comparative simulations of dissolved organic matter cycling in idealized oceanic, coastal, and...

Journal of Marine Systems 109–110 (2013) 109–128

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Journal of Marine Systems

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Comparative simulations of dissolved organic matter cycling in idealized oceanic,coastal, and estuarine surface waters

David P. Keller a,⁎, Raleigh R. Hood b

a IFM-GEOMAR, Leibniz-Institut für Meereswissenschaften, Düsternbrooker Weg 20, 24105 Kiel, Germanyb Horn Point Laboratory, University of Maryland Center for Environmental Science, P.O. Box 775, Cambridge, MD 21613, USA

⁎ Corresponding author. Tel.: +49 0431 600 4513.E-mail address: [email protected] (D.P. Keller)

0924-7963/$ – see front matter © 2012 Elsevier B.V. Alldoi:10.1016/j.jmarsys.2012.01.002

a b s t r a c t
a r t i c l e i n f o
Article history:Received 7 June 2011Received in revised form 4 November 2011Accepted 15 January 2012Available online 21 January 2012

Keywords:Dissolved organic matter cyclingEcosystem modelDissolved organic nitrogenDissolved organic carbon

In this paper we used a steady-state ecosystem model that simulates both dissolved organic carbon (DOC)and nitrogen (DON) cycling to study how the planktonic community structure, nutrient availability, anddissolved organic matter (DOM) loading affect these cycles in idealized oceanic, coastal, and estuarine surfacewaters. The model was able to reproduce DOM and planktonic biomass distributions, uptake rates, andproduction rates (including DOM) that fell within ranges reported for oceanic, coastal, and estuarine systems.Using a sensitivity analysis we show that DOM cycling was intricately tied to the biomass concentration,distribution, and productivity of plankton. The efficiency of nutrient remineralization and the availability ofinflowing nutrients and DON also played a large role in DOM cycling. In these simulations the largest autoch-thonous source of DOC was always phytoplankton exudation while important sources of DON varied consid-erably. In the oceanic simulations heterotrophic bacteria were particularly important for mediating DOMcycling because they were the primary agents that controlled nutrient recycling and supply (i.e., strongbottom-up control). In contrast, in the estuarine simulations mortality (mainly from grazing and virallysis) had the most influence on DOM production. However, DOM cycling was generally less dependent oninteractions between plankton in the estuarine case because of high nutrient and DOM loading. The coastalsimulations were somewhere in between. In all simulations competition between different size classes ofphytoplankton also played an important role in DOM cycling.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

Marine dissolved organic matter (DOM) is one of the Earth's majorreservoirs of bioreactive elements such as C, N, and P. While mostDOM is old (4000–6000 years) and very resistant to degradation(Bauer et al., 2002; Benner, 2002), some of it plays a more activerole in biogeochemical cycles with biological processes mediatingmany of the most important fluxes into and out of the DOM pool.Fifty percent, or more, of the carbon fixed by phytoplankton eventu-ally flows through the DOM pool where much of it is consumed bybacterioplankton (del Giorgio and Cole, 1998; Ducklow and Carlson,1992). Marine primary production is very dynamic with the annualmean global productivity of 45–50 Gt C being carried out by phyto-plankton with a biomass of~1 Gt C (Carr et al., 2006) and an estimat-ed turn over time (i.e., replacement time) of 2 to 6 days (Behrenfeldand Falkowski, 1997). Since marine phytoplankton contribute rough-ly half (48%) of the biosphere's net primary productivity (Behrenfeldet al., 2006b; Carr et al., 2006) these interactions with the DOM pool

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can potentially have a large influence on global biogeochemicalcycling. A significant amount of research has been conducted tounderstand DOM cycling in the oceans and we now have estimatesof many of the biological, chemical, and physical processes involved(Hansell and Carlson, 2002; Hansell et al., 2009). However, despitedecades of work there are still some fundamental aspects of DOMcycling that are poorly understood.

In the euphotic zone, where the most rapid DOM cycling occurs,several processes are responsible for DOM production. These includeextracellular release by phytoplankton (Baines and Pace, 1991;Nagata, 2000), grazer mediated release (i.e., “sloppy feeding”) andexcretion (Møller, 2005; Møller and Nielsen, 2001; Nagata andKirchman, 1991; Steinberg et al., 2000; Steinberg et al., 2002), releasevia viral lysis (Wommack and Colwell, 2000) and bacterially inducedcell lysis (Carlson, 2002), the solubilization of particles (Smith et al.,1992), and bacterial transformation and release (Tanoue et al., 1995).In coastal and estuarinewaters DOMmay also originate from terrestrialsources and marshes (Hopkinson et al., 1998; Mannino and Harvey,2000; Tzortziou et al., 2008). Furthermore, atmospheric depositioncan add significant quantities of DOM to coastal and estuarine waters(Bronk, 2002; Seitzinger and Sanders, 1999). Physical processes suchas upwelling and mixing can also act as a source of DOM to the surfaceby bringing DOM up from deeper waters.

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110 D.P. Keller, R.R. Hood / Journal of Marine Systems 109–110 (2013) 109–128

Dissolved organic matter in surface waters is removed throughseveral biotic and abiotic processes. Biotically, free-living heterotrophicbacterioplankton are the dominant consumers of DOM in the ocean(Azam et al., 1983; Nagata, 2000). In addition to bacteria, somephytoplankton also have the ability to take up DOM to supplementtheir metabolic needs (Mulholland et al., 2003). Abiotically,photochemical processes, through UV excitation, directly and indirectlyremove and transform DOM (Benner and Biddanda, 1998; Mopper andKieber, 2002). Dissolved organic matter may also be removed fromsurface waters by physical processes such as downwelling or mixing(Carlson et al., 2010). In addition, DOM can also potentially form gelsor absorb onto particles which may sink out of surface waters (Druffelet al., 1996; Verdugo et al., 2004).

Comparisons of DOM cycling in different marine environmentshave for the most part been conducted by compiling rates from indi-vidual studies (Benner, 2002; Berman and Bronk, 2003; Bronk, 2002;Carlson, 2002; Cauwet, 2002) or by focusing on a particular aspect ofthe DOM cycle (Hansell and Carlson, 1998; Loh and Bauer, 2000;Morán et al., 2002a, 2002b). This research has typically found thatthe processes involved in DOM cycling vary widely between oceanic,coastal, and estuarine systems because of differences in biologicalproductivity, the food web structure, ambient environmental condi-tions, or physical processes. How these general differences (Table 1)affect DOM cycling depends on the process and these are not fully un-derstood in the context of the whole ecosystem. Phytoplankton DOMrelease is thought to be a function of productivity, light intensity, andnutrient availability (Flynn et al., 2008). Grazer mediated DOM re-lease can be both a function of productivity and diet (Nagata andKirchman, 1991; Saba et al., 2009), as well as a size related processfor sloppy feeding (Møller, 2005). Bacterial production and consump-tion of DOM is highly dependent on the bioavailability and composi-tion of DOM (Carlson, 2002; Ducklow, 1999). The amount of DOMproduced by lysis depends on many factors (phytoplankton and

Table 1General ecosystem differences. The ratio of heterotrophic to autotrophic biomass is ab-breviated as H:A.

Oceanic Coastal Estuarine

Productivitya low moderate highFood web

Plankton sizeb small species variable larger speciesBiomassc (m−3) low variable highH:A biomassd high moderate low

EnvironmentLight availabilitye high variable lowInorganic nutrientavailabilityf

low moderate high

DOMconcentrationg low moderate highBulk DOM originh marine marine/ some

terrestrialestuarine/terrestrial

Benthic interactions no some manySalinity gradientsi no yes, variable yes, often strong

Physicsi verticalprocesses areimportant ashorizontal

gradients aresmall

both vertical andhorizontal

processes areimportant,

especially tidalmixing

both vertical andhorizontal processes

are important,especially stuarinecirculation and tidal

mixing

a Ducklow (2000); Field et al. (1998); Lalli and Parsons (1997).b Fukuda et al. (1998); Kimmel et al. (2006); Malone (1980); Quinones et al. (2003).c Ducklow (2000); Gasol et al. (1997); Malone (1980).d Gasol et al. (1997).e Devlin et al. (2008); Falkowski and Woodhead (1992); Kirk (1994); Lalli and

Parsons (1997).f Falkowski and Woodhead (1992); Garcia et al. (2010); Kelly (2001); Lalli and

Parsons (1997); Ryther and Dunstan (1971).g Bronk (2002); Carlson (2002).h Carlson (2002); Cauwet (2002).i Mann and Lazier (2006).

bacterial productivity, viral infection and decay rates, and the size ofthe lysed cell) which are all individually affected by environmentalconditions (Weinbauer, 2004; Wommack and Colwell, 2000).Environmental and physical differences also play an important rolein DOM cycling in these ecosystems by affecting abiotic processeslike photochemical reactions and the availability of DOM fromallochthonous sources.

In this paper we describe a modeling study of DOM cycling thatcompares how the planktonic community structure and nutrientloading effect the magnitude, rates, and importance of processesthat control DOM production, transformation, and loss in estuarine,coastal, and oceanic surface waters. We specifically focus on theroles that autochthonous phytoplankton extra-cellular release, non-grazing mortality, bacterial and phytoplankton viral lysis, andgrazer-mediated sloppy feeding, egestion, and excretion play in theproduction of DOM. In addition, we explore how DOM is transformedand removed by chemical and biological processes and how thisrelates to the overall production of DOM. The goal of this research isto understand how well known, general differences in these systemsaffect the key processes controlling the planktonic interactions thatregulate DOM cycling and determine what the important sources ofDOM are in each system.

2. Methods

To compare how the planktonic community structure, nutrientavailability, and DOM loading affect the magnitude, rates, and impor-tance of processes that control DOM cycling in estuarine, coastal, andoceanic surface waters we ran a model with three different sets of pa-rameters to simulate (at steady-state) typical oligotrophic oceanic,mesotrophic coastal, and eutrophic estuarine surface waters. Thento understand the importance of different processes in each of theseecosystems, a sensitivity analysis was performed. This methodologyallowed us to determine the importance of individual processes ineach ecosystem without the confounding effects and feedbacks ofconstantly changing environmental conditions, as would be found innon-steady state model. Since there is some variability within eachof these ecosystem types our simulations were designed to reproduce“idealized” versions of them with ecosystem properties and C and Nfluxes that fall within the range of measurements taken in each.Note that our goal was to reproduce the major differences betweenthese systems, not to simulate any particular marine region. However,since the light forcing and maximum growth rates (Tables 2 and 3)were set at values that would be found in temperate regions in thesummer, our steady-state simulations are more representative ofthe summer months than other seasons.

2.1. Model description

The ecosystem/biogeochemical model (Fig. 1) used in this study isa slightly modified version of an existing C-Nmodel (Keller and Hood,2011) that includes: (1) two size classes of phytoplankton (b20 μmand >20 μm) and zooplankton (b200 μm and >200 μm), (2) bacteria,(3) detritus, (4) viruses, (5) DIC, (6) ammonium, (7) nitrate, and (6)labile, semi-labile, and refractory DOC and DON. In this model plank-ton have fixed non-Redfield C:N ratios, while detritus and DOMstoichiometry is variable. Both the carbon and nitrogen cycles arefully constrained. Phosphorus cycling is not included in the model.A turbidostat-like numerical formulation was used to control nutrientand DOM inflow and all model runs were to steady-state. Dissolvedorganic matter cycling is treated in detail as described in Keller andHood (2011) with modifications only to the equations describingphotochemical processes and the uptake of DON by phytoplankton;the latter does not occur in these simulations because there is littleinformation to parameterize this process in the three systems andthe former are described below. The model processes that produce

Table 2Model parameters that are the same in the idealized ecosystems.

Description Symbol Value Units

PhytoplanktonMaximum large phytoplankton growth rate μPL 3.22 d−1

Maximum small phytoplankton growth rate μPS 4.19 d−1

Saturation const. for Nn uptake by PL KPLNn1.0 mmol N m−3

Saturation const. for A uptake by PL KPLA 0.5 mmol N m−3

Saturation const. for Nn uptake by PS KPSNn1.0 mmol N m−3

Saturation const. for A uptake by PS KPSA 0.2 mmol N m−3

ZooplanktonLarge zooplankton preference for PL ΦPL 0.19 DimensionlessLarge zooplankton preference for PS ΦPS 0.19 DimensionlessLarge zooplankton preference for D ΦD 0.19 DimensionlessLarge zooplankton preference for ZL ΦZL 0.19 DimensionlessLarge zooplankton preference for ZS ΦZS 0.19 DimensionlessLarge zooplankton preference for B ΦB 0.05 DimensionlessSmall zooplankton preference for PL φPL 0 DimensionlessSmall zooplankton preference for D φD 0.15 DimensionlessSmall zooplankton preference for ZS φZS 0.30 DimensionlessLarge zooplankton mortality mZL 0.04 d−1

DOM, detritus, and other parametersPartitioning of organic matter to D and DOM β1 0.75 DimensionlessLabile fraction of DOM production δ1 0.10 DimensionlessSemi-labile fraction of DOM production δ2 0.80 DimensionlessRefractory fraction of DOM production δ3 0.10 DimensionlessPartitioning of phytoplankton exudationto labile DOM

OL 0.40 Dimensionless

Partitioning of phytoplankton exudationto semi-labile DOM

OS 0.50 Dimensionless

Partitioning of phytoplankton exudationto refractory DOM

OR 0.10 Dimensionless

Partitioning of phytoplankton lysis product to D εD 0.375 DimensionlessPartitioning of lysis product to LC and LN εL 0.08 DimensionlessPartitioning of lysis product to SC and SN εS 0.030 DimensionlessPartitioning of lysis product to RC and RN εR 0.015 DimensionlessPartitioning of viral decay to DOM η 0.20 DimensionlessLabile fraction of DOM from DOM hydrolysis τ 0.9965 DimensionlessPartitioning of zooplankton DOM excretion tolabile and semi-labile pools

OZ 0.80 Dimensionless

Turbidostat flow rate h 0.01 m3 d−1

Table 3Model parameters that differ in the idealized ecosystems.

Description Symbol Units Value


DIN inflowNH4

+ concentration A0 mmol N m−3 0.1 2 50 a

NO3− concentration Nn

0 mmol N m−3 0.2 2 85 a

DOM inflowDON concentration DOM0 mmol N m−3 10 15 100C to N ratio b,c,d λDOM Dimensionless 14 17 10Labile fraction e,f ΞL Dimensionless 0.06 0.06 0.20Semi-labilefraction e,f

ΞS Dimensionless 0.30 0.30 0.30

Refractory fractione,f

ΞR Dimensionless 0.64 0.64 0.50

LightIrradiance I Wm−2 98.13 89.68 89.68

PhotochemistryDOC to DIC χUVC

d−1 0.001 0.004 0.004Refractory DOM tolabile

UV d−1 0 0.0015 0.0015

DON to NH4+ χUVN

d−1 0 0 0.0005

Large phytoplanktonMortality mPL d−1 0 0.01 0.03

Small phytoplanktonMortality mPS d−1 0.06 0.06 0.02

Large zooplanktonGrowth coefficient geZL Dimensionless 0.45 0.65 0.65

Small zooplanktonGrowth coefficient geZS Dimensionless 0.40 0.50 0.50Preference for Ps φPS Dimensionless 0.40 0.35 0.40Preference for B φB Dimensionless 0.15 0.20 0.15Mortality mZS

d−1 0.06 0.06 0.04

BacteriaGrowth efficiencyg ggeB Dimensionless 0.2 0.4 0.6Mortality mB d−1 0 0.02 0

VirusesPL infection rate ΨPL mmol−1 N m−3 d−1 3 0.58 0.58PS infection rate ΨPS mmol−1 N m−3 d−1 4 0.75 0.75Bacteria infectionrate

ΨB mmol−1 N m−3 d−1 3.0 1.0 1.0

Viral decay rate v h−1 0.2 0.08 0.08

a The very high inflowing estuarine nutrient concentrations were set to simulatesummertime nutrient availability in estuaries like the Chesapeake Bay where thereare high fluxes of inorganic nitrogen from sediments and anoxic waters (Kemp et al.,1990, 2009).

b This C:N ratio was used to account for the flux of DOM from deeper water to thesurface and from adjacent surface waters and was based on the mean oceanic C:Nratio reported by Bronk (2002).

c This C:N ratio was used to account for the flux of DOM from deeper water to thesurface and from adjacent coastal waters and was set based on the mean coastal C:Nratio reported by Bronk (2002).

d This C:N ratio was used to simulate the flux of DOM into an estuary from bothterrestrial and estuarine sources and was based on DOM measurements from themesohaline portion of Chesapeake Bay, USA (Bronk, 2002).

e The bioavailability of inflowing DOM in the oceanic and coastal simulations wasbased on DOM degradation experiments and measurements of DOM turnover andage (Benner, 2002; Carlson, 2002; Carlson and Ducklow, 1995; Lønborg et al., 2009).

f Keller and Hood (2011).g del Giorgio and Cole (1998).

111D.P. Keller, R.R. Hood / Journal of Marine Systems 109–110 (2013) 109–128

DOM include phytoplankton excretion and leakage, zooplankton“sloppy feeding” and excretion, viral lysis and decay, planktonmortality, and detritus decay. Bacteria consume DOM and are alsoresponsible for the ectoenzyme hydrolysis of semi-labile DOM,which produces both labile and refractory material. The modelequations are in the Appendix A and the parameters for the threeidealized ecosystems that differ from Keller and Hood (2011) arelisted in Tables 2 and 3.

The structure of the planktonic community in each idealizedecosystem was based on a study by Gasol et al. (1997) that examinedthe biomass distribution (in terms of carbon) of different marineplanktonic communities. In this study Gasol et al. (1997) found thatin the open ocean the mean biomasses of bacteria, protozoans(heterotrophic protists), and zooplankton, relative to that of phyto-plankton were 1.00±0.09, 0.54±0.09, and 0.51±0.05, respectively.In coastal waters they found that the mean biomasses of bacteria,protozoans (heterotrophic protists), and zooplankton, relative tothat of phytoplankton were 0.62±0.08, 0.27±0.02, and 0.87±0.18,respectively. As a starting point for our simulations we used measure-ments of phytoplankton biomass in the Sargasso Sea ecosystem forthe oceanic simulation, in the coastal waters of southern California,USA for the coastal simulation, and in the mesohaline region of Ches-apeake Bay, USA in August for the estuarine simulation. Parametersdetermining planktonic growth, mortality, and nutrient inflow(Tables 2 and 3) where then adjusted within measured ranges toachieve steady-state biomass distributions close to the those found byGasol et al. (1997) (see Supplemental for a comparative table and a

description of the tuning process). Since Gasol et al. (1997) did notcalculate biomass distributions in estuarine waters we used theircoastal biomass distribution as a starting point and set the largezooplankton biomass higher than in the coastal simulations to reflectthe higher abundances of large zooplankton in estuarine waters. To

Fig. 1. A schematic diagram of the ecosystemmodel. Gray lines indicate the flow of carbononly. Segmented lines indicate photochemical reactions (note these are not the same in allsimulations, see text). Dashed lines indicate sources of DOM. Hatched lines indicatezooplankton grazing. Processes within the DOM pool are shown on the right.


differentiate between large and small phytoplankton, for the oceanicrun we allowed small phytoplankton biomass to be 80% of the totalphytoplankton biomass following typical oceanic size based biomassand production distributions (Malone, 1980). In the coastal andestuarine runs small phytoplankton biomass was assumed to be lessthan 50% of the total phytoplankton biomass (i.e., summer biomassratios as in Keller and Hood, 2011).

Light conditions used in the model (Table 3) simulate the mid-summer average irradiance in the mixed layer of typical temperatecoastal/estuarine and oceanic waters, respectively (Stickney et al.,2000). Photochemical processes are assumed to be the only meansof turnover for refractory material in estuarine and coastal waterswhere refractory DOM is transformed into labile DOM. These formu-lations were used because research has shown that UV radiationtends to make terrestrially derived refractory material in coastal andestuarine environments more available for use by bacteria (Mopperand Kieber, 2002). In oceanic water where there is almost no terres-trially derived DOM, photooxidation studies have shown that “fresh-ly” produced DOM can become less available to bacteria (Benner andBiddanda, 1998; Obernosterer et al., 2001). However, oceanic refrac-tory DOM may also become more bioavailable due to UV exposure(Mopper and Kieber, 2002). Since the rates of these photochemicalreactions are poorly constrained, we have assumed that the processesbalance each other (i.e., as much DOM is photooxidized from thelabile pool to the refractory pool as is photooxidized from the refrac-tory pool to the labile one) and our formulation for the oceanic modelruns does not have DOM becoming more or less bioavailable due tophotochemistry. In all model runs, photochemical processes are alsoresponsible for the conversion of DOC to dissolved inorganic carbon.In humic-rich surface waters photochemical processes can releaseammonium from more refractory DON (Koopmans and Bronk,2002), so for the estuarine model runs we also allowed some DONto be photooxidized to produce ammonium.

The turbidostat formulated inflow of dissolved inorganic nitrogen(DIN) and DOM for each system (Table 3) was set to be representa-tive of the limited supply of new nutrients in open ocean water, themoderate supply of new nutrients in coastal waters, and the high sup-ply rate of new nutrients often found in estuarine waters. In additionto accounting for the flow of material from adjacent surface waters,inflowing DIN or DOM is assumed to also account for allochthonoussources such as river inflow or marshes (in the coastal and estuarinesimulations), atmospheric deposition, and the upwelling or mixing ofdeeper waters. Aside from the C:N ratio and bioavailability of DOM,these values were set during the tuning processes to achieve typicalnutrient and DOM concentrations.

2.2. Sensitivity analysis

The sensitivity of the simulations to variations in parameters thatplay an important role in DOM cycling was investigated by allowingthem to vary within ranges reported in the literature, when available.The parameters varied were the viral infection rates, the rate of viraldecay, the amount of DOM produced by sloppy feeding, the rates ofdetritus decay, non-grazing mortality rates, the amount of DOMproduced by zooplankton excretion, the amount of DOM produced byphytoplankton exudation, bacterial growth efficiency, and thezooplankton growth coefficients. Viral infection rates were variedfrom 50% and 20% mortality per day for bacteria and 10% and 0% perday for phytoplankton based on reported estimates of viral infection(Suttle, 1994; Suttle et al., 1990; Weinbauer, 2004; Wommack andColwell, 2000). Viral decay rates were varied from 1.0 to 0.01 h−1

based on the wide range of reported viral decay rates (Weinbauer,2004; Wommack and Colwell, 2000). The amount of DOM producedby sloppy feeding is a size-dependent processes (Møller, 2005, 2007)that is poorly constrained for the ecosystem as a whole. Therefore, weallowed this parameter to vary by±0.10 (dimensionless). The decayof detritus to DOM is also a poorly constrained process. Furthermore,under certain conditions more DOM may be forming gels andsubsequently becoming detritus (Verdugo et al., 2004) than there isdetritus decaying to DOM. Therefore, we let the rates of detritus decayvary between +0.10 and −0.05 d−1 for C detritus and +0.09 and−0.05 d−1 for N detritus (e.g., in some sensitivity tests DOM becamedetritus, while in others detritus decayed to DOM). Non-grazingmortality is also poorly constrained and is usually thought to be around0.05 d−1 for most phytoplankton and zooplankton (Le Quéré et al.,2005). Therefore, we varied non-grazing mortality from 0 to 0.10 d−1.Measurements of bacterial growth efficiency vary widely. However,most values are within 0.1 of the median value (Carlson, 2002; delGiorgio and Cole, 1998). Therefore, we varied bacterial growthefficiencies in the simulations by±0.1. The growth coefficients forzooplankton were also varied by±0.1 to keep them within a realisticrange (Anderson, 1992; Anderson and Hessen, 1995). The amount ofDOM that is released by phytoplankton varieswidelywith little system-atic variability across productivity regimes (Baines and Pace, 1991;Carlson, 2002). The amount of DOM that is excreted by zooplankton isdifficult to measure and there is insufficient data to estimate themagnitude of this source relative to others (Bronk, 2002; Carlson,2002). Since these two processes are so poorly constrained we variedthe parameters controlling them (∝, κZ, and σZ) by±0.10, which isenough to significantly change the simulated production of DOM, butnot large enough to make total DOM production unrealistically high.The sensitivity of the simulations to DOM loading was also investigatedby reducing the amount of inflowing DOM to zero. For easierinterpretation, the model output of these sensitivity runs wasnormalized to the results of the main runs.

3. Results

Below we compare the simulations and make comparisons toobservations from each ecosystem. However, since our simulations areidealized steady-state versions of these ecosystems it is not feasible,nor our goal, to fully validate each simulation here (see Keller andHood (2011) for a full validation of themodel performancewith seasonalforcing). Instead the focus here is to first demonstrate that these simula-tions reproduce themajor differences between the ecosystems (Table 1)and that the C andNfluxes fall within the range of observations. Thenwecompare how DOM cycling differs in each of these simulations.

3.1. Biomass

The simulated biomass distributions (Figs. 2 and 3) reflect thetuning of the model to the biomass distributions described in Section

Fig. 3. A comparative schematic showing the biomass (rounded rectangles; mmol Nm−3),productivity (hexagons; mg Cm−3 d−1), and the uptake, remineralization, and autochtho-nous DOM production fluxes (circles and squares) of carbon (mmol Cm−3 d−1) and nitro-gen (mmol Nm−3 d−1) in the oceanic, coastal, and estuarine simulations (blue top, greenmiddle, and brown bottom numbers, respectively).


2. For the oceanic simulation the carbon biomass was dominated bybacteria (35%) and small phytoplankton (25%), and to a lesser extentby large and small zooplankton (15% and 18%, respectively). Largephytoplankton constituted a relatively small fraction (7%) of theoceanic plankton biomass. For the coastal simulation the carbonbiomass was dominated by bacteria (25%) and large zooplankton(28%), and to a lesser extent by large and small phytoplankton (19%and 18%, respectively). Small zooplankton constituted a relativelysmall fraction (10%) of the total coastal biomass. For the estuarinesimulation the carbon biomass was dominated by large zooplankton(35%). Large and small phytoplankton and bacteria all had similarbiomass concentrations (20%, 17%, and 18%, respectively) with smallzooplankton constituting a relatively small fraction (10%) of thetotal estuarine biomass.

3.2. DOM and nutrient concentrations

In the oceanic, coastal, and estuarine simulations the majority ofthe DOC and DON was in the refractory pool with a smaller pool ofsemi-labile organic matter, and very little labile organic matter(Fig. 4). The total simulated oceanic DOC and DON concentrations(Fig. 3) were within the range (DOC: 60–90 mmol C m−3; DON:0.8–13 [mean: 5.8±2.0] mmol N m−3) of observed oceanic DOCand DON concentrations (Benner, 2002; Bronk, 2002). The totalsimulated coastal DOC and DON concentrations (Fig. 3) were alsowithin the range of observed coastal surface water DOC and DONconcentrations (DON: 9.9±8.1 mmol N m−3; C:N DOM pool: 17.7±4.3) (Bronk, 2002). In the estuarine simulation the total DOC andDON concentrations (Fig. 3) were also within the range of observedestuarine surface water DOC and DON concentrations (DON: 22.5±17.3 mmol N m−3; C:N DOM pool: 21.1±14.3)(Bronk, 2002;Carlson, 2002).

In all three simulations inorganic nitrogen concentrations werelow (Fig. 3) and the primary factor limiting primary production (seeSupplemental for calculations of light versus DIN limitation). Thelow ammonium and nitrate concentrations in the oceanic simulationwere similar to concentrations that are typically observed (Table 4) inoligotrophic oceanic surface waters around the world (Clark et al.,2008; Karl et al., 2001; Lipschultz, 2001; Mengesha et al., 1999;Metzler et al., 1997). In temperate coastal and estuarine surfacewaters a very wide range of inorganic nitrogen concentrations havebeen observed (Fisher et al., 1988; Harding, 1994; Thompson et al.,2009; Wafar et al., 2004) making it difficult to evaluate our idealizedcoastal and estuarine simulations. Nonetheless, our model results arewithin the range of many typical summer observations (Table 4).

3.3. Productivity

Primary and bacterial production rates in the simulations (Fig. 3)were within the very wide range of values that have been measured(Table 5) in oceanic, coastal, and estuarine waters (Behrenfeld et al.,2006a; Ducklow, 2001; Ducklow and Carlson, 1992; Ducklow andShia, 1993; Harding et al., 1986; Marañón et al., 2000). However, in

Bacteria

Phyto

Zoo

0 0.1 0.2 0.3 0.4 0.450

Oceanic C

Bioma

Fig. 2. The steady-state biomass (mmol C m−3) of bacteria, phytoplankton, and zooplanktonfor phytoplankton and zooplankton refer to their size classes as defined in the text.

the coastal simulation primary and bacterial productivity werelower than typically observed, while in the estuarine simulationbacterial productivity was high. The ratios of bacterial to primaryproduction in all simulations were also higher than the typicalobservations of 10–30% (Ducklow and Carlson, 1992; Ducklow andShia, 1993; Fuhrman, 1992). The comparatively low bacterial andprimary production rates in the coastal simulation were the resultof nitrogen limitation of primary productivity. Attempts made duringthe tuning processes to increase productivity resulted in eitherunrealistic DOM and biomass concentrations or prevented us fromreaching our targeted biomass distributions. Moreover, it is worthnoting that since the observations we used for comparison are biasedto sampling during periods of the year when productivity is highest(i.e., spring blooms), our simulated levels of productivity may benot be that atypical for the typically nutrient limited summer months.The high bacterial to primary production ratios in the simulationswere the result of them being tuned to the biomass distributionsfound by Gasol et al. (1997), which represent a mean biomass distri-bution from samples taken at different times of the year. While thismean biomass ratio may reflect the overall mean for the system it islikely different from ratios that would be measured at any one timein the field. This makes tuning the model to the mean biomass ratiouseful for a comparative study like this, but difficult to compare toreported measurements of productivity. Nonetheless, despite thesediscrepancies we achieved our main goal of simulating the overalldifferences between these systems (i.e., low, moderate, and highproductivity in the respective oceanic, coastal, and estuarine simula-tions; Fig. 3). However, to test if the model was capable of producing

1.800.90 1.35 0 4 8 12 16

oastal Estuarine

ss (mmol C m-3)

LargeSmall

in the oceanic, coastal, and estuarine simulations. The “large” and “small” designations

image of Fig.�2

DOM ConcentrationOceanic Coastal Estuarine

mm

ol D

OC

m-3

mm

ol D

ON

m-3

7060

504030

20100

5

4

3

2

1

0

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Fig. 4. Dissolved organic carbon and nitrogen concentrations, in terms of bioavailability, for the oceanic, coastal, and estuarine simulations. (a) oceanic DOC; (b) oceanic DON;(c) coastal DOC; (d) coastal DON; (e) estuarine DOC; (f) estuarine DON. The bars represent the range over which DOM concentrations varied during the parameter variationsensitivity analysis. Note the differences in scale.


(at steady-state) a bacterial production to primary production ratiothat falls within the range typically observed in oceanic surfacewater we also conducted an additional model run with the phyto-plankton and bacteria biomasses tuned to a Sargasso Sea meanbiomass distribution (bacteria to phytoplankton ratio of 0.4) reportedby Ducklow (2001). For this model run the bacterial to phytoplanktonbiomass ratio was 0.42 and bacterial production was 19% of totalprimary production which is within the range of 10–30% typicallyfound in oceanic waters (Fuhrman, 1992).

3.4. Nutrient uptake and regeneration

The total uptake (mmol Nm−3 d−1) of ammonium and nitrate byphytoplankton in the oceanic, coastal, and estuarine simulations (Fig. 3)was within the ranges reported for these ecosystems (Table 4). Uptakesof DOC, DON, and sometimes ammonium by bacteria (Fig. 3) reflect theequations governing their growth, which take into account the C:N ratioof labile DOM. As a result, in the oceanic and coastal simulations bacteria

Table 4Observed ammonium and nitrate concentrations and uptake rates. Observations belowthe detection limits are noted as “BD”.

Ecosystem [NH4+]

mmol N m−3NH4

+ uptakemmol N m−3 d−1

[NO3−]

mmol N m−3NO3

− uptakemmol N m−3 d−1

Oceanic 0.026a* 0.0009–0.0482d 0.024a* 0.0002–0.24d

Coastal BD—0.51b 0.031–1.38d BD—0.5b† 0.0014–0.26 d

Estuarine BD—3 c 2.4–31.2 e BD—3c 0.24–4.8 e

a Mean value calculated from average concentrations reported by Clark et al. (2008)for the N. Atl. Sub-Tropical Gyre, Metzler et al. (1997) for the Western S. Atlantic (oce-anic stations. 11 & 23), Mengesha et al. (1999) for the Western Indian Ocean (oceanicstations, intermonsoon period), Karl et al. (2001) for station ALOHA in the Pacific, andLipschultz (2001) at BATS.

b Glibert et al. (1991) for the Western N. Atlantic coast, Metzler et al. (1997) for theWestern S. Atlantic (coastal stations. 1, 61, & 39), and Mengesha et al. (1999) for theWestern Indian Ocean coast.

c Summer observations from Fisher et al. (1988) and Malone et al. (1996) for theChesapeake estuary and Gameiro et al. (2007) for the Targus estuary.

d Metzler et al. (1997).e Bronk et al. (1998).⁎ Some observations were below the detection limits, which varied depending on

methodology.† In a long-term analysis of Australian coastal waters Thompson et al. (2009) ob-

served that summer nitrate concentrations were typically less than 0.5 mmol N m−3.

were C limited, as is often found in these systems (Carlson and Ducklow,1996; Lønborg and Søndergaard, 2009), and remineralized much of theDON that they took up. In contrast, in the estuarine simulation bacteriawere N limited and also took up ammonium tomeet their stoichiometricrequirements.

In the oceanic simulation bacteria and zooplankton remineralizedammonium at a rate of 0.034 mmol N m−3 d−1 with bacteria respon-sible for 88% of it. This remineralization occurred at almost the samerate at which ammonium was being assimilated by phytoplankton(Fig. 3). Thus, these model results compare well with rates of ammo-nium regeneration and assimilation that have been extensivelymeasured at sea and been found to be of similar magnitude (Glibertet al., 1988; Varela et al., 2005). In the coastal simulation zooplanktonand bacteria remineralized ammonium at a rate of 0.04 mmol Nm−3 d−1 with zooplankton remineralizing 52% of it (48% fromsmall and 15% from large) and bacteria 37% of it. This rate of regener-ation was much less than the rate at which nutrients needed to beassimilated by phytoplankton (Fig. 3) to maintain the communitystructure and productivity typically found in coastal surface waters(i.e., the inflow of “new” DIN was needed by phytoplankton to supple-ment DIN from remineralization). In the estuarine simulation ammoni-umwas remineralized entirely by zooplankton at a rate of 0.87 mmol Nm−3 d−1 with small zooplankton responsible for 66% of it. In addition,photooxidation of DON produced ammonium at a rate of 80 nmol Nm−3 d−1. As in the coastal simulation, this rate of regeneration wasmuch less than the rate at which nutrients needed to be assimilated

Table 5Observed primary (PP) and bacterial (BP) production.

Production (mg C m−3 d−1)


Range Typical⁎ Range Typical Range Typical

PP 1.09–96a b4a,b,c 2.5–960† 75–300† 2.5–3000† 210–670†

BP 0.01–38b b2b,c 0.5–192b 15–60 c 0.5–600 b,c,d 42–134 d

⁎ oligotrophic gyres,† Calculated from BP based on a BP:PP ratio of 0.2.a (Behrenfeld et al., 2006a, 2006b; Marañón et al., 2000),b (Ducklow and Carlson, 1992).c (Ducklow, 2001),d (Ducklow and Shia, 1993).

image of Fig.�4


by phytoplankton (Fig. 3) to maintain the community structure andproductivity typically found in these surface waters.

3.5. DOM production

3.5.1. DOC productionIn all of the simulations the largest autochthonous source of DOC

was phytoplankton exudation (Fig. 5). The rates at which phyto-plankton produced DOC in all of the simulations were within thelarge range of reported phytoplankton exudation rates (0–24 mmol C m−3 d−1) (Carlson, 2002). In the oceanic simulation88% of this DOC was produced by small phytoplankton. In the coastalsimulation small phytoplankton were responsible for 70% of theexuded DOC. In the estuarine simulation small phytoplankton wereresponsible for 59% of the exuded DOC. While phytoplankton exuda-tion was the largest individual source of DOC in these simulations, itaccounted for only around half of the total DOC production. In theoceanic simulation total DOC production was dominated (56%) byheterotrophic processes which is consistent with other modelingstudies (Christian and Anderson, 2002). In the coastal and estuarinesimulations heterotrophic DOC production accounted for 50% and52% of the total, respectively. Of these heterotrophic processes virallysis stood out as being particularly important in the oceanic simula-tion (Fig. 5a). In the coastal simulation these heterotrophic processeswhere all of a similar magnitude (Fig. 5c). In the estuarine simulationsloppy feeding and viral lysis both stood out as important processes(Fig. 5e). In addition, in the coastal and estuarine simulations, refrac-tory DOC was converted to labile DOC through photooxidation atrates of 0.23 and 0.68 mmol C m−3 d−1. Photochemical processeswere also important for converting a small amount of DOC to DIC(data not shown) because they acted to turnover less bio-availableforms of DOC.

3.5.2. DON productionThe total production of DON in the oceanic, coastal, and estuarine

simulations (Fig. 3) is within the wide range of measured DON releaserates (0.005–9.3, ~0–8.6, and 0.8–7.5 mmol Nm−3 d−1, respectively)(Bronk, 2002). In the oceanic simulation phytoplankton exudation,

Oceanic Co

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Fig. 5. Dissolved organic matter production (mmol C or N m−3 d−1) from phytoplankton ex(SF), viral decay (VD) and detritus decay (D) at steady-state in the oceanic, coastal, and estuDOC production; (d) coastal DON production; (e) estuarine DOC production; (f) estuarine Dthe parameter variation sensitivity analysis.

viral lysis and decay, and the decay of detritus were the most impor-tant sources of DON with very little DON coming from zooplanktonexcretion or sloppy feeding (Fig. 5b). The ratio of DON release togross N uptake is 48% which is within the range and close to themean (41.4%) of observed oceanic DON release to gross N uptakeratios (Bronk, 2002). In the coastal simulation phytoplankton exuda-tion was the most important individual source of DON (Fig. 5d). TheDON release to gross N uptake ratio was 55% which is within therange (39±26%) of observed coastal DON release to gross N uptakeratios (Bronk, 2002). In the estuarine simulation phytoplanktonexudation, viral lysis and decay, and sloppy feeding were the mostimportant sources of DON with little DON coming from zooplanktonexcretion, mortality, and the decay of detritus (Fig. 5f). In additionin the coastal and estuarine simulations, refractory DON wasconverted to labile DON through photooxidation at rates of 0.04 and0.12 mmol N m−3 d−1, respectively. The few studies that havemeasured the conversion of refractory DON into more labilecompounds have suggested that rates can be quite high (Bronk,2002). However, given the paucity of data it is difficult to concludeif our rates are reasonable or not.

4. Sensitivity analysis results and discussion

The sensitivity analysis focuses on the response of both the plank-tonic community (biomass and productivity) and DOM cycling toperturbations. We included the response of the planktonic community(see Supplemental for additional Figures) in this analysis becauseplankton are responsible for many DOM production and consumptionprocesses. Sensitivity to varying DIN inflow is not shown here becausethe bottom-up effects that it has can already be seen in the parametervariation results discussed below.

4.1. Planktonic biomass and productivity sensitivity to parametervariations

Since nutrient availability was always the primary factor limitingprimary production in these simulations, the efficiency of nutrientremineralization and the availability of inflowing DIN always played a

astal Estuarine

oduction

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udation (PE), zooplankton excretion (ZE), mortality (M), viral lysis (L), sloppy feedingarine simulations. (a) oceanic DOC production; (b) oceanic DON production; (c) coastalON production. The bars represent the range over which DOM production varied during

Lrg.Zoo

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Fig. 7. Sensitivity of estuarine zooplankton (lrg. and sm. Zoo), phytoplankton (lrg. andsm. P), and bacterial (B) biomass (mmol C m−3) to variations in the zooplanktongrowth efficiency parameter. The y-axis scale represents normalized deviations fromthe main coastal simulation biomass concentration (i.e., −0.1 mmol C m−3 representsa biomass decrease of 0.1 mmol C m−3 for the parameter variation run). Parameter de-creases are in black (■) and parameter increases are in white (□).


large role in how sensitive plankton were to parameter variations.Moreover, the sources of remineralized nutrients played an importantrole in these dynamics. If one source was primarily responsible for theremineralization of nitrogen and DIN inflow was low, as in the oceanicsimulations, then parameter variations that affected that source wouldaffect other plankton as well through bottom-up effects. For example,in the oceanic simulation when variations in the rate of viral lysiscaused an increase or decrease in the biomass and productivity ofbacteria, then the biomass of all other plankton also increased ordecreased (Fig. 6), because of changes in the efficiency of nitrogen remi-neralization by bacteria. If thereweremultiple sources of remineralizednitrogen and DIN inflow was higher, as in the coastal and estuarinesimulations, then other plankton were not as effected by parametervariations that only effected one of these sources. Dissolved organicmatter played an important role in these dynamics because bacteriacan, but do not always, remineralize DON to inorganic forms. In theoceanic and coastal simulations where bacteria remineralized DONthere was a relationship between DON production, subsequentremineralization, and primary productivity. Parameter variations thataffected any one of these processes could thus affect plankton eitherdirectly or through bottom-up effects. In the estuarine simulationwhere bacteria did not remineralize DON they became less importantfrom a bottom-up perspective and there was not a strong response toparameter variations that affected them.

The structure of the planktonic community played an importantrole in these simulations by influencing the amount of top-downcontrol on the system. If grazing had a substantial impact on anyone group of plankton, as in the estuarine simulation, then parametervariations that effected the grazer also had an impact on the biomassand productivity of its prey (see Fig. 7 as an example). This thenaffected DON production and remineralization processes, includingremineralization due to grazing (i.e., zooplankton excretion) whichwas important in both the coastal and estuarine simulations (seeSection 3.4). However, variations in parameters that effected grazingnever had much of an effect on small zooplankton or bacterialbiomass. This is not a surprising result for bacterial biomass as trophiccascade experiments often do not result in significant changes in it(Zöllner et al., 2009). For small zooplankton, it is debatable as towhether or not they can actually be controlled by top-down effects(Sherr and Sherr, 2009). However, since small zooplankton havehigh growth rates and feed at multiple trophic levels in marinesystems (Sherr and Sherr, 2002; Tillmann, 2004), as in our model, itis not surprising that they are able to respond to changes in top-down control and maintain a stable biomass as in our simulations. It

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Viral Lysis

Fig. 6. Sensitivity of oceanic zooplankton (lrg. and sm. Zoo), phytoplankton (lrg. andsm. P), and bacterial (B) biomass (mmol C m−3) to variations in the rate of virallysis. The y-axis scale represents normalized deviations from the main coastal simula-tion biomass concentration (i.e., −0.1 mmol C m−3 represents a biomass decrease of0.1 mmol C m−3 for the parameter variation run). Parameter decreases are in black(■) and parameter increases are in white (□).

is also worth noting that the structure of the planktonic communityplayed a role in how much C and N were transferred through thefood web via either a “classical” type food chain (nutrients-phytoplankton-zooplankton) or through the microbial loop. Althoughboth food web pathways were always present (data not shown), themicrobial loop pathway was more important in the oceanic simula-tion than in the coastal and estuarine ones.

In many of the model runs there was also evidence of competitionbetween large and small phytoplankton during the parameter variations.Parameter variations often resulted in a decrease in the productivity andbiomass of one size class and an increase in the productivity and biomassof the other size class (see Fig. 7 as an example). Note that in some casesthese changes were also related to the effects of grazing. As a result ofthese dynamics in some of the runs large phytoplankton were evendriven to extinction because the parameter variation favored smallphytoplankton growth over that of large phytoplankton. For some ofthese parameter variations phytoplankton biomass and productivityalso became decoupled. An example of these dynamics can be seen inthe sensitivity of coastal plankton to variations in viral infection anddecay rates (Fig. 8). In these simulations the biomasses of smallphytoplankton and zooplankton did not change like those of the otherplankton even though they were also sensitive to these parametervariations. This occurred because when the viral infection rate wasincreased mortality increased for both size classes of phytoplankton butthe biomass of small phytoplankton stayed almost the same becausetheir productivity increased by 27% due to less competition from largerphytoplankton for nutrients. The biomass of small zooplankton thusstayed the same because they had the same amount of prey available tograze on. When the viral infection rate was decreased the oppositeoccurred with larger phytoplankton outcompeting smaller ones. Thefactors determining the coexistence of phytoplankton in our simulationsand their response to parameter variations are a result of the differenttraits that define them in our model (Tables 2 and 3) and the mortalitythat they experience. While our parameterizations of these traits arebasic, they do reflect some of the most important traits (mostly relatedto cell size) that allow for phytoplankton to coexist and compete inmarine waters (Litchman and Klausmeier, 2008; Litchman et al., 2010).

4.1.1. Key biomass and productivity sensitivity resultsIn the oceanic simulation the availability of nutrients and DOM,

the biomass and productivity of bacteria, and the efficiency of nutri-ent remineralization by bacteria were the most important factors indetermining the biomass and productivity of other plankton. Thestrong bottom-up control, and the importance of the microbial loop,in this simulation are consistent with some well recognized concepts

Viral Lysis

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Fig. 8. Sensitivity of coastal zooplankton (lrg. and sm. Zoo), phytoplankton (lrg. and sm.P), and bacterial (B) biomass (mmol C m−3) to variations in the rates of viral lysis anddecay. The y-axis scale represents normalized deviations from themain coastal simulationbiomass concentration (i.e., −0.1 mmol C m−3 represents a biomass decrease of0.1 mmol C m−3 for the parameter variation run). Parameter decreases are in black (■)and parameter increases are in white (□).


of the nutrient cycling (e.g., lots of recycling, little new input) and thefood web dynamics in oligotrophic oceanic waters that lack nitrogenfixers and are not subjected to frequent environmental perturbations(Azam et al., 1983; Clark et al., 2008; Fenchel, 2008; Kirchman, 2000;Mengesha et al., 1999; Sanders et al., 1992; Varela et al., 2005).Although there is still an ongoing debate about the importance oftop-down versus bottom-up control in oligotrophic waters, whichsuggest that under some conditions top-down control may be moreimportant than in our model (Banse, 1995; Marañón et al., 2000,2003; Sherr and Sherr, 2009), the results of this simulation are likelyvalid in many oligotrophic regions. Especially if these regions haveecosystems where bacterial biomass or productivity equals orexceeds that of phytoplankton.

In the coastal simulation the availability of nutrients and theefficiency of nutrient remineralization by both bacteria and smallzooplankton were the most important factors in determining thebiomass and productivity of the system. Plankton were not as effectedby parameter variations as they had been in the oceanic simulationbecause there were two, quite different, processes remineralizingnitrogen (i.e., grazing versus bacterial regeneration) and the systemwas less nitrogen limited (i.e., higher DIN inflow). Furthermore,there was more top-down control (mostly by small zooplankton)than in the oceanic simulation, which allowed for a more stablebalance between top-down and bottom-up processes, as is often thecase in nutrient limited systems where microzooplankton grazing isimportant (Juhl and Murrell, 2005). As a result of this balance theeffects of parameter variations tended to be smaller and more direct(i.e. no strong bottom-up or top-down responses). The analysis alsorevealed that competition between phytoplankton was important.The importance of competition is consistent with the generalobservation that, in temperate coastal waters, the biomass distribu-tion of large and small phytoplankton varies seasonally as differentenvironmental conditions favor one size class of phytoplankton overanother (Malone, 1980).

In the estuarine simulation where high nutrient inflow supportedhigh levels of productivity, the autochthonous factors thatdetermined the biomass and productivity of the system weredifferent than in the other simulations because bacteria becamenitrogen limited and took up ammonium instead of regenerating it.Thus, there was no bottom-up coupling between bacteria and highertrophic levels. Instead there was actually competition betweenbacteria and phytoplankton for inorganic nutrients, as well as compe-tition among phytoplankton. Grazing was also more important thanin the other simulations. However, during the sensitivity analysisthe effects of grazing on phytoplankton tended to have a positive ef-fect on one size class of phytoplankton and a negative effect on theother, keeping the overall biomass and productivity of phytoplanktonalmost the same. As a result of these dynamics and the high inflow ofDIN, parameter variations tended to affect plankton biomass andproductivity directly, with the exception of competitive responsesby phytoplankton, and there were fewer feedbacks than in the othersimulations. This weak coupling (or sometimes uncoupling) of theplanktonic community is often observed in estuarine systems. Inmany estuaries there is often no correlation between chlorophyll aand zooplankton biomass (David et al., 2006; Dolan and Coats,1990; Zhang et al., 2006). Moreover, in these systems heterotrophicutilization of organic matter often exceeds autochthonous autotro-phic production (e.g., the system is net heterotrophic)(Findlay et al.,1991; Kemp et al., 1997).

4.2. DOM cycling sensitivity

During the sensitivity analysis the dominant source of DOC in all ofthe simulations was always phytoplankton exudation (see Fig. 5range bars) with the only exception being an oceanic model runwhere the growth efficiency of bacteria was increased (DOC fromviral lysis was the dominant source for this run). Although it isquite difficult to experimentally determine individual sources ofDOC, this result agrees with the few studies that have attempted todetermine the importance of phytoplankton DOC production(Marañón et al., 2005). However, since this result makes it difficultto focus on the smaller variations in the other sources of DOM whencomparing them, we focus here on the sensitivity of DON productionto parameter variations. We have also chosen to focus on DONproduction because DON can be remineralized to inorganic forms ofnitrogen that limit primary productivity.

The results of the analysis indicate that, in addition to directeffects (i.e., varying a parameter that controls the amount of DONfrom a particular source), many of the variations in the amount ofDON produced were related to the effect that a parameter variationhad on the biomass and productivity of the source. For example, ifthe biomass and productivity of phytoplankton increased then theamount of DON produced by phytoplankton exudation wouldincrease as well. In addition, bacterial uptake of labile DON andhydrolysis of semi-labile DON also often changed with parametervariations and thus had a further moderating effect on the totalDON concentration.

4.2.1. Sensitivity to DOM inflowWhen DOM inflow was reduced to zero in each ecosystem the

sensitivity of planktonic biomass and productivity (data not shown),and the associated DOM production (Fig. 9), depended on howmuch of a bottom-up influence bacterial remineralization of DONhad on primary productivity. In addition, there was also the slighteffect of a reduced washout rate (i.e., less turbidostat outflow sincethe volume of inflow decreased), which mainly affected largezooplankton positively because of their low growth rate (e.g.,washout was a more important loss term for them). In the oceanicsimulation the biomass and productivity of most plankton decreasedwithout the “new” nitrogen supplied to the system by DON inflow. As

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Fig. 9. Comparisons of DON production between the main oceanic (a) and estuarine (b)model runs and runs where DOM inflow was zero. The main runs are in white (□) andthe runs with no DOM inflow are in black (■). The sources of DOM on the x-axis areviral lysis (Lysis), sloppy feeding (SF), natural mortality (Mort.), viral decay (V.Decay), zooplankton excretion (Excret.), phytoplankton exudation (Exud.), and thedecay of detritus (D. Decay).

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Fig. 10. Comparisons of DOC and DON concentrations between the main coastal modelrun and a run where DOM inflow was zero. The main run is in white (□) and the runwith no DOM inflow is in black (■).


a result DOM production from most sources decreased substantially(shown in Fig. 9a for DON prod.). These dynamics suggests that it isimportant not to overlook external sources of DON in oceanic waterssuch as those from atmospheric deposition, mixing, or upwellingwhen calculating “new” production as a means of understandingthe global carbon cycle (i.e., the paradigm introduced by Dugdaleand Goering, 1967). Furthermore, these results suggest thatallochthonous DOM plays an important role in oceanic DOM cycles.

In contrast, in the coastal and estuarine simulations the biomassand productivity of plankton did not decrease substantially, exceptfor bacterial productivity, when DOM inflow was stopped. As a resultDOM production did not decrease substantially when DOM inflowwas stopped (see Fig. 9b as an example for estuarine DON prod.).Moreover, DOM production from some sources, like sloppy feeding,actually increased in response to changes in the plankton dynamics(i.e., an increase in large zooplankton biomass and productivity dueto less loss fromwashout). These results suggest that while allochtho-nous DOM may be important food source for bacteria in coastal andestuarine waters its presence does not have a large impact on autoch-thonous DOM production. However, it is also worth noting that themodel does not take into account the affects of the chromophoriccomponent of DOM (CDOM) on water column light attenuation. Incoastal and estuarine waters where light attenuation by CDOM canbe high, reducing DOM inflow could increase light availability andpromote primary productivity, and the associated production ofDOM, if nutrients were not limiting. In addition, CDOM relatedchanges in light attenuation could also affect photochemical reactionsinvolving DOM.

Simulated DOM concentrations were of course affected by havingno DOM inflow. The concentrations of refractory DOM and semi-labile DOC always decreased, labile DOM concentrations remainedalmost the same, and the semi-labile DON concentration alwaysincreased (see Fig. 10 as an example). Labile DOM concentrations

did not change substantially because bacteria always took up asmuch labile DOM as their growth formulation allowed. RefractoryDOM concentrations decreased because little refractory DOM wasproduced in these simulations. Without an inflow of refractory DOMthe initial concentration decreased due to washout during thesteady-state spin-up with the remaining refractory DOM indicativeof howmuch new refractory DOM is produced (minus losses to wash-out and photochemical processes). The production of this refractoryDOM is important because it is a sink of both carbon and nitrogen(Jiao et al., 2010). Semi-labile DOM concentrations changed becausebacterial hydrolysis of it (the primary loss term for semi-labileDOM) decreased with bacterial productivity. Semi-labile DOMconcentrations were also affected by the C:N ratio of freshly producedDOM, changes in the sources of DOM that accompanied the loss ofinflowing DOM, and the model structure itself (i.e., the partitioningof DOM production). In addition, in some of the simulations photo-chemical processes played an important role transforming semi-labile and refractory DOM with DOC and DON transformationsoccurring at different rates.

4.2.2. Sensitivity to parameter variationsIn the oceanic simulation DON production (Fig. 11 and supple-

mental Table S 3) was especially sensitive to parameter variationsthat affected bacteria. In contrast, the production of DON was notvery sensitive to parameter variations that effected grazers. Due tothe feedbacks in this system the total concentrations of DOC andDON changed only slightly during the parameter variation runs(Fig. 4a, b range bars) even though the amount of DOM fromindividual sources often changed considerably (Fig. 5). Since bacterialbiomass and productivity were especially sensitive to viral lysis,which was controlled by the infection rate and the rate of viraldecay, variations in these parameters had an effect on the productionof DON from many sources (Fig. 11a, d, e, g). In addition, variations inthese parameters also had an effect on phytoplankton mortality soDON production was especially sensitive to them. A good exampleof this sensitivity and the underlying planktonic dynamics was the

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d-1

mimi

Fig. 11. Sensitivity of oceanic DON production sources to parameter variation runs. The x-axis shows the different parameter variation runs: infection rate (Ψi); sloppy feedingparameter (ω); detritus decay rate (χD); mortality rate (Si); viral decay rate (υ); zooplankton excretion parameters (κZ and σZ); phytoplankton exudation parameter (α); bacterialgrowth efficiency (ggeB); zooplankton growth efficiency (geZ). The y-axis scale represents normalized deviations from the main oceanic model run production rate at steady state(i.e., −0.1 mmol N m−3 d−1 represents a production decrease of 0.1 mmol N m−3 d−1 for the parameter variation run indicated on the x-axis). Parameter decreases are in black(■) and parameter increases are in white (□). The panels show DON production from (a) viral lysis; (b) sloppy feeding; (c) mortality; (d) viral decay; (e) zooplankton excretion;(f) detritus decay; and (g) phytoplankton exudation.


sensitivity of zooplankton excretion to variations in the rates of viralinfection and decay (Fig. 11e). When the viral lysis of phytoplanktonand bacteria decreased due to a decrease in the rate of infection, phy-toplankton and bacterial biomass and productivity increased (Fig. 6).This increase occurred because their mortality by viral lysis was

reduced (less top-down control) which in turn allowed bacteria toremineralize more DON to fuel primary productivity (more bottom-up control). Increased bacterial and primary production then provid-ed zooplankton with more prey, allowing them to increase their bio-mass and productivity. Subsequently, they excreted more DON.


However, the total concentration of DON did not increase much as aresult because DON production from viral lysis, viral decay, and phy-toplankton exudation decreased.

When the parameter was varied in the opposite direction (i.e.,higher rates of viral lysis) zooplankton DON excretion did notdecrease at the same magnitude as it had increased. This is becausesmall phytoplankton productivity actually increased in response tomore lysis (this is evident in Fig. 11g which shows an increase inphytoplankton exudation of DON in response to a higher rate ofviral lysis), which provided enough prey for zooplankton to maintainalmost the same rate of DON excretion. Small phytoplanktonproductivity increased, despite a decrease in their biomass (Fig. 6),due to less competition from large phytoplankton for inorganicnitrogen. This competitive effect occurred because large phytoplanktonwere more sensitive to increased viral lysis as a result of their slightlylower maximum growth rate and higher half saturation constant forthe uptake of ammonium. The results in this example are in goodagreement with the concept of viruses as sources of top-down controland DOM and also shows the bottom-up effect that these processescan have on someplankton (Suttle, 2007). Furthermore, as this exampleshows, the sensitivity of this system to parameter variations involvedmany feedbacks.

Overall there were a few key results from this analysis. First,bottom-up processes dominated DOM cycling in this simulationwith bacteria playing a key role. Second, competition betweenphytoplankton was important in the DOM dynamics. Third, althoughthe dynamics of the system were primarily dominated by bottom-upprocesses, some top-down processes like viral lysis had a strongimpact on DOM cycling, while other top-down processes like naturalmortality or zooplankton grazing did not. Fourth, the total concentra-tion of DOM did not vary much because as DOM production from onesource increased or decreased the amount of DOM from other sourceswould also increase or decrease by a similar amount.

In the coastal simulation DOM production was less sensitive toparameter variations (Fig. 12 and supplemental Table S 3) than inthe oceanic one. This system was more resilient because there weretwo, quite different, processes remineralizing nitrogen (e.g., grazingversus bacterial regeneration) and the system was less nitrogenlimited (i.e., higher DIN inflow). In addition, there was more top-down control due to grazing. As a result bottom-up effects did notoccur as they had in the oceanic simulation and the effects ofparameter variations on DOM cycling tended to be more direct.However, phytoplankton DON production (Fig. 12g) was still verysensitive to many of the parameter variations because of competitionbetween large and small phytoplankton and the differences in theirexudation rates. Although the system was less sensitive to top-down and bottom-up effects, important feedbacks still occurred aswas evident by only slight changes in the total concentrations ofDOC and DON during the parameter variation runs (Fig. 4 rangebars). Overall, there were several key results of this analysis. First,since the system was mesotrophic and the community structureallowed for multiple sources of remineralized nitrogen it was moreresilient to perturbations. Thus, the effects of parameter variationson DOM cycling tended to be more direct and small. Second,competition between phytoplankton was important in the DOMdynamics. Third, the total concentration of DOM did not vary muchbecause as DOM production from one source increased or decreasedthe amount of DOM from other sources would also increase ordecrease by a similar amount.

In the estuarine simulation DON production was sensitive to manyof the same parameter variations (Fig. 13 and supplemental Table S 3)as in the coastal simulation. This simulation responded much like thecoastal simulation because nitrogen was not as limiting as in theoceanic simulation and there were multiple remineralization sources(i.e., large and small zooplankton). Thus, the effects of parametervariations tended to be direct, with the exception of competitive

responses by phytoplankton. However, unlike in the coastal simula-tion bacteria were not a source of remineralized nitrogen. Insteadthey were nitrogen limited and actually took up ammonium incompetition with phytoplankton. These dynamics altered the compe-tition between phytoplankton for nutrients and made the productionof DON from phytoplankton exudation much more sensitive toparameter variations (Fig. 13g). Grazing by large zooplankton wasalso more important in these simulations than in the oceanic andcoastal ones and thus, had a substantial influence on competitionbetween phytoplankton and the DOM that they produced. Viral lysisplayed an important role in these dynamics because it was a sourceof mortality for both phytoplankton and bacteria, as well as a sourceof DOM. Note that DON production from viral decay was especiallysensitive to variations in the decay rate (Fig. 13d) with DON produc-tion from this source varying much more than for any other source ofDON. Since there were fewer feedbacks in this system because bacte-ria were not remineralizing DON and DOM inflow was high, theconcentrations of DOM tended to vary more in response to parametervariations (Fig. 4 range bars). Overall, there were several key resultsfrom this analysis. First, since the system was eutrophic and thecommunity structure allowed for multiple sources of remineralizednitrogen the effects of parameter variations on DOM productiontended to be more direct. Second, mortality processes, mainly fromgrazing by large zooplankton and viral infection, were important inDOM cycling. Third, competition between phytoplankton was impor-tant in the DOM dynamics and was affected by competition withbacteria and zooplankton grazing. Fourth, the total concentration ofDOM varied more in this simulation because the interactions betweenplanktonwere not as tightly coupled as in the other simulations, DOMinflow was higher, and bacteria were N limited.

Many of the key results of these simulations are difficult tovalidate because individual DOM sources cannot be easily measuredduring experiments with whole communities of plankton (Bronk etal., 1994; Carlson, 2002; Søndergaard et al., 2000). Moreover, experi-ments that seek to elucidate top-down and bottom-up control ofcertain processes usually add mass (e.g., nutrients, grazers, etc.) tothe system (Glibert, 1998), altering the nitrogen budget, whichmakes these studies difficult to directly compare to much of our sen-sitivity analysis where the total amount of nitrogen in the systems didnot change. Nonetheless, some comparisons and conclusions can bemade. In our simulations DOM from certain sources like phytoplank-ton changed with their biomass and productivity, a result that hasbeen shown in many culture experiments (Baines and Pace, 1991;Biddanda and Benner, 1997; Søndergaard et al., 2000). While othersources of DOM, like sloppy feeding or viral lysis, changed in responseto predator and prey or host and virus interactions, a result that hasalso been shown in culture (Bratbak et al., 1998; Møller, 2005;Wommack and Colwell, 2000). In addition, competitive effectsbetween phytoplankton in our simulations were important and hadan effect on DOM cycling, a result which is not unexpected consider-ing that different species of phytoplankton release DOM, of differingcomposition, at different rates (Baines and Pace, 1991; Biddandaand Benner, 1997).

Since the effects of nutrient and grazer manipulations on foodwebs have often been studied (Clark et al., 2008; Glibert, 1998;Landry et al., 1998), the results of this research, along with C and Ntracer studies, can be used to help validate some of our results,given what is known about biotic DOM production and consumptionprocesses. In oligotrophic waters nutrient additions often increase thebiomass and productivity of phytoplankton and their predators(Landry et al., 1998) so it is not surprising that when bacteria con-sumption of DOM and subsequent remineralization of DIN changedin our simulations the rest of planktonic community responded,along with DOM production. The importance of the microbial loop,the viral shunt, and nutrient recycling in oligotrophic waters arealso well established (Azam et al., 1983; Clark et al., 2008;

Ψi ωi χD Si ν α

κZ

σZ ggeB geZ Ψi ωi χD S νκZ

σZ α geZggeB

mm

ol N

m-3

d-1

mi mi

a) Viral Lysis b) Sloppy Feeding

c) Mortality d) Viral decay

e) Zooplankton Excretion f) Detritus decay


Fig. 12. Sensitivity of coastal DON production sources to parameter variation runs. The x-axis shows the different parameter variation runs: infection rate (Ψi); sloppy feedingparameter (ω); detritus decay rate (χD); mortality rate (Si); viral decay rate (υ); zooplankton excretion parameters (κZ and σZ); phytoplankton exudation parameter (α); bacterialgrowth efficiency (ggeB); zooplankton growth efficiency (geZ). The y-axis scale represents normalized deviations from the main oceanic model run production rate at steady state(i.e., −0.1 mmol N m−3 d−1 represents a production decrease of 0.1 mmol N m−3 d−1 for the parameter variation run indicated on the x-axis). Parameter decreases are in black(■) and parameter increases are in white (□). The panels show DON production from (a) viral lysis; (b) sloppy feeding; (c) mortality; (d) viral decay; (e) zooplankton excretion;(f) detritus decay; and (g) phytoplankton exudation.


Ψi ωi χD Si νκZ

σZ α geZggeBi i D i Ψi ωi χD Siν

κZ

σZ α ggeB geZm

mol

N m

-3 d

-1

m i mi

a) Viral Lysis b) Sloppy Feeding

c) Mortality d) Viral decay

e) Zooplankton Excretion f) Detritus decay


Fig. 13. Sensitivity of estuarine DON production sources to parameter variation runs. The x-axis shows the different parameter variation runs: infection rate (Ψi); sloppy feedingparameter (ω); detritus decay rate (χD); mortality rate (Si); viral decay rate (υ); zooplankton excretion parameters (κZ and σZ); phytoplankton exudation parameter (α); bacterialgrowth efficiency (ggeB); zooplankton growth efficiency (geZ). The y-axis scale represents normalized deviations from the main oceanic model run production rate at steady state(i.e., −0.1 mmol N m−3 d−1 represents a production decrease of 0.1 mmol N m−3 d−1 for the parameter variation run indicated on the x-axis). Parameter decreases are in black(■) and parameter increases are in white (□). The panels show DON production from (a) viral lysis; (b) sloppy feeding; (c) mortality; (d) viral decay; (e) zooplankton excretion;(f) detritus decay; and (g) phytoplankton exudation.



Kirchman, 2000; Suttle, 2007) so it is also not surprising that DOM cy-cling in our simulations was tied to these processes. In coastal watersexperiments suggest that grazing and nitrogen regeneration can en-hance the stability of the system by helping to balance top-downand bottom-up controls (Glibert, 1998). This stability was evident inour coastal simulations, as DOM cycling did not change as much asin the other simulations in response to parameter variations. In estu-arine waters where interactions between plankton are often weaklycoupled (see Section 4.1) the effects of parameter variations onDOM production in our simulations tended to be large and direct,reflecting this weak coupling. These dynamics likely play an impor-tant role in the substantial observed seasonal changes in estuarineDOM concentrations and sources (Bronk et al., 1998; Fisher et al.,1998; Malone et al., 1991).

In both the oceanic and coastal runs the concentrations of DOCand DON did not vary much when the model parameters werechanged even though the production of DOM from a particular sourcesometimes varied considerably. These results suggest that theabsolute DOC and DON concentrations are “robust” quantities inopen ocean and coastal systems even though the sources and sinksmay change substantially. This may help to explain the fact thatDOM concentrations in these environments are relatively constantin space and time (i.e., despite some variation in DOM concentrationsthere are no huge increases or decreases in the overall concentration,with latitudinal and seasonal changes of only a few mmol m−3;Bronk, 2002; Carlson and Ducklow, 1995; Carlson et al., 2010). In con-trast, in the estuarine case the model generated much more variabil-ity in DOC and DON concentrations during the sensitivity analysisbecause bacteria did not provide a bottom-up link to the food webby remineralizing DON (i.e., no feedback to influence DOM produc-tion). This may be why estuaries like the Chesapeake Bay experiencelarge fluctuations in DOM concentrations that cannot be accountedfor by mixing or river inflow (i.e., net annual accumulations of up to120 mmol DOC m−3, Fisher et al., 1998).

5. Summary and conclusions

The model was broadly tuned and parameterized to providesteady state solutions for idealized oceanic, coastal and estuarine sys-tems with the explicit goal of comparing the DOM cycling dynamicsin these different environments. These three different model imple-mentations were generated by tuning the model to reproduce broaddifferences in the observed biomass distributions as defined byGasol et al. (1997) and Malone (1980) under different forcing condi-tions (i.e., light availability and nutrient/DOM loading). The model re-sults were then analyzed using classic sensitivity analysis methods tocharacterize how the model behaves differently in oceanic, coastaland estuarine conditions.

We show that the model was able to reproduce the Gasol et al.(1997) biomass distributions in our oceanic, coastal and estuarineruns. Through comparisons with available data we demonstrate thatthe model generated nutrient and DOM concentrations and also up-take and production rates (for phytoplankton, bacteria and DOM)that fell within ranges reported for oceanic, coastal, and estuarine sys-tems. Differences in the forcing and parameterizations of the oceanic,coastal and estuarine runs gave rise to significant differences in DOMcycling that were intricately tied to differences in the biomass con-centration, distribution, and production of phytoplankton, zooplank-ton, and bacteria as well as the availability of nutrients and DOM.Abiotic processes such as photooxidation, which differed from systemto system, also played an important role in DOM cycling by alteringthe bioavailability of some DOM and by acting as a turnover mecha-nism for different pools of DOM.

In the oceanic simulation the availability of nutrients and DOM,the biomass and productivity of bacteria, and the efficiency of nutri-ent remineralization by bacteria were the most important factors in

determining the biomass and productivity of other plankton. As a re-sult, bacteria were the key to understanding how DOM cycling oc-curred in this system. Any parameter changes that increased ordecreased bacterial biomass or productivity generally also increasedor decreased the biomass and productivity of phytoplankton and zoo-plankton as well. This in turn impacted DOM production derived fromphytoplankton and zooplankton. The total concentration of DOM didnot vary much because as DOM production from one source increasedor decreased the amount of DOM from other sources would also in-crease or decrease by a similar amount. In this simulation DON inflowwas also an important source of “new” nitrogen that played an impor-tant role in the productivity of the system.

In the coastal simulation small zooplankton and bacteria bothplayed a key role in regenerating nutrients that sustain phytoplanktonproduction. Because there were two sources regenerating ammoniumfor phytoplankton growth, and nutrients were more abundant, thefood web and DOM production were more resilient to parameterperturbations (i.e., no strong top-down or bottom-up effects). Competi-tion between large and small phytoplankton was also very evident inthis simulation, which suggests the potential for substantial temporalchanges in the sources and sinks for DOM cycling in temperate coastalwaters, i.e., seasonally, as commonly observed. As in the oceanic simu-lation, the total concentration of DOM did not vary much because asDOM production from one source increased or decreased the amountof DOM from other sources would also increase or decrease by a similaramount. However, the addition of DOM to the system did not have as animportant impact on DOM cycling as it did in the oceanic simulationwith the effects mainly being limited to changing the communitystructure by reducing the outflow of the system.

In the estuarine simulation where nutrients and DOM wereabundant, interactions between plankton were different than inthe other simulations because bacteria became nitrogen limitedand took up ammonium instead of regenerating it. Thus, therewas no bottom-up coupling between the bacteria and highertrophic levels. Instead there was actually competition between bac-teria and phytoplankton for DIN. As in the other simulations, com-petition between phytoplankton also played an important role inDOM production and cycling. Mortality from grazing by largerzooplankton and viral lysis (including viral decay dynamics) werealso more important than in the other simulations. As a result ofthese dynamics parameter variations tended to affect DOM produc-tion directly and there were few feedbacks. Moreover, in contrast tothe oceanic and coastal simulations, the total concentration of DOMvaried more during the sensitivity analysis because the interactionsbetween plankton were not as tightly coupled as in the other simu-lations, DOM inflow was higher, and bacteria were N limited. How-ever, as in the coastal simulation, the addition of DOM to the systemdid not have as an important impact on DOM cycling as it did in theoceanic simulation with the effects mainly being limited to theresults of changing the community structure by reducing the out-flow of the system.

As our analysis shows the cycling of DOM in each system wasstrongly dependant on the structure of the planktonic food web andthe availability of nutrients and DOM. Table 6 highlights some of thekey results of these simulations. Despite the differences betweenthese systems there were a few general trends in DOM productionthat we observed in all three systems: 1) phytoplankton exudationwas almost always the most important individual source of DOC;2) viral lysis and decay were always important sources of DOM; and3) as shown in the sensitivity analyses the production of DOM froma particular source can vary in magnitude by a considerable amount.The importance of phytoplankton exudation as a source of DOC iswell known (Baines and Pace, 1991; Carlson, 2002; Søndergaard etal., 2000), although only a few studies have been able to determineits importance relative to other DOM producing processes. Thosethat have show that, as in ourmodel, it is themost important individual

Table 6Summary of the simulation results and sensitivity analysis.


Key process controlling planktonic interactionsand DOM cycling:

Remineralization of N bybacteria

Remineralization of N by bacteria andmicrozooplankton

Remineralization of N byzooplankton

Phytoplankton competition Phytoplankton competition Mortality (grazing and viral)Phytoplankton competitionBacteria are N limited

Important sources of DOC: Phytoplankton exudation Phytoplankton exudation Phytoplankton exudation

Important sources of DON: Phytoplankton exudation Phytoplankton exudationViral lysis Phytoplankton exudation Viral lysisViral decay All are similar in magnitude Viral decayDetritus decay Sloppy feeding


source of DOC (Marañón et al., 2005). The importance of viruses in oursimulations shows how large a role the estimated 20–40% of prokaryotemortality that is caused each day by lysis (Suttle, 1994) may play inDOM cycling. This result is also consistent with culture studies thatshow that viral lysis produces large amounts of DOM (Bratbak et al.,1998; Brussaard et al., 2005). Finally, the last result (3) has importantimplications for the cycling of DOM and even for the composition ofthe communities that utilize the DOM because the quality and bioavail-ability of DOM may be different for each DOM source. However, wecannot elaborate much further on this point, except to say that moreresearch is needed in this area, because our parameterizations whichpartition DOM production to the labile, semi-labile, and refractorypools were poorly constrained for most of the sources. It is also worthnoting here, that the bacterial community can have a profound effecton DOM cycling by acting either as a consumer or producer of DIN.This has important implications for DOM cycling under non-steadystate conditions where the bacterial community may switch betweenthese roles in response to changing environmental conditions.

While many of our results compare well to measured marineprocesses it is difficult to make too many detailed predictions aboutthe rates of DOM cycling because our model was run to a steady stateand many of the processes are poorly constrained. In addition, the pa-rameterization of the simulations resulted in the plankton dynamics al-ways being primarily controlled by nutrient availability (i.e., bottom-upcontrol), which is not always the case in marine environments. Themodel also does not account for phosphorus or other forms of nutrientlimitation (i.e., iron, vitamins, etc.). Furthermore, the model lacksphysical processes and a 3-D context both of which play a large role indetermining plankton dynamics, nutrient availability, and DOMconcentrations. Nonetheless, the results of this comparative modelingstudy are broadly consistent with our conceptual understanding ofhow ecosystem dynamics and DOM cycling differ between manyoceanic, coastal and estuarine systems. Furthermore, the model doesmake some specific predictions about 1) the role of bacteria andzooplankton in nutrient and DOM cycling; 2) the degree of competitionbetween large and small phytoplankton species and their role in DOMproduction; 3) the effect of viruses on the plankton dynamics andDOM cycling; and 4) the inherent variability of DOM concentrations inoceanic, coastal and estuarine waters. In addition, the sensitivity analysisprovides information about the response of DOM cycling to certainperturbations. Together these results may be useful indicators of howDOM cycling in an ecosystem can respond to changes in environmentalconditions. These predictions can also be viewed as testable hypothesesthat can be used to help guide future field studies.

Acknowledgements

We thank the DOMINO project members for helpful discussions ofDOM cycling. This research was supported by National Science

Foundation grant OCE-0221825. This is contribution number 4579from the University of Maryland Center for Environmental Sciences.

Appendix A

The model was integrated numerically over time with Stella™software using a fourth-order Runge–Kutta scheme. A 6-h time stepwas used and the model runs were allowed to reach a steady state.Unless described below the equations are as in Keller and Hood(2011).

A.1. Turbidostat forcing

There is a constant flow of water (h) with nutrients and DOM at aconcentration, i°, into a volume (V) of constantly mixed waterrepresenting the ecosystem, which for simplicity has been assumed tobe 1 m3. An equal rate of efflux occurs, so that the volume of water inthe ecosystem stays constant. Changes in nutrients and DOM(represented here as i) due to this inflow are thus:

∂i∂t ¼ iþ hio

Vð1Þ

and the change for any state variable, i, due to outflow is:

∂i∂t ¼ i−hi

Vð2Þ

A.2. Phytoplankton

The equation for large phytoplankton is:

∂PL

∂t ¼ αJPLQPLPL−mPL

PL−GZLPL−GZSPL

−ΨPLVPPL−

hPL

Vð3Þ

The equation for small phytoplankton is:

∂PS

∂t ¼ αJPSQPSPS−mPS

PS−GZLPS−GZSPS

−ΨPSVPPS−

hPS

Vð4Þ

In both of the above equations the first term represent growth.The remaining terms represent losses due to mortality, largezooplankton grazing, small zooplankton grazing, viral lysis, andecosystem outflow.

QPL¼ Q1

PLþ Q2

PLð5Þ


QPS¼ Q1

PSþ Q2

PSð6Þ

The uptake of nitrogen by phytoplankton, QPL or S, is designed so that

the uptake of nitrate, QPL or S

2 , is inhibited if the nitrogen requirements aremet by the uptake of ammonium, QPL or S

1 . Where

Q1PLorS

¼ AKPLorSA

þ Að7Þ

and if QPL or S

1 is less than one then

Q2PLorS

¼ Nn

KPL or SNnþ Nn

1−Q1PL or S

� �: ð8Þ

else

Q2PL or S

¼ 0: ð9Þ

A.3. Zooplankton

The equation for large zooplankton is:

∂ZL

∂t ¼ FZL−GZLZL−mZL

Z2L−

hZL

Vð10Þ

and the equation for small zooplankton is:

∂ZS

∂t ¼ FZS−GZLZS−GZSZS

−mZSZ2S−

hZS

Vð11Þ

In these equations the first term represents growth. The remainingterms represent losses due to predation, mortality, and ecosystemoutflow.

A.4. Bacteria

The equation for bacteria is:

∂B∂t ¼ Bgrowth−GZLB

−GZSB−mBB−ΨBVBB−

hBV

ð12Þ

In this equation the first term represents bacterial growth. Theremaining terms represent losses due to zooplankton grazing, mortality,viral lysis, and ecosystem outflow.

A.5. Detritus

Changes in nitrogenous detritus (mmoles N m−3) are modeled as:

∂DN

∂t ¼ 1−βNZ

� �INZL

þ INZS

� �þ ρD ωPL

GZLPLþωDL

GZLDNþωZL

GZLZLþmBB

� �

þβ1 mPLPL þmPS

PS þmZLZ2L þmZS

Z2S

� �

þεD ΨPLVPPL þΨPS

VPPS þΨBVBB� �

−χDNDN−GZLDN

−GZSDN− hDN

Vð13Þ

Changes in carbon detritus (mmoles C m−3) are modeled as:

∂DC

∂t ¼ 1−βCZ

� �ICZL

þ ICZS

� �

þρD ωPLλPGZLPL

þωDGZLPL þωDGZLDCþωZL

λZGZLZLþmBλBB

� �

þβ1 mPLλPPL þmPS

λPPS þmZLλZZ

2L þmZS

λZZ2S

� �

þβ5εV ΨPLVP λP−λVð ÞPL þΨPS

VP λP−λVð ÞPS þΨBVB λB−λVð ÞB� �

þεD ΨPLVPλPPL þΨPS

VPλPPS þΨBVBλBB� �

−χDCDC−GZLDC

−hDC

Vð14Þ

In these equations the positive terms represent the production ofdetritus from zooplankton fecal pellet production or egestion, sloppyfeeding, plankton mortality, and viral lysis (two terms for DC). Thenegative terms represent the loss of detritus due to photooxidation,large zooplankton grazing, small zooplankton grazing, and ecosystemoutflow.

A.6. Nutrients

The equation for nitrate is:

∂Nn

∂t ¼ −JPLQ2PLPL−JPSQ

2PSPS þϖ1Aþ hNo

n

V−hNn

Vð15Þ

In this equation the first two terms represent the uptake of nitrateby phytoplankton. The remaining terms represent the nitrification ofammonium to nitrate and the inflow and outflow of nitrate from theecosystem.

The equation for ammonium is:

∂A∂t ¼ κZ EZL

þ EZS

� �þ bχ þ χUVN

SN þ RNð Þ� ��−UA

−JPLQ1PLPL−JPS

Q1PSPS−ϖ1Aþ hAo

V−hA

V

ð16Þ

In this equation the positive terms represent the production oraddition of ammonium from zooplankton excretion, bacterial excretion,the photooxidation of DON (in the estuarine simulations only, notedwith *), and ecosystem inflow. The negative terms represent a loss ofammonium due to phytoplankton and bacterial uptake, nitrification,and ecosystem outflow.

A.7. Dissolved organic matter

Equations for labile (LC and LN), semi-labile (SC and SN), and refractory(RC and RN) DOM are:

∂LN∂t ¼ oL

�1−αð Þ JPL

QPLPL þ JPS

QPSPS

� �þ 1−β1ð Þ

× mPLPL þmPS

PS þmZLZ2L þmZS

Z2S

� ��þ oZ 1−κZð Þ EZL

þ EZS

� ��

þρL ωPLGZLPL

þωDGZLPNþωZL

GZLZLþmBB

� �þ δ1λDN

DN

þηυ V2P þ V2

B

� �þ εL ΨPL

VPPL þΨPSVPPS þΨBVBB

� �

þτμSSNλBBKS þ SC

−UN þ ΞLhDOMo

V−hLN

Vþ ζRNð Þ�� ð17Þ


∂LC∂t ¼ oL

�1−αð ÞλP JPLQPL

PL þ JPSQPSPS

� �þ EPL þ EPS þ 1−β1ð Þ

× mPLλPPL þmPS

λPPS þmZLλZZ

2L þmZS

λZZ2S

� ��þ oZ 1−σZð Þ

× RZLþ RZS

� �þ ρL ωPL

λPGZLPLþωDGZLDC

þωZLλZGZL ZL

þmBλBB� �

þδ1χDCDC þ ηυλV V2

P þ V2B

� �

þεL ΨPLVPλPPL þΨPS


þ τμSSCλBBKS þ SC

þβ2εVðΨPLVP λP−λVð ÞPL þΨPS

VP λP−λVð ÞPS

þΨBVB λB−λVð ÞBÞ−λBBgrowth−RB−χUVCLC

þΞLλDOMhDOM∘

V−hLC

Vþ ζRCð Þ��: ð18Þ

∂SN∂t ¼ oS

�1−αð Þ JPLQPL

PL þ JPSQPSPS

� �þ 1−β1ð Þ

× mPLPL þmPS

PS þmZLZ2L þmZS

Z2S

� ��þ 1−oZð Þ 1−κZð Þ EZL þ EZS

� �

þρS ωPLGZL PL

þωDGZLDNþωZL

GZLZLþmBB

� �þ δ2χDN

DN

þ 1−ηð Þυ V2P þ V2

B

� �þ εS ΨPL

VPPL þΨPSVPPS þΨBVBB

� �

− μSSNλBBKS þ SC

þ ΞShDOM∘

V−hSN

V− χUVN

SN� �� ð19Þ

∂SC∂t ¼ oS

�1−αð ÞλP JPLQPL

PL þ JPSQPS

PS

� �þ EPL þ EPS þ 1−β1ð Þ

× mPLλPPL þmPS

λPPS þmZLλZZ

2L þmZS

λZZ2S

� ��þ 1−oZð Þ 1−σZð Þ

× RZLþ RZS

� �þ ρS ωPL

λPGZL PLþωDGZLDC

þωZLλZGZLZL

þmBλBB� �

þδ2χDCDC þ 1−ηð ÞυλV V2

P þ V2B

� �

þεS ΨPLVPλPPL þΨPS


− μSSCλBBKS þ SC

þβ3εVðΨPLVP λP−λVð ÞPL þΨPS

VP λP−λVð ÞPS

þΨBVB λB−λVð ÞBÞ−χUVCSC þ ΞSλDOM

hDOM∘

V−hSC

V: ð20Þ

∂RN

∂t ¼ oR�1−αð Þ JPLQPL

PL þ JPSQPSPS

� �þ 1−β1ð Þ

× mPLPL þmPS

PS þmZLZ2L þmZS

Z2S

� ��þ ρR

× ωPLGZLPL

þωDGZLDNþωZL

GZLZLþmBB

� �þ δ3χDN

DN− ζRNð Þ��−χUVN

RN

� �� þ εR ΨPLVPPL þΨPS

VPPS þΨBVBB� �

þ 1−τð ÞμSSNλBBKS þ SC

þ ΞRhDOM∘

V−hRN

V: ð21Þ

∂RC

∂t ¼ oR�1−αð ÞλP JPL

QPLPL þ JPS

QPSPS

� �þ EPL

þ EPSþ 1−β1ð Þ

× mPLλPPL þmPS

λPPS þmZLλZZ

2L þmZS

λZZ2S

� ��

þρR ωPLλPGZLPL

þωDGZL DCþωZL

λZGZLZLþmBλBB

� �þ δ3χDC

DC

þεR ΨPLVPλPPL þΨPS


þβ4εV ΨPLVP λP−λVð ÞPL þΨPS

VP λP−λVð ÞPS þΨBVB λB−λVð ÞB� �

− ζRCð Þ�� þ 1−τð Þ μSSCλBBKS þ SC

−χUVCRC þ ΞRλDOM

hDOM∘

V−hRC

V:

ð22Þ

Dissolved organic matter is produced by phytoplankton excretionand leakage, zooplankton sloppy feeding, zooplankton excretion, virallysis of phytoplankton and bacteria, viral decay, plankton mortality,and detritus decay. Labile DOM can be consumed directly by bacteriaand phytoplankton. Semi-labile DOM requires ectoenzyme hydrolysisby bacteria to become available (labile) for consumption. Bacterialhydrolysis of semi-labile DOM transforms it into either labile orrefractory DOM upon hydrolysis. In the estuarine and coastal simula-tions photochemical processes are responsible for the conversion ofrefractory DOM into labile DOM (term noted by **). Photochemicalprocesses also convert some DOC into DIC, and in the estuarine

simulation DON into ammonium (term noted by *). In addition,DOM flows into and out of the ecosystem.

A.8. Viruses

The equation for bacterial viruses is:

∂VB

∂t ¼ εVΨBVBB−vV2B−

hVB

Vð23Þ

The equation for phytoplankton viruses is:

∂VP

∂t ¼ εV ΨPLVPPL þΨPS

VPPS

� �−vV2

P−hVP

Vð24Þ

In these equations the first term represents the production ofviruses during viral lysis. The next term represents the decay ofviruses and the final term represents the outflow of viruses fromthe ecosystem.

Appendix B. Supplementary data

Supplementary data to this article can be found online at doi:10.1016/j.jmarsys.2012.01.002.

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comparative simulations of dissolved organic matter cycling in idealized oceanic, coastal, and...

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