the size-based dynamics of plankton food webs. ii ......upwelling events during spring and summer...

Journal of Plankton Research Vol.13 no.5 pp 1039-1092, 1991

The size-based dynamics of plankton food webs. II. Simulations ofthree contrasting southern Benguela food webs

Coleen L.Moloney, John G.Field and Michael I.LucasMarine Biology Research Institute, University of Cape Town, Rondebosch 7700,South Africa

Abstract. Size-based models are used to simulate planktonic food webs for the euphoric zones ofthree contrasting southern Benguela ecosystems, coastal stratified waters of the Agulhas Bank, acoastal upwelling area, and an oligotrophic area representative of SE Atlantic oceanic waters. In themodels, phytoplankton are divided into four size categories and zooplankton into five Allparameters describing flows are derived using body-size relationships Nitrogen is assumed to belimiting in all three ecosystems, and other physical factors, such as light, are kept constant forsimplicity. Initial pulses of new nitrogen in the Agulhas Bank (10 mg-at N m~3) and coastal upwellingmodels (25 mg-at N m"3) respectively simulate mixing and upwelling. Continuous inputs of newnitrogen into the Agulhas Bank (0.6 mg-at N m~3 day"1) and oceanic models (0 1 mg-at N m"3

day ) simulate turbulent diffusion into the euphotic zone across the nutricline The Agulhas Banksimulations depict a fluctuating standing stock of phytoplankton, dominated by different size classesat different times Large (net-) phytoplankton are important in this model only during periods ofenhanced nitrogen supply. The phytoplankton bloom in the upwelling simulation is dominated bynano- and net-phytoplankton. The oceanic simulation depicts a phytoplankton communitydominated by small cells (<5 ujn), the populations of which remain at a relatively constant biomassof ~40 mg C m~3. In all three simulations the smallest autotrophs generally are responsible for thegreatest proportion of primary production. The simulated heterotroph communities consist ofvarying biomasses of different-sized organisms. The Agulhas Bank and coastal upwelling simulationsbecome dominated by micro- and meso-zooplankton, whereas small beterotrophs dominate theoceanic simulation. Different size classes of heterotrophs are important at different times in nitrogenregeneration, although zooflagellates (1-5 \un) were shown to be the most important regeneratorson average. In the stratified Agulhas Bank and SE Atlantic oceanic simulations, regeneratedproduction is generally greater than new production. In the Benguela upwelling simulation, newproduction is replaced by regenerated production as the phytoplankton bloom matures afterupwelling and new nitrogen is exhausted. Average /-ratios were greatest (—0 75) in newly upwelledwater, decreasing for coastal stratified (0.25) and oligotrophic oceanic regions (0.14). Simulatedmodel communities compare well with field observations in terms of standing stocks and sizecomposition, suggesting that the model results accurately reflect nature. Up to seven-step 'foodchains' occur in all three simulated ecosystems, but in practice most of the carbon is transferred viathree effective trophic categories Long transfer pathways are important for planktivorous pelagicfish production in the oceanic simulation, short pathways in the coastal upwelling simulation, andintermediate-length pathways in the simulation of stratified coastal waters. Very little (<13%) oftotal photosynthetically fixed carbon potentially is available to pelagic fish in the simulations, andmost carbon is lost through respiration and sinking. Nevertheless, the carbon potentially available topelagic fish in the upwelling simulation is sufficient to sustain the pelagic fish production measured inthe southern Benguela region. A revised food web model of plankton communities depicts therelative importance of different carbon and nitrogen flow pathways in different marine systems.

IntroductionThe Benguela ecosystem is situated off the west coast of southern Africa(Shannon, 1985). It is dominated by a coastal upwelling system which, incommon with upwelling systems in other eastern boundary current regions,supports productive commercial fisheries (Cushing, 1971). The offshore boun-dary of the Benguela system is the oceanic thermal front which generally existsalong the shelf break between Cape Point and Cape Frio; the surface waters of© Oxford University Press 1039

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C.L.Mokmey, J.G.FWd and M.I.Lucas

the SE Atlantic Ocean west of the zone influenced by coastal upwelling aresubtropical (Shannon, 1985). The Benguela system is usually divided intonorthern and southern sub-systems (Shannon, 1985), the southern Benguelaextending between Hondeklip Bay and Cape Agulhas (Shannon, 1985) (Figure1). The Benguela region supports important pelagic fisheries, with pilchard(Sardinops ocellatus) and anchovy (Engraulis japonicus capensis) dominatingpurse-seine catches at different stages in the history of the fishery (Crawford etal., 1987). In the southern Benguela, both species spawn on the western AgulhasBank (Figure 1), but early development and recruitment take place chiefly onthe west coast of southern Africa.

The spawning and recruitment areas have markedly different physical andbiotic environments, which display variations on a number of time scales.During summer, the period during which spawning of pelagic fish takes place,the water column of the Agulhas Bank is characterized by strong thermalstratification with a well-developed chlorophyll maximum at the thermocline(Carter et al., 1986). Primary production is limited by nitrogen availability andby light when self-shading occurs in the chlorophyll maximum layer (Shannonand Pillar, 1986; Carter et al., 1987). Nitrate is believed to enter the euphoticzone by diffusion from nitrate-rich water below the thermocline (Carter et al.,1986, 1987). The structure of the water column and its associated phytoplankton

15

20°-

o25 H

30 -

35 -

\\Capo Frto

NorthernBenguela

Bangusta upwalllngSouthernBenguela

" 8.E Atlantic

SOUTH ATLANTICOCEAN W. Agulhaa Bank

10 15° 20°

Fig. 1. Geographical positions of the three models in the southern Benguela region.

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Size-based dynamics of plankton food webs. II

community appear to be stable compared with that of the active upwelJing areasoff the west coast (Carter et al., 1987), and it is believed that this stability isimportant to anchovy spawning success (Carter et al., 1987). The Benguela frontaljet current is associated with upwelling, and transports eggs and larvaenorthwards from the western Agulhas Bank to the fish recruitment grounds onthe west coast (Shelton and Hutchings, 1982).

The west coast upwelling region of the southern Benguela is characterized byupwelling events during spring and summer (Brown and Hutchings, 1987a), andhas a cool (8-16°C), variable and unpredictable surface-water environment.Nitrate is the limiting nutrient in this system (Andrews and Hutchings, 1980).Nitrate-rich water is brought to the surface at the coast during upwelling andthen moves offshore, allowing the rapid development of phytoplankton blooms,usually of short duration (Olivieri, 1985; Brown and Hutchings, 1987b). Theupwelling region generally supports larger standing stocks of phytoplanktonthan are found on the Agulhas Bank (De Decker, 1973), and has a largerprimary production than the Agulhas Bank (Shannon and Field, 1985).

The pelagic ecosystems in these two neritic regions of the southern Benguelaare subjects of research aimed at understanding trophic processes affectingpelagic fish. The components of the food webs in these two areas are reasonablywell understood (Shannon and Pillar, 1986). Furthermore, a number of studieshave been carried out on the important processes occurring in the plankton.Measurements have been made of primary production (Brown, 1984; Brownand Field, 1985; Brown and Hutchings, 1987a,b; Carter et al., 1987; McMurrayet al., 1991), bacterioplankton production (Lucas et al., 1986, 1987; Painting etal., 1989; Verheye-Dua and Lucas, 1988), micro-zooplankton regeneration rates(Probyn, 1985, 1987; Probyn and Lucas, 1987) and grazing by large zooplankton(Verheye and Hutchings, 1988), and seasonal and monthly variations have beendemonstrated in the two regions (see Shannon and Pillar, 1986, for a review).However, although many of the components of the ecosystems have beenstudied in detail, it is still unclear how the whole system functions, because it isimpossible to measure all of the processes occurring in a time series adequate forall main processes. 'Snapshot' measurements are difficult to extrapolate, andexisting time series of data (e.g. Brown and Hutchings, 1987b; Carter et al.,1987; Lucas et al., 1987; Verheye-Dua and Lucas, 1988; Painting et al., 1989)allow for the construction of working hypotheses as to how the ecosystemsfunction, but cannot be used to test these hypotheses. Furthermore, differenttemporal and spatial scales in sampling programs make it inappropriate to treatall of the data as a 'system sample'.

In this study we use simulation models to complement the field studies beingundertaken in the southern Benguela. Our aim was to examine the size-baseddynamics of three contrasting planktonic food webs in the southern Benguela,using the simulation model that is described in the first paper of this two-partseries (Moloney and Field, 1991). This paper is divided into two main parts. Inpart I we use the model to test the hypothesis that differences in the rates ofnutrient supplied to phytoplankton populations result in plankton communitiesthat differ in size structure and display different patterns of dynamic fluctu-

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ations, as has been speculated for the plankton communities that occur on theAgulhas Bank and the upwelling areas. In addition, an oligotrophic ecosystemassumed to be representative of SE Atlantic oceanic waters is simulated tocompare with the two coastal ecosystems. Output from the simulations iscompared with field data, to assess whether model communities are realistic. Inpart II, the carbon and nitrogen flows resulting from the three simulations areused as 'data' for network analyses of the food web structures of the threeecosystems. We aim to assess the importance of phytoplankton cell sizes indetermining production rates and the structure of food webs, and to identify themajor pathways of carbon and nitrogen flows in different planktonic food webs,assessing the role of the microbial loop in carbon transfer and nitrogenregeneration. Finally, we aim to develop a descriptive model which summarizesthe dynamic features of carbon and nitrogen flows in plankton food webs.

I. Simulations and validation of biomasses and size composition

Nutrient dynamics of plankton communities are simulated for three hypotheticalBenguela food webs: the upper mixed layers of two coastal ecosystems (AgulhasBank and Benguela upwelling) and an oligotrophic oceanic ecosystem (SEAtlantic, Figure 1). Identical-size-based simulation models are used for the threesimulations. Details of the model structure are described elsewhere (Moloneyand Field, 1991), as are the derivations of model parameters (Moloney andField, 1989, 1991). A brief summary of the salient features of the model is givenbelow.

Model descriptionThe model plankton communities consist of four autotroph and five heterotrophsize classes (Figure 2). To facilitate discussion the size classes have been givennames: pico-phytoplankton (0.2-1 u-m), phyto-flagellates (1-5 u.m), nano-phytoplankton (5-25 u.m) and net-phytoplankton (25-125 p,m) for autotrophs;bacterioplankton (0.2-1 \ixn), zooflagellates (1-5 \im), nano-zooplankton (5-25 M-m), micro-zooplankton (25-125 jim) and meso-zooplankton (125-625 u-m)for heterotrophs. It must be emphasized that these names are for convenienceonly, and the size categories do not necessarily conform exactly to traditionalnomenclatures (e.g. Sieburth et al., 1978). Furthermore, the size categories arenot exclusive, and the representative taxa are not necessarily the dominant onesin each size category.

Transfers of carbon and nitrogen are simulated within the communities.Carbon is assumed to enter the system through carbon fixation by phytoplankton,and to leave the system as a result of sinking of faecal material and phytoplanktoncells, or dissipation through respiration (Figure 2a). Autotrophs secrete dissolvedcarbon (PDOC) during growth, and also are assumed to lose carbon to thepaniculate—and ultimately dissolved—pools respectively as a result of cellsenescence and lysis. Carbon flows into and out of heterotroph compartmentsthrough uptake of dissolved carbon and ingestion of carbon as biomass.Nitrogen inputs into the system occur through upwelling or diffusion of new

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C FIXATION

C FIXATION

C FIXATION

C FIXATION

SkUngatandmntacI I i I

(OETRrrAL POOL)

NITROGEN FLOWS

Excretion

SIZE

riwHfMp

(DeTRIT AL POOL).

StnUtgoffmomI I I I

(DETRrTAL POOLJ^I

1-5

25

12S 825

Fig. 2. Structure of the size-based model, showing the model compartments and carbon and nitrogenflows. The size scale is log to the base 5 (see Moloney and Field, 1991)

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C.L.Moloney, J.G.FWd and M.I.Lucas

nitrogen into the new nitrogen pool, and nitrogen is lost from the system throughsinking of faecal material through the thermocline. Nitrogen flows within thecommunities occur by uptake, ingestion and excretion (Figure 2b), with solubleexcreted nitrogen being recycled into the regenerated nitrogen pool.

The models are kept as simple as possible by assuming optimal light conditionsin the euphotic zone, with only nitrogen limiting phytoplankton growth. Thespatial resolution of the models is for one cubic meter of water; consequently'representative' depths in the water column have been chosen for each region(Figure 3). These are discussed in more detail below. In essence, the simulationsof the three regions differ only in terms of two factors, which are used tosimulate different environmental conditions: (i) method and amount of newnitrogen input to the systems; and (ii) ambient temperatures, which affect thevalues of rate parameters (see below). All parameters are presented in Tables Iand II. Simulations are executed over time horizons of 30 days (Agulhas Bankand oceanic) and 15 days (coastal upwelling), using a second order Runge-Kuttamethod with time increments of 0.05 days, i.e. ~1 h (see Moloney and Field,1991).

Agulhas Bank coastal stratified simulationThe Agulhas Bank model represents stratified conditions on the westernAgulhas Bank. The representative cubic metre occurs in the region of the sub-surface chlorophyll maximum layer (Figure 3a), which lies on or close to thenitracline (Carter et al., 1987; McMurray et al., 1991). Although lightattenuation at these depths is almost 80-90% (McMurray et al., 1991),

AGULHAS BANK BENGUELA S.E. ATLANTIC(coastal stratified) (coastal upwelling) (oligotrophic oceanic)

Temperaturemln (20*C) max mln (10"C) max min (25'C) max

DepthI 20m

1t1

111

i 1m

(If

1

20m

(

(pi'V50m r

IT- —s1m

J1

mln max mln max min max

Nitrate concentrations

Fig. 3. Idealized representations of cubic metres in the water column for each of the three simulatedsystems. Note that temperatures and depths are idealized 'averages', and will be variable in the field

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Size-based dynamics of planirtnn fo^j w c bs . II

production-irradiance curves (Carter et al., 1987) indicate that the phytoplank-ton community is dark-adapted, with almost maximum rates of photosynthesis at5% surface light, justifying the assumption of optimum light conditions in thisversion of the model. Furthermore, the position of the chlorophyll maximumlayer at the nitracline indicates that nitrogen is limiting. Most primaryproduction occurs at or just above the chlorophyll maximum layer (Carter et al.,1986,1987; McMurray etal., 1991), indicating that most nitrate diffusing into thesurface waters is taken up in this region. Carter et al. (1986) estimated that adaily nitrate flux of 6.2 mM m~2 day"1 was necessary to sustain primaryproduction in the euphotic zone on the Agulhas Bank. The chlorophyllmaximum layer is ~10 m thick (McMurray et al., 1991), and the nitrate flux isthus equivalent to 0.6 mg-at m"3 day"1; in the Agulhas Bank model diffusioninto the chlorophyll maximum layer is simulated by introducing new nitrogencontinuously at a rate of 0.6 mg-at N m~3 day"1. Occasional pulses of enhancednitrate concentrations may occur in the euphotic zone (Carter et al., 1987), andthis is simulated by starting the Agulhas Bank simulation with a relatively highnew nitrogen concentration of 10 mg-at N m"3. Temperatures of the surfacewaters on the Agulhas Bank are relatively warm (19-22°C; Swart and Largier,

Table I. Autotroph size classes, model parameters and initial standing stocks of the simulations

All simulationsAverage ESD (jun)Cell mass (pg C)C:N ratioInitial values (mg C m'3)Refuges (mg C m"3)K, uptake (mg N m"3)K ingestion (mg C m'3)Sinking (m day"')Lysis (day"1)PER (%)

Picc-phytopl.(0.2-1 pjn)

0.450.0406.00.50.10 00031

340.0000271

15

Phyto-flagellates(1-5 jun)

2 2156.00.50.10.12

500.00101

15

Agulhas Bank coastal stratified simulation (20°QV^ (day"1)Respiration (day"')Senescence (day"1)

Benguela coastal upwelhngV™, (day"1)Respiration (day ')Senescence (day"1)

8.03.8080

3 2150.32

simulation (10°C)4.01.9040

SE Atlantic oceanic simulation (25°C)V™, (day'1)Respiration (day ! )Senescence (day"1)

11.45.40.11

1.60.770.16

4.62 20.46

Nano-phytopl.(5-25 (im)

11566 050 14.1

740.0361

15

1.30.620.13

0.660 310.066

1.80.880.18

Net-phytopl(25-125 (un)

562100

6.0500.1

50109

0.821

15

0.530.250.053

0.260.120.026

0.750.350.075

Size-based parameters were estimated from regressions of Moloney and Field (1989,1991). Refugesfrom predation were set at small values (see Moloney and Field, 1991). V , ^ is maximum nutrientuptake rate, K and K, are half-saturation constants, PER is percentage extracellular release of totalcarbon fixed by phytoplankton (as PDOC). K values refer to predation on (not by) each size class

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Tabl

e II

. Het

erot

roph

size

clas

ses,

mod

el

All s

imul

ation

sAv

erag

e ES

D (t

un)

Cell

mas

s (p

g C)

CN

rat

ioIn

itial

valu

es (m

g C

m"3 )

Refu

ges

(mg

C m

"3 )K,

upt

ake

(mg

N m

"3 )K

inge

stion

(m

g C

m~3 )

Abs

orpt

ion

effici

ency

(da

y"')

para

met

ers

and

Bact

enop

l.(0

2-1

u.m

)

0.45

0.04

04.

00.

50.

1 0.000

3134 10

Agu

lhas

Ban

k co

astal

stra

tified

sim

ulati

on (2

0°C)

Vm

mx

(day

"')/-

„ (d

ay"')

Resp

iratio

n (d

ay '

)

80 - 3.8

Beng

uela

coa

stal u

pwell

ing

simul

ation

(10°

C)V

™ (

day'

1 )A

™,

(day

"1 )Re

spira

tion

(day

')

SE A

tlant

ic oc

eani

c sim

ulat

ion (2

5°C)

V-™

(day

"')

/m«

(day

"1 )Re

spira

tion

(day

')

4.0

- 1.9 11 - 5.4

initi

al st

andi

ng st

ocks

of t

he

Zoo f

lagell

ates

(1-5

fun)

2.2 0.41

4.5

0.5 0 1

- 50 0.85

- 79 17 - 39 8.7 -Il

l 25

simul

ation

s

Nan

o-zo

opl.

(5-2

5 (im

)

11 51 4.5 1 0 1

- 74 0.85

- 24 5.2 - 12 2.6

- 33 7.4

Micr

o-zo

opl.

(25-

125

urn)

5664

02 4.5

0.5 0 1

-10

9 0.85

- 70 1.6 - 35 0.78

- 10 2.2

Mes

o-zo

opl.

(125

-625

urn

)

280

800

000 4.

50.

5 0 1

- - 0 85

- 2.1 0.47

- 1.0 0.23

- 3.0 066

n r 2. 1 p a. § a 2

Size

-bas

ed p

aram

eters

wer

e es

timat

ed fr

om re

gres

sions

of M

olone

y an

d Fi

eld (1

989,

1991

). Re

fuge

s fro

m p

reda

tion

were

set a

t sm

all v

alues

(see

Molo

ney

and

Field

, 199

1). V

mu i

s max

imum

nut

rient

upt

ake r

ate,

/„„

is m

axim

um in

gesti

on ra

te, K

and

K, ar

e half

-satu

ratio

n co

nsta

nts.

K va

lues

refer

to pr

edat

ionon

(not

by)

each

size

clas

s

at University of California, San Diego on September 18, 2015http://plankt.oxfordjournals.org/Downloaded from


Size-based dynamics of plankton food webs, n

1987), and an ambient temperature of 20°C has been assumed for the modelsystem. The initial autotroph biomass spectrum is set with large organismsdominating, whereas the heterotroph biomass spectrum is flat, with smallbiomasses in each size class (Tables I and II).

Simulation output for 30 days is presented for autotrophs (Figure 4),heterotrophs (Figure 5) and nutrients (Figure 6), because the changes that occurare adequately represented during this time horizon. At the start of thesimulation, the autotrophs respond to the initial high concentration of newnitrogen by increasing rapidly (Figure 4a), but after the 'excess' new nitrogenhas been depleted (i.e. after day 2, Figure 6a), autotroph standing stocks arereduced, and fluctuate about a biomass of ~200 mg C m~3. The different sizecomponents of the autotroph and heterotroph assemblages fluctuate on differenttime scales (Figures 4 and 5), the smallest cells having the largest number offluctuations, but the shortest durations. Depletion of the high initial concen-trations in the new nitrogen pool occurs rapidly (Figure 6a), after whichdissolved nitrogen concentrations fluctuate between 0 and 2 mg-at m"3. In

800

Ed

a) Autotroph community

4 6

Key tosize classes

• 25-125 urnB 5-25 um0 1-5 urnD 0.2-1 um

8 10 12 14 16 18 20 22 24 26 28 30Time (days)

300 b) Plco-phytoplankton(0.2-1 jim)

300

10 15 20 25 30Time (days)

10 15 20 25 30Time (days)

300 d) Nano-phytoplankton(5-25 tim)

150 N*t-phytoplankton(25-125 |im)

10 15 20 25Time (days)

30 10 15 20 25 30Time (days)

Fig. 4. Changes in the standing stocks and size composition of the autotroph community in theAgulhas Bank simulation.

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CX.Mokmey, J.G.Fteld and M.I.Lucas

EdO )

300-

200-

100-

0-

I 8)

1Heterotroph

WAUAAJJ

community

• ^^^^^^^^^^ 1 a 1 " 1 ' 1 f I f 1 •

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Time (days)

50Key tosize classes

• 125-625 nm• 25-125 urn• 5-25 \xmS 1-5 umD 0.2-1 urn

9 40:E 30"

b) Bacterioplankton (0.2-1 um)

0 5 10 15 20 25 30Time (days)

c) Zoofiagellates (1-5 tun)

10 15 20 25Time (days)

30

d) Nano-zooplankton(5-25 um)

JUnU10 15 20 25 30Time (days)

200150-

50-0

e) Mlcro-zooplankton(25-125 um)

I 1 L L l l 1 1 110 15 20Time (days)

25

f) Meso-zooplankton(125-625 um)

10 15 20Time (days)

Fig. 5. Changes in the standing stocks and size composition of the heterotroph community in theAgulhas Bank simulation.

general, there is proportionately more regenerated than new nitrogen in thedissolved pool (see below for discussion of/-ratios). Carbon pool concentrations(Figure 6b) increase steadily throughout the simulation. This unrealistic result isdue to the assumptions of constant secretion rates of PDOC and constantsenescence rates of phytoplankton. Model bacterioplankton are not able toutilize all the carbon, allowing it to accumulate in the PDOC and POC pools.

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Size-based dynamics of plankton food webs,

E

D)

a) Nitrogen poolsE3 Reg.ND NewN

10 12 14 16 18 20 22 24 26 28 30Time (days)

1000CO

E6

b) Carbon pools

8 10 12 14 16 18 20 22 24 26 28 30Time (days)

Fig. 6. Changes in concentrations and compositions of the dissolved nitrogen and carbon pools in theAgulhas Bank simulation.

Other mechanisms of removing PDOC and POC from the euphotic zone are notmodelled. For example, it has been shown that dissolved organic matter maycome out of solution to form particulate aggregations ('marine snow') whichthen sink out of the euphotic zone (Suzuki and Kato, 1953; Sharp, 1973). Ourknowledge of the dynamics of PDOC production under natural conditions ispoor, and this is reflected in the unrealistic simulation output, for which aconstant percentage extracellular release (PER) has been assumed.

Comparisons with field data (Agulhas Bank)The model output indicates that standing stocks of plankton communities of theAgulhas Bank may vary rapidly, and that dominance by different size classeschanges. These results have been validated by recent detailed studies byMcMurray et al. (1991), in which it was noted that the Agulhas Bank did nothave as stable a summer community as had been generally believed. Theseauthors estimated 'average' phytoplankton biomasses in summer on the westernAgulhas Bank as ranging from 40 to 140 mg C m~3 (McMurray et al., 1991),which is very similar to the simulated range of 24-233 mg C m~3 from day 10onwards. Furthermore, the maximum simulated standing stock of 600 mg C m~3

during a nitrogen-enhanced period in the model compares well with maximumfield estimates of ~350 mg C m~3 (assuming a carbon:chlorophyll ratio of 50;

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C.L.Moloney, J.G.Fidd and M.I.Lucas

McMurray et al., 1991). The fluctuations in the model are supported to someextent by a short time-series of field observations from the Agulhas Bank(Carter et al., 1987), in which maximum measured chlorophyll concentrations ata sub-surface chlorophyll maximum layer fluctuated between 5 and >10 mg chlm~3 over a period of 3 days. Two 'peaks' occurred during this time. The firstlasted for at least 1.5 days and the second for 0.5 days, and they were separatedby an interval of ~0.5 days. This time scale is very similar to that shown insimulation output (Figure 4a), where small autotrophs primarily are responsiblefor the rapid fluctuations in standing stock.

In order to compare predicted phytoplankton biomass spectra with thoseobserved to occur on the Agulhas Bank, cell counts (numbers ml"1) ofphytoplankton species from six stations were obtained from Table 1 of Probynand Lucas (1987). Approximate cell volumes were estimated from celldimensions presented by Drebes (1974), and volumes were converted to carbonmasses using the equations of Strathmann (1967). Size categories representequivalent spherical diameters (ESDs); thus apparently large thread-like cellshave small ESDs, because these cells have small volumes (most fall into the sizeclass 5-25 u-m). This is not unrealistic, because such cells have large surface tovolume ratios and are thus adapted to take up nitrogen at low ambientconcentrations. There are similarities between these data and different timesegments of the simulation model (Figure 7). The size class 5-25 u.m dominatedat the two field stations where total standing stocks were relatively large (Figure7a), similar to the model (Figure 7b), although small size classes also were foundto dominate the simulations at certain times (see Figure 4). Biomasses ofphytoplankton cells <5 u,m from the Agulhas Bank range from practically zeroto ~14 mg C m~3 (based on cell counts, Figure 7a), which is similar to the modelbiomasses in Figure 7(b), but much smaller than the maximum biomasses in themodel (see Figure 4). It is possible that small cells have been underestimated inthe field data of Probyn and Lucas (1987) because phytoplankton cells <15 u.mare difficult to identify and enumerate (Davis and Sieburth, 1982). There are nofield data of pico-phytoplankton biomasses on the Agulhas Bank. The largestphytoplankton cells (25-125 u,m) are present in relatively large quantities in thesimulation output only because they are initialized with a large standing stock ina nitrogen-rich environment. This is supported by field data (Figure 7a), whichindicate that large phytoplankton cells are not important contributors to totalphytoplankton biomass on the Agulhas Bank.

For micro-heterotrophs, we converted cell counts from Probyn and Lucas(1987) to biomasses of organisms <5 u,m (1-50 mg C m~3, assuming most are3 p.m in diameter), 5-15 u.m (2-90 mg C m~3), 30 u.m (0.2-4.5 mg C m~3,mostly ciliates) and 150 \im length [1-16 mg C m"3, copepod nauplii, usinglength-weight conversions of James (1987)]. These ranges of measuredbiomasses are similar to those of the model; zooflagellate (1-5 n-m) biomassesrange from 0.1 to 70 mg C m~3, and ciliates (5-25 u,m) from 0.1 to 130 mg Cm~3. Micro-zooplankton (25-125 u.m) in the model have a maximum standingstock of 130 mg C m~3, and meso-zooplankton (125-625 u,m) a maximum of70 mg C m~3. For large heterotrophs, Pillar (1986) has estimated average

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EO

Iom

200

150-

100-

50"

a) Agulhas Bank field dataAulotrophsizes

ta 25-125 tunm 5-25 umD <5|ixn

60 37 133 107Stations

88 119

250

EdE.CO

1oin

200-

150-

100-

50-

b) Simulation resultsAutotrophsizes

• 25-125 n& 5-25 urnD 1-5 jimD 0 2-1

20.0 22.7 22.9 28.3 28.8 29.1Model days

Fig. 7. Comparison of field measurements with simulation results of biomasses and size compositionof the autotioph community of the Agulhas Bank, (a) Field measurements from six stations (afterProbyn and Lucas, 1987) (b) Simulation output at selected times.

copepod standing stocks on the Agulhas Bank to be 610 mg dry wt m~2 or244 mg C m~2. This can be compared with simulation estimates by extrapolatingto cover the depth of the chlorophyll maximum (~10 m), giving potentialmaximum integrated biomasses of model micro/meso-zooplankton of 2000 mg Cm"2. This size range incorporates organisms other than copepods, so the fieldbiomasses should be smaller than those of the model, especially because larvalfish fall into the same size ranges as zooplankton. At present the model does notallow for increased metabolic rates during activities such as feeding (seeMoloney and Field, 1991), and may underestimate respiratory losses and thusoverestimate standing stocks in motile organisms. No time series are availablefor field measurements of heterotroph standing stocks on the Agulhas Bank.

Benguela coastal up welling simulationThe Benguela upwelling version of the model represents conditions in the

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upwelling region on the west coast of South Africa. The simulation mimicks thesequence of phytoplankton bloom development as upwelled water movesoffshore and stratifies, as has been shown to occur in the southern Benguela(Brown and Hutchings, 1987a). In this model it is assumed that light is non-limiting and that nitrate initially is equally distributed in surface waters after anupwelling event. The representative cubic metre in the upwelling model occursat any depth in the surface mixed layer of the water column (Figure 3b), thisdepth ranging from ~ 10 to 60 m (Brown and Hutchings, 1987a). Upwelling ofnutrient-rich water into the euphotic zone is simulated by introducing a largeinitial concentration (25 mg-at N m~3) of new-nitrogen at the start of thesimulation, after which new-nitrogen input is zero. This value corresponds to thelargest measured concentrations of nitrogen in newly upwelled water off theCape Peninsula (Armstrong et al., 1987; Brown and Hutchings, 1987a).Conditions in cold upwelled water are simulated by assuming an ambienttemperature of 10°C (Brown and Hutchings, 1987a). A Gio of 2.0 (Parsons et al.,1984) is assumed, and rate parameters for uptake, ingestion and respiration arehalf those of the Agulhas Bank simulation (see Table I). As upwelled waterages, its temperature increases by ~5-8°C (Armstrong et al., 1987), whichaffects the values of rate parameters, but this effect is ignored in the presentsimulation to reduce model complexity. However, ignoring temperature changesmay result in underestimates of production and respiration rates. The autotrophbiomass spectrum is initialized with large cells dominant (as for the AgulhasBank model), simulating the seeding of newly upwelled water by diatoms(Pitcher, 1990; Table I). Heterotrophs are initialized at small biomasses; fororganisms <25 jim this is probably realistic, and for organisms >25 jtm thisintroduces a delay, which allows for gradual acclimation to increasing foodconcentrations as the phytoplankton bloom develops.

As expected from field observations, a phytoplankton bloom develops in themodel system as a result of the input of a pulse of new nitrogen (Figure 8).However, the structure of the simulated bloom is more complex than haspreviously been observed, displaying a number of peaks dominated by differentsizes of phytoplankton (Figure 8a). For the heterotroph community, there is asuccession from small to large heterotrophs with time (Figure 9a), reflectingpredator-prey relationships in the model, in which small prey are eaten bylarger organisms. Bacterioplankton display a large biomass peak of 110 mg Cm~3 on day 11.5. This increase results from rapid utilization of organic matter(released by senescent phytoplankton cells) at a time when bacterivores arereduced. The trends in bacterioplankton biomasses are similar to those for pico-phytoplankton, but bacterioplankton are generally less abundant than are pico-phytoplankton. The meso-zooplankton increase very slowly at the start of thesimulation (Figure 9f), and had not yet peaked at the end of 15 days; theypeaked on day 16.3 with a biomass of 92 mg C m~3.

New nitrogen was introduced only once at the beginning of the upwellingsimulation, and concentrations consequently decrease to zero after 6 days(Figure 10a). New nitrogen concentrations initially decrease slowly, but afterday 1 this process accelerates as a result of uptake initially by pico-1052




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1 2 3

Key tosize classes

4 5 6 7 8 9 10 11 12 13 14 15Time (days)

• 5-2S|im• 1 -5um• 0.2-1 ]m

600

0 2 4 6 8 10 12 14Time (days)

e) Phyto-flagtllatM (1-8 |im)

2 4 6 8 10 12 14Time (days)

d) Nano-phytoplankton(5-28 Jim)

400

0 2 4 6 8 10 12 14Time (days)

2 4 6 8 10 12 14Time (days)

Fig. 8. Changes in the standing stocks and size composition of the autotroph community in theBenguela upwelling simulation.

phytoplankton and bacterioplankton, and later by all phytoplankton size classes.The depletion of the new-nitrogen pool is 'stepped', because predators reducepicoplankton standing stocks during the initial stages of the bloom (days 1.5-3),and nitrogen uptake at these times is decreased. The regenerated nitrogen poolis depleted by phytoplankton and bacterioplankton, and replenished by allheterotrophs, resulting in fluctuating concentrations with time. The dissolvedcarbon pool increases from initial concentrations of 10 mg-at m~3 to unrealisti-cally large concentrations of ~500 mg-at C m~3 after 15 days (Figure 10b),similar to the result of the Agulhas Bank model. The accumulated carbon in thePDOC and DOC pools is potentially utilizable by bacterioplankton, but is nottaken up rapidly because bacterioplankton are nitrogen-limited at the end of thesimulation. Except for the PDOC pool, all other compartments in the simulatedupwelling system eventually will decline to zero, because some nitrogen (andcarbon) continually is lost in faecal material and dead phytoplankton cells, whichrapidly sink out of the model system.

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a) Heterotroph community

Key tosize classes

2••BmD

3 4125-625 um25-125 nm5-25 um1-5 Jim02-1 um

6 7 8 9Time (days)

10 11 12 13 14 15

b) Bacterloplankton (0.2-1 urn)

2 4 6 8 10 12 14Time (days)

o

200150;100-

400-c) Zooflagellates (1-5 um) d) Nano-zooplankton(5-25

4 6 8 10 12 14Time (days)

0 2 4 6 8 10 12 14Time (days)

4 6 8 10 12 14Time (days)

Eb

10080;60-40;20"0

f) Metozooplankton(125-625 um)

0 2 4 6 8 10 12 14Time (days)

Fig. 9. Changes in the standing stocks and size composition of the heterotroph community in theBenguela upwelling simulation.

Comparisons with field data (Benguela upwelling)A number of measurements have been made of phytoplankton, bacterioplank-ton and zooplankton biomasses in the Benguela upwelling region, and these canbe compared with those of the model. However, it is not valid to attemptdetailed comparisons between quantitative aspects of the simulations and field

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CO1

E

To•

E

30-

20"

10"

Size-based dynamics of plankton food webs. O

a) Nitrogen pools • DONQ RegN• NowN

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Time (days)

600 b) Carbon pools

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Time (days)

Fig. 10. Changes in concentrations and compositions of the dissolved nitrogen and carbon pools inthe Benguela upwelling simulation.

data, because time scales and standing stocks in the model may change ifdifferent size categories, assumptions and/or starting values are used (Moloneyand Field, 1991). Thus the two initial, rapid blooms of phytoplankton cells<5 u,m in the Benguela upwelling simulation (Figure 8) may be an artefact of amodel system in which the effects of light-limitation and photo-inhibition havebeen ignored. Alternatively, because the initial blooms are composed of verysmall cell sizes, they may have been overlooked in 'snapshot' sampling.

Despite the limitations imposed by simplification, the output of the model isremarkably similar to field estimates. The time sequence of phytoplanktonbloom development after upwelling in the southern Benguela region has beenstudied in detail in the field by Brown and Hutchings (1987a,b). Their results forfive different blooms indicate that these typically take ~3 days to develop and 3 -4 days to decline, making total bloom duration ~7 days. Recent microcosmwork by G.Pitcher (Sea Fisheries Research Institute, personal communication)has extended this period to 10-12 days, because of a much longer developmenttime. The simulation results are more consistent with the microcosm results thanthe field measurements, probably because physical conditions in a microcosmmore closely resemble those that are assumed for the simulation model. Forexample, we have assumed a constant temperature in the simulation for theduration of the bloom, whereas field temperatures increase as the water column

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C.L.Moloney, J.G.Fidd and M.I.Lucas

stratifies (Brown and Hutchings, 1987a). Maximum biomasses estimated fromfield data are typically 20 mg Chi a m"3 (Brown and Hutchings, 1987b) or -1000mg C m~3, assuming a C:Chl ratio of 50 for a developing phytoplankton bloom(Shannon and Field, 1985). This is very similar to the simulated maximum of1200 mg C m"3, and the size composition of this bloom is similar to thatobserved in field studies (Olivieri, 1985), if the pico-phytoplankton blooms areignored, as these small cells may have been overloooked in early field studies.

Bacterioplankton biomasses in the model are similar to field estimates,ranging from 10-80 mg C m~3 (Lucas et al., 1986), 30 mg C m~3 (Armstrong etal., 1987), 3.5-47 mg C m"3 (Probyn, 1987) and <20-50 mg C m"3 (Verheye-Dua and Lucas, 1988), all of which were measured on the west coast.Zooflagellate (<5 u,m ESD) standing stocks of up to 100 mg C m~3 wereobserved by Lucas et al. (1987) and Painting et al. (1989) in a microcosm study,and these compare well with the maximum biomass of the model zooflagellates,~160 mg C m"3 at day 2 (see Figure 9c). For large zooplankton, biomasses areusually presented per unit sea surface area, because they are sampled by meansof net hauls. To make the model biomasses comparable with the field ones, wehave assumed that integrated zooplankton production in the model is supportedby a 20 m deep euphotic zone, giving a maximum biomass of 1840 mg C m"2 formesozooplankton (125-625 u,m). This can be compared with field estimates of400-2000 mg C m~2 for zooplankton (Hutchings et al., 1984), 590 mg C m"2 forcopepods and euphausiids in the Cape Peninsula area and 980 mg C m"2 in theCape Columbine/St Helena Bay area (Pillar, 1986), 170 mg C m"2 forzooplankton 200-500 u.m and 1400 mg C m~2 for all zooplankton >200 u.m(Verheye and Hutchings, 1988). Thus simulation results and time spans forautotrophs and heterotrophs are of the same order of magnitude as fieldestimates in the upwelh'ng region, giving confidence in the model's ability tomimick real ecosystems.

SE Atlantic oceanic simulationThe oceanic simulation represents oligotrophic waters of the SE Atlantic, lyingwest of the oceanic thermal front which forms the seaward boundary of theupwelling region (see Figure 1). Oceanic waters generally are characterized by asmall, but relatively constant, nutrient supply (Kanda et al., 1985), resultingfrom the turbulent diffusion of nitrogen from below the thermocline in a watercolumn that is persistently stratified and well mixed above the pycnocline(Bienfang, 1985). It is difficult to estimate how fast nutrients are entrained fromdepth in oceanic waters (Dortsch etal., 1982; Kanda et al., 1985), but it has beenassumed that the flux in the oceanic simulation would be less than that of theAgulhas Bank model, because nutrient concentrations below the thermocline ina 'typical tropical profile' (Longhurst and Pauly, 1987) are substantially less thanthose measured on the Agulhas Bank. Consequently, the turbulent diffusionrate has been reduced, and a flux of 0.1 mg-at m"3 of new nitrogen has beenassumed for the oceanic simulation. The representative cubic metre occurs nearthe thermocline (Figure 3b). Sea temperatures in the surface waters of central

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ocean basin regions are generally warm, and an ambient temperature of 25°Chas been assumed. A Qi0 of 2 (Parsons et al., 1984) was used to modify all rateparameters; consequently the potential maximum process rates are faster than inthe Agulhas Bank and upwelling simulations.

The simulated oceanic autotrophic community is presented in Figure ll(a).Although fluctuations in standing stocks are apparent at the start of thesimulation, after —25 days the autotroph standing stocks are constant at —40 mgC m~3, comprising mainly pico-phytoplankton and phyto-flagellates. Bothpopulations show small rapid fluctuations, probably representing dampenedoscillations about equilibrium biomasses (Figure l ib and c). The coexistence ofthe two smallest phytoplankton size classes in the oceanic simulation is aconsequence of grazing control; zooflagellates limit the growth of competitivelysuperior pico-phytoplankton, allowing phyto-flagellates to become establishedin the steady-state community. Phytoplankton >5 u,m do not persist in theoceanic model (Figure lid and e) because the amount of nitrogen being supplied

Ed

Ed

100"80"60-40"20"o-


2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Time (days)Key tosize classes

3025 i2015-10:5"0

•HE3•

25-125 um5-25 nm1-5 urn0.2-1 nm

b) Pico-phytoplankton(0.2-1 um)

10 15 20Time (days)

25 30

c) Phyto-flagellates (1-5 um)

10 15 20Time (days)

Ed

d) Nano-phytoplankton(5-25 am)

10 15 20Time (days)

Ed

6050403020100

e) Net-phytoplankton(25-125 (im)

10 15 20Time (days)

Fig. 11. Changes in standing stocks and size composition of the autotroph community in the oceanicsimulation.

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is small, and the consequent slow carbon fixation rates of the large phytoplank-ton cells in the model are exceeded by respiratory losses. The net-phytoplanktonin this simulation appear to be relatively well represented during the first fewdays only because they were initialized with a relatively large biomass of 50 mgC m~3 (Table I). This starting biomass was chosen to be consistent with the othertwo simulations. The heterotrophic component of the oceanic model communityalso displays dampened oscillations, but overall standing stocks remain relatively

12 a) Heterotroph community

Key tosize classes

• 125-625 nm• 25-125 urn5 5-25 imS 1-5 nm• 02-1 urn

4 6 8 10 12 14 16 18 20 22 24 26 28 30Time (days)

b) Bacterloplankton (0.2-1 urn)

10 15 20Time (days)

25 30

Ed

c) Zooflagellates (1-5 urn) d) Nano-zooplankton(5-25 um)

10 15 20 25 30Time (days)

10 15 20 25 30Time (days)

Ed

0.6 q0.5:0.4-0.3:0.2:0.10.0

0

e) Mlcrozooplankton(25-125 um)

10 15 20Time (days)

Ed

1.00.80.60.40.20.0

25 30

Mesozooplankton(125-625 urn)

10 15 20Time (days)

25

Fig. 12. Changes in the standing stocks and size composition of the heterotroph community in theoceanic simulation.

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small at ~6 mg C m~3 (Figure 12a). The bacterioplankton, zooflagellates andnano-zooplankton stabilize at small biomasses, whereas the larger micro- andmeso-zooplankton decrease to the 'refuge biomass' (Table II, Figure 11).

The dissolved nitrogen pools fluctuate during the oceanic simulation (Figure13a), but do not exceed combined concentrations of 0.2 mg-at N m~3, and areusually ~0.1 mg-at N m~3. Continuous diffusion of new nitrogen replenishes thepool, but the new N is taken up immediately, so 'measured' concentrations arenegligible. The persistence of low concentrations of regenerated N and DON inthe dissolved pool is a model artefact; these concentrations represent inputs atthe end of each time step, before uptake by phytoplankton and bacterioplanktonhas been computed. The negligible concentrations reflect the rapid uptake andrecycling rates in the model community, with small organisms with fast growthand metabolic rates primarily being responsible for contributing and utilizingnitrogen from the dissolved pool. The dissolved carbon pool concentrationsincrease throughout the oceanic simulation (Figure 13b), similar to the results ofthe Agulhas Bank and Benguela upwelling models.

Comparisons with field data (oceanic)There are few field measurements of autotroph and heterotroph biomasses inoceanic waters in the SE Atlantic to compare with model output. Brown and

CO

E

76)

0.3

0.2"

0.1-

a) Nitrogen pools • DON• Reg. ND NewN

0.010 12 14 16 18 20 22 24 26 28 30

Time (days)

120CO

1

E6T5

•O)E

100"80-60"40"20"o-

b) Carbon pools • DOC• PDOC

10 12 14 16 18 20 22 24 26 28 30

Time (days)Fig. 13. Changes in concentrations and compositions of the dissolved nitrogen and carbon pools inthe oceanic simulation

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Hutchings (1987b) estimate a mean chlorophyll a concentration of 0.4 mg Chi am"3 in oceanic waters, equivalent to ~25 mg C m"3, assuming a carbon:chloro-phyll ratio of 50. This is within the range simulated by the model. In addition,the simulated phytoplankton standing stocks incorporate the range of varyingfield estimates from other oceanic regions. At six stations off Hawaii, Laws et al.(1984) measured phytoplankton standing stocks ranging from 3.2 to 150 mg Cm"3 and, in the equatorial Atlantic Ocean, Herbland et al. (1985) measuredchlorophyll standing stocks of ~5 mg chl m"3, which are equivalent to ~250 mgC m~3, again assuming a carbon:chlorophyll ratio of 50. Most of the autotrophs(75%) in the oceanic simulation were <5 u.m, with 27% being pico-phytoplank-ton and 48% phyto-Qagellates. These pico-phytoplankton and phyto-flagellatepercentages should be larger, because net-phytoplankton were assigned anartificially large initial biomass in the simulation, and their contribution tobiomass (23%) should be smaller. This size structure is similar to that observedfor oceanic phytoplankton, which are usually <3 u,m (Eppley et al., 1969;Bienfang and Takahashi, 1983; Herbland et al., 1985). In Hawaiian waters, 80%of the total chlorophyll is contained in cells <5 (j.m, and 50% of the totalchlorophyll in cells <1 u,m (Takahashi and Bienfang, 1983; Bienfang, 1985). Inthe central North Atlantic, on average, 79% of the biomass is due to cells<3 jj-m, 17% to cells 3-8 u-m, and only 4% to cells >8 \im (Murphy andHaugen, 1985). The domination by small cells in oceanic waters has beenpartially ascribed to the competitive advantage of small cells compared withlarge cells at low nutrient concentrations and negligible losses of small cells dueto sinking (Parsons and Takahashi, 1973; Bienfang and Takahashi, 1983;Bienfang, 1985). The simulation results support this hypothesis.

Simulated bacterioplankton standing stocks in the oceanic simulation areequivalent to a mean concentration of 3 x 107 cells I"1 (using the averagebiomass of 3.12 mg C m~3, and assuming one bacterium has a carbon mass of0.1 pg), which is less than measured counts of 6.5 x 108 cells I"1 for Pacificwaters (Laws et al., 1984). The structure of the model system is very simple, andnutrient enhancements through excretion by zooplankton or fish passingthrough an area are of necessity excluded from the model. Such effects wouldincrease bacterioplankton growth in the model, as would an increase in thediffusion rate of new nitrogen.

Ambient concentrations of nitrogenous nutrients such as ammonia and nitrateare often below detection limits in oligotrophic oceanic waters, implying thatthey are rapidly taken up (McCarthy and Goldman, 1979). Dissolved nitratestypically have ambient concentrations of 0.01 mg-at N m~3, although they mayreach concentrations of 3.22 mg-at N m~3 (Kanda et al., 1985). In the NorthPacific central gyre, nitrogen concentrations have been estimated to be <0.05mg-at m~3 (Eppley et al., 1977), although urea concentrations of 0.35 mg-at Nm~3 have been measured in north-western Pacific central waters (Mitamura andSaijo, 1980). NH4+ concentrations in Hawaiian waters are ~0.16 mg-at N m"3

(Bienfang and Takahashi, 1983). These measured values are similar to theaverage simulated nitrogen pool concentration of 0.1 mg-at m~3 (Figure 13a).The mean concentrations of new, regenerated and dissolved organic nitrogen

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respectively in the oceanic simulation were 0.0008, 0.04 and 0.06 mg-at N m"3,based on the assumption of a new N flux of 0.1 mg-at N m~3 day"1.

Comparison of average biomassesThe dynamic nature of the three simulated ecosystems has been demonstratedabove. However, in order to assess the major components of the planktoncommunities in the three ecosystems, mean biomasses of all biotic componentswere calculated for the durations of each simulation. Different size fractionsdominated the autotroph biomasses in the three ecosystems (Figure 14a). Thecoastal upwelling simulation was dominated by large autotrophs; net-phyto-plankton comprised 43% of total standing stock, and the combined net- andnano-phytoplankton comprised 74% of total standing stock. Thus autotrophs<5 u.m appeared unimportant in terms of standing stocks in the simulatedupwelling community. In contrast, the Agulhas Bank simulation had approxi-mately equal proportions of autotrophs <5 p.m (45%) and >5 u,m (55%). The

Ed

a) Mean autotroph biomasses

Agulhas Bank UpwellingSimulations

Oceanic

Airtotrophsizes

• 25-125 JimS 5-25 jim• 1-5 jun• 0.2-1 jim

Ed

b) Mean heterotroph biomasses

Agulhas Bank UpwellingSimulations

Oceanic

HBtarotrooh3J2B3

• 125-625 Jim• 25-125 urn3 5-25 urn• 1-5 jim• 0.2-1 jim

Fig. 14. Simulated mean biomasses (mg Cm 3) of size classes of (a) autotrophs and (b) heterotrophsin the three model communities

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oceanic autotroph size structure was dominated by cells <5 u.m, whichcomprised 74% of the total biomass (Figure 14a), with the phyto-flagellatesbeing the most important size class in terms of standing stocks (48% of total).The average biomass of net-phytoplankton in the oceanic simulation is probablyunrealistically large, because the simulation was initiated with a large biomassfor comparative purposes.

Average heterotroph standing stocks (Figure 14b) were largest in theBenguela upwelling simulation, which was dominated by the micro- and meso-zooplankton. The heterotroph community of the Agulhas Bank simulation had asmaller standing stock than that of the Benguela upwelling simulation, and wasequally represented by all size classes except bacterioplankton. The oceanicheterotroph community had a smaller mean biomass than the two coastalcommunities, as would be expected. Bacterioplankton were the most abundantheterotrophs in the simulated oceanic community, contributing 53% to totalheterotroph biomass. However, in all three simulations mean bacterioplanktonstanding stocks were relatively small, ranging from 1.8 mg C m~3 in the AgulhasBank simulation, to 4.9 mg C m~3 in the upwelling simulation, with the oceaniccommunity intermediate at 3.2 mg C m"3.

II. Carbon and nitrogen fluxes and network analyses

There is a vast literature describing planktonic data from many disparatesystems, and providing a large number of possible pathways for carbon andnitrogen to flow within plankton communities. However, it is difficult to obtainsufficient field data to carry out a whole-system study to assess the relativeimportance of the different pathways. Simulation models provide readilyavailable, repeatable data, the 'true' nature of which is known. In the sectionsbelow, we compare our simulated flows with carbon and nitrogen flows thathave been measured in the field, to show that the simulated flows are realistic, ashas been done above for the biomasses. The simulated flows are then analysed ina systems context, i.e. the relative importance of different pathways for carbonand nitrogen flows is assessed on a time scale that is appropriate for largeorganisms such as pelagic fish. This is done by calculating the instantaneous ratesof carbon and nitrogen entering and leaving each model compartment (mg m~3

day"1) at each time step (0.05 day) of the simulations, and then integrating theseflows over selected periods. Average daily flows were calculated by dividing theintegrated flows by the relevant number of days (mg m~3 day"1).

Primary productionPrimary production rates were estimated as the difference between simulatedcarbon fixation rates and PDOC production rates, because the fraction ofprimary production that was excreted as PDOC in the simulations was constant,and may have been overestimated. Furthermore, PDOC production is notmeasured during standard 14C incubations, and the simulated result should becomparable to field measurements. Where necessary, the simulated daily rateshave been converted to hourly rates by assuming 12 h of daylight.1062




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For the Agulhas Bank simulation, average hourly production is estimated tobe 42 mg C m~3 h"1, similar to the range of hourly production rates of between20 and 300 mg C m~3 h"1 measured by Carter et al. (1987) in the chlorophyllmaximum layer of the Agulhas Bank, and a maximum production rate of47.7 mg C m"3 h"1 measured by McMurray (1989) on the western Agulhas Bankin summer. For the Benguela upwelling region, Brown (1984) estimated anaverage, depth-integrated daily production of 4052 mg C m"2 day"1, or —100-400 mg C m~3 day"1, assuming a 10-40 m deep euphotic zone (Brown andHutchings, 1987a). This is similar to the simulated value of 556 mg C m~3 day"1.Average primary production rates estimated from the Benguela upwellingsimulation over selected periods are compared with field measurements in TableIII. The model production rates fall within the range of measured primaryproduction rates. For the simulated oceanic community the average productionwas estimated to be 210 mg C m"3 day"1, or 18 mg C m"3 h"1. Measuredphytoplankton production rates in Hawaiian waters range from 0.32 to 52 mg Cm"3 h"1 for a <3 u,m size fraction (Bienfang and Takahashi, 1983; Takahashiand Bienfang, 1983), and in a separate study Laws etal. (1984) measured rates of0.35-11 mg C m~3 h"1 for a total phytoplankton community (assuming a 12 hday). In the Sargasso Sea, Sheldon et al. (1973) estimated primary productionrates to be 2.8 mg C m~3 h"1, and in the North Pacific central gyre averageprimary production was estimated by two studies respectively to be 9 mg C m"3

day"1 (Marra and Heinemann, 1987) and 4.7 mg C m"3 day"1 (Laws et al.,1989). Thus the simulation results are similar to maximum measured values,indicating that output is of the right order of magnitude, but probablyoverestimates production in general. This implies that the assumed nitrate fluxof 0.1 mg-at N m"3 day"1 may be too large, or alternatively that factors such asphotoinhibition depress carbon fixation rates of oceanic phytoplankton in thefield. Production to biomass ratios in the simulated oceanic community were

Table HI. Comparison of average rates of primary production from the Benguela upwellingsimulation with average measured values and ranges from surface waters of the Benguela upwellingregion

Primary(mg C mAverage

195842

3.444577.9476134522

production" h"')

Measured range

_--

0.3-8 614-932-1813-1473-167--

Water type*

Newly upwelledMature upwelledAged upwelled

Newly upwelledMature upwelled (Oudekraal)Aged upwelled (Robbcn Island)Mature upwelledAged upwelled-Mature upwelled

Reference

This study, days 0-2This study, days 4-8This study, days 8-15

Brown (1984)Brown (1984)Brown (1984)Brown and Field (1986)Brown and Field (1986)Lucas et al. (1986)Armstrong et al. (1987)

Simulated daily rates were converted to hourly rates by assuming a 12 h productive day.•Classified according to nitrate concentrations and temperatures after Barlow (1982).

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—4.1 day"1. This value also is larger than field estimates of 0.89-1.50 day"1

(Laws et al., 1984) and 3 day"1 (Sheldon, 1984). However, despite somedifferences, the model estimates of primary production for all three ecosystemsare within the range of measured values, indicating that the simulated carbonflows are realistic.

The magnitudes of the average daily primary production rates in the threeecosystems are shown in Figure 15. These values are for optimal light regimes inthe surface waters of the three ecosystems, and should thus be larger thanaverage values that have been measured in the field and integrated over varyingdepths. Nevertheless, comparisons with field measurements have indicated thatthe values are of the right order of magnitude. It is apparent that pico-phytoplankton dominate primary production in all three simulated ecosystems,contributing >55% in the Agulhas Bank and oceanic simulations (Figure 15).McMurray et al. (1991) found that autotrophs <15 urn were responsible for 62%of total primary production on the Agulhas Bank in summer, and at the samestation they measured pico-phytoplankton (<5 uJn) as contributing 43%. Incontrast to their fast production rates, pico-phytoplankton standing stocks arerelatively small in the three ecosystems (Figure 14a). In general, as the size ofthe autotrophs increases, so their contribution to primary production decreases.The most remarkable example of this trend occurs in the upwelling simulation;net-phytoplankton comprise 43% of the standing stock of the autotrophcommunity, but contribute only 8.5% to the average gross primary production.

Utilization of regenerated nitrogenThe simulated uptake of regenerated nitrogen by different phytoplankton sizefractions is compared with field data from the Agulhas Bank (Figure 16) and theupwelling area (Figure 17), to assess whether simulated flows are realistic. Inboth model ecosystems, pico-phytoplankton dominate nitrogen uptake on

AutotrophCO

EO

| )

/uu600-500-400-300"200-100"

o-

Gross primary production

I ****mrmrmmmmm; - •

Agulhas Bank Upwelling

Simulations

Oceanic

• 25-125 \un& 5-25 jim• 1-5jun• 0.2-1 UJTI

Fig. 15. Simulated average daily primary production rates (mg C m~3 day ') by different autotrophsize classes in the three model communities.

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Size-based dynamics of plankton food webs, n

average, although their contribution is reduced at certain times. The field datafrom the Agulhas Bank (Figure 16b) agree with model predictions regarding theimportant role played by autotrophs <5 \im in nitrogen uptake, but the role ofnet-phytoplankton appears to be underestimated in the model. Large-celledphytoplankton in the Agulhas Bank model flourish only when ambient nitrogenconcentrations are high enough to support growth, which occurs only whennitrate-nitrogen is the dominant form. Large cells catabolize a smaller fraction oftheir biomass than small cells under conditions of scarce nutrients, and are ableto replenish reserves when nutrients are again abundant (Laws, 1975). Themodel does not include inactive, resting stages of large cells in the community,

6)

0-5 d 5-15 d 15-30 d 23-25 dAgulhas Bank simulations

0.12

119 107 88 60 37Agulhas Bank Stations

16

Autotrophsizes

• 25-125 umM 5-25 urnO 1-5 um• 0.2-1 um

Autotrophsizes

• 15-200 umS 5-15 um• 1-5 um• < 1 um

Fig. 16. Uptake rates of regenerated nitrogen (mg-at N m 3 IT1) by different size classes ofautotrophs in the chlorophyll maximum layer on the Agulhas Bank, (a) Average rates for differenttime segments of the Agulhas Bank simulation. Daily rates were converted to hourly rates assuminga 12 h productive day. (b) Size fractionated ammonium uptake rates at six stations on the AgulhasBank (after Probyn and Lucas, 1987)

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and may thus underestimate the role of the large cells, as is indicated by the fastnitrogen uptake rate measured for the size fraction 15-200 u,m by Probyn andLucas (1987), which is not represented in model output.

The dominance of pico-phytoplankton in nitrogen uptake in the Benguelaupwelling simulation (Figure 17b) is supported to some extent by field studiesfrom the same upwelling region. Probyn (1985) found that pico-phytoplanktonaccounted for 11% of the nitrogen taken up by the total phytoplanktoncommunity in inshore waters of the west coast, but in a more recent study(Probyn, 1987) the uptake of ammonium by pico-phytoplankton was estimatedto be small compared with that by phytoplankton >5 \Lvn (Figure 17b). Duringthe initial stages of the simulated phytoplankton bloom after upwelling (Figure17a), the pico-phytoplankton contribution was reduced, which is similar to the

CO

0.20

0-4 d 4-8 d 8-12 d 12-15 d

Benguela upwelling simulation

Autotrophsizes

• 25-125 nm3 5-25 urn• 1-5 m• 0.2-1 urn

Airtotrophsizes

• 15-200 nmm 5-15 urn• 1-5 nm• < 1 \im

0.00

Benguela upwelling stations

Fig. 17. Uptake rates of regenerated nitrogen (mg-at N m'3 h"1) by different size classes ofautotrophs in the Benguela upwelling region, (a) Average rates for different time segments of theBenguela upwelling simulation Daily rates were converted to hourly rates assuming a 12 hproductive day. (b) Size-fractionated ammonium uptake rates from four stations of the Benguelaupwelling region on the west coast of South Africa (after Probyn, 1987).

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Size-based dynamics of plankton food webs, II

field measurements (Figure 17b). Thus, although the magnitudes of the uptakerates by pico-phytoplankton may have been overestimated in the simulations,the similarities between simulation output and field data at certain timesindicates that more field data are required to understand fully the dynamics ofnitrogen uptake. Field measurements are limited in time and space, and verylittle is known of the short time-scale patterns of nitrogen uptake andregeneration (Probyn, 1985).

New versus regenerated productionThe proportion of total primary production that is due to the utilization of newnitrogen (Dugdale and Goering, 1967) is termed the /-ratio (Eppley andPeterson, 1979). This ratio provides an index of the degree of recycling in theeuphotic zone, with a recycling index r calculated as the ratio

r = (1 - M

The index r indicates the number of times a nitrogen molecule will cycle in theeuphotic zone before being lost by sinking. The average values of/and r in themodel systems are shown in Table IV. Regenerated nitrogen produced by largezooplankton and fish is not included in the simulations, but the contributions bythese large organisms are probably small compared to the contribution by micro-organisms (Azam et al., 1983), and this should not be a significant source oferror. In the Agulhas Bank model, new production comprises ~27% on averageof total production, similar to the value of 30% estimated for inshore waters(Eppley and Peterson, 1979). In general, the/-ratio at different periods of theAgulhas Bank simulation falls between 0.25 and 0.34, and r ranges from 1.9 to

Table IV. Estimated /-ratios and cycling indices r for different periods of the three simulatedecosystems

Simulation time / r

Agulhas Bank coastal stratified0-30 days 0.27 2.70-5 days 0.34 1 95-15 days 0.25 3.0

15-30 days 0.25 3.0

Benguela coastal upwellmg0-15 days 0 26 2.80-4 days 0.76 0.34-8 days 0.15 5.78-12 days 0 °°

12-15 days 0 °°

SE Atlantic oceanic0-30 days 0.14 6.10-5 days 0.18 4 6

25-30 days 0.14 6.1

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C.L.Moloney, J.G.FieM and M.I.Lucas

3.0. In the Benguela upwelling model, the nitrogen pool is not replenished withnew nitrogen, so regenerated nitrogen comes to dominate as the new nitrogen isused up, and after 8 days all of the primary production is regenerated production(/-ratio = 0). However, during the initial stages (days 0—4) of the simulatedupwelling bloom, the /-ratio was 0.74 (r = 0.35), similar to the value of 0.71estimated by Probyn (1985) for the upwelling region of the west coast. Theaverage value of / over the entire 15 day simulation of the upwelling bloom was0.26. The average /-ratio of 0.14 from the oceanic model (0-30 days) is less thanthat of the two coastal models, and supports the results of Probyn (1985) whoestimated a value of 0.13 for oceanic waters of the southern Benguela region.

Nitrogen regenerationNitrogen regeneration rates in the models are the rates at which nitrogen isexcreted by heterotrophs as a result of metabolic activity, and were estimated asthe nitrogen equivalents of carbon respiratory losses, although this may not berealistic for bacterioplankton (see Moloney and Field, 1991). These rates wereintegrated over relevant periods of the model, and an average rate estimated foreach period, similar to the calculations for average primary production ratesabove. The time-averaged contributions of different sizes of heterotrophs tonitrogen regeneration are presented in Figure 18. Larger amounts of nitrogenare regenerated in the upwelling model than in the Agulhas Bank and oceanicmodels. These results and the estimated/-ratios above support the observationsof Harrison (1980) and Probyn (1987) that the fastest rates of nitrogenregeneration occur in areas of high primary productivity, but that thesignificance of the regenerated nitrogen for phytoplankton growth is greatest inoceanic areas. In all three ecosystems, the zooflagellates appear to be the mostimportant size category, on average, for nitrogen regeneration. The amount of

•q7Ezto

Nitrogen regeneration ratesHaterotroohslzas

• 125-625 urn• 25-125 jimm 5-25 tunD 1-5 \ur\• 0.2-1 nm

Agulhas Bank Upwelling

SimulationsOceanic

Fig. 18. Comparison of average nitrogen regeneration rates (mg-at N m 3 day ') by different sizeclasses of heterotrophs for the Agulhas Bank, Benguela upwelling and SE Atlantic oceanicsimulations.

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nitrogen regenerated by the bacterioplankton is relatively small in the twocoastal simulations, and slightly larger in the oceanic simulation, wherebacterioplankton are responsible for ~35% of the total regenerated nitrogen.Heterotrophs in the 125-625 u.m ESD class do not contribute much to totalnitrogen regeneration, their average contribution being <2% in all threesimulations.

The proportion of regenerated nitrogen contributed by different heterotrophsize classes changes with time in the three model ecosystems, but the temporaldifferences are more pronounced in simulations of the two coastal ecosystems,reflecting the changing abundances of the size classes in the model communities.

0.20

0.000-5 d 5-15 d 15-30 d 23-25 d

Time segments of Agulhas Bank simulation

0.20

0.00119 107 88 60 37

Agulhas Bank Stations16

Heterotrophsizes

• 125-625 urn• 25-125 urnS 5-25 urn• 1-5 um• 0.2-1 um

Heterotrophsizes

• 15-200 umM 5-15 um0 1-5 um• < 1 um

Fig. 19. Contributions by different size dasses of heterotrophs to nitrogen regeneration (mg-at Nm~3 h"1) in the chlorophyll maximum layer of the Agulhas Bank, (a) Average regeneration rates fordifferent time segments of the Agulhas Bank simulation. Daily rates were converted to hourly ratesassuming a 24 h day. (b) Field measurements of ammonium excretion rates (after Probyn and Lucas,1987).

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These results are compared with field data of Probyn and Lucas (1987) andProbyn (1987) respectively for the Agulhas Bank (Figure 19) and Benguelaupwelling (Figure 20) simulations. Model results are consistent with theconclusions of these authors that organisms <15 u.m are the most importantremineralizers, due to their rapid regeneration rates. However, the smallorganisms are not consistently important, and the temporal variations suggestedby the models of Moloney et al. (1986) and Newell et al. (1988) are apparent inboth the simulation results and the field measurements. Such temporalvariations may be responsible for apparently conflicting evidence from 'snap-shot' sampling as to which size categories of heterotrophs are most important asremineralizers in the plankton.

1

CO

N.m

to

0

0

20"

15-•

0.10"

0*

05 "

0.00

Heterotrophsizes

• 125-625 urn• 25-125 urnB 5-25 urn• 1-5 urn• 0.2-1 urn

0-4 d 4-8 d 8-12 d 12-15 d

Time segments of Benguela upwelling simulation

0.20

CO

E

to6)

v 0.15-

0.10-

0.05-

0.00

b)Heterotrophsizes

• 15-200 umM 3-15 um• 1-3 um• < 1 urn

Benguela upwelling Stations

Fig. 20. Contributions by different size classes of heterotrophs to nitrogen regeneration (mg-at Nm"3 h"1) in the Benguela upwelling region, (a) Average regeneration rates for different timesegments of the Benguela upwelling simulation. Daily rates were converted to hourly rates assuminga 24 h day. (b) Field measurements of ammonium excretion rates (after Probyn, 1987).

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Time-averaged food web structureCarbon and nitrogen flow networks were constructed for the three simulatedecosystems. To obtain 'time-averaged' trophic structures, the carbon flows wereaveraged, and standardized to relative units, assuming that 100 units of carbon isfixed by autotrophs during each simulation (Figure 21). These trophic structuresaccount for some variability in time, but do not account for variability in space;consequently, the effect of patchy distributions on system functioning is not

Agulhas Bank simulation100

64.2 \ 23.3 j 3.1"

I AtfiOTROPH SIZE CLASSES

— / , _ , * 25— .ST- 46.3-1 27.143-0 7£|.i

(PDOC)3.1 — Q-3 I

SIZE dun md) 02-

j i I ,TI I 04, |HETEROTROPH SIZE CLASSES1.0 5 25 12125

.8-1

17.1-

Benguela Upwelllng simulation100

• HnUnf

"[-•8.4StZEQimeed)

(PDOC)3.5 —

SIZE ()im m<S) O2

46.2 | 23.0J 19.4 \ 8.5 |

L AUTOTROPH SIZE CLASSES

—ij,—i !5—,£"^38.9-1 27.0"151-2 74.4 I -

a.oj 11.51 9.et i.gt ^ ^vz.—r—I oV—i—r^

HETEROTROPH SIZE CLASSES U f c » 1 fl f1.0 5 25 125 625*^ ltJmK

Oceanic simulation100 " • trr*no*

SHZEQtm—d)

58.51 39.(4 OS* ^ 0 JAUTOTROPH SHE CLASSES

— iJO r» 25I(0)1(PDOC)

1 1 ^ —

SIZE Omfed) 0.2-

Fig. 21. Summaries of the average carbon flows through the simulated plankton communities of theAgulhas Bank (averaged over 30 days), Benguela upwelling region (15 days) and SE Atlanticoceanic region (30 days) The flows have been standardized to 100 units of carbon fixed (less PDOCsecreted).

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C.L.Motoney, J.G.FtekJ and M.I.Lucas

addressed in this paper. The relative contributions by different sizes ofautotrophs to total carbon fixation is shown; also the proportion of primaryproduction that reaches the heterotroph size classes (Figure 21). The flows fromthe autotrophs and heterotrophs of sizes 0.2-5 u,m to heterotrophs of sizes 1-5and 5-25 u,m may be overestimated in the simulation output, because the non-linear functions used to model predator-prey interactions occasionally resultedin more carbon being ingested than was available. This unrealistic result doesnot affect model output seriously, because prey populations decrease to therefuge biomass immediately after this occurs, followed shortly by the predatorpopulations. However, the cumulative effect of these excess flows is apparent inthe integrated flows, which have therefore been corrected to allow for them.

It is evident that in all three simulated ecosystems, a large proportion (70-90%) of the total carbon fixed during primary production is dissipated throughrespiration by autotrophs and heterotrophs in the plankton community (Figure21). Of the carbon reaching heterotrophs, the bulk (30-50%) occurs throughherbivory, with only a small proportion (<10%) being taken up by bacteria andmoving along the heterotroph size continuum through predation. For the largestheterotrophs in the models (25-625 u-m), only a very small fraction of the totalprimary production is grazed: 3.1% in the Agulhas Bank model, 11.7% in theBenguela upwelling model and practically nothing in the oceanic model. Thislatter result occurs because the bulk of the carbon in the oceanic model is fixedby autotrophs <5 u,m, which cannot be ingested by the large heterotrophs (apartfrom the gelatinous zooplankton which are not modelled here). Even smallerproportions of carbon (<1%) reach the large heterotrophs through predation.Thus, only small proportions of total primary production would be available topelagic fish in the simulations. On average, a larger proportion of primaryproduction is transferred to heterotrophs 25-625 u,m in the Benguela upwellingsimulation (12.5%) than in the Agulhas Bank (3.5%) and the oceanic (0.1%)simulations.

Network analysis of the carbon flow networksThe program NETWRK3 (Ulanowicz, 1986; Wulff et al., 1989) was used toanalyse the trophic structures of the carbon flow networks in the threesimulations. Inputs to the program were in the form of the standardized carbonflows presented in Figure 21, and also carbon flows integrated over varyingperiods to assess how the trophic structures change with time. The program usesthe flow data to produce a 'Lindeman transformation matrix' (Kay et al., 1989),which indicates how much of the feeding activity of each component of thenetwork takes place at each trophic level. In addition, the program condensesthe food webs into one-dimensional 'food chains' termed Lindeman spines(Ulanowicz and Kemp, 1979; Mann et al., 1989), by apportioning modelcompartments to integer trophic categories. These integer trophic categories areused to estimate average trophic efficiencies, and allow for the description ofcomplex food webs in terms of steps in a food chain. They are particularly usefulin identifying the most important pathways of material flows in the food webs.

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Sixe-based dynamics of plankton food webs, n

The degree to which each of the model components in the three simulations isdivided between the different trophic categories is given by the Lindemantransformation matrices presented in Tables V-VIII. Each percentage refers tothe proportion of feeding activity by each biotic component in each trophiccategory. For example, in Table V zooflagellates were 98.7% in category II and1.3% in category III. The network analysis results for three time segments of theAgulhas Bank simulation (Table V) indicate that trophic category II usually isthe most important trophic category for all heterotroph size classes, althoughthere are variations with time. For example, during the first five days of theAgulhas Bank simulation, nano-zooplankton and meso-zooplankton haverelatively large proportions (respectively 17.6 and 19.0%) of heterotrophs intheir diets, whereas in the time segment of 5-15 days, meso-zooplankton chieflywere herbivorous. Micro-zooplankton are 16.1 and 19.8% respectively rep-resented in trophic category III for the periods 5-15 days and 15-30 days.During the latter period, meso-zooplankton were relatively well represented(5.4%) in trophic category IV. The varying percentages for the trophiccategories occupied by each heterotroph size class reflect the dynamic nature ofthe simulated communities of the Agulhas Bank.

In the first time segment (0-4 days) of the upwelling simulation (Table VI),

Table V. Network analysis of carbon flows

Biotic component

0-5 daysAll autotrophs (0.2-125 u.m)Bactenoplankton (0.2-1 (un)Heterotrophs 1-5 ujnHeterotrophs 5-25 U4nHeterotrophs 25-125 ujnHeterotrophs 125-625 jim

5-15 daysAll autotrophs (0.2-125 (im)Bactenoplankton (0.2-1 \un)Heterotrophs 1-5 junHeterotrophs 5-25 y.mHeterotrophs 25-125 (imHeterotrophs 125-625 ujn

15-30 daysAll autotrophs (0.2-125 u.m)Bacterioplankton (0.2-1 jim)Heterotrophi 1-5 |unHeterotrophs 5-25 ujnHeterotrophs 25-125 (junHeterotropbs 125-625 jun

Trophic category*I II

100 0100.098782499.581.0

100 0100 095.178 079 497.3

100 0100 094.593.878.872.7

m

--1 3

17 40.4

18.9

--4.9

20.916.12 2

--5.55.9

19 821.5

IV

---0.20.090.08

---1.14.30 4

---0.31.25.4

V

----0.00010.02

----0.20 1

----0 070.3

VI

-

---0.00002

-----0.0006

-----0.02

Lindeman transformation matrices: the percentages contributed by each biotic compartment to eachtrophic category in the Agulhas Bank simulation at selected periods.•Indicates the number of steps from non-living matter in a hypothetical food chain (Mann el al.,1989): II represents herbivores and bacteria, ID-VI carnivores and bactenvores

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C.L.Moloney, J.G. Field and M.I.Lucas

Table VI. Network analysis of carbon flows

Biotic component

0-4 daysAll autotrophs (0.2-125 u-m)Bacterioplankton (0.2-1 pjn)Heterotrophs 1-5 |xmHeterotrophs 5-25 |imHeterotrophs 25-125 \unHeterotrophs 125-625 pjn

4-8 daysAll autotrophs (0.2-125 p.m)Bacterioplankton (0.2-1 (jun)Heterotrophs 1-5 (AmHeterotrophs 5-25 M riHeterotrophs 25-125 \xmHeterotrophs 125-625 (i.m

8-12 daysAll autotrophs (0.2-125 (im)Bacterioplankton (0.2-1 u.m)Heterotrophs 1-5 (unHeterotrophs 5-25 junHeterotrophs 25-125 (unHeterotrophs 125-625 i±m

12-15 daysAll autotrophs (0 2-125 (im)Bacterioplankton (0.2-1 ujn)Heterotrophs 1-5 tunHeterotrophs 5-25 UJTIHeterotrophs 25-125 (unHeterotrophs 125-625 urn

Trophic category*I

100.0-----

100 0-----

100.0-----

100.0-----

II

-100.09 7 698.362.999.8

-100.09 9 0

0.999.865.6

-100 0883900

0 884.4

-100.099537.5

5 197.7

III

--2 41.6

36.40.1

--1 0

98.10.001

34.3

--

11.78 8

89.30.1

--0.5

62.235.60 1

IV

---0.040.60.08

---1.00.20.0005

---1.28.7

13 9

---0.3

59.10.8

V

----0.020 001

----0.0020.06

----1.21 4

----0 31 3

VI

-----0.00004

-----0.0006

-----0 2

-----0006

Lindeman transformation matrices, the percentages contributed by each biotic compartment to eachtrophic category in the Benguela upwelling simulation at selected periods."Indicates the number of steps from non-living matter in a hypothetical food chain (Mann et al.,1989): II represents herbivores and bacteria, III-VI carnivores and bactenvores.

most heterotrophs were phytophagous, except for micro-zooplankton, whichobtained 37.1% of their carbon from other heterotrophs. During days 4-8,nano-zooplankton were almost exclusively carnivorous, and similarly for micro-zooplankton during days 8-12. At the end of the simulated phytoplanktonbloom (days 12-15), nano-zooplankton obtained ~62.2% of its carbon attrophic category III, and 59% at trophic category IV, indicating that food chainsincrease in length towards the end of the simulated phytoplankton bloom. Themeso-zooplankton were chiefly herbivorous during days 12-15, because largeautotrophs dominated biomasses during this period (Figure 8). As the largeautotrophs are grazed down, meso-zooplankton would become carnivorousagain.

For the oceanic simulation (Table VII), two time segments are presented; thefirst (days 0-5) reflects a period in which net-phytoplankton populations were

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Table VII. Network analysis of carbon flows

Biotic component

0-5 daysAll autotrophs (0.2-125 p.m)Bacterioplankton (0.2-1 MJH)Heterotrophs 1-5 jimHeterotrophs 5-25 (imHeterotrophs 25-125 urnHeterotrophs 125-625 \x.m

25-30 daysAll autotrophs (0 2-125 p.m)Bactenoplankton (0.2-1 \un)Heterotrophs 1-5 (imHeterotrophs 5-25 (unHeterotrophs 25-125 (unHeterotrophs 125-625 jun

Trophic category*I

100.0-----

100 0-----

n

-100.082096.340.092.9

-100.082.096.3

6.2909

III

--

18.03.0

57 82.9

--

18 03.0

9 0 30 6

IV

---0.71.84 1

---6.72.982

V

----040.1

----060.3

VI

-----0.03

-----0.06

Lindeman transformation matrices: the percentages contributed by each biotic compartment to eachtrophic category in the SE Atlantic oceanic simulation at selected periods.•Indicates the number of steps from non-living matter in a hypothetical food chain (Mann el al ,1989): II represents herbivores and bacteria, III-VI carnivores and bactenvores

Table VIII. Network analysis of carbon flows

Biotic component

Stratified Agulhas Bank simulationAll autotrophs (0.2-125 u,m)Bacterioplankton (0.2-1 (Jim)Heterotrophs 1-5 (junHeterotrophs 5-25 ujnHeterotrophs 25-125 UJHHeterotrophs 125-625 ujn

Trophic category*I II III

(averaged over 30 days)100.0

100.096.086587.6904

Benguela coastal upwelling simulation (averaged over 15 <All autotrophs (0.2-125 u.m)Bacterioplankton (0 2-1 jim)Heterotrophs 1-5 ujnHeterotrophs 5-25 ujnHeterotrophs 25-125 ujnHeterotrophs 125-625 i*m

100 0100.095.391.292.692.9

SE Atlantic oceanic simulation (averaged over 30 days)All autotrophs (0.2-125 u-m)Bacterioplankton (0.2-1 jun)Heterotrophs 1-5 |xmHeterotrophs 5-25 (imHeterotrophs 25-125 p.mHeterotrophs 125-625 u,m

100.0100.082296044999.4

--4.0

13.010.88.4

Jays)--4.78 46.76 6

--

17 83.3

52.90.2

rv

---0.51.61.0

---0 40 60 5

---0.71.80.3

V

----0070.2

----0.03004

----040 01

VI

-----0.0006

-----00002

-----0.002

Lindeman transformation matrices: the average percentages contributed by each biotic compartmentto each trophic category in the Agulhas Bank, Benguela upwelling and SE Atlantic oceanicsimulations, averaged over the periods of the simulations.'Indicates the number of steps from non-living matter in a hypothetical food chain (Mann et al.,1989): II represents herbivores and bacteria, III-VI carnivores and bactenvores

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unrealistically large, and the second period (days 25-30) represents the oceaniccommunity after it had 'stabilized' to some extent. The second time-segmentpresumably is more realistic than the first. In both periods, one- and two-stepfood chains dominate. Three- to five-step food chains also are present, butappear not to be as prevalent as the short food chains.

There are six effective trophic categories in each of the three simulatedecosystems (Tables V-VIII). When representing a food chain, it is customary toplace different species, groups of species or size classes into each trophic level.The trophic structure of the three models for such a representation is shown inFigure 22. This 'food chain' clearly is unrealistic, representing the stepwisethinking implied by food chains, rather than recognizing that in reality we aredealing with food webs. However, the degree to which the simple series inFigure 22 misrepresents the average trophic structure of the food webs" in themodels only becomes apparent when the flow networks are analysed. In all threesimulations, autotrophs occurred exclusively in trophic category I and bacterio-plankton only in trophic category II (Table VIII), as would be expected.However, despite the large number of potential trophic categories, mostheterotrophs in the three models occurred most frequently in trophic categoryII, because omnivory is assumed to occur in the plankton in the models. This isvery different from the food chain represented in Figure 22. In general, as thenumber of the trophic category increases (i.e. as the number of trophic stepsfrom autotrophs increases), so the proportions of carbon reaching that trophiccategory decrease.

It is evident that short food chains are important in all three simulations, butare more prevalent in the coastal upwelling food web, where the simulatedheterotroph community obtains >90% of its carbon directly from autotrophs onaverage (Table VIII). This percentage ranges between 86 and 96% in theAgulhas Bank simulation, and 45 and 99% in the oceanic simulation. Theseresults are based on the assumption that there is no selectivity of prey items,which may not necessarily be true. Furthermore, these representations areaverage pictures from relatively long periods, and over shorter time spans thetrophic structure may be quite different, as was shown in Tables V-VII.However, the time-averaged trophic structure should give the best indication ofwhat is the 'norm'.

The condensed food webs are represented as Lindeman spines in Figure 23,

BACTERIOPLANKTON

PARTICLE-FEEDING HETEROTROPHS

-625

Fig. 22. A food chain representation of the components of the plankton communities of the threesimulation models.

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together with estimated trophic efficiencies. In the representations in Figure 23the carbon has been standardized to 118 units, because 18 units (i.e. 15%) ofcarbon has been assumed to be secreted as PDOC (Moloney and Field, 1991).Because this percentage is unknown and probably variable, we have standard-ized carbon fixation (less PDOC excretion) at 100 units. It is evident that thefirst step (utilization of primary production) in the Lindeman spines is the mostefficient in all three simulations, with trophic efficiencies ranging from ~25 to50%. Subsequent steps are much less efficient, generally being <15%. Trophicefficiencies are not constant for each trophic transfer (as proposed by Slobodkin,1962), but generally decrease along the food web, as has been observed by Kayet al. (1989). The coastal simulations generally show greater trophic efficiencythan the oceanic simulation, and the upwelling simulation may have moreefficient utilization of primary production (the first step) than the Agulhas Banksimulation over short time periods.

DiscussionThis paper demonstrates that the size-based model described by Moloney andField (1991) can be used to simulate realistic representations of a variety ofplankton communities in the southern Benguela region. It is recognized thatsome features of the simulation output may not be realistic. This is notsurprising, because a large number of physical and biological processes operateunder natural conditions, whereas the simulation models concentrate only onthe nitrogen and carbon environments of the three model ecosystems.

118

AGULHAS BANK SIMULATION

45.6 3.6 1.6 0.0015 0.0002I n m IV - • V • • VI

38.8% 8.0% 4.4% 4.8% 3.4%

UnitsCarbon

TrophicEfficiencies

BENGUELA UPWELLING SIMULATION

118 54.2 4.0 0.18 0.006 0.00005Units

Carbon

I n -+ m rv V - • VI46.1% 7.4% 4.4% 3.6% 0.8% Trophic

Efficiencies

118 32.4

OCEANIC SIMULATION

3.5 0.06 0.0007 0.000006Units

CarbonI n m - • IV V -* VI

27.5% 10.9% 1.6% 1.2% 0.009% TrophicEfficiencies

Fig. 23. Lindeman spines obtained from network analyses of the time-averaged carbon flows in thethree simulated ecosystems Carbon flows have been standardized to 100 units of net carbon fixedplus 18 units of PDOC secretion. Trophic efficiencies are calculated by dividing outflows by inflows.

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Simulation models cannot reproduce accurately all that occurs in the field,although it may be possible to change parameter values and model assumptionsto produce output that exactly matches field measurements. This is a frequentlyexpressed and often valid criticism of simulation models; the models can bemade to produce almost any result. The models presented above are largely freefrom this criticism, because an independent criterion (body size) is used toestimate almost all parameters, and these are essentially the same for all threemodels. To maintain the objective nature of the analyses, parameters were notfine-tuned for each model, so that comparisons of the three simulatedecosystems are due chiefly to differences in model input, and not to differencesin the models themselves; by altering only the frequencies and amounts of newnitrogen input, and the values of rate parameters affected by temperature (C?io).three very different plankton communities have been simulated.

The structure of the simulated communities depends on the interplay of biotic(competition and predation) and abiotic (nutrient supply) influences, with thetime scales of importance depending mainly on body-size. Picoplankton(<1 (Jim) operate on time scales of hours (Tables I and II), and this is reflected inthe periodicity of their fluctuations (approximately once per day). As organismsize increases, so periodicities of standing stock fluctuations decrease and theirdurations increase. This has important implications for determining the timescales on which field sampling programmes should operate. If the time scale istoo long, important fluctuations may be overlooked, and the dynamics of thesystem may be misrepresented. On the other hand, if time scales are too short,standing stock fluctuations may be missed altogether, and the community mayappear permanently depauperate or permanently dominated by one or other ofthe size classes. We suggest that sampling should be done on an hourly basis overa period of a few days for organisms <~5 u.m, on a half-day basis over a periodof 10-15 days for organisms 5-25 \im, and on a 1-2 day basis over a period ofweeks for organisms >25 p.m when the dynamics of plankton communities arebeing studied. This implies that extrapolations from field measurements carriedout on a weekly or monthly basis may be valid only for the large size fractions ofthe plankton. The simulation models provide a useful framework within whichfuture field programs can be planned, illustrating the time scales required andthe possible manifestations of interactions through the entire plankton com-munity.

The three models demonstrate general features of coastal and oceanicecosystems that are supported by data from field studies. A dynamic, rapidlyfluctuating plankton community has been simulated for stratified conditionstypical of the western Agulhas Bank in summer. The simulated community isdominated by different autotroph and heterotroph size fractions at differenttimes. This is in contrast to the stable community that has been assumed to occuron the Agulhas Bank (e.g. Carter et al., 1987), but is supported by recent fieldstudies by McMurray et al. (1991), who found that considerable fluctuationsoccurred during summer in western Agulhas Bank waters. In the Agulhas Bankmodel, net-phytoplankton appear to be important only if they are seeded intosurface waters in relatively large biomasses at the same time as a localized

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upwelling or mixing event, when nitrogen concentrations in the euphotic zoneare enhanced. Pico-phytoplankton are consistently present in the Agulhas Bankmodel, but with varying standing stocks. This is similar to the situation describedby Joint and Pomroy (1983) as being typical of a stratified water column on thecontinental shelf in temperate regions in summer. These authors believe thatpico-phytoplankton are probably important in many more areas than have thusfar been reported.

Plankton communities in upwelling environments are regarded as highlyvariable (e.g. Harrison et al., 1981). This is reflected in the simulation results,which portray the rapid development and decay of phytoplankton blooms afterupwelling that have been noted in upwelling ecosystems from different areas(e.g. Blasco et al., 1981; Brown and Hutchings, 1987b). However, although thesimulated plankton community may be regarded as being variable on time scalesof weeks, i.e. the time scale on which upwelling events occur, on shorter timescales of days it appears less variable than the simulated communities of theAgulhas Bank and oceanic ecosystems. This is partially due to simplifyingassumptions in the model; there is no diffusion of nitrate into the euphotic zoneafter the simulated upwelling event, light and other nutrients are assumed to benon-limiting, and physical processes are ignored. However, the magnitude andduration of the large-celled phytoplankton bloom suggests that organisms of size125-625 u,m in the upwelling simulation may have a less variable foodenvironment during their life span than those in the stratified coastal and oceanicsimulations. The main difference between the Benguela upwelling and othersimulated ecosystems is that large-celled phytoplankton are able to growmaximally in the nitrogen-rich conditions found in the water column afterupwelling. The presence of a relatively persistent large-celled phytoplanktonbloom in the upwelling model allows heterotrophs of sizes >125 u,m to attainlarge standing stocks. This has important implications for pelagic fish pro-duction.

The Benguela upwelling simulation depicts a succession of different sizeclasses of phytoplankton after upwelling. Traditionally, this succession has beenviewed from a taxonomic viewpoint, and individual species and higher taxa havebeen used to describe the succession, e.g. diatoms to dinoflagellates (Sukhanovaet al., 1978). The simulation results show that, at least on one level, thesuccession can be described by size-dependent effects. Species-dependent effectswill also be important, but these should be considered with size effects whenattempting to explain the succession phenomenon, as has been suggested byWatson and McCauley (1988) from their studies on the structure of phytoplank-ton communities in lakes. The phytoplankton of the southern Benguela havebeen regarded as being diatom-dominated (Shannon and Pillar, 1986). How-ever, recent studies have shown that nano- and pico-phytoplankton may also beimportant, and may dominate both standing stocks (Hutchings et al., 1984;Probyn, 1985; Mitchell-Innes and Winter, 1987) and primary production (Norris,1983, quoted in Shannon and Pillar, 1986; Probyn, 1987). The dynamics of thesedifferent size fractions of phytoplankton have been studied only recently (e.g.Probyn and Lucas, 1987).

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C.L.Motoney, J.G.FieM and M.I.Lucas

The size structure of phytoplankton communities

Marine ecologists generally recognize the need to distinguish between differentsizes of phytoplankton (Platt, 1985; Harris, 1986), and many studies in recentyears have used size-fractionated samples to estimate standing stocks andprimary production (e.g. Malone, 1977; Furnas, 1982, 1983; Larsson andHagstrom, 1982; Bienfang and Takahashi, 1983; Gieskes and Kraay, 1983;Bienfang, 1985; Herbland et al., 1985; Probyn, 1985,1987; Harrison and Wood,1988; Hopkinson et al., 1989). In coastal and oceanic waters small phytoplank-ton have been identified at times as comprising the major proportion of standingcrop (e.g. Gieskes and Kraay, 1983; Mitchell-Innes and Winter, 1987) and ofprimary production (Glibert et al., 1982; Joint and Pomroy, 1983). Whatdetermines the size structure of phytoplankton communities? In the modelecosystems, competition for nitrogen and grazing pressure are the two mostimportant factors. Nitrogen supply determines growth rates of phytoplankton,whereas grazing losses determine community biomasses.

Large cells are not as efficient as small cells in utilizing dissolved nitrogen atlow concentrations because of their small half-saturation constants (Moloneyand Field, 1991); the simulation results suggest that this is sufficient to restrictthe growth of large-celled phytoplankton (25-125 u.m) in the stratified coastal(Agulhas Bank) and oceanic (SE Atlantic) models. However, there are factors(not explicitly represented in the models) that favour the growth of large cells. Inenvironments in which nutrients are supplied in pulses, the pulses interspersedby conditions of reduced or absent nutrients, large organisms should befavoured, because they have large storage capacities and reduced metabolicdemand relative to small cells, and thus are well suited to surviving periods ofreduced nutrient concentrations (Laws, 1975) or starvation (Kooijman, 1986).In periodically eutrophic environments such as upwelling regions, largephytoplankton cells survive periods of reduced nutrients by developing dormantcysts, which form the seeding crop when they, together with nutrients, aretransported back into the euphotic zone (Estrada and Blasco, 1985; Pitcher,1990). The formation of dense cysts has been suggested as a mechanism by whichnet-phytoplankton ensure that they are not adverted off the continental shelf,but sink rapidly to the bottom to await a mixing or upwelling event that willtransport them into the euphotic zone in nutrient-rich water (Anderson et al.,1985; Pitcher, 1990), allowing them to capitalize at times when environmentalconditions are optimum. Such effects are apparent at the start of the AgulhasBank simulation and during the Benguela upwelling simulation. These resultssuggest that net-phytoplankton blooms occur in periodically stratified watersonly after nutrient-enrichment (when nitrate generally is the dominant nutrient).Pico-phytoplankton presumably do not have as strong a selection pressure asnet-phytoplankton to form dense cysts to ensure that they remain on thecontinental shelf, because they are able to grow in nutrient-deficient oceanicwaters. In addition to the sinking of cysts, live cells also sink through the watercolumn, and large cells sink faster than small cells. The net-phytoplankton thus

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comprise an important downward flux of material in the water column (Michaelsand Silver, 1988).

Pico-phytoplankton appear particularly suited to outcompete large cellsbecause of their fast growth rates, and also are better adapted to live in low-nutrient environments, because of their small half-saturation constants (Eppleyet al., 1969; Stockner, 1988). Given these size-based differences, why do smallcells not outcompete large cells in coastal regions? Model output suggests thatpredatory control of small cells precludes them from dominating for long periodsin eutrophic waters. Small cells are eaten by small grazers which can respondrapidly; large phytoplankton cells are grazed by relatively slow-growing, largezooplankton and fish. In oceanic waters and stratified coastal waters, such as onthe Agulhas Bank, pico-phytoplankton are present as an important but variableproportion of the phytoplankton community. In eutrophic coastal areas, pico-phytoplankton may dominate the phytoplankton for limited periods, as wasshown by the Benguela upwelling simulation. However, predator control sets uposcillations and prevents them from persisting, and larger phytoplankton cellsform the bulk of the biomass of protracted phytoplankton blooms.

It should be noted that these size-based differences are founded on generalprinciples applicable to a large size range of cells; on a more detailed scale it ispossible that taxonomic and other differences become more important than sizein determining uptake dynamics (see Furnas, 1983). Such detail is beyond thescope of this model, because only four size categories of autotrophs have beenused, and these incorporate a range of taxonomic forms of similar sizes.Furthermore, no differences between uptake capabilities of ammonium andnitrate are assumed in the models. Such differences have been found to occur ina number of studies (e.g. Bienfang, 1975; McCarthy et al., 1977; Price et al.,1985), and will be important in a more detailed resolution than is represented bythese models. In the simulations, net-phytoplankton were found to dominatebiomasses only when new nitrogen was abundant. This reflects the fact that net-phytoplankton require large amounts of nitrogen for maximal growth, and largeconcentrations occurred only when nitrate was dominant. Thus, differences inuptake capacities of different-sized cells are sufficient to explain some of thepreferences that have been inferred from observations of dominant phytoplank-ton and nitrogen species.

Primary production rates of oceanic ecosystemsCentral oceanic regions traditionally have been regarded as biological deserts(Parsons et al., 1984; Eppley, 1981; Kerr, 1986), with small standing stocks ofliving organisms and correspondingly little productivity. However, some studieshave indicated that primary production rates in the open ocean may be rapid(Sheldon and Sutcliffe, 1978; Morris, 1981), and in oligotrophic waters offHawaii production rates approach the maximum values measured in laboratorystudies (Bienfang and Takahashi, 1983; Laws et al., 1984). This has resulted insome controversy regarding the accuracy and reliability of the different methodsused to measure primary productivity (Eppley, 1981; Sheldon, 1984; Smith et

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C.L.MoJoney, J.G.FIeW and M.I.Lucas

al., 1984), as well as the underlying processes determining production rates(Goldman etal., 1979; McCarthy and Goldman, 1979; Jackson, 1980; Landry etal., 1984; Kanda et al., 1985).

The model output for the oceanic ecosystem was consistent with the evidencethat production rates in oceanic waters are probably fast rather than slow, inkeeping with the observation that small cells characteristically have fast specificgrowth rates. Jackson (1980) suggested that oceanic pico-phytoplankton havelost the ability to grow rapidly, but Bienfang and Takahashi (1983) refute thisstatement on the basis of their studies in Hawaiian waters. The maximumspecific instantaneous growth rates of individual cells in the simulation areestimated to be 11 day"1 for sizes 0.2-1 u,m and 4.6 day"1 for sizes 1-5 u.m at25°C (derived empirically; Moloney and Field, 1989, 1991). This is faster thanthe average simulated whole community growth rate of 4.1 day"1, reflecting thedamping influence of reduced nutrient supply on growth rates. The phytoplank-ton cells, although growing rapidly, grow at sub-optimal rates, as was reasonedby Landry et al. (1984) for growth of bacteria and flagellates in Hawaiian waters.

The hypothesis that phytoplankton in warm, surface oceanic waters have fastprimary production rates is supported by size-based arguments. Low nutrientsupply rates result in a phytoplankton community dominated by small cells,which have fast growth rates, even when growing sub-optimally. In real systemsthe basic model would be complicated by other factors such as nutrient pulsescaused by isolated mixing events and patchy excretion by large zooplankton andfish, but these complications should modify the basic structure, not determine it.Sheldon et al. (1973) stated that if the definitive relationships that exist betweengrowth rates, size and temperature could be formalized, it would not benecessary to rely on conventional measurements of rate processes to understandsystem functioning. This simple example shows how a simulation model can beused to explore a system from a different perspective from that in which mostdata are collected, and thus objectively provide hypotheses to be tested by fielddata.

The role of the microbial loopThe microbial loop in planktonic food webs represents the transferral of carbon(or energy) to large metazoans via uptake of PDOC by bacterioplankton andsubsequent ingestion by increasing sizes of microbial heterotrophs (Azam et al.,1983). However, this 'food chain' is considered to be a component of a largermicrobial food web (Sherr and Sherr, 1988), and we use a broad definition of themicrobial loop, comprising only the heterotrophic portion of the microbial foodweb. In this section we discuss the average role of the microbial loop on the basisof the output produced by the three simulation models. Average is used here in astochastic sense, to express the proportion of the total carbon flows that followsany given pathway, integrated over a suitable time interval. In the food-webanalyses in this study, it has been shown that the fast metabolism of smallorganisms precludes the micTobial loop from being an efficient pathway forcarbon flows to large zooplankton and pelagic fish, but contributes substantially

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Size-based dynamics of plankton food webs. D

to nitrogen regeneration. These results support the conclusions of a number ofrecent studies (e.g. Ducklow et al., 1986; Nagata, 1988; Pomeroy and Wiebe,1988; Sherr and Sherr, 1988). However, carbon transfer in long 'food chains'(such as the microbial loop) would be expected to be inefficient, because energyis dissipated (through respiration) at each step (Caron et al., 1985). Thus ourobservation that <1% of photosynthetically fixed carbon reaches heterotrophs>25 pjn via heterotrophs <25 u.m in the simulation models (Figure 21) is notsurprising. However, as pointed out by Sherr and Sherr (1988), the role of themicrobial loop as a link or sink for carbon is a non-issue, because ultimately allcarbon for ingestion by metazoans such as pelagic fish must come from themicroscopic part of the food web, because all primary producers in the planktonare microscopic organisms. An important issue, which we address below, is therelative importance in different marine ecosystems of long pathways viaheterotrophs compared with short transfer routes through herbivory.

The results from our three simulated food webs indicate that the microbialloop as a carbon source for heterotrophs >25 u.m is more important in theoceanic simulation than in the two coastal simulations (Figure 21). In the oceanicsimulation, very little (0.1%) of the total primary production reaches hetero-trophs >25 u.m, but nearly all of this carbon is derived from heterotrophs,because large autotrophs do not persist in the oceanic simulation. In contrast,only 11.4% of the carbon reaching large heterotrophs is derived from themicrobial loop in the Agulhas Bank simulation, and even less (6.4%) in theupwelling simulation. Probyn et al. (1990) presented a model of nitrogentransfers through a planktonic food web in aged upwelled waters, using datafrom Benguela communities dominated by pico- and nano-phytoplankton. Theyestimated that carnivory on micro-zooplankton contributed ~14% to theproduction of meso-zooplankton, which is similar to our estimate of 11.4% forthe Agulhas Bank simulation, dominated also by small-celled phytoplankton.Thus the microbial food web is inefficient as a carbon pathway to largeheterotrophs, but is the only available pathway when autotrophs are all small,and is consequently important in oceanic ecosystems. In coastal ecosystems, themicrobial food web is not the most important source of carbon, becauseherbivory dominates carbon transfers, as a result of the greater availability ofautotrophs relative to heterotrophs. Nevertheless, small heterotrophs comprisesome fraction of the carbon ingested by large heterotrophs, being moreimportant in the stratified Agulhas Bank simulation than in the Benguelaupwelling simulation. In all three simulations the microbial food web was foundto be very important in nitrogen regeneration, contributing >75% on average.

The trophic position of anchovy (and other planktivorous fish) in the southernBenguela regionMuch of marine ecological research is directed towards understanding the pro-cesses which affect the productivity offish (Fenchel, 1987). This is a central themeof the Benguela Ecology Programme (Siegfried and Field, 1982), where a speciesof considerable interest is the Cape anchovy (Engraulis japonicus capensis), which

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forms the basis of an important purse seine fishery in the southern Benguelaupwelling region. Extensive feeding studies by James (1987) indicate thatanchovy are not only filter-feeders, but can selectively feed on meso-zooplankton, especially calanoid copepods and euphausiids. These zooplanktonprimarily depend on phytoplankton for growth, but it is not known whatproportion of primary production yields zooplankton carbon and ultimately iseaten by anchovy. The model results have indicated that most primaryproduction may be due to pico- and nano-phytoplankton (Figure 15), andrespiration and sinking losses may account for relatively large proportions oftotal carbon flows (Figure 21). Thus, in the models, only a minor proportion oftotal primary production is potentially available to pelagic fish (Figure 21). Thesouthern Benguela region supports a relatively large pelagic-fish production(Crawford et al., 1987), and this large fish production, characteristic of upwellingareas (Cushing, 1971), must be explained chiefly in terms of the amount ofavailable primary production. Is the simulated available production largeenough to sustain the pelagic fish biomasses in the southern Benguela?

It is relatively simple to assess the trophic position of anchovy on the basis ofwhere their food occurs. Assuming that they feed on the largest phytoplankton(25-125 u.m) and zooplankton (125-625 u,m) size classes in the model, anchovyoccupy trophic categories II-VI in all model systems. However, most of theirtrophic function would be concentrated in categories II and III, because theirmajor prey size classes occur mainly in categories I and II. We used productionestimates from the Benguela upwelling simulation to assess potential pelagic fishproduction. Estimated primary production in the upwelling simulation was556 mg C m~3 day"1, and of this production 12.5% was estimated to be availableto pelagic fish (Figure 21), making the average available production 69.5 mg Cm~3 day"1. If we assume that the productive depths in the upwelling region arebetween 10 and 20 m (Brown and Hutchings, 1987a), this gives a range of 695-1390 mg C m~2 day"1. The productively active area of the southern Benguelahas been estimated to be 40 000 km2 (Shannon and Field, 1985), giving anestimate of total available carbon of 2.78-5.56 x 107 t C day"1. The averagedaily production was estimated from a 2 week period covering the developmentand decay of a phytoplankton bloom, and may be converted to an estimatedannual production by multiplying by 270, assuming an upwelling season of 6months and half the production for the remaining 6 months. The carbon valueswere converted to wet mass using a carbon:wet mass conversion of 14.3(Moloney and Field, 1991), giving an estimate of annual available production of110-220 x 106 t year"1. Most planktivorous fish in the southern Benguela feedmainly on zooplankton (e.g. James, 1987), and we have estimated that trophicefficiency of the third trophic category should be ~ 1 % (Figure 23), so estimatedannual pelagic fish production ranges from 1.1 to 2.2 million tonnes. This can becompared with commercial catches of ~400 000 tonnes for the last 20 years inthe southern Benguela (Crawford et al., 1987), —18-36% of total fishproduction. The proportion of pelagic fish available to a fishery in upwellingregions is generally believed to be between 25 and 50% (see Moloney and Field,1985). Our estimates are of the same order of magnitude. Thus, despite large

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She-based dynamics of plankton food webs, n

losses in the microscopic component of the pelagic food web, the simulatedavailable production is realistic when extrapolated to a scale appropriate topelagic fish.

From the simulation results, it appears that the plankton communities arecharacterized by being dominated by few trophic steps, although long transferpathways are also present (Table VIII). Thus, for sufficient primary productionto be transferred to support a large production of pelagic fish, a necessaryprerequisite is that large-celled phytoplankton should occur at the base of short'food chains'. These results corroborate the arguments of Ryther (1969) thatupweUing regions are productive because of short food chains and large-celledphytoplankton; in the simulations the proportion of total primary productionavailable to pelagic fish is —3.5 times larger in the upweUing simulation than inthe Agulhas Bank simulation (Figure 21). However, the simulated ecosystemsare complex food webs in which the traditional diatom-copepod-fish 'foodchain' accounts for ~10% of total primary production, instead of being themajor transfer pathway.

A revised food web modelA semi-quantitative, modified version of the size-based food web described byAzam et al. (1983) is presented in Figure 24. The food web is represented as aseries of seven food chains, each of which ends at pelagic fish. We havetruncated the food web at this point for simplicity of presentation, and becauseour studies concentrated on small organisms. However, the model can beextended to include apex predators such as large predatory fish, marinemammals and seabirds. Each row in the food-web matrix corresponds to atrophic category, and each row of diagonal arrows to one trophic transfer. Thehorizontal and vertical arrows represent uptake and release of dissolvednutrients. All arrows in the model represent material or energy flows; the widerthe arrow, the larger the flow. We have used carbon and nitrogen flows in thisdiscussion to be consistent with our simulation models.

In the model, primary producers cover as wide a range of sizes as the micro-heterotrophs which form the microbial loop (0.2-125 u,m); thus each of thecomponents of the microbial loop eats not only heterotrophs but also autotrophssmaller than itself. In keeping with the general size-basis of our food web,organism size increases and growth rate decreases as one moves along the foodchains (Figure 24). Thus as one moves from left to right in the diagram, thespatial- (observation areas) and temporal- (sampling intervals) scales ofrelevance to research studies respectively increase from.cm3 to km2, and fromminutes to months or years. It is difficult to study all interactions between allcomponents of the food web because of these differences, and descriptivemodels are limited because they cannot adequately depict the relativeimportance of different interactions in the food web. Simulation models areuseful tools for investigating dynamic features such as these, and the results ofour simulations of three contrasting planktonic food webs are summarized onthe right of Figure 24.

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Our models have shown that long food chains result in little carbon beingtransferred to pelagic fish. We noted that different marine planktonic systemsmay have one or more of the seven food chains operating; our simulatedecosystems contained all seven in varying degrees of importance at differenttimes (Tables V-VII). What were the features that determined which of the foodchains transferred the most carbon to pelagic fish? We hypothesize that ambientnutrient concentrations and the magnitudes of the supply rates are importantfactors, and that these in turn determine the size structure of the phytoplanktoncommunity. If nutrient concentrations are low, the phytoplankton are small, andthe only available pathways to pelagic fish are food chains 5, 6 and 7 (Figure24). This is the situation in oligotrophic oceanic waters. On the other hand, ifnutrient concentrations are high, the dominant phytoplankton are large species,and food chains 1, 2 and 3 can occur, but will only be important if food/preyconcentrations are large enough, as in upwelling regions. For intermediatenutrient concentrations, such as in the Agulhas Bank simulation, large-celledphytoplankton cannot dominate, and all sizes of autotrophs are found in thesystem. In this case, food chains 1 and 2 were not important, because biomassesof large phytoplankton were small.

In general, the number of potential trophic pathways or food chains increasesas the maximum size of the autotrophs increases. If food chain 1 (Figure 24)occurs, it is probable that all of the food chains are present to some degree (seeTables V-VII), because pico-phytoplankton are ubiquitous components ofmarine plankton communities (Johnson and Sieburth, 1979; Joint and Pomroy,1983). This implies that productive ecosystems have more complex food websthan unproductive ones, which is opposite to the traditional view of planktonicfood webs (e.g. King, 1987). The most efficient pathways in carbon transfer arethe shortest pathways (food chains 1 and 2), with estimated trophic efficienciesof 25-50% and 5-15%. Food chains of increasing length decrease in terms oftrophic efficiency, but the converse is true for nitrogen regeneration (Figure 24).Long food chains involve the most nutrient cycling, because longest food chainsoriginate at the smallest organisms which are prolific remineralizers because oftheir fast turnover rates. Thus the importance of recycled nitrogen increasesfrom upwelling to oceanic regions (Figure 24), as is documented by /-ratiostudies (Harrison, 1980; Probyn, 1987).

AcknowledgementsWe thank Peter Ryan for commenting on an early draft of the manuscript, andSuzanne Painting and Trevor Probyn for helpful discussion. Financial andlogistical support was supplied by the Foundation for Research Development,through the Systems Analysis Project of the Benguela Ecology Programme, andby the University of Cape Town.

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