Limits of our knowledge, part 2: Selected frontiers in marine organic biogeochemistry

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Review article

Limits of our knowledge, part 2: Selected frontiers in marine organic biogeochemistry Stuart G. Wakehama, , Cindy Leeb ⁎

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Skidaway Institute of Oceanography, University of Georgia, 10 Ocean Science Circle, Savannah, GA 31411-1011, United States School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 11794-5000, United States

ARTICLE INFO

ABSTRACT

Keywords: Marine organic geochemistry Marine organic biogeochemistry Dissolved organic matter Particulate organic matter Archaea Compound-specific isotopes

The advent of new sampling tools, analytical methods, and data handling capabilities that have been applied to marine chemistry since the 1970s along with coordinated, international and interdisciplinary research programs has led to explosive growth of marine organic biogeochemistry. Here we briefly summarize the history of stepwise growth as illustrated by community-wide workshop reports and symposia, highlighting evolving recommendations for future research put forth in those reports. Following that, we present examples of four frontiers that have been explored recently, focusing as much as possible at the molecular level and on marine water columns: (i) how analytical advances and informatics tools provide new insight into the chemical nature and cycling of dissolved organic matter; (ii) how evolving studies of suspended and sinking particles play an important role in understanding ocean biogeochemistry; (iii) the new symbiosis between marine microbiology, analytical chemistry and organic geochemistry as illustrated by the archaea, their habitats, lipid biomarkers, and influence on geochemical cycles; and (iv) how advances in compound-specific measurements of carbon, nitrogen, hydrogen, and sulfur isotopes shed new light on sources and behavior of marine organic matter. We cite selected recent (primarily the past two decades) research examples as a basis for further reading and to project into the future some aspects of these research areas that could be further developed. Throughout, we highlight how new analytical and sampling methods allowed these fields to progress.

1. Introduction “..it might be helpful to remind ourselves regularly of the sizable incompleteness of our understanding, not only of ourselves as individuals and as a group, but also of nature and the world around us” - Norman Hackerman, 1974. In the mid-1970s, the combined advent of new analytical and sampling tools applied to marine chemistry and the development of coordinated and interdisciplinary research programs led to the emergence of a new field, marine organic geochemistry (MOG). In 1975, Max Blumer, one of the founders of that field, pointed out that not only do we not know very much about the natural world, but that we can never expect to know everything (Blumer, 1975). He suggested that limitations of available analytical techniques would be the major roadblock to understanding the chemical complexities of nature. Now we walk a tortuous path through the massive amount of information amassed in the 43 years since the Blumer paper was published. Some of the analytical tools available now were hardly dreamed of then. But

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when all is said and done, can we conduct “realistic studies of nature that acknowledge the limitations of our present analytical powers and the gaps in our understanding”? Here we first present a brief overview of the timeline of MOG's stepwise growth through community-wide workshop reports and symposia, with attention to evolving recommendations for future research. Following that, we reflect in a somewhat peripatetic fashion on our nearly four decades as marine organic geochemists (and now marine organic biogeochemists), not by reviewing all aspects of marine organic biogeochemistry – a daunting task – but rather by presenting molecularlevel examples of four frontiers (dissolved organic matter; particle sources and dynamics; archaeal biogeochemistry; marine isotope biogeochemistry) that have seen major methodological advances and have been explored in the past few decades. We emphasize the marine water column, but that is not to say that freshwater systems and sedimentary environments are not important to marine organic biogeochemistry. We cite (although not exhaustively) recent (roughly the past two decades) selected research examples as a basis for further reading, and to project

Corresponding author at: 9091 Olympus Beach Rd NE, Bainbridge Island, WA 98110, United States. E-mail addresses: [email protected] (S.G. Wakeham), [email protected] (C. Lee).

https://doi.org/10.1016/j.marchem.2019.02.005 Received 6 November 2018; Accepted 19 February 2019 0304-4203/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Stuart G. Wakeham and Cindy Lee, Marine Chemistry, https://doi.org/10.1016/j.marchem.2019.02.005

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into the future some aspects of these research areas that could be further developed. We especially highlight how new analytical and sampling methods led to progress in these fields.

(BC) as a ubiquitous component of refractory organic matter; the significant role of polymer gel particles in the microbial loop; biogeochemical interaction between dissolved and particulate material; and particle dynamics and sedimentation processes in the ocean. Since 2003, there have been special sessions or mini-symposia covering aspects of marine organic geochemistry during regularly scheduled conferences, but there has not, to our knowledge, been a community-wide and cross-disciplinary symposium or workshop assessing the progress and future needs in marine organic biogeochemistry. To bridge this gap in a small way, we have chosen to discuss four among the many recent research frontiers in organic biogeochemistry as examples of the type of interdisciplinary research, at the molecular level to the extent possible, that has been made feasible by the development of new sampling, analytical, and computational methods: (i) How analytical advances and informatics tools provide new insight into the chemical nature and cycling of DOM; (ii) How evolving studies of suspended and sinking particles play an important role in ocean biogeochemistry; (iii) How a symbiosis between marine microbiology, analytical chemistry and organic geochemistry is leading to a new understanding of the archaea, their habitats, lipid biomarkers, and influence on geochemical cycles; (iv) How compound-specific measurements of isotopes of carbon, nitrogen, hydrogen, and sulfur shed new light on sources and behavior of marine organic matter. The cross-disciplinary nature of marine organic biogeochemistry is evident in the overlap between the four sections below.

2. A brief history Some areas of marine organic geochemistry have seen tremendous progress, others remain intractable. The state-of-the-art in the mid1970s was summarized in the NATO/Office of Naval Research symposium proceedings (Andersen, 1977) dedicated to Max Blumer. Plenary and discussion sessions established recommendations, more often conceptual rather than detailed or experimental, on four emerging themes: (i) inputs; (ii) inventories; (iii) transport; (iv) fate and recycling. Several questions were asked. What do we know well? What is needed in the near future, and which efforts promise the greatest returns? How are our concepts changing? Where will the complexity of problems or conceptual limitations lead to continued ignorance that will hinder progress? How does persistent ignorance moderate our interpretations of nature and our recommendations for future work? What scientific backgrounds could yet be applied to marine questions? Attempts were made to assess the processes affecting organic substances in the marine environment, but all too often the words “little is known about…” were repeated. Molecular-level analyses were limited and usually without the chemical detail and environmental context needed to adequately characterize processes and their rates. Recommendations included (i) further development of analytical capabilities that would determine the extent of progress into the future, (ii) strengthened international cooperation among biochemists, biologists, microbiologists, organic geochemists, oceanographers and limnologists, and (iii) coordinated sampling expeditions to study the organic chemistry of the atmosphere, water column, sediments, and the interfaces between these. Recommendations from this NATO/ONR symposium were subsequently integrated with recommendations of a second MOG workshop, sponsored by the NSF Office for the International Decade of Ocean Exploration, into a summary of research needs in marine organic chemistry that included sections on production, transport, and transformation (Gagosian et al., 1978). Since then, several follow-up assessments have been made. Papers in Duursma and Dawson (1981) made clear that advances in sampling methodologies and the detection of specific organic compounds were leading to an explosion of field and laboratory data on the composition, concentrations, sources, and fates of organic substances in marine systems. However, new investigations were needed to advance our understanding of the organic geochemistry of particulate organic matter (POM) and dissolved organic matter (DOM), especially macromolecular material. Two years later, Gagosian (1983) reviewed the status of marine organic geochemistry, followed by Farrington (1987). In 1990, an NSF-sponsored MOG workshop was published as a special issue of Marine Chemistry (Farrington, 1992). Areas of needed research that were highlighted included (i) global biogeochemical cycles, (ii) water column transformations, and (iii) molecular paleontology. In 2003, a conference was convened upon the tragic passing of John Hedges to celebrate his life and science via a cross-disciplinary exchange of ideas and new concepts to address analytical, methodological and conceptual questions pivotal to the field of marine organic biogeochemistry. Proceedings of this symposium form a special issue of Marine Chemistry (Benner et al., 2004). Emphasis was given to state-ofthe-art methods in use since the 1990 workshop, particularly with regard to the “molecularly uncharacterized carbon” (MUC; Hedges et al., 2000; Lee et al., 2004) fraction of organic matter. New paradigms were discussed regarding: the origin of organic matter in the ocean; new applications of biomarkers and their isotopic compositions; the physical and compositional changes; organo-mineral interactions and exposure to oxygen involved in stabilization and protection of OM; black carbon

3. Dissolved organic matter: composition and new paradigms Dissolved organic matter (DOM) is the largest reservoir of organic carbon in the ocean (Carlson and Hansell, 2015), roughly 200-fold greater than marine biomass and 20-fold bigger than particulate organic matter (POM). The sizeable number of DOM sources that exist greatly complicates our progress in understanding its composition, e.g., rivers, sediments, phytoplankton exudation; zooplankton excretion and sloppy feeding; leaching from fecal pellets, etc. The large number of processes affecting DOM greatly complicates our progress in understanding its cycling, e.g., photoxidation, microbial chemoautotrophy, chemoheterotrophy and decomposition (Carlson and Hansell, 2015; Kujawinski et al., 2016; Steinberg and Landry, 2017). The biologically labile fraction, perhaps < 1% of the DOM inventory, may have reactivity lifetimes of minutes whereas biologically resistant DOM can be sequestered in the deep ocean for millennia (Jiao et al., 2014). 3.1. Sources and composition DOM is present in very low concentration in the ocean, typically 30–80 μmol C kg−1, and it is chemically complex, making defining both its composition and cycling dynamics challenging (reviews by Carlson and Hansell, 2015, and Repeta, 2015). Estimates are that 60–70% of DOM has now been characterized, albeit only at the organic functional group level (e.g., carbonyls, amides, aromatics/alkenes, aliphatics). DOM is operationally defined as the fraction not retained by filtration (traditionally through 0.45 or 0.7 μm filters). Techniques that pass filtered seawater through solid hydrophobic or mineral phases, e.g., SPE or solid phase extraction (Dittmar et al., 2008; Repeta, 2015; Benner and Amon, 2015), selectively concentrate hydrophobic or surface-active material. Ultrafiltration, with and without tangential flow, and reverse osmosis/electrodialysis systems using semipermeable membranes concentrates high molecular weight DOM (UDOM or HMWDOM; typically > 1000 Da) that includes gels and colloids and separates it from lower molecular weight DOM (LMWDOM; < 1000 Da). Low molecular weight compounds (e.g., amino acids, simple sugars, lignin phenols, some lipids; with and without preliminary hydrolysis or chemical oxidation steps) may be measured

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chromatographically by, for example, gas chromatography, high pressure liquid chromatography and anion exchange chromatography, and will not be discussed in detail here (but see Repeta, 2015). Significant analytical advances over the past decade have revolutionized the characterization of bulk or HMWDOM, notably nonselective Fourier transform infrared spectrometry (FTIR), high field nuclear magnetic resonance spectroscopy (NMR), Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and liquid chromatography tandem mass spectrometry (LC-MS/MS) (e.g., Mopper et al., 2007; Nebbioso and Piccolo, 2013; Hertkorn et al., 2013; Petras et al., 2017). High resolution 1H NMR of DOM yields characteristic spectra with strong resonances for non-exchangeable hydrogen bound to saturated carbon with nearby heteroatoms (N and O). The major functional forms of carbon in DOM are revealed by 13C NMR to include: C bound only to C and H atoms, but also amides, amines and methoxy groups; carbon bound to heteroatoms; olefinic and aromatic C; and carbonyl derivatives (esters, amides, carboxylic acids). That amide functional groups dominate nitrogen in DOM has been demonstrated via 15N NMR. FT-ICR-MS of DOM (often SPE-DOM) provides elemental formulae, involving primarily C, H, and O and lesser amounts of N and S, which may then be converted into molecular formulas (Fig. 1). Elemental compositions are visualized in van Krevelen plots of H/C and O/ C ratios where they may be compared with the same ratios in possible biochemical precursors (lipids, proteins, carbohydrates), between samples and across various spatial scales (Fig. 2). H/C and O/C ratios that diverge from precursors indicate DOM that has been degraded or transformed. Landry and Tremblay (2012) used a HPLC-FTIR method for examining the organic composition of UDOM to show that significant changes in organic functional group distributions occur depending on molecular size and origin of the DOM.

Fig. 2. Van Krevelen diagram of H/C and O/C molar ratios obtained by FT-ICRMS of DOM from Great Dismal Swamp surface water. Blue points are for whole water DOM only; red points are for peaks that occur in both whole water DOM and in C18 solid phase extracted DOM. Major organic compound classes are indicated by the circles. After Sleighter and Hatcher (2008). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

New computational tools are required to extract compositional information from extremely complex NMR and FT-ICR-MS data sets; this combination of analyses and informatics is collectively termed “omics”, and to date is most often applied to marine organisms (e.g., Gaillard and Potin, 2014; Maciel et al., 2016). With respect to DOM, Kido Soule et al. (2015) and Longnecker and Kujawinski (2017) have described analytical strategies for environmental metabolomics using FT-ICR-MS to investigate low molecular weight metabolites, indentified or as yet unknown, in DOM as indicators of metabolic pathways resulting from natural and anthropogenic stressers in the environment. Johnson (2017) used LC-MS/MS to survey dissolved (and particulate) metabolites in shallow and deep waters along two transects in the North and South Atlantic. In some cases there were relationships between abundances of dissolved and particulate metabolites indicating release from particles (cellular biomass), whereas in other cases there were no apparent relationships. Johnson also noted the need for future work linking metabolomics with community composition, nutrient availability and other omics data sets. Investigations routinely employ NMR and FT-ICR-MS techniques in parallel. The current paradigm for HMWDOM is that carbohydrates dominate, that carbohydrate compositions vary little among samples, but that relative abundances of carbohydrate vary with sampling location and depth (e.g., Aluwihare and Meador, 2008; Abdulla et al., 2013; Hertkorn et al., 2013). There appear to be two major components of the polysaccharide fraction of HMWDOM: acylated polysaccharides (APS) in which N-acetyl amino acids are bound with neutral and amino sugars (yielding amide-N), and non-acylated heteropolysaccharides, which include carboxyl-rich aliphatic matter or CRAM (after Hertkorn et al., 2006) with smaller amounts of aromatic and aromatic N-heterocyclics. CRAM seems to be the most abundant identified component of DOM. Hydrolysis is often required to elucidate simple sugar compositions, whereas methylation and reductive cleavage can be used to determine branching (e.g., Quan and Repeta, 2007). Combined NMR and FT-ICR-MS analyses confirm that increasing depth in the water column tends to conserve increasingly branched CRAM and heteroatom-containing components at the expense of progressively lost carbohydrates, contributing to the resistance of DOM to biodegradation (Hertkorn et al., 2013). Metabolic profiling via NMR and FT-ICR-MS shows that bacterial DOM in bioassay experiments has a chemical composition and structural diversity similar to refractory natural DOM in seawater (Lechtenfeld et al., 2015). Roughly one-half of the

Fig. 1. High resolution electrospray ionization FT-ICR-MS of deep (4000 m) and surface (2 m) UDOM from the Pacific Ocean. Hundreds to thousands of individual signals are visible. Spacings between ions that are 14.0156 Da represent methylene groups (CH2)n, 2.0157 Da spacings are double bond equivalents/H2, 1.0034 Da spacings are 13C vs 12C, and 0.0364 Da spacings are due to exchange of CH4 versus oxygen. The deep sample shows a more complex pattern. The upper expansion shows molecular formulae CnHmOq for prominent peaks that are indicative of refractory carboxyl-rich alicyclic molecules. After Hertkorn et al. (2006).

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molecules in DOM consist solely of carbon, hydrogen and oxygen, but one-third of the molecules contained nitrogen. Mentges et al. (2017) quantified compositional diversity of DOM determined by FT-ICR-MS conceptually using three measures: richness of molecular formulas, abundance-based diversity, and functional molecular diversity. Molecular diversity of DOM decreases with age > 1 year; systematic changes in the molecules' abundance distribution result from increased heterotrophic degradation; and homogeneity with respect to chemical properties (molecular size, oxidation state and degree of saturation) increases in more degraded DOM.

continuum (Verdugo, 2016; Orleanna and Leck, 2015; Baltar et al., 2016). Gels that form at the sea surface can link biological productivity and microbial degradation and can impact cloud properties (as cloud condensation nuclei), radiative balance and global climate (Orleanna and Leck, 2015). Gels formed in the oceanic water column and that are rich in carbohydrates, amino acids and lipids may form metabolic hotspots as part of the microbial loop (e.g., Busch et al., 2017). Gels that reach a certain size/density threshold may be involved in marine snow formation or adhere to sinking particles providing a mechanism for passive transport of semi-labile DOM to the ocean's interior, as will be discussed more extensively in the POM (Section 4). Thus, Benner and Amon (2015) described a size-chemical composition-reactivity continuum in which DOM progresses via heterotrophy, particle aggregation and gel formation from a highly structured cellular state into a seeming random and chemically complex molecular state that is widely distributed throughout the ocean.

3.2. DOM cycling DOM in seawater is often operationally partitioned into “labile”, “semi-labile” and “refractory”, and the chemical compositions of these fractions is fundamental to understanding their cycling (Carlson and Hansell, 2015). Labile (or reactive) DOM is most abundant in surface waters and consists of easily-characterized low-molecular-weight biochemicals (simple sugars and carbohydrates, amino acids and proteins, vitamins, etc.) released by metabolic processes of phytoplankton, zooplankton, and bacteria or photooxidation of upwelled DOM. Labile DOM may also be produced via deep-water chemoautotrophy. Through the “microbial loop”, this material turns over on very short timescales (hours to days), making it a small fraction of the oceanic DOM reservoir. Semi-labile DOM requires hydrolytic or degradative steps for chemical characterization, cycles more slowly (annual to decadal timescales), and thus generally is not present in the deep ocean. Refractory DOM is resistant to the chemical and microbial processes that degrade labile and semi-labile DOM. It can be formed both biotically and abiotically. This material contributes most to the export of DOM into the deep ocean where it dominates the carbon inventory and is primarily responsible for the old radiocarbon age (several thousand years) for deep-water DOM (Druffel et al., 2016; Walker et al., 2016). It is also the fraction that is receiving the most attention from the NMR and FT-ICR-MS techniques described briefly above. The link between microbial community structure and DOM composition and cycling continues to receive considerable attention. Heterotrophic microbial transformations are important for formation of biorefractory DOM (Kaiser and Benner, 2012; see also carbon and nitrogen isotopes (Sections 6.1 and 6.2, respectively) below). Arnosti (2011) and more recently Hoarfrost and Arnosti (2017) describe how heterotrophic microbial communities use extracellular enzymes to initialize degradation of high molecular weight DOM, showing a range of heterotrophic enzymatic capabilities for degradation of polysaccharides over diverse water column depths and geographic latitudes. Alternately, bacteria also apparently take up large oligosaccharides intact and perform the enzymatic hydrolysis within the periplasmic space; such mechanisms would minimize loss of both enzymes and degradation products to the external environment (Reintjes et al., 2017). Kujawinski (2011) and Kujawinski et al. (2016) provide a dual perspective – microbiologist vs. organic geochemist – on the role that microbes play in DOM formation and transformation, relying heavily on advances in high resolution mass spectrometric techniques and on recent laboratory probing experiments addressing assimilation of labile DOM (e.g., parallel metagenomics, metabolomics and lipidomics). Jiao et al. (2011, 2014) review the processes thought to be involved in the formation of recalcitrant DOM, its relationship to the biological pump, the resulting range of recalcitrance for DOM, and the global implications of this refractory DOM on long-term global carbon sequestration. The cycling of DOM is not exclusively biotic. Colloidal macromolecular material (primarily of biological origin) may assemble abiotically to form three-dimensional polymeric hydrogels, also called marine microgels. These include transparent exopolymer particles (TEP) and self-assembled microgels (SAG) that bridge the DOM-POM

3.3. DOM as bioactive signaling molecules Low molecular weight DOM can play a role in mediating biological processes in the ocean as signaling molecules. Cytotoxic allelochemicals produced as secondary metabolites suppress growth or reproduction of competitors, predation, intercellular signaling, etc. (Van Donk et al., 2011; Ianora et al., 2012; Singh and Thakur, 2016; see also quorum sensing below). Bioactive compounds may be released intentionally or as a result of cell damage or breakage, and include lipoxygenase/hydroperoxide lyase oxidation products of fatty acids (collectively termed oxylipins: polyunsaturated aldehydes (PUAs), hydroxyl and epoxy fatty acids), and dimethylsulfoniopropionate (DMSP) and saxitoxins (Ianora et al., 2011; Singh and Thakur, 2016). In some cases, the inventory of allelopathic metabolites exuded by a single organism can be quite extensive. For example, the chemically-mediated competition resulting from exposure of the marine diatom, Thallasiosira pseudonana, to the red tide dinoflagellate, Karenia brevis, was investigated by NMR and MS metabolomic and proteomic analyses (Poulson-Ellestad et al., 2014). A wide variety of metabolic pathways, enzymes, and metabolites were affected by K. brevis allelopathy; some were surpressed whereas others were enhanced. Diatom-derived PUAs may have negative effects on copepod reproduction (e.g., Ianora et al., 2015; Brugnano et al., 2016), and hydroxyl-acids affect gene expression and embryo development of a sea urchin (Varrella et al., 2016). Allelopathy by polyunsaturated alcohols produced by brown algae of the genus Lobophora is involved in the bleaching of scleractinian corals (Vieira et al., 2016). Pezzolesi et al. (2017) report on production of PUAs in benthic diatoms and implications for grazing by benthic invertebrates. Diatom-derived polyunsaturated aldehydes trigger complex cascading effects involving herbivorous microzooplankton and ultimately copepods (Franzè et al., 2018). Such a “foodweb loophole” (Irigoien et al., 2005) would impact food web dynamics and the export of phytoplankton-derived carbon into the ocean's interior. Allelopathy may become more important in response to climate change. Ritson-Williams et al. (2016) report that increased seawater temperature enhances the detrimental effects of microcolin A of benthic cyanobacterial origin on coral larval recruitment; hypoxia and pH might also affect allelopathic function. Allelopathic interactions are not limited to phytoplankton and zooplankton predators; they are also important for higher animal species. Sponges produce allelochemicals that inhibit metabolic processes in corals (e.g., Singh and Thakur, 2016), and some marine macrophytes produce cytotoxins that inhibit the growth of harmful algal bloom (HAB) dinoflagellates (Ben Gharbia et al., 2017). Needless to say, marine allelochemicals are also of great interest for their potential therapeutic properties in human health. Most of these compounds are derived from marine invertebrates (Ruiz-Torres et al., 2017), but e.g.,

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cyanobacteria can produce a variety of highly toxic metabolites, some with anticancer activities (Zanchett and Oliveira-Filho, 2013), and diatom-derived PUAs can activate cell death in human cancer cells (Sansone et al., 2014) as well as negatively impacting human health. Such a topic is well beyond the scope of this article. Quorum sensing (QS) is a bacterial cell–cell communication process that drives microbial community structure and regulates behavioral ecology, including biofilm formation, virulence, antibiotic resistance, swarming motility, and secondary metabolite production (Dobretsov et al., 2009; Hmelo, 2017; Whiteley et al., 2017). Using small, hormone-like bioactive compounds (“autoinducers”) such as acylated homoserine lactones, furanosyl-borate diesters, and hydroxy ketones, bacteria can coordinate gene expression and cooperate with one another in group-beneficial behaviors. QS is closely linked to biofilms that contain an environment conducive to bacterial habitation; corals, microbial mats and marine snow are ideal sites for such biofilms. QS by bacteria using acylated homoserine lactone molecules in laboratory incubations (Hmelo and Van Mooy, 2009) and associated with marine sinking particulate matter is involved in regulating production of extracellular hydrolytic enzymes by heterotrophic bacteria (Hmelo et al., 2011; Krupke et al., 2016). Johnson et al. (2016) used a metabolomics approach to demonstrate that a marine bacterium grown on algal-derived dimethylsulfoniopropionate (DMSP) alters its metabolism to produce a QS metabolite (among other secondary metabolites), with the suggestion that such metabolic shifts might potentially influence carbon cycling by affecting POM degradation and sinking. QS-mediated interactions influence a variety of algal-bacterial symbiotic relationships (Zhou et al., 2016). Epibiotic bacteria associated with the cyanobacterium, Trichodesmium, have been shown to use QS to regulate the activity of alkaline phosphatases involved in the acquisition of phosphate from dissolved-organophosphorus molecules (Van Mooy et al., 2011). On the other hand, marine organisms also secrete QS inhibitors (Dobretsov et al., 2011). For example, whereas QS is often active in formation of exopolysaccharides in microbial biofilms that form on sessile organisms like corals and sponges (e.g., Di Donato et al., 2016), some soft corals and sponges excrete furanosesterterpenes that inhibit QS in biofilms (Quintana et al., 2015). Secretion of indole-3-acetic acid synthesized by a marine bacterium (Sulfitobacter sp.) from endogenous and diatom-derived tryptophan promotes cell division by that diatom (Pseudo-nitzschia multiseries) (Amin et al., 2015). A recent metagenomics survey shows that QS autoinducers are indeed chemically diverse and geographically and ecologically widespread within the marine environment (Doberva et al., 2015).

4.1. Sources of suspended and sinking particles Much of the marine particle research by organic geochemists in the past few decades has focused on identifying organic biomarkers as source indicators. Although traditional organic geochemical approaches using structural elucidation and experimental manipulations remain important, especially multiproxy approaches, complementary “omics” approaches are becoming widely used. Genomic approaches using the absolute biomarker, DNA, are elucidating the role of microorganisms as sources and processors of detrital particulate organic matter. Most research has focused on the microbial community associated with small particles (e.g., Heidelberg et al., 2010). However, Mestre et al. (2017) examined the spatial variability of prokaryotic communities along a particle size continuum. Microbial community composition varied with particle size: bacteria are more diverse in larger size-fractions, whereas archaea were more diverse in smaller particles. Several studies (e.g., Pelve et al., 2017; Farnelid et al., 2018; Mestre et al., 2018) have shown that the prokaryotic community of bulk particle samples is much more similar in time and space than that of hand-picked individual particles, and different from the surrounding seawater; the largest sinking particles appear to be the most similar throughout the water column. These genomic approaches have distinct advantages for individual particles where the amount of material is insufficient for traditional organic geochemical analyses, and because the specificity of genomic approaches is extreme. Pitcher et al. (2011b) combined organic geochemical with genomic approaches. Using HPLC-MS/MS analyses of intact polar lipids and gene-based DNA/RNA analyses of suspended particulate matter from the oxygen minimum zone of the Arabian Sea, they showed that the distributions of ammonium-oxidizing Thaumarchaeota and anaerobic ammonium-oxidizing anammox bacteria were separated with respect to depth and water column dissolved oxygen concentrations (Fig. 3). Depth distributions of hexose-phosphohexose crenarchaeol (see Archaeal Section 5 for details), and archaea-specific 16S rDNA and amoA functional genes are most abundant at the oxycline where dissolved oxygen concentration gradients are highest. By contrast, concentrations of phosphatidylcholine-monoether ladderane, an anammox-specific lipid, and anammox bacteria-specific 16S rRNA and hzo (the enzyme hydrazine oxidoreductase) functional genes are highest at the lowest O2 concentrations in the core of the oxygen minimum zone. Further application of organic biomarkers in combination with genomics is certainly in the future. Proteomic approaches that analyze the structure, function and interactions of proteins in particles are also increasing. Nunn and Timperman (2007) described how useful this mass spectrometric method of determining the primary structure of proteins promises to be in elucidating the source of proteins in the environment. For example, Dong et al. (2010) characterized suspended particles in the South China Sea using shot-gun proteomics to identify the various proteins on the particles and their biological sources. Moore et al. (2012) used a LCMS/MS based approach to identify metabolic, binding/structural, and transport-related protein groups in suspended and sediment trap particles and sediments of the Bering Sea in a study of the transport, deposition and preservation of particulate protein. Although large sample sizes are needed for proteomic analyses, the method, like genomic approaches, is very specific, and sample requirements are decreasing. Lipidomics and metabolomics have been, to date, more often used in marine ecology, but the protocols are ripe for application to particle research. For example, Hunter et al. (2018) showed via lipidomics that the diatom, Thallasiosira pseudonona, produces non‑phosphorus-containing phospholipids (sphingolipids) as well as several novel diglycosylceramide lipids when grown under P-depleted conditions. These lipids may prove to be useful as biomarkers for investigating carbon

4. Detrital particle sources and dynamics Knowledge of the organic compounds in particulate organic matter has greatly improved our understanding of particle sources and dynamics. Early work concentrated on describing the organic composition of both suspended and sinking particles (reviewed in Wakeham and Lee, 1993; Wakeham et al., 2000; Volkman and Tanoue, 2002), and this knowledge allowed us to address questions about the source of detrital particles and their dynamics in ways that were not previously possible. In the following subsections, we discuss some of the work in the past few decades on organic compounds that can identify particle sources (biomarkers) and what organic compounds can tell us about organic matter decomposition, particle transport rates and mechanisms, and aggregation and disaggregation. Last, we discuss recent mathematical modeling approaches using organic composition to better understand particle dynamics. We realize that organisms are particles too, but we limit most of our discussion to non-living, detrital particles.

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Fig. 3. Combined lipid biomarker and genetic analyses for suspended particulate matter in the northern Arabian Sea show that ammonium-oxidizing thaumarchaea (see Archaea Section 5) and anaerobic ammonium-oxidizing (anammox) bacteria are offset with respect to depth and dissolved oxygen concentration. Depth profiles of (a) dissolved oxygen; (b) archaeal-specific HPH-hexose-phosphohexose crenarchaeol (HPH-crenarchaeol, XXVIII in Supplemental Fig. 1); (c) Thaumarchaeotal 16S rDNA and amoA gene abundances; (d) anammox-specific C20-[3]-phosphatidycholine-monoether ladderane (XXIX), and (e) anammox bacteria 16rDNA and hydrazine oxidoreductase (hzo) gene abundances. Adapted from Pitcher et al. (2011c).

cycling in the P limited ocean. Llewellyn et al. (2015) used a metabolomic approach as a proof-of-concept to characterize marine microbial particulate organic matter via polar and lipid metabolite profiles in the English Channel. These authors pointed out the advantages of using a broad-based profiling approach rather than just specific compound analysis. Schubotz et al. (2018) used a similar lipidomics approach to characterize the intact polar lipid composition of suspended particulate matter in the Eastern Tropical North Pacific, with emphasis on in-situ microbial sources within the oxygen minimum zone. In addition to these omics approaches, there has been interest in adapting X-ray spectromicroscopic approaches for organic particle research (Brandes et al., 2004; Abramson et al., 2009), especially to investigate mineral-bound material. The association of organic matter, particularly amino acids and amines, with carbonate and silicate minerals in marine organisms and particles has been known for some time (King Jr., 1977; Carter and Mitterer, 1978), and organic nitrogen compounds are known to be important in silicate formation in organisms (Kröger et al., 2000, 2001). Ingalls et al. (2003, 2006a) investigated the silicate-bound amino acid fraction (presumably in diatoms) in Southern Ocean particles and found that silicate-bound amino acids were not preferentially preserved between shallow and deep traps, possibly because of silica dissolution but also because there was little remineralization of either silica-bound and non-silica bound organic carbon. Even with these new techniques, most organic geochemical approaches to marine particle research still involve determination of chemical composition and concentration, and the large number of identified organic biomarkers has proven very useful in studying particle processes. Lipids and pigments are the most commonly used source indicators due to their specificity. In the past few decades, these compounds have often been used to investigate spatial heterogeneity,

seasonal changes, and community structure of organisms that are sources for particles in, for example, the North Atlantic (Mayzaud et al., 2014) and the Mediterranean Sea (Tolosa et al., 2004; Liu et al., 2009b; Wakeham et al., 2009). As will be discussed further in Sections 5 and 6, distributions and isotopic compositions of archaeal ether lipids have shown the limited exchange between suspended and sinking particles in the Black Sea (Wakeham et al., 2003). Sinking particles (and underlying sediments) in the deep anoxic Black Sea contain the archaeal lipid signature of surface water production, whereas the deep suspended particle pool contains compounds diagnostic of archaeal anaerobic oxidation of methane. Likewise, Hurley et al. (2018) employed ether lipid distributions on particulate matter from the Atlantic Ocean to evaluate export of GDGTs to the sediments, with implications for sources and particle dynamics. We suspect that future organic biomarker development will be “targeted” even more than in the past to certain organisms of significance. As just one potential example, giant phaeodarians were recently suggested as major exporters of biogenic silica in some systems (Stukel et al., 2018; Biard et al., 2018). Phaeodarians could thus be targeted, for example via lipidomics, to determine whether there is a suitable biomarker or suite of biomarkers to investigate this very important oceanographic process. In the meantime, well-known biomarkers are currently in use to investigate oceanographic processes as we discuss in later subsections. One area of research that needs further work is to establish the source(s) of “molecularly-uncharacterized material” (MUC; Hedges et al., 2000; Lee et al., 2004). This uncharacterized material is not susceptible to analysis by traditional chromatography-based organic geochemical techniques and is formed by several complex but poorly understood processes (Fig. 4). MUC makes up a larger fraction of marine particles with depth (Wakeham et al., 1997, 2000) (Fig. 5), thus reducing the proportion of recognizable biomarkers that are useful to

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condensation of smaller labile molecules, as was traditionally thought (Tegelaar et al., 1989), nor a result of non-selective degradation with “amino” carbon dominating the resistant material (Hedges et al., 2001). In subsequent work, Hwang et al. (2006) supported the idea that biological incorporation of old DOC is a source of uncharacterized material on marine particles. However, using the same multiproxy approach, Roland et al. (2008) later suggested that uncharacterized material in particles varies greatly between open ocean and coastal sites, with nonselective preservation being more important initially, but contributions from resuspended particles and DOC sorption varying with location. In the absence of suitable compound-specific analytical techniques for MUC, organic functional group compositions may nonetheless be obtained. Hedges et al. (2001) applied one-dimensional solid-state 13C NMR to show that sinking POM in the equatorial Pacific underwent minimal compositional change with depth (cf. molecular analyses by Wakeham et al., 1997). Subsequently, Minor et al. (2003) used solidstate 13C NMR and direct temperature-resolved mass spectrometry (DTMS) on particles from the same study to further demonstrate that the organic composition of sinking POM varied little with depth and that extensive geopolymerization did not occur. On the other hand, some of the original upper-ocean organic matter composition persisted at depth, consistent with protection due to minerals (e.g., Armstrong et al., 2002; see Section 4.3). Liu et al. (2009b) later used even more advanced solidstate NMR techniques to show that at least for sinking particles in the Mediterranean Sea, there must be yet another mechanism at work. Organic matter that was preserved at depth was not mainly intact labile biopolymers but organic matter that had been modified, rearranged, or selectively preserved, and furthermore, enhanced lipid components were not observed in the NMR spectra. The quest for new analytical tools to characterize MUC continues. For example, Tremblay et al. (2011) describe a rapid and non-destructive technique for characterizing organic functional groups in particles deposited on filters using attenuated total reflectance infrared spectroscopy (ATR-FTIR), a technique that may make possible more extensive surveys of POM composition. Raman spectroscopy may provide yet another route to information about the chemical composition of POM. Raman spectroscopy is now widely used to analyze marine aerosols that contain, among other things, organic compounds physically ejected from the marine microlayer (e.g., Deng et al., 2014), and has been used to identify microplastics in the ocean and the extent of their degradation (Lenz et al., 2015). An intriguing application of Raman spectroscopy has been described in a lipidomics assessment of organic composition of algae using Raman spectroscopy (Wu et al., 2011; Parab and Tomar, 2012). Whether such a technique might be suitable for marine POM remains to be assessed. Multiproxy investigations of marine particles that combine organic geochemical and isotopic markers are now routine (see also isotope Section 6). Since the early work on isotopic composition of biochemical classes (Wang et al., 1996, 1998) and individual compounds (e.g., Freeman et al., 1994; Bidigare et al., 1997; Tolosa et al., 1999) isolated from particles, measurements of compound-specific δ13C and Δ14C values of particulate samples have increased. For example, Cavagna et al. (2013) measured δ13C of individual sterols as well as bulk POC in marine particles from the Southern Ocean. They were able to investigate how food chain changes might alter carbon export, and also suggested that such measurements might be useful as a proxy for seawater DIC concentrations. Wakeham and McNichol (2014) used compound-specific 13C and 14C compositions of lipids (fatty acids, alkenones, hydrocarbons, sterols and fatty alcohols) in sinking particles to constrain the relative inputs of organic carbon from marine biomass, terrigenous vascular plants and relict kerogen. In the North Pacific gyre (Close et al., 2013) and Eastern Tropical North Pacific (Close et al., 2014), compound specific δ13C and Δ14C analyses of individual lipids showed that submicron-size particles, thought to be primarily bacterial in origin, dominate the total lipids found in deep waters, suggesting

Microbial Detritus

Macromolecules

Minerals

Uncharacterized Organic Matter

Black Carbon

Aggregates

? Unmeasured

Fig. 4. The uncharacterized organic matter in marine particles is not susceptible to analysis by traditional chromatography-based organic geochemical techniques. Its formation in the water column may be affected by aggregation and disaggregation, formation of bio- and geo-macromolecules, interactions between organic substances and inorganic mineral matrices, production of organic materials in the form of microbial biomass or detritus, black carbon, and the fact that some organic materials are simply not routinely measured. Adapted from Lee et al. (2004).

b

a POC Flux, mg/m d2 0.01 1.0

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100

20

40

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Amino Acid

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Surface sediment Fig. 5. Cumulative biochemical class distributions as a percent to of total organic carbon in sediment trap material from the Equatorial Pacific (after Wakeham et al., 1997). The figure shows that as total POC flux decreases with depth, the characterized compounds such as amino acids, lipids and carbohydrates contribute less of the total organic carbon with depth. Material not molecularly uncharacterized in this study increases with depth. The major change in composition occurs in the “twilight zone” (the epipelagic zone) where POC flux decreases very little.

investigating biogeochemical processes. Using a multiproxy approach (measurement of δ13C and Δ14C of biochemical classes isolated from sinking marine particles), Hwang and Druffel (2003) suggested that this material may be “lipid-like” in origin, and not derived from abiological

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surface derived fresh degraded aggregation

0.2-0.5 µm

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grazing mesopelagic

fast sinking

0.5-53 µm >53 µm

disaggregation and lysis

deep derived 0.2-5 mm Fig. 7. Application of principal components analysis to marine particles collected in the equatorial Pacific Ocean (from Sheridan et al., 2002). The degradation index of particles (blue) decreases with depth from suspended particles to sinking particles to sediment. Phyto- and zooplankton endmembers (green) are from other studies. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 6. Lipids and their δ13C- and Δ14C-values in size-fractionated particulate matter may be used to better understand the role of small particles in the carbon cycle. Submicron (0.2–0.5 μm) particles in the North Pacific Subtropical Gyre are distinct with respect to fatty acid and isotope compositions from larger suspended (0.5–53 μm) and sinking (> 53 μm) POM. A conceptual model derived from this analysis shows that passive or active (grazing) aggregation, sinking, and subsequent disaggregation of surface-derived POM transports OC to the mesopelagic ocean. Much of the material exported out of surface waters is submicron in size and its lysis/degradation fuels mesopelagic microbial communities. Adapted from Close et al. (2013).

samples of various sources and decomposition status, suspended and sinking particles among them (e.g., Sheridan et al., 2002; Ingalls et al., 2003; Goutx et al., 2007; Abramson et al., 2010; Xue et al., 2011). Although the majority of organic geochemical research on POM decomposition and cycling has been conducted on marine sediments, organic analyses have provided much insight into particle decomposition as well. For example, Sheridan et al. (2002) used the DI (PC1) to clearly show the increase in organic matter degradation state with depth from the surface phytoplankton to surface suspended and sinking particles and to the sediments (Fig. 7). The DI of the particles increased with depth, but more slowly for the sinking than for the suspended particles. Whatever the mechanism controlling this midwater degradation, it was apparent that organic matter alteration in midwaters, and not cycling within the euphotic zone, had the larger effect on organic composition of suspended particles. In Southern Ocean samples, Ingalls et al. (2003) showed that the mineral preservation of organic matter observed in sediments (e.g., Mayer, 1994) starts in the water column. They found that amino acids in silicate and calcium carbonate biomineral fractions of sinking particles were an insignificant fraction of total amino acids in plankton, a small fraction of total amino acids in particles in surface waters, but increased with depth in the water column and into sediments, where mineral-bound amino acids were as much as 50% of the total. This clearly demonstrated that mineral binding plays a significant role in the preservation of amino acids throughout the water column and surface sediments. PCA further suggested that the greatest change in THAA composition occurred between the sediment surface floc layer and deeper sediments where particles had the longest residence time. In addition to preservation by minerals, it has also been suggested that bacterial cell membrane proteins might be selectively preserved in detrital organic matter (e.g., Nunn et al., 2010). Tang and Lee (2016) suggested that at least for cyanobacteria, selective preservation of glycoprotein was not as important as physical protection by S-layer protein; this surface layer protein is widely found in both Eubacteria and Archaea. Particle decomposition studies have not been limited to amino acids or to microbial decomposition. Abiotic photo-oxidation and auto-oxidation, in addition to microbial decomposition, of lipids have been documented in both suspended and sinking particles. In the Mediterranean Sea, Marchand et al. (2005) and, later, Rontani et al. (2009, 2013) compared the extents of photo-oxidation, auto-oxidation,

that small particles are important to export, that bacteria overprint the original phytoplankton-dominated signature, or that the submicron particles are preserved better during transit (Fig. 6). Using compoundspecific nitrogen isotopes instead, McCarthy et al. (2007) used δ15N patterns of amino acids to show that 1.5–2 trophic transfers occurred between phytoplankton and sinking particles collected from the equatorial Pacific. 4.2. Decomposition of POM Much early work in marine organic geochemistry focused on how organic matter degrades, so we have known for some time the general picture of degradation, but new findings continue to emerge. For individual organic compounds to be useful in studies of POM decomposition, it is best if they make up a large portion of the total organic matter and also decompose to known products. While amino acids are not usually specific source indicators, their well-known compositional changes during decomposition have long been used to measure the extent of degradation of organic matter in marine systems. To quantify these changes, Dauwe and Middelburg (1998) and Dauwe et al. (1999) used principal component analysis (PCA) to explore the systematic changes in amino acid composition that are observed during degradation in marine sediments and defined the first principal component (PC1) as a Degradation Indicator (DI). Since that time, the DI has been used as a measure of general organic matter degradation in marine and freshwater organisms, particles, and sediments. However, Ingalls et al. (2003) showed that the DI was a relative rather than absolute indicator of decomposition in particles from Southern Ocean waters where diatom abundance is high, since the high glycine content in diatom cell walls can make particles look more degraded than they actually are. PCA in general is now commonly applied to biochemical data sets in organic geochemistry to quantitatively investigate differences between

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Fig. 8. Conceptual model of biotic and abiotic degradation processes that affect phytoplankton-derived lipids in euphotic and aphotic zones of the water column. The relative importance of aerobic bacterial decomposition, photo-oxidation (hν), and auto-oxidation (RO•) acting on sinking aggregates and small suspended particles are shown by the relative sizes of the colored arrows. Adapted from Rontani et al. (2013).

Aeolian input of degraded RO* terrestrial material

sea surface

photic zone

bacteria

phytoplankton

phytoplankton

bacteria

aggregation disaggregation

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sinking particles inefficient transfer of 1O2 to bacteria increases RO* biodegradation

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zooplankton 1O

1O

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efficient transfer of 1O2 to bacteria decreases RO* biodegradation

aphotic zone

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RO*

deposition of degraded material

preservation of undegraded labile material

and microbial decomposition of sterols and alkenones in sinking particles (Fig. 8). Much of the concentration decrease for lipid biomarkers with depth relative to POC was due to abiotic processes rather than microbial decomposition. Sterols could be useful biomarkers for estimating the ratio of abiotic to biotic degradation, particularly in suspended particles, which have longer residence times than sinking particles. In the equatorial Pacific, photodegradation and autoxidation were important for lipids in both suspended and sinking particles; furthermore terrestrial material was also abiotically degraded (Rontani et al., 2011). Photodegradation was even important in the Arctic (Rontani et al., 2012). Pigments in sediment trap material and suspended particles generally show little spatial compositional difference, but rapid degradative losses with depth, especially at the sediment surface (Lee et al., 2000; Sheridan et al., 2002; Ingalls et al., 2006a; Abramson et al., 2009). Tamburini et al. (2009) investigated the effect of hydrostatic pressure on chloropigments (as well as POC, carbohydrates, lipids, amino acids and TEP) using a Particle Sinking Simulator. They found that POC-normalized concentrations of chloropigments, carbohydrates and TEP decreased more slowly under conditions that simulated increasing pressure with depth than under atmospheric conditions. However, POC-normalized wax and steryl esters increased only under pressure, suggesting biochemical responses of prokaryotes to the increasing pressure regime. More recently, Bale et al. (2015) examined the distribution of Chl-a transformation products in sinking and suspended particles during the North Atlantic spring bloom using highresolution HPLC with multistage mass spectrometry (LC–MSn). Large differences in the distribution of Chl-a and its transformation products between surface suspended particles and sinking material were observed. Surface suspended particle samples from both inside and outside a main bloom patch showed high spatial variability in Chl-a

concentrations, but not in Chl transformation products, although the proportions of demetallated and de-esterified transformation products in suspended particles increased with depth. Goutx et al. (2007) applied an innovative approach to examine POM degradation. After collecting particles in a sediment trap, an elutriator (as described in Peterson et al., 2005) was used to separate particles into different sinking velocity classes. The separated particles were then allowed to degrade. Amino acid, pigment, lipid, and carbohydrate concentrations initially varied with settling velocity but became more similar with time, and compounds indicative of a more degraded state increased in abundance. Biogenic opal in the more degraded particles dissolved faster than in fresher particles, suggesting that loss of organic matter may expose opal to dissolution. This was indeed shown later in decomposition experiments with diatoms where measurements of amino acids, pigments, and lipids indicated the presence of two opal phases, one that protected organic matter and one that did not (Moriceau et al., 2009). Opal is not the only mineral that protects organic matter. Engel et al. (2009) added calcifying and non-calcifying cultures of Emiliania huxleyi to roller tanks to simulate particle sinking. They found that decomposition of particulate C and N, pigments, TEP, and amino acids was greater in experiments without carbonate shells than in those with carbonate shells. Several studies have indirectly evaluated decomposition by monitoring the controls in experiments designed to test the effect of poisons on preventing degradation. Using various poisons and preservatives, Liu et al. (2006) measured loss of amino acids, chlorophyll and lipids in poisoned relative to unpoisoned material (diatoms and sinking particles). In unpoisoned samples, concentrations of POC, PN and amino acids were 50% lower than in unpoisoned controls. Production of γaminobutyric acid after one month indicated that biological degradation was occurring. Fatty acids, but not sterols, were also degraded

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substantially. On the other hand, Chl a increased after one month by about 30%, suggesting the release of free Chl from Chl-protein complexes via decomplexation or degradation of proteins. Specifically using mercuric chloride, a common poison in sediment trap studies, they found that dissolution of POC to DOC still occurred and Chl-a allomer was formed during particle degradation. Pelve et al. (2017) also compared decomposition in poisoned and unpoisoned sinking particles, but in particles that were allowed to degrade in situ. Using genomic and metagenomic approaches, they identified the major microorganisms present in the poisoned trap (initial composition) and the unpoisoned traps (the decomposing organic matter). There was a clear succession of microrganisms, with initial particles dominated by eukaryotes and their associated microbes, followed by specific, reproducible bacterial succession events. Nunn et al. (2010) in the lab and Moore et al. (2012) in the field compared metaproteomic methods with traditional amino acid analytical techniques based on acid hydrolysis. These studies followed algal protein decomposition and could differentiate between algal and bacterial proteins over time. Survival of algal proteins in water column particles and sediments appeared to be selective, with membrane- and organelle-bound proteins being more resistant to degradation, thus enhancing the overall preservation of protein during particle transport and incorporation into sediments (Moore et al., 2012). Bergauer et al. (2018) combined metaproteomic and genomic methods to investigate microbial uptake of organic matter throughout the oceanic water column, an approach that may be applicable to future particle studies. The apparent uptake of solutes throughout the water column suggests that even though the microbial community structure varies with depth, the organic composition and substrate specificities of the solute transporters do not (their relative abundance does change with depth). When this type of approach is adapted for particles, we may learn much about exactly how particulate organic matter is decomposed in the ocean.

associated flux is seasonal. There has been considerable discussion about the relative contributions of slow versus fast-settling particles to POC fluxes. In the coastal Mediterranean Sea, analyses of POC, lipids, amino acids, and pigments in IRS sediment traps designed to separate sinking particles by different sinking velocity (Peterson et al., 1993, 2005, 2009) showed that more of the particle flux was due to faster-sinking particles (Wakeham et al., 2009). The slow- and fast-sinking particles had different compositions. Fast-sinking material was similar in composition to fecal pellets and aggregated material that sinks as the spring bloom terminates. More-slowly sinking particles appeared to be more bacterially-degraded. As mentioned earlier, the elutriation experiments by Goutx et al. (2007) also showed different chemical compositions for particles that settled at different rates. However, Alonso-González et al. (2010b) found that for pigment and amino acid compositions in particles sampled in the Canary Current regions, slow and fast-sinking particles had similar degradation states. Slowly settling particles were more (60%) of the annual POC export and their longer residence time in surface waters would result in more POC being respired rather than being transferred to deeper waters. Using Marine Snow Catchers (MSC), Cavan et al. (2018) observed that lipid compositions of larger, fastersinking particles in and beneath the mixed layer are distinct from slower-sinking particles. These authors suggested that particles in the mixed layer may not be directly formed from phytoplankton cells but instead may be aggregates of smaller, slow-sinking particles. The contrasting compositions of particles with different sinking rates calls into question the paradigm of constant exchange between particles, as has been suggested (e.g., Hill, 1998). For compositions to be different, physical and biological exchange between fast- and slowsinking particles must be incomplete. Abramson et al. (2010) tested this idea with PCA of pigment and amino acid data from the Mediterranean Sea. Sinking particles collected by sediment trap were composed of mostly fecal pellets, some phytoplankton aggregates, and diatoms, while suspended particles collected by in-situ pumps appeared to be more enriched in fresh phytoplankton and contained indicators of calcium carbonate-bearing organisms. These compositional differences were highest during the high flux periods in spring. During the low-flux summer period, the contribution of fecal pellet indicators to sinking particles was lower, whereas microbial degradation products were higher in both particle types at this time, suggesting reduced particle exchange during periods of low flux when fecal pellets were not as abundant. Suggestions that minerals may protect sinking particulate organic matter (Armstrong et al., 2002; Klaas and Archer, 2002) have engendered considerable research on POC-mineral interactions, some of

4.3. Mechanisms and rates of particle transport Because of the importance of understanding the ocean's biological pump (e.g., Honjo et al., 2014), considerable effort has been directed to discerning the rates and mechanisms of particle, and in particular POC, transport. Single phytoplankton cells are generally too small and of too low density to sink at appreciable rates on their own (Smayda, 1970), but they can form aggregates with other phytoplankton or with heavier biological particles, such as zooplankton shells and fecal pellets (e.g., Alldredge and Silver, 1988; Passow and Carlson, 2012). In addition, there is evidence for a strong relation between flux of organic matter and mineral dust (e.g., Lee et al., 2009; Pabortsava et al., 2017). Although much has been published on these topics, again we focus here on recent studies that used specific organic compounds as exploratory tools. One area where individual organic compounds are useful for investigating physical particle transport pathways is their ability to differentiate between fresh surface-derived vs. older resuspended material. For example, Fabres et al. (2008) used particulate pigments and amino acids collected in sediment traps in the Gulf of Lions to demonstrate the importance of seasonality to the transfer of POM from the shelf to the slope and basin, and particularly the presence in spring of the dense water cascading phenomenon at this site. Later at the same location, Pasqual et al. (2011) used amino acids, pigments and lignin phenols in sediment trap particles as “freshness” indicators to evaluate the relative importance of the contribution of phytoplankton associated with wind-driven mixing vs. that of dense-water cascading to overall transfer of OM down canyons. Amino acids and pigments in trap samples analyzed by Alonso-González et al. (2010a, 2013) showed enhanced flux in eddy-dominated areas near the Canary Islands compared to areas with no eddies, and observed that this enhanced eddy-

Fig. 9. Dissolved organic carbon (DOC) can self-assemble to form gel particles (adapted from Verdugo, 2016). DOC polymers assemble first, forming nanogels that are stabilized by entanglements and cross-linking with Ca2+. These DOC polymer networks can further interpenetrate neighboring nanogels, forming microgels in a reversible process. Microgels are thought to eventually form the larger, more stable macrogels, like TEP.

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which use specific organic compounds as tracers. As mentioned earlier, Ingalls et al. (2006a) using chemical means on Southern Ocean particles and Abramson et al. (2009) using X-ray spectroscopy of diatoms both found direct evidence for mineral-protected organic matter, mostly amino acids or protein. As also noted earlier, Engel et al. (2009) found that decomposition of pigments, TEP, and amino acids in a coccolithophore was faster in cells without carbonate shells than in those with carbonate shells. Using a global data set of > 1000-m sediment trap fluxes, Klaas and Archer (2002) observed that calcium carbonate has a higher carrying capacity for sinking organic matter than silica on a global basis. Diatom silicification, physiological status and decomposition pathways may thus decrease water column OM preservation by opal, although this may be a detection problem since diatom organic matter can be well-preserved in marine sediment (Ingalls et al., 2006a). Keil et al. (2016) used a peptidomic approach in addition to more traditional methods to explore reasons for the higher apparent particle flux they observed in oxygen minimum zones relative to other areas of the Arabian Sea. They suggested that several mechanisms were important, among them incorporation of mineral ballast, although the major factors appeared to be the oxygen effect and/or changes in the efficiency of the microbial loop including the addition of chemoautotrophic carbon to the sinking flux in the upper 500 m. Gel particles were briefly mentioned in the previous DOM section (Section 3), and this is also an area of considerable importance in marine particle research due to the role of macrogels in aggregation and sinking of POC. We still do not completely understand the biochemical basis for aggregation. In a review of the physical properties of marine microgels, Verdugo (2016) described how DOC can self-assemble to form microgels (Fig. 9). These microgels, which likely consist of large undegraded organic polymers, are thought to eventually form the larger, more stable macrogels like TEP (transparent exopolymer particles, Alldredge et al., 1993), CSP (Coomassie blue stainable particles, Long and Azam, 1996), and eventually, marine snow aggregates (Alldredge and Silver, 1988). Passow (2002) described how TEP aids in the aggregation of solid particles, thus promoting particle sedimentation and the transport of organic carbon to the deep sea and sediments. Because TEP is visualized with a dye that is specific for acidic polysaccharides, it was clear that these compounds were present, and early organic chemical analysis confirmed that (Mopper et al., 1995). Passow (2002) summarized TEP composition as being highly surface-active material enriched in fucose, rhamnose and to a lesser degree arabinose but depleted in glucose and galactose, and with traces of uronic acids. TEP acidity was thought to result predominantly from the presence of sulfate half-ester groups (Zhou et al., 1998), although the TEP studied in those experiments was prepared by bubbling instead of being produced by organisms. CSP have been less frequently studied, although they can now be quantified spectrometrically (Cisternas-Novoa et al., 2014, 2015), and again we know that they are rich in protein given the dye used to visualize them. However, although macrogels correlate with coagulation efficiency in laboratory experiments (Passow and Alldredge, 1995; Engel, 2000), exactly how the organic composition determines the stickiness of a particle is still a mystery. Several recent studies have analyzed specific organic compounds in macrogels and their precursors, particularly the monosaccharide composition (e.g., Borchard and Engel, 2015), and some have related these compositions to coagulation efficiency (Chow et al., 2015) or stickiness (Vieira et al., 2008). Stickiness appears to depend heavily on polysaccharide reactivity as initially proposed by Mopper et al. (1995). Vieira et al. (2008) found in a fresh-water reservoir that the deoxy, methylated sugars fucose and rhamnose, known for their hydrophobic properties, represented more than half of the terminal units of the extracellular polysaccharides they studied. Uronic acids were also common; these compounds give a negative charge to polysaccharides. Uronic acids can also form cationic bridges with

metals, which increases aggregation by particle capture (Passow, 2002). The organic matrix of the large mucilaginous aggregates common in the Mediterranean Sea are among the best studied. For example, Giani et al. (2012) characterized more than half of the total organic carbon as carbohydrates, proteins, lipids and humic and fulvic acids, but these compositions were not specifically related to stickiness. As particles degrade, they become stickier (Vieira et al., 2008; RochelleNewall et al., 2010), giving us clues as to how composition affects stickiness. Further progress in this area will require combining chemical, physical, and biological measurements. Sampling techniques remain a major issue in particle research and need improvement (see also Lee, 2019). Sediment traps can over- or under-collect particles in surface waters, depending on conditions. Particles caught using the Marine Snow Catcher (MSC) mentioned above may not be directly comparable with sediment trap particles as the MSC does not efficiently collect the rare fastest-sinking particles. Free-drifting, surface-tethered net traps (Peterson et al., 2005) are now being employed to collect large amounts of sinking particles from at least as deep as the mesopelagic ocean for subsequent ship-board incubations (e.g., Keil et al., 2016). The recently developed PHOtosynthesis and Respiration Comparison-Yielding System (PHORCYS) is a large-volume (i.e., > 2.5 L), light and dark chamber, incubation system for autonomous measurement of rates of primary production and respiration (Collins et al., 2018) that may have potential applications for in situ incubations of particulate matter. When measuring suspended particle concentrations, bottles and pumps can give different particle results. For example, suspended POC concentrations measured in small volume Niskin bottles are often higher than those measured by large volume in situ pumps. Using pigment and lipid compounds, Liu et al. (2009a) found that differences in how these two methods collect zooplankton may explain this systematic discrepancy in POC between bottle and pump techniques. They also compared different pump inlets and suggested that inlet design can affect the efficiency and retention of large particles. Pigments, fatty acids, and amino acids were also used to investigate the possible use of the 210Po/210Pb disequilibrium to measure particle flux (Stewart et al., 2007), much as 234Th/238U disequilibrium has been used (since Coale and Bruland, 1985). PCA and a correlation coefficient matrix showed that the distribution of polonium in sinking particles was influenced by fresh phytoplankton-derived, nitrogen-rich organic matter as well as sulfur-containing amino acids. This work supported using 210Po as a tracer of the flux of organic matter, but flux results were different from those of Th. Differences between the Th and Po methods in a recent study showed a factor of 3 difference, with higher POC estimates generally derived from 234Th, with differences thought to be due to integration time scales and the history of bloom events (Tang et al., 2018). 4.4. Statistical and mathematical modeling approaches Statistical approaches to analyzing organic geochemical data have become the norm in many geochemical studies, as illustrated by a large portion of the papers published in the past 10–20 years, and are quite necessary given the volume of data engendered in many analytical techniques. Many papers specifically address the use of such techniques in marine organic geochemistry. Sleighter et al. (2010), Kido Soule et al. (2015), and Longnecker and Kujawinski (2017) (among many others) discuss how to handle ultrahigh resolution mass spectrometry data when considering DOM composition, but similar techniques could be applied to POM. Xue et al. (2011) discuss the advantages and pitfalls in using PCA and cluster analysis in interpreting organic geochemical data. They give examples using sediment trap particle data and discuss the use of scree plots and cluster analysis to determine the optimum number of principal components needed. Although many textbooks

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cover general statistical methods, more approaches and applications specific to marine organic biogeochemical data are still needed. Mathematical models have been present in organic geochemical studies for some time and have been applied to sinking particles. First attempts to model depth changes in the flux of organic carbon in the sea (Martin et al., 1987) were quickly followed by attempts to model fluxes of specific organic compounds (Wakeham and Lee, 1993). The only similarity in methods, however, was that both models ignored the complicated surface waters. The Martin curve, a simple normalized power function, has been widely used in global carbon models. However, exponential models are also common (Banse, 1990; Armstrong et al., 2002; Lutz et al., 2002) and seem more intuitive for those who study decomposition. Recent attempts to further model fluxes of specific organic compounds have been rare, although considerable data exist for such a venture. Major difficulties, however, are the same as for total organic carbon (see Kriest and Oschlies, 2008), e.g., uncertainty of sinking speed, particles with different sizes (and different compound/ particle ratios), and different size-dependent sinking velocities. A different modeling approach was taken by Wang et al. (2017) in an effort to constrain particle exchange and organic matter remineralization rate constants using chloropigment data from sediment traps, so as to better understand aggregation and diaggregation process in the Mediterranean Sea. They adapted an earlier model for thorium in sediment traps (Wang et al., 2016a) and used chloropigment concentration data and the degradation rate of chlorophyll to pheopigments to show that thorium and pigments sorb and desorb differently from particles, which is not surprising considering that thorium sorbs to the surface of particles while chlorophyll is an integral part of phytoplankton-derived particles. Later, Wang et al. (2018 and in prep) applied their modeling approach to pigments from large and small suspended particles obtained from in-situ pumps. Aggregation and disaggregation rate constants obtained from pump particles differed little from those calculated from trap particles, but were very different from rate constants calculated from thorium data. It is clear that a multi-tracer approach will shed more light on questions about particle aggregation processes. The wealth of particulate organic geochemical data may be extremely helpful in this regard.

5. Archaeal lipids in marine biogeochemistry: confluence of biology and chemistry Interest in the diversity and distributions of the archaea, their cellular structure and metabolism, their biochemistry, and their role in biogeochemical cycles in the ocean and on land has exploded as molecular genomic and biogeochemical analytical tools have improved, from a few publications per year prior to 1970 to ~1500 per year by 2010 (Cavicchioli, 2011). This research began in earnest in the 1970s as molecular-level gene characteristics became the primary factor in biological classification, and, based on RNA and DNA sequencing, Woese and Fox (1977) (see also Woese et al., 1990) proposed that the Archaea constitute one of the three “domains” of life on Earth (along with Bacteria and Eukarya). It should be stressed, however, that the “tree of life” is constantly being revised, including a suggestion that Archaea is no longer a valid taxon as Archaea and Eukarya are now within several phyla of Arkarya (Forterre, 2015). 5.1. Metabolic roles and habitats of archaea Archaea are particularly abundant in the ocean. They differ from bacteria in their ability to better adapt to living in inhospitable habitats of chronic energy stress by adjusting their membrane permeability and fluidity and specific catabolic pathways (Valentine, 2007). The domain Archaea is represented by numerous phyla (e.g., 26 archaeal phyla vs 92 bacterial phyla, Hug et al., 2016). For the sake of this discussion, the Euryarchaeota include methanogens, methane oxidizing archaea, denitrifiers, sulfate reducers, iron oxidizers and organotrophs. The Crenarchaeota include extremophiles – thermophiles, halophiles, acidophiles. The most recently recognized major divisions are the mesophilic ammonium-oxidizing Thaumarchaeota and the hyperthermophilic anaerobic Korarchaeota (e.g., Brochier-Armanet et al., 2008, 2011). The number of complete genomic sequences of archaea has increased from a few in 1997 to nearly 120 in 2010 (Brochier-Armanet et al., 2011); about 20 new genomes were sequenced in 2010 alone. Phylogenetic analyses such as fluorescence in-situ hybridization, Archaeal membrane

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Fig. 10. Chemical structures of monolayer and bilayer membranes of archaea and bacteria showing the basic glyceryl-ether and isoprenoid moieties of archaeal membranes (above the dashed line) and the glyceryl-ester and acyl moieties of bacterial membranes (below the dashed line).

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quantitative PCR, small-subunit (16S) rRNA gene sequencing and DNA libraries have documented the high diversity of archaea (Swan and Valentine, 2009) and demonstrate that archaea are ubiquitous in marine (e.g., Hugoni et al., 2013; Zhang et al., 2015a; Yilmaz et al., 2016), freshwater (e.g., Bomberg et al., 2008; Zhang et al., 2015b), and terrestrial (e.g., Jung et al., 2014; Shi et al., 2016; Rodrigues et al., 2016) habitats. Euryarchaeota typically dominate the ocean's shallow water column, with Thaumarchaeota more abundant below the euphotic zone (e.g., Beman et al., 2011; Zhang et al., 2015a) and in the deep sediment subsurface biosphere (e.g., Parkes et al., 2014; Lauer et al., 2016). Whereas some marine archaea are thermophiles or hyperthermophiles, many Thaumarchaeota must be psychrophilic and/or piezotolerant since their deep-sea habitat is cold. Few marine archaea have been cultured (e.g., Sørensen and Teske, 2006; Auguet et al., 2010; Yoshinaga et al., 2015) so their specific metabolic functions and biochemical makeup are poorly defined. What is known often derives from 16S rRNA gene sequences obtained from molecular surveys (e.g., Stahl and de la Torre, 2012) or the relatively few species that have been cultivated.

isoprenoidal glycerol dialkanol diethers (XX, GDDs) (Liu et al., 2012b), and so-called S-GDGTs (XXI) (Liu et al., 2016) have been reported in POM and sediments. Branched non-isoprenoid GDGTs with the 1,2-diO-alkyl-sn-glycerol configuration in soils and peats (e.g., Liu et al., 2010; Peterse et al., 2011) and anoxic marine waters (Liu et al., 2014) are possibly bacterial in origin. Analysis of intact polar lipids (IPLs) by HPLC-electrospray chemical ionization-MS (HPLC-ESI-MS) (e.g., Rütters et al., 2002; Sturt et al., 2004) has allowed routine identification of the monohexose (XXII), dihexose (XXIII) and phosphohexose (XXIV) polar head groups of intact archaeal polar lipids in addition to the alkyl side chains (e.g., Zhu et al., 2016; Sollai et al., 2015, 2019). Novel analytical approaches continue to add to our inventory and utility of archaeal lipids. For example, Becker et al. (2013) describe an ultra-high-performance liquid chromatography (UHPLC–APCI-qToFMS) separation of isomers of archaeal and bacterial membrane ether lipids, in particular GDGTs. Zhu et al. (2013, 2014) developed a RP-ESIMS (reversed phase liquid chromatography–electrospray ionizationmass spectrometry) protocol for the analysis of raw lipid extracts; they reported polar head groups (e.g., phosphatidylcholine, phosphatidylglycerol, phosphatidylinositol, hexose and dihexose) and alkyl moieties modified with different numbers of cycloalkyl moieties, hydroxyl and alkyl groups and double bonds. This method was subsequently used to identify a new class of unsaturated-GDGTs in marine sediments and water column particulate matter (Zhu et al., 2016). Wörmer et al. (2013, 2014) coupled laser desorption ionization and Fourier transform ion cyclotron mass spectrometry (LDI FTICR-MS) for direct, extraction-free analysis of caldarchaeol and crenarchaeol and used the ratio caldarchaeol:crenarchaeol to derive a high resolution (250 μm scale) record of the water column distribution of planktonic archaea preserved in sediments. Using these distributions, they were able to document export of archaeal IPLs to the sediments and the environmental conditions (stratification, light regime, temperature, redox) that affect both distribution and export. Chen et al. (2016) described a reverse phase liquid chromatography-electrospray ionizationmultiple reaction ion monitoring mass spectrometry (RP-LC-ESI-MRMMS) method that increases sensitivity for fingerprinting IPL-GDGTs. Radović et al. (2016) developed a method using atmospheric pressure photoionization in positive mode FTICR-MS for rapid screening of archaeal GDGTs and GDDs in recent marine sediments. A coupled high temperature flame-ionization gas chromatography/time-of-flight mass spectrometry (HTGC-FID/ToFMS) method has recently been described for analysis of archaeal and bacterial GDGTs in environmental samples (Lengger et al., 2018). Analytical capabilities for identifying assorted GDGTs often outpace our ability to characterize their sources. In many instances, GDGT sources in marine systems are known (see below), but in others, they are not. For example, in a study of GDGTs in suspended particles in the Black Sea and Cariaco Basin, Liu et al. (2014) observed that relative abundances of isoprenoid GDGTs changed little down the water column whereas abundances of several types of branched GDGTs increased in the anoxic zones (Fig. 11). The isoGDGTs are apparently produced by Thaumarchaea in both oxic and anoxic zones, but the branched-GDGTs may be produced only in anoxic waters. The specific microbial sources for branched-GDGTs are unknown. However, since branched-GDGTs also have terrestrial sources, lateral transport of branched-GDGTs from the adjacent continental margins into the deep anoxic zones cannot be completely ruled out. Determination of the configuration of the glycerol moieties (archaeal 2,3-sn-glycerol-1phosphate vs bacterial 1,2-sn-glycerol-3-phosphate) might be useful in distinguishing GDGT origins.

5.2. Unique membrane structures – how analytical advances allowed their discovery The organic biogeochemistry of archaeal lipids has been largely driven by advances in analytical capabilities (reviewed by Schouten et al., 2013). In a lipidomic perspective on the biochemical pathways of archaeal lipid biosynthesis, Pearson (2014) describes biosynthetic pathways in relation to metabolic profiling and gene sequencing for a large variety of geochemically relevant lipids in diverse organisms. The unique structures of archaeal lipids could not have been determined with the analytical techniques available in Max Blumer's time. Lipids in archaeal membranes are unique, comprised of a 2,3-sn-glycerol-1phosphate (G-1-P) backbone, ether linkages, and isoprenoid hydrocarbon chains (Fig. 10) (e.g., Koga and Morii, 2007). Bacterial and eukaryotic membrane lipids in contrast are characterized by a 1,2-snglycerol-3-phosphate (G-3-P) backbone that is ester-linked to fatty acid chains. The isoprenoid side chains, diglycerol dialkyl glycerol tetraethers (GDGT), are therefore characteristic of archaeal membranes. Acyclic isoprenoid hydrocarbons that might be derived from archaeal membranes [e.g., C20-2,6,10,14-tetramethylhexadecane (phytane, I in Appendices A and B), C25-2,6,10,15,19-pentamethyleicosane (PMI, II), C30-2,6,10,14,19,23-hexamethyltetracosane (squalane, III)] were first identified among free extractable hydrocarbons in marine sediments and particulate matter by gas chromatography-mass spectrometry (GC-MS) (e.g., Risatti et al., 1984; Wakeham, 1990). Protocols employing cleavage of ether lipids (using BBr3 or HI) followed by reduction (LiAlH4) to hydrocarbons allowed for determinations of C40biphytane (IV-VIII) backbones of GDGTs (e.g., IX-XIII; see also Schouten et al., 2013 and Rush and Sinninghe Damsté, 2017 and references cited below for additional structures of archaeal lipids), initially in Messel shale kerogen and petroleums (e.g., Chappe et al., 1979) and subsequently in a marine sponge (De Long et al., 1998) and POM and sediments (Hoefs et al., 1997; Wakeham et al., 2003). Direct analysis of GDGT core lipids (the isoprenoid skeletons minus the polar head groups) by HPLC-APCI-MS (high performance liquid chromatography-atmospheric pressure chemical ionization-mass spectrometry; Hopmans et al., 2000; Schouten et al., 2007) has revealed the complexity of GDGTs in nature. Acyclic (e.g., GDGT-0, IX) and cyclic isoprenoid GDGTs (GDGT-1-crenarchaeol, X-XIII), branched non-isoprenoid GDGTs (e.g., XIV, XV) (Liu et al., 2010), mono- and dihydroxyl GDGTs (e.g., XVI, XVII) (Huguet et al., 2013; Zhu et al., 2016; Liu et al., 2012a), H-GDGTs (XVIII) (Schouten et al., 2008), polyunsaturated GDGTs (XIX) (Zhu et al., 2016; Liu et al., 2016),

5.3. Role of archaea in geochemical cycles – evidence from archaeal lipids Archaea play fundamental roles in global cycles of carbon and

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Fig. 11. Relative abundances of isoprenoid (isoGDGTs) and various non-isoprenoid branched GDGTs [regular branched (B-GDGT) and so-called overly-branched (OB) and sparsely-branched (SBGDGTs)] in suspended particulate matter in the water columns of the Black Sea and Cariaco Basin. Iso-GDGTs are the most abundant compounds the oxic and anoxic zones, indicating their Thaumarchaeotal sources throughout the water column. Branched-GDGTs, on the other hand, increase in relative abundance in the anoxic zones and suggest in-situ production. However, the source organisms are as yet unidentified. Terrigenous inputs for some branched GDGT's cannot be ruled out if they are transported laterally to the deep anoxic zones. From Liu et al. (2014).

nitrogen, and to a lesser extent sulfur, much more than we can describe in this report (e.g., Teske and Sørensen, 2008; Offre et al., 2013). They employ energy metabolisms using both inorganic and organic electron donors and acceptors. Many Crenarchaeota, Euryarchaeota and Thaumarchaeota grow autotrophically (Berg et al., 2010), some are facultative autotrophs, others are obligate heterotrophs. Their metabolic pathways may be determined through a combination of organic biogeochemical and stable isotope analysis. Aerobic ammonium-oxidizing Thaumarchaeota are widely distributed in nature (e.g., Wuchter et al., 2006; Lam and Kuypers, 2011; Sintes et al., 2013). They are important nitrifiers in the ocean (and soils) and are a major source of GDGTs (Rush and Sinninghe Damsté, 2017). Recent results by Lincoln et al. (2014a) suggest planktonic euryarcheota may also be a GDGT source, although there is a lack of consensus regarding this conclusion (Schouten et al., 2014; Lincoln et al., 2014b). Crenarchaeol (XIII) is the most widely accepted biomarker for Thaumarchaeota based on the similarity between crenarchaeol concentrations (particularly as its intact form, hexose-phosphohexose (HPH)-crenarchaeol, XXVIII) and those of the genetic indicators, 16 s rDNA and amoA (the functional gene encoding the initial step in ammonium oxidation) (e.g., Pitcher et al., 2011a, 2011b, 2011c; Sollai et al., 2015, 2019). Thaumarchaeotal chemoautotrophy, fixing dissolved inorganic carbon (DIC), was indicated by stable isotope compositions of isoprenoid biphytane side-chains of GDGTs, including crenarchaeol, in seawater, particulate matter, and sediments (e.g, Hoefs et al., 1997; Wakeham et al., 2007; Pearson, 2010) where biphytane δ13C values are similar to δ13C values of DIC. Autotrophy was subsequently confirmed by stable isotope (13C and D) probing (SIP) experiments using archaea enriched from marine sediments (e.g., Wuchter

et al., 2003; Park et al., 2010; Pitcher et al., 2011c; Kellermann et al., 2012) and cultures (De La Torre et al., 2008; Könneke et al., 2012), and by natural radiocarbon analyses of GDGT in the deep ocean (e.g., Ingalls et al., 2006b; Shah et al., 2008; Hansman et al., 2009). Incubations using 13C-labeled bicarbonate with and without nitrification inhibitors show the strong coupling between autotrophy and nitrification (Pitcher et al., 2011b). Archaea also function as obligate heterotrophs, using organic carbon for growth. The δ13C values for sedimentary archaeal GDGTs overlap with those of sedimentary organic carbon (Biddle et al., 2006; Zhuang et al., 2016), consistent with heterotrophy. Culturable heterotrophic archaea are extremophiles belonging to the Euryarchaeota and the Crenarchaeota (Offre et al., 2013). Takano et al. (2010) showed that GDGT's glycerol backbone is synthesized de novo in sedimentary heterotrophic archaea, whereas the isoprenoid chains originate from recycled organic carbon. Lin et al. (2013) tracked 13C-labeled glucose and 13 C-labeled biomass from a cyanobacterium into diglycosyl-glycerol dibiphytanyl glycerol tetraethers (2G-GDGTs) in marine sediments. Isoprenoid glycerol dialkanol diethers (GDDs) in marine sediments are biosynthetic intermediates or degradation products associated with GDGTs, consistent with benthic archaea recycling sedimentary lipids heterotrophically, but with low activity (Lin et al., 2013; Lengger et al., 2014). SIP using D2O in anoxic sediments showed heterotrophic incorporation of D into archaeal biphytanes (Wegener et al., 2016). Euryarchaeotes and crenarchaeotes inhabiting hydrothermal vent (or hot spring) areas may function as lithoautotrophs by either oxidizing H2S or metal sulfides or reducing sulfate or thiosulfate, or as chemoheterotrophs by using sulfur to oxidize simple reduced carbon compounds (Offre et al., 2013). The GDGTs of these vent organisms tend to

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be dominated by GDGT-0 although in many cases GDGT 0–4 are found (e,g., Koga, 2012; Schouten et al., 2013; Boyd et al., 2013; Lincoln et al., 2013). Euryarchaeota acting both as methanogens and methanotrophs are central to the biogeochemistry of methane, and as all things archaeal, the phylogeny and specific mechanisms of methanogenesis/methanotrophy are diverse and complex (Caldwell et al., 2008; Valentine, 2011; Offre et al., 2013). Two primary pathways, acetoclastic methanogenesis and CO2 reduction, produce most biogenic methane, with varying isotopic fractionations relative to carbon source (Pearson, 2010). Lipids common to methanogens (note that some are also present in methanotrophs) include, for example, the core diether archaeol (XXV, 2,3-diO-phytanyl-sn-glyceryl), hydroxyarchaeol (XXVI, 2-O-3-hydroxyphytanyl-phytanylglyceryl) and its free hydrocarbon derivatives phytane, PMI and squalane, and GDGT-0 (Risatti et al., 1984; Koga and Morii, 2007; Schouten et al., 2013). They are generally enriched in 13C (e.g., Hinrichs et al., 2003; Wakeham et al., 2003, 2007) in pelagic water columns and sediments depending on δ13C of the carbon source, reflecting either autotrophy or heterotrophy (Pearson, 2010). Zhuang et al. (2016) used 16S rRNA gene sequences, distributions and isotopic compositions of methanogen lipids (intact polar and polyunsaturated hydroxyarchaeols with the glycosidic and phosphatidic headgroups common to methanogens) and stable isotope probing in Orca Basin sediments to conclude that extreme depletions of δ13Clipid were due to methylotrophic methanogenesis using primarily methanol as the major methanogenic pathway. Anaerobic oxidation of methane (AOM) is mediated by three distinct clades of Euryarchaeota (anaerobic methanotrophic archaea, ANME-1, −2, −3) acting in concert with bacterial partners, usually sulfate reducers but denitrifiers as well (Raghoebarsing et al., 2006; Knittel and Boetius, 2009). Lipids of methanotrophic archaea in marine particulate matter and sediments can be similar to lipids of methanogens, including crocetane (XXVII, 2,6,11,15-tetramethylhexadecane) PMI, squalane and archaeol and hydroxyarchaeol and various GDGTs. ANME-2 archaea tend to have high abundances of crocetane and hydroxyarchaeol but low abundances of archaeol and GDGTs, with a high ratio of archaeol:hydroxyarchaeol being indicative of ANME-1 archaea (Blumenberg et al., 2004; Niemann and Elvert, 2008). In the anoxic Black Sea, ANME-1 archaea appear responsible for AOM below ~500 m depth whereas ANME-2 may dominate above 600 m (Schubert et al., 2006; Wakeham et al., 2007). Rossel et al. (2011), among others, have reported that AMNE-1 methanotrophic archaea preferentially synthesize GDGTs with glycosidic and phosphoglycerol polar head groups, but ANME-2 and -3 clusters produce diethers dominated by archaeol-based lipids (see also Lin et al., 2013; Kellermann et al., 2016). Lipids of methanotrophic archaea are substantially depleted in 13C (Hinrichs et al., 2003; Wakeham et al., 2003; Schubotz et al., 2011). Our understanding of the biogeochemistry of both methanogenic and methanotrophic archaea continues to evolve. For example, Yanagawa et al. (2011) observed a niche separation of ANME-1 and ANME-2 in sediments of a gas hydrate field in the Sea of Japan; ANME1 dominate AOM in sulfate-poor sediments whereas ANME-2 prefer sediments with higher sulfate concentrations. Lloyd et al. (2011) and more recently Timmers et al. (2017) describe the reversibility of the methanogenesis pathway commonly used by ANME such that methanogens oxidize methane. Evidence of the physiological diversity of ANME-1 and ANME-2 and co-occurrence of methanogenesis and methanotrophy was investigated via stable isotope (13C) experiments using a variety of substrates (Bertram et al., 2013). We have learned much about the archaea using organic geochemical biomarkers, although there is clearly even more to learn.

6. Isotopes in marine biogeochemistry: old toolbox, new tools 6.1. Carbon Oceanic distributions of isotopes of carbon, the stable isotopes (12C and 13C) and radiocarbon (14C), reflect the multiplicity of pathways of carbon assimilation by living organisms and the mass transfer of OC between reservoirs. Natural abundance stable carbon isotopes (δ13C) give insight into carbon source, carbon assimilation pathways and carbon flow in marine ecosystems and food webs (Ohkouchi et al., 2015). Natural-abundance radiocarbon analyses (Δ14COC or fraction modern, Fm) add the dimension of “age” to the character of organic matter and help define the residence time and redistribution of OC. The 13 C compositions of biomass and bulk particulate and sedimentary OC continue to be used to gain insight into OM sources, carbon fixation pathways, biogeochemical alteration and transport mechanisms, and paleoenvironmental conditions in aquatic systems (e.g., Williams et al., 2014; Oczkowski et al., 2016; Havig et al., 2018). However, the last two decades have seen the advent of isotopic analysis of individual compounds. Compound-specific 13C isotope analyses (CSIA) and compound-specific radiocarbon analyses (CSRA) can combine the advantages of source-specificity of biomarkers, δ13Cbiomarker-derived information on carbon flow, and Δ14Cbiomarkerderived ages (Close, 2019). CSIA of 13C in environmental materials and SIP experimental manipulations are now used in wide-ranging biogeochemical studies e.g., phototrophic carbon fixation of inorganic carbon into biomass, heterotrophic alteration of organic substances, and employing δ13Cbiomarker-values in paleoceanographic reconstructions. Stable carbon isotope systematics have been reviewed by Freeman (2001), Pearson (2010) and Ohkouchi et al. (2015). One of the most widely used applications of 13C-CSIA is the long-chain alkenonebased proxy for paleo-CO2 reconstruction (reviewed by Pagani, 2014). This is possible because alkenones are biosynthesized by only a few algae, primarily Emiliania huxleyi and Gephyrocapsa oceanica, and the systematics of photosynthetic carbon isotope fractionation are well known. 13C SIP of polar lipid fatty acids (PLFAs, phospholipid fatty acids) has been used to explore microbial heterotrophy and methanotrophy (reviewed by Evershed et al., 2006). Wegener et al. (2016) reviewed applications using bioreactive inorganic and organic substrates with single (13C) and dual (13C and 1H) isotope labels in SIP of membrane lipids to track the activity and function of microorganisms. Although not yet widely used in marine applications, two major advances have greatly enhanced the utility of SIP. The incorporation of labeled (13C or 15N) substrates into biomass and subsequent recovery of labeled DNA has been combined with metagenomics analysis; this has allowed unprecedented recognition of environmental microbial community diversity (Friedrich, 2006; Chen and Murrell, 2010). RNA-SIP combines the sequence based phylogenetic resolution of DNA-SIP with high copy biomarkers, small subunit ribosomal RNA molecules, for culture-independent functional phylogeny (Whiteley et al., 2006; Dumont et al., 2011). Understanding the methane cycle is one area where 13C-CSIA has proven to be invaluable. Biomass produced by CO2-reducing methanogenic euryarchaeota may be depleted in 13C relative to source CO2. CSIA of the biphytane derived from GDGT-0 biosynthesized by acetoclastic methanogenic archaea typically has δ13C values in the −25 to −20‰ range in the water column and sediments whereas CO2-reducing methanogens produce archaeol, hydroxyarchaeol and GDGT-1 with δ13C values between ~ −50 and − 35‰ (e.g., Niemann and Elvert, 2008; Naeher et al., 2014). Archaeol, hydroxyarchaeol and biphytanes derived from GDGT-2 and GDGT-3 of archaea mediating the anaerobic oxidation of methane (ANME) are highly diagnostic since the methane itself can have δ13C values down to −120‰ and the GDGTs

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may have δ13C values down to −50‰. Polar lipid fatty acids and nonisoprenoidal mono and dialkyl glycerol ethers (MAGEs, DAGEs) of sulfate-reducing bacteria (SRB) in systems where the archaea and SRB cooperate syntrophically are somewhat less depleted than biphytanes, indicating transfer of carbon from the archaea (Zhang et al., 2002; Niemann and Elvert, 2008; Naeher et al., 2014). Differences in isotope fractionation in archaeal and SRB lipids allow for distinguishing ANME1 from ANME-2 and ANME-3 (Niemann and Elvert, 2008). In an example of single/dual labeling SIP, Wegener et al. (2012) conducted incubations using 13C-CH4, 13C-DIC and D2O to track the incorporation of label into bacterial PLFA and into archaeol-derived phytane and GDGT-derived biphytanes during bacterial-archaeal AOM syntrophy. These experiments confirm that methane plays a central role as an energy source, but a minor role as a carbon source, in AOM communities. Archaea are also key players in the marine nitrogen cycle. Biomarkers for autotrophic ammonia-oxidizing archaea (AOA) have been the subject of numerous 13C-CSIA investigations. The cyclohexyl biphytane of crenarchaeol has δ13C values in the Black Sea and Cariaco Basin POM that are similar to δ13C values of other autotrophic lipids (Wakeham et al., 2003, 2004). Following Wuchter et al.'s (2003) demonstration that “marine Crenarchaeota” (now Thaumarchaeota) in North Sea surface water incorporate 13C-labeled HCO3– autotrophically into biphytanes, Park et al. (2010) confirmed autotrophy by the ammonia-oxidizing archaeon, Candidatus Nitrosopumilus maritimus, enriched from continental margin sediments. Pitcher et al. (2011c) investigated the uptake of 13C-labeled HCO3−into the cyclohexyl biphytane of crenarchaeol by Thaumarchaeota in suspended particulate matter. Incorporation of 13C varied in parallel with abundance of AOA genes and intact polar lipid-derived GDGTs, all of which were highest in winter. Uptake of 13C was markedly reduced when nitrification inhibitors were applied to incubations. In cultivated N. maritimus, Könneke et al. (2012) reported low δ13C values for bulk biomass and biphytanes compared to HCO3−. Interestingly, the monosaccharides (e.g., mannose, glucose, and inositol) comprising the glycosidic headgroups of the GDGTs of N. maritimus are enriched relative to biomass and biphytanes, suggesting an alternate biosynthetic pathway. A competing process by which ammonium is consumed is its anaerobic oxidation by anammox bacteria. There are few reports of δ13C values for the linearly concatenated cyclobutane ladderane lipids of anammox bacteria in natural samples, but Schouten et al. (2004) measured δ13C values for the [3]-ladderane FAME in Black Sea suspended particulate matter (SPM). In the same study, two anammox bacteria, Candidatus Brocadia anammoxidans and Candidatus Scalindua sorokini (this isolated from the Black Sea), were found to use CO2 as their carbon source, with isotope fractionation of 45–49‰ between ladderane lipids and CO2. Rattray et al. (2009) investigated ladderane lipid biosynthesis by tracking uptake of 2-13C-labeled acetate in a culture of Candidatus Brocadia fulgida. Using PLFA analysis and high-field 13 C nuclear magnetic resonance spectroscopy on isolated PLFAs these authors found that ring moieties of ladderane fatty acids are produced via a different route than straight-chain fatty acids C16:0 and i-C16:0; labeling of the cyclobutane rings of ladderane fatty acids suggested a biosynthetic pathway that does not incorporate acetate. The bulk of 13C-CSIA studies of archaeal lipids to date have required chemolysis to release the C40 biphytanes from intact GDGTs before using GC-IRMS. Pearson et al. (2016) have developed a method for measuring δ13C values of intact GDGTs. GDGTs from a series of Recent sediments were purified by orthogonal dimensions of HPLC followed by spooling wire microcombustion-isotope ratio mass spectrometry (SWiM-IRMS). The δ13CGDGT values were heavy compared to other marine lipids but are inconsistent with expectations from δ13CGDGT values in the overlying water columns. This suggests multiple GDGT sources (see also Shah et al., 2008) and, notably in the case of

Fig. 12. Stable isotope probing experiments with H13CO3− show that thiosulfate-utilizing chemoautotrophs are important in the oxygen deficient waters of the Cariaco Basin. Among the sulfur species tested, thiosulfate enhances the assimilation of 13C, increased polar lipid fatty acid concentrations, and increases incorporation of 13C into specific fatty acids and mid-chain methoxylated fatty acids, which are suggested to be indicators of chemoautotrophy. a) Chemoautotrophic carbon fixation rates, b) abundances and 13C label uptake for major fatty acids and combined 9/10-methoxy-C16 fatty acids for control with H13CO3−2 but without sulfur amendments, and c) the same with S2O3−2 added. Fatty acid nomenclature (e.g., C18:1ω7) is the number of carbons: number of double bonds followed by location of the double bond. Adapted from Wakeham et al. (2010).

crenarchaeol, autotrophy by Thaumarchaeota cannot be its sole source. This same SWiM-IRMS technique has been applied as a proof-of-concept to fingerprint whole protein δ13C values for two photoautotrophs, Allochromatium vinosum DSM180 and Synechocystis sp. PCC6803, grown on CO2 (Mohr et al., 2014). In addition to providing source information, 13C-CSIA measurements aid in unravelling bacterial processes. McCarthy et al. (2004) used 13C-CSIA of amino acids to evaluate heterotrophic processing in sinking POM and DOM from the equatorial Pacific. There were clear differences between the two carbon pools: sinking POM contained the signature of bacterial alteration and resynthesis of “new” OM; DOM better retained a photoautotrophic character over time and into the deep ocean. In the Cariaco Basin, sulfur-oxidizing bacteria are

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important chemoautotrophs within the chemocline. Depleted δ13C values of PLFAs C16:ω7c, C16:ω7t and 9-methoxy-C16:0 + 10-methoxyC16:0 in suspended POM, and SIP experiments with 13C-HCO3– showing label uptake in the presence of thiosulfate (but not sulfite or elemental S), were interpreted as resulting from sulfur-metabolizing chemoautotrophs (Fig. 12) (Wakeham et al., 2010, 2012). As noted above, Close et al. (2013, 2014) measured δ13C of phospholipid and glycolipid fatty acids associated with size-fractionated POM in the water column of the Eastern Tropical North Pacific (ETNP). The δ13C values of fatty acids from membrane polar lipids biosynthesized by surface water organisms were overprinted by lipids produced by bacterial heterotrophs and chemoautotrophs during transit down the water column. The submicron (< 2 μm) component of exported POM plays a more significant role in the global biological pump that previously thought, with the bacterial signature thus obtained from CSIA reinforcing interpretations of particle dynamics based on δ13C values of bulk OC (e.g., Williams et al., 2014, for the ETNP). Accelerator mass spectrometry (AMS) has revolutionized the utility of natural abundance radiocarbon for studying biogeochemical cycling in the dissolved and particulate carbon of the ocean (McNichol and Aluwihare, 2007; Druffel et al., 2016). Compound-specific radiocarbon analysis (CSRA), usually requiring preparative GC or HPLC followed by off-line AMS, is used to constrain relative inputs from marine biomass, terrigenous vascular-plant and relict-kerogen sources (Ingalls and Pearson, 2005; Mollenhauer and Rethemeyer, 2009). For example, 14C and 13C compositions of lipid biomarkers (fatty acids, alkanes, alkenones, fatty alcohols, and sterols) were measured in sediment trapsurface sediment pairs from the Arabian Sea, the Black Sea and the Southern Ocean (Wakeham and McNichol, 2014). Isotope mass balances showed that autochthonous marine biomass dominated (60–100% of OC) the sediment trap material, with smaller contributions from allochthonous terrigenous (3–8%) and relict (4–16%) sources. Sediments contained lower proportions (66–90%) of marinederived OC but higher amounts of terrigenous (3–17%) and relict OC (7–13%). Thus, even in these open ocean settings where marine biomass overwhelmingly dominates, pre-aged terrigenous and relict OC accumulates in sediments. Long-chain PLFA from sediments from the Southern California Bight have younger 14C ages than long-chain alkanes, consistent with the PLFAs being derived from bacterial uptake of surface-produced OM whereas the alkanes reflected older terrigenous OM (Druffel et al., 2010). Kusch et al. (2016) measured 14C of alkenones, GDGTs, and short-chain fatty acids in Black Sea sediments and found little contribution from pre-aged components, not surprisingly since the sources are primarily marine and unaffected by lateral transport. Autotrophy by deep water or sediment Thaumarchaeota is indicated by Δ14C values of GDGT (Ingalls et al., 2006b; Shah et al., 2008) and DNA (Hansman et al., 2009) that are commensurate with measured Δ 14C values for DIC, yet significantly different from surface water-derived algal lipids. Considerable work has evaluated the mobilization and export of terrigenous OC to the ocean, especially in coastal environments (Bianchi, 2011; Blair and Aller, 2012; Galy et al., 2015), with CSRA being an essential tool. Vascular plant biomarkers (long chain n-alkanes and fatty acids) in Black Sea sediments were older than the more labile, algal lipids (short-chain fatty acids), and that 14C ages of the terrigenous component increased with increasing distance from river mouths (Kusch et al., 2010). Tao et al. (2015) determined abundances and 14C and 13C compositions of bulk OM and vascular plant long-chain alkanes, fatty acids and lignin in suspended POM of the Yellow River, and subsequently Tao et al. (2016) measured the same parameters in sediments from the Bohai Sea and Yellow Sea. There, a coupled carbon-isotope model was used to partition OC into three components that reflect different origins (modern OC, ancient sedimentary rocks and fossil fuel) and differential mobilization and dispersion processes. Feng et al. (2015) used CSRA of vascular plant

Fig. 13. Schematic of coastal marine food web analysis using CSIA-AA shows the relative constancy of δ15N values of “source” amino acids (i.e., δ15Nphenylalanine) but enrichment in δ15N values of “trophic” amino acids (i.e., δ15Nglutamic acid) over four trophic transfers (indicated by dashed trophoclines) from microalgae to moray eel. Adapted from Chikaraishi et al. (2014).

biomarkers (plant wax, lipids, suberin, cutin, lignin and hydroxyl phenols) in Arctic sediments and found that radiocarbon content of terrestrial biomarkers was highly dependent on specific OM sources and degradation status. Although most CSRA analyses have been for lipids and are conducted off-line by AMS, off-line HPLC-AMS methods for purification of lignin phenols (Ingalls et al., 2010) and individual amino acids (Bour et al., 2016) have recently been described and hopefully will be more available in the future. 6.2. Nitrogen Stable nitrogen isotope analysis provides information about the organic N source and food web structure in aquatic ecosystems (reviewed by Ohkouchi et al., 2015). Analysis of δ15N in bulk organic matter, although useful for estimating trophic positions of organisms in food webs, is limited because, like carbon isotopes, it integrates across the entire N-containing inventory of the sample. Because most organic nitrogen compounds are not easily extracted from marine samples, only δ15N analysis of amino acids (CSIA-AA) is now a routine tool in the isotope tool box and has been applied to studies of terrestrial and aquatic food webs, animal migration patterns, ancient human diet, environmental variability, and sources and degradation of marine organic matter (Ohkouchi et al., 2017). The CSIA-AA approach relies on the differential isotope fraction of individual AAs during their specific biochemical – anabolic and catabolic – reactions during autotrophy or heterotrophy. The basis for using CSIA-AA in foodweb investigations relies on the differentiation between “source” AAs and “trophic” AAs (Popp et al., 2007; Chikaraishi et al., 2009; reviews by McMahon and McCarthy, 2016; Ohkouchi et al., 2017). Metabolism of trophic amino acids (e.g., alanine, valine and glutamic acid) by heterotrophs involves enzymatic

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cleavage of the α‑carbon‑nitrogen bond (transamination), which strongly fractionates the N isotopes relative to a δ15NAA baseline in the diet. Metabolism of source AAs (e.g., phenylalanine and methionine) does not involve this cleavage so these AAs are only slightly fractionated relative to the diet during heterotrophy. Thus, the trophic position of organisms along a trophic chain is revealed by comparing δ15NAA values of source AAs vs trophic AAs. For example, Chikaraishi et al. (2014) measured δ15NAA of 39 species in coastal marine (and terrestrial) food webs (Fig. 13). A cross-plot of marine values of δ15Nphenylalanine vs δ15Nglutamic acid ranging from primary producer (macroalgae) through strict herbivores, omnivores to top carnivore (moray eel in this example) reveals that, in addition the usually recognized effects of herbivory on isotope compositions, there is a significant degree of trophic omnivory among what are usually considered carnivorous species. The authors point out that a more complete understanding of the complex networks of multiple food chains will require combining trophic position estimates (using δ15NAA) with accurate food source estimates (using δ13CAA). Décima et al. (2017) further extended the δ15NAA food web analysis to heterotrophic protists by examining the δ15N trophic fractionation of alanine in chemostat cultures containing algal diets and dinoflagellate or ciliate grazers. Several factors influence the trophic transfer scenario. The assumption that δ15NAA values of the original primary diet is uniform is not always the case. In a broad survey of δ15NAA values in prokaryotic and eukaryotic phytoplankton, McCarthy et al. (2013) found that although all of the studied algae revealed a similar δ15NAA pattern and fit the “source” vs “trophic” distinction, there were significant variations between the two phytoplankton groups. Specifically, fractionations for glutamine+glutamic acid were different from those of other proteinaceous AAs (e.g., leucine, isoleucine, valine, etc), perhaps because glutamine+glutamic acid serves as a biosynthetic precursor for the other AAs. This observation led to the suggestion that δ15NAA patterns might have diagnostic utility for distinguishing prokaryotic from eukaryotic algal groups. More recently, Hetherington et al. (2017) examined a pelagic food web involving four trophic groups (euphausiids, myctophids, squids, and tunas) along a north-south transect in the eastern tropical Pacific with known primary productivity and nitrogen cycling dynamics. Nitrogen concentrations (and denitrification) were a key control on δ15NAA (and δ15Nbulk) at the base of the food web and these effects propagated up the food chain; productivity appeared less important. This observation demonstrated the need for combining the δ15NAA data of food web studies with broader ecosystem analyses. Hannides et al. (2013) compared δ15NAA (and δ13CAA) values of midwater zooplankton and SPM in the North Pacific gyre (see also Choy et al., 2015 for epipelagic and mesopelagic fish). Suspended particles were strongly enriched as a function of depth, which was attributed to heterotrophic degradation rather than a source change or trophic transfer at depth. On the other hand, isotopic fractionation consistent with trophic transfer was observed in the zooplankton. Because δ15Nphenylalanine values were significantly different in zooplankton vs SPM, it was concluded that the dominant OM source for midwater zooplankton was not SPM but rather surface-derived, either via sinking particles, carnivory, or vertically migrating zooplankton feeding at the surface at night. The role of SPM as a nutrition source for the mesopelagic and bathypelagic ocean was further addressed by Gloeckler et al. (2018). CSIA-AA analyses of micronekton (several fishes and an octapod) showing increased fractionation with depth indicated that SPM becomes progressively more important in the deep-water food web. An important corollary of this report is that SPM might constitute an important component of the “carbon deficit” (in which sinking POM flux from surface waters does not meet deep water carbon demand) in the ocean's interior. How important is microbial processing in affecting δ15N values in

marine organic nitrogen (ON)? In an early application of CSIA-AA, McCarthy et al. (2007) explored relationships between source and processing of non-living OM, comparing δ15NAA in plankton, PON and HMW-DON. In addition to supporting the “source-trophic” distinction of AAs during zooplankton heterotrophy, δ15NAA gives information about bacterial heterotrophic reworking of POM. Isotopic signatures preserved in detritus might be useful in distinguishing microbial from eukaryotic heterotrophy. And finally, there were distinct differences between particulate and dissolved proteinaceous material. Zooplankton heterotrophy, involving 1.5–2 trophic transfers at depth, appeared to be the primary source of sinking proteinaceous material, including repackaging and aggregation as discussed above. Proteinaceous material in DON was more difficult to source, but in general seemed to involve less reworking than for PON. Yamaguchi and McCarthy (2018), however, have updated this paradigm using a CSIA-AA technique with improved precision. In their study, δ15NAA signatures of HMW-DON from both surface and mesopelagic depths showed heterotrophic bacterial sources, with the deeper DON more heavily altered. Production of HMW-DON appeared to be related to subsurface nitrate concentrations whereas PON production was more closely related to N fixation and recycling within the mixed layer. Values of δ15NAA also suggested that some fraction of mesopelagic HMW-DON might derive, again via bacterial degradation/alteration, from suspended PON. In laboratory experiments, Yamaguchi et al. (2017) examined the effects of growth substrate on microbial processing of OM by five cultured heterotrophic bacteria and several chemolithotrophic organisms (one fungus, one bacterium, and three archaea). When grown on ammonium, δ15N values in the microbes were similar to values typical for algae. When grown on free amino acids, isotope fractionations in the microbes were those expected for trophic transfer between diet and consumer (e.g., ~ − 8‰ for phenylalanine vs ~ − 0.1‰ for glutamic acid). These results indicated that δ15N patterns in natural samples might be useful for distinguishing between microbial de novo synthesis of AAs from inorganic N vs. assimilation of ON (amino acids) from the environment. 6.3. Hydrogen Environmental water is the primary source of hydrogen for biosynthesis in photoautotrophic organisms (reviewed by Sachse et al., 2012; Sessions, 2016). Organic matter thus produced is depleted in deuterium, the heavy isotope of hydrogen (2H or D) compared to water. The hydrogen isotope composition (D/H ratio or δD value) of water reflects a balance between isotope fractionation during evaporation, which produces water vapor that is depleted in D, and condensation of water vapor (as rainfall) that is enriched in D. The δD values of water used in biosynthesis thus vary depending on the hydrological cycle (precipitation amount, air mass trajectories, evaporation). However, because organic matter is a complex mixture of compounds that record diverse photosynthetic pathways and environmental factors, deriving environmental and biogeochemical information from δD values of bulk organic matter is problematic. Compound-specific isotope ratio mass spectrometry of δD values of lipid biomarkers (e.g., Sessions et al., 1999) has helped reduce this complexity and provided information about geochemistry and trophic structure in modern and ancient ecosystems. D/H ratios of lipids reflect the heterogeneity of biosynthetic pathways and intracellular isotope fractionations available to photosynthetic plants (Sachse et al., 2012). Temperature affects rates of evaporation and evapotranspiration and thus δD values of environmental water; rates of respiration and photosynthesis affect δD values of intracellular water. Isotope fractionation in alkenones of cultured haptophyte algae depends, in addition to the δD of water, on temperature, salinity and growth rate (Schouten et al., 2006). D/H ratios (and concentrations) of lipids in cultured algae

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varied among both species and the specific lipid measured, suggesting that other variables in addition to δD values of water, such as nutrients, light and temperature, might also affect D/H fractionation during lipid biosynthesis (Zhang and Sachs, 2007; Zhang et al., 2009b). D/H fractionation during isoprenoid (branched) lipid synthesis is affected by nitrogen limitation, but D/H fractionation during acetogenic (linear) lipid synthesis is not. Measurements of δD values of lipids in hypersaline ponds of Christmas Island, the tropical Pacific Ocean (Sachse and Sachs, 2008), the Chesapeake Bay (Sachs and Schwab, 2011), and in saline and hypersaline lakes (Nelson and Sachs, 2014a, 2014b) show D/ H fractionation to be also affected by salinity. Laboratory investigations confirm the salinity effect (Maloney et al., 2016; Sachs et al., 2016). Salinity appears to affect δD values of intracellular water. Growth phase affects the response to salinity of D/H fractionation (Chivall et al., 2014), and D/H fractionation of some lipids responds to irradiance (Sachs et al., 2017), possibly by affecting the metabolic pathway by which specific lipids are biosynthesized. Biosynthesis of microbial lipids incurs large variations in H isotope fractionation that suggests that lipids are linked to energy metabolism (Sessions et al., 2002). In cultures of various eukaryotes and bacteria, Zhang et al. (2009a) found strong D-enrichment of lipids in aerobic heterotrophs, strong D-depletion in chemoautotrophs, and intermediate D-depletion by photoautotrophs. This large range in fractionations was hypothesized to result not from isotope effects within the biosynthetic pathway but rather D/H ratios of NADH/NADPH used for biosynthesis. Heinzelmann et al. (2015) measured fatty acid δD values in different cultivated microorganisms. Fatty acids produced by heterotrophs were enriched in D to their growth water, whereas photoautotrophs were depleted in D relative to water, and chemoautotrophs were still further depleted. Dawson et al. (2015) and Osburn et al. (2016) expanded the range of investigated microorganisms by measuring D/H fractionations of lipids in nitrate-respiring and sulfate-reducing bacteria using a range of growth substrates. There were significant differences in isotope fraction patterns between nitrate-reducing and sulfate-reducing organisms. Kellermann et al. (2016) conducted SIP experiments using D2O to show that phosphatidyl glycerol archaeol is a key intact membrane lipid in actively growing anaerobic methanotrophic archaea (ANME-1) whereas intact GDGTs were more important in AMNE-1 at stationary growth. The SIP approach of Wegener et al. (2016) mentioned above used dual stable isotope labeling (D2O and 13C-DIC) by measuring D/H and 13C/12C fractionations into polar lipid fatty acids and GDGT-derived biphytanes in anaerobic methanotrophic communities. Lipid δD values also aid in identifying and apportioning inputs of lipids to sediments (parallel with δ13C values). For example, Chikaraishi et al. (2005) used dual δ13C-δD compositions of sterols to estimate proportions of algal vs land plants in river sediments. Jones et al. (2008) measured δD values of individual fatty acids from SPM and surface sediments in the Santa Barbara and Santa Monica Basins and Gulf of Santa Catalina off the coast of southern California. In SPM, D depletion increased with increasing chain length, possibly reflecting reduced dissolution or degradation of higher carbon number homologs. Two fatty acids of likely bacterial origin (iso-15:0 and n-15:0) were strongly enriched relative to other fatty acids (e.g., 16:0 and 18:0), suggesting unknown fractionation during bacterial biosynthesis. Li et al. (2009) investigated δD values of lipids in Santa Barbara Basin sediments, including n-alkanes, n-alkanols, alkenols, short and longchain fatty acids, linear isoprenoids, sterols and hopanoids. n-Alkyl lipids of marine origin were generally depleted in D relative to plant leaf waxes. Bacterial fatty acids, however, were enriched compared to even numbered algal counterparts, suggesting in-situ biosynthesis by unknown, possibly anaerobic, organisms. Empirical relationships between D/H fractionations of lipids

biosynthesized by photoautotrophs and environmental water can be used as proxies for paleohydrology (Sachse et al., 2012). The δD values of lipids of aquatic organisms and vascular plant leaf waxes in sediments provide a means of inferring changes in rainfall, runoff, salinity, and by extension, climate. Long-chain alkenone δD values have been used to reconstruct surface-water freshening and salinity changes in the eastern Mediterranean Sea during the last interglacial (~120 kya; van der Meer et al., 2007) and δD values of dinosterol in Black Sea sediments have been used to infer variations in freshwater input and subsequent salinity variations during the late Holocene (van der Meer et al., 2008). Hydrologic changes over the past 27,000 years in the Pacific Ocean, in particular the position of the Intertropical Convergence Zone in the tropical Pacific and precipitation patterns in equatorial south America, have been reconstructed using δD values of alkenones from a sediment core from the Panama Basin (Pahnke et al., 2007). Sachs and coworkers (Sachs et al., 2009; Smittenberg et al., 2011; Atwood and Sachs, 2014) used δD values of environmental water and lipid biomarkers in sediments from islands in Micronesia and the Galapagos to reconstruct rainfall patterns over various time scales that reflect changes in frequency and intensity of El Niño/Southern Oscillation events and changes in the position of the Intertropical Convergence Zone. Although regulated by incompletely understood physiological and climate factors, leaf wax lipid (usually n-alkanes and nalkanoic acids) δD values preserved in sediments can provide (qualitative) information about changes in terrestrial hydrology. For example, the 20,000-year record of n-C29 alkane δD values recovered from a marine sediment core from the Congo Fan was used to infer wetter (more negative δD values) or drier (more positive δD values) conditions associated with changes in Atlantic Ocean meridional temperature gradients, i.e., varying monsoon rainfall in central Africa due to variations in the strength of southerly trade winds (Schefuß et al., 2005). Supporting these results, Tierney et al. (2008) reported a similar record for n-C28 alkanoic acid δD values in a core from Lake Tanganyka, which indicated a role for Indian Ocean sea-surface temperature on rainfall patterns in the Congo Basin over the past 60,000 years. A survey of marine sediments along a transect off southwest Africa further tested the leaf wax n-alkane proxy for past precipitation, showing that in addition to continental precipitation, the resulting plant type distributions were important determinants of sediment n-alkane δD values (Vogts et al., 2016). Although the focus on D/H biogeochemistry has been on lipids of photosynthesizing organisms, protein biosynthesis also involves fixation of H from water through enzyme catalyzed reductions through NAD(P)H. Fogel et al. (2016) tracked D/H fractionation during incorporation into individual amino acids (released by hydrolysis of protein for analysis) in cultured Escherichia coli and found that δD values of nonessential amino acids varied linearly with δD of growth water whereas δD values of essential amino acids were identical to those of the diet. Analytical constraints, however, have generally precluded extensive investigations of D/H fractionations during protein biosynthesis, i.e., the conventional GC-IRMS used for lipids is not amenable to protein analysis. Fischer et al. (2013) recently described a combined metabolomics and proteomic analysis scheme based on electrospray mass spectrometry for investigating D/H fractionation in proteins and phospholipids in a mixed chemoautotrophic, acidophilic biofilm reactor. Lipids were isotopically light compared to growth water but proteins were still further depleted. Isotope fractionation patterns in proteins differed between coexisting microorganisms, suggesting variations in the specific biosynthetic pathways used, thereby providing an additional tool for studying metabolic characteristics of living organisms. This technique has yet to be applied to marine systems.

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6.4. Sulfur

values of that were substantially lighter isotopically than pore water sulfide and extractable and residual OM but similar to co-existing pyrite. The likely explanation for the observed δ34S values for organic sulfur compounds is the normal kinetic isotope effect of irreversible addition of HS− to form a CeS bond; however, the offset between the organic sulfur compounds and extractable and residual OM suggests a second sulfurization pathway that has yet to be elucidated. Raven et al. (2016) later used δ34S values of the aforementioned C20 highly branched thiophene and an unidentified compound along with unextractable “protokerogen” in sediment trap material from the Cariaco Basin to suggest that the abiogenic sulfurization of lipids associated with particulate matter occurs on time scales of days and that the Ssource may be polysulfides.

Sulfur plays a key role in the redox balance of the Earth, with the major aqueous reservoir being the oceans (Sievert et al., 2007). Organic sulfur compounds are important products of myriad biogeochemical processes. Two processes affect sulfur isotope fractionation. Assimilatory sulfate reduction (direct uptake of sulfate during biosynthesis into biomass) dominates in oxygenated waters and is accompanied by small 34S/32S isotope fractions of 1–3‰ relative to SO42− (Tcherkez and Tea, 2013). Anaerobic dissimilatory sulfate reduction is the main sulfur metabolism process on Earth and is characterized by large isotope fractionations, e.g., up to 72‰ relative to H2S (Bradley et al., 2016; Weber et al., 2016). As yet relatively few studies involve compound-specific sulfur isotopes, largely due to analytical constraints. Early investigations required tedious isolation of compounds followed by off-line isotope analysis. Recently, Amrani et al. (2009) developed an on-line coupled gas chromatography/multicollector-inductively coupled plasma mass spectrometry (ICPMS) method for compound-specific analysis of δ34S of volatile organic compounds. They used this method to measure δ34S values of dimethylsulfide (DMS) and its algal precursor dimethylsulfioniopropionate (DMSP) in seawater from various oceanic environments in order to constrain the contribution of oceanic DMS to atmospheric aerosols. DMS plays an important role in the carbon cycle in the upper ocean, and as the major source of biogenic S from the ocean to the atmosphere, it modulates global climate through aerosol dynamics. In oxygenated seawater, DMS is produced by enzymatic cleavage of DMSP, the δ34S values of which are similar to the S source, sulfate. Oduro et al. (2012) used δ34S values of DMSP and sulfate in cultures of several algae to show that DMSP is linked to metabolic pathways of methionine production. DMSP δ34S values were homogeneous across a range of environments, and since there was little isotope fractionation between DMSP and DMS, the authors concluded that the flux of biogenic DMS from seawater to atmosphere is constant and distinguishable from anthropogenic sources of atmospheric S. In a more recent study of DMS-DMSP cycling in stratified Lake Kinneret (Israel), however, δ34S values for DMS and DMSP were different, and δ34S values of DMS were closer to δ34S values of H2S (Sela-Adler et al., 2016). This result indicates an alternate source of DMS in stratified water bodies, that is methylation of H2S. Diagenetic sulfurization of organic compounds (“sequestration”) involves abiotic incorporation of reduced inorganic sulfur into functionalized organic molecules and enhances the preservation of biomarker lipids (Sinninghe Damsté and de Leeuw, 1990; Werne et al., 2000, 2003) and carbohydrates (van Dongen et al., 2003). Traditionally, these reactions were thought to occur over geological time frames, similar to kerogen formation, but new work indicates more rapid sulfurization can occur. Werne et al. (2008) used HPLC isolation followed by off-line isotope analysis to measure δ34S values of two lipids, a sulfurized triterpenoid thiane (malabaricatriene, XXX) and a C25 highlybranched isoprenoid thiophene (XXXI) in Holocene and late Pleistocene sediments of the euxinic Cariaco Basin. The δ34S values were enriched in 34S compared to bulk organic and inorganic S pools, indicating multiple pathways by which inorganic S is incorporated into organic matter. In more recent work using a modification of the GC-ICPMS method of Amrani et al. (2009) mentioned above, Raven et al. (2015) observed that several C20 highly branched isoprenoid thiophenes (probably sulfurized phytol), C25-highly branched thiolanes (probably sulfurized C25-highly branched isoprenoid alkenes), and a triterpenoid thiane in a Holocene sediment core from the Cariaco Basin, had δ34S

7. Concluding remarks The preceding has been a “brief” survey of some exciting new advances in marine organic biogeochemistry over the past 10–15 years. As the field is now large and rapidly expanding, we have not even touched on many issues and future needs, and we now understand why no one has reviewed the entire field! Just a few topics among these issues are:

• The organic biogeochemistry of the marine microlayer and aerosols (e.g., Engel et al., 2017; Wurl et al., 2017); • Metals in organic biogeochemistry (e.g., Buck et al., 2017; Heal • •

et al., 2018), including a role in formation of microgels (Orleanna and Leck, 2015); Identification and biogeochemistry of new organic proxies for paleoceanography (e.g., Eglinton and Eglinton, 2008; Sachs et al., 2013); Continued development and application of analytical (e.g., Baczynski et al., 2018) and informatics (e.g., Wang et al., 2016b) tools to marine organic biogeochemistry. There are now many journals dedicated to myriad aspects of omics (e.g., www. omicsonline.org), with a few being relevant to marine science.

Finally, Max Blumer was right on all counts: 1) there is still so much that we don't know about the natural world, 2) we can never expect to know everything, and 3) limitations of the available analytical techniques are still the major roadblock to understanding the chemical complexities of nature. In this review, we have tried to make clear how new analytical (and sampling) techniques have been fundamental in paving the way for big steps in our understanding of the ocean. We have come so far since Blumer's, 1975 paper, but there is still so far to go. Always appropriate, Blumer (1975) quoted Saint Avvaiyar, an Indian poetess: “What we have learnt is like a handful of Earth, while what we have yet to learn is like the whole World”. Declarations of interest None. Acknowledgements We thank the Chemical Oceanography Program of the National Science Foundation for support for our research over the past 40 years, and especially the foresight of Neil Andersen and Don Rice in leading the charge. Many colleagues and friends have contributed to our careers over the years, and we thank them. Dee King at Skidaway Institute of Oceanography assisted in preparation of this manuscript.

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Appendix A

I phytane

II PMI

III squalane

IX GDGT-0 caldarchaeol

IV biphytane a

V biphytane b

X GDGT-1

VI biphytane c XI GDGT-2

VII biphytane d XII GDGT-3

VIII biphytane e XIII crenarchaeol

XIV B-GDGT-1050

XV B-GDGT-1018

XVI OH-GDGT-1

XVII 2OH-GDGT-1

XIX unsaturated biphytane from ether cleavage (tentative)

XVIII H-GDGT (tentative)

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Appendix B

XX GDD-1

XXII monohexose

XXV archaeol

XXI S-GDGT

XXIII dihexose

XXIV phosphohexose

XXVI hydroxyarchaeol

XXVII crocetane

XXVIII Hexose-phosphohexose-crenarchaeol (HPH-crenarchaeol)

XXIX C20-[3]-phophatidylcholine monoether ladderane

XXX malabaricatriene

XXXI C25-HBI thiophene

https://doi.org/10.1016/0967-0637(93)90129-q. Alonso-González, I.J., Arístegui, J., Lee, C., Calafat, A., 2010a. Regional and temporal variability of sinking organic matter in the subtropical northeast Atlantic Ocean: a biomarker diagnosis. Biogeosciences 7, 2101–2115. https://doi.org/10.5194/bgd-611089-2009. Alonso-González, I.J., Arístegui, J., Lee, C., Sanchez-Vidal, A., Calafat, A., Fabrés, J., Sangrá, P., Masqué, P., Hernández-Guerra, A., Benítez-Barrios, V., 2010b. Role of slowly settling particles in the ocean carbon cycle. Geophys. Res. Lett. 37, L13608. https://doi.org/10.1029/2010GL043827. Alonso-González, I.J., Arístegui, J., Lee, C., Sanchez-Vidal, A., Calafat, A., Fabrés, J., Sangrá, P., Mason, E., 2013. Carbon dynamics within cyclonic eddies: insights from a biomarker study. PLoS One 8, e82447. https://doi.org/10.1371/journal.pone. 0082447. Aluwihare, L.I., Meador, T., 2008. Chemical composition of marine dissolved organic nitrogen. In: Capone, D., Bronk, D., Mulholland, M., Carpenter, E. (Eds.), Nitrogen in the Marine Environment, 2nd ed. Elsevier, pp. 95–140. https://doi.org/10.1016/ B978-0-12-372522-6.00003-7. Amin, S.A., Hmelo, L.R., van Tol, H.M., Durham, B.P., Carlson, L.T., Heal, K.R., Morales, R.L., Berthiaume, C.T., Parker, M.S., Djunaedi, B., Ingalls, A.E., Parsek, M.R., Moran,

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