biogeochemistry of marine dissolved organic matter || modeling dom biogeochemistry

635 Crown Copyright © 2015 and Elsevier Inc. All rights reserved.Biogeochemistry of Marine Dissolved Organic Matter, http://dx.doi.org/10.1016/B978-0-12-405940-5.00015-7

C H A P T E R

15Modeling DOM Biogeochemistry

Thomas R. Anderson⁎, James R. Christian†, Kevin J. Flynn‡

*National Oceanography Centre Southampton, Southampton, UK†Fisheries and Oceans Canada, Canadian Centre for Climate Modelling and Analysis,

Victoria, British Columbia, Canada‡Centre for Sustainable Aquatic Research, Swansea University, Swansea, UK

C O N T E N T S

I Introduction 635

II Modeling Approaches 637A Models Without an Explicit

Ecosystem 637B Models With an Explicit Ecosystem 642

III Modeling the Role of DOM in Ocean Biogeochemistry 648

IV Lability in Focus: Concepts and Definitions 653A Physiological Considerations 653B Modeling Implications 655

V Discussion 656

Acknowledgments 661

References 661

I INTRODUCTION

Up until the 1950s, the prevailing view of mi-crobial ecology in the ocean was summed up by Keys et al. (1935): “It appears that in the sea there is generally an equilibrium such that only minimal bacterial activity at the ex-pense of dissolved organic matter (DOM) takes place.” Bacterial numbers, enumerated by plate counts, were typically a mere 100 per

cubic centimeter (e.g., Reuszer, 1933). As Krogh (1934) put it, “…the number of bacteria present in ocean water is extremely small,” continuing “… dissolved organic matter is not a suitable food.” In similar vein, Harvey (1950) remarked that the numbers of bacteria in the sea are “limited by the great dilution of DOM serving as food in the water” and that attachment to particles provided a more favorable substrate for microbes. Changes in outlook came about

636 15. MODElINg DOM BIOgEOCHEMISTRy

in the following two decades with the realiza-tion that, based on direct microscope counts, numbers of bacteria were orders of magnitude higher than previously thought (e.g., Jannasch and Jones, 1959), along with the introduction of 14C methods (Parsons and Strickland, 1961) and wet oxidation techniques (Menzel and Vaccaro, 1964) that demonstrated the dynamic nature of DOM in marine environments (e.g., Banoub and Williams, 1973). A fuller appreci-ation of the importance of the microbial loop was well on the way with Pomeroy’s (1974) key paper. Further important publications followed which elaborated the ideas in more detail (e.g., Azam et al., 1983; Sorokin, 1977; Williams, 1981). Yet, there remained a consid-erable reluctance among biologists to reject the notion that food webs in the ocean are more complex than copepods feeding on phyto-plankton (Sieburth, 1977).

This new paradigm regarding the importance of microbial processes and DOM dynamics was em-braced slowly by biologists, and even more slowly by the modeling community. The first models of the marine ecosystem were developed by Richard Fleming and Gordon Riley in the 1940s and were followed, during the next three decades, by the nu-trient-phytoplankton-zooplankton (NPZ) model of John Steele (e.g., Steele and Henderson, 1981). By the 1980s, computer simulations were becom-ing widespread. Most models continued to focus on the classical food chain of phytoplankton and zooplankton, as presented by Steele, without in-cluding DOM or bacteria (e.g., Franks et al., 1986; Hofmann and Ambler, 1988). There were, however, exceptions. Vézina and Platt (1988) conducted an inverse analysis of food web dynamics at stations in the English Channel and the Celtic Sea, conclud-ing that microbial grazers were a major link in the transfer of carbon to mesozooplankton. Ducklow et al (1989) used a similar approach to assess food web dynamics in warm core rings. Other research-ers took the first steps in creating dynamical, dif-ferential equation models of DOM and bacteria (Moloney et al., 1986; Pace et al., 1984).

The model of Fasham et al. (1990) provided an important step forward, incorporating bac-teria and DOM, as well as an improved repre-sentation of recycling of inorganic nutrients by dividing N between nitrate and ammonium. Parameterizing DOM in models was, however, a major challenge and remains so today. Four years later, Berman and Stone (1994) wrote: “Theoretical ecologists have hardly begun to ex-plore the implications of Pomeroy’s ‘new para-digm’ of aquatic food webs. An explanation for this lies in the difficulties of modeling systems that are of a truly complex nature.” Since then, ecosystem models have continued to proliferate, but Berman’s statement is, we suggest, relevant even today. The NPZ models have been replaced by plankton-functional-type (PFT) models in which phytoplankton are divided into separate state variables for simulating groups such as diatoms, coccolithophores, and N2-fixers (e.g., Gregg et al., 2003; Le Quéré et al., 2005). Most recently, the basic paradigm of the eukaryote plankton community has been called into ques-tion by a new emphasis on mixotrophy, with es-timates that more than half of protistan primary producers are actively mixotrophic (Flynn et al., 2013). These mixotrophs use bacteria as a source of N, P, and Fe in support of photosynthetic car-bon fixation, which substantially alters our view of the roles of DOM and bacteria in energy flow in pelagic food webs (Hartmann et al., 2012; Mitra et al., 2014).

Over the decades, the representation of DOM in models has also been progressively elabo-rated, including fractions of different lability and a variety of parameterizations of the pro-duction and fate of DOM in ocean ecosystems. Pragmatically, DOM is often associated with a filter pore size of <0.2-0.7 μm, rather than a state of dissolution. Using this operational defini-tion of DOM presents various challenges from a modeling perspective. Many models today, including those used in Earth System models (ESMs), do not even include DOM or any explicit representation of the microbial loop. Others do,

II MODElINg APPROACHES 637

but they also use apparently rudimentary as-sumptions about a very complex system. There is no right or wrong approach in this regard, although it is generally the case that increased mathematical complexity in models must be jus-tified by specific conceptual objectives and not simply a desire for increased realism. Here, we examine the current state of the art, as well as the historical background, of modeling DOM in marine systems. The ongoing development of new schemes and associated parameterizations for representing DOM in models is discussed in context of the general trend toward increasing complexity in marine ecosystem models.

II MODELING APPROACHES

As we will show, there is an enormous range of approaches to modeling DOM in use, with the perennial issue of model complexity provid-ing an ongoing subject of debate. At its crux is that models should be constructed with a level of complexity appropriate for the subject or hypothesis of interest (Allen and Fulton, 2010). Many studies have focused on biogeochemical cycling at basin or global scales, often using rel-atively simple representations of the marine eco-system. This is not surprising given, in the words of Gordon Riley, the great pioneer in marine eco-system modeling (Anderson and Gentleman, 2012), the “difficulty of deriving a system of mathematical equations subtle enough to meet the demands of widely varying environments and at the same time simple enough to be us-able for practical application” (Riley et al., 1949). Alternatively, because marine microbial ecology, and the associated cycling of DOM, is multifac-eted, complex models have also been favored in many instances.

A Models Without an Explicit Ecosystem

The use of modeling studies to examine the dis-tribution of nutrients and carbon in the ocean

need not necessarily involve the use of explicit ecosystem models. Early studies involving ocean general circulation models (OGCMs) calculated export by, for example, restoring nutrients to observed fields (Najjar et al., 1992) or using a simple Michaelis-Menten-type function of nutri-ent concentration (Bacastow and Maier-Reimer, 1990). Similar approaches have proved useful for studying the transport and fate of DOM in the ocean. These models specified production of DOM in surface waters and then examined its distribution in the ocean under the influences of circulation, mixing, and remineralization.

The first task facing modelers is to compart-mentalize DOM into discrete entities that can be subject to simulation and represent the DOM pool as a whole. An immediate problem is the fact that the bulk DOM pool is largely uncharac-terized (Chapter 2). Given the difficulty in char-acterizing DOM biochemically, the concept of lability may appear as a useful means of classi-fying DOM based on how readily different com-pounds are utilized by heterotrophic microbes. Conveniently for modelers, the bulk DOM pool has often been categorized into three fractions on the basis of turnover rates: labile, semi- labile, and refractory (Carlson and Ducklow, 1995; Cherrier et al., 1996; Kirchman et al., 1993). The early work of Ogura (1975), for example, showed that DOM decomposition in coastal seawater during bottle incubations occurred in two distinct phases with rates of 0.1-1 day−1 and 0.007 day−1, with a third fraction remaining un-utilized. Thus, labile material degrades rapidly on time scales of hours to days, semi-labile DOM turns over on seasonal timescales while the re-fractory fraction remains for centuries and may be treated as biologically inert on the timescales of most ocean model simulations. The concept of lability, which depends both on biochemical characteristics of DOM and the ecophysiology of microbial communities, is addressed in detail in Section IV. Suffice to say at this point that frac-tionation according to lability is challenging. For example, low molecular weight (LMW) DOM


can be highly labile and its dynamics described within the time step used in most ecosystem models. Alternatively semi-labile/refractory LMW DOM is likely the remnants of high mo-lecular weight (HMW) material after the more labile fractions have been successively stripped away (Amon and Benner, 1996) and may turn-over on timescales appropriate for biogeochem-ical models. This raises an important question of whether models describing DOM in marine ecosystems should be categorized as those that attempt to describe biological processes (requir-ing suitable biological descriptions and appro-priately small time steps) or biogeochemical processes (which for the most part will inevita-bly be concerned with longer lived DOM and less-labile remnants of DOM that would be out-puts from detailed biological process models)? Pragmatically, Earth system scale models must fall into the latter camp (if only because their integration time step is far too long to do other-wise). However, as we shall see the history of the topic is rather schizophrenic in this regard, with constructions and parameterizations that argu-ably give acceptable results but not necessarily from realistic model structures.

We start by describing the model of Anderson and Williams (1999) as a characteristic early ex-ample of the methodology of modeling DOM in the ocean. This model, in combination with the complementary study of Anderson and Williams (1998; see next section), provided the basis for many of the models that followed, including models in use today (e.g., Keller and Hood, 2011, 2013). The model (Figure 15.1) has carbon as its base currency and contains three DOM fractions, labile, semi-labile, and refractory, as well as ex-plicit heterotrophic bacteria. Primary production is specified as a fixed rate input (there is no ex-plicit ecosystem), the fate of which is allocated between the labile and semi-labile DOC pools, exported particulate organic carbon (POC) and CO2 with fractions of 14%, 41%, 10%, and 35%, respectively. These DOC fractions are then mixed downwards through a one-dimensional water

column based on a prescribed profile of eddy dif-fusivity. Organic carbon is also exported to depth via sinking particles (10% of primary production). Losses from sinking detritus, which are distrib-uted through the water column according to the hyperbolic function of Martin et al. (1987), are partitioned to DOC rather than direct remineral-ization to CO2 with allocations of 10% and 90% to the labile and semi-labile pools, respectively. Both of these pools are utilized for growth by bacteria. It is assumed that the semi-labile pool is not taken up directly but instead enters the labile pool via enzymatic hydrolysis. Uptake of labile DOC is then calculated according to Michaelis-Menten kinetics (as is hydrolysis of semi-labile DOC) and is then used for growth with a fixed efficiency of 14% (the remainder released as CO2). Bacteria are subject to mortality as a nonlinear function of bio-mass, with 51% allocated to DOC. Finally, a small fraction (0.35%) of the DOC processed by bacte-ria is allocated to the refractory pool, the sole loss term for which is photooxidation by ultraviolet radiation at the ocean surface.

The modeled vertical profile of DOC shows good agreement with data collected in the Atlantic

Labile DOC Semi-labile DOC

Bacteria

Primary production

Detritus

Refractory DOC

CO210%

35%

14% 41%

14%86%

49%

46%

H

S S

M

CO2

G

CO2

90%

5%

10%

FIGURE 15.1 Model flow diagram showing the rela-tive allocations of C between different pathways. S, solubi-lization; H, enzymatic hydrolysis; G, growth; M, mortality. Dotted arrows are quantitatively minor fluxes associated with the refractory pool. Based on the model of Anderson and Williams (1999).


and Pacific oceans (Figure 15.2). The vertical gradi-ent is maintained almost entirely by the semi- labile pool and results both from downward mixing of semi-labile DOC produced in the euphotic zone and DOC derived from solubilization of sinking detritus. Most of the organic C mixed downward is consumed by bacteria and remineralized to CO2 within the upper 250 m of the water column, with associated turnover rates of between 0.01 and 0.001 day−1 in this depth range. It should be noted, however, that Anderson and Williams (1999) com-mented that their model may have underestimated downward transport due to lack of representation of physical processes such as subduction. Below

250 m, most of the semi-labile DOC in the water column arose from turnover of sinking particles. A large pool of refractory DOC, ~40 mmol m−3, is maintained throughout the water column with concentrations decreasing slightly near the ocean surface due to photooxidation. In reality, it may be that refractory DOM is slowly remineralized, as shown by DOC gradients along the deep-ocean conveyor in the deep ocean (Hansell and Carlson, 1998). Recent work has also suggested that local-ized sinks of refractory DOM may control some of the patterns observed in the deep sea (Hansell and Carlson, 2013). There is little information available about the processes responsible for this remineral-ization (Hansell, 2013).

The model of Anderson and Williams (1999) illustrates many of the key challenges associated with representing DOM in ocean biogeochemi-cal models: (1) the choice of which DOM pools to include and their associated definition; (2) choice of model currency and associated stoichi-ometry for models with multiple currencies; (3) parameterization of the turnover for each DOM pool; (4) choice of DOM source terms to include such as those via primary production in the eu-photic zone, via detritus turnover, and via bacte-rial mortality (lysis), and their allocation among the different DOM pools.

DOM modeling studies that lack an explicit ecosystem (Table 15.1) simulate distributions of carbon and nutrients in the ocean, in both organic and inorganic form, and how these are influenced by circulation, vertical mixing, and DOM turnover rates. In this way the po-tential for C storage in the deep ocean can be examined along with the possible response of C storage to changing climate. The main fo-cus of these models has therefore been the semi-labile pool, although in some instances slow- turnover biodegradable DOM is divided between semi-labile and semi-refractory pools (Hansell et al, 2012; Roussenov et al., 2006). A few models also include the refractory pool in order to directly compare with measured DOM. As a word of caution, however, different authors

0 10 20 30 40 50 60 70 80 90

0 10 20 30 40 50 60 70 80 90

Dep

th (m

)DOC (mmol m–3)

0

200

400

600

800

1000

Labile

Semi-labile

Refractory

FIGURE 15.2 Vertical profile of DOC (mmol C m−3) as simulated by the model of Anderson and Williams (1999), split into its component parts: labile, semi-labile, and re-fractory. Data are for various profiles in the Atlantic (solid points) and Pacific oceans (open points). Taken from Christian and Anderson (2002).


use different definitions for DOM pools. Efforts have been made to better constrain and unify these definitions based on data and improved estimates of turnover (Hansell et al., 2012, and see Chapter 3). The semi-labile pool, for exam-ple, has been variously described as both labile (e.g., Six and Maier-Reimer, 1996) and refractory (e.g., Levy et al., 1998; Walsh and Dieterle, 1994). Regarding currencies, most models include C for the obvious reason that DOM is a potentially important determinant of the ocean influence on atmospheric CO2. Other elemental fluxes can be inferred by invoking the canonical Redfield ratio (Redfield et al., 1963), although DOM gen-erally does not follow Redfield stoichiometry (Hopkinson and Vallino, 2005; Williams, 1995). DOM may also be important as a means of trans-porting nutrients into the oligotrophic gyres (see Section III) in which case N or P provide the main focus (e.g., Williams et al., 2011). The fundamen-tal problem is, however, that DOM represents a suite of literally many 1000s of different chemi-cals about whose identities, concentrations, and

dynamics we know virtually nothing. And that is before one includes the microparticulates that are mixed in with the <0.2 μm size fraction, and perhaps excluding TEP and other material that arguably comprises “dissolved” material but does not pass the size fractionation.

The primary loss mechanism for DOM in the ocean is uptake by heterotrophic bacteria. All eu-karyotic microorganisms, the protists, (Marchant and Scott, 1993; Sherr, 1988) and perhaps some metazoa (Wright and Manahan, 1989) have the potential to take up dissolved or colloidal organic matter, but it is not known how wide-spread or quantitatively significant this process is. Flynn and Berry (1999) sought to explain the apparent ubiquity of dissolved free amino ac-ids (DFAA) uptake by phytoplankton through modeling a leak-recovery process. Through this, it can be seen that the leakage of metabolites such as DFAA (and sugars) across membranes with gradients running from high (mM) inter-nal concentrations to low (nM) external concen-trations presents a risk to the growth of the cell.

TABLE 15.1 Model Characteristics: Models Without Ecosystem

Reference Type DOM Pools Lifetime (years) DOM Sources Currencies

Yamanaka and Tajika (1997) 3D SL, R 2, 16a EuZ, Drem Redfield C:P

Anderson and Williams (1999) 1D L, SL, R B, B, 1.8b EuZ, Drem, Bmort C only

Bendtsen et al. (2002) 1Dc SL, R B, 150a Drem, Bmort C only

Schlitzer (2002) 3D SL 2 EuZ C only

Roussenov et al. (2006) 3D SL, SR 0.5, 6-12 EuZ N, (P)

Kwon and Primeau (2006) 3D SL 1 EuZ Redfield C:P

Najjar et al. (2007) 3D SL 2 EuZ Redfield C:P

Kwon and Primeau (2008) 3D SL 1.7 EuZ Redfield C:P

Williams et al. (2011) 3D SL, R 0.5, 6-12 EuZ N only

Hansell et al. (2012) 3D SL, SR, R 1.5, 20, 16 k EuZ C only

DOM sources, EuZ: euphotic zone; Drem, detritus remineralization; Bmort, bacteria mortality.Lifetime: B: variable, mediated by explicit bacteria state variable.aEuZ only.bAt ocean surface, decreasing exponentially with depth within EuZ.cBendtsen et al. (2002) model is of aphotic zone only.


The recovery of these leaked metabolites can ex-plain the “critical inoculum” problem often seen in algal culturing, where a sufficient load of mi-croalgal cells must be introduced for the leak-re-covery process to work to the advantage of the individual cell; if this does not occur then these organisms effectively bleed DFAA from their in-ternal high-concentration metabolite pools to the external water (Flynn and Berry, 1999).

Setting aside the leak-recovery mechanism (which for biogeochemical modeling can be ig-nored as only the net loss is consequential), the first decision to make regarding the modeling of DOM turnover is whether or not to explicitly in-clude heterotrophic bacteria. This represents one of the examples of schizophrenia mentioned ear-lier; because an explicit description of the DOM-linked biological C pump involving microbes must surely operate with time-steps far below those in ESMs. Indeed, it is difficult to reliably model bacteria in marine systems, a topic we dis-cuss in detail when describing ecosystem-based models (Section II.B). In many instances, there-fore, modelers have chosen to omit bacteria and use fixed (specified) lifetimes for DOM pools. The chosen lifetimes (turnover = 1/lifetime) typ-ically vary between 0.5 and 2 years (Table 15.1), a fourfold range (Table 15.1). In reality, lability and associated turnover are variable because DOM represents a spectrum of compounds and as a result of physiological aspects of the bac-terial community (Section IV). The models of Roussenov et al. (2006) and Hansell et al. (2012) therefore chose to have separate semi-labile and semi-refractory pools. Nevertheless, despite this apparently simplified approach, modeled dis-tributions of carbon and nutrients are generally reasonable. Of the models listed, only that of Anderson and Williams (1999) included a labile pool. The refractory pool is rarely included in models because, at least according to conven-tional wisdom, it is biologically inert over time scales of hundreds to tens of thousands of years. The global inventory of refractory DOM, despite its large size, may be relatively insensitive to

climate change over the next 200 years if photo-oxidation is the sole loss term. Nevertheless, a new and upcoming challenge for biogeochem-ists and modelers is that of the microbial carbon pump (MCP), as proposed by Jiao et al. (2010). In the MCP framework, repetitive processing of DOM by microbes transforms reactive organic carbon into refractory DOM, potentially build-ing up a reservoir for carbon storage. The pro-cesses involved include viral lysis and associated release of microbial cell wall material, the selec-tive action of ecto- and exo-enzymes and losses associated with protist grazing (see Chapter 3).

The models listed in Table 15.1 each use sim-ple methods to prescribe DOM input into the eu-photic zone (in one case, Bendtsen et al. (2002), the euphotic zone was ignored altogether with the aphotic ocean as the sole focus). The most common approach is to derive either new or export production (the two are theoretically equivalent, at least at steady state), either by restoring nutrients to observed surface concen-trations (e.g., Kwon and Primeau, 2006, 2008; Najjar et al., 1992) or as a simple function of surface nutrient concentrations (e.g., Roussenov et al., 2006; Schlitzer, 2002; Yamanaka and Tajika, 1997). The resulting export flux is divided be-tween particulate and dissolved organic mat-ter, with a perhaps surprisingly high allocation to the latter in most instances, for example, 50% (Schlitzer, 2002), 50-65% (Roussenov et al., 2006), 67% (Najjar et al., 2007 and Yamanaka and Tajika, 1997), or 74% (Kwon and Primeau, 2006). The model of Hansell et al. (2012) focused only on the production of DOM described as the square root of primary production and did rep-resent sinking particulate organic matter (POM) in their analyses.

The early models listed in Table 15.1 exhibit a key difference compared to those published later, namely they include detritus turnover as a source of DOM in the deep ocean. As detritus sinks through the water column both attached bacteria and detritivorous zooplankton atten-uate its flux. The former solubilize particulate


matter using hydrolytic enzymes (Smith et al, 1992) and much of this solubilized material may be lost to surrounding waters thereby fuelling growth of free-living bacteria (Cho and Azam, 1988; Unanue et al., 1998). Hansman et al. (2009), for example, noted that at 950 m in the meso-pelagic zone of the North Pacific subtropical gyre, free-living bacteria utilize carbon derived primarily from sinking particles. Zooplankton also release DOC as a result of breakup of food while grazing (“sloppy feeding”), excretion, and voiding of fecal material that includes dissolved substrates (Jumars et al., 1989). Both bacteria and zooplankton may play important roles in the processing and remineralization of organic C within the mesopelagic zone. Giering et al. (2014) used a simple steady state model to an-alyze data collected at the Porcupine Abyssal Plain site in the North Atlantic (49 °N, 16° 30′W). The model indicates that, although most remin-eralization can be attributed to microbes, detri-tivorous zooplankton may consume half of the sinking POC flux. Much of this POC consumed by zooplankton is however released as sus-pended detritus and DOM, thereby stimulating the microbial loop. Thus, the reality would ap-pear to be that a significant fraction of detritus turnover gives rise to the production of DOM.

B Models With an Explicit Ecosystem

Models with an explicit ecosystem (Tables 15.2 and 15.3) are intended to directly address in some detail the production and cycling of DOM in the euphotic zone, as well as its export to the ocean interior. As with models that do not incorporate an explicit ecosystem, it is neces-sary to choose which DOM pools to include and whether or not to opt for multiple curren-cies. Additional considerations are (1) whether to explicitly include bacteria and, if so, how to model their consumption and remineralisation of DOM and (2) the definition of DOM source terms and which pools (in terms of lability and stoichiometry) to allocate them to.

Models that have no explicit treatment of bac-teria (Table 15.2) are similar to those described in Section II.A above in that DOM fractions are assigned fixed (or temperature-dependent) life-times, but differ in that the production of DOM is related explicitly to ecosystem dynamics. Again, it is possible in these models to omit the labile pool and focus solely on the semi-labile DOM fraction. The chosen lifetimes for semi- labile pool are, however, often relatively short (<0.5 year) in these models, perhaps indicating a shift in emphasis from modeling carbon distri-butions in the ocean to modeling the dynamics of marine ecosystems. An interesting approach adopted by Pahlow et al. (Pahlow and Vézina, 2003; Pahlow et al., 2008) modeled DOC and DON labilities dynamically as the fractions of total DOM available for bacterial utilization. Freshly produced DOM was assumed to be en-tirely labile, with lability then decreasing over time according to first order decay coefficients. Some of the models, including more recent additions to the literature (Llebot et al., 2010; Shigemitsu et al., 2012), include only a single labile pool thereby providing a (relatively sim-plistic) representation of the microbial loop. It is notable that many of the models with an explicit ecosystem incorporate considerable complex-ity in the ecosystem representation (e.g., multi-ple phytoplankton and zooplankton functional types), yet choose to use simple, empirical ap-proaches to modeling DOM (Table 15.2).

Models with explicit descriptions of bacteria (Table 15.3) invariably include a state variable for labile DOM as it is this pool that is used directly for growth. A number of contrasting approaches have been used for modeling substrate uptake and utilization by bacteria. A popular choice is the Monod model (Monod, 1942) which was de-veloped by a French biologist, Jacques Monod, who showed that the growth rate of bacteria in culture, μ, can be described by:

(15.1)m

m=

+maxS

K SS

,


where S is substrate concentration in the sur-rounding environment, μmax is the maximal growth rate and KS is the substrate concentra-tion at which μ = μmax/2. Growth and uptake are assumed to operate in tandem, which is strictly only the case under steady-state conditions (i.e., Monod’s cultures of bacteria). The model also assumes that growth is limited by only one factor (substrate); a most unlikely event in nature and certainly inappropriate for varying sources of DOM, noting that the single half saturation constant used is surrogate for perhaps many 10s of half saturation values for different com-ponents of DOM. Fasham et al. (1990) extended the basic formulation of Monod to address the simultaneous use of organic and inorganic ni-trogen by defining the ratio, η, of the two which occurs for balanced growth:

(15.2)

where ωx is the growth efficiency for C or N and θDOM and θB are the C:N ratios of DOM and bac-teria, respectively. This formulation assumes that if there is sufficient NH4

+, dissolved inor-ganic nitrogen (DIN), and DON are taken up in fixed ratio (η = 0.6). If not, DIN and DON jointly limit the bacterial growth rate. Respiration was parameterized as a constant biomass-specific loss of ammonium. A potential problem with the Fasham approach is that bacteria are un-likely to use ammonium if sufficient labile DON is available, depending on the concentration of the types of DON (DFAA could repress con-sumption of NH4

+). If DON availability is used in this N-based model as a surrogate for DOC

hw qw q

= −C

N B

DOM 1,

TABLE 15.2 Model Characteristics: Models with Ecosystem but Without Explicit Bacteria

Reference

Type

Food Web Structurea

DOM Poolsb

DOM Currenciesc

DOM Sourcesd

DOM Lifetime (years)e

Kawamiya et al. (1995) 1D NPZD S N P⁎, D# 0.09♣

Six and Maier-Reimer (1996) 3D NPZD S P:(C) P#, Z# 0.11♀

Aumont et al. (2003) 3D 3N2P2ZD S C P#, Z#, D# 0.27

Moore et al. (2004) 3D 4N3PZ2D (L), S C:N:P P#, Z†,# 0.27

Christian (2005) 1D NPZ L, S C:N:P P#, Z† 0.007, 2.7

Schmittner et al. (2005) 3D NPZD S N P#, Z†,# 5.8

Dutkiewicz et al. (2005) 3D 3N2PZD S P:Fe P#, Z†,# 0.14

Huret et al. (2005) 3D NPZD S N P⁎, Z#,†, D# 0.05

Schartau et al. (2007) 0D NP(Z + B)D L C:N P#, Z#, D# 0.011-0.015

Salihoglu et al. (2008) 1D 3N3P2Z2D S N:P:(C) P⁎, Z#, D# 0.15

Druon et al. (2010) 3D NPZ2D (L), S C:N P⁎, Z†, D# 0.18-0.36♣

Llebot et al. (2010) 0D 2N2PZD L N:P P#, Z#, D# 0.027

Shigemitsu et al. (2012) 1D 3N2P3Z2D L N P⁎, D# 0.018♣

aState variables: nutrient (N), phytoplankton (P), zooplankton (Z), bacteria (B), detritus (D), bacteria (B) are listed if present. Others not listed.bDOM pools: labile (L, lifetime hours to days), semi-labile (S, weeks to months), refractory (R, decades and longer); terminology may differ in original texts.cNitrogen (N), phosphorus (P), carbon (C), iron (Fe); parentheses indicate fixed C/N or C/P ratios. Ammonium and nitrate not considered to be separate currencies.dDOM sources: * via production; # via mortality (turnover) terms; †as fraction of grazing.eDOM lifetime: ♣ value for 0 °C, with Q10 = 2; ♀ maximum rate as function of nutrients.


availability, then DON availability could indi-rectly support NH4

+ use. An additional problem in models that have N as their sole currency is that in reality the consumption of ammonium requires a concurrent assimilation of DOC (i.e., DOM with a sufficiently high C:N that there remains a demand for N even after respiratory and other considerations). Fasham’s approach was nevertheless popular during the 1990s but, with increasing emphasis on the carbon cycle, was superseded by stoichiometric approaches. Experimental work by Goldman et al (1987) showed that excretion of N by bacteria decreases when DOM has a high C:N ratio indicating spar-ing of nutrient elements for growth while pref-erentially meeting the costs of respiration using C-rich substrates (Anderson, 1992). Based on these studies stoichiometric approaches were developed for modeling zooplankton (e.g., Anderson and Hessen, 1995; Hessen, 1992) and mineralization of soil organic matter (e.g., DeRuiter et al., 1993). Depending on θB and θDOM, either N or C can limit growth. If N is limiting, N excretion is zero. Indeed, NH4

+ may be taken up to supplement DON for growth. Conversely, N is excreted under C-limiting conditions, that is, when N-rich substrates are utilized. The net excretion of nitrogen, EB, is then:

(15.3)

where UC is DOC uptake. A review of the liter-ature by del Giorgio and Cole (1998) indicated a mean value for bacterial growth efficiency (BGE) of 0.15, with values in models varying between 0.15 and 0.3 (Table 15.3). In some cases, BGE is not a fixed parameter in models but in-stead varies as a result of separate basal and activity- related respiration terms (Luo et al., 2010; Polimene et al., 2006). Only a few models adopt a cell quota approach to bacterial stoichiometry (Blackford et al, 2004; Luo et al., 2010; Polimene et al., 2006). Nutrient uptake is calculated accord-ing to Michaelis-Menten kinetics and growth is a function of the internal quotas of elements such

as N and P. In contrast, the model of Flynn (2005) assumes fixed C:N:P but handles the fate of labile and semi-labile components by priority, giving a compact model that also describes different respi-ration rates and hence different growth efficiency depending on which nutrient is limiting and the degree of limitation.

Models that have a semi-labile pool often use a Monod approach whereby semi-labile DOM is hydrolyzed to form labile DOM, which is then suitable for uptake by bacteria. Values for the kinetic parameters, namely the maximum rate of hydrolysis and the half saturation constant, are not easily obtained and represent an amal-gam of many different processes. Connolly et al. (1992) and Connolly and Coffin (1995) provided estimates for a variety of coastal and freshwater environments from which values for Santa Rosa Sound (FL, USA) were applied to the English Channel by Anderson and Williams (1998) and subsequently also used by some of the models which followed. Another approach is to allow bacteria to simultaneously take up both labile and semi-labile DOM directly for growth, but assuming that only a small fraction of the latter, for example, 1%, is bioavailable at any one time (e.g., Luo et al., 2010).

DOM is produced within marine ecosys-tems from a variety of sources (Figure 15.3; see Chapter 3), many of which are not well under-stood or quantified. Four main categories can be identified: phytoplankton exudation and mortal-ity, grazer-associated losses, bacteria lysis, and mortality and detrital turnover. A variety of differ-ent combinations of these terms have been used in models, along with different methods of parame-terization (Table 15.3). An immediate problem is that these processes are themselves poorly under-stood and further confounded with all the chal-lenges over the description of what constitutes DOM in the first instance. Detrital turnover is, for example, often mediated by grazers together with bacterial activity, while most grazing-associated losses and mortality through cell lysis will liber-ate material as microparticulates or colloids, not

E UB CDOM

C

B

1=

q

wq

− ,


as true DOM (noting that the definition of DOM is operationally defined by filter pore size, tradi-tionally <0.2 μm diameter, and not with a state of dissolution).

Nearly all models with an explicit ecosys-tem include phytoplankton exudation, which involves both passive “leakage” and active re-lease. The former results from the permeability of the plasma membrane to LMW compounds (Bjørnsen, 1988) whereas the active release of “overflow” carbon as DOC may occur as a re-sult of metabolic instabilities (Williams, 1990). Accordingly, a key modeling choice has been as to whether to represent exudation as a “prop-erty tax” (some fraction of biomass per unit time), as suggested by Bjørnsen (1988), or as an “income tax” (a fraction of photosynthe-sis). Both approaches have been widely used in models (Table 15.3). In reality, release of all forms of DOM from an intact living cell rep-resents an imbalance between internal versus ex-ternal concentration gradients and whether the DOM molecular weight is small enough to pass through the plasma membrane unaided (ca. < 300 Daltons, which would include the bulk of primary metabolites which one would expect to also be highly labile). Such metabolites may often show peak internal concentrations during

unbalanced growth, such as during N-refeeding (Flynn, 1990). One may also expect (see Flynn and Berry, 1999) organisms to have active up-take systems to recover such metabolites as these may not only represent a loss of valuable mate-rial and energy but also provide an unwelcome attractant to grazers (e.g., Martel, 2006). If the molecular weight of the organic matter is larger than that permitted by passive diffusion then some form of reverse pinocytosis is required, or a specific structural feature such as a mucocyst. It appears that all protists can engage in osmot-rophy, and that the bulk of organisms tradition-ally labeled as “phytoplankton” are mixotrophic (Flynn et al., 2013) and hence have phagocytic and pinocytic capabilities. As the rejection of partly digested material from a living cell cannot be passive, the release of HMW “DOM” should be viewed as active. Metabolic “instabilities” may thus apply to either or neither categories and is not a useful differentiator. Both the prop-erty tax and income tax approaches are part of a common explanation that sees leakage as a func-tion of the concentration of the internal metab-olite pool. Thus, from the models described by Flynn and Berry (1999) and Flynn et al. (2008), there is leakage (mainly of DOC) that increases (transiently) during nutrient stress and during N-refeeding (DON), while there will always be some level of background leakage of metabolites from living cells.

High (net) release rates may be a common feature of oligotrophic waters (Christian, 2005; Teira et al., 2001), possibly as a result of contin-ued use of photosynthetic machinery after nu-trient exhaustion (Wood and Van Valen, 1990). This observation is consistent also with the leak-recovery mechanism described in Flynn and Berry (1999) where thin suspensions of or-ganisms are more likely to show a net “bleed” of organics. Exuded polymers may serve a number of fitness-promoting functions such as structural defences, storage, virus repellents, and protec-tive metabolites (toxins) (Hessen and Anderson, 2008), few if any of which are well understood.

DOC

Refractory

LabileSemi-labile

BacteriaPhytoplankton

Detritus Pellets

Zooplankton

Lysis

Exudation

Solubilization Solubilization

Grazer-associatedlosses

Uptake

Lysis

Photo-oxidation

FIGURE 15.3 Idealized food web illustrating the four main DOC production terms (phytoplankton exudation, grazer-associated processes, lysis, detrital solubilization) and the two principal loss routes (bacterial uptake, photoo-xidation). Taken from Christian and Anderson (2002).


The model of Polimene et al. (2006) explicitly assigns a fraction of DOC to represent capsu-lar material, extracellular polysaccharides that produce a mucilaginous protective envelope and are assumed to be relatively refractory to hydrolysis and decomposition (Stoderegger and Herndl, 1998).

Most models also include grazer-associated losses to DOM, either via grazing or as mortality of the grazers themselves. A fixed fraction of the ingested material, typically between 20% and

40%, may be allocated to so-called sloppy feed-ing. Strictly, this term refers only to the release of dissolved compounds when prey cells are bro-ken by mouthparts of crustacean zooplankton. However, grazer released DOM also includes the direct exudation of DOM by grazers, as well as DOM resulting from egestion and dissolution of fecal material (Jumars et al., 1989). Strom et al. (1997) estimated that between 16% and 37% of algal C is released as DOC during grazing by phagotrophic protozoa. Parameterizing DOM

TABLE 15.3 Model Characteristics: Models with Ecosystem Including Explicit Bacteria

Reference

Type

Food Web Structure

DOM Pools

DOM Currencies

DOM Sources

Bacteria Modela

Fasham et al. (1990) 0D NPZDB L N P⁎, Z#, D# Monod(L), FDM

Anderson and Williams (1998)

0D NPZDB L, S C:N P⁎,#, Z†, D#, B# Monod(L), SLH, AW98 (ω = 0.27)

Levy et al. (1998) 1D NPZDB L, S N P⁎, Z#, D# Monod(L), SLH, FDM

Bissett et al. (1999) 1D N4PB L, R C:N P#, Z† Monod(L), FDM

Tian et al. (2000) 1D N2P2Z2DB L C:(N) P#, Z† Monod(L), ω = 0.15

Vallino (2000) 0D NPZDB L, S C:N P⁎, D# Monod(L), SLH, ωmax = 0.8042b

Anderson and Pondaven (2003)

1D NPZDB L, S C:N P⁎,#, Z†,#, D#, B# Monod(L), SLH, AW98 (ω = 0.17)

Pahlow and Vézina (2003), Pahlow et al. (2008)

1D NPZDB (L + S + R) C:N P⁎, Z#,†, D# M-P(L + S + R), AW98 (ω = 0.25)

Blackford et al. (2004) 1D 3N4P3ZDB L, S C:N:P P⁎,#, Z†, D#, B# M-M(L), SLH, ω = fn(active, basal resp)

Polimene et al. (2006) 0D 3N4P3ZDB L, 2S C:N:P P⁎,#, Z†, B# M-M(L + 2SL), ω = fn(active, basal resp)

Grégoire et al. (2008) 3D 2N3P2ZDB L, S C:N P⁎,#, Z†, D#, B# Monod(L), SLH, AW98 (ω = 0.17)

Luo et al. (2010) 1D 2N3P2ZDB L, S, R C:N:P P#, Z†,#, D#, B# M-M(L + SL), ω = fn(active, basal resp)

Keller and Hood (2011) 0D N2P2ZDB L, S, R C:N P⁎,#, Z†,#, D#, B# Monod(L), SLH, AW98 (ω = 0.30)

Nomenclature and symbols as for Table 15.2: food web structure and DOM pools, currencies, and sources.aBacteria models: FDM (approach of Fasham et al. (1990): Equation 15.2); AW98 (method of stoichiometric balancing of Anderson and Williams, 1998); SLH (semi-labile material not consumed directly by bacteria, but hydrolyzed to labile); ω (BGE), M-M (Michaelis-Menten uptake, used with cell quota model); M-P (Mayzaud-Poulet uptake, used with cell quota model); resp (respiration).bBGE declines as N availability declines.


production via “sloppy feeding,” egestion, ex-cretion, and dissolution of fecal pellets in not straightforward. Microzooplankton, for exam-ple, use a variety of feeding mechanisms in-cluding pallium feeding tube feeding and direct engulfment (Hansen and Calado,1999) which may be expected to entail negligible sloppy feeding losses. Grazing by protists may actually result in the voiding of partly consumed prey and secondary metabolites (likely semi-labile DOM), material which may subsequently be consumed again if other food material is un-available (Flynn and Davidson, 1993). Grazing in marine systems is often dominated by the microzooplankton, except in areas where large algal blooms occur (e.g., Verity et al., 1993). Release of DOM from fecal pellets may also be an important source, with as much as 50% of C in pellets being rapidly solubilized (Urban-Rich, 1999), although just how much of this material is really dissolved, as opposed to being fine sus-pended particulates, remains unknown.

When it comes to zooplankton non-grazing loss terms, namely excretion and mortality (in-cluding model closure that represents all higher trophic levels in the food chain), there is once again considerable variation among models. The model of Fasham et al. (1990), for example, in-cluded a zooplankton excretion term (0.1 day−1) in which 75% was released as ammonium and 25% as DON. The same model allocated zooplankton mortality, the closure term, as 33% to sinking detritus and the remainder to ammonium repre-senting the cumulative losses associated with an infinite chain of predators. The same approach was taken by Anderson and Pondaven (2003) but with allocations of 29%, 33%, and 38% to detri-tus, inorganic nutrients, and DOM respectively. Thus, some models have chosen to include a fraction of zooplankton mortality as DOM (e.g., Blackford et al, 2004) whereas others have not (e.g., Grégoire et al, 2008; Tian et al, 2000). The justification of all these values is not clear and often represents little more than guestimates or values derived from tuning models to data.

Some recently published models include non-grazing bacterial mortality as a source of DOM. It is usually parameterized as a con-stant biomass-specific rate (e.g., Anderson and Williams, 1998; Grégoire et al., 2008) and rep-resents viral infection and subsequent lysis. This process may account for 10-50% of bacte-rial mortality (Fuhrman, 1999) with the result-ing lysis products fuelling microbial production (Noble and Fuhrman, 1999). Anderson and Ducklow (2001) included viral lysis of both phy-toplankton and bacteria in their simple steady-state model of the microbial loop, concluding that production of DOC by this pathway is rel-atively small. Keller and Hood (2011) explicitly included viruses as a state variable in a complex model of DOM cycling, with lysis of bacteria contributing to both DOM and detritus pools. There is the continual problem of gauging just what of a lysed microbe is truly dissolved, versus being micro-particulate.

Finally, there is the decision whether to allo-cate detritus breakdown to DOM, which has al-ready been addressed in some detail in Section II.A. above. It is interesting to note that many of the recently published models with an ex-plicit ecosystem (Table 15.3) include detritus as a source of DOM, in contrast to models without an explicit ecosystem that do not (Table 15.2).

Fluxes to DOM produced via the various processes described above have to be divided between pools of different lability. “Passive” exudation (though likely often allied to active recovery) is often assumed to consist solely of small, labile molecules such as sugars and amino acids (e.g., Anderson and Williams, 1998; Billen and Becquevort, 1991). This can be distinguished from “active” exudation of DOC that can also include polymeric such as extracellular polysac-charides and may be allocated to the semi-labile pool because it is devoid of nutrients to support bacterial growth (e.g., Polimene et al., 2006). Christian (2005) found that under high-light, low nutrient conditions this DOC source must be pri-marily labile to prevent excessive accumulation


of DOC. There is marked variation in the par-titioning between labile and semi-labile pools between models, even more so than with alloca-tion of the other DOM source terms. For exam-ple, Anderson and Williams (1998) used a labile fraction of 10% for non-phytoplankton processes compared to Anderson and Pondaven (2003) and Grégoire et al. (2008) who used labile frac-tions of 70% and 65%, respectively. Keller and Hood (2011) partitioned just 25% of zooplank-ton sloppy feeding and bacterial non-grazing mortality, but 40% of phytoplankton mortality, to labile DOM. Inevitably, these poorly known parameters are set by tuning to data, giving rise to at least some of the variability seen between models. Ultimately, as we shall turn to, lability depends on chemical structure and the need of organisms to utilize the structure. The latter is often linked to the presence of other nutrients (glucose is only “labile” when there is sufficient N, P (etc.) available to support its consumption and assimilation). However, a complicating fac-tor affecting the longevity of DOM is the variable assimilation efficiency that is linked to nutrient sufficiency. Thus, P-limited bacterial growth can be far less efficient at assimilating DOC (Flynn, 2005; Pirt, 1982).

III MODELING THE ROLE OF DOM IN OCEAN BIOGEOCHEMISTRY

There have been several important develop-ments in global-scale modeling of the role of DOM in ocean biogeochemical cycles since the first edition of this book (Christian and Anderson, 2002). First is the completion of the Ocean Carbon Cycle Model intercomparison project phase II (OCMIP-2, Najjar et al., 2007) which used a common biogeochemistry model with the key objective to diagnose the effects of intermodel differences in ocean circulation on modeled distributions of dissolved inorganic carbon (DIC) and oxygen. The experiment fea-tured global ocean circulation models with a

common, rather simplistic, scheme for biogeo-chemistry. The OCMIP-2 “biology” (Table 15.1) does not include explicit biology but calculated new production by nutrient restoring (Section II.A). A large fraction (two thirds) of this new production was allocated to DOM while the remainder, POM, was remineralized at various depths according to a parameterized (“Martin curve”) representation of particle flux; the ba-sic scheme is the same as used by Najjar et al. (1992). It is important to note that an alloca-tion of two thirds of new production to DOM does not entail an equivalent 2:1 ratio of DOM to POM in the export flux. While 100% of the POM is exported, DOM produced in the surface layer is advected laterally by the model currents such that total carbon export from the euphotic zone in OCMIP-2 models is around 80% par-ticulate, consistent with observations (Hansell et al., 2009; Najjar et al., 2007). The remineraliza-tion lifetime of DOM was set at 2 years, which is relatively high compared to other similar modeling studies (Table 15.1). The intercom-parison study concluded that the semi-labile DOC distribution is “highly sensitive to ocean circulation, particularly the exchange of water between the mixed layer and the thermocline” and that there is need for better observational constraints, particularly “quantifying the DOM pool” and “constrain(ing) C:N and C:P ratios of semi-labile DOM” (Najjar et al., 2007). The chosen model structure may in fact have made the modeled distributions of organic and inor-ganic carbon more sensitive to ventilation pro-cesses than they would otherwise have been in a model more heavily weighted toward particu-late export. Najjar et al. (2007) noted that models that include mixed-layer physics have greater relative export of DOM as a result of seasonal convection. The exact time scale chosen for rem-ineralization of DOC also affects the sensitivity of ocean tracer distributions to modeled circula-tion processes.

A second important development has been the incorporation of ocean biogeochemistry,

III MODElINg THE ROlE Of DOM IN OCEAN BIOgEOCHEMISTRy 649

in some cases including DOM, into coupled ocean-atmosphere models. This entails a huge computational burden and so has led to the devel-opment of Earth System Models of Intermediate Complexity (EMICs). Decreased complexity is included in one or both components of the cou-pled ocean-atmosphere system, making EMICs suitable for experiments of greater duration than is generally possible with full dynamic cou-pled models (see Chapter 1). Representations of ocean and terrestrial biogeochemistry have also been included in recent years. While early EMICs used extremely simplified ocean models (e.g., Stocker et al., 1994), many of those now in use include a three-dimensional OGCM (albeit with coarse resolution) along with a highly sim-plified atmosphere model.

In recent years, several experiments have been published using EMICs that include DOM in the ocean biogeochemistry model (Matsumoto et al., 2008; Ridgwell et al, 2007; Schmittner et al., 2005). In one instance, a sophisticated data assimilation scheme was applied to estimate the parameter values that would allow the model to best repro-duce the present-day global ocean distributions of phosphate and alkalinity, as well as other tracers of physical circulation (Ridgwell et al., 2007). The OCMIP-2 approach to representing DOM was used, with DOM as 66% of produc-tion and a turnover time of 2 years, but without optimization of these parameters. Optimization was carried out for the POC flux parameters, al-though changes from their a priori values were small (~10% for both the remineralization length scale and the fraction of export subject to remin-eralization). The C:P ratio was assumed to be in Redfield proportion, 106:1. The assumptions of a high production of DOM and a low C:P ratio are not as artificial as they may appear at first glance (see Najjar et al., 2007), although they were not tested in the parameter optimization experiment. Matsumoto et al. (2008) reevaluated some of the physical parameters of this model with respect to their effect on ocean ventilation and subsurface tracer distributions.

Most recently, an ensemble of model simu-lations was carried out that comprised the 5th Coupled Model Intercomparison Experiment (CMIP5).The aims of this comparison were to evaluate the performance of models in simulat-ing the recent past, provide projections of future climate change and to understand the impact of key feedbacks such as clouds and the carbon cycle in those projections. Output from climate modeling groups around the world is being de-posited into a globally accessible public-domain archive (Taylor et al., 2012). The models in ques-tion are not EMICs but, rather, fully dynamic coupled models. This is the first experiment of its kind that includes ocean biogeochemical fields. To date, output from more than 15 mod-els have been submitted, of which at least 12 include DOC. This data resource is new and, as such, there is little or no published literature to draw upon. Hence, we include here a brief syn-thesis of these model results.

The global mean DOC profiles (for waters >200 m deep; semi-labile DOC only) of CMIP5 models can generally be grouped according to the attenuation length scale (Figure 15.4). Several models have elevated concentrations of semi-labile DOC within the upper 100 m, which rapidly drop off to near zero in the waters below, while others have appreciable concentrations throughout the upper kilometer that attenuate gradually through the thermocline. Most of the models have surface concentrations of semi-la-bile DOC of 10-25 μM, although a few are higher and one is substantially lower. Najjar et al. (2007) estimated the “observed” global mean surface concentration of semi-labile DOC as 29 μM; this is within the range of CMIP5 models although, in general, the models seem to have a slight low bias as compared to the OCMIP-2 models, most of which exhibit rapid attenuation of DOC with depth (Najjar et al, 2007). The relatively deeper penetration of DOC in the CMIP5 models may reflect greater ventilation of the intermediate and deep ocean due to higher resolution and improved mixing parameterizations, as well as


more widespread inclusion of dynamical mixed-layer models.

The annual mean geographic distribution of surface DOC is shown in Figure 15.5, along with the seasonal cycles of zonally averaged surface DOC in Figure 15.6 (as in Figure 15.4, shelf waters with depths <200 m are excluded). The models show striking differences, with only half reproducing the generally observed trend of high concentrations in the tropics, decreasing towards the poles (Hansell et al., 2009). Others have high but transient accumulations in the mid-latitudes in summer, while some models have high concentrations both in the tropics and in summer in the mid-latitudes. Of all the models, only GISS-E2-R-CC reproduces the high concentrations of DOC in the stratified waters of the Arctic as a result of terrigenous inputs via rivers (Raymond et al., 2007). It is possible that the other models are underestimating the global mean due to poor representation of the Arctic,

neglecting fluvial DOC sources (see Chapter 14 regarding Arctic DOM). Many of the models show DOC concentrations (excluding the re-fractory baseline) near zero in the surface wa-ters of the Southern Ocean for most or all of the year (Figure 15.5), a consequence of upwelling of deep water. Winter observations in the Ross Sea by Carlson et al. (2000) show uniform con-centrations (at ~41 μM) down to 800 m, the data corresponding to “background” (i.e., refractory) concentrations. Significant accumulations did occur in spring and summer in the upper 200 m although it should be noted that the Ross Sea, even well beyond the continental shelf, is gen-erally more productive than most open ocean regions of the Southern Ocean. Nonetheless, sev-eral models do show significant accumulations in the Southern Ocean during austral summer (Figure 15.6). There are insufficient observations to know whether these modeled seasonal accu-mulations of DOC are realistic or not. There are few data available that allocate DOC to different classes of lability in a manner that is consistent with model formulation.

Several groups submitted more than one model, with identical biogeochemistry but dif-ferent ocean circulation models (or the same ocean but different atmospheres) and in most cases they are sufficiently similar that it was not considered useful to display both in Figures 15.5 and 15.6. The differences between two or three variants of the same model are generally much smaller than the differences among different bio-geochemical models (Figure 15.4). Thus, while the effects of circulation biases on subduction and subsurface remineralization of DOM may be important in terms of its role in biogeochemical cycling, modeled global distributions of DOC are mostly a function of the biogeochemical model used. The difference in resolution between the two versions of the MPI-ESM is quite large (about 1.4 × 0.8º vs. 0.5 × 0.5º) but the difference in the mean DOC profiles is very small (Figure 15.4). In both GFDL-ESM2 and GISS-E2, the attenuation length scale (i.e., the penetration depth of surface

0 5 10 15 20 25 30 35 403500

3000

2500

2000

1500

1000

500

0

DOC (mmol m−3)

Dep

th (

m)

CESM1−BGCCNRM−CM5GFDL−ESM2GISS−E2−CCIPSL−CM5MPI−ESMNorESM1−ME

FIGURE 15.4 Global mean profiles of DOC concentra-tion from CMIP5 models (mean for 1986-2005). The black dot and horizontal line represent an observation-based esti-mate of the global mean surface concentration of 29 ± 5 μM. Where a modeling group submitted DOC data from more than one model, all models are shown in the same color and the names in the legend are approximate (not official CMIP5 names). Refractory DOC was subtracted from the GFDL models (the only ones that were posted as total DOC rather than semi-labile) as the global mean for depths >3000 m.

III MODElINg THE ROlE Of DOM IN OCEAN BIOgEOCHEMISTRy 651

produced DOC) is greater in the z-level (GFDL-ESM2M, GISS-E2-R-CC) than in the isopycnic (GFDL-ESM2G, GISS-E2-H-CC) models.

Ocean biogeochemical models have historically had difficulty simulating physical processes that

introduce nutrients into the surface layer of the oligotrophic subtropical gyres, and may signifi-cantly underestimate new production in stratified regions (Hood and Christian, 2008). Proposed res-olutions to this paradox have included mesoscale

CESM1−BGC

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CNRM−CM5

10

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GFDL−ESM2M

20

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IPSL−CM5A−LR

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80

GISS−E2−R−CC

20

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80

MPI−ESM−LR

20

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NorESM1−ME

0

5

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FIGURE 15.5 Global maps of annual mean surface semi-labile DOC concentration (mmol m−3) from eight CMIP5 models (mean for 1986-2005). Where a modeling group submitted DOC data from more than one model, only one is shown here, except for GISS-E2, because the two versions differ quite markedly. The models selected to represent multimodel groups are GFDL-ESM2M, IPSL-CM5A-LR, and MPI-ESM-LR.


upwelling (e.g, McGillicuddy and Robinson, 1997) and N2 fixation (Karl et al., 1997). The latter can only support a limited amount of additional production before phosphorus becomes limiting, even given a relatively flexible N/P stoichiometry (Christian, 2005). Horizontal advection of DOM has at times been suggested as a mechanism for transporting additional “new” N or P to the cen-ters of the gyres (e.g., Abell et al, 2000; Hayward, 1991; Peltzer and Hayward, 1996), but strong ev-idence to support this hypothesis has been lack-ing. Modeling studies in the last few years have

provided a more quantitative underpinning of this conjecture, at least for the Atlantic Ocean (e.g., Roussenov et al., 2006; Torres-Valdés et al., 2009; Williams et al., 2011). As noted by Hood and Christian (2008), “models can provide estimates of quantities that are not directly observable, such as fluxes of nutrients by different physical pro-cesses.” The results of these experiments show that in at least some regions, horizontal advec-tion of DOM from outside the gyres is a source of “new” nutrients comparable to vertical trans-port of inorganic nutrients, although these tend

CESM1−BGC

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itud

e (°

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itud

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N)

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itud

e (°

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itud

e (°

N)

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itud

e (°

N)

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itud

e (°

N)

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itud

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F A J A O

−60−30

03060

−60−30

03060

−60−30

03060

−60−30

03060

−60−30

03060

2468101214

FIGURE 15.6 Annual cycle of zonal mean surface semi-labile DOC concentration (mmol m−3) from eight CMIP5 models (same models as in Figure 15.5).

IV lABIlITy IN fOCuS: CONCEPTS AND DEfINITIONS 653

to be the regions with the lowest total production (Torres-Valdés et al., 2009). The main process in-volved is Ekman transport from the tropics to the subtropics (Roussenov et al., 2006), and there is strong asymmetry across the basin with the transport and remineralization of DOM concen-trated on the western side (Torres-Valdés et al., 2009). The largest gradients (i.e., divergence) in meridional transport of DON and DOP are found between about 20° S and 30° N latitude, but par-ticularly for P this transport can provide a source supporting more than 50% of particulate export in low-productivity regions up to 40-45° N (Torres-Valdés et al., 2009). Williams et al. (2011) illustrate the pathways by which DOM is transported into the gyres, which in the North Atlantic are strongly associated with the Gulf Stream and its associ-ated mesoscale circulation. The supply of DOP appears to exceed (relative to phytoplankton re-quirements) the supply of DON in some regions, which could potentially help to sustain additional inputs of new N from N2 fixation (Abell et al, 2000; Mahaffey et al, 2004; Roussenov et al., 2006).

IV LABILITY IN FOCUS: CONCEPTS AND DEFINITIONS

A Physiological Considerations

The usual approach taken by modelers is to divide bulk DOM into distinct pools, each with a defined lability (e.g., labile, semi-labile, semi-refractory, refractory; see Hansell, 2013 or Chapter 3) specified either as a fixed turnover rate or fixed kinetic parameters. It may then be assumed, for example, that semi-labile material is converted to labile by the action of microbial hydrolytic enzymes (Section II). A well known example is the activity of alkaline phosphatase, an enzyme common in marine prokaryote and eukaryote microbes, which cleaves P from DOP, leaving the phosphate to be taken up and the remaining organic component outside of the cell. Just how realistic is this type of approach? In order to answer this question, it is worth

considering the reasons why DOM accumulates in marine environments. The most obvious hy-pothesis is that the biochemical structure of the material prevents rapid degradation. An alter-native hypothesis, proposed by Thingstad et al. (1997), is that of the “malfunctioning microbial loop” whereby bacteria are unable to consume otherwise degradable DOC released by the food web. They proposed that bacterial growth rate (and thereby consumption of DOM) is kept low by competition with phytoplankton for limiting nutrients. It may be that bacteria in some ma-rine environments are limited by phosphorus, especially in areas such as the Mediterranean (Zweifel et al., 1993; Zohary and Robarts,1998). Another explanation is simply that the concen-tration of the individual compounds is too low to support net uptake against the concentration gradients (e.g., the leak-recovery argument of Flynn and Berry, 1999). While the concentration of bulk DOM may be high, that of the individual components, which are the subjects of transport kinetics, are several orders of magnitude lower (Flynn and Butler, 1986). Indeed, the concen-trations of individual DFAA appear to reflect most closely the inverse of their “desirability” as substrates for consumption. This causes prob-lems for experimental studies. Does one study the flux of a component of DOM that one can measure (perhaps because its flux is low) or one present at vanishingly low values (in which instance which substrate is released, and thence consumed, most)? Factors controlling DOM accumulation are reviewed in the Chapters 3 and 7 and will be described briefly here.

In order to appreciate the differences between the various hypotheses, we distinguish between potential and functional lability. Potential lability refers to whether a compound could ever be read-ily taken up and used by an organism. Functional lability of a compound refers to whether there is the means to readily use it at a point in space and time, and depends on a range of factors includ-ing biomass of consuming organisms, substrate concentration, expression of uptake pathways, and competing nutrient sources.


The potential lability of a compound depends on its biochemical composition and biophysi-cal structure which dictates how readily it may be catabolized if nothing were to interfere with that consumption. A distinction can be made here between substrates that can be taken up di-rectly versus those that require prior hydrolysis by extracellular enzymes. Only molecules below a certain size (ca. a few hundred Daltons) can be transported directly although small size does not in itself guarantee high potential lability. Histidine, for example, is not so readily catabo-lized (laying at the end of a unique biochemical pathway), as the other basic amino acids (Flynn, unpublished; but see Flynn and Butler, 1986); this protein amino acid is thus functionally semi-labile. Of the forms of DOM that cannot be directly taken up, most contain components that can be utilized after partial enzymatic hydro-lysis and so molecular weight is not therefore indicative of potential lability although such compounds could be considered as semi-labile depending on the energetic and metabolic con-straints on the degradation step.

Recalcitrance is typically related to chemi-cal structures. An example is the tertiary bond, which presents a steric hindrance to terminal oxidation, and is perhaps a universally difficult structure to break (Alexander, 1994). The sub-stitution of sulfate, nitrate, and the halogens chlorine and bromine as xenophores may sim-ilarly impede catabolism (Alexander, 1994), as may toxins that have complex multi-bond ring configurations. Finally, whereas many of the compounds that accumulate are in principle bi-ologically degradable, the cost of doing so may in practice outweigh the benefits in terms of en-ergy and/or nutrients obtained (e.g., Floodgate, 1995), especially when considering the availabil-ity of alternatives. In large measure this expla-nation is another facet of the low-concentration issue mentioned above; if concentrations were high enough it would be likely that some organ-isms would evolve mechanisms to extract ele-ments and energy from these compounds.

The consumption and utilization of any one component of DOM by microbes is thus influ-enced by various factors associated with poten-tial and functional lability: supply sufficient to support use, suitability for use, the specific need for particular compounds and the absence of competing alternative compounds. Intracellular concentrations of substrates in microbes tend to be in the low mM range in order to efficiently sup-port enzymatic processes (e.g., Bjørnsen, 1988; Flynn, 1990). Regarding substrate concentrations in the surrounding environment, it is interesting to compare marine systems with research carried out on non-marine (especially medically import-ant) bacteria (e.g., Egli et al., 1993; Harder and Dijkhuizen, 1982). External substrate concentra-tions in typical microbiology laboratory studies tend also to be in the mM range, which is orders of magnitude higher than concentrations of indi-vidual substrates observed in natural aquatic en-vironments (typically nM, or lower). Thus, while Egli et al. (1993) detected “no apparent limiting nutrient concentration” for the utilization of sug-ars by E. coli, substrate concentrations recorded as “below detection” in biomedical studies are typically still far in excess of those in the ocean. The point to note here is that it is the concen-tration of substrates individually that counts, rather than in the total, because each is taken up using specific transporters (Driessen et al., 2000; Flynn and Butler, 1986). It may also be that in some instances individual substrates are present in concentrations that are too low for net con-sumption (see molecular diversity hypothesis in Chapter 7). This situation may be exacerbated by temperature in the deep ocean because sub-strate affinity may decrease at low temperatures (Aksnes and Egge, 1991; Nedwell, 1999).

Different sources of nutrition are favored, via investment in appropriate transporters and through (de)repression of biochemical path-ways, by different microbial communities, so what is semi-labile for one may be labile for another (Carlson et al., 2011). A good example is provided by observations of DOC dynamics

IV lABIlITy IN fOCuS: CONCEPTS AND DEfINITIONS 655

at the Bermuda Atlantic Time Series site where semi-labile DOC in surface waters is resistant to bacterial degradation whereas it is more readily utilized by bacteria from 250 m depth (Carlson et al., 2004). Bacteria can be divided into several different groups (Cottrell and Kirchman, 2003; Giovannoni and Stingl, 2005), each of which has its own characteristic pattern of substrate utiliza-tion (Elifantz et al., 2005). They invest in transport systems that selectively target different resources and so will vary for communities inhabiting, for example, different parts of the water column. Some bacteria are specialists operating on specific C compounds, with others having a general strat-egy (Gómez-Consarnau et al., 2012). Rapid shifts are seen in bacterial community structure in re-sponse to changing environmental conditions such as altered nutrient status (Teira et al., 2010).

The presence of preferred carbon sources pre-vents the expression of catabolic systems that enable the use of secondary substrates. Enzymes should only be synthesized for substrates that are present in sufficient quantities to warrant the cost of synthesis (Floodgate, 1995; Lengeler, 1993). Regulation is achieved by various mechanisms including transcription activation and repression and control of translation by an RNA-binding protein, in different bacteria (Görke and Stülke, 2008). A good example is the acquisition of phos-phorus by marine bacteria. The preferred source of P for growth is inorganic phosphate but this element can also be obtained from DOP, which is dominated by two classes of compounds, phos-phorus esters, and phosphonates. Phosphonates contain a highly stable CP bond, in contrast to the more labile COP phosphate ester bond. DOP in surface water exhibits a high proportion of phosphonates relative to phosphate esters, in-dicating that utilization by bacteria is a selective process with phosphate esters as the preferred source (Clark et al., 1998). Conditions where inorganic P (and presumably phosphate esters) is scarce lead to the introduction of genes con-trolling phosphonate uptake and proteins that cleave the CP bond (McGrath et al., 1997), an

adaptation that appears widespread in marine bacteria (Martinez et al., 2010). The derepression of these genes and associated enzymes thus ren-ders phosphonates functionally labile, a course of action that would be energetically wasteful under conditions when other sources of P are in plentiful supply. The same mechanism, induc-tion of phosphonate transport under P-stressed conditions, is also seen in the diazotroph Trichodesmium (Dyrham et al., 2006). In similar fashion, the synthesis of hydrolytic enzymes is repressed when concentrations of readily utiliz-able substrates fulfill bacterial requirements for metabolism and growth (Gajewski and Chróst, 1995; Unanue et al., 1999). Under variable condi-tions, repression, and derepression of metabolic pathways leads to the exhibition of diauxic selec-tivity, where a substrate is only used when the preferred source is exhausted (Egli et al., 1993). The activity of ecto- and exo-enzymes (like the leak-recovery mechanism of Flynn and Berry, 1999) may also have a biomass-dependancy in-teraction because each enzyme molecule benefits more than just the individual cell, while the en-zymes themselves are substrates for degradation.

B Modeling Implications

As described above, the division of the bulk DOM pool into a limited number of state vari-ables (labile, semi-labile, etc.) of predefined la-bility is simplistic because (1) lability is dynamic, depending on the availability of different sub-strates and bacteria physiology and (2) substrates are not utilized in bulk but, rather, individual compounds may be taken up selectively to meet bacterial requirements.

The use of fixed kinetics for the labile pool may be reasonable in some cases, especially where this represents primary metabolites such as sugars, amino sugars, protein amino acids, nucleotides, ATP, etc. As used in models, the labile pool usu-ally also includes HMW compounds (combined primary metabolites) that require extracellular hydrolysis with no account taken of possible


repression of its use by early products of nutri-ent assimilation and other primary metabolites (Flynn, 1991; Magasanik, 1988). Semi-labile DOM is often the primary focus for modelers because of its roles in seasonal accumulation in surface waters, vertical distribution of DOM in the water column, and export of carbon to the deep ocean. In models where this pool is assigned a fixed life-time, ranging from months to years (Section II), semi-labile DOM is effectively defined as being comprised exclusively of substrates that are of low potential lability from the outset. It is easy to envisage DOM lifetimes being maintained or increasing as material is exported below the eu-photic zone into the deep ocean as compounds of low potential lability remain unused by bacteria. Such a picture is simplistic because it is functional lability that may define much of the semi-labile pool. Substrates that are functionally semi-labile in the euphotic zone may become functionally labile in deeper waters as transporters for their uptake by bacteria are derepressed. Other models simulate the degradation of semi-labile material to labile, usually invoking the action of hydro-lytic enzymes, which seems entirely reasonable as combined primary metabolites will be acted on in this way. However, the conversion of semi-labile to labile DOM is parameterized as a fixed rate or by applying a fixed kinetic parameters that does not account for aspects of functional lability re-lating to competing substrates. In some instances induction of mechanisms to enable consumption of newly available substrates may occur, while in other instances a lack of repression signals from within the cell may result in expression of an ability to exploit an alternative nutrient source should such a source become available.

Most current models assume that dissolved or-ganic nutrient elements (N, P, Fe, etc.) and carbon are taken up in proportion to their bulk availability, in the same way as different food particles are (in-correctly assumed to be) consumed by zooplank-ton. This assumption ignores the importance of the concentration of individual chemical moieties and also of selectivity between those chemicals.

The advantage of this assumption, however, is that the fate of the material consumed can then be calculated in accord with stoichiometric for-mulations (e.g., Anderson, 1992; Touratier et al., 1999). When nutrient elements such as N or P are limiting, excess carbon is left unutilized and may be consumed via the increased respiration that ac-companies nutrient stress (Neijssel and Tempest, 1975; Pirt, 1982; Flynn, 2005) or release of C-rich polymers, notably polysaccharides (Decho, 1990). Laboratory studies have shown that bacteria have low C growth efficiency when confronted by large amounts of primary metabolites such as glucose (e.g., Goldman et al., 1987; Tezuka, 1990). Nevertheless, it is likely the case that when bac-teria experience a heterogeneous resource envi-ronment, as is usually the case in the ocean, they strip out selected substrates. If this stripping is done to obtain nutrient elements, it would lead to an accumulation of DOC (of decreasing lability) that would not be predicted using conventional stoichiometric models. This could be seen as a facet of so-called malfunctioning microbial loop of Thingstad et al. (1997). Thus, the assumption of stoichiometric linkage between C and other elements, without the potential for nutrient strip-ping, is likely untenable for N, and is certainly so for P.

V DISCUSSION

Our survey of the literature has highlighted both similarity and diversity in the approaches taken for modeling DOM in marine systems. The similarity lies in the convenient approach of dividing DOM into labile, semi-labile, and re-fractory pools, although it should be remarked that differences in terminology exist between studies. The diversity, which is enormous, is in the chosen formulations for DOM production and consumption. How, one may ask, is it possi-ble to reliably simulate the dynamics of DOM in marine systems given this diversity in formula-tions and associated parameterization? More

V DISCuSSION 657

fundamentally, we lack the chemical data (con-centrations and fluxes) against which to config-ure and test such models. Unsurprisingly then, where common approaches exist, these formu-lations and associated parameterizations remain wide open to question. As we have shown, for example, lability is a dynamic concept and yet, with little in the way of alternatives, modelers today continue to use “labile,” “semi-labile,” and “refractory” pools as they were first concep-tualized in the 1990s. Indeed, despite the current emphasis on increasing complexity and realism in models, it is interesting to note that some of the models in use today have chosen not only this basic construction, but also adopt some of the associated early parameterizations. For exam-ple the model of Anderson and Williams (1998), which includes a fixed C growth efficiency for bacteria, Michaelis-Menten consumption of la-bile and semi-labile pools and stoichiometric regulation of consumption/remineralization of ammonium, remains a popular choice (e.g., Grégoire et al., 2008; Keller and Hood, 2011).

To some extent, the differences seen in DOM parameterization between models can be at-tributed to objectives and focus. Simple ap-proaches have often, for example, been used in OGCMs (Section III). Multiple DOM source terms, the whole issue of lability, a mixed bac-terial community, and a heterogeneous envi-ronment all serve to make this a very complex and challenging system to model. Our under-standing of the web of interactions is incomplete and so the parameterization of flows and the compartmentalization of DOM into meaning-ful structures in models are far from straight-forward. Before addressing these difficulties, it is worth emphasizing that models have been relatively successful at simulating DOM in the ocean. On what basis have they apparently been so effective? There is a parallel here, we believe, with the nutrient-phytoplankton- zooplankton-detritus (NPZD) ecosystem models. Despite their simplicity, NPZD models have been largely successful as simulating bulk system properties

such as chlorophyll and primary production, which are constrained by nutrient availabil-ity, light, and grazing (Anderson, 2005; Franks, 2002). In the case of DOM, production is con-strained largely by primary production, graz-ing, and detritus turnover although modelers have a free hand to allocate between the labile and semi-labile pools. Turnover rates are in re-ality variable but, nevertheless, mean lifetimes of months to years for semi-labile material are considered robust and reliable estimates. Simple models using DOM production terms as frac-tions of primary production, grazing and detri-tus, with a fixed turnover rates, may therefore do remarkably well.

The future development of marine biogeo-chemical models of DOM does, however, re-quire assessment of the considerable difficulties of modeling this complex system. For example, there is the issue of just what constitutes DOM in the first place. Modelers have steered clear of the difficulties of characterizing DOM bio-chemically, adopting stoichiometric approaches to describing DOM instead. Processes such as phytoplankton exudation and release via messy feeding may therefore be assigned, for example, different C:N ratios thereby allowing simulation of the accumulation of C-rich DOM that is char-acteristic of many systems (e.g., Williams, 1995). The simulation of DOM consumption by bacte-ria is simplistic in that uptake is calculated as a function of bulk concentration whereas in reality DOM constitutes many compounds each with its own kinetic constraints. Bacteria selectively remove certain compounds; with time there will be an inevitable change in composition of DOM, with an increasing proportion of less energeti-cally and less nutritionally favored chemicals remaining in solution.

Perhaps the most thorny issue for modelers is that of DOM lability and how to compartmen-talize DOM into meaningful state variables in this regard. If one considers the traditional defi-nitions, then exclusion of labile DOM in some models may be argued as reasonable on the basis


that this pool has a fast turnover (hours to days) and so contributes little to C export via mixing or subduction. However, if one considers the role of DOM and bacteria in supporting primary pro-duction through the growth of the mixotrophic protists (Mitra et al., 2014), then describing the dynamics of bacteria growth over hours-days-weeks certainly warrants consideration; primary production supported by labile DOM could cer-tainly contribute to C export and to the MCP. The new paradigm for planktonic primary production proposed by Mitra et al. (2014) places bacteria and DOM in a new light, and should act as a stimu-lus for DOM research and modeling. At the other end of the spectrum, the question of whether to include refractory DOM depends on the times-cales of interest. Its turnover rate is so slow that it appears unnecessary to include it dynamically in models addressing climate change within the next 200 years, although recent work that points to localized removal of otherwise defined refrac-tory DOC in the deep ocean (Hansell and Carlson, 2013) and emphasis on the MCP merits attention (Jiao et al., 2010). It is the semi-labile pool that is usually of greatest interest because of its role in carbon export and transport of nutrients within oligotrophic gyres. Yet, as we have discussed (Section IV), the factors that render DOM semi- labile are multifaceted such that there is a need to separate potential and functional lability. The latter depends on various factors including sup-ply sufficient to support use, suitability for use, the specific need for particular compounds and the absence of competing alternative compounds. The way forward is, however, by no means clear. Matters are only complicated further by the fact that microbes constitute a mixed assemblage with varying growth requirements and, moreover, the factors controlling growth are not completely understood. Bacteria may be separated into sev-eral major groups (Cottrell and Kirchman, 2003; Giovannoni and Stingl, 2005), each with its own physiological attributes (Pinhassi and Hagström, 2000) and even so the individual organisms bio-chemistry changes in response to external factors.

An understanding of the relationship between bacteria community structure and biogeochemi-cal function is therefore crucial for model devel-opment (Fuhrman and Steele, 2008; Höfle et al., 2008; Mitra et al., 2014).

The greatest diversity in approach by mod-elers is seen in the specification of source terms and turnover rates of the chosen DOM pools, as well as the associated representation of bac-teria. Perhaps the most surprising difference seen between models is in the specification of the fate of detritus. In some models detritus turnover goes to DOM whereas in others rem-ineralization is directly to inorganic nutrient and CO2. Modeling studies by Anderson and Williams (1999) and Bendtsen et al. (2002) sug-gest that the majority of DOM present at depth is derived from turnover of sinking detritus rather than from DOM exported via mixing from the surface ocean. At first sight, it seems surprising that many modeling studies, espe-cially those using OGCMs, have chosen not to include detritus turnover as a source term of DOM. Of the models listed in Table 15.1, for ex-ample, all of those published in the last 10 years chose this option. Depending on objectives, it may be that this choice is not particularly sig-nificant. If the aim is to model distributions of inorganic nutrients and carbon, including those in the deep ocean, then it may not matter much if detritus is remineralized directly to nutrient and CO2. DOM is merely an intermediary. If, on the other hand, the aim is to model DOM distributions (e.g., Hansell et al, 2012), then we would argue that remineralization of detritus to DOM should be included. Further, detritus turnover is a significant source of nutrients and carbon in the euphotic zone. For example, Yool et al. (2011) simulated global biogeochemistry using an intermediate-complexity ecosystem model in which the turnover of detrital-N was allocated to DIN. Their model suggested that detritus turnover accounted for 35% of the source terms of DIN (0-100 m), with phyto-plankton mortality, and zooplankton grazing,

V DISCuSSION 659

excretion, and mortality accounting for 15%, 23%, 13%, and 13% respectively. Further work is needed to assess the sensitivity of predicted biogeochemical fields to the choice of whether to allocate detritus turnover to DON or DIN. Similar arguments can be made for carbon. The accumulation of DOC in surface waters is potentially significant in, for example, the sea-sonal drawdown of DIC and pCO2 (Anderson and Pondaven, 2003).

The reality is that detritus turnover is di-vided between DOM and inorganic nutrients, rather than 100% either way, the fractionation depending on a number of processes, and crit-ically upon the time scale in question. Both bac-teria and zooplankton are likely responsible for this turnover and organic matter is recycled via various pathways including DOM, suspended POM, and remineralization to inorganic forms (Giering et al., 2014). Particle-attached bacte-ria release large amounts of DOM to the sur-rounding water as the solubilization products of enzymatic hydrolysis (Cho and Azam, 1988; Smith et al., 1992). Zooplankton consume detri-tus, recycling it both as fecal material and DOM (Lampitt et al., 1990). Improved understanding and quantification of these processes is needed in order to formulate and parameterize the pro-duction and fate of DOM in the ocean.

Another point of departure in DOM models is whether or not bacteria are explicitly repre-sented. In some cases, modelers have chosen to circumvent the difficulties of representing bac-teria by not including them as a state variable, parameterizing DOM turnover using fixed rates (e.g., Llebot et al., 2010; Salihoglu et al., 2008). There is little evidence to suggest that these models are performing any worse than those that do include explicit bacteria. Nevertheless, it is unsurprising that models aimed at under-standing the dynamics of the microbial loop and DOM cycling in marine systems usually favor the explicit representation of bacteria. Certainly descriptions of primary production, by protists other than by diatoms, which involve bactivory

(Flynn et al., 2013; Mitra et al., 2014) warrant a renewed consideration of the whole issue of bac-teria PFT models. New parameterizations have been developed, for example, by dividing bac-terial respiration into basal and active fractions giving rise to variable BGE depending on sub-strate availability (e.g., Flynn, 2005; Polimene et al., 2006; Luo et al., 2010). Our understanding of BGE, and notably the stoichiometric control thereof, nevertheless remains rudimentary de-spite excellent reviews (e.g., del Giorgio and Cole, 1998). The use of Michaelis-Menten ki-netics and Blackman-style limitation of growth are also open to question. Although there have been calls for a more considered development of models for the bacterial consumption of multi-ple substrates (Button, 1993; Egli et al., 1993; van Dam et al., 1993), Michaelis-Menten remains the norm and may be criticized in the same way as equivalent models of phytoplankton growth (Flynn, 2003). Quota models may help (e.g., Luo et al., 2010) although other factors may also be important, such as the fact that bacteria are a mixed assemblage and simple responses to nu-trient pulses may not be expected in the natural environment. The modeling of substrate uptake, bacterial nutrition, and resulting growth effi-ciency is probably more challenging than that for any other member of the plankton because the range of potential substrates and nutrient in-teractions is so great. Complex models have been developed (i.e., Vallino et al., 1996) that describe bacterial utilization of DOM based on growth rate optimisation subject to constraints on en-ergy, redox reactions, substrate uptake kinetics, and the C:N of bacteria. The C:N:P model of Flynn (2005) enables considerations of changes in growth efficiency with C, N, and/or P stress. The considerable variability in substrates expe-rienced by bacteria in their natural environment makes models such as these difficult to employ in an ecosystem context.

Despite all the above, several reasons for the inclusion of DOM can be proposed. DOC plays a significant role in export to the deep ocean


(Hansell et al., 2009). The paradigm shift sug-gested by Mitra et al. (2014) sees an overturn of the concept of competition between the micro-bial loop and the “traditional food chain.” The new paradigm (Figure 15.7) sees an intermedi-ate relationship, not a competition, between pri-mary production and bacteria mediated through DOM, a concept put forward by Flynn (1988). The seasonal accumulation of DOM alters nutrient inventories and pCO2 in surface waters. Lateral transport of dissolved organic nutrients may af-fect primary production and nutrient budgets in the oligotrophic gyres. Most marine ecosystem models today do therefore incorporate DOM, al-though there are exceptions (e.g., Liu and Chai, 2009; Yool et al., 2011). The performance of two versions of a global ocean box model, with and without DOM, was compared by Popova and Anderson (2002). Export fluxes were relatively insensitive to the choice of whether or not to include DOM because they were dominated by sinking particles. Significant changes were nev-ertheless noted in primary production and the f-ratio, giving rise to changes in alkalinity that could be important when calculating pCO2 and

the resulting air-sea flux of CO2. Similar find-ings were made by Schmittner et al. (2005). The case for modeling DOM and the microbial loop is clear. The problem is that it is, as we have shown, it is a complex business. When explain-ing why DOM was excluded from their global biogeochemical model, Yool et al. (2011) cited the previous edition of this book (Christian and Anderson, 2002), summarizing our conclusions as “Assigning equations and parameter values for DOM cycling is fraught with difficulty given our limited understanding of interactions within microbial communities and of the physiology of heterotrophic bacteria.”

Models have played a key role in the quanti-fication and understanding of the biogeochemi-cal cycling of DOM in the ocean. Yet, as we have shown, published equations and parameter-izations are disparate and, if one were feeling uncharitable, can easily be criticized as being simplistic or unrealistic. Referring to this lack of consensus, Christian and Anderson (2002) com-mented that “the mathematical formulation of these terms remains tentative and speculative.” Although the description of DOM cycling has

FIGURE 15.7 Schematic showing the detailed involvement of bacteria and DOM for the supply of nutrients to support primary production (yellow arrows) in the traditional paradigm for the linkage between marine primary production, DOM, and bacteria (a) versus that in the new paradigm suggested in (b) by Mitra et al. (2014). In the former, bacteria compete with primary producers for DIN, DIP, and Fe while their growth is supported by DOM released with primary production. Nutrients are then regenerated via the activity of microzooplankton. In the new paradigm, there is a direct support of pri-mary production via nutrient acquisition in bactivory, with the traditional functionality of microalgae and microzooplankton merged in protist mixotrophs (see also Hartmann et al., 2012). Black arrows indicate predatory links.

Growth

Growth

DIN

DIPFe

DICBacteria

DOM

Microzooplankton(a)

Microalgae

DOM

DIC

Growth

DIN

DIP

Fe

Bacteria

Mixotroph

(b)

REfERENCES 661

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biogeochemistry of marine dissolved organic matter || modeling dom biogeochemistry

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