biogeochemistry of marine dissolved organic matter || modeling dom biogeochemistry

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  • 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 Pomeroys (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. Vzina 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 Pomeroys 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 Bermans 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 Qur 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

  • 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 day1 and 0.007 day1, 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

  • 638 15. MODElINg DOM BIOgEOCHEMISTRy

    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 weigh

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