pattern in the diversity index with DO, peaking at suboxic (11 μM O2) conditions. Shifts in the bacterial communities were also evident in response to the availability of DO. Changes in the operational taxonomic units (OTUs) that were
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less abundant accounted for a large part of the community dissimilarity. It was further demonstrated that the relative abundance of OTUs affiliated with Gammaproteobacteria was correlated positively with DO, while that with Deltaproteobacteria was inversely correlated with DO. Additional analysis of the 16S rRNA gene amplicon sequences conducted for uppermost sediment samples in 2011 confirmed these patterns of bacterial diversity in response to DO conditions. It was further demonstrated that Desulfobacteraceae within Deltaproteobacteria was the most abundant bacterial family
across the sediment samples, and that Woeseiaceae was the most abundant family within Gammaproteobacteria. These results strongly suggest that DO availability of bottom
water plays a fundamental role in shaping the bacterial community, and that Woeseiaceae may be a responsible bacterial member for BOCINT in the sediment surface (Chapter IV).
Temporal dynamics of the SRB community, which was thought to be largely responsible for COCINT, was further examined with a terminal restriction fragment length polymorphism (T-RFLP) analysis of dsrA genes. The SRB community was significantly different between the two sediment layers, while no significant shifts in the community structure were observed under varying DO conditions. Another batch of bacterial 16S rRNA gene amplicon sequences revealed Desulfococcus, a member of SRB with a high tolerance to oxygen, was the most predominant Deltaproteobacteria across the uppermost
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sediment samples. Considering the predominance of shared OTUs across the SRB community in the sediment (0–10 mm) regardless of the bottom-water DO, some SRB that are physiologically tolerant to a wide range of DO conditions may have dominated and masked the influence of other SRB in the sediment (Chapter V).
From these results, it was clearly demonstrated that DO availability of bottom water exerted fundamental impacts on potential SOC (WSOCINT) consisting of COCINT and BOCINT. It was further demonstrated that responsible microorganisms (at genera or family level) for COCINT and BOCINT in surface sediment of Omura Bay were Desulfococcus and Woeseiaceae, respectively. Future research should involve validation of the above- mentioned findings in different coastal areas, and integration of shifts in sediment microbial activities (respiration) and community composition into ecosystem modeling under varying DO concentrations in bottom water in order to better predict the possible ecosystem consequences imposed by global trends in ocean deoxygenation.
6 I. General Introduction
Coastal hypoxia
Formation of oxygen-depleted water masses in bottom environments is a widespread phenomenon in coastal areas around the world (Diaz and Rosenberg 2008). Oxygen depletion in bottom water is lethal to macrobenthic animals, eliminates sensitive species, and therefore weakens secondary and/or higher production through the grazing food web in sediment ecosystems (Wu 2002; Levin 2003; Diaz and Rosenberg 2008). In contrast to the devastation inflicted upon benthic fauna, hypoxic conditions would enhance diversion of energy flow into the microbial food web driven by prokaryotic microorganisms (Middelburg and Levin 2009) . Although microbial metabolic process largely contributes to the cycles such as carbon nitrogen and sulfur in bottom environments, the formation of bottom-water hypoxia also causes changes in the microbial community, consequently, these cycles are drastically changed (Diaz and Rosenberg 2008; Wright et al. 2012). In addition, not only microbial aerobic but also anaerobic respiration contribute to the formation and maintenance of bottom-water hypoxia (Diaz and Rosenberg 2008).
However, microbial community and activity have not been fully delineated in relation to bottom-water hypoxia in coastal areas.
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Sediment oxygen consumption in coastal bottom and its relationship with hypoxia
Bottom-water hypoxia develops when the consumption of oxygen by organisms and chemical processes in the water and sediment exceeds the supply of oxygen. Although the contribution of oxygen consumption in the water column to total oxygen consumption is often higher than sediment oxygen consumption (SOC) (Murrell and Lehrter 2011), SOC accounted for 20-81% of total O2 consumption below the pycnocline (Dortch et al.
1994; Rivera et al. 2010; Murrell and Lehrter 2011). Hence SOC can significantly contribute to the formation of bottom-water hypoxia in coastal areas. Murrel and Lehrter (2010) estimated SOC with sediment cores from the hypoxic Louisiana Continental Shelf, and found that SOC increased when bottom-waters were re-oxygenated. This indicates that SOC rate potentially increases under bottom-water hypoxia due to accumulation of reduced compounds such as sulfides. The increased potential SOC may contribute to re- formation of bottom-water hypoxia. Indeed, prompt reformation of bottom-water hypoxia are often observed (e.g. Rabalais et al. 2007) after mixing of water column and replenishment of oxygen due to strong winds. However, little is known about the relationship between potential SOC and coastal hypoxia. In addition, whole SOC
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(WSOC) should be composed of biological O2 consumption (BOC), which is mainly driven by aerobic microbial community respiration, and chemical O2 consumption (COC), which is mainly caused by oxidation of sulfide ions derived from sulfate reducing bacterial activity. However, as far as I know, potential BOC and COC have not been evaluated simultaneously in previous studies on coastal hypoxia.
Methodology of sediment oxygen consumption
There are direct and indirect methods to determine SOC. Direct methods include measuring dissolved oxygen concentration in the overlying water of (1) laboratory- incubated sediment cores or (2) in-situ benthic chambers that are placed over the sediment surface. However, these methods are not only labor intensive but also costly to perform for multiple samples. Alternatively, a respiratory dehydrogenase assay that determines in vivo electron transport system activity (ETSA) has been used as an indirect method to
infer SOC (INT reduction method; Trevors 1984; Wada et al. 2012). The principle of the INT reduction method is based on the reduction of a tetrazolium salt, 2-(4-iodophenyl)- 3-(4-nitro-phenyl)-5-phenyl tetrazolium chloride (INT) by NADH dehydrogenase and succinate dehydrogenase present in the ETS (Martínez-García et al. 2009). The red- colored reduced form of INT (INT-formazan, INT-F) can be measured
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spectrophotometrically. Thus, the INT reduction method can be less costly and labor- intensive compared with the direct methods mentioned above. Given the INT reduction method is much simpler to perform and allows estimation of relative contribution of BOC and COC to WSOC compared with the direct method, the former can be advantageous over the latter for realistic estimate of SOC in the field. In a previous study, Wada et al.
(2012) applied the INT reduction method to estimate WSOC in an enclosed sea, Omura Bay, Japan. Wada et al. (2012) used a stoichiometric relationship between INT-F formation and the theoretical reducing equivalent to infer WSOC. However, the WSOC inferred from the INT reduction method were far below those obtained with other direct method (Wada et al. 2012; Mori et al. 2015). In order to make better use of the INT reduction method to infer SOC, a quantitative comparison between the data obtained with this method and that from a direct measurement of SOC needs to be conducted. For planktonic samples, an empirically derived conversion factor of 12.8 was obtained from a linear regression between INT-F formation (INT-F) and O2 consumption (R) rates (Martínez-García et al. 2009), and it has been effectively used to estimate pelagic community respiration (Baltar et al. 2015; Lønborg et al. 2016; Martínez-García 2017).
Such quantitative comparisons have, however, not been made so far for SOC estimation
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with INT reduction method. Therefore, the relationship between INT-F formation and O2
consumption rates in sediments needs to be confirmed before using INT reduction method.
Dynamics of sediment bacterial community composition in seasonal hypoxia
A number of studies reported the impacts of deoxygenation for planktonic BCC (Beman and Carolan 2013; Spietz et al. 2015; Laas et al. 2016), there is currently limited knowledge about how sediment BCC would respond to changing levels of DO in the water overlying seafloor. Mahmoudi et al. (2015) found sediment BCC of the Caspian Sea in permanently hypoxic regions was different from that in oxic regions. Similarly, Devereux et al. (2015) reported significant differences in sediment BCC between normoxic and hypoxic periods in the northern Gulf of Mexico. Jessen et al. (2017) also found changes in relative abundance of some phylogenetically distinct groups of bacteria (e.g.., Flavobacteriia, Gammaproteobacteria, and Deltaproteobacteria) in response to decreasing oxygenation in the Crimean shelf break of the Black Sea.
Although these pioneering works demonstrated distinct shifts in sediment BCC between oxic and hypoxic conditions, their findings are based on snapshot observations and the details of how oxygen availability affects sediment BCC are poorly constrained. In order
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to understand the responses of sediment BCC to spreading hypoxia in the coastal sea bottom more precisely, it is necessary to examine the BCC in a defined location for a longer period of time.
Sulfate reducing bacteria diversity and community structure in seasonal hypoxia
Sulfate-reducing bacteria (SRB) are one of the most important of these microbes and strongly contribute to the carbon and sulfur cycles in benthic environments (Muyzer and Stams 2008). Moreover, SRB are largely responsible for oxygen dynamics both in the sediment and the water column. The dissolved sulfide produced by SRB is highly reactive to dissolved oxygen (DO), leading to a great deal of DO consumption at the sediment–
water interface and thus contributing to the formation of a hypoxic water mass in the bottom environment (Jørgensen 1982; Roden and Tuttle 1992; Ferdelman et al. 1999). A number of studies (e.g. Sass and Cypionka, 2007) have demonstrated that diverse SRB employ a variety of physiological mechanisms to respond to the presence of O2. Furthermore, in aquatic environments, phylogenetically and physiologically different SRB can be found along either a vertical or a horizontal gradient of DO conditions. In contrast to the spatial dynamics of SRB diversity found in the surface sediment, little is
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known about how SRB community composition would change in response to temporal variation of DO in bottom water.
Objective of the study
In my thesis work, I raised the following questions (i)-(iv) and conducted experiments to gain insights for the questions.
i. Does INT reduction method give realistic estimates for SOC in hypoxic condition?
ii. How does SOC change in seasonally hypoxic coastal area?
iii. How do BCC and diversity change in seasonally hypoxic coastal sediment?
iv. How do SRB change in response to temporal variation of DO in bottom water?
In chapter II, I conducted three experiments as follows. Firstly, I made a comparison between whole INT reduction rate and WSOC for INT reduction assay with sediment samples. Secondly, I confirmed the relationship between chemical INT reduction rate and COC with sediment samples. Finally, I conducted time-series
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experiment to determine the optimal incubation time to properly estimate INT reduction rate with the INT method.
In chapter III, I examined that temporal change in the potential WSOC, COC and BOC separately under bottom-water hypoxia in an enclosed bay, Omura Bay, Japan. I used INT reduction assay according to the method of Wada et al. (2012) as a proxy of SOC index.
In chapter IV, I used a community fingerprinting method, automated ribosomal intergenic spacer analysis (ARISA), to assess bacterial community dynamics (Fisher and Triplett, 1999). Based on the combination of ARISA and Sanger sequencing of the bacterial 16SrRNA genes franked with ITS, I delineated the shift in the sediment bacterial community and demonstrated changes in their diversity in response to DO availability in the bottom water of Omura Bay. Based on these results, I discuss the significance of the findings in the context of the recent trend in coastal deoxygenation around the world.
In chapter V, I examined the SRB community in Omura Bay, with a terminal restriction fragment length polymorphism (T-RFLP) analysis of dsrA genes amplified from the sediment DNA, in order to reveal the temporal dynamics of the SRB community in relation to the formation of bottom-water hypoxia.
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Finally, I discussed achievement of this study and future outlook regarding (i) improvement of INT reduction method for SOC measurement, (ii) interaction between SOC and hypoxia, and (iii) interaction among BCC, SRB and hypoxia.
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II. Application of INT reduction assay to estimate sediment oxygen consumption rate in coastal area
Introduction
Sediment oxygen consumption (SOC) rate is one of the most widely studied parameters to assess benthic community metabolism and carbon mineralization processes.
SOC can be divided into two components. One is biological oxygen consumption (BOC) and the other is chemical oxygen consumption (COC). As each component of the SOC reflects different microbial respiratory activities, it is worth to determine the contribution of each to the whole sediment oxygen consumption (WSOC). In practice, COC can be measured as SOC in the presence of a fixative for microorganisms. BOC will be determined by subtracting the COC from the WSOC without fixatives (Mori et al. 2015).
There are direct and indirect methods to determine SOC. Direct methods include measuring dissolved oxygen concentration in overlying water of (1) a laboratory- incubated sediment cores or (2) an in situ benthic chambers that are placed over the sediment surface. However, these methods are not only labor intensive but also costly to perform for multiple samples. Alternatively, a respiratory dehydrogenase assay that determines electron transport system activity (ETSA) has been used as an indirect method to infer SOC (e.g., Broberg 1985; Relexans 1996).
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The principle of the ETSA assay is based on the reduction of a tetrazolium salt, 2- (4-iodophenyl)-3-(4-nitro-phenyl)-5-phenyl tetrazolium chloride (INT) by NADH dehydrogenase and succinate dehydrogenase present in the ETS (Packard 1985). The red- colored reduced form of INT (INT-formazan, INT-F) can be measured spectrophotometrically. Thus, ETSA assay can be less costly and labor-intensive compared with the direct methods mentioned above. There are two practical ways to access ETSA with sediment samples: one requires extraction of the membrane-bound respiratory enzymes from raw or deep-frozen specimen before conducting the enzyme assay (in vitro ETSA) (Broberg 1985; Relexans 1996; Kinoshita et al. 2003), and the other is to measure INT-reducing activity of non-destructed sediment samples (INT reduction method; (Trevors 1984; Wada et al. 2012).
Historically, in vitro ETSA assay was first introduced to obtain quantitative, yet potential respiratory activities of marine plankton (Packard 1971) and later applied for sediment samples (Pamatmat and Bhagwat 1973). Regardless of the samples, the in vitro ETSA assay is conducted with a saturation level of electron donors (NADH, NADPH, and succinate), and thus would yield maximum rates of the ETS activities. By assuming one mole of INT-F is formed via one electron reduction that is equivalent to 1/2 mole of O2 reduction, the in vitro ETSA can be converted to potential oxygen consumption.
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Although SOC inferred from the in vitro ETSA assay can be comparable to the WSOC estimated with direct method, it is unable to infer relative contributions of BOC and COC to the WSOC due to the methodological constraints.
On the other hand, INT reduction method is generally used to obtain metabolic index of intact microbial cells based on the extent of INT-F formation. This method has also been utilized for both planktonic and sediment samples similar to the case of in vitro ETSA method (Martínez-García et al. 2009; Wada et al. 2012). In the INT reduction assay, the extent to which the microbial cells reduce INT can be quantified either by extracting INT-F from the sample and measuring it with a spectrophotometer or by counting the microbial cells with INT-F deposits to obtain their relative abundance under a microscope (Tabor and Neihof 1982; Dufour and Colon 1992; Wada et al. 2006; Neto et al. 2007). As this method does not need exogenous substrates for the assay, the extent of INT-F deposition inside the cell membrane should reflect availability of intrinsic reducing power in the cytosol, and therefore provide more realistic estimates of biological respiration of the specimen.
When the INT reduction method is applied to estimate SOC, however, it is necessary to consider that INT can be reduced not only by aerobic but also anaerobic respiratory activities (Fukui and Takii 1989). It is of particular importance that INT can
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be reduced non-biologically by sulfide ions that had been originated from sulfate reduction by sulfate reducing bacteria (Merlin et al. 1995). This gives a basis to discriminate COC from WSOC in sediment samples with the INT reduction assay (Mori et al. 2015). Another point that may complicate the interpretation of the INT reduction is an apparent cellular toxicity of INT. Development of INT-F deposits inside cell membranes could cause cell breakage and death of some microorganisms (Martínez- García et al. 2009; Villegas-Mendoza et al. 2015).
Despite these possible complications to infer SOC, a good correlation between the amount of INT-F and SOC has been demonstrated (Trevors 1984). Given the INT reduction method is much simpler to perform and allows estimation of relative contribution of BOC and COC to WSOC compared with the in vitro ETSA method, the former can be advantageous over the latter for realistic estimate of SOC in the field. In a previous study, Wada et al. (2012) applied the INT reduction method to estimate WSOC in an enclosed sea, Omura Bay, Japan. Wada et al. (2012) used stoichiometric relationship between INT-F formation and theoretical reducing equivalent to infer WSOC. However, the WSOC inferred from the INT reduction method were far below those obtained with other direct method (Wada et al. 2012; Mori et al. 2015). Although, the causes of underestimation are not clear, Wada et al. (2012) raised a possibility that incubation period
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(24–25 h) they used might have been unnecessarily long, leading to underestimation of the WSOC. In addition, the aerobic respiratory chains of sediment microorganisms probably reduce not only INT but also dissolved oxygen (DO) when DO and INT co-exist in the assay sample.
In order to make better use of the INT reduction method to infer SOC, a quantitative comparison between the data obtained with this method and that from a direct measurement of SOC needs to be conducted. For planktonic samples, an empirically derived conversion factor of 12.8 was obtained from a linear regression between INT-F formation (INT-F) and O2 consumption (R) rates (Martínez-García et al. 2009), and it has been effectively used to estimate pelagic community respiration (Baltar et al. 2015;
Lønborg et al. 2016; Martínez-García 2017). Such quantitative comparisons have, however, not been made so far for SOC estimation with INT reduction method.
In the present study, I aimed at determining the R/ INT-F ratio for sediment samples with INT reduction method. To this end, I conducted following three experiments. Firstly, I made a comparison between whole INT reduction rate (WIR) and WSOC for INT reduction method with sediment samples. Secondly, I confirmed the relationship between chemical INT reduction rate (CIR) and COC with sediment samples. Finally, I conducted
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time-series experiment to determine the optimal incubation time to properly estimate INT reduction rate with the INT reduction method.
21 Materials and methods
Measurement of whole INT reduction rate
In order to avoid artifacts resulting from the heterogeneity in abundance and distribution of microbial cells and reactive sulfides in sediment samples, sediment was homogenized before use as follows. First, five sediment cores were collected from the central Omura Bay (St. 21: 32°55’.390” N, 129°51’.350” E, in Fig. 1) in September 2015 at 20 ± 1 m depth with an acrylic tube (31 cm long with 26 mm inner diameter) by scuba diving which were sliced into to 0-5 cm layer and then pooled. The sediment was suspended with 1 L of filter sterilized (0.22 μm) artificial seawater, and stirred continuously for 24h. Aeration was performed simultaneously with an aquarium aerator, in order to reduce rapid INT reduction due to high concentrations of reduced compounds in the samples. Following the centrifugation at 1610 × g for 10 min (LMC-3000, Biosan), ten aliquot samples were stored at -80 °C until further treatments.
After thawing, sediment slurries were prepared by mixing an aliquot of sediment sample (3 g dry weight) and 1 L of aerated and filter sterilized artificial seawater. Ninety mL of the slurry was transferred into a 100-ml incubation bottle and amended with 10 ml of 0.1% INT. A total of six slurries were prepared. Incubations were done in the dark condition for 24h at different temperature (10, 15, 20, 26, and 30 °C). Experiment was
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done in triplicate. After the incubation, slurries were centrifuged at 1610 × g for 10 min (LMC-3000, Biosan). The supernatant was filtered through a cellulose acetate membrane filter (25 mm in diameter, pore size 0.22 lm, Advantec, A020A025A) and kept below - 20 °C until analysis. The remaining sediment was also kept below -20 °C until analysis.
Extraction of the reduced form of INT (INT-formazan, INT-F) from the filter and sediment were conducted according to the method of Wada et al. (2012). Briefly, the INT- F on the filter was extracted with 3 mL of 99.8 % isopropanol and the absorbance at 485 nm was determined with a spectrophotometer (U-2800, Hitachi). In contrast, 1 g of frozen sediment sample (wet weight) was mixed with 2 mL of methanol and disrupted with an ultrasonic disruptor (10 pulses of 4 s with 1 s interval, amplitude 80%, on a Q125, QSONICA). After centrifugation of the sample at 1610 × g for 10 min (LMC-3000, Biosan), the supernatant was transferred to a clean polypropylene tube. The extraction procedure was repeated three times for one sediment sample, and all the supernatant (methanol containing INT-F) was pooled for the spectrophotometric analysis as described above. The amount of sediment used for extracting INT-F was weighed after drying at 60 °C for more than 24 h. The INT-F concentration in the extract was calculated by applying a standard curve previously elaborated using five different concentrations (ranging from 0.5 to 50 µM) of pure INT-F (Sigma-Aldrich) dissolved in isopropanol and
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methanol. Subsequently, concentrations of INT-F were calculated from the following equations:
For water samples:
INTF μmol bottle−1day−1 = 𝐼𝑁𝑇𝐹 × 𝑉 ×𝑊𝑣𝐹𝑣 (A)
Where INTF is the concentration of INT-F in isopropanol used (μM). V is the volume of isopropanol used for extracting INT-F (L). Fv s is the volume of sample filtered (mL) and Wv is the volume of water sample in incubation.
For sediment samples:
INTF μmol bottle−1day−1 = 𝐼𝑁𝑇𝐹 × 𝑉 ×𝐷𝑤𝐸𝑤 (B)
where INTF is the concentration of INT-F in methanol (μM). V is the volume of methanol used for extracting INT-F (L). Ew is the dry weight of sediment sample used for extracting INT-F (g) and Dw is the dry weight of all sediment in an incubation bottle.
In order to estimate per gram sediment oxygen consumption rate, I used the following equation:
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INTF μmol g−1day−1 =𝐴 + 𝐵 𝐷𝑤
Where A and B were estimated INT-F formation rate of water and sediment in incubation bottle, respectively. Dw is the dry weight of the sediment used for extraction.
Measurement of whole sediment oxygen consumption rate
WSOC rate was estimated in parallel with WIR measurement. After 90 mL of the sediment slurry was transferred into a 100 mL-incubation bottle as described above, 10 mL of ultra-pure Milli-Q water (Millipore) was added into the bottle instead of INT solution. The incubation bottles were capped with dissolved oxygen probe (Pro20, YSI) with special care to avoid oxygen penetration and were mixed continuously with a magnetic stirrer (MC-303N, Scinics). Oxygen consumption rates for each incubation were obtained from the slopes of the linear regressions, which were calculated in R (R Core Team 2015).
Measurement of chemical INT reduction rate
Sediment samples were collected with a G.S. type core sampler, Ashura. (Rigosha), equipped with three polycarbonate tubes (57 cm long with 82 mm inner diameter) from
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the central Omura Bay (St. 21: 32°55’.390” N, 129°51’.350” E, in Fig. 1) in August 2017.
Each sediment core was sliced at the depth of 0-5 cm and the sliced sediment was pooled and stored frozen in plastic bag at –20 °C until further processing.
After thawing, sediment slurries were prepared by the same way as described above in the section for whole INT reduction assay. Additional aeration was given at room temperature for various periods (0, 0.25, 0.5, 2, 4, 7 and 24 h), so as to obtain sediment samples containing various concentrations of intrinsic sulfide. Eighty mL of sediment slurry was transferred into a 100 mL-incubation bottle. A total of six samples were fixed by adding 10 mL of formalin (formaldehyde: 3.7% w/v final concentration). After 15 minutes, 10 mL of 0.1% INT solution was added to the bottles and capped. The bottles were mixed continuously with a magnetic stirrer (MC-303N, Scinics) during incubation in a water bath at 26°C and in the dark for 24 h. After the incubation, slurries were centrifuged at 1610 × g for 10 min (LMC-3000, Biosan). The supernatant was filtered through a cellulose acetate membrane filter (25 mm in diameter, pore size 0.22 lm, Advantec, A020A025A) and kept below -20 °C until analysis. The remaining sediment was also stored below -20 °C until analysis.
Extraction of INT-F and following calculation of INT reduction rate were conducted as described above (See section of “Measurement of whole INT reduction rate”).
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Measurement of chemical oxygen consumption rate and acid volatile sulfides
COC rate was also estimated in parallel with CIR rate measurement. After 80 mL of sediment slurry was transferred into a 100 mL-incubation bottle and fixed by adding 10 mL of formalin. After 15 min, 10 mL of ultra-pure Milli-Q water (Millipore) was added into the bottle instead of INT solution. Incubation was done in the same way for chemical INT reduction measurement (26 °C, in dark condition, 24 h). Oxygen concentrations in the bottles were monitored using a fiber-optic oxygen sensor (Firesting O2, Pyro Sciences, Aachen, Germany). Oxygen consumption rates for each incubation were obtained from the slopes of the linear regressions, which were calculated in R (R Core Team 2015).
Acid-volatile sulfide (AVS) in sediment slurry was fixed with zinc acetate when incubation was started, and were measured spectrophotometrically by using the methylene blue method (Kondo et al. 1990).
Time-course experiments
Time-course experiments were performed to determine the optimal incubation time for SOC measurements using the INT-reduction assay of Wada et al. (2012).
Sediment core samples were collected from the central Omura Bay (St. 21, Fig. 1) with
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an acrylic tube (31 cm long with 26 mm inner diameter) by scuba diving on August 7th and September 8th in 2014. In this investigation, I followed the protocol described by Wada et al. (2012) with some modifications. Briefly, after the overlying water had been replaced with 45 mL of aerated and filter-sterilized (0.22 μm) artificial seawater (TetraMarine Salt Pro, Tetra), 5 mL of 0.1% INT solution (w/v) was added into overlying water and then gently mixed by pipetting up and down. Upon replacing the water, special care was taken to avoid disturbance at the sediment surface. Triplicate core samples in acrylic tubes for each station were incubated in the laboratory at 26°C in dark conditions, and stopped at different incubation times (0, 2, 4, 6, and 24 h). After the incubation, the overlying water was siphoned out to a sterile plastic tube and 20 mL was filtered through a cellulose acetate membrane filter (25 mm in diameter, pore size 0.22 μm, Advantec, A020A025A) and kept below -20°C until analysis. The sediment cores were then vertically extruded and sliced into horizontal sections with 0-5 mm depth. The sediment slices were kept below -20°C. INT-F extraction and following calculation were done as described above.
28 Results
Relationship between WSOC and whole INT reduction
WSOC ranged from 4.2 to 56.5 μmol O2 g-1 day-1, while the WIR ranged from 1.2 to 3.3 μmol INTF g-1 day-1. Both WSOC rate and WIR rate increased with incubation temperature (Table 1). A significant (P < 0.05) linear relationship was found between WIR and WSOC rates with a slope of 29.1 R/INT-F (Fig. 2). A type II regression model was also applied. In this case, a slope was 32.9 (P < 0.01).
Relationship between COC and chemical INT reduction
COC ranged from 51.5 to 94.3 μmol O2 g-1 day-1, while the CIR rate ranged from 1.6 to 15.8 μmol INTF g-1 day-1. A significant (P < 0.01) linear relationship was found between CIR and COC with a slope of 2.57 R/ INT-F (Fig. 3). A type II regression model was also applied. In this case, a slope was 2.69 (P < 0.01). Both COC rate and CIR rates increased with sulfide content (Table 2, Fig. 4). Sulfide contents ranged from 0.034 to 0.573 mg S g-1. It was found that aeration of the sediment samples effectively reduced the amount of sulfide. Sulfide contents were significantly correlated with both COC and CIR (COC: r = 0.84, P < 0.05, CIR: r = 0.97, P < 0.001) (Table 2, Fig. 4).
29
Relationship between whole INT reduction rate and period of incubation As a results of time course experiments, significant (P < 0.001) linear relationships were found between WIR rates and incubation time (h) with slopes of 0.03 for August and 0.05 for September samples, respectively (Fig. 5).
Discussion
The result from the first experiment clearly demonstrated a significant positive correlation between WSOC and WIR. The second experiment further revealed a significant positive correlation among COC, CIR and sulfide concentration in the sediment. These results provide empirical evidence that the INT reduction was dependent on SOC in the sediment samples, corroborating that INT reduction assay can be a simple, fast and high-throughput method to infer SOC potential under hypoxic condition (Mori et al. 2015).
Given the R/ INT-F value for WSOC and WIR (29.1), it became feasible to directly infer whole DO consumption rates of sediment samples from WIR data of INT reduction method. However, relative contributions of COC and BOC to WSOC at present still need to be indirectly inferred from comparison of INTFs formed in the presence (CIR) and absence of formaldehyde (WIR), as the present study failed to simultaneously compare
30
O2 consumption and INT reduction rate for WSOC and COC with aliquoted samples that are prepared at the same time. If we apply the R/INT-F ratio between WSOC and WIR (29.1) and that between COC and CIR (2.57) at the same time, contribution of BOC to WSOC would become much higher than that of COC inferred from the ratio of CIR to WIR. Although all of the sediment samples (slurries) had been prepared in the same way between the 1st and 2nd experiments, incubation temperature was different in the first place.
Besides, sulfide concentration and/or microbial community in the sediment might have been different between the two experiments.
As for the result of time-course experiment, amount of INT-F in the sediment linearly increased with time until 24 h (Fig. 5). Therefore, incubation period of 24–25 h does not seem to be the main cause of underestimation for WSOC. Although Martinez et al. (2009) pointed out prolonged incubation times caused underestimation of INT reduction rate due to cell breakage after accumulation of the INT-F crystals inside microbial cell membrane, no appreciable decline in the INT-F formation was found even after 24 h. This may reflect differences in physiological and phylogenetic characteristics of microorganisms between sediment and water column. Another possible reason for the underestimation of WSOC may be related to the competition between INT and dissolved oxygen for reduced compounds from sediment (Mori et al. 2015).
31
Due to concerns mentioned above, INT reduction rate was used as a proxy for SOC, but the conversion from INT reduction rate to O2 consumption rate was avoided in the next section.
32
Fig.1 Sampling location (St. 21 marked as “center”) in Omura Bay, West Kyushu, Japan.
33
Fig. 2 Whole sediment O2 consumption rates (WSOC, μmol g-1 day-1) versus INT reduction rates (INT-F, μmol g-1 day-1). Regression line: WOC = 29.1 INTF – 33.8, r = 0.93, (P < 0.05). Error bars represent the standard error.
34
Fig. 3 Chemical O2 consumption rate (COC, μmol g-1 day-1) versus chemical INT reduction rate (INTF, μmol g-1 day-1). Regression line: COC = 2.57 INTF + 52.4, r = 0.88, (P < 0.01). Error bars represent the standard error.
35
Fig. 4 Sulfide (S, mg S g-1) versus chemical O2 consumption rate (COC, μmol g-1 day-
1) (a). Sulfide (S, mg S g-1) versus chemical INT reduction rate (CIR, μmol g-1 day-1).
Regression line for (a): COC = 70.4 S + 54.2, r = 0.84, (P < 0.05). Regression line for (b): CIR = 28.0 S + 0.58, r = 0.97, (P < 0.001). Error bars represent the standard error.
36
Fig. 5 Whole INT reduction rate (WIR, μmol g-1 day-1) versus incubation time (h).
Regression line for August samples (filled circles): WIR= 0.03 h + 0.81, r =0.99, (P <
0.005). Regression line for September (open circles): WIR = 0.05 h + 0.72, r = 0.96, (P
< 0.01). Error bars represent the standard error.
37
Table 1 Average values of whole INT reduction rate (WIR) and O2 consumption rate (WSOC). Values in parenthesis indicate standard error.
Temperature (°C)
WIR (μmol INTF g-1 day-1)
WSOC (μmol O2 g-1 day-1)
10 1.2 (0.10) 4.2 (0.05)
15 1.6 (0.08) 8.3 (0.04)
20 2.0 (0.34) 18.4 (0.36)
26 2.6 (0.18) 56.5 (3.04)
30 3.3 (0.61) 54.1 (0.56)
38
Table 2 Average values of chemical INT reduction rate (CIR), O2 consumption rate (COC) and sulfide content in sediment slurries. Values in parenthesis indicate standard error.
Aeration time (h)
CIR (μmol INTF g-1 day-1)
COC (μmol O2 g-1 day-1)
Sulfide (mg S g-1)
0 15.8 (1.26) 94.3 (0.09) 0.573 (0.033)
0.25 12.2 (0.29) 77.0 (0.07) 0.333 (0.046)
0.5 8.2 (0.16) 85.9 (0.08) 0.258 (0.017)
2 4.9 (0.13) 58.2 (0.06) 0.219 (0.075)
4 3.0 (0.11) 61.9 (0.06) 0.064 (0.007)
7 2.5 (0.20) 51.5 (0.05) 0.100 (0.002)
24 1.6 (0.19) 62.1 (0.06) 0.034 (0.003)
39
III. Dynamics of microbial community respiration in sediment at Omura Bay in response to seasonal hypoxia
Introduction
Formation of oxygen-depleted water masses in bottom environments is a widespread phenomenon in coastal areas around the world (Diaz and Rosenberg 2008). Oxygen- depleted water mass, in other word, bottom-water hypoxia develops where the consumption of oxygen by organisms and chemical processes in water and sediment exceeds the supply of oxygen. Although the contribution of oxygen consumption in water column to total oxygen consumption is often higher than sediment oxygen consumption (SOC) (Murrell and Lehrter 2011), SOC accounted for 20-81% of total O2 consumption below pycnocline (Dortch et al. 1994; Rivera et al. 2010; Murrell and Lehrter 2011).
Hence SOC can significantly contribute to the formation of bottom-water hypoxia in coastal area.
However, it has been noticed that SOC apparently decline at the hypoxic conditions (Lichtschlag et al. 2015). On the other hand, sulfide derived from activity of sulfate reducing bacteria would built up in the pore water of sediment, diffuse out into the overlying water under oxygen depleted condition and reach the upper-oxygenated water, resulting in the maintenance and expansion of bottom-water hypoxia (Roden and Tuttle
40 1992).
Murrel and Lehrter (2010) estimated SOC with collected sediment cores from the hypoxic Louisiana Continental Shelf, and found SOC increased when bottom-waters were re-oxygenated. This indicates that SOC rate potentially increases under bottom- water hypoxia due to accumulation of reduced compounds such as sulfide. The increased potential SOC may contribute to re-formation of bottom-water hypoxia. Indeed, prompt reformation of bottom-water hypoxia are often observed (e.g. Rabalais et al. 2007) after temporal mixing of water column and replenishment of oxygen due to strong winds.
However, little is known about the relationship between potential SOC and bottom-water hypoxia. In addition, SOC should be composed of biological O2 consumption (BOC), which is mainly driven by aerobic microbial community respiration, and chemical O2
consumption (COC), which is mainly caused by oxidation of sulfide ions derived from sulfate reducing bacterial activity. However, as far as I know, potential BOC and COC have not so far been evaluated separately in the previous studies on coastal hypoxia. In order to clarify the relationship between SOC and bottom-water hypoxia, it is instrumental to clarify dynamics of potential BOC and COC under bottom-water hypoxia.
Here I examined that temporal change in the potential Whole SOC (WSOC), COC and BOC separately under bottom-water hypoxia in an enclosed bay, Omura Bay, Japan.
41
I used INT reduction method that was described in chapter II.
42 Material and methods
Study site and sampling
Surface sediment samples and environmental parameters in the water column were collected in a center region of Omura Bay (St. 21, 32°55.390ʹN, 129°51.350ʹE, Fig. 1).
During summer months from 2011–2013 (Table 3), ten sediment cores were collected at each sampling from the center location at 20 ± 1 m depth with an acrylic pipe (31 cm long with 26 mm inner diameter) by scuba diving or TFO gravity corer. Reference cores were also collected from 11 m depth at a fringe site (32°51’.520” N, 129°52’.210” E, in Fig. 1) 8.3 km away from the center (July, November of 2011 and August of 2013). The vertical profiles of DO, temperature, salinity and chlorophyll a in the sampling sites were obtained using a Conductivity Temperature Depth (CTD) profiler (AAQ, JFE-Advantec Co, Kobe, Japan).
All core samples were kept at in situ temperature and carefully brought to the laboratory within 3 h after sampling, during which time samples were handled carefully to avoid direct exposure to sunlight and other physical disturbances. Upon return to the laboratory, three replicate sediment cores were extruded from cores down to either 5 or 7 mm depth from the top. The top 0–7 mm (in 2011) or 0–5 mm (in 2012 and 2013) layer of sediment was pooled and regarded as the uppermost sediment layer. The pooled
43
sediment layers from three cores were stored -20°C until DNA extraction and total organic carbon (TOC) measurement. A portion of the pooled sediment was fixed with glutaraldehyde for bacterial counting (See “Bacteria counting” for more detail). It was unfortunately not possible to keep the depth range of the uppermost surface and subsurface layers constant throughout the study, mainly because different devices were used to dissect the cores (Wada et al. 2012). Nevertheless, the uppermost sediment layers (0–7 mm) in 2011 mostly overlapped with those in the other 2 years (0–5 mm), and the number of cores in 2011 contributed only 30% of the total number (6 out of 20) of cores examined. Therefore, it does not seem unrealistic to assume that combining the 2011 data with those of 2012 and 2013 would not be comparable to combining data among sediment layers of identical depth (0–5 mm).
The INT reduction method with sediment core
Sediment INT reduction method was performed to reveal dynamics of SOC in Omura Bay. I followed the protocol described by Wada et al. (2012) with some modifications. Triplicate cores were used for measurement of whole INT reduction rate (WIR). After the overlying water had been replaced with 40 mL of aerated and filter- sterilized (0.22 μm) artificial seawater (TetraMarine Salt Pro, Tetra), 5 mL of 0.1% INT
44
solution (w/v) and 5 mL of MQ water were added into overlying water and then gently mixed by pipetting up and down (for WSOC). In contrast, triplicate or replicate cores were used for measurement of chemical INT reduction rate (CIR). After the overlying water had been removed, 40 mL of aerated and filter-sterilized (0.22 μm) artificial seawater (TetraMarine Salt Pro, Tetra) and 5 mL of formalin were added into core and waited for 10 min. Subsequently, 5 mL of 0.1% INT solution (w/v) was added into overlying water and then gently mixed by pipetting up and down. Upon replacing the water, special care was taken to avoid disturbance at the sediment surface. Core samples in acrylic tubes for each sampling were incubated in the laboratory at 26°C in dark conditions for 24 h. After the incubation, the overlying water was siphoned out to a sterile plastic tube and 20 mL was filtered through a cellulose acetate membrane filter (25 mm in diameter, pore size 0.22 μm, Advantec, A020A025A) and kept below -20°C until analysis. The sediment cores were then vertically extruded and sliced into horizontal sections with 0-5 mm depth. The sediment slices were kept below -20°C. INT-F extraction and following calculation were done as described above (See “Whole INT reduction versus oxygen consumption experiments” in chapter II).
45
Biological INT reduction rate (BIR) was calculated by subtracting CIR from WIR.
In order to avoid possible complication, calculated WIR, CIR and BIR as a proxy for SOC were represented as “WOCINT”, “COCINT” and “BOCINT”, respectively.
Sediment organic carbon and acid-volatile sulfides analysis
TOC of the sediment samples was treated according to the method of Wada et al.
(2016). Briefly, TOC of the sediment samples was determined on a Perkin Elmer 2400 CHNS/O Series II Analyzer (Perkin Elmer, Shelton, CT, USA) with acetanilide (C = 71.09%, N = 10.36%) as a standard. Prior to analysis, sediment samples were freeze-dried in a vacuum low-temperature oven (DRV320DA, Advantech, Tokyo, Japan) then passed a sieve with 500-μm-mesh and pulverize with a porcelain mortar and pestle. Samples were then put into silver capsules (Santis, Teufen, Switzerland) and treated with 1 M HCl for 24 h to remove carbonates.
Acid-volatile sulfides (AVS) in sediment samples were fixed with zinc acetate, and were measured spectrophotometrically by using the methylene blue method (Kondo et al.
1990).
46 Bacteria counting
Each sediment slice was mixed with filter-sterilized sea water containing glutaraldehyde (final conc. 2 %) and kept at 4°C until use. The fixed sediments were treated according to the method of Wada et al. (2016) with slight modifications. Briefly, to liberate the bacterial cells from sediment particles, Tween 20 (Bacto Tween 20, Difco) was added to the sample at a final concentration of 1 mg L−1. After mixing for 1 min, the sample was subjected to ultra-sonication (Q125, QSONICA) with five cycles of 5 s at 40% amplitude followed by centrifugation at 1600×g for 30 s. The supernatant was diluted 50-fold with filtered sterilized seawater. A 200 μL sample of the supernatant was then mixed with 4’,6-diamidino-2-phenylindole (DAPI), with a final concentration of 5 μg mL−1, and kept for 30 min at room temperature (ca. 25°C). The filter was then placed on a glass slide and embedded in non-fluorescent immersion oil. Bacterial cells were examined with an epifluorescent microscope (BX51, Olympus, Tokyo, Japan) with UV excitation. At least 400 single cells were counted on each slide at a magnification of 1000×.
Statistical analysis
To assess effect of environmental parameters on potential SOC, I used a backward stepwise regression to select the most predictive variables for SOC (WOCINT, COCINT
